Solar Engineering Of Thermal Processes

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

Neutrosophic set, initiated by Smarandache, is a novel tool to deal with vagueness considering the truth-membership T, indeterminacy-membership I and falsity-membership F satisfying the condition $$0\le T+I+F\le 3$$. It can be used to characterize the uncertain information more sufficiently and accurately than intuitionistic fuzzy set. Neutrosophic set has attracted great attention of many scholars that have been extended to new types and these extensions have been used in many areas such as aggregation operators, decision making, image processing, information measures, graph and algebraic structures. Because of such a growth, we present an overview on neutrosophic set with the aim of offering a clear perspective on the different concepts, tools and trends related to their extensions. A total of 137 neutrosophic set publication records from Web of Science are analyzed. Many interesting results with regard to the annual trends, the top players in terms of country level as well as institutional level, the publishing journals, the highly cited papers, and the research landscape are yielded and explained in-depth. The results indicate that some developing economics (such as China, India, Turkey) are quite active in neutrosophic set research. Moreover, the co-authorship analysis of the country and institution, the co-citation analysis of the journal, reference and author, and the co-occurrence analysis of the keywords are presented by VOSviewer software.

A bibliometric analysis of neutrosophic set: two decades review from 1998 to 2017

In real world applications, soft computing is an inspirational domain for encoding imprecision and uncertainty. Soft computing procedures integrated with medical applications can support the existing medical systems to allow solutions for unsolvable problems. Fuzzy techniques have extensive solutions for the medical domain applications; however incorporating a new neutrosophic approaches in the medical domain proves its superiority. The current study reported the main neutrosophic sets (NS) definitions along with different medical applications based on NS. In addition, an extensive discussion for the possibility of prolonging the abilities of the fuzzy systems using the neutrosophic systems was included. The preceding studies established that the NS has a significant role in medical images de-noising, clustering, and segmentation. As a future scope, it was suggested that the neutrosophic medical systems can be exploited for neutrosophic scores; continuous truth/indeterminate/falsity versions of conventional score schemes. The integrated methods of the NS in medical domain would lead to tabular or rule-based mapping from input to output variables. The qualitative simulation of the reported studies established that the neutrosophic model based diagnosis is promising aspirants for future research. Furthermore, the current work highlighted the main medical image processes that can be developed using the NS, including de-noising, thresholding, segmentation, clustering and classification. The general algorithms that can be used to include NS in each task were proposed.

A survey of the state-of-the-arts on neutrosophic sets in biomedical diagnoses

FPGA devices have emerged as a popular platform for the rapid prototyping of biological Spiking Neural Networks (SNNs) applications, offering the key requirement of reconfigurability. However, FPGAs do not efficiently realise the biologically plausible neuron and synaptic models of SNNs, and current FPGA routing structures cannot accommodate the high levels of interneuron connectivity inherent in complex SNNs. This paper highlights and discusses the current challenges of implementing scalable SNNs on reconfigurable FPGAs. The paper proposes a novel field programmable neural network architecture (EMBRACE), incorporating low-power analogue spiking neurons, interconnected using a Network-on-Chip architecture. Results on the evaluation of the EMBRACE architecture using the XOR benchmark problem are presented, and the performance of the architecture is discussed. The paper also discusses the adaptability of the EMBRACE architecture in supporting fault tolerant computing.

/pdf/a-reconfigurable-and-biologically-inspired-paradigm-for-1ykt5li2vq.pdf

A reconfigurable and biologically inspired paradigm for computation using network-on-chip and spiking neural networks

Halogenated Bromide (Br −) was subsequently dosed to aqueous electrolyte solution to enforce the capacitance features of activated carbon (AC) electrodes in electric double-layer supercapacitor devices for electrical energy storage Physicochemical properties for AC with sodium sulfate (Na2SO4) and KBr redox additive were assessed by different characterization tools such as XRD, SEM, EDS, surface roughness, and BET techniques Cyclic voltammetry (CV), galvanostatic charge–discharge (GCD), electrochemical impedance spectroscopy (EIS), and the stability after 1000 cycles have been used to monitor the electrochemical behaviors of the prepared electrodes The presence of halogen atom exhibited large faradaic resembling battery like-type in charge–discharge curves In the aqueous solution, the investigated electrode showed a high specific capacitance of 9578 F g−1 at a specific current of 046 A g−1 using a certain concentration of KBr added to Na2SO4 The single AC electrode showed specific energy of 133 Wh kg−1 and specific power of 8596 W kg−1 Besides, the AC electrode displayed excellent long-term stability in Na2SO4@KBr electrolyte, preserving retention capacitance of 174% AC/AC symmetric supercapacitor cell demonstrated excellent electrochemical performance, including specific energy of 5715 Wh kg−1, specific capacitance of 127 F g−1, specific power of 5262 W kg−1 at a specific current of 38 A g−1, and 828% capacitance retention after 10,000 cycles

Activated carbon electrode with promising specific capacitance based on potassium bromide redox additive electrolyte for supercapacitor application

An approach to reducing the computation time taken by neural nets for the searching process is introduced. We combine both fast and cooperative modular neural nets to enhance the performance of the detection process. Such an approach is applied to identify human faces automatically in cluttered scenes. In the detection phase, neural nets are used to test whether a window of 20/spl times/20 pixels contains a face or not. The major difficulty in the learning process comes from the large database required for face/nonface images. A simple design for cooperative modular neural nets is presented to solve this problem by dividing these data into three groups. Such division results in reduction of computational complexity and thus decreasing the time and memory needed during the test of an image. Simulation results for the proposed algorithm show a good performance.

Fast modular neural nets for human face detection

In this study, we present and establish a gold surface plasmon polariton (SPP) GaAs photodetector that achieves high internal quantum efficiency (IQE). At a wavelength of 600 nm, the IQE with the SPP was 85%, while the IQE without the SPP was 42%, an enhancement of 43%. Also, at a wavelength of 675 nm, the IQE with SPP was 82%, whereas the IQE without SPP was 45%, which constitutes an increase of 37%. Such excellent performance is ascribed to the subwavelength scope of the optical power in the photoconductive-based gold SPP GaAs that provides high IQE. Moreover, the recombination of the SPP in the photodetector provides greater photocurrent and responsivity.

High-Performance Enhancement of a GaAs Photodetector Using a Plasmonic Grating

A new approach to the face recognition problem is presented through combining Fourier descriptors with principal component analysis (PCA) and neural networks. Here the faces are vertically oriented frontal view with scaling, orientation, expression, and illumination changes. There are many research activities on face recognition using the face space which is described by a set of eigenfaces. Each face is efficiently represented by its projection onto the space expanded by the eigenfaces and has a new descriptor. Previous work on eigenface has shown that it performs well only with changes in expression, but results are poor in the case of rotating, or scaling the input face. In order to enhance the performance of the eigenfaces technique to accommodate other variations of the input face, the Fourier vector of each face is projected in the eigenspace. Neural networks are used to recognize the face through learning the correct classification of these new descriptors. A real-time system has been created which combines the face detection and recognition techniques. A recognition rate of 91% has been achieved over real tests. It is also shown that our proposed system behaves accurately in the case of rotated or scaled faces as well as for changes in expression.

https://ieeexplore.ieee.org/iel5/6758/18070/00838951.pdf

Integrating Fourier descriptors and PCA with neural networks for face recognition

In this paper, we introduce a powerful solution for complex problems which required to be solved using neural nets. This is done by using modular neural nets (MNNs) that divide the input space into several homogenous regions. Such an approach is applied to implement XOR functions, 16 logic functions on one bit level, and a 2-bit digital multiplier. Compared to previous nonmodular designs, a salient reduction in the order of computations and hardware requirements is obtained.

M.A. Abo-Elsoud

Papers

Activated carbon electrode with promising specific capacitance based on potassium bromide redox additive electrolyte for supercapacitor application

Fast modular neural nets for human face detection

High-Performance Enhancement of a GaAs Photodetector Using a Plasmonic Grating

Integrating Fourier descriptors and PCA with neural networks for face recognition

Modular neural networks for solving high complexity problems