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 this paper, we propose a predictive resource metric region (RMR) based resource metric estimation method, which utilizes a resource metric mapping function (RMMF), both of which permit efficient inter-working between the physical layer and higher layers. The RMR can provide the acceptable resource region where QoS and acceptable link quality can be guaranteed with an achievable resource margin to be utilized in terms of capacity margin, the degree of confidence (DCL) of user, second-order statistics of Eb/Io. With predicted capacity margin and variance, DCL can deliver decision parameters with which an adaptive QoS based admission control can perform well taking capacity and resource availability into account in a dynamic and predictive manner.

Radio resource metric estimation for wireless CDMA communication systems maximising radio resource utilisation

This work proposes a new iterative application of the Chase algorithm on Reed-Solomon (RS) product codes. Reliability information of bit positions is calculated only once. Although the bit error rates (BER) achieved are not as good as the results obtained with the recent turbo product decoding (TPD) algorithm, decoding complexity is far less. Simulation results are presented for a communications system using binary phase shift keying (BPSK) on an additive white Gaussian noise (AWGN) channel for both hard and soft decoding iterations and their performances are compared with turbo product decoding.

Iterative application of the Chase algorithm on Reed-Solomon product codes

Measuring markers of stress such as pH and redox potential are important when studying toxicology in in vitro models because they are markers of oxidative stress, apoptosis and viability. While surface enhanced Raman spectroscopy is ideally suited to the measurement of redox potential and pH in live cells, the time-intensive nature and perceived difficulty in signal analysis and interpretation can be a barrier to its broad uptake by the biological community. In this paper we detail the development of signal processing and analysis algorithms that allow SERS spectra to be automatically processed so that the output of the processing is a pH or redox potential value. By automating signal processing we were able to carry out a comparative evaluation of the toxicology of silver and zinc oxide nanoparticles and correlate our findings with qPCR analysis. The combination of these two analytical techniques sheds light on the differences in toxicology between these two materials from the perspective of oxidative stress.

SERS as a tool for in vitro toxicology

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( An overview ) Time-Frequency Reassignment and Synchrosqueezing

While rate-compatible punctured low-density parity-check (RCP-LDPC) codes offer high flexibility in terms of code rate at a relatively low cost in implementation complexity, they are reported to require more decoding iterations than unpunctured LDPC codes. In this paper1 we consider layered belief propagation decoding and propose efficient check node layering that significantly accelerates the decoding convergence of RCP-LDPC codes. We show that the proposed layering outperforms both random layering and conventional BP decoding. The performance improvements become more distinctive at high rates and they come at no additional implementation cost.

Stephen McLaughlin

Papers

Radio resource metric estimation for wireless CDMA communication systems maximising radio resource utilisation

Iterative application of the Chase algorithm on Reed-Solomon product codes

SERS as a tool for in vitro toxicology

( An overview ) Time-Frequency Reassignment and Synchrosqueezing

Fast Decoding of Rate-Compatible Punctured LDPC Codes