Browsing by Author "Sripati Acharya, U."

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    Classification of FSO channel models using radial basis function neural networks and their ber performance with Luby transform codes
    (2012) Prakash, G.; Kulkarni, M.; Sripati Acharya, U.; Kalyanpur, M.N.
    Free Space Optical (FSO) communication systems offer a license free and cost effective access performance. FSO links can suffer from data packet corruption and erasure. Error control codes can help to mitigate turbulence induced fading and can improve the error performance of such links. Various statistical models have been proposed to describe the atmospheric turbulence channels. The choice of the appropriate model for varying level of turbulence is dependent on the atmospheric parameters. In this paper we classify the channels using Radial Basis Function Neural Networks to decide the best fit. We then investigate the error performance of FSO channels modeled as Gamma- Gamma and K distribution functions with Luby Transform encoding which are rateless codes. Simulation results are used to compare the performance of different modulation schemes with Luby Transform encoding and also to classify the appropriate distribution function for the channel model. © 2012 by IJAI (CESER Publications).
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    Digital and Analog Communication Systems
    (Pearson Education, 2012) Couch, Leon W.; Kulkarni, Muralidhar; Sripati Acharya, U.
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    Systolic-Architecture-Based Matrix Multiplications and Its Realization for Multi-Sensor Bias Estimation Algorithms
    (Springer Science and Business Media Deutschland GmbH, 2021) Gopala Swamy, B.; Sripati Acharya, U.; Srihari, P.; Pardhasaradhi, B.
    The accelerators are gaining predominant attention in the HW/SW designs and embedded designs due to the less power consumption and parallel data processing capabilities compared to standard microprocessors and FPGA’s. In this paper, MSSKF (Multi-sensor Schmidt–Kalman filter)-based coupled bias estimation problem is considered for single target multiple sensors case. Here MSSKF augments the state vector and bias vector for bias estimation, results in computationally expensive as the dimensions of the state and sensors increases. Hence to address the computational complexity, digital signal processing (DSP) architectures are proposed and accelerated the algorithm to meet the real-time constraints. In the MSSKF algorithm, the overload of the algorithm is due to state covariance prediction and innovation covariance prediction. To realize the state covariance and innovation covariance, a folded DSP architecture and parallel processing based folded DSP architecture are proposed, respectively. The matrix multiplications are addressed with systolic arrays to gain the advantage of latency and parallel processing. Moreover, MSSKF using systolic array architectures simulated and synthesized in Vivado 2018.1 using Verilog and implemented on FPGA-Zynq-7000 board. The performance of the systolic-based accelerator realization was compared with normal matrix multiplication. © 2021, Springer Nature Singapore Pte Ltd.