Processing Networks


Book Description

The state of the art in fluid-based methods for stability analysis, giving researchers and graduate students command of the tools.




Scheduling and Congestion Control for Wireless and Processing Networks


Book Description

In this book, we consider the problem of achieving the maximum throughput and utility in a class of networks with resource-sharing constraints. This is a classical problem of great importance. In the context of wireless networks, we first propose a fully distributed scheduling algorithm that achieves the maximum throughput. Inspired by CSMA (Carrier Sense Multiple Access), which is widely deployed in today's wireless networks, our algorithm is simple, asynchronous, and easy to implement. Second, using a novel maximal-entropy technique, we combine the CSMA scheduling algorithm with congestion control to approach the maximum utility. Also, we further show that CSMA scheduling is a modular MAC-layer algorithm that can work with other protocols in the transport layer and network layer. Third, for wireless networks where packet collisions are unavoidable, we establish a general analytical model and extend the above algorithms to that case. Stochastic Processing Networks (SPNs) model manufacturing, communication, and service systems. In manufacturing networks, for example, tasks require parts and resources to produce other parts. SPNs are more general than queueing networks and pose novel challenges to throughput-optimum scheduling. We proposes a "deficit maximum weight" (DMW) algorithm to achieve throughput optimality and maximize the net utility of the production in SPNs. Table of Contents: Introduction / Overview / Scheduling in Wireless Networks / Utility Maximization in Wireless Networks / Distributed CSMA Scheduling with Collisions / Stochastic Processing networks




Wireless Sensor Networks


Book Description

Publisher Description




Signal and Image Processing with Neural Networks


Book Description

The first book to offer practical applications of neural networks to solve problems in digital signal processing and imaging. A highly practical book with a minimum of math and a wealth of examples. Disk includes a complete program for training, testing, and using neural networks along with C++ subroutines for all techniques discussed and source for the book's example code.




Signal Processing and Networking for Big Data Applications


Book Description

This unique text helps make sense of big data using signal processing techniques, in applications including machine learning, networking, and energy systems.




Learning from Data Streams


Book Description

Processing data streams has raised new research challenges over the last few years. This book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. Applications in security, the natural sciences, and education are presented. The huge bibliography offers an excellent starting point for further reading and future research.




Applied Neural Networks for Signal Processing


Book Description

A comprehensive introduction to the use of neural networks in signal processing.




Information Processing in Sensor Networks


Book Description

This volume contains the Proceedings of the 2nd International Workshop on Information Processing in Sensor Networks (IPSN 2003). The workshop was held at the Palo Alto Research Center (PARC), Palo Alto, California, on April 22–23, 2003. Informationprocessinginsensornetworksisaninterdisciplinaryresearcharea with deep connections to signal processing, networking and protocols, databases and information management, as well as distributed algorithms. Because of - vances in MEMS microsensors, wireless networking, and embedded processing, ad hoc networks of sensors are becoming increasingly available for commercial andmilitaryapplicationssuchasenvironmentalmonitoring(e.g.,tra?c,habitat, security), industrial sensing and diagnostics (e.g., factories, appliances), inf- structure maintenance (e.g., power grids, water distribution, waste disposal), and battle?eld awareness (e.g., multitarget tracking). From the engineering and computing point of view, sensor networks have become a rich source of problems in communication protocols, sensor tasking and control, sensor fusion, distributed databases and algorithms, probabilistic reasoning, system/software architecture, design methodologies, and evaluation metrics. This workshop took a systemic approach to address crosslayer issues, from the physical sensor layer to the sensor signal processing and networking levels and then all the way to the applications. Following the successful 1st Workshop on Collaborative Signal and Inf- mation Processing in Sensor Networks at PARC in 2001, this new workshop brought together researchers from academia, industry, and government to p- sent and discuss recent work concerning various aspects of sensor networks such as information organization, querying, routing, and self-organization, with an emphasis on the high-level information processing tasks that these networks are designed to perform.




Artificial Neural Networks in Food Processing


Book Description

Artificial Neural Networks (ANNs) is a powerful computational tool to mimic the learning process of the mammalian brain. This book gives a comprehensive overview of ANNs including an introduction to the topic, classifications of single neurons and neural networks, model predictive control and a review of ANNs used in food processing. Also, examples of ANNs in food processing applications such as pasteurization control are illustrated.




Speech Processing, Recognition and Artificial Neural Networks


Book Description

Speech Processing, Recognition and Artificial Neural Networks contains papers from leading researchers and selected students, discussing the experiments, theories and perspectives of acoustic phonetics as well as the latest techniques in the field of spe ech science and technology. Topics covered in this book include; Fundamentals of Speech Analysis and Perceptron; Speech Processing; Stochastic Models for Speech; Auditory and Neural Network Models for Speech; Task-Oriented Applications of Automatic Speech Recognition and Synthesis.