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

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

Cognitive radios hold tremendous promise for increasing spectral efficiency in wireless systems. This paper surveys the fundamental capacity limits and associated transmission techniques for different wireless network design paradigms based on this promising technology. These paradigms are unified by the definition of a cognitive radio as an intelligent wireless communication device that exploits side information about its environment to improve spectrum utilization. This side information typically comprises knowledge about the activity, channels, codebooks, and/or messages of other nodes with which the cognitive node shares the spectrum. Based on the nature of the available side information as well as a priori rules about spectrum usage, cognitive radio systems seek to underlay, overlay, or interweave the cognitive radios' signals with the transmissions of noncognitive nodes. We provide a comprehensive summary of the known capacity characterizations in terms of upper and lower bounds for each of these three approaches. The increase in system degrees of freedom obtained through cognitive radios is also illuminated. This information-theoretic survey provides guidelines for the spectral efficiency gains possible through cognitive radios, as well as practical design ideas to mitigate the coexistence challenges in today's crowded spectrum.

Breaking Spectrum Gridlock With Cognitive Radios: An Information Theoretic Perspective

Information Theory and Reliable Communication

Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

Accurate three-dimensional displacement measurements of bridges and other structures have received significant attention in recent years. The main challenges of such measurements include the cost and the need for a scalable array of instrumentation. This paper presents a novel Hybrid Inertial Vision-Based Displacement Measurement (HIVBDM) system that can measure three-dimensional structural displacements by using a monocular charge-coupled device (CCD) camera, a stationary calibration target, and an attached tilt sensor. The HIVBDM system does not require the camera to be stationary during the measurements, while the camera movements, i.e., rotations and translations, during the measurement process are compensated by using a stationary calibration target in the field of view (FOV) of the camera. An attached tilt sensor is further used to refine the camera movement compensation, and better infers the global three-dimensional structural displacements. This HIVBDM system is evaluated on both short-term and long-term synthetic static structural displacements, which are conducted in an indoor simulated experimental environment. In the experiments, at a 9.75 m operating distance between the monitoring camera and the structure that is being monitored, the proposed HIVBDM system achieves an average of 1.440 mm Root Mean Square Error (RMSE) on the in-plane structural translations and an average of 2.904 mm RMSE on the out-of-plane structural translations.

Measurement of Three-Dimensional Structural Displacement Using a Hybrid Inertial Vision-Based System

Mathematical Foundations for Signal Processing, Communications, and Networking describes mathematical concepts and results important in the design, analysis, and optimization of signal processing algorithms, modern communication systems, and networks. Helping readers master key techniques and comprehend the current research literature, the book offers a comprehensive overview of methods and applications from linear algebra, numerical analysis, statistics, probability, stochastic processes, and optimization. From basic transforms to Monte Carlo simulation to linear programming, the text covers a broad range of mathematical techniques essential to understanding the concepts and results in signal processing, telecommunications, and networking. Along with discussing mathematical theory, each self-contained chapter presents examples that illustrate the use of various mathematical concepts to solve different applications. Each chapter also includes a set of homework exercises and pointers to further readings for additional topics and applications. This text helps readers understand fundamental and advanced results as well as recent research trends in the interrelated fields of signal processing, telecommunications, and networking. It provides all the necessary mathematical background to prepare students for more advanced courses and train specialists working in these areas.

Mathematical Foundations for Signal Processing, Communications, and Networking

Emergency services are being improved by the Federal Communications Commission (FCC) by obligating wireless service providers to be E911 compliant by December 2005. We propose a system which is not only compliant with the E911 requirements, but also provides enhanced functionality to relay an emergency call arising from a user outside the cellular coverage area via another user within the range of the network. Simulations based on the GSM wireless technology are described and results show that cellular ad-hoc relay for emergencies (CARE) achieves a system efficiency of over 90%.

http://lyle.smu.edu/~rajand/Pubs/care_vtc04.pdf

Cellular ad-hoc relay for emergencies (CARE)

We derive a simple characterization of the diversity-multiplexing (D-MG) tradeoff in three multiuser scenarios -multiple access, broadcast and relay assuming channel knowledge at the transmitters. Specifically, we study the D-MG tradeoff of protocols in literature that opportunistically use the channel knowledge to maximize the information rate. We show that for the multiple access channel with K users, a feedback of log K" bits is sufficient to maximize the multiuser diversity gain. The multiuser diversity gains in all three channels are obtained without explicit cooperation between users.

/pdf/multiuser-diversity-in-wireless-networks-a-diversity-3uh31yrlrj.pdf

Multiuser Diversity in Wireless Networks: A Diversity-Multiplexing Perspective

In this paper, a practical multiple input multiple output Dirty paper coding (MIMO-DPC) scheme is designed to cancel the effect of additive interference that is known perfectly to the transmitter. The proposed system uses a trio of encoders — a LDPC code, a vector quantizer implemented as a convolutional decoder and an Orthogonal space time block code (STBC) to achieve temporal coding gain, interference cancelation and spatial diversity, respectively. First, we derive the equivalent noise seen by the receiver using an equivalent lattice based dirty paper code. Then the optimal value of the power inflation factor, which is one of the key system parameters used to minimize the equivalent noise seen by the receiver is derived. Furthermore, we analytically prove that the equivalent noise seen by the receiver tends to 0 for large number of receive antennas. Performance results in the case of various number of receiver antennas are presented and show that significant reduction in bit error probabilities can be obtained over a system that uses no interference cancelation.

/pdf/multiple-input-multiple-output-dirty-paper-coding-system-1fdfzljwi2.pdf

Dinesh Rajan

Papers

Measurement of Three-Dimensional Structural Displacement Using a Hybrid Inertial Vision-Based System

Mathematical Foundations for Signal Processing, Communications, and Networking

Cellular ad-hoc relay for emergencies (CARE)

Multiuser Diversity in Wireless Networks: A Diversity-Multiplexing Perspective

Multiple input multiple output Dirty paper coding: System design and performance