Classification of gross motor function and manual ability in children with cerebral palsy.
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It is caused by a lesion that occurred in the developing brain, usually before birth but also during or after. Cerebral palsy manifests itself early in life, during infancy or preschool years with delayed or aberrant motor progress and it is non-progressive, which means that at the time of the diagnosis, the disturbance that incited the cephalic lesion is no longer active. At the moment there is no cure for cerebral palsy (Bax 2005).
Cerebral Palsy is a condition which affects approximately 2 out of every 1000 newborns. The total number of children with cerebral palsy has remained stable since 1970, but at the same time there has been a consistent rise in the risk of cerebral palsy associated with preterm infants (Thornhill 2009). Since it was first reported by Little in 1861, it has been widely documented and it has attracted research interest.
According to the World Health Organization (WHO), more than one billion people of the world’s population lives with a disability and this number is rising as the population grows, the increase of chronic health conditions, and the life expectancy becomes higher (World report on disability, 2011).
There are 3 possibilities to restore function:
Using remaining muscular pathways as substitute for paralyzed muscles. The use of eye or hand movement can give control over communication devices.
Using EMG, above the level of lesion, as control for paralyzed muscles.
Provide the brain with a new form of communication and control that uses no muscular paths.
Despite the motor limitations of the physically disabled, most of the time the brain activity remains intact. Brain-Computer Interfaces (BCI) are communication devices that translate signals from the brain or nervous system (e.g. Electroencephalogram (EEG)) into electrical signals For the control of devices, allowing people to regain some form of control and regain interaction with the environment (Rao 2010, Wolpaw 2002). Even though, BCIs where initially developed as assistive devices for people with severe neuromuscular disorders, such as brain or spinal cord injury, cerebral palsy, muscular dystrophies, multiple sclerosis, and numerous other conditions; the increasing interest in non-medical application looking for an improved technology for Human-Computer Interaction (HCI) such as exoskeletons, robot or wheelchair control, or augmented reality (Lotte 2013), has generated clinical, scientific and commercial interest in the use of BCI’s for an augmentative communication and control technology.
Despite, that BCIs have shown their possibility as communication and control device through spelling devices (Donchin 2000), used as control of prosthesis (Tenore 2008), web browsing (Mugler 2010), for control in a virtual reality environment (Lotte 2009) and for entertainment (Rao 2010), there are still more possible applications and room for improvement, using combined technology (e.g. Hybrid BCIs), improving or creating new classification algorithms, and better recording technology. In this chapter we give a brief description of the recording technology, pattern selection, current classification algorithms used for BCI and the state of the art as well as future technology.
Basic components of a BCI. The image illustrates the map between the input and output through the translating algorithm. Signals are acquired by electrodes and then translated into a control signal for an external device (e.g. wheelchair, neuroprosthesis or exoskeleton) using a sequence of processing steps.
The diagnosis of CP is made largely through clinical observations. The natal history is of vital importance for the identification of reasons for concern and the determination of the cases which merit closer monitoring. Failure to meet gross motor milestones is often the initial concern of parents. Significantly delayed motor milestones, persistence of primitive reflexes, and abnormal postural reactions are additional reasons for concern and referral to a neurologist or expert in neurodevelopment for evaluation. Clusters of symptoms or evolving abnormal movement patterns may be indications of CP and thus should be explored further with diagnostic instruments.
Instruments like the Hammersmith Neurological Examination (Dubowitz 1999), the Amiel Tison Neurological Assessment (Tison 2002) or the INFANIB (Infant Neurological International Battery for the Assessment of Neurological Integrity in Infancy) (Ellison 1994) have proven extremely valuable in the earlier identification of the difficulties that at-risk neonates and, as a result, a better targeted, early intervention.
These instruments offer a neurological or neuromotor exploration of the neonate and the infant, assessing the existence of primitive reflexes, the automatic system or any other involuntary movements, that appear in normal infants and should be integrated by the 9th month of life. Their persistence past that age is a reflection of abnormalities in terms of control in the central nervous system and may indicate cerebral palsy. The persistence of primitive reflexes causes changes in muscle tone and the position of limbs, which makes it interfere with the development of voluntary motor movements by causing changes in muscle tone and the position of the limbs. Failure to develop protective reflexes such as the parachute response or an asymmetrical response is also taken into consideration.
\n\t\t\t\tThe instruments also take into consideration the age when the infant met the motor milestones (head control, sitting, voluntary grasp, ability to kick, rolling, crawling, standing and walking) and may include some items of attention, sensory function or self regulation, as well as muscle tone and posture are considered.
Cerebral Palsy is a symptom complex with various types and degrees of motor impairment. Depending on the area of the brain that has been affected, according to the SCPE (Elison P 2004, SCPE working group 2002) we may identify the predominant motor characteristics of the condition as of three types: Spastic, dyskinetic and ataxic.
Spastic CP results from defects or damage occurring in the brain\'s corticospinal pathways, also described as upper motor neuron damage. Spastic CP accounts for almost 84% of all cases of CP, with cognitive impairments seen in approximately 30% of the cases with CP (SCPE working group 2000, Palisano 1997). Although increased , as well as muscle tone is the predominant feature observed, hyperreflexia, clonus, extensor Babinski response, and persistent primitive reflexes are commonly seen.
Dyskinetic and ataxic CP are caused by damage to nerve cells outside of the pyramidal tracts in the basal ganglia or the cerebellum. Dyskinetic CP is then further divided into athetoid and dystonic. It accounts for 15% to 20% of all cases of CP, with dyskinetic accounting for 10% to 15% and ataxic approximately 5%. The resulting disability is global with abnormal tone regulation, postural control, and coordination (SCPE Working Group, Palisano 1997)
It is actually quite common to see many different combinations of types of CP, since this depends on the area of brain damage; the types overlap very frequently, which can make it very difficult to precisely label the resulting disability within the typical subtypes. As a result, when not one type dominates we make reference to a “mixed” category.
What is of particular interest for the parents of the children affected is evaluating the functional consequences of the condition. The Gross Motor Function Classification System (GMFCS) (Eliasson 2006) was developed as an evaluation tool in order to offer a prognosis or to assess differences in motor functions after an intervention. It recognizes motor function as dependent on age due to the expected change of the developing child. It separates clusters of periods (0-2, 2-4, 4-6 and 6-12 years of age).
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Level I | \n\t\t\tChild´s ability to walk is not affected | \n\t\t\tChild handles objects easily and successfully | \n\t\t
Level II | \n\t\t\tChild´s ability to walk is slightly affected | \n\t\t\tChild handles objects with somewhat reduced quality | \n\t\t
Level III | \n\t\t\tChild walks with assistive device | \n\t\t\tChild handles objects with difficulty | \n\t\t
Level IV | \n\t\t\tLimited self-mobility with assistive device | \n\t\t\tChild handles only a few, adapted objects | \n\t\t
Level V | \n\t\t\tNo self-mobility | \n\t\t\tChild cannot handle objects | \n\t\t
Classification of gross motor function and manual ability in children with cerebral palsy.
In this chapter we have cited the classification of a child´s gross motor function between 6 and 12 years of age, which is divided into five levels, based on functional mobility or activity limitation. Particular emphasis is made on the function of sitting and walking. Children in level I are the most independent (motor function) and children in Level V are the least according to the
The Manual Ability Classification System for Children with Cerebral Palsy (MACS) (Bottcher 2010) is widely used to evaluate and classify how children with cerebral palsy use their hands to handle objects in daily activities. Like the Gross Motor Function Classification System, MACS describes five levels. The levels are based on the children’s self-initiated ability to handle objects and their need for assistance or adaptation to perform manual activities in everyday life.
A child with cerebral palsy often has other conditions related to developmental brain abnormalities, such as intellectual disabilities. Almost 50% of children with CP have an average intelligence, 20% have an intelligence slightly lower than average (borderline intelligence). The rest 30% its not mentioned if its more inteligent or not than average. Most patients that have spastic tetraparetic, discinetic and ataxic have a severe mental discapacity (SCPE Working Group 2002)
There have been studies that prove that children with CP with average intelligence have attentional deficits or problems with the executive functions, which may partially account for the behavioral problems that sometimes present. (Guzzetta 2001) They might have deficits in visioperceptive functioning. The child has difficulties recognizing the spatial relations between objects, as well as between objects and his own body. This results frequently in a constructive dyspraxia. The saccadic movement of the eye to focus on an object that appears periferically at the previous point of focus are slow and dyspraxic, which constitutes an added difficulty in order to achieve the perceptive integration. The proprioceptive-visual integration of the parietal lobe is necessary in order to orient the movements and postures of the upper limbs to reach for and manipulate the surrounding objects and starting the proceeding automatic movement that experience and repetition offers. These deficits are completely independent from the vision problems that may coexist (Guzzetta 2001).
Language problems are also common and their severity depends on the timing that the lesion took place, in the prelinguistic period or later, when the linguistic function has already started to form.
Although there are many compelling reasons to give the diagnosis as early as possible (parents frustration of handling a child with abnormal tone such as feeding, sleep, and temperament problems, plan in advance for long-term treatments and management options that may be needed by the child, possible increased insurance benefits and in some cases federal assistance, benefits that come from an early intervention) the diagnosis should not be formally made until the second year of age. For the SCPE in Europe minimum age of 4 years old is required to make a diagnosis so that transitory alterations of neurodevelopment or degenerative diseases may not be confused with CP (SCPE Working Group 2002).
The diagnosis has an impact on the life of the family and, of course, the child. The major issue of concern is, for most parents, walking. Once confronted with the diagnosis, the first question that parents ask their child´s health care specialist if the child is going to walk. Children will CP will experience some degree of difficulty with movement. This can range from problems like clumsiness that does not disrupt everyday life activities all the way to difficulties with walking. The child may move slowly, may need to use a walking aid or a wheelchair.
Simple activities like dressing, bathing, eating can be a real challenge to the child with CP and their family. The activities can take longer, especially if the child needs more assistance, physical help or specialized equipment.
Language problems are common among children with CP. Children may have difficulties with both verbal and non verbal aspects of language. The expression and understanding of the formal aspects of language can be affected (for example articulation or denomination) which may eventually lead to problems with reading and writing or even interfere with the child´s ability to communicate verbally. The other aspect of language that can be impaired is pragmatics, which refers to the ability to place words in the context of one´s own mind and the interlocutor´s, which creates problems in the child´s social adjustment.
The aim is for every child with CP to achieve their potential. Depending on the child´s individual characteristics decisions must be taken that will determine whether he would benefit more from mainstream placement in a school or from a placement in a more specialized environment that could tend to his needs.
In order for the interface to be able to read the brain signal, the child needs to be focused. Not always is it possible for all children to emit a signal strong enough so that it can be captured by the interface. The emission of a strong signal depends on the attention of the child which can be negatively affected by a variety of factors which have no relation with the interface but which affect its ability to read the brain signals.
The attention of the child can be hindered by three main factors which are at constant interplay and affect the prefrontal cortex and the ability of the child to focus on a particular task. The three main factors are:
Cognitive
Emotional
Behavioral
Although the cognitive function of children with CP has not been systematically studied, and more research is needed, there is evidence suggesting that children with CP and normal intelligence present impairments in executive functions. Executive functions are the brain functions that regulate and control impulse, anticipate consequences, put attention, regulate emotion, allow flexibility, plan and monitor results. Executive functions are highly fragile because they are the last cognitive area to mature. They involve the prefrontal cortex and they rely on an extensive interconnectivity with other parts of the brain. Damage to that area results in slower information processing, and a decrementation in sustained attention performance, which is necessary for the reading of the signal by the interface (Guzzetta 2001).
In the case of an intellectual disability, which as we have seen affects almost half of the children with CP, we cannot speak of attention problems. The degree of cognitive impairment is such that the attention processes cannot reach the required level so that it can be captured by the interface.
The attentional processes are also going to be affected by the emotional problems that the child may be experiencing (Parkes 2008). This is also an issue that has not been researched but there is enough evidence to suggest that children with CP, like children with some sort of a disability in general, are more likely to suffer from depression, anxiety and low self esteem. This is associated with the severity and visibility of the condition, which affects the child´s ability to control his own body and the way his peers may perceive him as being different from them. (McDermott 1996) The lack of social support, the anxiety of the parents, the child´s inability to use words to express his emotions are all factors that put the child at increased risk to experience emotional problems. Emotional problems hinder the ability of a child to focus and pay enough attention so as to send a strong signal to the interface.
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Children with CP have behavioral problems like being defiant and disobedient. The behavior problems reported by parents were 5 times more likely in children with cerebral palsy compared with children having no known health problems. Behavioral problem are associated with some kind of combination of the impairment, the environment and interpersonal relationships. Damage to the prefrontal cortex affects, as we have seen, cognitive flexibility, the abilities for strategic planning, tolerance to frustration, behavioral inhibition (hyperactivity-impulsivity) as well as the associated impairment of inattention. The child that has trouble maintaining his attention on the signal is more likely to refuse to try or abandon the task.
It is important to mention epilepsy as one of the factors that cause behavior and attention difficulties in children with cerebral palsy.
Epilepsy affects 7 to 50% children with cerebral palsy. Epilepsy, in itself, takes away part of the vitality of the brain, with the frequent crises affecting the cognitive abilities of the child. Behavior and attention difficulties are highly common in children with cerebral palsy who have epilepsy. Furthermore, the crises are frequently a motive for the child to stop receiving education. Medical treatment for epilepsy can be helpful, keeping in mind that although antiepileptic drugs may impair the cognitive functions of the child, with the careful monitoring of the physician and the new medical intervention, this side effect would be very infrequent.
There are some limits that can be solved using the brain activity. This activity allows the communication between the processor and the person.
Through the recording and processing of direct brain electrical activity via signal processing and machine learning algorithms, BCIs enables communication and control to assistive devices. Although the aim of a BCI is to identify and translate brain electrical signals into commands, it is not a thought-reading device or systems able to literally translate arbitrary cognitive activities. BCIs are design for translation of well characterized a priori defined brain activity patterns through the use of machine learning techniques and patterns recognition methods into commands.
Considered as a control system, a BCI has an input (e.g. EEG), an output (e.g. control signal), and components that translate input into output, a protocol that determines the timing operation and in some cases some feedback is provided to the user (Figure 1.).
Exemplification on EEG (a), ECoG (b) and Single-neuron recording (c) electrode placement over the head
Invasively or noninvasively brain activity is recorded either from recording electrical activity through electrodes (EEG, Electrocorticography (ECoG) or from single-neuron recordings within the brain), recording magnetic fields using magnetoencephalography (MEG)), or recording metabolic activity reflected in changes in blood flow (positron emission tomography (PET), functional magnetic resonance imaging (fMRI) and functional Near Infrared (fNIR)). Despite the fact that MEG, PET, fMRI and fNIR have shown success for BCI applications these techniques are still technically demanding and expensive technologies that require sophisticated equipment that can be operated only in special facilities. Furthermore, PET, fMRI and fNIR techniques depend on metabolic processes, such as blood flow, having long latencies and thus less suitable for the control of BCIs.
On the other hand, the non-invasive EEG and the invasive ECoG and single neuron recordings (Figure 2.), are methods that have relative low costs, are simpler to use and have higher temporal resolutions, making them more practical to the use with BCIs.
Invasive techniques such single-neuron recording and ECoG take recordings over the cortex; while single-neuron recording records the activity within the cortex, ECoG records the activity over the cortical surface of the brain. Single-neuron recordings and ECoG does not record single neuron activity but records activities over small regions of the brain giving them a high spatial resolution, and as it is implanted directly over the cortex, they have a high bandwidth, high SNR and high amplitude. Since ECoG electrodes do not penetrate the cortex, recorded signals are also not subjected as heavily to immune response, possess lower risk to implant as well. Furthermore, maintaining long term reliable recording with implantable electrodes is difficult.
Although, ECoG has a higher spatial resolution compared to EEG (i.e. 1.25 - 1.4mm vs centimeters) higher frequency bandwidth ([19, 11] (i.e. 0−500Hz vs. 0−40Hz), have higher signal amplitude (50 − 100µV maximum vs. 10 − 20µV maximum), and being less susceptible to artifacts (i.e. EMG, EOG or electrical devices), EEG has become the most common source for brain activity due to its none invasiveness (requiring no craniotomy (surgical incision of the skull)), being more practical for everyday situations. EEG measures the potential over the scalp, reflecting the collective activity over large population of neurons located underneath the sensor position.
Brain signal recordings, like EEG or ECoG, are obtained with electrodes attach from the surface of the skull or to the surface of the brain measuring difference over the potential that reflect the activity within the brain. The electrodes are connected to biosignal amplifier where they are amplified and go through an analog-digital conversion. These signals are sent to the signal processing system that is in charge to perform the feature extraction and classification. Finally, a signal will be send to the control system as final output. The BCI can be design to present feedback that is beneficial to learn the BCI control faster.
The electrodes measure a difference in potential (i.e. the voltage) between two electrodes. The difference in potential reflects neural activity below the electrode. There are different EEG electrode montages. Usual EEG recordings use unipolar montage rather than bipolar electrodes, meaning that they use a common reference for all electrodes. A ground is added to keep the voltage levels close to the amplifier ground voltage level. The reference and ground can be positioned everywhere within the array of electrodes, but they are normally placed either over the ear or the mastoids (the temporal bone behind the ear). There also exist a bipolar and Laplacian montage that each electrode represents the difference between the electrode and its surrounding electrodes (see Figure 3).
EEG recordings electrodes usually use small metal plates made out of gold or Ag/AgCl. Alternative materials such as Tin have been used, but they present drifting noise below 1HZ, making them unsuitable for some applications, such as Slow Cortical Potentials. The electrodes could be either passive or active (i.e. pre-amplified with gain 1-10) disks that are connected through a cable to the biosignal amplifier. Active electrodes are less susceptible to environmental noise, and can work with higher skin impedance than passive electrodes. There exist also dry and wet electrodes. As the dry electrodes normally use an array of pins to go through the hair have contact with the skin, the wet electrodes use a gel that reduces the impedance and make a better connection to the skin. Even though dry electrodes have the advantage that require less preparation and cleaning time (not requiring conductive gel) and They are proven to be an alternative for EEG recordings (Zander 2002), More in-depth research is necessary for their successful dailybased application. daily-base application.
Examples of non-invasive BCI, with visual stimuli and virtual control of ball movement
The standard EEG electrodes naming and position on the scalp are according to the international 10-20 electrode system (Jasper 1958). The system ensures that different laboratories share the same names over electrode positioning. It is based on arcs dividing the scalp in an array, using the Nasion and Inion as longitudinal reference points (i.e. front and back respectively) and the left and right Pre-Auricular points as lateral reference points (Figure 4a.). The intersection between the longitudinal line and the lateral is called the Vertex, and at this point it is located over the center. The 10-20 system identify each point using each lobe (Frontal-F, Temporal-T, Central-C, Parietal-P and Occipital-O) and each hemisphere (Left-Odd, Right-Even numbers and z or zero over the midline) as a marking. An extension to this configuration is using 70 electrodes (Figure 4b.), subdividing in between the 10-20 arcs (using the combinations of the letters for reference). In addition, the letters A and Fp are used to identify the earlobes and frontal polar sites respectively There is also the AF marking the subdivision in between the Frontal and Frontal polar site
Currently electrode caps are mainly intended to be used in a laboratory environment, therefore being expensive, the electrode and cap placement is difficult and need special preparation, making them not practical for an everyday use. Current commercial caps, such as the Emotive Epoc, they present an alternative for an everyday use being more economically accessible, they have the drawback that they use wet electrodes that dry quickly, and they are difficult to position and remain stable, which is problematic as it gives a high variance over recordings, as well as the same recording, and they are not possible to use in a different configuration. For the remaining of this section we will only focus on laboratory caps and EEG recording, as these are the most commonly used for BCI.
\n\t\t\t\t\tEEG Electrode Montage (
Since EEG is further away from the neurons it has low spatial resolution and very noisy overview of ongoing brain activity. There are mainly two sources of noise while performing EEG recordings: Environment and Physiological artifacts.
To design a BCI, we need to decide on the type of signal, the location, the desired feature and the appropriate classification technique.. In this section a description of the different types of signals, the different types of feature extraction that has been used, and finally a brief description of the different machine learning algorithms available is presented.
The two major types of EEG signals used in BCI are Evoked Potentials (EPs) and changes in the spontaneous oscillatory EEG activity, also known as event-related desynchronization (ERD), and event-related synchronization (ERS) (Pfurtscheller 1999(2)).
EPs are electrical potential shifts that are time-locked to perceptual events, such as a rare visual or audio stimulus. Time-locked implying here that the time between the event and the time potential shift is approximately constant. Due to its low Signal-to-Noise Ratio (SNR) are typically analyzed by averaging EEG data over time beginning of the perceptual event for duration over 1s. There are different types of EPs based on the source of stimulus (e.g. visual, auditory or tactile)
On the other hand, oscillatory activity can be voluntary induced by the user (e.g. imagination of kinesthetic body movement, aka motor imagery, Neuper 2005). Such imagery usually generates a decrease or increase in power in a particular frequency band (ERD or ERS (ERDS) respectively). ERS are normally associated with an ERD appearing either after the termination of the movement or simultaneously to the ERD, but in other areas of the cortex. Although, Oscillatory patterns detection is less robust and reliable compare to EP, which as synchronous signal (i.e. knowing its time and shape) requires little adaptation and its detection is robust, as an asynchronous BCI it allows the user to send information at their own pace, unlike synchronous BCIs that require to follow the cues or prompts from the system.
Figure 5 illustrates the use of EP and ERDS for achieving brain-computer interaction in physical and virtual environment.
Examples of evoke potentials with either lights or screen squares flickering at different frequencies
Within these two ways of brain signal extraction there are four main strategies to consider for input at a BCI system. extraction there exist mainly 4 common strategies are considered for input of a BCI system, a) Motor imaginary. b) Slow Cortical Potentials, c) the P300 wave of visual evoke potentials, and d) Steady State Visual Evoked Potential (SSVEP)
It is believed that the mechanisms of brain operations are characterized by groups of neurons synchronizing themselves to a certain physiological frequency (Engel 2001). These oscillations has been divided into different frequency bands and are referred as brain rhythms (Delta [0.1 − 4Hz], Theta [4 − 7Hz], Alpha [8 − 12Hz], Mu [8 − 13Hz] Although the Alpha and Mu rhythm occur over the same frequency, one is located over the resting visual cortex at the back of the scalp, while the second is found over the motor cortex
Slow cortical potentials (SCPs) are slow voltage changes generated over the cortex. These changes in potential occur over 0.5−10s. These potentials can be divided in Negative SCPs, typically associated with movement and other typical cortical activation, and Positive SCPs that is associated with a reduce cortical activation, the viability of the use of SCPs that after a period of learning user has gain control selecting words or pictograms from a computerized language (Birbaumer 2000) or used with patients Suffering from a locked-in condition such as amyotrophic lateral sclerosis (ALS) (Kübler 1999). The drawback of using SCPs is that It requires a long training process that allows the user to gain control, a normal training can go for several weeks or even months. The normal training for a SCP based BCI users first learn to move a cursor vertically on a monitor selecting targets at the top or the bottom of the screen. Next, a split keyboard into two where an area is selected, the selected characters are once more split into two and once more selected, and this is done until the final choice is made.
Example of different types of normal EEG rhythms (Lotte 2009) and Primary Motor and somatosensory cortical homunculus
The P300 (P3) wave of visual evoke potentials (henceforth referred only as P300) is a positive wave that appears 300ms after a stimulus is presented (Figure 7a.). It was first described by Sutton (Sutton 1965). The most known paradigm for P300 is the one described by Farwell-Donchin 1988, where characters and numbers are represented in a six by six grid (Figure 7b.) where the rows and columns are repeatedly flashed and when the character containing the chosen character is flashed a P300 is evoked. These characters (number or letters) can be replaced with different symbols and used not only as spelling device, but for navigation or for control tasks, using symbols as arrows or object selection. P300 can be used as a lie detector, providing certain stimuli (e.g. picture, phrase or word related to the lie) that P300 is generated if the subject has knowledge of the stimuli presented (Farwell 2001, Farwell 2012).
As a normal procedure, the P300 of different repetitions are averaged, to reduce the effect of artifacts, or the presence of different mental activity that could masked the ongoing P300. Different features and classification techniques (e.g. Linear Discriminant Analysis or Supported vector machines, Krusienski 2006) has been used for P300 based systems, these techniques will be described in a following section.
P300 wave and the classical P300 spelling paradigm described by
Steady State Visual Evoked Potential (SSVEP) are brain responses to visual stimulus (e.g. flickering LEDs or phase-reversing checkerboards), flashing at constant frequencies between approximately 6 − 100Hz. SSVEP is a frequency-locked signal that manifests itself as an increase of the EEG amplitude of the stimulated frequency over the occipital lobe. Classification of SSVEP is done either using FFT-spectrum analysis or by the use of canonical correlation analysis (CCA) or finally by using of the minimal energy approach.
SSVEP have shown to be independent to eye movement, making them a good alternative for people with well preserve eye acuity but are incapable to moving their eyes (Brendan 2008). Some drawbacks while using SSVEP is that if a computer screen is used only frequencies that entire division over its base refresh rate (e.g. a 60Hz screen only 30, 20, 15, 12, 10 or lower are possible). A second drawback is that SSVEP are usually developed with a short number of flickering channels trying to avoid distraction and hence lower performance.
Even though the amount of electrodes, the number of tasks performed, the high sample frequency required, the classes and the different patterns make that the amount of data recorded large, the normal training data set is short. Identifying, Selecting and extracting the relevant properties or features of the signals that better describe the EEG signals are essential steps in the design of a BCI. The correct selection of the features is crucial, if the features extracted from EEG are not relevant and do not accurately describe the EEG signals employed, the classification algorithm will have trouble selecting the class or label the user intended. The feature extraction could be divided in two main groups: temporal and frequential methods, a third group can be added as hybrid between temporal and frequency techniques.
Features that present a time dependent variation can be treated using a temporal method. The changes can be as the ones that occur on P300 wave, which depend of the flashing of the selected command to 300ms later to be generated. The main temporal methods are the parametric models (e.g. AR or AAR) modeled the signal using a weighted sum of values, the Hjorth parameters that describe the dynamics of the signal by the use of three measures (activity, mobility and complexity) and finally the signal amplitude method that concatenates the electrodes amplitude into a feature vector that is used as input into the classification algorithm.
The different oscillations or rhythms that characterize the EEG signals present variations while performing a mental task (e.g. motor imagery) or with a steady state evoked potential that a change in the oscillation is highly related to the stimulus frequency. Frequency methods are commonly used for the ease of application and computational speed. The most commonly used methods are power spectral densities and band powers. The third method uses a feature that can be located both in time and frequency domain. This method uses the Short Time Fourier Transform or the Wavelet transform to have a time-frequency representation of the signal.
After the features have been selected the next step is to translate them into a command. This translation can use regression/classification methods. There are different classification methods and they can be divided using their classifier properties into: Linear classifiers, neural networks, non-linear Bayesian classifiers, nearest neighbor classifiers and combinations of classifiers (Lotte 2007).
Linear classifiers are discriminant algorithms that use linear functions to separate between classes. The most common used for BCI are Linear Discriminant Analysis (also known as Fisher’s LDA) and supported vector machines. These two methods separate the data using hyperplanes, for two-classes they are divided depending on the side of the hyperplane they are located (see Figure 8.). For LDA and SVM the popular method to solve a multiclass situation (N-number of classes) is selecting a class and separating it from the rest, this technique is referred “One Versus the Rest” (Schlögl 2005). This technique is very computational efficient and suitable for online classification. One drawback of LDA is when it deals with complex nonlinear EEG data (Garcia 2003). Even though SVM is originally linearly, it can be expanded using the “kernel trick”. The trick consists of mapping the data into another space, using a kernel function. For BCI usually the Gaussian or radial basis function K(x,y)=exp[−||x−y||/ (2σ 2)]. This trick gives a better generalization, but has lower speed execution (Lotte 2007).
LDA and SVM hyperplanes that separate between two classes (circles and crosses).
LDA has been used for motor imagery (Pfurtscheller 1999), for a multiclass asynchronous motor imagery (Scherer 2004), as well as for P300 (Congedo 2006).
The second most used for classification method for BCI is using Neural Networks (NN). NN are non-linear classifiers that use assembly of neurons to produce the boundaries. The most used technique is the Multilayer Perceptron (Bishop 1995), that uses an input layer where the features are inserted, some hidden layers for processing and finally an output that defines the class (Figure 9.). Even though NNs can adapt to any number of classes and composed with enough neurons they can approximate any function, they are susceptible to over training and noise (Bishop 1995).
A conventional feed-forward artificial neural network (ANN´s) is a system constructed by a finite number of basic elements called neurons, which are grouped in layers. Every neuron is highly interconnected in the whole topology; the structure has a number of inputs and outputs that depends on the system that will be approximated.
A neuron is the basic element in an artificial neural network that simulates biological neurons which receives electrical impulses which are received through its dendrites, from other neuron’s axons. Those electrical impulses are added in order to have a final potential. This potential must exceed a certain level to have the neuron generate an electrical impulse on its axon. If the level required is not met, then the axon of that neuron doesn’t fire its axon. Neurons can be divided as: dendrites which are channels of input signals, core cell that processes all these signals and axons that transmit output signals of the processed information came from dendrites.
The ANN´s are applied to approximate normally a non-linear system as universal approximations. The first step to design an ANN´s is to train the neural network in order to fix the interconnection namely weights which are between the neurons. The training process can take a lot of time in the case of the back-propagation algorithm. After training the ANN´s the response could show a high-quality behavior, when a new input signals is presented to the system.
In other words the ANN´s could generalize any input signal. These ANN´s mimic the human brain, on the basic process of learning and generalization. Normally the process of training the ANN´s is slow and defining the correct topology could be complicated. The main advantages of artificial neural networks are:
Ability to generalize and learn.
Acquire knowledge from internal and external parameters.
Ability to learn from examples and adapt to situations based on its findings.
Generalization of knowledge to Production of adequate responses to unknown situations.
Artificial neural networks solve complex problems that are difficult to manage by approximation.
Produce linear or non-linear relationships
Fault Tolerance
An extension to this technique is the Restricted Boltzmann Machines (RBM) that have a bidirectional connection between the layers (see Figure 9b.), this quality allows the RBMs to be train as normal NN and retrained using back propagation (Hinton 1986). Success of NN can be seen in (Kalcer 1993, Pfurtscheller 1996,
Hsu 2012) while for RBM in (Balderas 2011).There are mainly types Bayesian classifiers used for BCI systems: Bayes quadratic and Hidden Markov Models (HMM). Both these classifiers produce nonlinear decision boundaries. Furthermore, they are generative, which allows them to reject uncertain samples more efficiently than discriminative classifiers (Lotte 2007). While Bayesian assign the class to the feature vector with the highest probability, HMM is probabilistic automaton that can provide the probability of observing a given sequence of feature vectors (Rabiner 1989, Lotte 2007).
Neural Networks architectures having Multilayer Perceptron and RBM
Nearest Neighbor classifiers are also used in BCIs with the k Nearest Neighbor (kNN) and Mahalanobis Distance (MDist) as preferred Nearest Neighbor classifier methods. kNN assign to an unseen point the dominant class among its kNN within the training set. kNN algorithms are sensitive to the curse of dimensionality making them fail in several BCI experiments, however they may perform efficiently with low-dimensional feature vectors (Lotte 2007). Mahalanobis Dististance based classifiers use Mahalanobis distance to assign a class to a feature vector to the nearest prototype. Mahalanobis Distance has been used to detect motor imagery of the hand giving accuracies over the 80% (Ming 2009).
Combinations of classifiers are proposed trying to reduce the variance and thus increase classification accuracy. Voting, Boosting, Stacking and Random subspaces. Voting consists of assign different classifiers the input feature vector and select the class with the higher majority of votes (hence the name). Boosting uses several classifiers in cascade where the errors committed by previous classifier are focus by each classifier. Stacking uses several classifiers (level-0 classifiers) running through the input vector. The output of the different classifiers is then use as input vector for a meta-classifier (or level-1 classifier) which is responsible for the final decision. Lastly Random subspaces uses subsets of the original feature vector as training set for different classifiers and the final decision is made by majority voting.
The design of a BCI comes with two major challenges, the non-stationary and inherent variability of the EEG signals. Data from the same experimental paradigm but recorded at different instances are likely to exhibit differences due to; for instance; shift of the electrodes positions between sessions or changes in the sensor mechanical properties of the electrodes (e.g. change in the impedances). Adding to this problem the noisy nonlinear superposition of the measured EEG activity can mask underlying neural patterns and hamper their detection. The user current mentally state (e.g. due to tiredness, workload or stress) may impact in the ability to focus and generate specific mental events. Due to these factors, statistical signal processing and machine learning techniques play a crucial role in recognizing EEG patterns and translating them into control signals.
A normal training of a BCI uses information from a first or previous sessions EEG recordings are used to pre-train the pattern recognition algorithms for classification or regression. On a posterior session user uses the trained algorithm for control. One of the drawbacks is that the variability of brain activity requires that the system is robust enough to handle the changes. Adding to this, the high adaptability of the brain gives the problem on how much has to be relegated for the system and how much left for the brain. It has been shown that using invasive over single neuron or a population of neurons the can rapidly learn to generate an appropriate pattern for a fix task. The same adaptation using EEG could take months to have a similar level of performance (Kübler 1999). Adding to this normal neuromuscular activity depends of feedback to have a successful control. A strategy to improve the control over the BCI system has to have a control that uses feedback. A good strategy is to use co-adaptive training, with a self-optimizing pattern detector and user adaptation, using new data to update the system. So new data is collected in different session and used to update the classifier to user’s most recent brain patterns. Feedback can be provided during the new session to helping to generate more distinct EEG patterns, which increases detection performance. An Online adaptation can be included to provide a faster update of the training parameters and have a faster co-adaptation.
There exists almost no reason why different technology could be combined, combining different patterns (e.g. EP and motor imaging) or different recording technology, combining invasive and non-invasive, or using different electrophysiological signals with the combination of BCI technologies.
A case study was made using a commercial HCI, the amplifier Emotiv EPOC, which can record EEG signals as well as movement from the head with an incorporated gyroscope. The interface was created using the amplifier gyroscope signals as control for the displacement and the direction for an electric wheelchair (see Figure 10.).
Example of the control with the movements of the head, translated with a gyroscope into the control of an electric wheelchair.
The gyroscope counts with two rotation axes that were used for displacement and turn. Also the velocity of displacement and turn was control depending on the amount of rotation the gyroscopes detect from the movement of the head.
The wheelchair counts with the displacement control of the two back wheels, giving it advance and turn control. This control was adapted to be controlled directly from a DAQ that has a direct interface with Labview.
The case study was divided in three areas: Signal acquisition, Signal processing, and control signal (Figure 11.). For the signal acquisition we use the amplifier driver connected with Simulink (Matlab). Both signals were filtered and amplified using the rotation left-right for the turn and the rotation frontal-backward for forward or backward motion. The online process was done in the same interface that was used for recording in Simulink (Figure 12a.), which finally send the signals to Labview using a UDP protocol. Labview was finally in charge of the control signal (Figure 12b.) that had control over the wheelchair wheels.
Control Process
Matlab-Simulink and Labview interfaces
The control was first tested on a free environment and later on a simple maze (Figure 13.). Testing the manageability to make turns and understand the commands.
Interfaces de Matlab-Simulink y Labview
Even though the control with the Emotiv EPOC was limited, the viability of developing an HCI was shown using the gyroscope signals as control signals for a wheelchair.
Entangled photon pairs are one of the key elements for research and in emerging quantum applications with successful results in quantum foundations [1, 2], quantum communication [3, 4, 5], and quantum information [6, 7, 8]. Thus far, nonlinear crystals exhibiting spontaneous parametric down-conversion (SPDC) [9, 10, 11] have been the main source of generating entangled photon pairs for use in these areas. This type of source results in photon pairs that exhibit near-unity entanglement fidelity, high degrees of single-photon purity and indistinguishability in each emission mode, and high temporal correlation. Moreover, these sources perform at or near room temperature. However, there are fundamental limitations to such sources, which limit their performance and scalability for use in quantum photonics; an ideal source is imperative for optimal performance. One key feature of an ideal source of entangled photons is the ability to perform on-demand, i.e., source triggering and extraction of light must be possible with near-unity efficiency. SPDC sources follow a stochastic process and therefore generate entangled photon pairs at random. Moreover, the probability of multiphoton generation follows a Poisson distribution, and thus entanglement fidelity, single-photon purity, and photon indistinguishability [12] degrade when the pump power is increased [13]. As a result, these sources only operate at extremely low pair-production efficiencies,
Semiconductor quantum dots [15] are capable of generating pairs of entangled photons based on a process called the biexciton (
The XX-X cascade. In the
Over the past three decades, QDs have been extensively studied with recent advancements, as compared to other solid state quantum emitters [18, 19, 20, 21], and have produced sources which exhibit features closest to an ideal photon source [22]. The first generation of QDs was self-assembled [23, 24, 25], which resulted in QDs with various sizes and imperfect symmetry due to the random nature of the formation process [13]. Moreover, since the bulk semiconductor material possessed a high refractive index, these self-assembled QDs typically suffered from isotropic emission and total internal reflection at the semiconductor-air interface and thus exhibited a low light-extraction efficiency of
Recent developments in micro- and nanoscale crystal growth and fabrication have resulted in structures which have improved the performance of QDs considerably. Enhancement of the spontaneous emission of QDs was first achieved by coupling an ensemble of QDs [27], and later a single QD, to a micro-cavity [28]. More recently, the coupling of QDs to micro-pillar cavities has achieved light-extraction efficiencies as high as 80% [29]. Also, such structures allow for proper control of the charge noise around the QD and thus the suppression of detrimental dephasing processes from the moving charge carriers. Excitingly, as a result, photons with
However, such performance comes at a price. Due to Coulomb interactions [17],
Another important feature of QDs affecting the measured entanglement is the fine-structure splitting (FSS) of the
XX-X cascade in the presence of FSS. (a)
where
Due to the random nature of the growth process, self-assembled QDs have long suffered from large base asymmetries, which resulted in FSS values larger than the
To reveal the true potential of QDs, proper excitation schemes are needed in addition to engineering sophisticated photonic structures. Until recently, off-resonant excitation had been widely used to generate entangled and single photons from QDs in photonic structures. This scheme excites charge carriers to energy levels above the bandgap of the host semiconductor, and relaxation of the resulted
Direct population of
Schematics of resonant TPE. A linearly polarized pulse is tuned to a virtual state halfway between
In this review, we focus on attempts to improve the performance of entangled photon generation in by embedding them in photonic nanowires, as well as the effects of different excitation schemes in the performance of such sources. Additionally, we will also cover the improvements achieved in photon extraction efficiency, reduction of the dephasing processes, suppression of multiphoton emission, and enhancing entanglement fidelity of nanowire QD based entangled photon sources.
Embedding QDs in tapered nanowires was initially developed by using top-down approaches via reactive-ion etching [55, 56]. Such photonic structures allow for coupling of the QD emission to the waveguide’s fundamental mode in a broad range of wavelengths,
A novel bottom-up approach to growing tapered nanowires was used in the work by Reimer et al. [57]. This innovative approach allowed, for the first time, the positioning of a QD on the symmetry axis of the nanowire and at a desired height with a precision of ∼100 nm (Figure 4). In this method, the growth of the nanowire core, InP, is initiated by a gold particle which defines the core of the nanowire and ultimately the size of the QD,
Schematic of the bottom-up nanowire growth process and SEM image of a tapered nanowire (right). The growth process is initiated by a gold particle, which defines the dimensions of the QDf. After the quantum dot is grown the waveguide shell and the tapered tip are fabricated around the QD by controlling the growth parameters. This growth process ensures that the QD is placed on-axis of the tapered nanowire waveguide for efficient light extraction.
In terms of brightness, a value of Measuring multiphoton emission and photon indistinguishability of entangled photon sources. In order to quantify the multiphoton emission of a source, the second-order correlation function is measured based on a setup first introduced by Hanbury Brown and Twiss [59] (Box 1.
with
In addition to single-photon emission, for an ideal entangled photon source, the emitted photons in each mode should exhibit perfect indistinguishability. For measuring this feature, the Hong-Ou-Mandel setup is used. Using a setup similar to that the HBT (
Following the method introduced by James et al. [60], the first results in measuring the degree of entanglement in bottom-up grown nanowire QDs were reported in 2014 by Versteegh et al. [61]. In this work, using an above-bandgap excitation scheme, the fidelity of the emitted
Two-photon quantum state tomography setup. The setup consists of two pairs of
It is important to note that neither of the above-mentioned works addresses the ultimate entanglement fidelity achievable for nanowire QDs. In addition to the projection measurements, a more in-depth analysis is needed in order to reveal the underlying physical mechanisms such as dephasing due to nuclear spins and charge carriers through spin-flip processes. Moreover, the effect of
In an attempt to shed light on these finer aspects of generation of entangled photons in nanowire QDs, Fognini et al. [45] studied an InAsP QD embedded in an InP photonic nanowire, revealing the effects of dephasing,
QD emission spectra. (a) Emission spectrum by excitation via a green laser. Excitations at two different energy levels, wurtzite InP bandgap at 830 nm and donor/acceptor levels at
Following a similar setup to the one used by Jöns et al. [62] (Figure 5), Fognini et al. [45] conducted two-photon quantum state tomography on the
Dephasing-free entanglement (a) showing the correlation measurements
Despite the fact that the value for concurrence does not reach near unity and that after a peak around
Starting with the state described by Eq. (2), the expected values for 36 possible projection correlations,
where
To construct the density matrix of the two-photon quantum state, Eq. (4) gives the correlations in all 36 bases with the effect of the detectors’ timing resolution function included. However, two additional factors should be included,
where
Figure 7c shows the calculated
In stark contrast, under non-resonant excitation at the wurtzite InP bandgap, conducting two-photon quantum state tomography reveals the detrimental effect of the surrounding charge noise on the entangled state. By comparing Figure 8a and b, it becomes clear that shortly after the excitation laser moves to the InP bandgap, the detrimental effects of the excessive charge carriers become evident,
Effect of the excitation scheme and detection system. (a) Comparison of the theoretical model and results from quasi-resonant excitation indicate suppression of dephasing during the
As mentioned earlier, the drop observed in the measured concurrence is the result of the low timing resolution of the detectors. Therefore, it is expected that once the detection system is improved, an enhancement in the measured concurrence will be observed. Figure 8c shows the result of a simulation when the features of the detection system and/or the excitation scheme have changed. The red curve shows the actual system at hand, quasi-resonant excitation, with
The way in which the curve of concurrence vs. time is affected by the detectors’ response function
where
In the alternate approach, the uncertainty in timing of the arrival of the photons can be interpreted as an uncertainty in measuring the energy of
Detectors’ timing resolution and energy uncertainty. The detectors’ timing resolution,
In an attempt to realize on-demand entanglement, we have performed performed resonant two-photon excitation on the same sample used by Fognini et al. [45]. The spectrum of the source under resonant TPE is given in Figure 10a. As it is evident from comparing this spectrum with the spectra under non-resonant excitation shown in Figure 6, the abundance of charge carriers surrounding the QD is significantly suppressed, leading to a lower intensity of the
Resonant two-photon excitation of a nanowire QD. (a) The spectrum of the QD under resonant TPE. The
Moreover, under resonant TPE, the multiphoton emission is significantly suppressed. Figure 10c and d show the results of the second-order correlation function performed on the QD once excited at the donor/acceptor levels and under resonant TPE. For resonant TPE,
The impressive potential for nanowire QDs in detecting entangled photon pairs with near-unity entanglement fidelity is illuminated by the results of the resonant two-photon excitation. Notably, we are now at a point where we can make a comparison between SPDC sources and state-of-the-art QDs in different structures, i.e., self-assembled, micropillar cavities, nanowires, etc. As mentioned earlier, the Poissonian nature of photon-pair emission in SPDC sources limits the performance of such sources to extremely low pair-extraction efficiencies. On the other hand, recent advances in QD growth in various photonic structures have resulted in achieving high entanglement fidelity and high pair-extraction efficiencies, simultaneously. Hüber et al. [67] have reported on measuring an entanglement fidelity of
The result of such a comparison is shown in Figure 11. The blue circles show different values reported for entanglement fidelity vs. pair-extraction efficiency for SPDC sources. The values are taken from [69] and [14]. The dashed line shows the theoretical limit of such sources, following a Poisson distribution for the probability of multiphoton emission [70]. The two solid red squares indicate the result of two measurements performed on nanowire QDs by Jöns et al. [62] and Fognini et al. [45]. The latter work shows both an improvement in the measured entanglement fidelity and an improvement in pair-extraction efficiency. Based on the results shown by Fognini et al. [45] and the improvements gained by performing resonant TPE, we can predict measuring near-unity entanglement fidelity once two important modifications are implemented: the resonant TPE scheme is employed, and the detection system is improved to a fast and low-noise one. The final result that we predict by implementing these two changes is shown by the hollow red square. This is an extrapolation of results reported thus far on nanowire QDs based on the enhancement achieved in pair-extraction efficiency and entanglement fidelity, as well as the analysis presented in Figure 8c. Therefore, it is confidently predicted that nanowire QDs have the potential to surpass and outperform that of SPDC sources, revealing the significant potential of these sources for quantum communication purposes.
Performance of state-of-the-art entangled photon sources. Comparison between various quantum light sources in terms of entanglement fidelity and pair-extraction efficiency. Blue circles represent SPDC sources, values taken from [
In this chapter, we have given a historical overview of previous methods for attaining pairs of entangled photons from a QD, as well as included the latest research and subsequent recent advances toward enhancement of the performance from such sources. Thus far, several photonic structures have been developed in order to improve the low pair-extraction efficiency of self-assembled QDs, among which bottom-up grown nanowire QDs exhibit considerable promise. Based on the detailed studies of these sources under different excitation schemes along with understanding the effects of detection systems and multiphoton emission on the measured value of entanglement fidelity, we predict nanowire QDs can undoubtedly outperform SPDC sources, once excited via resonant TPE and detected by fast, low-noise detectors.
Admittedly, despite the fact that the results that indicate near-unity fidelity are achievable by nanowire QDs, the finite value of
Excitingly, this research shows that despite the challenges experienced thus far in generating on-demand and optimally entangled photon pairs, the results gained from resonant excitation of a nanowire QD have in fact revealed the enormous potential these sources have to outperform their predecessors. This research and the realization of optimally entangled photon pairs it offers have given quantum foundations, quantum communication, and quantum information a quantum leap forward.
The authors gratefully acknowledge the Swiss National Science Foundation, Industry Canada, Natural Sciences and Engineering Research Council of Canada (NSERC) and Transformative Quantum Technologies (TQT), for their funding and support.
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\\n\\nAt this step you will also be asked to accept the Copyright Agreement.
\\n\\n5. LANGUAGE COPYEDITING, TECHNICAL EDITING AND TYPESET PROOF
\\n\\nYour manuscript will be sent to Straive, a leader in content solution services, for language copyediting. You will then receive a typeset proof formatted in XML and available online in HTML and PDF to proofread and check for completeness. The first typeset proof of your manuscript is usually available 10 days after its original submission.
\\n\\nAfter we receive your proof corrections and a final typeset of the manuscript is approved, your manuscript is sent to our in house DTP department for technical formatting and online publication preparation.
\\n\\nAdditionally, you will be asked to provide a profile picture (face or chest-up portrait photograph) and a short summary of the book which is required for the book cover design.
\\n\\n6. INVOICE PAYMENT
\\n\\nThe invoice is generally paid by the author, the author’s institution or funder. The payment can be made by credit card from your Author Panel (one will be assigned to you at the beginning of the project), or via bank transfer as indicated on the invoice. We currently accept the following payment options:
\\n\\nIntechOpen will help you complete your payment safely and securely, keeping your personal, professional and financial information safe.
\\n\\n7. ONLINE PUBLICATION, PRINT AND DELIVERY OF THE BOOK
\\n\\nIntechOpen authors can choose whether to publish their book online only or opt for online and print editions. IntechOpen Compacts, Monographs and Edited Books will be published on www.intechopen.com. If ordered, print copies are delivered by DHL within 12 to 15 working days.
\\n\\nIf you feel that IntechOpen Compacts, Monographs or Edited Books are the right publishing format for your work, please fill out the publishing proposal form. For any specific queries related to the publishing process, or IntechOpen Compacts, Monographs & Edited Books in general, please contact us at book.department@intechopen.com
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\n\nPlease complete the publishing proposal form. The completed form should serve as an overview of your future Compacts, Monograph or Edited Book. Once submitted, your publishing proposal will be sent for evaluation, and a notice of acceptance or rejection will be sent within 10 to 30 working days from the date of submission.
\n\n2. SUBMIT YOUR MANUSCRIPT
\n\nAfter approval, you will proceed in submitting your full-length manuscript. 50-130 pages for compacts, 130-500 for Monographs & Edited Books.Your full-length manuscript must follow IntechOpen's Author Guidelines and comply with our publishing rules. Once the manuscript is submitted, but before it is forwarded for peer review, it will be screened for plagiarism.
\n\n3. PEER REVIEW RESULTS
\n\nExternal reviewers will evaluate your manuscript and provide you with their feedback. You may be asked to revise your draft, or parts of your draft, provide additional information and make any other necessary changes according to their comments and suggestions.
\n\n4. ACCEPTANCE AND PRICE QUOTE
\n\nIf the manuscript is formally accepted after peer review you will receive a formal Notice of Acceptance, and a price quote.
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\n\nWe will send you your price quote and after it has been accepted (by both the author and the publisher), both parties will sign a Statement of Work binding them to adhere to the agreed upon terms.
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\n\nAfter we receive your proof corrections and a final typeset of the manuscript is approved, your manuscript is sent to our in house DTP department for technical formatting and online publication preparation.
\n\nAdditionally, you will be asked to provide a profile picture (face or chest-up portrait photograph) and a short summary of the book which is required for the book cover design.
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\n\nThe invoice is generally paid by the author, the author’s institution or funder. The payment can be made by credit card from your Author Panel (one will be assigned to you at the beginning of the project), or via bank transfer as indicated on the invoice. We currently accept the following payment options:
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\n\n7. ONLINE PUBLICATION, PRINT AND DELIVERY OF THE BOOK
\n\nIntechOpen authors can choose whether to publish their book online only or opt for online and print editions. IntechOpen Compacts, Monographs and Edited Books will be published on www.intechopen.com. If ordered, print copies are delivered by DHL within 12 to 15 working days.
\n\nIf you feel that IntechOpen Compacts, Monographs or Edited Books are the right publishing format for your work, please fill out the publishing proposal form. For any specific queries related to the publishing process, or IntechOpen Compacts, Monographs & Edited Books in general, please contact us at book.department@intechopen.com
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Those nanoparticles with small size to large surface area (1–100 nm) have several potential functions. These days, sustainable agriculture is needed. The development of nanochemicals has appeared as promising agents for the plant growth, fertilizers and pesticides. In recent years, the use of nanomaterials has been considered as an alternative solution to control plant pests including insects, fungi and weeds. Several nanomaterials are used as antimicrobial agents in food packing in which several nanoparticles such as silver nanomaterials are in great interest. Many nanoparticles (Ag, Fe, Cu, Si, Al, Zn, ZnO, TiO2, CeO2, Al2O3 and carbon nanotubes) have been reported to have some adverse effects on plant growth apart from the antimicrobial properties. In food industries, nanoparticles are leading in forming the food with high quality and good nutritive value.",book:{id:"9012",slug:"applications-of-nanobiotechnology",title:"Applications of Nanobiotechnology",fullTitle:"Applications of Nanobiotechnology"},signatures:"Alaa Y. Ghidan and Tawfiq M. Al Antary",authors:null},{id:"68760",doi:"10.5772/intechopen.88744",title:"Nanofibrous Scaffolds for Skin Tissue Engineering and Wound Healing Based on Synthetic Polymers",slug:"nanofibrous-scaffolds-for-skin-tissue-engineering-and-wound-healing-based-on-synthetic-polymers",totalDownloads:1273,totalCrossrefCites:5,totalDimensionsCites:15,abstract:"Nanofibrous scaffolds are popular materials in all areas of tissue engineering, because they mimic the fibrous component of the natural extracellular matrix. In this chapter, we focused on the application of nanofibers in skin tissue engineering and wound healing, because the skin is an organ with several vitally important functions, particularly barrier, thermoregulatory, and sensory functions. Nanofibrous meshes not only serve as carriers for skin cells but also can prevent the penetration of microbes into wounds and can keep appropriate moisture in the damaged skin. The nanofibrous meshes have been prepared from a wide range of synthetic and nature-derived polymers. This review is concentrated on synthetic non-degradable and degradable polymers, which have been explored for skin tissue engineering and wound healing. These synthetic polymers were often combined with natural polymers of the protein or polysaccharide nature, which improved their attractiveness for cell colonization. The nanofibrous scaffolds can also be loaded with various bioactive molecules, such as growth factors, hormones, vitamins, antioxidants, antimicrobial, and antitumor agents. In advanced tissue engineering approaches, the cells on the nanofibrous scaffolds are cultured in dynamic bioreactors enabling appropriate mechanical stimulation of cells and at air-liquid interface. This chapter summarizes recent results achieved in the field of nanofiber-based skin tissue engineering, including results of our research group.",book:{id:"9012",slug:"applications-of-nanobiotechnology",title:"Applications of Nanobiotechnology",fullTitle:"Applications of Nanobiotechnology"},signatures:"Lucie Bacakova, Marketa Zikmundova, Julia Pajorova, Antonin Broz, Elena Filova, Andreu Blanquer, Roman Matejka, Jana Stepanovska, Petr Mikes, Vera Jencova, Eva Kuzelova Kostakova and Alla Sinica",authors:null},{id:"52066",doi:"10.5772/64394",title:"Supported Gold Nanoparticles as Promising Catalysts",slug:"supported-gold-nanoparticles-as-promising-catalysts",totalDownloads:3092,totalCrossrefCites:5,totalDimensionsCites:11,abstract:"In recent times, gold nanoparticles (AuNPs) either in the form of colloids or as supported nanoparticles are being extensively used as efficient redox catalyst materials. Catalysis particularly using supported gold nanoparticles (AuNPs) has attracted immense research interest due to their unique properties and greater potentiality that is directly related to their particle size. The primary objective of this chapter is to provide comprehensive overview about gold metal nanoparticles (AuNPs) and their application as promising catalysts. This chapter contains six sections in total. Section 1 starts with a general introduction, recent progress, and brief summary of the application of supported AuNPs as promising catalysts for different applications. Section 2 briefs the properties and stability of gold nanoparticles. Section 3 reviews the preparation methods of supported AuNPs for a wide range of catalytic applications. Section 4 describes briefly some of the most commonly reported supported AuNPs for different applications. Section 5 concentrates on our own results related to the application of supported AuNPs in heterogeneous catalysis. In this section, the oxidation of cyclohexane (CH) and benzyl alcohol (BA) to adipic acid (AA), benzaldehyde (BAl), and ammoxidation of 2-methylpyrazine to 2-cyanopyrazine are discussed. Finally, Section 6 describes, main points and outlook are summarized.",book:{id:"5310",slug:"catalytic-application-of-nano-gold-catalysts",title:"Catalytic Application of Nano-Gold Catalysts",fullTitle:"Catalytic Application of Nano-Gold Catalysts"},signatures:"Ahmad Alshammari and Venkata Narayana Kalevaru",authors:[{id:"178547",title:"Dr.",name:"Ahmad",middleName:null,surname:"Alshammari",slug:"ahmad-alshammari",fullName:"Ahmad Alshammari"},{id:"180753",title:"Dr.",name:"V. Narayana",middleName:null,surname:"Kalevaru",slug:"v.-narayana-kalevaru",fullName:"V. Narayana Kalevaru"}]},{id:"50852",doi:"10.5772/63729",title:"Synthesis of Gold Nanoparticles Using Amino Acids by Light Irradiation",slug:"synthesis-of-gold-nanoparticles-using-amino-acids-by-light-irradiation",totalDownloads:3602,totalCrossrefCites:3,totalDimensionsCites:9,abstract:"The synthesis of nanoparticles is generally carried out by chemical reduction, which is effective but uses a number of toxic substances, making the process potentially harmful to the environment. Thus, as part of the search for environmentally friendly or green synthetic methods, this chapter aimed to present the synthesis of gold nanoparticles (AuNPs) using only HAuCl4, Milli-Q water, white light from a xenon lamp, and amino acids. A total of 21 amino acids were studied, and the shapes and sizes of the resultant nanoparticles were evaluated. The products were characterized by ultraviolet-visible (UV-Vis) and fluorescence spectroscopy, zeta potential measurements, and transmission electron microscopy. The synthesis of the AuNPs was successful with 18 amino acids, and the best results were obtained with aspartic acid, arginine, threonine, tryptophan, and valine. The nanoparticles were spherical and their sizes ranged from 5 to 100 nm. Changes in pH were required to improve the stability of the colloidal suspensions.",book:{id:"5310",slug:"catalytic-application-of-nano-gold-catalysts",title:"Catalytic Application of Nano-Gold Catalysts",fullTitle:"Catalytic Application of Nano-Gold Catalysts"},signatures:"Lilia Coronato Courrol and Ricardo Almeida de Matos",authors:[{id:"183894",title:"Ph.D.",name:"Lilia",middleName:null,surname:"Courrol",slug:"lilia-courrol",fullName:"Lilia Courrol"},{id:"185446",title:"MSc.",name:"Ricardo",middleName:null,surname:"Matos",slug:"ricardo-matos",fullName:"Ricardo Matos"}]},{id:"51091",doi:"10.5772/64081",title:"Nanoporous Gold Films as Catalyst",slug:"nanoporous-gold-films-as-catalyst",totalDownloads:2026,totalCrossrefCites:2,totalDimensionsCites:6,abstract:"Nanoporous gold (NPG) is reviewed as a catalyst. Various preparation methods were first reviewed for NPG and its structure. Applications of this catalyst in CO oxidation, hydrogen oxidation, hydrogen production are discussed. Regarding CO oxidation, detailed studies on reaction mechanism and density functional theory (DFT) calculations were also reviewed. Not only as a model reaction but also practical aspects of removing CO residue in hydrogen stream are discussed. Beyond those simple reactions, the application of NPG to more complicated reactions such as alcohol oxidation is reviewed. Selective aerobic oxidation of gas‐phase alcohols is first reviewed and reactions in liquid phase are discussed. Finally, future prospects of NPG as a catalyst for more complicated reactions such as organic synthesis are briefly discussed.",book:{id:"5310",slug:"catalytic-application-of-nano-gold-catalysts",title:"Catalytic Application of Nano-Gold Catalysts",fullTitle:"Catalytic Application of Nano-Gold Catalysts"},signatures:"Sang Hoon Kim",authors:[{id:"183817",title:"Dr.",name:"Sang Hoon",middleName:null,surname:"Kim",slug:"sang-hoon-kim",fullName:"Sang Hoon Kim"}]}],mostDownloadedChaptersLast30Days:[{id:"68970",title:"Applications of Nanotechnology in Agriculture",slug:"applications-of-nanotechnology-in-agriculture",totalDownloads:3459,totalCrossrefCites:7,totalDimensionsCites:19,abstract:"Nanotechnology has gained intense attention in the recent years due to its wide applications in several areas like medicine, medical drugs, catalysis, energy and materials. Those nanoparticles with small size to large surface area (1–100 nm) have several potential functions. These days, sustainable agriculture is needed. The development of nanochemicals has appeared as promising agents for the plant growth, fertilizers and pesticides. In recent years, the use of nanomaterials has been considered as an alternative solution to control plant pests including insects, fungi and weeds. Several nanomaterials are used as antimicrobial agents in food packing in which several nanoparticles such as silver nanomaterials are in great interest. Many nanoparticles (Ag, Fe, Cu, Si, Al, Zn, ZnO, TiO2, CeO2, Al2O3 and carbon nanotubes) have been reported to have some adverse effects on plant growth apart from the antimicrobial properties. In food industries, nanoparticles are leading in forming the food with high quality and good nutritive value.",book:{id:"9012",slug:"applications-of-nanobiotechnology",title:"Applications of Nanobiotechnology",fullTitle:"Applications of Nanobiotechnology"},signatures:"Alaa Y. Ghidan and Tawfiq M. Al Antary",authors:null},{id:"72461",title:"Role of Nanobiotechnology in Drug Discovery, Development and Molecular Diagnostic",slug:"role-of-nanobiotechnology-in-drug-discovery-development-and-molecular-diagnostic",totalDownloads:1028,totalCrossrefCites:2,totalDimensionsCites:6,abstract:"Nano-biotechnology has already tested its magnitude in a number of sections of existence science and biotechnology field. It is no longer hyperbole to say that in future, nano-scale method would in reality take the associated science area to the subsequent level. Since, there are technical hurdles present; despite the fact that scientists are giving their great to overcome such problems. Applications of nano-biotechnology have already been discussed in this chapter. Future potential are really associated with innovative amendment of such applications. Despite of some impedance, this technology presents giant hope in the future. It performs most important position in distinct sorts of biomedical application such as shipping of drug, gene therapy, biosensors, biomarkers and molecular imaging. It additionally leads to innovations in this field. The fundamental lookup goal of this discipline would be the innovation of early analysis approach and cure with target-specific remedy therapy. Although there would possibly be some safety worries with admire to the in vivo use of nanoparticles, research are in region to decide the nature and extent of adverse events.",book:{id:"9012",slug:"applications-of-nanobiotechnology",title:"Applications of Nanobiotechnology",fullTitle:"Applications of Nanobiotechnology"},signatures:"Deepak Kumar Dash, Rajni Kant Panik, Anil Kumar Sahu and Vaibhav Tripathi",authors:[{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu"},{id:"250558",title:"Dr.",name:"Deepak Kumar",middleName:null,surname:"Dash",slug:"deepak-kumar-dash",fullName:"Deepak Kumar Dash"},{id:"314683",title:"Dr.",name:"Rajnikant",middleName:null,surname:"Panik",slug:"rajnikant-panik",fullName:"Rajnikant Panik"},{id:"316679",title:"Dr.",name:"Vaibhav",middleName:null,surname:"Tripathi",slug:"vaibhav-tripathi",fullName:"Vaibhav Tripathi"}]},{id:"51930",title:"Gold-Catalysed Reactions",slug:"gold-catalysed-reactions",totalDownloads:1934,totalCrossrefCites:0,totalDimensionsCites:1,abstract:"In recent years, there have been three significant pieces of research which helped propel gold catalysis research into the forefront: the discoveries that gold/silica can catalyse the hydrogenation of pentene, that gold on carbon can be used in the hydrochlorination of acetylene and that deposition-precipitation (DP) methods can be used to prepare nanogold on titania capable of enabling the oxidation of CO at very low temperatures. The synthesis of small gold particles, their characterisation and peculiar properties are considered together with their behaviour as heterogeneous catalysts for a variety of reactions. Some of the issues concerning the practical application of gold catalysts are also discussed.",book:{id:"5310",slug:"catalytic-application-of-nano-gold-catalysts",title:"Catalytic Application of Nano-Gold Catalysts",fullTitle:"Catalytic Application of Nano-Gold Catalysts"},signatures:"J.A. Moma, T.A. Ntho and Michael Scurrell",authors:[{id:"179872",title:"Prof.",name:"Mike",middleName:null,surname:"Scurrell",slug:"mike-scurrell",fullName:"Mike Scurrell"},{id:"183973",title:"Dr.",name:"John",middleName:null,surname:"Moma",slug:"john-moma",fullName:"John Moma"},{id:"183974",title:"Dr.",name:"Thabang",middleName:"Abraham",surname:"Ntho",slug:"thabang-ntho",fullName:"Thabang Ntho"}]},{id:"52066",title:"Supported Gold Nanoparticles as Promising Catalysts",slug:"supported-gold-nanoparticles-as-promising-catalysts",totalDownloads:3092,totalCrossrefCites:5,totalDimensionsCites:11,abstract:"In recent times, gold nanoparticles (AuNPs) either in the form of colloids or as supported nanoparticles are being extensively used as efficient redox catalyst materials. Catalysis particularly using supported gold nanoparticles (AuNPs) has attracted immense research interest due to their unique properties and greater potentiality that is directly related to their particle size. The primary objective of this chapter is to provide comprehensive overview about gold metal nanoparticles (AuNPs) and their application as promising catalysts. This chapter contains six sections in total. Section 1 starts with a general introduction, recent progress, and brief summary of the application of supported AuNPs as promising catalysts for different applications. Section 2 briefs the properties and stability of gold nanoparticles. Section 3 reviews the preparation methods of supported AuNPs for a wide range of catalytic applications. Section 4 describes briefly some of the most commonly reported supported AuNPs for different applications. Section 5 concentrates on our own results related to the application of supported AuNPs in heterogeneous catalysis. In this section, the oxidation of cyclohexane (CH) and benzyl alcohol (BA) to adipic acid (AA), benzaldehyde (BAl), and ammoxidation of 2-methylpyrazine to 2-cyanopyrazine are discussed. 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The synthesis of the AuNPs was successful with 18 amino acids, and the best results were obtained with aspartic acid, arginine, threonine, tryptophan, and valine. The nanoparticles were spherical and their sizes ranged from 5 to 100 nm. 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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:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{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. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"10",type:"subseries",title:"Animal Physiology",keywords:"Physiology, Comparative, Evolution, Biomolecules, Organ, Homeostasis, Anatomy, Pathology, Medical, Cell Division, Cell Signaling, Cell Growth, Cell Metabolism, Endocrine, Neuroscience, Cardiovascular, Development, Aging, Development",scope:"Physiology, the scientific study of functions and mechanisms of living systems, is an essential area of research in its own right, but also in relation to medicine and health sciences. The scope of this topic will range from molecular, biochemical, cellular, and physiological processes in all animal species. Work pertaining to the whole organism, organ systems, individual organs and tissues, cells, and biomolecules will be included. Medical, animal, cell, and comparative physiology and allied fields such as anatomy, histology, and pathology with physiology links will be covered in this topic. 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