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Table Of Integrals Series And Products

We study a certain random groeth model in two dimensions closely related to the one-dimensional totally asymmetric exclusion process. The results show that the shape fluctuations, appropriately scaled, converges in distribution to the Tracy-Widom largest eigenvalue distribution for the Gaussian Unitary Ensemble.

Shape Fluctuations and Random Matrices

By K. A. Brownlee London: John Wiley & Sons Ltd. 1960. Pp. xv + 543. Price 126s. This book is by the author of Industrial Experimentation, a book which has long been popular in the United Kingdom. It differs from most in offering adequate proofs using 'easy' mathematics.

https://iopscience.iop.org/article/10.1088/0031-9112/14/2/026/pdf

Statistical Theory and Methodology in Science and Engineering

Equalization refers to any signal processing technique used at the receiver to combat intersymbol interference in dispersive channels. This paper reviews the applications of artificial neural networks (ANNs) in modeling nonlinear phenomenon of channel equalization. The literature associated with different feedforward neural network (NN) based equalizers like multilayer perceptron, functional-link ANN, radial basis function, and its variants are reviewed. Feedback-based NN architectures like recurrent NN equalizers are described. Training algorithms are compared in terms of convergence time and computational complexity for nonlinear channel models. Finally, some limitation of current research activities and further research direction is provided.

Channel Equalization Using Neural Networks: A Review

Finite fields play a fundamental role in some of the most fascinating applications of modern algebra to the real world. These applications occur in the general area of data communication, a vital concern in our information society. Technological breakthroughs like space and satellite communications and mundane matters like guarding the privacy of information in data banks all depend in one way or another on the use of finite fields. Because of the importance of these applications to communication and information theory, we will present them in greater detail in the following chapters. Chapter 8 discusses applications of finite fields to coding theory, the science of reliable transmission of messages, and Chapter 9 deals with applications to cryptology, the art of enciphering and deciphering secret messages. This chapter is devoted to applications of finite fields within mathematics. These applications are indeed numerous, so we can only offer a selection of possible topics. Section 1 contains some results on the use of finite fields in affine and projective geometry and illustrates in particular their role in the construction of projective planes with a finite number of points and lines. Section 2 on combinatorics demonstrates the variety of applications of finite fields to this subject and points out their usefulness in problems of design of statistical experiments. In Section 3 we give the definition of a linear modular system and show how finite fields are involved in this theory.

Introduction to finite fields and their applications: Theoretical Applications of Finite Fields

An improved energy aware distributed unequal clustering protocol for heterogeneous wireless sensor networks

This letter presents a new filtering scheme based on contrast enhancement within the filtering window for removing the random valued impulse noise. The application of a nonlinear function for increasing the difference between a noise-free and noisy pixels results in efficient detection of noisy pixels. As the performance of a filtering system, in general, depends on the number of iterations used, an effective stopping criterion based on noisy image characteristics to determine the number of iterations is also proposed. Extensive simulation results exhibit that the proposed method significantly outperforms many other well-known techniques.

A Contrast Enhancement-Based Filter for Removal of Random Valued Impulse Noise

Eigenvalue based double threshold spectrum sensing under noise uncertainty for cognitive radio

The design of large-scale sensor networks interconnecting various sensor nodes has spurred a great deal of interest due to its wide variety of applications. Data fusion is a critical step in designing a wireless sensor network as it handles data acquired by sensory devices. Wireless sensor networks allow distributed sensing and signal processing while collaborating during energy efficient operations. Wireless sensor networks are battery powered; therefore prolonging the network lifetime through an energy aware node organization is highly desirable. The main goal of a topology control scheme in wireless sensor networks is to reduce power consumption in order to extend network lifetime. Our aim is to provide a better understanding of the current research issues in this field. The paper provides a more detailed look at some existing data fusion and topology management algorithms. The most important design issues of data fusion and topology control are also highlighted.

/pdf/data-fusion-and-topology-control-in-wireless-sensor-networks-4vfo45l09o.pdf

Data fusion and topology control in wireless sensor networks

The performance of an image filtering system depends on its ability to detect the presence of noisy pixels in the image. Most of the impulse detection schemes assume the presence of salt and pepper noise in the images and do not work satisfactorily in case of uniformly distributed impulse noise. In this paper, a new algorithm is presented to improve the performance of switching median filter in detection of uniformly distributed impulse noise. The performance of the proposed scheme is demonstrated by the results obtained from computer simulations on various images. Keywords—Switching median filter, Impulse noise, Image filtering, Impulse detection.

Rajoo Pandey

Papers

An improved energy aware distributed unequal clustering protocol for heterogeneous wireless sensor networks

A Contrast Enhancement-Based Filter for Removal of Random Valued Impulse Noise

Eigenvalue based double threshold spectrum sensing under noise uncertainty for cognitive radio

Data fusion and topology control in wireless sensor networks

An Improved Switching Median filter for Uniformly Distributed Impulse Noise Removal