Exploring and identifying structure is even more important for multivariate data than univariate data, given the difficulties in graphically presenting multivariate data and the comparative lack of parametric models to represent it. Unfortunately, such exploration is also inherently more difficult.

Multivariate Density Estimation

Year 2009 marks the completion of 50 years of the invention of CORDIC (coordinate rotation digital computer) by Jack E. Volder. The beauty of CORDIC lies in the fact that by simple shift-add operations, it can perform several computing tasks such as the calculation of trigonometric, hyperbolic and logarithmic functions, real and complex multiplications, division, square-root, solution of linear systems, eigenvalue estimation, singular value decomposition, QR factorization and many others. As a consequence, CORDIC has been utilized for applications in diverse areas such as signal and image processing, communication systems, robotics and 3-D graphics apart from general scientific and technical computation. In this article, we present a brief overview of the key developments in the CORDIC algorithms and architectures along with their potential and upcoming applications.

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50 Years of CORDIC: Algorithms, Architectures, and Applications

From the Publisher: 
This is undoubtedly the most accessible book on digital signal processing (DSP) available to the beginner. Using intuitive explanations and well-chosen examples, this book gives you the tools to develop a fundamental understanding of DSP theory. The author covers the essential mathematics by explaining the meaning and significance of the key DSP equations. Comprehensive in scope, and gentle in approach, the book will help you achieve a thorough grasp of the basics and move gradually to more sophisticated DSP concepts and applications.

数字信号处理 = Understanding digital signal processing

Although arithmetic coding offers extremely high coding efficiency, it provides little or no security as traditionally implemented. We present a modified scheme that offers both encryption and compression. The system utilizes an arithmetic coder in which the overall length within the range [0,1) allocated to each symbol is preserved, but the traditional assumption that a single contiguous interval is used for each symbol is removed. Additionally, a series of permutations are applied at the input and the output of the encoder. The overall system provides simultaneous encryption and compression, with negligible coding efficiency penalty relative to a traditional arithmetic coder

Secure Arithmetic Coding

Many countries will experience the effects of an aging population, resulting in a high demand of healthcare facilities. Development of novel biomedical technologies is an urgent necessity to improve diagnostic services for this demographic. Electrocar diogram (ECG) and temperature recording have been used for more than 50 years in medical diagnosis to understand various biological activities [1], [2]. A more recent development, electronic pill technology, requires the integration of more complex systems on the same platform when compared to conventional implantable systems. A small miniaturized electronic pill can reach areas such as the small intestine and can deliver real time video images wirelessly to an external console. Figure 1 shows an electronic pill system (i.e., wireless endoscopy) for a medical monitoring system. The device travels through the digestive system to collect image data and transfers the data to a nearby computer for display with a distance of one meter or more. A high resolution videobased capsule endoscope produces a large amount of data, which can then be delivered over a high-capacity wireless link.

Easy-to-Swallow Wireless Telemetry

Density estimation is a fundamental part of statistical analysis and data mining In high-dimensional domains, parametric methods and the commonly used non-parametric methods like histograms or Kernel estimators fail to perform properly In this paper, we present computationally efficient data structures for efficient implementation of the Bayesian sequential partitioning (BSP), as a framework for density estimation in high-dimensional domain Simulation results are presented to analyze the performance for large high-dimensional datasets

Efficient data structures for density estimation for large high-dimensional data

Raptor codes have been proven very suitable for mobile multimedia content delivery, and yet they have not been analyzed in the context of embedded systems. At the heart of Raptor codes for binary erasure channel (BEC) is the matrix inversion operation. This paper analyzes the performance, energy profile and resource implication of two matrix inversion algorithms for the Raptor decoder on a system on a chip (SoC) platform with a soft-core embedded processor. We show how the cache size, matrix memory type and organization affect the two algorithms under consideration.

Analysis and implementation of Raptor codes on embedded systems

In this paper asynchronous design techniques are employed to implement a multiplierless FIR filter. Suitability of modular, micropipelined based design style for mapping of the DSP algorithms into VLSI hardware has been demonstrated. In this design global clock has been eliminated, thereby, reducing the complexity associated with the clock distribution network.

Micropipeline architecture for multiplier-less FIR filters

Efficient density estimation over an open-ended stream of high-dimensional data is of primary importance to machine learning. In general, parametric methods for density estimation are not suitable for high dimensions, and the widely used non-parametric methods like kernel density estimation (KDE) method fail for high-dimensional datasets. In this paper we present a framework for density estimation over stationary and non-stationary high-dimensional data streams. It is based on a blockized implementation of the Bayesian sequential partitioning (BSP) algorithm. The proposed framework satisfies the general design criteria for systems with the mission of online machine learning and data mining over data streams.

Online density estimation over high-dimensional stationary and non-stationary data streams

The performance of turbo hybrid-ARQ (HARQ) Type-I, Type-II and Type-III schemes are examined for wideband communications based on an orthogonal frequency division multiplexing (OFDM) system. In addition, various non-linear clustering strategies for minimizing the effects of peak-to-average power (PAP) ratio are compared and examined based on their throughput performance.

Saeid Nooshabadi

Papers

Efficient data structures for density estimation for large high-dimensional data

Analysis and implementation of Raptor codes on embedded systems

Micropipeline architecture for multiplier-less FIR filters

Online density estimation over high-dimensional stationary and non-stationary data streams

Turbo punctured hybrid-ARQ strategies over OFDM systems