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

On robust estimation of the location parameter

It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.

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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Stable non-Gaussian random processes , by G. Samorodnitsky and M. S. Taqqu. Pp. 632. £49.50. 1994. ISBN 0-412-05171-0 (Chapman and Hall).

In a mobile ad hoc network, a method of routing data and a mobile unit for use according to the method are arranged to find the relay capacity of a node and the lifetime of the node, and thereby determine whether the node can relay the data The method and unit can find the relay efficiency of nodes and this information is used to route data traffic efficiently through a number of nodes along a route

Mobile telephone network

This paper investigates new transmission techniques for clustered feedback-based wireless networks that are characterized by energy and secrecy constraints. The proposed schemes incorporate multiuser diversity gain with an appropriate power allocation (PA) in order to support a defined Quality-of-Service (QoS) and jointly achieve lifetime maximization and confidentiality. We show that an adaptive PA scheme that adjusts the transmitted power using instantaneous feedback and suspends the transmission when the required power is higher than a threshold significantly prolongs the network lifetime without affecting the QoS of the network. In addition, the adaptation of the transmitted power on the main link improves the secrecy of the network and efficiently protects the source message from eavesdropper attacks. The proposed scheme improves network's confidentiality without requiring any information about the eavesdropper channel and is suitable for practical applications. Another objective of the paper is the energy analysis of networks by taking into account processing and maintenance energy cost at the transmitters. We demonstrate that the combination of PA with an appropriate switch-off mechanism, that allows the source to transmit for an appropriate fraction of the time, significantly extends the network lifetime. All the proposed protocols are evaluated by theoretical and simulation results.

/pdf/a-feedback-based-transmission-for-wireless-networks-with-1dncdx94k1.pdf

A Feedback-Based Transmission for Wireless Networks with Energy and Secrecy Constraints

Multifractal analysis is a powerful tool used in signal processing Multifractal models are essentially characterized by two parameters, the multifractality parameter c 2 and the integral scale A (the time scale beyond which multifractal properties vanish) Yet, most applications concentrate on estimating c 2 while the estimation of A is in general overlooked, despite the fact that A potentially conveys important information Joint estimation of c 2 and A is challenging due to the statistical nature of multifractal processes (ie the strong dependence and non-Gaussian nature), and has barely been considered The present contribution addresses these limitations and proposes a Bayesian procedure for the joint estimation of (c 2 , A) Its originality resides, first, in the construction of a generic multivariate model for the statistics of wavelet leaders for multifractal multiplicative cascade processes, and second, in the use of a suitable Whittle approximation for the likelihood associated with the model The resulting model enables Bayesian estimators for (c 2 , A) to also be computed for large sample size Performance is assessed numerically for synthetic multifractal processes and illustrated for wind-tunnel turbulence data The proposed procedure significantly improves estimation of c 2 and yields, for the first time, reliable estimates for A

/pdf/a-bayesian-approach-for-the-joint-estimation-of-the-4j7jy7ccrh.pdf

Stephen McLaughlin

Papers

Mobile telephone network

A Feedback-Based Transmission for Wireless Networks with Energy and Secrecy Constraints

A Bayesian approach for the joint estimation of the multifractality parameter and integral scale based on the Whittle approximation

Adaptive Bayesian decision feedback equalizer for dispersive mobile radio channel

Radio Resource Allocation with Resource Metric Estimation for Multimedia CDMA Systems