Описание: Measuring systems are an essential part of all automated production systems. They can serve to ensure the quality of production, or they can be used to assure the reliability and safety in various areas. The same applies in principle for fields of telecommunication, energy production and distribution, health care, etc. Similarly, no serious scientific research in the field of natural and technical sciences can be performed without objective data about the investigated object, which is usually acquired using measuring system. Demands on the speed and accuracy of measurement increase in all areas in general. These are the grounds for publishing this book.
Advanced Distributed Measuring Systems: Exhibits of Application offers eight up-to-date examples of typical laboratory, industrial, and biomedical applications of advanced measuring and information systems, including virtual instrumentation. The book arose based on the most interesting papers from this area published at IDAACS 2011 conference. However, single chapters include not only system design solution in wider context but also relevant theoretical parts, achieved results, and possible future ways of design and development.
Technical topics discussed in the book include: - embedded applications; - small distributed systems; - automotive distributed system; - distributed monitoring systems based on wireless networks; - synchronization in large DAQ systems; - virtual instrumentation.
Advanced Distributed Measuring Systems: Exhibits of Application is ideal for personnel of firms dealing with control systems, automotive electronics, airspace instrumentation, health care technology, etc. as well as academic staff and postgraduate students in electrical, control, and computer engineering.
Описание: The most important theoretical aspects of Image and Signal Processing (ISP) for both deterministic and random signals, the theory being supported by exercises and computer simulations relating to real applications.
Автор: Amitava Chatterjee; Hadi Nobahari; Patrick Siarry Название: Advances in Heuristic Signal Processing and Applications ISBN: 364244525X ISBN-13(EAN): 9783642445255 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
There have been significant developments in the design and application of algorithms for both one-dimensional signal processing and multidimensional signal processing, namely image and video processing, with the recent focus changing from a step-by-step procedure of designing the algorithm first and following up with in-depth analysis and performance improvement to instead applying heuristic-based methods to solve signal-processing problems.
In this book the contributing authors demonstrate both general-purpose algorithms and those aimed at solving specialized application problems, with a special emphasis on heuristic iterative optimization methods employing modern evolutionary and swarm intelligence based techniques. The applications considered are in domains such as communications engineering, estimation and tracking, digital filter design, wireless sensor networks, bioelectric signal classification, image denoising, and image feature tracking.
The book presents interesting, state-of-the-art methodologies for solving real-world problems and it is a suitable reference for researchers and engineers in the areas of heuristics and signal processing.
Автор: Amitava Chatterjee; Hadi Nobahari; Patrick Siarry Название: Advances in Heuristic Signal Processing and Applications ISBN: 364237879X ISBN-13(EAN): 9783642378799 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Chap. 1: Nonconvex Optimization via Joint Norm Relaxed SQP and Filled Function Method with Application to Minimax Two-Channel Linear Phase FIR QMF Bank Design.- Chap. 2: Robust Reduced-Rank Adaptive LCMV Beamforming Algorithms Based on Joint Iterative Optimization of Parameters.- Chap. 3: Designing OFDM Radar Waveform for Target Detection Using Multiobjective Optimization.- Chap. 4: Multiobject Tracking using Particle Swarm Optimization on Target Interactions.- Chap. 5: A Comparative Study of Modified BBO Variants and Other Metaheuristics for Optimal Power Allocation in Wireless Sensor Networks.- Chap. 6: Joint Optimization of Detection and Tracking in Adaptive Radar Systems.- Chap. 7: Iterative Design of FIR Filters.- Chap. 8: A Metaheuristic Approach to Two-Dimensional Recursive Digital Filter Design.- Chap. 9: A Survey of Kurtosis Optimization Schemes for MISO Source Separation and Equalization.- Chap. 10: Swarm Intelligence Techniques Applied to Nonlinear Systems State Estimation.- Chap. 11: Heuristic Optimal Design of Multiplier-less Digital Filter.- Chap. 12: Hybrid Correlation-Neural Network Synergy for Gait Signal Classification.- Chap. 13: Image Denoising Using Wavelets: Application in Medical Imaging.- Chap. 14: Signal Separation with A Priori Knowledge Using Sparse Representation.- Chap. 15: Definition of a Discrete Color Monogenic Wavelet Transform.- Chap. 16: On Image Matching and Feature Tracking for Embedded Systems: State of the Art.
Автор: Ohta, Jun Название: Smart CMOS Image Sensors and Applications, Second Edition ISBN: 1498764649 ISBN-13(EAN): 9781498764643 Издательство: Taylor&Francis Рейтинг: Цена: 19906.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides the state-of-the-art of CMOS image sensors and applications. The book describes the fundamentals of CMOS image sensors, optoelectronic device physics, and introduces typical CMOS image sensor structures, such as an active pixel sensor (APS).
This book focuses on scheduling algorithms for parallel applications on heterogeneous distributed systems, and addresses key scheduling requirements – high performance, low energy consumption, real time, and high reliability – from the perspectives of both theory and engineering practice. Further, it examines two typical application cases in automotive cyber-physical systems and cloud systems in detail, and discusses scheduling challenges in connection with resource costs, reliability and low energy.
The book offers a comprehensive and systematic treatment of high-performance, low energy consumption, and high reliability issues on heterogeneous distributed systems, making it a particularly valuable resource for researchers, engineers and graduate students in the fields of computer science and engineering, information science and engineering, and automotive engineering, etc. The wealth of motivational examples with figures and tables make it easy to understand.
Описание: This book focuses on scheduling algorithms for parallel applications on heterogeneous distributed systems, and addresses key scheduling requirements - high performance, low energy consumption, real time, and high reliability - from the perspectives of both theory and engineering practice.
Networking and Architectures.- Accelerating GPU-Based Out-of-Core Stencil Computation with On-the-Fly Compression.- Routing with Ant Colony Optimization in Wireless Mesh Networks.- A light-weight scheme for detecting component structure of network traffic.- Evaluating the performance and conformance of a SYCL implementation for SX-Aurora TSUBASA.- Bayesian Optimization Based Task Scheduling in Heterogeneous Computing Systems.- Optimizing Uplink Bandwidth Utilization for Crowdsourced Livecast.- A Batched Jacobi SVD Algorithm on GPUs and Its Application to Quantum Lattice Systems.- A Molecular Dynamics Based Multi-Scale Platelet Aggregation Model and Its High-throughput Simulation.- Approximation and Polynomial Algorithms for Multi-Depot Capacitated Arc Routing Problems.- Zero-shot Face Swapping with De-identification Adversarial Learning.- An user-driven active way to push ACL in Software-Defined Networking.- Photonic Computing and Communication for Neural Network Accelerators.- Performance Comparison of Multi-layer Perceptron Training on Electrical and Optical Network-on-Chips.- The design and implementation of reconfigurable quaternary logic processor.- A 3D Dubins Curve Constructing Method Based on Particle Swarm Optimization.- Software Systems and Technologies.- Towards Conflict-Aware Workload Co-execution on SX-Aurora TSUBASA.- A Learning-Based Scheduler for High Volume Processing in Data Warehouse using Graph Neural Networks.- Adaptive Updates for Erasure-Coded Storage Systems Based on Data Delta and Logging.- Matching Program Implementations and Heterogeneous Computing Systems.- FastDCF: A Partial Index based Distributed and Scalable Near-Miss Code Clone Detection Approach for Very Large Code Repositories.- Towards Optimal Fast Matrix Multiplication on CPU-GPU Platforms.- Temperature Matrix-based Data Placement Using Improved Hungarian Algorithm in Edge Computing Environments.- Realtime Physics Simulation of Large Virtual Space with Docker Containers.- A deep reinforcement learning-based approach to the scheduling of multiple workflows on non-dedicated edge servers.- A MVCC Approach to Parallelizing Interoperability of Consortium Blockchain.- An Effective and Reliable Cross-Blockchain Data Migration Approach.- Algorithm for the Facility Location Problem with Origin and Destination.- Reinforcement Learning-based Auto-scaling Algorithm for Elastic Cloud Workflow Service.- Optimal Energy Efficiency Strategy of mm Wave Cooperative Communication Small Cell based on SWIPT.- Providing Low Latency Execution Guarantees under Uncertainty in Serverless Platforms.- High resolution patient-specific blood flow simulation in a full-size aneurysmal aorta based on a parallel two-level method.- Optimizing Data Locality by Executor Allocation in Reduce Stage for Spark Framework.- TEFRED: A temperature and energy cognizant fault-tolerant real-time scheduler based on deadline partitioning for heterogeneous platforms.- Algorithms and Applications.- Social Recommendation via Graph Attentive Aggregation.- MACSQ: Massively Accelerated DeepQ Learning on GPUs using on-the-fly State Construction.- Model-based Multi-agent Policy Optimization with Dynamic Dependence Modeling.- Multi-index federated aggregation algorithm based on trusted verification.- Few-shot Generative Learning by Modeling Stereoscopic Priors.- Distributed fair k-Center clustering problems with outliers.- Multi-zone residential HVAC control with satisfying occupants' thermal comfort requirements and saving energy via reinforcement learning.- Approximating BP Maximization with Distorted-Based Strategy.- Streaming Algorithms for Maximization of a Non-Submodular Function with a Cardinality Constraint on the Integer Lattice.- Adaptable Focal Loss for Imbalanced Text Classification.- Roman Amphitheater Classification Using Convolutional Neural Network and Data Augmentation.- Data-Hungry Issue in Personalized Product Search.- Jointl
Описание: This book presents a study of the use of optimization algorithms in complex image processing problems. The problems selected explore areas ranging from the theory of image segmentation to the detection of complex objects in medical images. Furthermore, the concepts of machine learning and optimization are analyzed to provide an overview of the application of these tools in image processing. The material has been compiled from a teaching perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics, and can be used for courses on Artificial Intelligence, Advanced Image Processing, Computational Intelligence, etc. Likewise, the material can be useful for research from the evolutionary computation, artificial intelligence and image processing communities.
