{"product_id":"analog-vlsi-circuits-for-the-perception-of-visual-motion-isbn-9780470854914","title":"Analog VLSI Circuits for the Perception of Visual Motion","description":"Although it is now possible to integrate many millions of transistors on a single chip, traditional digital circuit technology is now reaching its limits, facing problems of cost and technical efficiency when scaled down to ever-smaller feature sizes. The analysis of biological neural systems, especially for visual processing, has allowed engineers to better understand how complex networks can effectively process large amounts of information, whilst dealing with difficult computational challenges.  \u003cp\u003eAnalog and parallel processing are key characteristics of biological neural networks. Analog VLSI circuits using the same features can therefore be developed to emulate brain-style processing. Using standard CMOS technology, they can be cheaply manufactured, permitting efficient industrial and consumer applications in robotics and mobile electronics.\u003c\/p\u003e \u003cp\u003eThis book explores the theory, design and implementation of analog VLSI circuits, inspired by visual motion processing in biological neural networks. Using a novel approach pioneered by the author himself, Stocker explains in detail the construction of a series of electronic chips, providing the reader with a valuable practical insight into the technology.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAnalog VLSI Circuits for the Perception of Visual Motion\u003c\/i\u003e:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eanalyses the computational problems in visual motion perception;\u003c\/li\u003e \u003cli\u003eexamines the issue of optimization in analog networks through high level processes such as motion segmentation and selective attention;\u003c\/li\u003e \u003cli\u003edemonstrates network implementation in analog VLSI CMOS technology to provide computationally efficient devices;\u003c\/li\u003e \u003cli\u003esets out measurements of final hardware implementation;\u003c\/li\u003e \u003cli\u003eillustrates the similarities of the presented circuits with the human visual motion perception system;\u003c\/li\u003e \u003cli\u003eincludes an accompanying website with video clips of circuits under real-time visual conditions and additional supplementary material.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eWith a complete review of all existing neuromorphic analog VLSI systems for visual motion sensing, \u003ci\u003eAnalog VLSI Circuits for the Perception of Visual Motion\u003c\/i\u003e is a unique reference for advanced students in electrical engineering, artificial intelligence, robotics and computational neuroscience. It will also be useful for researchers, professionals, and electronics engineers working in the field.\u003c\/p\u003e  \u003cb\u003eForeword.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003ePreface.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Artificial Autonomous Systems.\u003c\/p\u003e \u003cp\u003e1.2 Neural Computation and Analog Integrated Circuits.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Visual Motion Perception.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Image Brightness.\u003c\/p\u003e \u003cp\u003e2.2 Correspondence Problem.\u003c\/p\u003e \u003cp\u003e2.3 Optical Flow.\u003c\/p\u003e \u003cp\u003e2.4 Matching Models.\u003c\/p\u003e \u003cp\u003e2.4.1 Explicit matching.\u003c\/p\u003e \u003cp\u003e2.4.2 Implicit matching.\u003c\/p\u003e \u003cp\u003e2.5 FlowModels.\u003c\/p\u003e \u003cp\u003e2.5.1 Global motion.\u003c\/p\u003e \u003cp\u003e2.5.2 Local motion.\u003c\/p\u003e \u003cp\u003e2.5.3 Perceptual bias.\u003c\/p\u003e \u003cp\u003e2.6 Outline for a Visual Motion Perception System.\u003c\/p\u003e \u003cp\u003e2.7 Review of aVLSI Implementations.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Optimization Networks.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 AssociativeMemory and Optimization.\u003c\/p\u003e \u003cp\u003e3.2 Constraint Satisfaction Problems.\u003c\/p\u003e \u003cp\u003e3.3 Winner-takes-all Networks.\u003c\/p\u003e \u003cp\u003e3.3.1 Network architecture.\u003c\/p\u003e \u003cp\u003e3.3.2 Global convergence and gain.\u003c\/p\u003e \u003cp\u003e3.4 Resistive Network.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Visual Motion Perception Networks.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Model for Optical Flow Estimation.\u003c\/p\u003e \u003cp\u003e4.1.1 Well-posed optimization problem.\u003c\/p\u003e \u003cp\u003e4.1.2 Mechanical equivalent.\u003c\/p\u003e \u003cp\u003e4.1.3 Smoothness and sparse data.\u003c\/p\u003e \u003cp\u003e4.1.4 Probabilistic formulation.\u003c\/p\u003e \u003cp\u003e4.2 Network Architecture.\u003c\/p\u003e \u003cp\u003e4.2.1 Non-stationary optimization.\u003c\/p\u003e \u003cp\u003e4.2.2 Network conductances.\u003c\/p\u003e \u003cp\u003e4.3 Simulation Results for Natural Image Sequences.\u003c\/p\u003e \u003cp\u003e4.4 Passive Non-linear Network Conductances.\u003c\/p\u003e \u003cp\u003e4.5 Extended Recurrent Network Architectures.\u003c\/p\u003e \u003cp\u003e4.5.1 Motion segmentation.\u003c\/p\u003e \u003cp\u003e4.5.2 Attention and motion selection.\u003c\/p\u003e \u003cp\u003e4.6 Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Analog VLSI Implementation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Implementation Substrate.\u003c\/p\u003e \u003cp\u003e5.2 Phototransduction.\u003c\/p\u003e \u003cp\u003e5.2.1 Logarithmic adaptive photoreceptor.\u003c\/p\u003e \u003cp\u003e5.2.2 Robust brightness constancy constraint.\u003c\/p\u003e \u003cp\u003e5.3 Extraction of the Spatio-temporal Brightness Gradients.\u003c\/p\u003e \u003cp\u003e5.3.1 Temporal derivative circuits.\u003c\/p\u003e \u003cp\u003e5.3.2 Spatial sampling.\u003c\/p\u003e \u003cp\u003e5.4 Single Optical Flow Unit.\u003c\/p\u003e \u003cp\u003e5.4.1 Wide-linear-range multiplier.\u003c\/p\u003e \u003cp\u003e5.4.2 Effective bias conductance.\u003c\/p\u003e \u003cp\u003e5.4.3 Implementation of the smoothness constraint.\u003c\/p\u003e \u003cp\u003e5.5 Layout.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Smooth Optical Flow Chip.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Response Characteristics.\u003c\/p\u003e \u003cp\u003e6.1.1 Speed tuning.\u003c\/p\u003e \u003cp\u003e6.1.2 Contrast dependence.\u003c\/p\u003e \u003cp\u003e6.1.3 Spatial frequency tuning.\u003c\/p\u003e \u003cp\u003e6.1.4 Orientation tuning.\u003c\/p\u003e \u003cp\u003e6.2 Intersection-of-constraints Solution.\u003c\/p\u003e \u003cp\u003e6.3 Flow Field Estimation.\u003c\/p\u003e \u003cp\u003e6.4 DeviceMismatch.\u003c\/p\u003e \u003cp\u003e6.4.1 Gradient offsets.\u003c\/p\u003e \u003cp\u003e6.4.2 Variations across the array.\u003c\/p\u003e \u003cp\u003e6.5 Processing Speed.\u003c\/p\u003e \u003cp\u003e6.6 Applications.\u003c\/p\u003e \u003cp\u003e6.6.1 Sensor modules for robotic applications.\u003c\/p\u003e \u003cp\u003e6.6.2 Human–machine interface.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Extended Network Implementations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Motion Segmentation Chip.\u003c\/p\u003e \u003cp\u003e7.1.1 Schematics of the motion segmentation pixel.\u003c\/p\u003e \u003cp\u003e7.1.2 Experiments and results.\u003c\/p\u003e \u003cp\u003e7.2 Motion Selection Chip.\u003c\/p\u003e \u003cp\u003e7.2.1 Pixel schematics.\u003c\/p\u003e \u003cp\u003e7.2.2 Non-linear diffusion length.\u003c\/p\u003e \u003cp\u003e7.2.3 Experiments and results.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Comparison to Human Motion Vision.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Human vs. Chip Perception.\u003c\/p\u003e \u003cp\u003e8.1.1 Contrast-dependent speed perception.\u003c\/p\u003e \u003cp\u003e8.1.2 Bias on perceived direction of motion.\u003c\/p\u003e \u003cp\u003e8.1.3 Perceptual dynamics.\u003c\/p\u003e \u003cp\u003e8.2 Computational Architecture.\u003c\/p\u003e \u003cp\u003e8.3 Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA Variational Calculus.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB Simulation Methods.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eC Transistors and Basic Circuits.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eD Process Parameters and Chips Specifications.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eReferences.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e \"This new book provides unconventional and fresh perspectives on how to understand perception and build simple artificial perceptual systems using VLSI circuits.\" (\u003ci\u003eThe Neurimorphic Engineer,\u003c\/i\u003e March 2007  \u003cp\u003e\u003cb\u003eAlan A. Stocker\u003c\/b\u003e is the author of \u003ci\u003eAnalog VLSI Circuits for the Perception of Visual Motion\u003c\/i\u003e, published by Wiley.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988732592357,"sku":"NP9780470854914","price":158.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470854914.jpg?v=1761781373","url":"https:\/\/k12savings.com\/es\/products\/analog-vlsi-circuits-for-the-perception-of-visual-motion-isbn-9780470854914","provider":"K12savings","version":"1.0","type":"link"}