Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

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

Quantization is a process that maps a continous or discrete set of values into approximations that belong to a smaller set. Quantization is a lossy: some information about the original data is lost in the process. The key to a successful quantization is therefore the selection of an error criterion – such as entropy and signal-to-noise ratio – and the development of optimal quantizers for this criterion.

Quantization

Introduces a new measure of conditional connectivity for large regular graphs by requiring each vertex to have at least g good neighbors in the graph. Based on this requirement, the vertex connectivity for the n-dimensional cube is obtained, and the minimal sets of faulty nodes that disconnect the cube are characterized. >

Conditional connectivity measures for large multiprocessor systems

Machine-to-machine (M2M) communication, also referred to as Internet of Things (IoT), is a global network of devices such as sensors, actuators, and smart appliances which collect information, and can be controlled and managed in real time over the Internet. Due to their universal coverage, cellular networks and the Internet together offer the most promising foundation for the implementation of M2M communication. With the worldwide deployment of the fourth generation (4G) of cellular networks, the long-term evolution (LTE) and LTE-advanced standards have defined several quality-of-service classes to accommodate the M2M traffic. However, cellular networks are mainly optimized for human-to-human (H2H) communication. The characteristics of M2M traffic are different from the human-generated traffic and consequently create sever problems in both radio access and the core networks (CNs). This survey on M2M communication in LTE/LTE-A explores the issues, solutions, and the remaining challenges to enable and improve M2M communication over cellular networks. We first present an overview of the LTE networks and discuss the issues related to M2M applications on LTE. We investigate the traffic issues of M2M communications and the challenges they impose on both access channel and traffic channel of a radio access network and the congestion problems they create in the CN. We present a comprehensive review of the solutions for these problems which have been proposed in the literature in recent years and discuss the advantages and disadvantages of each method. The remaining challenges are also discussed in detail.

A Survey of Traffic Issues in Machine-to-Machine Communications Over LTE

We propose a new spectrum-sensing technique based on the sample autocorrelation of the received signal. We assume that the received signal is oversampled and allow for frequency offset between the local oscillator and the carrier of the primary signal. We evaluate the performance of this algorithm for both additive white Gaussian noise (AWGN) and Rayleigh-fading channels and study its sensitivity to carrier frequency offset. Simulation results are presented to verify the accuracy of the approximation assumptions in our analysis. The performance of the proposed algorithm is also compared with those from the energy detector, the covariance detector, and the cyclic-autocorrelation detector. The results show that our algorithm outperforms the covariance detector and the cyclic autocorrelation detector. It also outperforms the energy detector in the presence of noise power uncertainty or in the case of unknown primary signal bandwidth. Finally, we investigate three diversity combining techniques, namely 1) equal gain combining, 2) selective combining and 3) equal gain correlation combining. Our simulations show that for detection probabilities of interest (e.g., > 0.9), a system with a four-branch diversity achieves a signal-to-noise ratio (SNR) gain of more than 5 dB over the no-diversity system that uses the same number of received signal samples.

Autocorrelation-Based Spectrum Sensing for Cognitive Radios

Traditional design rules for cellular networks are not directly applicable to code division multiple access (CDMA) networks where intercell interference is not mitigated by cell placement and careful frequency planning. For transmission quality requirements, a minimum signal-to-interference ratio (SIR) must be achieved. The base-station location, its pilot-signal power (which determines the size of the cell), and the transmission power of the mobiles all affect the received SIR. In addition, because of the need for power control in CDMA networks, large cells can cause a lot of interference to adjacent small cells, posing another constraint to design. In order to maximize the network capacity associated with a design, we develop a methodology to calculate the sensitivity of capacity to base-station location, pilot-signal power, and transmission power of each mobile. To alleviate the problem caused by different cell sizes, we introduce the power compensation factor, by which the nominal power of the mobiles in every cell is adjusted. We then use the calculated sensitivities in an iterative algorithm to determine the optimal locations of the base stations, pilot-signal powers, and power compensation factors in order to maximize the capacity. We show examples of how networks using these design techniques provide higher capacity than those designed using traditional techniques.

/pdf/multicell-cdma-network-design-2i1jznzcp1.pdf

Multicell CDMA network design

Wireless sensor networks are prone to node misbehavior arising from tampering by an adversary (Byzantine attack), or due to other factors such as node failure resulting from hardware or software degradation. In this paper, we consider the problem of decentralized detection in wireless sensor networks in the presence of one or more classes of misbehaving nodes. Binary hypothesis testing is considered where the honest nodes transmit their binary decisions to the fusion center (FC), while the misbehaving nodes transmit fictitious messages. The goal of the FC is to identify the misbehaving nodes and to detect the state of nature. We identify each class of nodes with an operating point (false alarm and detection probabilities) on the receiver operating characteristic (ROC) curve. Maximum likelihood estimation of the nodes' operating points is then formulated and solved using the expectation maximization (EM) algorithm with the nodes' identities as latent variables. The solution from the EM algorithm is then used to classify the nodes and to solve the decentralized hypothesis testing problem. Numerical results compared with those from the reputation-based schemes show a significant improvement in both classification of the nodes and hypothesis testing results. We also discuss an inherent ambiguity in the node classification problem which can be resolved if the honest nodes are in majority.

/pdf/decentralized-hypothesis-testing-in-wireless-sensor-networks-14ialeb7eo.pdf

Mort Naraghi-Pour

Papers

Conditional connectivity measures for large multiprocessor systems

A Survey of Traffic Issues in Machine-to-Machine Communications Over LTE

Autocorrelation-Based Spectrum Sensing for Cognitive Radios

Multicell CDMA network design

Decentralized Hypothesis Testing in Wireless Sensor Networks in the Presence of Misbehaving Nodes