Studies on signal processing with applications

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Digital signal processing (DSP) techniques have found a variety of applications over the last 20 years in the processing of signals in such diverse areas as biomedical image processing, acoustics, telecommunications, image and video signal processing, and seismology.  The availability of high-resolution, inexpensive, high-speed A/D converters and powerful digital signal processors in VLSI form has provided the necessary impetus for the ever-expanding applications of DSP in these areas.  Functions commonly performed in a telephone exchange like tone generation, detection, filtering, trans-multiplexing and echo cancellation required for long-band terrestrial links and satellite communication links are now implemented using one-dimensional (1-D) DSP techniques.  Speech recognition and speech coding techniques relying totally on 1-D DSP techniques have also become popular in view of research advances in the development of suitable complex algorithms and the availability of hardware in VLSI to support their real-time implementation.  Multidimensional (m-D) DSP techniques are being increasingly used in the processing of m-D signals such as pictures and moving pictures obtained from satellites, x-rays, and other sources. Videophone and HDTV are some examples of products that have gained from the research and development advances in the 2-D and 3-D DSP area.  The overall objectives of the proposed research are to develop a variety of efficient and reliable algorithms for filtering, coding and processing of signals (such as speech, image, video and biomedical signals) with a view to VLSI implementation. For this purpose, we will continue our work regarding (a) application of the residue number system in the VLSI design implementation for specific DSP applications, (b) wavelet-based noise reduction in images and video sequences, which should be useful in improving the quality of medical videos, (c) memory-based systolic architectures for various fast discrete domain transforms, (d) video coding for wireless networks, and (e) development of algorithms for biomedical applications. These should be beneficial to the telecommunication, signal processing, VLSI and wireless sectors of the industry.