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.

/pdf/hyperspectral-unmixing-overview-geometrical-statistical-and-2lb11fr9l4.pdf

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, the spatial and temporal dispersion and correlation properties of a wideband dynamic directional indoor channel at 5.2 GHz are presented based on extensive measurement campaigns in four different environments under various propagation scenarios. Channel spatio-temporal dispersions are assessed in terms of the rms delay spread (DS) and rms azimuth spread (AS), respectively, while the correlation properties are determined by the spatio-temporal correlation coefficient. A strong correlation is observed with the presence of the line-of sight (LOS) path but degraded when the LOS is obstructed. The variations of the spatio-temporal dispersions with mobility are also investigated where higher values of spatio-temporal dispersions were found when the transmitter and receiver separation increased particularly in a more cluttered environment. The average values of the rms DS and rms AS for all investigated environments and scenarios are also given. These parameters can be used as empirical values for the 5 GHz band WLAN systems.

Spatio-temporal dispersion and correlation properties for the 5.2 GHz WLAN indoor propagation environments

We propose an analytical model for differentiated service in a wireless mobile ad-hoc network (MANET) with a Rayleigh-fading channel. The system is modelled using a multidimensional Markov chain to support real-time applications. It is also used to demonstrate that various types of data arrival process can be modeled by a Markov modulated arrival process (MMAP). Numerical analyses are drawn to estimate the packet drop probability, effective throughput and packet queuing delay. We validate the scheme by simulation under different link utilizations using the IEEE 802.11b DCF ad hoc model. Analytical and simulation results are compared to determine the accuracy of the presented methods. Both show that better quality-of-service (QoS) can be achieved for high priority traffic.

An analytical model for differentiated services in wireless mobile ad-hoc network

Distributed channel-adaptive fair allocation (DCAFA) is proposed to extend the IEEE 802.11e enhanced distributed coordination function (EDCF), by halving the contention window (CW) after zeta consecutive successful transmissions to reduce the collision probability when channel is busy. The scheme computes an adaptive threshold function for each priority queue by taking into account the channel load. Simulation results show that DCAFA outperforms EDCF in-terms of global throughput, throughput of traffic class, frame discard probability and average delay in medium and high load cases. They also show that the proposed analytical model is accurate and achieves a high degree of fairness among applications of same priority level

Distributed Channel-Adaptive Fair Allocation for IEEE 802.11e WLAN

https://www-ljk.imag.fr/membres/Thomas.Oberlin/files/icassp13_presentation.pdf

Analysis and Reconstruction of strongly modulated Multicomponent Signals with the Short-Time Fourier Transform

We demonstrate 2.5-dimensional, 180° field-of-view non-line-of-sight reconstructions of large-scale scenes using time-correlated single-photon detection and pulsed illumination along an arc at a small opening where a vertical wall edge meets a floor plane.

Stephen McLaughlin

Papers

Spatio-temporal dispersion and correlation properties for the 5.2 GHz WLAN indoor propagation environments

An analytical model for differentiated services in wireless mobile ad-hoc network

Distributed Channel-Adaptive Fair Allocation for IEEE 802.11e WLAN

Analysis and Reconstruction of strongly modulated Multicomponent Signals with the Short-Time Fourier Transform

Edge-Resolved Transient Imaging