Giridhar D. Mandyam

The discrete Laguerre transform: derivation and applications

The discrete Laguerre transform (DLT) belongs to the family of unitary transforms known as Gauss-Jacobi transforms. Using classical methodology, the DLT is derived from the orthonormal set of Laguerre functions. By examining the basis vectors of the transform matrix, the types of signals that can be best represented by the DLT are determined. Simulation results are used to compare the DLTs effectiveness in representing such signals to that of other available transforms in applications such as data compression and transform-domain adaptive filters.

Lossless Image Compression Using the Discrete Cosine Transform

In this paper, a new method to achieve lossless compression of two-dimensional images based on the discrete cosine transform (DCT) is proposed. This method quantizes the high-energy DCT coefficients in each block, finds an inverse DCT from only these quantized coefficients, and forms an error residual sequence to be coded. The number of coefficients used in this scheme is determined by using a performance metric for compression. Furthermore, a simple differencing scheme is performed on the coefficients that exploits correlation between high energy DCT coefficients in neighboring blocks of an image. The resulting sequence is compressed by using an entropy coder, and simulations show the results to be comparable to the different modes of the lossless JPEG standard.

Analysis of impact on handset transmitter design of the high-speed data requirements in the IS-95-B CDMA wireless standard

As part of the evolution of the IS-95 CDMA wireless communications towards third-generation technology, high-speed data requirements were defined for both the uplink and downlink in the proposed IS-95-B revision. Although the requirements for the downlink are straightforward, the uplink requirements have considerably larger impact on the handset transmitter and base station receiver design. The impacts on the transmitter are examined, from baseband to RF frequencies. In particular, waveform quality and emissions are examined through high-level simulation to determine the tradeoffs involved implementation of a handset design which can support IS-95-B high speed data requirements for the uplink in the 1.9 GHz band.

A two-stage scheme for lossless compression of images

A two-stage lossless image compression scheme is presented, the first stage of which decorrelates the raw image data by using a two-dimensional, causal linear predictor, resulting in significant entropy reduction. The second stage uses standard coding techniques. Different two-stage schemes are compared with respect to their performances on several images, using bilevel coding, arithmetic coding, and adaptive Huffman coding.

Channel decoding implementation in downlink of CDMA systems

In this paper, an analytical model for expected convolutional code performance for IS-95-A CDMA systems is given. This model incorporates both the aspects of random and deterministic puncturing associated with IS-95-A, and variable rate source coding which is utilized in IS-95-A CDMA. As a result of this analysis, particular implementation issues are addressed for channel decoding in IS-95-A.

Implementation of raised cosine filters using orthonormal expansions

Discrete-time sequences, in particular Laguerre sequences, are effective for representing sequences in the form of orthogonal expansions. Taking advantage of the network realization of a Laguerre expansion, it is shown that approximating raised-cosine filters by means of Laguerre expansions provides considerable savings in hardware implementations without adding significant distortion.

On the Laguerre representation of noisy EMP signals

The notion of representing discrete-time electromagnetic pulse (EMP) signals using orthonormal Laguerre sequences for the noiseless case was introduced by Mandyam, Ahmed and Armstrong (see ibid., vol.37, no.11, p.592-95, 1995). The motivation for doing so is that EMP signals and Laguerre sequences are both characterized by decaying exponentials. Very efficient representation of EMP signals is possible using this approach. The paper extends this representation to noisy EMP sequences where the noise is often additive and white-it is shown that noise suppression is possible using Laguerre representation.

Lossless Image Compression Using Predictive Codebooks

Abstract This paper describes a method for lossless image compression where relative pixel values of prediction regions in a set of training images are stored as a codebook. In order to achieve decorrelation of the pixels comprising an image, each pixels prediction neighborhood is assigned to a neighborhood in the codebook, and the difference between the actual pixel value and the predicted value from the codebook is coded using an entropy coder. Using the same codebook, one can achieve perfect reconstruction of the image. The method is tested on several standard images and compared with previously published methods. These experiments demonstrate that the new method is a suitable alternative to existing lossless image compression techniques.

Exploration of bit reoptimization schemes in transform-based speech coding

Transform-based speech coding is an attractive alternative to conventional (i.e. linear-predictive) speech coding schemes due to its simplicity and its flexibility with respect to particular frequency components. The classic transform-based speech coding algorithm gives a well-defined coding procedure which yields good results. However its optimal bit assignment procedure has a good degree of variability. In this work several bit reoptimization schemes are explored, and a reoptimization scheme is proposed that is proven by simulations to be beneficial.

Application of genetic algorithms to Volterra filter structure determination for adaptive systems

In this paper a genetic algorithm is used to determine the form of an adaptive Volterra filter that will best model a particular system. A fitness functional is defined that quantifies the performance of a particular Volterra system structure in the adaptation process, and using this functional, a genetic algorithm is utilized to find the best possible Volterra system structure for a given data set. Simulations are done to demonstrate the effectiveness of this approach to this problem.