Faculty Publications

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    Iterative or Turbo decoding of parallel concatenated binary error control codes in Binary Phase Shift Keyed (BPSK) Communication System corrupted by both Additive White Gaussian Noise (AWGN) and oscillator phase noise is discussed. As an example of application of the discussed theory, iterative decoding technique of Parallel Concatenated block Codes (PCBC) is applied to a coherent Optical Code Division Multiple Access (OCDMA) network in the presence of Multiple Access Interference (MAI), detector shot noise and laser phase noise. Remarkable improvement in performance resulting from the use of turbo decoding in the OCDMA network is also discussed.
    (Medknow Publications, Performance of iteratively decoded parallel concatenated error control codes in phase noise corrupted BPSK systems) Kumar, M.S.; Bhat, K.N.H.; Umesh, G.
    2004
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    High-speed and parallel approach for decoding of binary BCH codes with application to Flash memory devices
    (2012) Kumar, H.; Sripati, U.; Rajesh Shetty, K.
    In this article, we propose a high-speed decoding algorithm for binary BCH codes that can correct up to 7bits in error. Evaluation of the error-locator polynomial is the most complicated and time-consuming step in the decoding of a BCH code. We have derived equations for specifying the coefficients of the error-locator polynomial, which can form the basis for the development of a parallel architecture for the decoder. This approach has the advantage that all the coefficients of the error locator polynomial are computed in parallel (in one step). The roots of error-locator polynomial can be obtained by Chien's search and inverting these roots gives the error locations. This algorithm can be employed in any application where high-speed decoding of data encoded by a binary BCH code is required. One important application is in Flash memories where data integrity is preserved using a long, high-rate binary BCH code. We have synthesized generator polynomials for binary BCH codes (error-correcting capability, s) that can be employed in Flash memory devices to improve the integrity of information storage. The proposed decoding algorithm can be used as an efficient, high-speed decoder in this important application. © 2012 Taylor & Francis.
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    Performance analysis of stack decoding on block coded modulation schemes using tree diagram
    (2012) Prashantha, K.H.; Vineeth, U.K.; Sripati, U.; Rajesh, Sh.K.
    The channel encoder adds redundancy in a structured way to provide error control capability. Modulator converts the symbol sequences from the channel encoder into waveforms which are then transmitted over the channel. Usually channel coder and modulator are implemented independently one after the other. But in a band limited channel better coding gains without sacrificing signal power are achieved when coding is combined with modulation. Block Coded Modulation (BCM) is such a scheme that results from the combination of linear block codes and modulation. In this paper we are proposing a stack decoding of rate 2/3 and rate 1/2 BCM schemes using tree structure and performance is compared with the Viterbi decoding that uses trellis representation. Simulation result shows that at reasonable bit error rate stack decoder performance is just 0.2 to 0.5 dB inferior to that of Viterbi decoding. Since stack decoding is a near optimum decoding scheme and whose decoding procedure is adaptable to noise level, we can consider this method in place of Viterbi decoding which is optimum and its decoding complexity grows exponentially with large code lengths. © K.H. Prashantha, U.K. Vineeth, U. Sripati, Sh.K. Rajesh, 2012.
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    Gradient-oriented directional predictor for HEVC planar and angular intra prediction modes to enhance lossless compression
    (Elsevier GmbH journals@elsevier.com, 2018) Shilpa Kamath, S.; Aparna., P.; Antony, A.
    Recent advancements in the capture and display technologies motivated the ITU-T Video Coding Experts Group and ISO/IEC Moving Picture Experts Group to jointly develop the High-Efficiency Video Coding (HEVC), a state-of-the-art video coding standard for efficient compression. The compression applications that essentially require lossless compression scenarios include medical imaging, video analytics, video surveillance, video streaming etc., where the content reconstruction should be flawless. In the proposed work, we present a gradient-oriented directional prediction (GDP) strategy at the pixel level to enhance the compression efficiency of the conventional block-based planar and angular intra prediction in the HEVC lossless mode. The detailed experimental analysis demonstrates that the proposed method outperforms the lossless mode of HEVC anchor in terms of bit-rate savings by 8.29%, 1.65%, 1.94% and 2.21% for Main-AI, LD, LDP and RA configurations respectively, without impairing the computational complexity. The experimental results also illustrates that the proposed predictor performs superior to the existing state-of-the-art techniques in the literature. © 2018 Elsevier GmbH
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    Index modulation aided multi carrier power line communication employing rank codes from cyclic codes
    (Elsevier B.V., 2020) Raghavendra, M.A.N.S.; Shripathi Acharya, U.
    In a multi-carrier power line communication (mPLC) with dominant Narrowband and Impulse noise, crisscross errors can be clearly observed. In this work, mPLC employing Rank codes with Index modulation (mPLC-IM) has been considered to provide a reliable high data rate communication over the powerline channel. The rank codes required for this implementation have been derived from cyclic codes over GF(qm) viewed as m×n matrices over GF(q). Encoding has been performed by employing the Galois Field Fourier Transform (GFFT) domain description of cyclic codes. This scheme is able to correct a variety of crisscross errors in mPLC-IM The GFFT approach provides an additional degree of freedom that is offered by choice of free transform component indices. It can be used to design an index key scheme which can enhance the physical layer security of an mPLC system. In the absence of knowledge of the index key, it is observed that the probability of error reaches an error floor of ?10?2, highlighting the need for index key for appropriate decoding. Further, a novel check matrix construction is proposed and used in devising a decoding strategy. It is observed that the proposed decoder is capable of correcting any errors of rank ??m?12?. In mPLC-IM with OFDM, the proposed codes over GF(24) provide an asymptotic gain of approximately 3 dB when compared to the uncoded system. For mPLC-IM with multi-tone Frequency Shift Keying (FSK), the proposed RC over GF(24) provides a 25% improvement in symbol error rate (SER) at lower values of p (probability of occurrence of narrowband noise) when compared to Reed-Solomon (RS) based Constant Weight (CW) CW(13,6,5)2?RS[15,14,2]16 codes. Further, a SER improvement of around 30% is achieved using rank codes (RCs) over GF(28) when compared with CW(9,4,4)2?RS[15,14,2]16. In the presence of dominant background noise, the BER graphs show that the proposed codes are equivalent (slightly superior) in performance as that of Low Rank Parity Check (LRPC)/Gabidulin based designs. In the presence of dominant impulse noise, the proposed system is providing significant gain when compared with the Linearly Pre-coded Orthogonal Frequency Division Multiplexing (LP-OFDM) system and LRPC based scheme. Additionally, simulation results show that, in the absence of an index key, the probability of error reaches the error floor, highlighting the need for index key for appropriate decoding. This can be viewed as the code capable of providing an additional layer of security. © 2019 Elsevier B.V.
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    NucleiSegNet: Robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images
    (Elsevier Ltd, 2021) Lal, S.; Das, D.; Alabhya, K.; Kanfade, A.; Kumar, A.; Kini, J.R.
    The nuclei segmentation of hematoxylin and eosin (H&E) stained histopathology images is an important prerequisite in designing a computer-aided diagnostics (CAD) system for cancer diagnosis and prognosis. Automated nuclei segmentation methods enable the qualitative and quantitative analysis of tens of thousands of nuclei within H&E stained histopathology images. However, a major challenge during nuclei segmentation is the segmentation of variable sized, touching nuclei. To address this challenge, we present NucleiSegNet - a robust deep learning network architecture for the nuclei segmentation of H&E stained liver cancer histopathology images. Our proposed architecture includes three blocks: a robust residual block, a bottleneck block, and an attention decoder block. The robust residual block is a newly proposed block for the efficient extraction of high-level semantic maps. The attention decoder block uses a new attention mechanism for efficient object localization, and it improves the proposed architecture's performance by reducing false positives. When applied to nuclei segmentation tasks, the proposed deep-learning architecture yielded superior results compared to state-of-the-art nuclei segmentation methods. We applied our proposed deep learning architecture for nuclei segmentation to a set of H&E stained histopathology images from two datasets, and our comprehensive results show that our proposed architecture outperforms state-of-the-art methods. As part of this work, we also introduced a new liver dataset (KMC liver dataset) of H&E stained liver cancer histopathology image tiles, containing 80 images with annotated nuclei procured from Kasturba Medical College (KMC), Mangalore, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India. The proposed model's source code is available at https://github.com/shyamfec/NucleiSegNet. © 2020 Elsevier Ltd
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    L, r-Stitch Unit: Encoder-Decoder-CNN Based Image-Mosaicing Mechanism for Stitching Non-Homogeneous Image Sequences
    (Institute of Electrical and Electronics Engineers Inc., 2021) Chilukuri, P.K.; Padala, P.; Padala, P.; Desanamukula, V.S.; Pvgd, P.R.
    Image-stitching (or) mosaicing is considered an active research-topic with numerous use-cases in computer-vision, AR/VR, computer-graphics domains, but maintaining homogeneity among the input image sequences during the stitching/mosaicing process is considered as a primary-limitation major-disadvantage. To tackle these limitations, this article has introduced a robust and reliable image stitching methodology (l,r-Stitch Unit), which considers multiple non-homogeneous image sequences as input to generate a reliable panoramically stitched wide view as the final output. The l,r-Stitch Unit further consists of a pre-processing, post-processing sub-modules a l,r-PanoED-network, where each sub-module is a robust ensemble of several deep-learning, computer-vision image-handling techniques. This article has also introduced a novel convolutional-encoder-decoder deep-neural-network (l,r-PanoED-network) with a unique split-encoding-network methodology, to stitch non-coherent input left, right stereo image pairs. The encoder-network of the proposed l,r-PanoED extracts semantically rich deep-feature-maps from the input to stitch/map them into a wide-panoramic domain, the feature-extraction feature-mapping operations are performed simultaneously in the l,r-PanoED's encoder-network based on the split-encoding-network methodology. The decoder-network of l,r-PanoED adaptively reconstructs the output panoramic-view from the encoder networks' bottle-neck feature-maps. The proposed l,r-Stitch Unit has been rigorously benchmarked with alternative image-stitching methodologies on our custom-built traffic dataset and several other public-datasets. Multiple evaluation metrics (SSIM, PSNR, MSE, L_{\alpha,\beta,\gamma } , FM-rate, Average-latency-time) wild-Conditions (rotational/color/intensity variances, noise, etc) were considered during the benchmarking analysis, and based on the results, our proposed method has outperformed among other image-stitching methodologies and has proved to be effective even in wild non-homogeneous inputs. © 2013 IEEE.
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    A Novel Approach for Asymmetric Quantum Error Correction With Syndrome Measurement
    (Institute of Electrical and Electronics Engineers Inc., 2022) Mummadi, M.; Rudra, B.
    Most of the quantum error correction methods are symmetric. Symmetric methods are implemented by considering the amplitude of bit flip(X) and phase flip(Z) errors as same. With the quantum experiments, it is observed that the amplitude of Z errors are more compared to X errors. Due to which the need of asymmetric error correction has increased. This paved a path for the development of asymmetric error correction methods. In this paper, we discussed the concept of asymmetric quantum error correction (AQEC) and proposed an efficient approach for AQEC with encoding, syndrome measurement and decoding operations with increased fidelity to 85.89% and reduced circuit depth to 48%. © 2013 IEEE.
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    A Nested Texture Inspired Novel Image Pattern Based Optical Camera Communication
    (Institute of Electrical and Electronics Engineers Inc., 2022) Salvi, S.; Geetha, V.
    Pattern recognition is a vital component of display-based optical camera communication. However, as the distance between the transmitter LED panel and the receiver camera increases, the visibility of the pattern reduces. Decoding each pattern requires extensive computational resources. This paper proposes a nested texture-inspired novel binary hierarchical image pattern classification-based modulation for optical camera communication to provide efficient pattern classification and achieve longer communication distance. Custom 8× 8 patterns are generated at the transmitter based on input data using nested outer and inner patterns. On the receiver side, multi-threaded pattern matching is performed simultaneously for inner and outer patterns for faster decoding. A simulator is built to test the performance of linear search and two variants of binary search for pattern matching. A hardware prototype and Matlab app are designed to perform experiments to test the performance of the proposed technique in a real-world environment. Experiments were conducted to select optimal camera parameter values for the best signal to noise ratio (SNR) and to analyze the impact of pattern intensity profile on decoding at various distances. The proposed pattern communication technique showcased 48.6% and 42.5% improvement in bit error ratio (BER) compared to Data Matrix and QR Code Pattern-based communication techniques, respectively. © 2013 IEEE.