Abstract-In this paper, the problem of designing finite-impulse-response (FIR) equalizers for multiple-input multiple-output (MIMO) FIR channels is considered. It is shown that an arbitrary MIMO frequency-selective channel can be rendered FIR equalizable by a suitable filter bank (FB) precoding operation that introduces redundancy at the transmitter. The expression for the minimum redundancy required to ensure FIR invertibility is derived. The analysis is extended to the case of MIMO multicarrier modulation. Optimum zero-forcing (ZF) and minimum mean-squared error (MMSE) solutions for the FIR equalizer are derived. Simulation results are provided to demonstrate that the proposed scheme achieves better performance than the block-processing methods while supporting a higher data rate.Index Terms-Filter bank, finite-impulse-response (FIR) equalization, multiple-input multiple-output (MIMO), polynomial matrix, pseudocirculant matrix, Smith form.

Particle filtering is very reliable in modelling non-Gaussian and non-linear elements of physical systems, which makes it ideal for tracking and localization applications. However, a major drawback of particle filters is their computational complexity, which inhibits their use in real-time applications with conventional CPU or DSP based implementation schemes. The re-sampling step in the particle filters creates a computational bottleneck since it is inherently sequential and cannot be parallelized. This paper proposes a modification to the existing particle filter algorithm, which enables parallel re-sampling and reduces the effect of the re-sampling bottleneck. We then present a high-speed and dedicated hardware architecture incorporating pipe-lining and parallelization design strategies to supplement the modified algorithm and lower the execution time considerably. From an application standpoint, we propose a novel source localization model to estimate the position of a source in a noisy environment using the particle filter algorithm implemented on hardware. The design has been prototyped using Artix-7 field-programmable gate array (FPGA), and resource utilization for the proposed system is presented. Further, we show the execution time and estimation accuracy of the high-speed architecture and observe a significant reduction in computational time. Our implementation of particle filters on FPGA is scalable and modular, with a low execution time of about 5.62 µs for processing 1024 particles (compared to 64 ms on Intel Core i7-7700 CPU with eight cores clocking at 3.60 GHz) and can be deployed for real-time applications.

Retiming is a transformation which can be applied to digital filter blocks that can increase the clock frequency. This transformation requires computation of critical path and shortest path at various stages. In literature, this problem is addressed at multiple points. However, very little attention is given to path solver blocks in retiming transformation algorithm which takes up most of the computation time. In this paper, we address the problem of optimizing the speed of path solvers in retiming transformation by introducing high level synthesis of path solver algorithm architectures on FPGA and a computer aided design tool. Filters have their combination blocks as adders, multipliers, and delay elements. Avoiding costly multipliers is very much needed for filter hardware implementation. This can be achieved efficiently by using multiplierless MCM technique. In the present work, retiming which is a high level synthesis optimization method is combined with multiplierless filter implementations using MCM algorithm. It is seen that retiming multiplierless designs gives better performance in terms of operating frequency. This paper also compares various retiming techniques for multiplierless digital filter design with respect to VLSI performance metrics such as area, speed, and power.

MUSIC is the divine way of portraying the most beautiful about this world". With that being said, the diversity in this language of music is immense, to say the least. Broadly, one would be well aware of the classification between Indian classical music and western music. In music Information Retrieval (MIR), raga classification has a tremendous role in understanding the fundamentals of Indian classical music and in a multitude of other tasks like database organisation of music files to music recommendation systems. The paper incorporates a variety of techniques like ANN, CNN, Bi LSTM and XGBoost models for the task of Raga Identification from a Carnatic Classical Instrumental audio (CCIA). The work is initially carried out on a set of 10 ragas and then extended to largely available 15 ragas of the dataset. The data samples for the same were obtained from the standard data set. This task showed state-of-the-art results with an accuracy of 97% for a set of 15 Ragas. The astounding results were obtained without performing source separation on the musical audio track. The process was carried out on the Ragas pertaining to Carnatic Classical music, a division of Indian classical music.

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