\r\n\tThis book will intend to look at different migrant patterns, voluntary and involuntary migration, over the last three centuries. What influenced people to leave their home countries, family, and friends and settle somewhere else? The book may include histories of the 19th century, consider tragedies and movements activated by political events in the 20th century, and/or look at recent events of the 21st century. Push and pull factors are important points. While most of us may be influenced in a negative way by the current happenings in Eastern Europe, the Russian invasion and resulting tragedies also demonstrate some very positive human traits – the preparedness of Ukraine’s surrounding countries to help those in need and to provide a safe place for the present. \r\n\tWhether one looks at voluntary or involuntary migration into any country, after a period of adjustment, migrants do play a positive role. The research found that migrants contribute to the economy (food, shelter, employment, tax) and enrich a country’s cultural norms. Prerequisites for successful settlements are that the host society adopts a tolerant approach and that the migrants recognize the law and the language of the host country. Nothing is ever easy or without controversy, but I am a migrant (German Australian), and life in Australia has been relatively harmonious. Issues that could be considered in the book are multicultural societies (do monocultural societies still exist?) and theories of acculturation versus integration (settlement processes). \r\n\tTwo further issues are very important in relation to human migration. There is climate change, global warming, and the environment, which clearly affect people’s movement. Small island populations are very concerned about rising sea levels. 2021 has also seen floods costing human lives: Turkey (August 2021), Brazil (December 2021), Chile (January 2021), and South India (November 2021), to name but a few. In Australia (March 2022), farms and whole townships in New South Wales and Queensland have been flooded for the second time in five years, and plans to resettle these towns are considered. Official and social media provide ample coverage of the events, which leads me to the next issue. There is today’s very important role of the media, of the official and social media. We are constantly bombarded with images of human war tragedies and flood victims. People in industrialized, western countries must be the best-informed populace. How far do the images and up-to-date TV news influence us, make us change our behavior, and perhaps even consider us more generous than we have been? \r\n\tClimate change and the media are relatively new to the human migration debate, but both issues play important parts, and some interesting discussions are appreciated. \r\n\t
",isbn:"978-1-80356-618-4",printIsbn:"978-1-80356-617-7",pdfIsbn:"978-1-80356-619-1",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"9836df9e82aa9f82e3852a60204909a8",bookSignature:"Dr. Ingrid Muenstermann",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11433.jpg",keywords:"Voluntary Migration, Involuntary Migration, Push Factors, Pull Factors, Receiving Countries, Human Rights Violations, Migrants' Acculturation, Migrants' Integration, Young People's Movement, Climate Change, War, Psychological Consequences",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 13th 2022",dateEndSecondStepPublish:"June 10th 2022",dateEndThirdStepPublish:"August 9th 2022",dateEndFourthStepPublish:"October 28th 2022",dateEndFifthStepPublish:"December 27th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"15 days",secondStepPassed:!1,areRegistrationsClosed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"Dr. Ingrid Muenstermann is a Casual Academic at the College of Nursing and Health Sciences, Flinders University of South Australia, with a rich research background in relation to migration. 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Dr. Muenstermann has a special interest in all things equity. Of particular interest have been, and still are, new settlers to Australia with a special focus on German immigrants. The decline of the natural environment and increased societal self-interest led her to consider universal social responsibility. Lately the concept of aging and how to retire gracefully, that is, to maintain a certain standard of living, have been on her mind. 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\n
1. Introduction
\n
Aging is a complex process that is associated with an accumulation of the effects of cellular stresses and an increased stress response. The aging in the brain includes structural and functional changes, which together cause brain pathologies in the elderly. These changes are also thought to be critical risk factors in the development of cognitive disorders [1, 2]. It is well known that the cerebrovascular system supports brain function [3]. Vascular phenotypic and functional changes caused by aging make the brain more susceptible to neurodegenerative diseases, particularly to cognitive decline [4]. Dysregulation of cerebral blood flow (CBF) is one factor in the pathogenesis of vascular cognitive impairment (VCI) [5]. However, definition, diagnostic criteria, and treatments for VCI have not been firmly established. Thus, strategies in translational medicine and the clinical approach to VCI patients and the current aged society need to be established. This is because according to the World Alzheimer Report 2015, it was estimated that 46.8 million people worldwide suffer from dementia, and this number is expected to increase to 74.7 million by 2030 and 131.5 million by 2050 [6]. The report also stated that the incidence of dementia, including Alzheimer’s disease (AD) and vascular dementia (VaD), will increase by 45%. As a result of the increase in the size of the aging population, the incidence of dementia and the onset or prevalence of neurodegenerative diseases are increasing. These diseases have become important social concerns and represent both a current and a future social and economic burden.
\n
In the aging process, age-related cerebrovascular dysfunction results from multiple pathophysiological changes. The first of them is oxidative stress and inflammation. Excessive oxidative stress is well known to contribute to vascular aging in both animals [4, 7, 8, 9, 10] and humans [11, 12]. It is an overproduction of reactive oxygen species (ROS) rather than accumulation of ROS. In cellular senescence, inflammatory mediators such as chemokines and cytokines are secreted in an autocrine or paracrine manner. This is often referred to as the senescence-associated secretory phenotype (SASP) [13, 14, 15]. The second is the narrowing of the vascular lumen caused by atherosclerotic plaques, which is referred to as atherosclerosis in large vessels or arteriosclerosis in small vessels [16, 17, 18]. Technically a further factor is endothelial cell senescence induced by the SASP in the aging process. This arises as a result of the action of pro-inflammatory cytokines such as interleukin (IL)-1 and IL-6 [19]. Under aging, atherosclerotic plaques are prone to arise in the human aorta and coronary arteries, which contain senescent endothelial cells [20]. The senescent phenotypes of endothelial cells can be physiologically classified as having either an anti-inflammatory phenotype or a pro-inflammatory senescent phenotype [21]. Recently, numerous studies have focused on the status of the immune system during aging [22, 23, 24]. This chapter introduces the current knowledge about cognitive decline according to cerebrovascular aging relevant to cellular senescence and the changes in the SASPs. It also provides approaches on how senescent vessels exposed to the SASP enhance age-related cerebrovascular degeneration and vascular damage-derived cognitive impairment.
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\n
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2. Aging and vessels
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Cellular senescence is the state in which normal cells cease to divide. It can be thought of as a type of programmed cell cycle arrest. The senescent cells go through changes in gene expression and secretion of soluble factors in response to excessive stresses [25]. The secreted soluble factors from senescent cells are referred to as the SASP [26, 27, 28]. The SASP includes interleukins (IL) and chemokines such as IL-1, IL-6, IL-8, monocyte chemoattractant protein (MCP)-2, and macrophage inflammatory protein (MIP)-1 [29]. In addition, nitric oxide, growth factors, and several matrix metalloproteinases have also been identified in the SASP [14]. Several studies have reported that SASP factors have roles in inducing normal cells to acquire a senescent phenotype and can act in a paracrine manner to affect the activity of other nearby cells [30]. This might suggest that SASP factors do not merely arise as a result of cell senescence but could rather act to promote the senescence phenotype in normal cells.
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Although it still remains equivocal whether neurodegenerative diseases arise as a result of, or from, aging-related changes, it is clear, however, that the prevalence of neurodegenerative diseases that show a cognitive decline is positively associated with aging. Increases in oxidative stress and inflammation in response to aging trigger cellular senescence and the appropriate downstream responses. A series of these insults can aggravate age-related or neurodegenerative pathogenesis with several vascular diseases [31].
\n
\n
2.1 Endothelium
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The endothelium is one of the vessel constituents and is important in vascular structure because of its high versatility. The endothelium has multifunctional roles in maintaining blood fluidity and metabolic homeostasis. The features are important in regulating the delivery of water and nutrients through the whole body [32]. The endothelium also plays an important role in the resolution of inflammatory responses. Morphological and functional changes in the endothelium are also involved in the development of numerous pathological disorders [33, 34]. Endothelial cells are located in the inner layer of the vasculature and can come into contact with blood-containing macrophages. Therefore, endothelial cells are the first target of cytokines circulating in the blood. Simultaneously, endothelial cells also secrete pro-inflammatory cytokines, such as IL-1, IL-6, IL-8, IL-15, MCP-1, and tumor necrosis factor (TNF)-α [29]. In fact, endothelial cell-derived pro-inflammatory cytokines and chemokines are important in recruiting immune cells to the site and play a role in wound healing, angiogenesis, and inflammatory diseases. In addition to their inflammatory function, endothelial cells also produce pro-thrombotic mediators and cellular adhesion molecules (ICAM-1, intercellular adhesion molecule-1; PAI-1, plasminogen activator inhibitor-1) and enhance the adhesion and transmigration of immune cells into underlying tissues. It has also been reported that senescent endothelial cells have reduced expression levels of nitric oxide (NO) as well as reduced expression levels and phosphorylation of endothelial NO synthase (eNOS) [35, 36].
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It is well accepted that senescence in vascular endothelial cells is a common factor in various age-related diseases. Associated with aging, endothelial cells dynamically change the inflammatory phenotype to SASP [21]. A recent study has demonstrated that alterations in toll-like receptor (TLR) and TLR ligands during aging might determine the state of the inflammatory process [29]. Since endothelial cells are not immune cells, they cannot recognize foreign antigens via TLRs or present antigens through MHC II molecules to T and B cells [37]. On the other hand, endothelial cells have been shown to upregulate the mRNA levels of TLR-2, TLR-4, TLR-7, TLR-8, TLR-9, and TLR-10 during aging [38]. Since aging is a chronic inflammatory condition in the lower level, nonimmune endothelial cells could play a role in controlling and maintaining immune homeostasis. Taken these reasons, endothelial cells could be a therapeutic target to allow for recovery of a disruption in immune homeostasis.
\n
Endothelial cells derived from patients with severe coronary artery disease have also been shown to be mostly senescent endothelial cells with reduced levels of telomeric DNA-binding factor 1 (TRF1) and increased telomere oxidation [20]. When TRF1 was overexpressed in a cellular aging model, namely, human umbilical vein endothelial cells’ (HUVECs) passage for a long period of time, the telomere-associated DNA damage foci and the SASP were decreased [39, 40]. These results suggest that telomere dysfunction and the SASP occur prior to cellular senescence and induce vascular dysfunction following cardiovascular disease (CVD) development.
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2.2 Atherosclerosis
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Atherosclerosis and its associated clinical outcomes such as vascular stiffness are initiated and progressed through dysfunction in senescent endothelial cells [29]. During oxidation in the vessel, there are changes in its physicochemical properties including lipid charge, size, and content. Oxidized low-density lipid (oxLDL) becomes different from natural LDL. The oxLDL stimulates endothelial cells to induce the expression of adhesion molecules such as E-selectin and vascular cell adhesion molecule-1 (VCAM-1) on the surface of the artery [41]. Numerous senescent endothelial cells are detected in the aorta of the human with atherosclerotic plaques [42, 43]. The formation of atherosclerotic plaques is initiated by macrophages that infiltrate into the arterial intima in response to oxLDL present in the vessel [69]. Lipid-laden foam cells become pro-inflammatory, and macrophages secrete pro-inflammatory cytokines. The recruited macrophages move into the artery, and consequently, the atherosclerotic plaque size and complexity are getting larger [44].
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Atherosclerosis occurs following chronic exposure to cellular stressors. It has been reported that SASP can be restrained simply by inhibiting TNF-α without any transmission of senescence signals in an autocrine or paracrine manner [45]. Anti-TNF-α treatment has been demonstrated to physiologically reduce the non-cell autonomous effects of SASP. In patients treated with adalimumab (Humira, a human monoclonal antibody against TNF-α), epigenetic modifications were triggered. The molecular mechanisms are identified as TNF-α signaling in senescent endothelial cells, which raises the possibility of therapeutic approaches for age-associated diseases. In senescent human umbilical vein endothelial cells (HUVECs), the levels of miR-146a-5p and miR-126-3p show higher than younger HUVECs. Additionally, the levels of miR-146a-5p are increased in both senescent and young HUVECs following lipopolysaccharide (LPS) exposure, whereas the level of miR-126-3p is decreased only in senescent HUVECs and is unchanged in young HUVECs [45]. This study also showed that changes in the levels of several microRNAs (miRNAs) do not arise as a result of just treatment with LPS or an anti-TNF-α antibody. These data suggest that some miRNAs work in an age-dependent manner in vessels and endothelial cells.
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2.3 Vascular aging
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From a classical perspective, the aged vasculature is viewed as having worsened vasodilation, arterial stiffness, remodeling of the extracellular matrix, intimal thickening, and endothelial cell dysfunction [46]. The effects of vascular aging have been explored extensively and have been attributed to the number of different causes with genome instability and mTOR being two of the major causes. Studies with mTOR-inhibiting drugs in vessels have shown that such drugs have a deleterious effect on endothelial function in patients who have advanced arterial aging and inserted coronary stents [47, 48, 49]. Compared to human studies, cultured endothelial cells have augmented anti-inflammatory effects to mTOR inhibitors with a dose-dependent manner and have increased cytostatic effect [50]. It also showed that rapamycin, an mTOR inhibitor, induces the expression of PAI-1 in mice as well as in cultured endothelial cells [51]. When the Atg7 gene, one of the mediators of the autophagic process, is deleted in mice, vascular aging is accelerated [52]. In a mouse model of Hutchinson-Gilford progeria, autophagy is activated by AMPK activation and inhibition of mTOR [53]. These results showed that the mTOR–AMPK signaling pathway might be a link to the regulation of autophagy in age-related diseases. Based on the results described above, it is hard to firmly establish that rapamycin plays a preventive role on cellular senescence.
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Telomere shortening is known to be a hallmark of cellular senescence. Telomeres are significantly shorter in several endothelial cells and vascular smooth muscle cells (VSMCs), which is a clear marker of SASP in aged vasculature. The shortened telomeres in mouse vascular tissue have been shown to be sufficient to induce endothelial dysfunction [54], whereas human VSMCs still have a normal phenotype sustaining plaque stability regardless of telomere length [55]. Although telomere shortening is common in vascular aging and CVD, it remains unclear whether telomere shortening is sufficient to lead to cellular senescence and vascular degeneration in aged vessels [56].
\n
In the patients with severe coronary artery disease, most of endothelial cells demonstrated the features of senescent endothelial cells that reduced levels of telomeric DNA-binding factor 1 (TRF1) and increased telomere oxidation as mentioned above [20, 39, 40]. These results suggest that telomere dysfunction and the SASP occur prior to cellular senescence and induce vascular dysfunction following CVD development.
\n
The causes of vascular aging and vascular aging-related phenotypes can be summarized as follows:
Oxidative DNA damage: during aging, the onset of CVD led by changes in vascular endothelial cells results in an impairment of endothelium-dependent vasodilation, an overproduction of pro-inflammatory and pro-thrombotic factors, and an increase in oxidative stress [57]. In the human population, age is an important and independent risk factor for CVD [58].
Telomere shortening: an association between telomere shortening and CVD risk factors has been found in atherosclerosis, arterial stiffness, type 1 and type 2 diabetes, and obesity [59, 60, 61, 62].
Genome instability: a failure in DNA damage repair occurs in cells and tissues during the aging process [63].
\n\n
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3. Aging and cerebrovascular function
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As aging progresses, cerebrovascular function declines which can increase the possibility for ischemic stroke, intracerebral hemorrhages (ICHs), microbleeds, and cognitive decline [64]. Healthy functional cerebral vessels can coordinate with CBF and appropriately supply blood to the brain [65]. However, cerebrovascular aging has the following features, which can lead to age-related cerebrovascular diseases: (i) endothelial senescence, (ii) oxidative stress and inflammation, (iii) microvascular rarefaction, (iv) arterial stiffness, (v) vascular lumen narrowness, and (vi) CBF reduction. Each feature according to cerebrovascular aging is described in detail in the following subsections.
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3.1 Cerebral vasculature
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Cerebral vessels play a critical role in mediating between the whole body and the brain by transporting molecules between the blood and brain [5]. The brain vasculature that supplies blood to the brain tissue consists of two blood supply systems. One is the internal carotid artery system. This system is responsible for approximately 70% of the total CBF. The other system is the vertebral artery system, which is responsible for approximately 30% of the total CBF. These two major blood systems converge at Willis’ circle, which allows communication between the left and right brain hemispheres, and branch out into the whole brain through cerebral arteries [66]. The most common structural feature of cerebral vessels is the blood–brain barrier (BBB), which comprises tight junctions and adherens junctions [5]. These tight junctions exist between endothelial cells, the basal membrane, pericytes, and the astrocyte end feet [67].
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The BBB is important because it prevents harmful molecules from entering the brain tissue from the systemic circulation. Accordingly, a malfunction in BBB permeability has been reported in neurodegenerative disorders and cognitive decline that leads to dementia [68, 69, 70]. CBF is regulated in response to blood pressure through cerebral autoregulation. It was demonstrated that there were minor differences between CBF and blood pressure (within 10 mmHg of blood pressure) among healthy humans in the plateau region [71]. The report also suggested that hypertension and a higher pulsatile rate might disrupt cerebral autoregulation, which make subjects prone to neurodegenerative diseases because the aging brain is subject to hypoperfusion [71].
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3.2 Cerebral vascular aging
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Healthy functional cerebral vessels can coordinate with CBF and appropriately supply blood to the brain [65]. As aging progresses, however, cerebrovascular function declines, which can increase the possibility of ischemic stroke, intracerebral hemorrhages (ICHs), microbleeds, and cognitive decline [64]. Cerebrovascular aging has the following features, which can lead to age-related cerebrovascular diseases:
Endothelial senescence: many studies have shown the presence of senescent endothelial cells in aged cerebrovascular lesions, which is triggered by the accumulation of ROS [31] and modulated by inflammatory factors, as described above.
Oxidative stress and inflammation: in vessels’ walls, ROS increase nicotinamide adenine dinucleotide phosphate (NADPH) oxidase activity associated with aging [7, 72, 73]. These findings have been reported in both nonclinical and clinical studies. It is also well known that the vasculature can be easily damaged by vasculopathy, including atherosclerosis [74, 75, 76].
Microvascular rarefaction: this is a condition where the microvascular network and its density are reduced. Rarefaction has been detected in some brain subregions, particularly in the hippocampus. Since the hippocampus is involved in memory, it could lead to memory loss [77, 78]. Circulating endothelial progenitor cells have been shown to be diminished by aging, which is linked to white matter changes and a decline in cognitive function [79, 80].
Arterial stiffness: this is a major characteristic of vascular aging. Vascular stiffness increases blood flow velocity and blood pressure. Arterial stiffness results in an increase in systolic pressure and a decrease in diastolic pressure.
Vascular lumen narrowness: the accumulation of toxic molecules in the vessel walls makes the vascular lumen narrow. There have been clinical studies such as cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) that have examined Notch3 molecules in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy. The high-temperature requirement A serine peptidase 1 (HTRA1) is also explored in cerebral autosomal recessive arteriopathy and leukoencephalopathy [81, 82, 83].
CBF reduction: generally, hypoperfusion in the cerebral circulation is suggested to lead to cognitive impairment [84]. In cases of mild hypoperfusion, synaptic plasticity is impaired by a reduction in protein synthesis during learning and memory consolidation [2]. Under severe hypoperfusion, there is failure in the formation of the action potential, disruption in the acid–base balance, occurrence of neuronal edema, and accumulation of neurotic molecules [85].
\n\n
With respect to neural activity, cerebrovascular reactivity (CVR) can be used to measure the response levels of brain blood vessels to various stimuli. Very recent evidence has shown that cerebral vessel contractility and dilation decrease as aging progresses and have effects on the neurovascular damage mediated by NO [86]. In addition, there is an induction of vasoconstrictive factors in the cerebral endothelium [87]. Since the vessel wall undergoes structural changes through the normal aging process, it is a natural and frequently found phenomenon that the basement membrane becomes thicker but smooth muscle cells and elastin layer are thinner [88]. These different responses between young and old adults have been demonstrated using blood-oxygen-level-dependent functional MRI (BOLD fMRI) data [89]. As a result of these collective findings, it is now considered that the vasculature plays a critical secondary cause in many neurodegenerative diseases, particularly in neurovascular dysfunction. Therefore, there needs to be an increase in recognition and a focus on cognitive decline after vascular damage to develop newer therapeutic approaches.
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4. Aging and vessel-related cognitive decline
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Dementia is an irreversible cognitive condition. According to a statistical report, 7.7 million people are newly diagnosed with dementia every year [90]. Among these, patients with vascular cognitive impairment and dementia (VCID) compose over 20% of the total dementia patient population [91]. By 2030, the number of older people (>60 years of age) is predicted to increase by 56% compared to the number in 2015, and it will continue to grow year by year. Finally, by 2050, our society will become a superaged society, and it is evident that the prevalence of neurodegenerative diseases will increase. Cognitive-related diseases, such as AD or VaD, will increase by 45% in 2050 compared to 2015 [92].
\n
Mild cognitive impairment (MCI) is included as a cognitive-related disease in the older population. Although MCI is also considered as a pre-step proceeding to dementia, patients with MCI still fortunately have a chance of recovery or at least have a chance to delay the progression of the disease. Therefore, new strategies are urgently needed to diagnose and treat patients with MCI. Some blood factors such as MCP-1 or IL-6 have been suggested to be biomarkers for estimating the progression in cognitive decline because the vascular blood factors are modified in patients with MCI [93]. Some clinical studies have shown that VCID occurs in 25–30% of aged people who have had a previous stroke [94, 95]. Stroke is known to be the second leading cause of cognitive dysfunction. Furthermore, a clinical history of stroke increases the risk of cognitive dysfunction up to fivefold [96, 97]. Therefore, the symptoms of poststroke dementia could be related to the occlusion site, occlusion type, occlusion numbers, and lesion volume in the brain. There are studies that have shown that poststroke cognitive decline is related to the pathology of cerebrovascular disease and dementia, although the mechanisms involved in poststroke dementia are complex [90, 98].
\n
Aging is complex and vulnerable to cognitive decline as well as brain disorders [99]. A recent study concluded that cognitive impairment in aged adults with depression is correlated with the SASP profile [100]. This study showed that the levels of the SASP were highest in participants with both late-life depression (LLD) and MCI (Figure 1). This study suggests that cognitive impairment in LLD is linked to an aging-specific molecular profile, which might be an indicator for aging people with LLD who develop dementia [100]. Recent clinical studies have reported that depression and cognitive impairment in aging are associated with the regulation of the SASP: immune-inflammatory response [101], proteostasis [102], signal transduction, and oxidative stress [103].
\n
Figure 1.
Aging, cerebrovascular burden, and developing cognitive decline to vascular dementia. With aging, the number of resident senescent cells displaying SASP increases. The familial genetic backgrounds and vascular risk factors acquired through individual lifestyle or harmful habits, such as smoking, increase vulnerability to vascular damage and neuronal dysfunction. Combined with aging factors, such as SASP, during the aging process, vascular damage and neuronal diseases could lead to susceptibility to cognitive decline, which consequently contributes to the progression of vascular dementia. Abbreviations: SASP, senescence-associated secretory phenotype; APOE4, apolipoprotein E4; PSEN1, presenilin 1; VSMC, vascular smooth muscle cell; EC, endothelial cell.
\n
\n
\n
5. Conclusions
\n
Clinically and pathologically, vessel diseases including atherosclerosis are important diseases in a rapidly aging world. Age-related cerebrovascular dysfunctions result from multiple pathophysiological alterations. The clear one thing is that vascular aging and the aged brain vessels are vulnerable to damages and harmful factors such as the SASPs. Once the cerebral vessels have experienced insults, cognitive decline is eventually followed. The source of insults can be SASP particularly in the aging process. Despite efforts to develop therapeutic targets, it is not possible to identify the processes contributing to the onset of vascular disease and its progression of cognitive decline. Our aging society needed more fundamental approaches for treating aging-related neurodegenerative diseases containing dementia. Preferential treatment might be a preventive chance to neurodegenerative diseases. In the present time when dementia becomes an important issue in public heath, economics, social aspects, as well as the political fields, it should be possible to develop preventing and also therapeutic strategies against progressive dementia with a careful focus on treating vascular health by modulating the SASP.
\n
\n
Acknowledgments
\n
This study was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07048587).
\n
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"aging, cerebrovascular, cognitive impairment, senescence-associated secretory phenotype, endothelial cells",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/69646.pdf",chapterXML:"https://mts.intechopen.com/source/xml/69646.xml",downloadPdfUrl:"/chapter/pdf-download/69646",previewPdfUrl:"/chapter/pdf-preview/69646",totalDownloads:780,totalViews:0,totalCrossrefCites:1,totalDimensionsCites:1,totalAltmetricsMentions:0,impactScore:1,impactScorePercentile:56,impactScoreQuartile:3,hasAltmetrics:0,dateSubmitted:"April 12th 2019",dateReviewed:"September 23rd 2019",datePrePublished:"October 19th 2019",datePublished:"May 13th 2020",dateFinished:"October 19th 2019",readingETA:"0",abstract:"Brain function is supported by the cerebrovascular system, and changes in vascular phenotype and function through aging process make the brain more susceptible to neurodegenerative diseases, particularly cognitive decline. Correspondingly, the incidence of dementia and the prevalence of neurodegenerative diseases have also increased. In aging, the vessels have been exposed to the inflammatory state by harmful factors referred to as the senescence-associated secretory phenotype (SASP). Aging is a complex process that is associated with accumulated cellular stresses and an increased stress response. The aging in the brain includes structural and functional changes, which cause brain pathologies in the elderly. Particularly, damaged neurovascular event can be a consequent trigger in the pathology of vascular cognitive impairment. This chapter introduces the current knowledge on cognitive decline according to cerebrovascular aging relevant to endothelial senescence and the changes in the SASPs.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/69646",risUrl:"/chapter/ris/69646",book:{id:"9364",slug:"new-insight-into-cerebrovascular-diseases-an-updated-comprehensive-review"},signatures:"Kyoungjoo Cho",authors:[{id:"176556",title:"Ph.D.",name:"Kyoungjoo",middleName:null,surname:"Cho",fullName:"Kyoungjoo Cho",slug:"kyoungjoo-cho",email:"nehemier@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Aging and vessels",level:"1"},{id:"sec_2_2",title:"2.1 Endothelium",level:"2"},{id:"sec_3_2",title:"2.2 Atherosclerosis",level:"2"},{id:"sec_4_2",title:"2.3 Vascular aging",level:"2"},{id:"sec_6",title:"3. Aging and cerebrovascular function",level:"1"},{id:"sec_6_2",title:"3.1 Cerebral vasculature",level:"2"},{id:"sec_7_2",title:"3.2 Cerebral vascular aging",level:"2"},{id:"sec_9",title:"4. Aging and vessel-related cognitive decline",level:"1"},{id:"sec_10",title:"5. Conclusions",level:"1"},{id:"sec_11",title:"Acknowledgments",level:"1"},{id:"sec_14",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'\nKalaria RN. Linking cerebrovascular defense mechanisms in brain ageing and Alzheimer’s disease. Neurobiology of Aging. 2009;30(9):1512-1514\n'},{id:"B2",body:'\nIadecola C, Park L, Capone C. Threats to the mind: Aging, amyloid, and hypertension. Stroke. 2009;40(3 Suppl):S40-S44\n'},{id:"B3",body:'\nCatchlove SJ, Parrish TB, Chen Y, Macpherson H, Hughes ME, Pipingas A. Regional cerebrovascular reactivity and cognitive performance in healthy aging. 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Neurobiology of Aging. 2015;36(2):592-600\n'},{id:"B93",body:'\nClarke MC, Figg N, Maguire JJ, Davenport AP, Goddard M, Littlewood TD, et al. Apoptosis of vascular smooth muscle cells induces features of plaque vulnerability in atherosclerosis. Nature Medicine. 2006;12(9):1075-1080\n'},{id:"B94",body:'\nAllan LM, Rowan EN, Firbank MJ, Thomas AJ, Parry SW, Polvikoski TM, et al. Long term incidence of dementia, predictors of mortality and pathological diagnosis in older stroke survivors. Brain: A Journal of Neurology. 2011;134(Pt 12):3716-3727\n'},{id:"B95",body:'\nSavva GM, Stephan BC, Alzheimer’s Society Vascular Dementia Systematic Review G. Epidemiological studies of the effect of stroke on incident dementia: A systematic review. Stroke. 2010;41(1):e41-e46\n'},{id:"B96",body:'\nMerino JG. Dementia after stroke: High incidence and intriguing associations. Stroke. 2002;33(9):2261-2262\n'},{id:"B97",body:'\nQu Y, Zhuo L, Li N, Hu Y, Chen W, Zhou Y, et al. Prevalence of post-stroke cognitive impairment in China: A community-based, cross-sectional study. PLoS One. 2015;10(4):e0122864\n'},{id:"B98",body:'\nDeramecourt V, Pasquier F. Neuronal substrate of cognitive impairment in post-stroke dementia. Brain: A Journal of Neurology. 2014;137(Pt 9):2404-2405\n'},{id:"B99",body:'\nLopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153(6):1194-1217\n'},{id:"B100",body:'\nDiniz BS, Reynolds CF 3rd, Sibille E, Lin CW, Tseng G, Lotrich F, et al. Enhanced molecular aging in late-life depression: The senescent-associated secretory phenotype. The American Journal of Geriatric Psychiatry: Official Journal of the American Association for Geriatric Psychiatry. 2017;25(1):64-72\n'},{id:"B101",body:'\nDiniz BS, Sibille E, Ding Y, Tseng G, Aizenstein HJ, Lotrich F, et al. Plasma biosignature and brain pathology related to persistent cognitive impairment in late-life depression. Molecular Psychiatry. 2015;20(5):594-601\n'},{id:"B102",body:'\nStelzhammer V, Haenisch F, Chan MK, Cooper JD, Steiner J, Steeb H, et al. Proteomic changes in serum of first onset, antidepressant drug-naive major depression patients. The International Journal of Neuropsychopharmacology. 2014;17(10):1599-1608\n'},{id:"B103",body:'\nBot M, Chan MK, Jansen R, Lamers F, Vogelzangs N, Steiner J, et al. Serum proteomic profiling of major depressive disorder. Translational Psychiatry. 2015;5:e599\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Kyoungjoo Cho",address:"kcho0611@kgu.ac.kr",affiliation:'
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1. Introduction
This chapter stands as an introduction to the field of biometrics which is rising as an advanced layer to many user- and enterprise-centric security systems. In fact, conventional authentication methods, such as traditional passwords, have long been a weak point for security systems. Biometrics aims to answer this issue by linking proof-of-identity to our physiological traits and behavioral patterns. It is therefore important to present the concepts and primitives of performance metrics due to their impact on secure biometric systems. Thus, a brief overview is given to describe the main biometric traits along with their properties as well as the various biometric system operating modalities and the relatively known vulnerabilities. Finally, the criteria for performance evaluation have been defined to determine the system accuracy and security which are related to the applicability in real-world deployments.
2. Biometric traits
Various biometric modalities have been developed over the years making the biometric technology landscape very vibrant. Prominent examples of physiological/biological and behavioral biometric characteristics, which have been the purpose of major real-world applications, are illustrated in Figure 1.
Figure 1.
Examples of physiological/biological and behavioral traits applied in biometric recognition applications.
2.1 Physiological/biological (intrinsic) human characteristics
Biological biometrics make use of traits at a genetic and molecular level which may include features like DNA or blood, whilst physiological biometrics involve the individual physical traits like a fingerprint, iris, or the shape of the face. On the other hand, behavioral biometrics are based on patterns unique to each person, for example, how an individual walks, speaks, or even types on a keyboard. Some examples of biometric traits are briefly described below.
Fingerprint: Fingerprint recognition, which measures a finger’s unique pattern, is one of the oldest forms of biometric identification. This trait appears as a series of dark lines and white spaces when captured from the device and it consists of a set of ridges and valleys located on the surface tips of a human finger to uniquely distinguish individuals from each other. The fingerprint features are generally categorized into— (i) macroscopic ridge flow patterns (core and delta points), (ii) minutia features (which consists of the ridge bifurcations/trifurcation and the ridge endings), and (iii) pores and ridge contour attributes (incipient ridges, pore, shape, and width). Fingerprints of identical twins are different and so are the prints on each finger of the same person [1].
Face: Facial features use the location and shape (geometry) of the face, including the distance between the eyes, the distance from the chin to the forehead, or other measures that involve eyebrows, nose, lips, and jawline [2]. This kind of recognition is a nonintrusive method with reasonable authentication performance in commercially available systems. However, several constraints may be imposed by the systems on how the facial images are obtained to work properly, for example, controlled illumination and background. Moreover, its susceptibility to change due to factors such as aging or expression may present a challenge [3].
Hand geometry: This trait is based on the geometric characteristics of the hand such as the length and width of fingers, their curvature, and their relative position to other features of the hand. Though once a dominant method of biometric measurement due to the requirement of the low complexity in feature extraction and low-cost imaging, modern advances in biometrics have replaced its relevance in most applications [4]. Furthermore, such a biometric trait is not known to be very distinctive and hand geometry-based recognition systems cannot be scaled up for systems requiring the identification of an individual from a large population. In addition, hand-geometry features from both hands are expected to be similar, as their anatomy is quite similar [5].
Iris: Systems based on this trait are among the most accurate biometric systems available. This human characteristic refers to the colored part in the eye that consists of thick, thread-like muscles characterized by unique folds and patterns that can be used to identify and verify the identity of humans. Furthermore, this biometric trait is stable because iris patterns do not vary during the course of a person’s life and are not susceptible to loss, manipulation, or theft, making an iris recognition system robust to spoofing attacks. One interesting point worth noting is that even the two eyes in the same person have different patterns [6].
Ear acoustic: The main purpose of this kind of recognition system is to map one aspect within acoustic ear recognition, namely the performance of the ear characteristics bands and peaks. An ear signature is generated by probing the ear with inaudible sound waves which are reflected bouncing in different directions and picked up by a small microphone. The shape of the ear canal determines the acoustic transfer function which forms the basis of the signature. The recognition process is also possible, whilst the subject is on the move and caters to the protection of secrecy, which expands the applicability of this technology [7].
Vascular patterns: This biometric trait has been largely investigated for its advantages over other features. In fact, the vascular pattern of the human body is unique to every individual, even between identical twins [8], remains steady during the course of a person’s life, and lies underneath the human skin ensuring confidentiality and robustness to counterfeiting, as opposed to other intrinsic and extrinsic biometric traits that are more vulnerable to spoofing, thus leading to important security and privacy concerns [9]. To acquire the network structure of blood vessels underneath the human skin, a vascular-based recognition system uses near-infrared light to reflect or transmit images of blood vessels, since they are almost invisible in normal lighting conditions [10]. The most commonly used vascular biometric solutions use hand-oriented modalities, such as finger vein, palm vein, hand dorsal vein, and wrist vein recognition, as well as eye-oriented modalities, such as retina and sclera recognition [11].
Electrocardiogram (ECG): This trait considers the human heart and body anatomic features form the shape of the ECG signal typically acquired using a few electrodes, amplifiers, filters, and a data acquisition module, and which reports the strength and timing of the electrical activity of the heart [12]. However, scientific findings to date throw doubt on the specificities of real-world application scenarios and acceptability by the potential end users, which pose several constraints and questions.
Deoxyribonucleic acid (DNA): DNA matching is based on a common molecular biology method named short tandem repeat (STR)2 analysis, which is used to compare allele repeats at specific locations on a chromosome in DNA between two or more samples [14, 15]. DNA-based biometric recognition has been widely used in forensic science and scientific investigation due to its very high accuracy, despite the fact that identifications require tangible physical samples and cannot be done in real time.
2.2 Behavioral (extrinsic) human characteristics
Keystrokes, handwriting, gait, how a person uses a mouse, and other movements are some of the behavioral traits that a biometric system may analyze to assess the individual’s identity.
Gait: This characteristic may be changeable over a large time span due to various reasons, such as weight gain [16]. Thus, it can be used in low-security applications for massive crowd surveillance as it can quickly identify people from afar based on their walking style, even harnessing the potential of a large number of surveillance cameras installed in public locations into a biometric system. In fact, such a system does not require the individuals to be cooperative, nor that they wear any special device or equipment to be recognized [17].
Mobile interactions: It is based on the unique ways in which users swipe, tap, pinch-zoom, type, or apply pressure on the touchscreen of mobile devices like tablets and phones, thus providing characteristic patterns that may be used to identify people, even considering further features deriving from on-board sensors such as GPS, gyroscope, and accelerometers [18], which can also be configured to collect data in passive mode. Therefore, mobile interactions-based biometrics focuses not so much on the outcome of the user’s actions but rather on the way a user performs those actions.
Signature: Signature recognition is the most widely accepted method for documents authentication and it makes use of shorter handwriting probes compared to text-independent writer recognition methods, but it requires to write the same sign every time. A signature authentication scheme can be categorized into two methods—(i) off-line or static (the signature is digitized after the writing process) and (ii) online or dynamic (the signature is digitized during the writing process). Signature biometric features are extracted by analyzing curves, edges, spatial coordinates, inclination, the center of gravity, pen pressure, and pen stroke of the signature samples in both off-line and online applications. However, dynamic information like writing speed and stroke order is available only in online signatures [19].
Mouse dynamics: It makes use of patterns in mouse or trackpad cursor movement including clicks, trajectories, direction changes, tracking speed, and the relationships between them. Mouse-generated movement features are relatively stable for the same individual and different compared to other users, as such can be used to authenticate individuals [20]. These methods are most often used to continuously verify the user’s identity.
Keystrokes: Keystroke dynamics (also known as typing biometrics) include the tracking of the rhythm used to type on a keyboard. Two events constitute a keystroke event—key down and key up. The first one occurs when an individual presses a key, whilst the second one is associated with the event that occurs when the pressed key is released. Making use of these events, a set of inter-key and intra-key features known as delay times, hold times, and key down-key downtimes can be extracted. In general, keystroke recognition will work on the computer or virtual keyboards, mobile phones, smartwatches, and touchscreen panels, providing a low-cost authentication method that can be easily deployed in a variety of scenarios [21].
Voice: Voice recognition technology falls under both the physiological and behavioral biometric categories. Voice biometric recognition allows to distinguish among humans’ voice for personal authentication as voice features include physical characteristics such as vocal tracts, nasal cavities, mouth, and larynx [22]. Behaviorally, the way a person speaks or says something, for example, tone, movement variations, accent, pace, and so on, is also considered unique to each individual. Using data from both physiological and behavioral biometrics creates, therefore, a precise vocal signature, though mismatches may occur due to illness or other factors.
2.3 Properties of biometric traits
The main requirements that should be satisfied before a trait can be characterized as suitable for its applicability in a biometric recognition system, are briefly discussed as follows [23].
Universality: Every individual or at least most of them, accessing the biometric application should possess the characteristic.
Distinctiveness (or uniqueness): The given trait should be sufficiently different across individuals comprising the user population. Otherwise, the proportion of times the biometric system grants access to unauthorized individuals would be unacceptably high.
Permanence: The biometric trait of an individual should be sufficiently invariant (with respect to the matching criterion) over a period of time. This implies that the given trait should not change significantly over time otherwise the proportion of times the biometric system denies access to authorized individuals would be unacceptably high.
Collectability: The biometric trait can be measured quantitatively with particular regard to the easiness of obtaining the biometric data using suitable devices that do not cause undue inconvenience to the user.
Even though any human characteristic can be used as a biometric trait as long as the previous requirements are satisfied, in real-world biometric recognition applications there are a number of other issues that should be considered, such as:
Performance: This is a property aimed at assessing the verification or identification accuracy, the computational time required for a single recognition, as well as the operational and environmental factors that may affect or not the recognition accuracy and speed.
Acceptability: It indicates the extent to which people are willing to accept the use of a specific biometric application as well as their willingness to provide their biometric data. Nowadays, this is a crucial aspect to be considered due to the current pandemic situation caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [24], raising questions about how safe using touch-based biometric systems really is as touching the sensors can potentially spread viruses. As a consequence, less-constrained biometrics will likely be the preferred modality, whilst there may be less demand for other solutions that rely on physical contact with a reader.
Circumvention: This property reflects how easily the system can be deceived through potential spoofing attacks. It refers to the ways in which an attacker can endeavor to bypass a biometric system and finally attack the weak spot of such a system in order to gain unauthorized access.
Real-life biometric recognition systems ought to meet the requirements of accuracy, speed, and resource constraints, be harmless to the users, be accepted by the intended population as well as sufficiently robust to various fraudulent methods and attacks to the system [25].
Table 1 is reported a comparison study of the most popular traits based on the characteristics of biometric entities [26].
Biometric trait
Universality
Uniqueness
Permanence
Collectability
Performance
Acceptability
Circumvention
Fingerprint
M
H
H
M
H
M
H
Face
H
L
M
H
L
H
H
Hand geometry
M
M
M
H
M
M
M
Iris
H
H
H
M
H
L
L
Ear
M
M
H
M
M
H
M
Vascular patterns
H
H
M
M
H
M
L
DNA
H
H
H
L
H
L
L
Gait
M
L
L
H
L
H
M
Signature
L
L
L
H
L
H
H
Keystroke dynamics
L
L
L
M
L
M
M
Voice
M
L
L
M
L
H
H
Table 1.
Comparison study of the most common traits based on the characteristics of biometric entities.
H = High; M = Medium; L = Low.
3. Biometric system operating modes
A biometric system can provide two kinds of operating modes (identity management functionalities), namely, verification and identification. Biometric systems can indeed automatically authenticate3 or identify subjects in a reliable and fast way and are, therefore, suitable to be used in a wide range of applications to face the risks of unauthorized logical or physical access and identity theft, as well as new threats such as terrorism or cybercrime [27]. Figure 2 provides a high-level view of a generic biometric recognition system as well as all its basic building blocks, whilst Figure 3 depicts the enrollment and the biometric recognition schemes of the authentication and identification modalities.
Figure 2.
Basic building blocks of a generic biometric recognition system.
Figure 3.
Different operating modes of a biometric system—(a) enrollment mode, (b) authentication mode (the dashed line is an optional operation aimed at updating a specific user’s template), and (c) identification mode.
3.1 Authentication
In the authentication mode, the purpose of the biometric system is to verify whether an individual’s claimed identity is genuine or not (binary classification). Thus, the captured biometric data (query) is compared only with the biometric template(s) stored in the system database and corresponding to the claimed identity (one-to-one or one-to-few comparison). Given a claimed identity I and a query feature set xQ, the biometric system has to be categorized IxQ into “genuine” or “impostor” class. Let xIE be the stored biometric template corresponding to the identity I (i.e., the enrolled user with identity I). The similarity measure between xQ and xIE gives, as a result, a matching score. Hence, the biometric system applies the decision rule given by
IxQ∈genuine,ifsxQxIE≥ξ,impostor,otherwise,E1
where S represents a similarity function and ξ represents a pre-defined threshold at which the system is intended to operate. The authentication mode is typically employed for positive recognition, where the aim is to prevent multiple people from using the same identity [28].
3.2 Identification
In the identification mode, the purpose of the biometric system is to recognize an individual’s identity by searching the templates of all the enrolled individuals in the system database for a match (one-to-many comparison) without the subject having to claim an identity.
This operating mode can be further split into negative and positive identification—in the negative identification (also known as screening), the user is considered to be hiding her/his true identity from the biometric system, whilst in the positive identification, the user tries to positively identify herself/himself to the system without explicitly claiming an identity. Given a query feature set xQ, the biometric system has to determine the identity Ik∀k∈12…nn+1 where I1I2…In are identities of the enrolled users in the system, whilst In+1 represents the failure case where no identity can be assigned for the given query (open-set identification). Hence, assuming that xIkE is the stored template corresponding to the identity Ik, the biometric system applies the decision rule given by
xQ∈Ik,ifmaxksxQxIkE≥ξ,In+1,otherwise,E2
where s represents a similarity function and ξ represents a pre-defined threshold at which the system is intended to operate.
The identification mode is typically employed for screening4, where the aim is to prevent a single person from using multiple identities [28].
4. Vulnerabilities
Biometric-based cybersecurity solutions ensuring tight access control are essential in preventing intrusions and unauthorized accesses. However, even though a biometric system enhances user convenience and security, does not necessarily mean that it is also exempt from security and privacy issues. Many security measures in biometric systems are designed to protect one or more facets of the CIA triad, which is a common framework that refers to confidentiality, integrity, and availability [31].
Confidentiality is roughly equivalent to privacy. Measures undertaken to ensure confidentiality are designed to prevent sensitive information from reaching unauthorized people. It is perhaps the most obvious aspect of the CIA triad when it comes to security; but correspondingly, it is also the one which is attacked most often. Confidentiality covers a wide spectrum of access controls and measures that protect data from getting misused by any unauthorized access. Cryptography and encryption methods are an example of an attempt to prevent illegitimate access ensuring the confidentiality of (sensitive) data.
Integrity of information refers to the ability to protect information from being modified or destroyed by unauthorized parties, thus ensuring nonrepudiation and authenticity of the information. Thus, integrity involves maintaining the consistency and trustworthiness of data. One type of security attack is to intercept some important data and make changes to it before sending it on to the intended receiver.
Availability of information refers to ensuring that only legitimate and authorized parties are able to access the information when needed. Problems affecting the information system could make it impossible to access information, thereby making the information unavailable. Some types of security attacks attempt to deny access to the appropriate user, either for the sake of inconveniencing them, or because there is some secondary effect.
Biometric recognition systems implicitly (and effectively) address the authentication problem included in the last issue of the CIA triad, which consists in guaranteeing access to data only to authorized users. The reason for this is because biometric traits are (generally) not susceptible to loss, manipulation, or theft, and therefore overcome the security issues affecting the conventional methods for personal authentication, such as knowledge-based and token-based systems. However, it must be kept in mind that a biometric-based security solution is composed of several different components and the recognition module, which is only capable of addressing the authentication aspect, is just one of them. Thus, a logical structure-based approach of biometric systems is used to describe the eight points of attacks illustrated in Figure 4.
An attack on the biometric sensor consists of presenting a fake biometric trait (e.g., an artificial characteristic) to perform a spoofing attack aimed to either avoid detection (false negative) or masquerade as another (false positive). Methods used to prevent spoofing attacks include layered biometrics, liveness, and combining biometrics and conventional authentication methods such as passwords, tokens, or smart cards [32].
The connection between the biometric sensor and the subsequent modules of the system may be attacked to allow input of a stored digital biometric signal. This data can be obtained, for instance, by performing an eavesdropping (disclosure) attack [31].
Attacks on the feature extractor can be used either to create impostors or to evade detection. Hence, knowledge of the algorithms involved in this module5 may be used to forge features in presented samples to cause computation of incorrect features. To achieve this, an attacker can replace the feature extractor with a Trojan horse program that produces the desired feature sets.
An attack on the output of the previous module consists of spoofing the legitimate biometric feature set to replace it with a synthetic one.
Vulnerabilities of template database concern modifying the storage (modifying, removing, or adding templates), copying stored data for future use (identity theft or directly using the acquired information to gain access), or modifying the identity to which the biometric is assigned.
The channel between the template database and the matching module is similarly vulnerable to the previous one, however, the attack against data transmission may be easier than against the template storage, especially in the case of an adversary able to intercept any information communicated by the system by observing the data (passive eavesdropping). Encryption is crucial in this case, but may still be vulnerable to key discovery [33].
The matcher module is responsible for computing a similarity score between two biometric templates in order to confer the likelihood that they are from the same subject. Even though it may not be possible to do it easily, an attack against the matcher can be possible in specific cases. For instance, it is possible to replace the matcher module with a Trojan horse program that always outputs high scores thereby defying system security [34].
An attack on the final decision module means that if the final decision can be inserted or blocked by the attacker then the authentication system function will be overridden. If it is instead reviewed by a human operator, a DoS (denial of service) attack may be performed to mislead it or to force it to mistrust the output of the system [35].
Figure 4.
Attack points of a general biometric system.
5. Criteria for performance evaluation
The reliability and validity of a biometric scheme as well as the selection of a certain biometric trait for an application are determined by specific measures that are used to evaluate the recognition accuracy and effectiveness as addressed in ISO/IEC Standards [36]. Accordingly, to evaluate the accuracy of the proposed method based on a single-sample approach for unimodal biometric systems, each sample in the database should undergo a one-to-one matching test against every single stored sample. Hence, a comparison between a subject with a real identity Ir and a subject with claimed identity Ic is aimed at testing the hypothesis:
H0:Ir=IcversusH1:Ir≠IcE3
where H0 is the null hypothesis that the user is who s/he claims to be (genuine or intra-class matching), whilst H1 is the alternative hypothesis that the user is not who s/he claims to be (impostor or inter-class matching). To test the hypothesis in (3), it is required to compute a similarity measure, sQT where large (respectively, small) values of s indicate that the template T of the claimed identity Ic in the database and the biometric query Q of a real user Ir are close to (far from) each other. Formally, the verification problem consists of determining if a claimed identity I with biometric data Q belongs to the class H0 or not:
IQ=H0,ifsQT≥ξ,H1,otherwise.E4
Precisely, given a threshold ξ, all matching values s lower (respectively, greater) than ξ lead to the rejection (acceptance) of the null hypothesis [37]. Therefore, whether the hypothesis is accepted or not, the test is prone to two kinds of error:
false acceptance rate (FAR), that is the probability of accepting the null hypothesis H0 when input is not valid (type-I error),
false rejection rate (FRR), that is the probability of rejecting the null hypothesis H0 when input is valid (type-II error).
Let H0 and H1 be the labels that denote the genuine and impostor classes, respectively. Assume also that the psH0 and psH1 represent the probability density functions of the genuine and impostor scores, respectively. Then the FAR and FRR distributions are given by:
FARξ=ps≥ξH1=∫ξ+∞psH1ds,E5
FRRξ=ps<ξH0=∫−∞ξpsH0ds.E6
The false acceptance and false rejection rates are functions of the system threshold ξ and are closely related because the increase of one implies the decrease of the other. Hence, for a given biometric system, it is not possible to decrease both these errors at the same time by varying the threshold ξ [25]. The separation between the two distributions (or classes) indicates the ability of the system to distinguish the genuine user samples from those of the impostors. Indeed, the separation also provides a hint on the threshold point that maximizes the variance between the two classes in order to correctly mark a user sample image as authentic or impostor [23].
The genuine acceptance rate (GAR) is instead the probability of accepting the null hypothesis H0 when input is valid, hence it can be used as an alternative to FRR:
GARξ=ps≥ξH0=1−FRRξ.E7
Depending on the security level required by the final application (i.e., forensics, surveillance and homeland security, civilian, or high-security applications), the same biometric system may operate at different threshold values (ξ), as illustrated in Figure 5.
Figure 5.
Examples of biometric system error rates: (a) FAR and FRR for a given threshold ξ are displayed over the genuine and impostor score distributions and (b) typical operating points of different biometric applications are displayed on a DET curve aimed at relating FAR and FRR at different threshold values.
Hence, in order to evaluate the biometric system performance as a function of the threshold ξ, the following curves can be considered:
The receiver operating characteristic (ROC) is a graphical plot that illustrates the trade-off between false acceptance and false rejection rates when the threshold varies, whilst the intersection point for which rejection and acceptance errors are equal is named equal error rate (EER). The curve is generated by plotting the genuine acceptance rate against the false acceptance rate at various threshold settings,
The detection error trade-off (DET) is another graphical plot that illustrates the false rejection rate against the false acceptance rate at various threshold values. The two axes are scaled nonlinearly by their standard normal deviates6 or just by logarithmic transformation.
Furthermore, the above-mentioned ROC and DET curves are threshold-independent, allowing performance comparison of different biometric systems under similar conditions [23], as illustrated in Figure 6. Given a set of thresholds ξi∣smin≤ξi≤smax∀i∈12…n where smin and smax are the minimum and maximum scores, respectively, in a given set of match scores si∣0≤si≤1∀i∈12…n. Then, it is possible to generate a ROC curve computing the overall false acceptance and false rejection rates for each threshold value ξ as follows:
Figure 6.
Example of vascular-based biometric systems performance comparison [4]. Comparative graph of—(a) DET curves generated by plotting FRR against FAR and (b) ROC curves generated by plotting GAR against FAR.
FAR=1N∑k=1NFARξ,E8
FRR=1N∑k=1NFRRξ,E9
where N represents all identities being evaluated by the system and
FARξ=no.of FARsno.of impostor accessesE10
FRRξ=no.of FRRsno.of genuine accesses.E11
Since biometric systems cannot jointly provide a false acceptance rate equal to zero and a perfect verification/identification rate, the system threshold must be adjusted for the given application considering the trade-off between accuracy and false positives. Once the threshold has been set, the system can be evaluated by means of common measures that are used to assess the classification accuracy and effectiveness. In this context, we are interested in confirming or denying the identity of a subject leading thus to a dichotomous binary classification problem, where the labels are P (genuine) and N (impostor) and the predictions of the classifier are summarized in a 2×2 contingency table known as confusion matrix [38] (expanded in Table 2):
Predicted class
P
N
Total
Actual class
P
TP
FN (Type-II error)
TP + FN
N
FP (Type-I error)
TN
FP + TN
Total
TP + FP
FN + TN
Table 2.
Example of confusion matrix for a dichotomous binary classification problem.
M=TPFNFPTNE12
which completely describes the outcome of the classification task. This contingency table may be expressed using raw counts of the number of records from class times each predicted label is associated with each actual class. As illustrated in Table 2, the confusion matrix reports:
true positive (TP), the probability of correctly accepting the null hypothesis;
true negative (TN), the probability of correctly rejecting the null hypothesis;
false positive (FP), the probability of falsely rejecting the null hypothesis;
false negative (FN), the probability of falsely accepting the null hypothesis.
Based on the entries in the confusion matrix, the total number of correct predictions carried out by the model is TP+TN, whilst the number of incorrect predictions is FP+FN [39]. Therefore, if.
M=n+00n−E13
where obviously n+=TP+FN and n−=FP+TN, then the classification has been perfectly done. Conversely, if the confusion matrix is as follows
M=0n+n−0E14
it represents the worst case (perfect misclassification).
Several measures have been defined to assess the quality of a prediction [40], aimed at conveying into a single figure the structure of M. The most used functions are briefly described as follows.
Precision also known as positive predictive value (PPV) counts the true positives, how many samples are properly classified within the same cluster (closeness of the measurements to each other)
PPV=TPTP+FP.E15
Sensitivity also known as recall or true positive rate (TPR) refers to the proportion of the samples properly classified as true positives out of the actual number of true positives
TPR=TPTP+FN.E16
F-measure combines precision and recall in a single metric, indeed, it is the harmonic mean of precision and sensitivity and as a function of M, has the following form:
F1=2PPV⋅TPRPPV+TPR=TPTP+12FN+FPE17
where the worst case (F1=0) is achieved for TP=0, whilst the best case (F1=1) is reached for FN=FP=0.
Accuracy represents the ratio between the correctly predicted instances and all the instances in the dataset, whose range is between 0 (worst case) and 1 (best case):
ACC=TP+TNTP+TN+FP+FN.E18
Matthews correlation coefficient is the measure of the quality of binary (two-class) classifications:
MCC=TP⋅TN−FP⋅FNTP+FPTP+FNTN+FPTN+FNE19
it is a correlation coefficient between the actual and predicted binary classifications and it returns a value between −1 (worst case) and 1 (best case).
Accuracy and F-score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets [40]. Hence, among all the parameters described above, the Matthews correlation coefficient (MCC) is the only one that takes into account true and false positives and negatives and is generally regarded as a balanced measure that can be used even if the classes are of very different sizes [41].
6. Conclusions
Biometric-based technologies make use of unique behavioral (extrinsic) and/or physiological/biological (intrinsic) attributes to overcome the security issues affecting the conventional methods for identity authentication. Even though biometrics has been in use for decades, the advent of technology has expanded its application from primarily criminal identification to a wide range of everyday tasks, becoming a regular security process of our nowadays life. Accurate authentication or identification is fundamental to physical security, cyber security, military applications (e.g., biometric-driven lethal autonomous weapon systems), financial transactions, contracts and employment, public services, criminal justice, national security, and more. The approaches that have been proposed in literature depend on the type and the number of the underlying biometric traits, which, in general, cannot be easily transferred between people, and thereby represents a highly secure unique identifier. As a matter of fact, various biometric modalities have been developed over the years making the biometric technology landscape very vibrant. In this book chapter, we have provided an overview of the most commonly used biometric traits along with their properties, the various biometric system operating modalities as well as various limitations and weaknesses related to these systems. Indeed, biometric technologies have a number of vulnerabilities that underscore the concerns over their employment and may result in the failure of the technology to perform as anticipated. We have also discussed how the system threshold must be adjusted for the given application considering the trade-off between accuracy and false positives since biometric systems cannot jointly provide a FAR equal to zero and a perfect recognition rate. Finally, the criteria for performance evaluation have been defined to determine the system’s accuracy and security which are related to the applicability in real-world deployments, even though the existing evaluation metrics are more related to the data quality than the security aspects of the overall system. However, despite the risks, biometrics provide very compelling security solutions remaining a growing way to verify identity offering tons of promise for the future of cybersecurity.
Conflict of interest
The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Abbreviations
DET
Detection error trade-off
DNA
Deoxyribonucleic acid
ECG
Electrocardiogram
FAR
False acceptance related
FN
False negative
FRR
False rejection rate
FP
False positive
GAR
Genuine acceptance rate
MCC
Matthews correlation coefficient
NGI
Next-generation identification
PPV
Positive predictive value
ROC
Receiver operating characteristic
SARS-CoV-2
Severe acute respiratory syndrome coronavirus 2
STR
Short tandem repeat
TN
True negative
TP
True positive
TPR
True positive rate
\n',keywords:"biometrics, authentication, identification, human traits, evaluation criteria, pattern recognition system, security, vulnerabilities",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/80031.pdf",chapterXML:"https://mts.intechopen.com/source/xml/80031.xml",downloadPdfUrl:"/chapter/pdf-download/80031",previewPdfUrl:"/chapter/pdf-preview/80031",totalDownloads:132,totalViews:0,totalCrossrefCites:0,dateSubmitted:null,dateReviewed:"November 19th 2021",datePrePublished:"February 2nd 2022",datePublished:null,dateFinished:"January 13th 2022",readingETA:"0",abstract:"With the proliferation of automated systems for reliable and highly secure human authentication and identification, the importance of technological solutions in biometrics is growing along with security awareness. Indeed, conventional authentication methodologies, consisting of knowledge-based systems that make use of something you know (e.g., username and password) and token-based systems that make use of something you have (e.g., identification card), are not able to meet the strict requirements of reliable security applications. Conversely, biometric systems make use of behavioral (extrinsic) and/or physiological (intrinsic) human characteristics, overcoming the security issues affecting the conventional methods for personal authentication. This book chapter provides an overview of the most commonly used biometric traits along with their properties, the various biometric system operating modalities as well as various security aspects related to these systems. In particular, it will be discussed the different stages involved in a biometric recognition process and further discuss various threats that can be exploited to compromise the security of a biometric system. Finally, in order to evaluate the systems’ performance, metrics must be adopted. The most widely used metrics are, therefore, discussed in relation to the provided system accuracy and security, and applicability in real-world deployments.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/80031",risUrl:"/chapter/ris/80031",signatures:"David Palma and Pier Luca Montessoro",book:{id:"11195",type:"book",title:"Recent Advances in Biometrics",subtitle:null,fullTitle:"Recent Advances in Biometrics",slug:null,publishedDate:null,bookSignature:"Prof. Muhammad Sarfraz",coverURL:"https://cdn.intechopen.com/books/images_new/11195.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80355-457-0",printIsbn:"978-1-80355-456-3",pdfIsbn:"978-1-80355-458-7",isAvailableForWebshopOrdering:!0,editors:[{id:"215610",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sarfraz",slug:"muhammad-sarfraz",fullName:"Muhammad Sarfraz"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Biometric traits",level:"1"},{id:"sec_2_2",title:"2.1 Physiological/biological (intrinsic) human characteristics",level:"2"},{id:"sec_3_2",title:"2.2 Behavioral (extrinsic) human characteristics",level:"2"},{id:"sec_4_2",title:"2.3 Properties of biometric traits",level:"2"},{id:"sec_6",title:"3. Biometric system operating modes",level:"1"},{id:"sec_6_2",title:"3.1 Authentication",level:"2"},{id:"sec_7_2",title:"3.2 Identification",level:"2"},{id:"sec_9",title:"4. Vulnerabilities",level:"1"},{id:"sec_10",title:"5. Criteria for performance evaluation",level:"1"},{id:"sec_11",title:"6. Conclusions",level:"1"},{id:"sec_15",title:"Conflict of interest",level:"1"},{id:"sec_14",title:"Abbreviations",level:"1"}],chapterReferences:[{id:"B1",body:'Maltoni D, Maio D, Jain AK, Prabhakar S. Handbook of Fingerprint Recognition. London, UK: Springer Science & Business Media; 2009'},{id:"B2",body:'Zhao W, Rama Chellappa P, Phillips J, Rosenfeld A. 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Optimal classifier for imbalanced data using matthews correlation coefficient metric. PLoS One. 2017;12(6):e0177678'}],footnotes:[{id:"fn1",explanation:"STR is the DNA sequence of the short repeat region of the sequence in the noncoding region of the human genome [13]."},{id:"fn2",explanation:"Throughout this book chapter, the term authentication will be used as a synonym for verification."},{id:"fn3",explanation:"In some real scenario, such as latent palmprint matching [29], it is preferable to use a semi-automated approach aimed at providing the top n identities that best match to the given template for further analysis by a human expert. Alternatively, it is possible to consider all the identities whose corresponding match scores exceed the threshold ξ that leads to a challenging task in a quite large database (e.g., FBI’s next generation identification (NGI) system, which provides the world’s largest repository of biometric and criminal history information [30])."},{id:"fn4",explanation:"Since biometric recognition algorithms are likely susceptible to reverse engineering techniques, it is possible to conduct off-line experiments on a copy of the biometric software to be hacked in order to achieve the objective [32]."},{id:"fn5",explanation:"In the normal deviate scale, the threshold values ξ correspond to linear multiples of standard deviation σ of a Gaussian distribution. Thus, if the FAR and FRR distributions are Gaussian, the corresponding DET curve would be linear [25]."}],contributors:[{corresp:"yes",contributorFullName:"David Palma",address:"david.palma@uniud.it",affiliation:'
D.P. and P.L.-M. designed the research; D.P. performed the research and wrote the paper. The results and the paper were analysed and reviewed by P.L.-M. All authors have read and agreed to the published version of the manuscript.
D.P. and P.L.-M. designed the research; D.P. performed the research and wrote the paper. The results and the paper were analysed and reviewed by P.L.-M. All authors have read and agreed to the published version of the manuscript.
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As this section deals with legal issues pertaining to the rights of individual Authors and IntechOpen, for the avoidance of doubt, each category of publication is dealt with separately. Consequently, much of the information, for example definition of terms used, is repeated to ensure that there can be no misunderstanding of the policies that apply to each category.
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Copyright is the term used to describe the rights related to the publication and distribution of original Works. Most importantly from a publisher's perspective, copyright governs how Authors, publishers and the general public can use, publish, and distribute publications.
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IntechOpen only publishes manuscripts for which it has publishing rights. This is governed by a publication agreement between the Author and IntechOpen. This agreement is accepted by the Author when the manuscript is submitted and deals with both the rights of the publisher and Author, as well as any obligations concerning a particular manuscript. However, in accepting this agreement, Authors continue to retain significant rights to use and share their publications.
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By accepting the agreement terms Authors retain their copyright on their Work but grant broad publishing and distribution rights to the publisher.
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IntechOpen makes the publication available online under an appropriate license.
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Agreement samples are listed here for the convenience of prospective Authors:
The following definitions apply in this Copyright Policy:
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TERMS
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All Works published on the IntechOpen platform and in print are licensed under a Creative Commons Attribution 3.0 Unported and Creative Commons 4.0 International License, a license which allows for the broadest possible reuse of published material.
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All Works are published under the CC BY 3.0 and CC BY 4.0 license. However, please note that book Chapters may fall under a different CC license, depending on their publication date as indicated in the table below:
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The same principles apply to Works published under the CC BY-NC-SA 3.0 license, with the caveats that (1) the content may not be used for commercial purposes, and (2) derivative works building on this content must be distributed under the same license. The restrictions contained in these license terms may, however, be waived by the copyright holder(s). Users wishing to circumvent any of the license terms are required to obtain explicit permission to do so from the copyright holder(s).
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DISCLAIMER: Neither the CC BY 3.0 license, CC BY 4.0, nor any other license IntechOpen currently uses or has used before, applies to figures and tables reproduced from other works, as they may be subject to different terms of reuse. In such cases, if the copyright holder is not noted in the source of a figure or table, it is the responsibility of the User to investigate and determine the exact copyright status of any information utilised. Users requiring assistance in that regard are welcome to send an inquiry to permissions@intechopen.com.
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The copyright to Books, Journals and other compilations is subject to separate copyright from those that exist in the included Works.
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Share — copy and redistribute the material in any medium or format
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Share — copy and redistribute the material in any medium or format
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Users wishing to reuse, modify, or adapt the Video Lectures in a way not permitted by the license are welcome to contact us at permissions@intechopen.com to discuss waiving particular license terms.
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All software used on the IntechOpen platform, any used during the publishing process, and the copyright in the code constituting such software, is the property of IntechOpen or its software suppliers. As such, it may not be downloaded or copied without permission.
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Unless otherwise indicated, all IntechOpen websites are the property of IntechOpen.
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All content included on IntechOpen Websites not forming part of contributed materials (such as text, images, logos, graphics, design elements, videos, sounds, pictures, trademarks, etc.), are subject to copyright and are property of, or licensed to, IntechOpen. Any other use, including the reproduction, modification, distribution, transmission, republication, display, or performance of the content on this site is strictly prohibited.
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It performs very complex tasks while occupying about 2 liters of volume and consuming very little energy. The computation tasks are performed by special cells in the brain called neurons. They compute using electrical pulses and exchange information between them through chemicals called neurotransmitters. With this as inspiration, there are several compute models which exist today trying to exploit the inherent efficiencies demonstrated by nature. The compute models representing spiking neural networks (SNNs) are biologically plausible, hence are used to study and understand the workings of brain and nervous system. More importantly, they are used to solve a wide variety of problems in the field of artificial intelligence (AI). They are uniquely suited to model temporal and spatio-temporal data paradigms. This chapter explores the fundamental concepts of SNNs, few of the popular neuron models, how the information is represented, learning methodologies, and state of the art platforms for implementing and evaluating SNNs along with a discussion on their applications and broader role in the field of AI and data networks.",book:{id:"10372",slug:"biomimetics",title:"Biomimetics",fullTitle:"Biomimetics"},signatures:"Khadeer Ahmed",authors:[{id:"320026",title:"Dr.",name:"Khadeer",middleName:null,surname:"Ahmed",slug:"khadeer-ahmed",fullName:"Khadeer Ahmed"}]},{id:"65418",title:"Opening the “Black Box” of Silicon Chip Design in Neuromorphic Computing",slug:"opening-the-black-box-of-silicon-chip-design-in-neuromorphic-computing",totalDownloads:1565,totalCrossrefCites:3,totalDimensionsCites:3,abstract:"Neuromorphic computing, a bio-inspired computing architecture that transfers neuroscience to silicon chip, has potential to achieve the same level of computation and energy efficiency as mammalian brains. Meanwhile, three-dimensional (3D) integrated circuit (IC) design with non-volatile memory crossbar array uniquely unveils its intrinsic vector-matrix computation with parallel computing capability in neuromorphic computing designs. In this chapter, the state-of-the-art research trend on electronic circuit designs of neuromorphic computing will be introduced. Furthermore, a practical bio-inspired spiking neural network with delay-feedback topology will be discussed. In the endeavor to imitate how human beings process information, our fabricated spiking neural network chip has capability to process analog signal directly, resulting in high energy efficiency with small hardware implementation cost. Mimicking the neurological structure of mammalian brains, the potential of 3D-IC implementation technique with memristive synapses is investigated. Finally, applications on the chaotic time series prediction and the video frame recognition will be demonstrated.",book:{id:"6875",slug:"bio-inspired-technology",title:"Bio-Inspired Technology",fullTitle:"Bio-Inspired Technology"},signatures:"Kangjun Bai and Yang Yi",authors:[{id:"239041",title:"Prof.",name:"Yang",middleName:null,surname:"Yi",slug:"yang-yi",fullName:"Yang Yi"},{id:"245542",title:"Mr.",name:"Kangjun",middleName:null,surname:"Bai",slug:"kangjun-bai",fullName:"Kangjun Bai"}]},{id:"58622",title:"Bio-inspired Adaptable Facade Control Reflecting User's Behavior",slug:"bio-inspired-adaptable-facade-control-reflecting-user-s-behavior",totalDownloads:1587,totalCrossrefCites:0,totalDimensionsCites:1,abstract:"The purpose of this research is to develop the process of methodology in designing adaptable façade. This study focuses on the processes of façade operation control for each resident’s unit according to the user’s lifestyle. This study aims to develop the design methods that are applicable to the adaptable facade, which is inspired by the design inspiration of the biomimicry. The ideal façade to increase comfort in internal space is an adaptable façade that can constantly respond to changes in the environments. This chapter attempts in active adoption of adaptable facade that makes it possible to respond to changing requirements and environments, eventually enabling the creation of customized services for users. This chapter explores the processes of designing an adaptable façade controlled by three rules inspired by the behaviors of flocks of birds. This chapter shows how adopted bird intelligence can produce various façade controls. Also, this chapter demonstrates biomimetic façade control that has been implemented by behavior-based design. Through this demonstration, this chapter identifies the potentials of biomimetic design in facade using rules of bird flocking as source of design inspiration. This study concludes that a behavior-based approach provides flexibly responding façade to environments increasing users’ quality of life.",book:{id:"5902",slug:"interdisciplinary-expansions-in-engineering-and-design-with-the-power-of-biomimicry",title:"Interdisciplinary Expansions in Engineering and Design With the Power of Biomimicry",fullTitle:"Interdisciplinary Expansions in Engineering and Design With the Power of Biomimicry"},signatures:"Hyunsoo Lee and Nayeon Kim",authors:[{id:"220502",title:"Prof.",name:"Hyunsoo",middleName:null,surname:"Lee",slug:"hyunsoo-lee",fullName:"Hyunsoo Lee"},{id:"220507",title:"Ms.",name:"Nayeon",middleName:null,surname:"Kim",slug:"nayeon-kim",fullName:"Nayeon Kim"}]}],onlineFirstChaptersFilter:{topicId:"690",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:99,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:288,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"24",title:"Sustainable Development",doi:"10.5772/intechopen.100361",issn:null,scope:"
\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
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\r\n\tThis Series focuses on covering research and applied research involving the five Ps through the following topics:
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\r\n\t1. Sustainable Economy and Fair Society that relates to SDG 1 on No Poverty, SDG 2 on Zero Hunger, SDG 8 on Decent Work and Economic Growth, SDG 10 on Reduced Inequalities, SDG 12 on Responsible Consumption and Production, and SDG 17 Partnership for the Goals
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\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
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\r\n\t3. Inclusivity and Social Equality involving SDG 4 on Quality Education, SDG 5 on Gender Equality, and SDG 16 on Peace, Justice and Strong Institutions
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\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
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\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
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\r\n\tThe series also seeks to support the use of cross cutting SDGs, as many of the goals listed above, targets and indicators are all interconnected to impact our lives and the decisions we make on a daily basis, making them impossible to tie to a single topic.
",coverUrl:"https://cdn.intechopen.com/series/covers/24.jpg",latestPublicationDate:"May 23rd, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:0,editor:{id:"262440",title:"Prof.",name:"Usha",middleName:null,surname:"Iyer-Raniga",slug:"usha-iyer-raniga",fullName:"Usha Iyer-Raniga",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRYSXQA4/Profile_Picture_2022-02-28T13:55:36.jpeg",biography:"Usha Iyer-Raniga is a professor in the School of Property and Construction Management at RMIT University. Usha co-leads the One Planet Network’s Sustainable Buildings and Construction Programme (SBC), a United Nations 10 Year Framework of Programmes on Sustainable Consumption and Production (UN 10FYP SCP) aligned with Sustainable Development Goal 12. The work also directly impacts SDG 11 on Sustainable Cities and Communities. She completed her undergraduate degree as an architect before obtaining her Masters degree from Canada and her Doctorate in Australia. Usha has been a keynote speaker as well as an invited speaker at national and international conferences, seminars and workshops. Her teaching experience includes teaching in Asian countries. She has advised Austrade, APEC, national, state and local governments. She serves as a reviewer and a member of the scientific committee for national and international refereed journals and refereed conferences. She is on the editorial board for refereed journals and has worked on Special Issues. Usha has served and continues to serve on the Boards of several not-for-profit organisations and she has also served as panel judge for a number of awards including the Premiers Sustainability Award in Victoria and the International Green Gown Awards. Usha has published over 100 publications, including research and consulting reports. Her publications cover a wide range of scientific and technical research publications that include edited books, book chapters, refereed journals, refereed conference papers and reports for local, state and federal government clients. She has also produced podcasts for various organisations and participated in media interviews. She has received state, national and international funding worth over USD $25 million. Usha has been awarded the Quarterly Franklin Membership by London Journals Press (UK). Her biography has been included in the Marquis Who's Who in the World® 2018, 2016 (33rd Edition), along with approximately 55,000 of the most accomplished men and women from around the world, including luminaries as U.N. Secretary-General Ban Ki-moon. In 2017, Usha was awarded the Marquis Who’s Who Lifetime Achiever Award.",institutionString:null,institution:{name:"RMIT University",institutionURL:null,country:{name:"Australia"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:5,paginationItems:[{id:"91",title:"Sustainable Economy and Fair Society",coverUrl:"https://cdn.intechopen.com/series_topics/covers/91.jpg",isOpenForSubmission:!0,editor:{id:"181603",title:"Dr.",name:"Antonella",middleName:null,surname:"Petrillo",slug:"antonella-petrillo",fullName:"Antonella Petrillo",profilePictureURL:"https://mts.intechopen.com/storage/users/181603/images/system/181603.jpg",biography:"Antonella Petrillo is a Professor at the Department of Engineering of the University of Naples “Parthenope”, Italy. She received her Ph.D. in Mechanical Engineering from the University of Cassino. Her research interests include multi-criteria decision analysis, industrial plant, logistics, manufacturing and safety. She serves as an Associate Editor for the International Journal of the Analytic Hierarchy Process. She is a member of AHP Academy and a member of several editorial boards. She has over 160 Scientific Publications in International Journals and Conferences and she is the author of 5 books on Innovation and Decision Making in Industrial Applications and Engineering.",institutionString:null,institution:{name:"Parthenope University of Naples",institutionURL:null,country:{name:"Italy"}}},editorTwo:null,editorThree:null},{id:"92",title:"Health and Wellbeing",coverUrl:"https://cdn.intechopen.com/series_topics/covers/92.jpg",isOpenForSubmission:!0,editor:{id:"348225",title:"Prof.",name:"Ann",middleName:null,surname:"Hemingway",slug:"ann-hemingway",fullName:"Ann Hemingway",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035LZFoQAO/Profile_Picture_2022-04-11T14:55:40.jpg",biography:"Professor Hemingway is a public health researcher, Bournemouth University, undertaking international and UK research focused on reducing inequalities in health outcomes for marginalised and excluded populations and more recently focused on equine assisted interventions.",institutionString:null,institution:{name:"Bournemouth University",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null},{id:"93",title:"Inclusivity and Social Equity",coverUrl:"https://cdn.intechopen.com/series_topics/covers/93.jpg",isOpenForSubmission:!0,editor:{id:"210060",title:"Prof. Dr.",name:"Ebba",middleName:null,surname:"Ossiannilsson",slug:"ebba-ossiannilsson",fullName:"Ebba Ossiannilsson",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6LkBQAU/Profile_Picture_2022-02-28T13:31:48.png",biography:'Professor Dr. Ebba Ossiannilsson is an independent researcher, expert, consultant, quality auditor and influencer in the fields of open, flexible online and distance learning (OFDL) and the "new normal". Her focus is on quality, innovation, leadership, and personalised learning. She works primarily at the strategic and policy levels, both nationally and internationally, and with key international organisations. She is committed to promoting and improving OFDL in the context of SDG4 and the future of education. Ossiannilsson has more than 20 years of experience in her current field, but more than 40 years in the education sector. She works as a reviewer and expert for the European Commission and collaborates with the Joint Research Centre for Quality in Open Education. Ossiannilsson also collaborates with ITCILO and ICoBC (International Council on Badges and Credentials). She is a member of the ICDE Board of Directors and has previously served on the boards of EDEN and EUCEN. Ossiannilsson is a quality expert and reviewer for ICDE, EDEN and the EADTU. She chairs the ICDE OER Advocacy Committee and is a member of the ICDE Quality Network. She is regularly invited as a keynote speaker at conferences. She is a guest editor for several special issues and a member of the editorial board of several scientific journals. She has published more than 200 articles and is currently working on book projects in the field of OFDL. Ossiannilsson is a visiting professor at several international universities and was recently appointed Professor and Research Fellow at Victoria University of Wellington, NZ. Ossiannilsson has been awarded the following fellowships: EDEN Fellows, EDEN Council of Fellows, and Open Education Europe. She is a ICDE OER Ambassador, Open Education Europe Ambassador, GIZ Ambassador for Quality in Digital Learning, and part of the Globe-Community of Digital Learning and Champion of SPARC Europe. On a national level, she is a quality developer at the Swedish Institute for Standards (SIS) and for ISO. She is a member of the Digital Skills and Jobs Coalition Sweden and Vice President of the Swedish Association for Distance Education. She is currently working on a government initiative on quality in distance education at the National Council for Higher Education. She holds a Ph.D. from the University of Oulu, Finland.',institutionString:"Swedish Association for Distance Education, Sweden",institution:null},editorTwo:null,editorThree:null},{id:"94",title:"Climate Change and Environmental Sustainability",coverUrl:"https://cdn.intechopen.com/series_topics/covers/94.jpg",isOpenForSubmission:!1,editor:null,editorTwo:null,editorThree:null},{id:"95",title:"Urban Planning and Environmental Management",coverUrl:"https://cdn.intechopen.com/series_topics/covers/95.jpg",isOpenForSubmission:!0,editor:{id:"181079",title:"Dr.",name:"Christoph",middleName:null,surname:"Lüthi",slug:"christoph-luthi",fullName:"Christoph Lüthi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRHSqQAO/Profile_Picture_2022-04-12T15:51:33.png",biography:"Dr. Christoph Lüthi is an urban infrastructure planner with over 25 years of experience in planning and design of urban infrastructure in middle and low-income countries. He holds a Master’s Degree in Urban Development Planning from the University College of London (UCL), and a Ph.D. in Urban Planning & Engineering from TU Berlin. He has conducted applied research on urban planning and infrastructure issues in over 20 countries in Africa and Asia. In 2005 he joined Eawag-Sandec as Leader of the Strategic Environmental Sanitation Planning Group. Since 2015 he heads the research department Sanitation, Water and Solid Waste for Development (Sandec) at the Swiss Federal Institute of Aquatic Research and Technology (Eawag).",institutionString:"Swiss Federal Institute of Aquatic Science and Technology, Switzerland",institution:null},editorTwo:{id:"290571",title:"Dr.",name:"Rui Alexandre",middleName:null,surname:"Castanho",slug:"rui-alexandre-castanho",fullName:"Rui Alexandre Castanho",profilePictureURL:"https://mts.intechopen.com/storage/users/290571/images/system/290571.jpg",biography:"Rui Alexandre Castanho has a master\\'s degree in Planning, Audit, and Control in Urban Green Spaces and an international Ph.D. in Sustainable Planning in Borderlands. Currently, he is a professor at WSB University, Poland, and a visiting professor at the University of Johannesburg, South Africa. Dr. Castanho is a post-doc researcher on the GREAT Project, University of Azores, Ponta Delgada, Portugal. He collaborates with the Environmental Resources Analysis Research Group (ARAM), University of Extremadura (UEx), Spain; VALORIZA - Research Center for the Enhancement of Endogenous Resources, Polytechnic Institute of Portalegre (IPP), Portugal; Centre for Tourism Research, Development and Innovation (CITUR), Madeira, Portugal; and AQUAGEO Research Group, University of Campinas (UNICAMP), Brazil.",institutionString:"University of Johannesburg, South Africa and WSB University, Poland",institution:{name:"University of Johannesburg",institutionURL:null,country:{name:"South Africa"}}},editorThree:null}]},overviewPageOFChapters:{paginationCount:11,paginationItems:[{id:"81920",title:"Rethinking an Approach for Sustainable Globalization",doi:"10.5772/intechopen.105141",signatures:"Parakram Pyakurel",slug:"rethinking-an-approach-for-sustainable-globalization",totalDownloads:5,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Globalization and Sustainability - Recent Advances, New Perspectives and Emerging Issues",coverURL:"https://cdn.intechopen.com/books/images_new/11476.jpg",subseries:{id:"91",title:"Sustainable Economy and Fair Society"}}},{id:"81297",title:"Legumes Cropping and Nitrogen Fixation under Mediterranean Climate",doi:"10.5772/intechopen.104473",signatures:"Fernando Teixeira",slug:"legumes-cropping-and-nitrogen-fixation-under-mediterranean-climate",totalDownloads:3,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Food Systems Resilience",coverURL:"https://cdn.intechopen.com/books/images_new/10897.jpg",subseries:{id:"91",title:"Sustainable Economy and Fair Society"}}},{id:"81493",title:"Rust Disease Classification Using Deep Learning Based Algorithm: The Case of Wheat",doi:"10.5772/intechopen.104426",signatures:"Shivani Sood, Harjeet Singh and Suruchi Jindal",slug:"rust-disease-classification-using-deep-learning-based-algorithm-the-case-of-wheat",totalDownloads:42,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Food Systems Resilience",coverURL:"https://cdn.intechopen.com/books/images_new/10897.jpg",subseries:{id:"91",title:"Sustainable Economy and Fair Society"}}},{id:"81428",title:"Observatory of Sustainable Development in Postgraduate Study Programs in Baja California",doi:"10.5772/intechopen.104641",signatures:"Rodolfo Martinez-Gutierrez, Maria Marcela Solis-Quinteros, Maria Esther Ibarra-Estrada and Angel Ernesto Jimenez-Bernardino",slug:"observatory-of-sustainable-development-in-postgraduate-study-programs-in-baja-california",totalDownloads:9,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Globalization and Sustainability - Recent Advances, New Perspectives and Emerging Issues",coverURL:"https://cdn.intechopen.com/books/images_new/11476.jpg",subseries:{id:"91",title:"Sustainable Economy and Fair Society"}}}]},overviewPagePublishedBooks:{paginationCount:0,paginationItems:[]},openForSubmissionBooks:{},onlineFirstChapters:{paginationCount:17,paginationItems:[{id:"81791",title:"Self-Supervised Contrastive Representation Learning in Computer Vision",doi:"10.5772/intechopen.104785",signatures:"Yalin Bastanlar and Semih Orhan",slug:"self-supervised-contrastive-representation-learning-in-computer-vision",totalDownloads:12,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"79345",title:"Application of Jump Diffusion Models in Insurance Claim Estimation",doi:"10.5772/intechopen.99853",signatures:"Leonard Mushunje, Chiedza Elvina Mashiri, Edina Chandiwana and Maxwell Mashasha",slug:"application-of-jump-diffusion-models-in-insurance-claim-estimation-1",totalDownloads:2,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Data Clustering",coverURL:"https://cdn.intechopen.com/books/images_new/10820.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"81557",title:"Object Tracking Using Adapted Optical Flow",doi:"10.5772/intechopen.102863",signatures:"Ronaldo Ferreira, Joaquim José de Castro Ferreira and António José Ribeiro Neves",slug:"object-tracking-using-adapted-optical-flow",totalDownloads:10,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Information Extraction and Object Tracking in Digital Video",coverURL:"https://cdn.intechopen.com/books/images_new/10652.jpg",subseries:{id:"24",title:"Computer Vision"}}},{id:"81558",title:"Thresholding Image Techniques for Plant Segmentation",doi:"10.5772/intechopen.104587",signatures:"Miguel Ángel Castillo-Martínez, Francisco Javier Gallegos-Funes, Blanca E. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. 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