Performance analysis of reconstruction algorithms.
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These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\\n\\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\\n\\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
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IntechOpen and Knowledge Unlatched formed a partnership to support researchers working in engineering sciences by enabling an easier approach to publishing Open Access content. Using the Knowledge Unlatched crowdfunding model to raise the publishing costs through libraries around the world, Open Access Publishing Fee (OAPF) was not required from the authors.
\n\nInitially, the partnership supported engineering research, but it soon grew to include physical and life sciences, attracting more researchers to the advantages of Open Access publishing.
\n\n\n\nThese books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\n\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\n\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
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It is divided into seven parts. In the first part comprising the first two chapters,\nalternative concepts and equations for multiport network analysis and characterization are\nprovided. A thru-only de-embedding technique for accurate on-wafer characterization is\nintroduced. 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Forward problems are used to explain the propagation of photons within a tissue and to calculate the optical flux at the tissue boundary. The reconstruction of the tissue image is the inverse problem, from light measurements at the boundary phantom surface, tissue absorption, scattering factors, and optical flow [1]. The inverse problem is difficult to solve due to the issue of fairness. This indicates that the problem is not properly configured. Appropriate problem characteristics include the existence of a solution, the uniqueness of the solution, and the constant reliance on the data [2]. The third property determines the stability of the solution and is important for determining the inverse problem. The ill-posedness problem occurs when the problem solution does not depend on the data indefinitely. Small changes to the data can make a big difference in the solution in this case. Regularization method is used to solve this problem, which is a regularization method that introduces additional information in order to create well-posed data [3, 4]. Diffuse optical imaging [5, 6, 7, 8] is a technique that uses an MRI scan and X-rays generate spatially decomposed images and uses high-resolution complementary structural information to improve low-resolution functional images. A set of fiber optics has been connected the object’s boundary in experimental systems. The light source was a near-infrared (NIR) laser source that was diffused on the phantom, the scattered rays were measured with a photodetector [9]. Regularization method [10] is used to remove the ill-posedness, with the Levenberg–Marquardt method (LM) being one of the most commonly used methods. Following the regularization process, the Split Bregman reconstruction method [10, 11, 12] is used to reconstruct a soft tissue image. The sparsity regularization technique for image reconstruction in DOT is described by Bo Bi et al. [9]. Gehre et al. [13] investigates the possibility of sparsity constraints in the inverse problem of deriving distributed conductivity from critical potential measurements in electrical impedance tomography (EIT). Chamorro et al. [14] proposed an Algebraic reconstruction technique—Split Bregman (ART-SB) algorithm solved the L1-regularized problem. Wang et al. evaluated the Split Bregman iteration algorithm for the L1 norm regularization inverse problem in electrical impedance tomography. Figueiredo et al. [15] investigated the use of Split Bregman iterative algorithms for the L1-norm regularized inverse problem of electrical impedance constrained quadratic programming ill-tomography formulation.
In most cases, the measurement data in DOT reconstruction is derived from the numerical solution of the forward problem. Regularization techniques are used to eliminate the obtrusive inverse problem variables. The measurement technology of optical devices is so limited that their existence cannot be accurately determined from all angles. Instead, it gets the average contact angle data for the phantom. The purpose is to reconstruct the image from known scattering and absorption coefficients, which are assumed to be known. The reconstructed result is obtained by comparing the true value to the measured value. A variety of practical reconstruction algorithms have been developed in tomography to implement the process of reconstructing a 3D object from a projection [16]. These algorithms are mainly based on the mathematics of statistical knowledge of the data acquisition process and the geometry of the data imaging system [17].
The inverse problem is used to reconstruct an image in the following ways by estimating the scattering coefficient, absorption coefficient, and optical flux [18, 19]. The noise level is present in the actual measurement; both actual measurement data and actual data are shown here [9]. The following nonlinear equation is used to solve the inverse problem of DOT for
The inverse problem of DOT is inappropriate and uses regularization techniques to reconstruct the image [8], i.e. the Tikhonov functional feature that is minimized for the coefficient. R (
Over the set,
The standard reconstruction method is considered using Eqs. (3) and (4).
Traditional norm-squared penalties are believed to reduce the following functions,
In the inverse problem of DOT, the Levenberg–Marquardt regularization method [20] is used. The forward operator is linearized around the initial estimation for each;
where Eq. (7) denotes the Taylor remainder for the linearization around and the Frechet derivative is obtained by substituting the above equation and ignoring the higher-order remainder [13].
The Euler equation for discrete problems is
Solving this (9) yields the final Equation [16].
That is,
where
Sparsity reconstruction function can be minimized as
Such that d=
where
By dividing the minimization of (12) and d separately, the sub-problem can be minimized.
Consider
Consider
Consider
Minimize
The variational equation is given as
L1 is solved efficiently by the contraction operator; that is
Where the shrinkage operator is defined as
The rate of spilt Bregman is highly dependent on the rate of dissolution
Because the DOT image has poor spatial resolution due to severe ill-posedness, the regularization technique is used in conjunction with reconstruction algorithms to reconstruct the images.
for
for
i. Compute the Frechet derivative
end for.
i. Compute
ii. Update
iii. Check the stopping criterion.
end for.
Regularization parameters
While
For each
end while.
The split Bregman method entails locating the Fréchet derivative, which is nothing more than the first order derivative function. The algorithm is built with regularization parameters in mind.
The reconstructed image can be obtained using the Levenberg–Marquardt algorithm by providing optical flux, scattering coefficient, and absorption coefficient values, which are then compared to distinguish between normal soft tissue and cancer-affected tissue. The forward mesh has more nodes to extract all of the optical parameters of the tissue, whereas the inverse mesh has fewer nodes for reconstruction. As shown in Figure 1, the forward mesh has 1097 nodes and 2095 elements, and the reverse mesh has 286 nodes and 522 elements.
Mesh diagram of inverse problem.
Figure 2 depicts the reconstruction based on absorption and scattering coefficient measurements. The image is reconstructed using optical properties of human tissue such as absorption coefficient and scattering coefficient (and (r) = 2). The reconstructed image is based on the absorption and scattering coefficient values. It is possible to predict normal tissue and cancer-affected tissue by examining the reconstructed image with absorption and scattering coefficients. When the absorption and scattering coefficients are higher, the tumor is classified as malignant or benign soft tissue tumor.
Levenberg–Marquardt regularization and standard reconstruction.
The split Bregman algorithm with sparsity regularization efficiently solves the DOT image reconstruction problem. Figure 3 depicts a Spilt Bregman reconstruction from scattering coefficients. The scattering coefficient value distinguishes the variation of abnormal tissue to normal tissue. According to Figure 3, the abnormal tissue scattering coefficient ranges from 150 to 210, whereas the normal tissue scattering coefficient ranges from 0 to 20. When compared to other reconstruction algorithms, the Bregman algorithm produces more accurate results. The reconstructed image’s resolution is determined by calculating the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), relative solution error norm (RE), and CPU time. SNR is calculated as follows:
Spilt Bregman regularization with sparsity reconstruction.
The CNR is a metric used to assess image quality. The mean and standard deviation values are used to calculate it. CNR is calculated as follows:
where are the image signal intensities and is the standard deviation of pure image noise. The Relative solution error norm is computed as follows:
Table 1 compares parameters used to evaluate the performance of reconstruction algorithms. The Split Bregman method has a higher SNR than the Gauss Newton algorithm and improves CNR more than the Gauss Newton method. To achieve better performance, the RE of a reconstructed image should be low. Because the Gauss Newton method has a high RE value, it is not an optimal solution for image reconstruction. Finally, when compared to the Gauss Newton method, the Split Bregman method requires less CPU time to execute. The graph of the performance analysis of the Split Bregman and Gauss Newton algorithms is shown in Figure 4.
Parameters | Split Bregman method | Gauss Newton |
---|---|---|
SNR | 9.2327 | 4.3402 |
CNR | 66.947 | 39.743 |
RE | 0.0508 | 0.2141 |
CPU time (s) | 72.231 | 75.197 |
Performance analysis of reconstruction algorithms.
Performance analysis of reconstruction algorithms.
The solution to diffuse light transport through tissues is provided by iterative non-linear reconstruction of diffuse optical tomography using a finite element forward model. The efficiency of the forward solver has a significant impact on reconstruction performance and reconstruction time, which is critical in making optical tomography a viable imaging modality in clinical diagnosis. Standard regularization (Levenburg-Marquadt) with a small anisotropy factor identifies the scattering coefficient better than sparsity regularization in the inverse model. Sparsity regularization (Split Bregman) localizes the inclusion position and has high anisotropy factor g while forward-peaking region. The absorption and scattering coefficient values of the reconstructed it is analyzed to determine the difference between normal soft tissue and cancerous tissue. Increasing the number of measurements by adding more photo detectors is one way to improve the quality of a reconstructed image. Finally, a regularization technique is used to remove the ill-posedness problem, and a Split Bregman reconstruction algorithm is used to achieve a high-resolution image.
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Reaching far beyond information technology’s traditional role in business strategy, the implications of this big data phenomenon are considered through an exploration into what big data is, how it is currently being used by existing firms, and how it factors into strategic thinking. As different organizational approaches have developed toward big data, we use resource-based theory and organizational learning as anchoring perspectives to link this phenomenon with traditional strategic management. We also identify four avenues for future scholarship as the nature of business moves increasingly digital.",book:{id:"8649",slug:"strategy-and-behaviors-in-the-digital-economy",title:"Strategy and Behaviors in the Digital Economy",fullTitle:"Strategy and Behaviors in the Digital Economy"},signatures:"Mattew J. Mazzei and David Noble",authors:[{id:"287735",title:"Dr.",name:"Matthew",middleName:null,surname:"Mazzei",slug:"matthew-mazzei",fullName:"Matthew Mazzei"},{id:"287736",title:"Dr.",name:"David",middleName:null,surname:"Noble",slug:"david-noble",fullName:"David Noble"}]},{id:"68451",title:"Digital Transformation: Digital Leadership Role in Developing Business Model Innovation Mediated by Co-Creation Strategy for Telecommunication Incumbent Firms",slug:"digital-transformation-digital-leadership-role-in-developing-business-model-innovation-mediated-by-c",totalDownloads:1702,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"Incumbents have a challenge to sustain their business due to new attractive business model offered by new entrants. Incumbent firms are required to transform their existing business to a new paradigm of business which is digital business through developing new capability in business model. In developing innovation in the business model, there is a challenge for incumbents due to existing legacy business and routine process. The fastest way in developing new capabilities through collaboration is called co-creation strategy. In driving co-creation strong culture and visioning of digital leader is required. The study of the digital leadership role in developing business model innovation and co-creation strategy was limited; hence this study has an objective to assess the role of digital leadership, whether it will direct or indirect through co-creation strategy in developing business model innovation. The study was conducted on 88 senior leader respondents. The statistical data analysis used SmartPLS application. The result explained that digital leadership impacts indirectly through co-creation strategy on developing business model innovation. Co-creation strategy plays a mediating role in the relationship between business model innovation and digital leadership.",book:{id:"8649",slug:"strategy-and-behaviors-in-the-digital-economy",title:"Strategy and Behaviors in the Digital Economy",fullTitle:"Strategy and Behaviors in the Digital Economy"},signatures:"Leonardus Wahyu Wasono Mihardjo and Sasmoko Sasmoko",authors:[{id:"276909",title:"Dr.",name:"Leonardus",middleName:"W Wasono",surname:"Mihardjo",slug:"leonardus-mihardjo",fullName:"Leonardus Mihardjo"},{id:"284255",title:"Prof.",name:"Sasmoko",middleName:null,surname:"Sasmoko",slug:"sasmoko-sasmoko",fullName:"Sasmoko Sasmoko"}]},{id:"74563",title:"The Trouble with Minding Markets: Emotional Finance in Context",slug:"the-trouble-with-minding-markets-emotional-finance-in-context",totalDownloads:452,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The term ‘Emotional Finance’ normally denotes a methodological approach advocated by Richard Taffler and David Tuckett, which they intended as a challenge both to Behavioral Finance and to mainstream finance and economics. In the wake of the Great Financial Crisis, Emotional Finance received a warm reception from regulators, the financial press, and the CFA Institute. Nearly a decade on, their ideas have largely failed to achieve traction in the academic literature, and continue to struggle to find empirical validation. Their approach is essentially an application of Kleinian psychoanalysis to financial markets, albeit without the terminological rigor that psychoanalytic practitioners might expect. Because their approach is inherently interdisciplinary, it has rarely been subject to scrutiny, as few psychoanalytic commentators feel qualified to comment on financial markets, and fewer finance academics feel comfortable commenting on the psychoanalytic theory. This chapter characterizes the main theoretical claims of Emotional Finance, and subjects each of them to scrutiny, finding them largely untenable. Although financial bubbles are commonplace and emotional responses to markets unremarkable, the subsidiary arguments advanced by advocates of Emotional Finance to support their primary claims are found wanting. The interpretative strategy of Emotional Finance is fundamentally flawed. Although it is fruitful to analyze the role of emotions in financial markets, more precise, rigorous and realistic approaches to these problems are needed.",book:{id:"8148",slug:"investment-strategies-in-emerging-new-trends-in-finance",title:"Investment Strategies in Emerging New Trends in Finance",fullTitle:"Investment Strategies in Emerging New Trends in Finance"},signatures:"D’Maris Coffman",authors:[{id:"325580",title:"Prof.",name:"D'Maris",middleName:null,surname:"Coffman",slug:"d'maris-coffman",fullName:"D'Maris Coffman"}]},{id:"66027",title:"Implementation of a Digital Workplace Strategy to Drive Behavior Change and Improve Competencies",slug:"implementation-of-a-digital-workplace-strategy-to-drive-behavior-change-and-improve-competencies",totalDownloads:2074,totalCrossrefCites:6,totalDimensionsCites:11,abstract:"Digital technologies are integrated in many aspects of life and work and present benefits and challenge for organizations, employers, and employees. In order to have advantages from digital transformation, organizations should be creative for new working environments and their culture around digital developments in the workplace in order not to lose clients, productivity, and employees. Some keys of success of digital workplaces are an effective implementation of a digital workplace strategy with a changed learning and culture as an incentive for staff behavior. This should suit to technological solutions and support its adoption and use it for work, communication, and cooperation. Entrepreneurship education should be also adapted to digital transformation in order to prepare employees and employers for digital workplaces.",book:{id:"8649",slug:"strategy-and-behaviors-in-the-digital-economy",title:"Strategy and Behaviors in the Digital Economy",fullTitle:"Strategy and Behaviors in the Digital Economy"},signatures:"Ileana Hamburg",authors:[{id:"80248",title:"Dr.",name:"Ileana",middleName:null,surname:"Hamburg",slug:"ileana-hamburg",fullName:"Ileana Hamburg"}]}],onlineFirstChaptersFilter:{topicId:"64",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:320,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:133,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:16,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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