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

Matrix Differential Calculus with Applications in Statistics and Econometrics

It is now well known that employing channel adaptive signaling in wireless communication systems can yield large improvements in almost any performance metric. Unfortunately, many kinds of channel adaptive techniques have been deemed impractical in the past because of the problem of obtaining channel knowledge at the transmitter. The transmitter in many systems (such as those using frequency division duplexing) can not leverage techniques such as training to obtain channel state information. Over the last few years, research has repeatedly shown that allowing the receiver to send a small number of information bits about the channel conditions to the transmitter can allow near optimal channel adaptation. These practical systems, which are commonly referred to as limited or finite-rate feedback systems, supply benefits nearly identical to unrealizable perfect transmitter channel knowledge systems when they are judiciously designed. In this tutorial, we provide a broad look at the field of limited feedback wireless communications. We review work in systems using various combinations of single antenna, multiple antenna, narrowband, broadband, single-user, and multiuser technology. We also provide a synopsis of the role of limited feedback in the standardization of next generation wireless systems.

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An overview of limited feedback in wireless communication systems

Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology.

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Deep learning for computational biology

Compressed sensing (CS) utilizes the sparsity of magnetic resonance (MR) images to enable accurate reconstruction from undersampled k-space data. Recent CS methods have employed analytical sparsifying transforms such as wavelets, curvelets, and finite differences. In this paper, we propose a novel framework for adaptively learning the sparsifying transform (dictionary), and reconstructing the image simultaneously from highly undersampled k-space data. The sparsity in this framework is enforced on overlapping image patches emphasizing local structure. Moreover, the dictionary is adapted to the particular image instance thereby favoring better sparsities and consequently much higher undersampling rates. The proposed alternating reconstruction algorithm learns the sparsifying dictionary, and uses it to remove aliasing and noise in one step, and subsequently restores and fills-in the k-space data in the other step. Numerical experiments are conducted on MR images and on real MR data of several anatomies with a variety of sampling schemes. The results demonstrate dramatic improvements on the order of 4-18 dB in reconstruction error and doubling of the acceptable undersampling factor using the proposed adaptive dictionary as compared to previous CS methods. These improvements persist over a wide range of practical data signal-to-noise ratios, without any parameter tuning.

MR Image Reconstruction From Highly Undersampled k-Space Data by Dictionary Learning

During the initial development of the IEEE 802.11n (11n) amendment for improving the throughput of wireless LANs, a lot of excitement existed surrounding the potential higher throughput (i.e., faster downloads), and increased range (distance) achievable. However, delays in the development of this standard (which began in 2003, and is still in the final draft stages) as well as vendor, customer reluctance to adopt the pre-11n offerings in the marketplace, have generally slowed interest in this next-generation technology. However, there is still much to be excited about. The latest draft of IEEE 802.11n (Draft 3.0) offers the potential of throughputs beyond 200 Mbps, based on physical layer (PHY) data rates up to 600 Mbps. This is achieved through the use of multiple transmit and receive antennas, referred to as MIMO (multiple input, multiple output). Using techniques such as spatial division multiplexing (SDM), transmitter beamforming, and space-time block coding (STBC), MIMO is used to increase dramatically throughput over single antenna systems (by two to four times) or to improve range of reception, depending on the environment. This article offers an exposition on the techniques used in IEEE 802.11n to achieve the above improvements to throughput and range. First, the current generation WLAN devices (11a/b/g) are described in terms of the benefits offered to end users. Next, the evolution of the Tin amendment is discussed, describing the main proposals given, and illustrating reasons for the delay in standardization. Then, the changes to the PHY for 11n are presented. A description of channel modeling with MIMO is shown, followed by the signal processing techniques employed, including MIMO channel estimation and detection, space-time block coding (STBC), and transmitter beamforming. Simulation results are presented which illustrate the benefits of these techniques, versus the existing a/g structures, for both throughput and range. Finally, a brief section outlining considerations for the rapid prototyping of a baseband design based on the 802.11n PHY is presented. We conclude with a discussion of the future for 11n, describing the issues addressed with Drafts 2.0 and 3.0, as well as its place in a wireless market with WiMAX and Bluetooth.

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Wireless LAN Comes of Age: Understanding the IEEE 802.11n Amendment

to Nonlinear Systems.- Polynomial Models of Nonlinear Systems.- Volterra and Wiener Nonlinear Models.- Nonlinear System Identification Methods.- to Adaptive Signal Processing.- Nonlinear Adaptive System Identification Based on Volterra Models.- Nonlinear Adaptive System Identification Based on Wiener Models (Part 1).- Nonlinear Adaptive System Identification Based on Wiener Models (Part 2).- Nonlinear Adaptive System Identification Based on Wiener Models (Part 3).- Nonlinear Adaptive System Identification Based on Wiener Models (Part 4).- Conclusions, Recent Results, and New Directions.

Adaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches

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Adaptive Nonlinear System Identification

In this paper, we present a new hardware-oriented, modified diamond search (HMDS) algorithm, for fast integer pel, motion estimation in H.264/AVC. We also present our co-designed, low power very large scale integration (VLSI) architecture for HMDS. The goal of HMDS is to enable the support of high quality video on low power mobile devices and low bit rate applications which typically use H.264/AVC baseline profile at levels 1-2. Our experiments use standard test sequences ranging from QCIF to high-definition 1280 × 720p video. The proposed VLSI architecture is prototyped as an field-programable gate array (FPGA)-based field programable system-on-chip. Our results show that HMDS on average has better rate-distortion performance and speedup, compared to previous state-of-the-art fast motion estimation algorithms, while its losses compared to full search motion estimation, are insignificant. Our prototyped architecture is more hardware-efficient than previous FPGA-based architectures in terms of power consumption, area, throughput, and memory utilization. We also show that its performance in terms of maximum frequency, minimum frequency, transistor count, and power consumption are comparable to that of state-of-the-art architectures implemented on application-specific integrated circuits.

Algorithm and Architecture Co-Design of Hardware-Oriented, Modified Diamond Search for Fast Motion Estimation in H.264/AVC

The affine projection class of algorithms (APA) provides faster convergence than LMS-based adaptive filters. Its convergence analysis is not as extensively studied as Normalized LMS (NLMS), and remains an active area of research. For tractability, most works on APA make many assumptions on the statistics of the input, as well as correlation between signals. Here we consider the effect of the correlation between filter coefficients and past measurement noise on MSE error. The effect of this correlation was found to be dependent on step-size mu, increasing or decreasing the predicted MSE depending on whether mu is less than or greater than 1, irrespective of the input statistics. Simulations are used to verify the analysis results presented.

Tokunbo Ogunfunmi

Papers

Wireless LAN Comes of Age: Understanding the IEEE 802.11n Amendment

Adaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches

Adaptive Nonlinear System Identification

Algorithm and Architecture Co-Design of Hardware-Oriented, Modified Diamond Search for Fast Motion Estimation in H.264/AVC

On the Convergence Behavior of the Affine Projection Algorithm for Adaptive Filters