http://www.eecs.ucf.edu/~dcm/Teaching/COT4810-Spring2011/Literature/SensorNetworksSurvey.pdf

Wireless sensor networks: a survey

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Publisher Summary This chapter provides an account of different neural network architectures for pattern recognition. A neural network consists of several simple processing elements called neurons. Each neuron is connected to some other neurons and possibly to the input nodes. Neural networks provide a simple computing paradigm to perform complex recognition tasks in real time. The chapter categorizes neural networks into three types: single-layer networks, multilayer feedforward networks, and feedback networks. It discusses the gradient descent and the relaxation method as the two underlying mathematical themes for deriving learning algorithms. A lot of research activity is centered on learning algorithms because of their fundamental importance in neural networks. The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue. It closes with the discussion of performance and implementation issues.

Neural Networks for Pattern Recognition

Data Mining - Concepts and Techniques.

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

String patterns of leading digits

This paper explores the application ofwavelets to a variety of real life problems and more specifically to image processingproblems A general review of the construction and analysis of wavelet analysis will be presented The issues like multiresolutionanalysis in the context of sensor integration and pattern recognition and other salient features of the images using wavelets will be discussed in detail1 GENERAL INTRODUCTIONFourier analysis has been the dominant principal analyticaltool in the mathematical sciences and engineering, offering a rich framework with powerful techniques to study properties of functions and operators, and signals via their spectra In some *Dr Iyengar is currently Chairman of the Department of ComputerScience and is the coauthor of a well known text book on Wavelets published by CRC Press He has published over 220 papers research papers in first rate Computer Science, IEEE Journals, and various refereed proceedings He is an IEEE Fellow, Williams

Wavelets: an important tool of mathematical analysis for image/signal processing applications

In recent years, there is an ever-increasing concern about energy consumption and its environmental impacts, reliable energy supply, and sustainable development of energy and power networks. These issues motivate the evolution of Smart Grid (SG) as a novel means to worldwide electricity grid [1]. In this context, optimal operation of the power systems depends on finding the power flow through the transmission lines in the network. DC power flow has been widely used to tackle the power flow problem in the transmission networks.

Error Detection of DC Power Flow Using State Estimation

Abstract The high demand for user-centric applications such as secure cloud storage laid the foundation for the development of user-centric security schemes with multiple security features in recent years. But, the current state-of-art techniques primarily emphasized only one type of security feature i.e., either homomorphism or non-malleability. To fill this gap and provide a common platform for both homomorphic and non-malleable cloud applications, we have introduced a new public key-based probabilistic encryption switching method. The proposed scheme generates $${\mathcal {O}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>O</mml:mi> </mml:math> ( m +2 log N ) bits of ciphertext where $$m\in {\mathbb {N}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>m</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> and $$m&lt;n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>m</mml:mi> <mml:mo>&lt;</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> , $$n\in {\mathbb {N}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> is the plaintext size, N is the RSA composite. The superior performance of the proposed scheme has been tested in comparison to existing methods and is reported in this paper. 

/pdf/a-probabilistic-public-key-encryption-switching-scheme-for-36u0ah5f.pdf

A probabilistic public key encryption switching scheme for secure cloud storage

k-Systems methodology takes an arbitrary finite valued multivariate real function and transforms it into a dimensionless (0,1) interval function with associated "marginal" equations. Computations are then performed on this function and its marginal equations invoking solely the principle of maximum entropy. Many traditional statistical measures are thereby computed in this context. The claim is that this constitutes a correct framework for these measures.This theory has many statisticians throwing up their hands in consternation. The principle of maximum entropy is somewhat mysterious itself, but now the k-systems (0,1) function no longer represents relative frequencies, and the principle of maximum entropy has only been applied and rationalized for relative frequencies. Puzzling questions arise: Just what is this methodology doing, why does it seem to do it so well, and why and when is it correct? In this paper we explore the rationale behind k-systems.

S. Sitharama Iyengar

Papers

String patterns of leading digits

Wavelets: an important tool of mathematical analysis for image/signal processing applications

Error Detection of DC Power Flow Using State Estimation

A probabilistic public key encryption switching scheme for secure cloud storage

Why k-systems methodology works