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Digital Systems

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ISBN: 9781789845402 9781789845419 Year: Pages: 164 DOI: 10.5772/intechopen.74915 Language: English
Publisher: IntechOpen
Subject: Computer Science
Added to DOAB on : 2019-10-03 07:51:52

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This book provides an approach toward the applications and principle theory of digital signal processing in modern intelligent systems, biological engineering, telecommunication, and information technology. Assuming the reader already has prior knowledge of signal processing theory, this book will be useful for finding novel methods that fit special needs in digital signal processing (DSP). The combination of signal processing and intelligent systems in hybrid structures rather than serial or parallel processing provide the best mechanism that is a better fit with the comprehensive nature of human. This book is a practical reference that places the emphasis on principles and applications of DSP in digital systems. It covers a broad area of digital systems and applications of machine learning methods including convolutional neural networks, evolutionary algorithms, adaptive filters, spectral estimation, data compression and functional verification. The level of the book is ideal for professional DSP users and useful for graduate students who are looking for solutions to their design problems. The theoretical principles provide the required base for comprehension of the methods and application of modifications for the special needs of practical projects.

Neural information processing with dynamical synapses

Authors: --- ---
Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889193837 Year: Pages: 178 DOI: 10.3389/978-2-88919-383-7 Language: English
Publisher: Frontiers Media SA
Subject: Neurology --- Science (General)
Added to DOAB on : 2015-12-03 13:02:24
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Experimental data have consistently revealed that the neuronal connection weight, which models the efficacy of the firing of a pre-synaptic neuron in modulating the state of a post-synaptic one, varies on short time scales, ranging from hundreds to thousands of milliseconds. This is called short-term plasticity (STP). Two types of STP, with opposite effects on the connection efficacy, have been observed in experiments. They are short-term depression (STD) and short-term facilitation (STF). Computational studies have explored the impact of STP on network dynamics, and found that STP can generate very rich intrinsic dynamical behaviors, including damped oscillations, state hopping with transient population spikes, traveling fronts and pulses, spiral waves, rotating bump states, robust self-organized critical activities and so on. These studies also strongly suggest that STP can play many important roles in neural computation. For instances, STD may provide a dynamic control mechanism that allows equal fractional changes on rapidly and slowly firing afferents to produce post-synaptic responses, realizing Weber's law; STD may provide a mechanism to close down network activity naturally, achieving iconic sensory memory; and STF may provide a mechanism for implementing work-memory not relying on persistent neural firing. From the computational point of view, the time scale of STP resides between fast neural signaling (in the order of milliseconds) and rapid learning (in the order of minutes or above), which is the time scale of many important temporal processes occurring in our daily lives, such as motion control and working memory. Thus, STP may serve as a substrate for neural systems manipulating temporal information on the relevant time scales. This Research Topic aims to present the recent progress in understanding the roles of STP in neural information processing. It includes, but no exclusively, the studies on investigating various computational roles of STP, the modeling studies on exploring new dynamical behaviors generated by STP, and the experimental works which help us to understand the functional roles of STP.

Recent Developments of Nanofluids

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ISBN: 9783038428336 9783038428343 Year: Pages: VIII, 150 Language: Englisch
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Mathematics --- Physics (General) --- Chemistry (General)
Added to DOAB on : 2018-08-24 15:50:37
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Over the past two decades, there has been increased attention in the research of nanofluid due to its widely expanded domain in many industrial and technological applications. Major advances in the modeling of key topics such as nanofluid, MHD, heat transfer, convection, porous media, Newtonian/non-Newtonian fluids have been made and finally published in the special issue on recent developments in nanofluids for Applied Sciences. The present attempt is to edit the special issue in a book form. Although, this book is not a formal textbook even than it will definitely be useful for research students and university teachers in overcoming the difficulties occurring in the said topic while dealing with the nonlinear governing equations. On one side the real world problems in mathematics, physics, biomechanics, engineering and other disciplines of sciences are mostly described by the set of nonlinear equations whereas on the other hand, it is often more difficult to get an analytic solution or even a numerical one. This book has successfully handled this challenging job with latest techniques. In addition the findings of the simulation are logically realistic and meet the standard of sufficient scientific value.

Advanced Memristor Modeling: Memristor Circuits and Networks

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ISBN: 9783038971047 9783038971030 Year: Pages: 172 DOI: doi.org/10.3390/books978-3-03897-103-0 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General)
Added to DOAB on : 2019-05-22 16:48:48
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The investigation of new memory schemes, neural networks, computer systems, and many other improved electronic devices is very important for the future generation’s electronic circuits and for their widespread application in all the areas of industry. In this respect, the analysis of new efficient and advanced electronic elements and circuits is an essential field of highly developed electrical and electronic engineering. The resistance-switching phenomenon, observed in many amorphous oxides, has been investigated since 1970 and is a promising technology for constructing new electronic memories. It has been established that such oxide materials have the ability for changing their conductance in accordance with the applied voltage, and for memorizing their state for long-time interval. Similar behaviour has been predicted for the memristor element by Leon Chua in 1971. The memristor is proposed in accordance with symmetry considerations and the relationships between the four basic electric quantities—electric current i, voltage v, charge q, and magnetic flux Ψ. The memristor is an essential passive one-port element together with the resistor, inductor, and capacitor. The Williams HP research group has made a link between resistive switching devices and the memristor proposed by Chua. A number of scientific papers related to memristors and memristor devices have been issued, and several memristor models have been proposed. The memristor is a highly nonlinear component. It relates the electric charge q and the flux linkage, expressed as a time integral of the voltage. The memristor element has the important capability for remembering the electric charge passed through its cross-section and its respective resistance, when the electrical signals are switched off. Due to its nano-scale dimensions, non-volatility, and memorizing properties, the memristor is a sound potential candidate for application in computer high-density memories, artificial neural networks, and many other electronic devices.

Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources

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Book Series: Karlsruher Forschungsberichte aus dem Institut für Hochleistungsimpuls- und Mikrowellentechnik ISSN: 21922764 ISBN: 9783731504672 Year: Volume: 8 Pages: XIII, 231 p. DOI: 10.5445/KSP/1000051503 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Technology (General)
Added to DOAB on : 2019-07-30 20:02:02
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In this work, an innovative real-time microwave control approach is proposed, to improve the temperature homogeneity under microwave heating. Multiple adaptive or intelligent control structures have been developed, including the model predictive control, neural network control and reinforcement learning control methods. Experimental results prove that these advanced control methods can effectively reduce the final temperature derivations and improve the temperature homogeneity.

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

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ISBN: 9783038972860 9783038972877 Year: Pages: 250 DOI: 10.3390/books978-3-03897-287-7 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2018-10-19 11:45:03
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More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers.This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy.

AI based Robot Safe Learning and Control

Authors: --- --- --- --- et al.
ISBN: 9789811555039 Year: Pages: 127 DOI: 10.1007/978-981-15-5503-9 Language: English
Publisher: Springer Nature
Subject: Agriculture (General) --- Computer Science
Added to DOAB on : 2020-06-16 23:57:53
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This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors’ papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities.

Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS)

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ISBN: 9783039213757 9783039213764 Year: Pages: 344 DOI: 10.3390/books978-3-03921-376-4 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:15
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This book contains the latest research on machine learning and embedded computing in advanced driver assistance systems (ADAS). It encompasses research in detection, tracking, LiDAR

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Vehicle-to-X communications --- Intelligent Transport Systems --- VANET --- DSRC --- Geobroadcast --- multi-sensor --- fusion --- deep learning --- LiDAR --- camera --- ADAS --- object tracking --- kernel based MIL algorithm --- Gaussian kernel --- adaptive classifier updating --- perception in challenging conditions --- obstacle detection and classification --- dynamic path-planning algorithms --- joystick --- two-wheeled --- terrestrial vehicle --- path planning --- infinity norm --- p-norm --- kinematic control --- navigation --- actuation systems --- maneuver algorithm --- automated driving --- cooperative systems --- communications --- interface --- automated-manual transition --- driver monitoring --- visual tracking --- discriminative correlation filter bank --- occlusion --- sub-region --- global region --- autonomous vehicles --- driving decision-making model --- the emergency situations --- red light-running behaviors --- ethical and legal factors --- T-S fuzzy neural network --- road lane detection --- map generation --- driving assistance --- autonomous driving --- real-time object detection --- autonomous driving assistance system --- urban object detector --- convolutional neural networks --- machine vision --- biological vision --- deep learning --- convolutional neural network --- Gabor convolution kernel --- recurrent neural network --- enhanced learning --- autonomous vehicle --- crash injury severity prediction --- support vector machine model --- emergency decisions --- relative speed --- total vehicle mass of the front vehicle --- perception in challenging conditions --- obstacle detection and classification --- dynamic path-planning algorithms --- drowsiness detection --- smart band --- electrocardiogram (ECG) --- photoplethysmogram (PPG) --- recurrence plot (RP) --- convolutional neural network (CNN) --- squeeze-and-excitation --- residual learning --- depthwise separable convolution --- blind spot detection --- machine learning --- neural networks --- predictive --- vehicle dynamics --- electric vehicles --- FPGA --- GPU --- parallel architectures --- optimization --- panoramic image dataset --- road scene --- object detection --- deep learning --- convolutional neural network --- driverless --- autopilot --- deep leaning --- object detection --- generative adversarial nets --- image inpainting --- n/a

Statistical Analysis and Stochastic Modelling of Hydrological Extremes

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ISBN: 9783039216642 9783039216659 Year: Pages: 294 DOI: 10.3390/books978-3-03921-665-9 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Meteorology and Climatology
Added to DOAB on : 2019-12-09 16:10:12
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Hydrological extremes have become a major concern because of their devastating consequences and their increased risk as a result of climate change and the growing concentration of people and infrastructure in high-risk zones. The analysis of hydrological extremes is challenging due to their rarity and small sample size, and the interconnections between different types of extremes and becomes further complicated by the untrustworthy representation of meso-scale processes involved in extreme events by coarse spatial and temporal scale models as well as biased or missing observations due to technical difficulties during extreme conditions. The complexity of analyzing hydrological extremes calls for robust statistical methods for the treatment of such events. This Special Issue is motivated by the need to apply and develop innovative stochastic and statistical approaches to analyze hydrological extremes under current and future climate conditions. The papers of this Special Issue focus on six topics associated with hydrological extremes: Historical changes in hydrological extremes; Projected changes in hydrological extremes; Downscaling of hydrological extremes; Early warning and forecasting systems for drought and flood; Interconnections of hydrological extremes; Applicability of satellite data for hydrological studies.

Keywords

rainfall --- monsoon --- high resolution --- TRMM --- drought prediction --- APCC Multi-Model Ensemble --- seasonal climate forecast --- machine learning --- sparse monitoring network --- Fiji --- drought analysis --- ANN model --- drought indices --- meteorological drought --- SIAP --- SWSI --- hydrological drought --- discrete wavelet --- global warming --- statistical downscaling --- HBV model --- flow regime --- uncertainty --- reservoir inflow forecasting --- artificial neural network --- wavelet artificial neural network --- weighted mean analogue --- variation analogue --- streamflow --- artificial neural network --- simulation --- forecasting --- support vector machine --- evolutionary strategy --- heavy storm --- hyetograph --- temperature --- clausius-clapeyron scaling --- climate change --- the Cauca River --- climate variability --- ENSO --- extreme rainfall --- trends --- statistical downscaling --- random forest --- least square support vector regression --- extreme rainfall --- polynomial normal transform --- multivariate modeling --- sampling errors --- non-normality --- extreme rainfall analysis --- statistical analysis --- hydrological extremes --- stretched Gaussian distribution --- Hurst exponent --- INDC pledge --- precipitation --- extreme events --- extreme precipitation exposure --- non-stationary --- extreme value theory --- uncertainty --- flood regime --- flood management --- Kabul river basin --- Pakistan --- extreme events --- innovative methods --- downscaling --- forecasting --- compound events --- satellite data

Memristors for Neuromorphic Circuits and Artificial Intelligence Applications

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ISBN: 9783039285761 / 9783039285778 Year: Pages: 244 DOI: 10.3390/books978-3-03928-577-8 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2020-06-09 16:38:57
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Artificial Intelligence (AI) has found many applications in the past decade due to the ever increasing computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses. Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. The so-called Spiking Neural Networks behave similarly to how the brain functions and are very energy efficient. Up to this moment, both spiking and conventional neural networks have been implemented in software programs running on conventional computing units. However, this approach requires high computing power, a large physical space and is energy inefficient. Thus, there is an increasing interest in developing AI tools directly implemented in hardware. The first hardware demonstrations have been based on CMOS circuits for neurons and specific communication protocols for synapses. However, to further increase training speed and energy efficiency while decreasing system size, the combination of CMOS neurons with memristor synapses is being explored. The memristor is a resistor with memory which behaves similarly to biological synapses. This book explores the state-of-the-art of neuromorphic circuits implementing neural networks with memristors for AI applications.

Keywords

memristor --- artificial synapse --- neuromorphic computing --- memristor-CMOS hybrid circuit --- temporal pooling --- sensory and hippocampal responses --- cortical neurons --- hierarchical temporal memory --- neocortex --- memristor-CMOS hybrid circuit --- defect-tolerant spatial pooling --- boost-factor adjustment --- memristor crossbar --- neuromorphic hardware --- memristor --- compact model --- emulator --- neuromorphic --- synapse --- STDP --- pavlov --- neuromorphic systems --- spiking neural networks --- memristors --- spike-timing-dependent plasticity --- RRAM --- vertical RRAM --- neuromorphics --- neural network hardware --- reinforcement learning --- AI --- neuromorphic computing --- multiscale modeling --- memristor --- optimization --- RRAM --- simulation --- memristors --- neuromorphic engineering --- OxRAM --- self-organization maps --- synaptic device --- memristor --- neuromorphic computing --- artificial intelligence --- hardware-based deep learning ICs --- circuit design --- memristor --- RRAM --- variability --- time series modeling --- autocovariance --- graphene oxide --- laser --- memristor --- crossbar array --- neuromorphic computing --- wire resistance --- synaptic weight --- character recognition --- neuromorphic computing --- Flash memories --- memristive devices --- resistive switching --- synaptic plasticity --- artificial neural network --- spiking neural network --- pattern recognition --- strongly correlated oxides --- resistive switching --- neuromorphic computing --- transistor-like devices --- artificial intelligence --- neural networks --- resistive switching --- memristive devices --- deep learning networks --- spiking neural networks --- electronic synapses --- crossbar array --- pattern recognition

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