Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Optimization of WDM lightwave systems (BAC) design using error control coding(Academic Press Inc., 2007) Mruthyunjaya, H.S.; Umesh, G.; Kumar, M.In a binary asymmetric channel (BAC) it may be necessary to correct only those errors which result from incorrect transmission of one of the two code elements. In optical fiber multichannel systems, the optical amplifiers are critical components and amplified spontaneous emission noise in the optical amplifiers is the major source of noise in it. The property of erbium doped fiber amplifier is nearly ideal for application in lightwave long haul transmission. We investigate performance of error correcting codes in such systems in presence of stimulated Raman scattering and amplified spontaneous emission noise with asymmetric channel statistics. Performance of some best known concatenated coding schemes is reported. © 2006 Elsevier Inc. All rights reserved.Item Noniterative content-adaptive distributed encoding through ML techniques(Society of Motion Picture and Television Engineers, 2018) Sethuraman, S.; Nithya, V.S.; Venkata Narayanababu Laveti, D.Distributed encoding is desirable for content preparation cloud workflows to reduce turnaround times. Content-adaptive bit allocation strategies have been proposed to achieve efficiencies in storage and delivery. Many of these methods tend to be iterative in nature and consume significant additional compute resources. There is a need to limit this increase in computational complexity. In this paper, we propose a noniterative codec-agnostic approach that employs machine learning techniques to achieve average bitrate savings and a target consistent quality by selecting a content-adaptive bitrate and resolution for each adaptive bitrate (ABR) segment within each ABR representation in a manner that makes it equally suitable for live and on-demand workflows. Test results are presented over a wide range of content types. Initial results indicate that the proposed approach can recover 85% of the bit savings possible with more exhaustive techniques while its computational complexity is only 15%-20% of two-pass variable bitrate (VBR) encoding. © 2002 Society of Motion Picture and Television Engineers, Inc.Item A High Performance Early Acknowledged Asynchronous Pipeline using Hybrid-logic Encoding(Elsevier B.V., 2020) Girija Sravani, K.; Rao, R.This paper details a novel asynchronous pipelining methodology that maximizes the throughput buffering capacity and robustness of gate-level pipelined systems. The data paths in the proposed pipeline style are encoded using hybrid logic encoding scheme, which incorporates simplicity of the single-rail encoding and robustness of the dual-rail encoding. The control path that provides the synchronization between pipeline stages is constructed based on the simple and high-speed early acknowledgment protocol. Further, the proposed pipeline accommodates isolate phase to achieve 100% storage capacity. Two test cases: A 4-bit,10-stage FIFO and a 16-bit adder, have been designed in 90 nm technology to validate the proposed pipeline style. The FIFO has been laid out in the UMC 180 nm process using the cadence tool suite. The post-layout results of FIFO show 12.5% better throughput than the high capacity single-rail pipeline. Simulation results of the adder also reveal that the proposed structure achieves the throughput of 3.44 Giga-items/sec, which is 44.18% higher than the APCDP (Asynchronous pipeline based on constructed critical path) and 11.9% higher than the high capacity single-rail pipelines. © 2019 Elsevier B.V.Item Distributed video coding based on classification of frequency bands with block texture conditioned key frame encoder for wireless capsule endoscopy(Elsevier Ltd, 2020) Sushma, B.; Aparna., P.Wireless capsule endoscopy (WCE) has provided remarkable improvement in diagnosing gastrointestinal disorders by scanning the entire digestive tract. The system still need refinement, to upgrade the quality of images, frame rate and battery life. The principal component of the system that can address these issues is low complexity video compressor. Motivated by low computational complexity requirements of WCE video encoding, this paper presents a distributed video coding framework based on frequency bands classification. The lower frequency bands are used to generate good quality side information (SI) as they exhibit high temporal correlation. This reduces the complexity of hash generation at the encoder, thus eliminating the latency in SI creation. Apart from this, SI creation involves only a simple block search and doesn't depend on Wyner–Ziv (WZ) bands. Also different approach for distributed coding of sub-sampled chroma components of WZ frame is proposed. Low complexity JPEG based key frame encoding is proposed that take advantage of WCE image textural properties to reduce the complexity of encoding smooth blocks by 81% at the quantization and encoding stage. Other novel features include use of discrete Tchebichef transform (DTT), Golomb–Rice code for entropy coding. Performance evaluation shows that the proposed method achieves 60% improvement in compression over Motion JPEG with low computational complexity. © 2020 Elsevier LtdItem Robust transmission using channel encoding towards 5G New Radio: A telemetry approach(Elsevier Ltd, 2021) Sharma, V.; Arya, R.K.; Kumar, S.This paper presents a robust channel encoding scheme under adaptive modulation and coding for a massive machine type communication device in 5G new radio. For the very first time, mode-selection and distance statistics algorithms have been simultaneously evaluated, in which together it provides the closest approximation of efficient adaptive modulation and coding with robust transmission. The prediction of optimum adaptive modulation and coding is based on the analysis of uplink packet using distance statistics, and downlink packet using mode-selection mechanism. The performance of 5G new radio by incorporating OFDM subcarrier has been evaluated using analytical as well as simulation approach. Mode-selection algorithm has been considered to predict the environmental condition under a fading channel while the distance statistics provide feedback of the previously transmitted channel condition. The result of both the approaches provide a better bit error rate for adaptive modulation & coding profile under 1/4, 1/18, 1/16 and 1/32 cyclic prefix. © 2021Item 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.Item Contribution of frequency compressed temporal fine structure cues to the speech recognition in noise: An implication in cochlear implant signal processing(Elsevier Ltd, 2022) Poluboina, V.; Pulikala, A.; Pitchai Muthu, A.N.The study investigated the effect of proportionally frequency compressed encoding of temporal fine structure information on speech perception in noise using vocoder simulations of cochlear implant signal processing. The study proposed a pitch synchronous overlap-add algorithm (PSOLA) for downward frequency shifting of TFS. The speech recognition scores (SRS) were measured at −10 dB, 0 dB, and +10 dB for eight signal processing conditions corresponding to sinewave vocoder without TFS (NO-TFS), four unshifted TFS conditions including full band TFS, TFS up to 2000, 1000, and 600 Hz, and three conditions with PSOLA which shifted 2000, 1000 and 600 Hz TFS to 1000, 500 and 300 Hz respectively. The original envelope was unchanged across the conditions. SRS at +10 dB and −10 dB SNR reached ceiling and floor respectively, in most conditions. Hence, SRS at 0 dB SNR was compared across the conditions. The results showed that the SRS was highest with full band TFS and lowest for the NO-TFS condition.The SRS for TFS 600 Hz shifted to 300 Hz through PSOLA was higher than the NO-TFS condition. Study findings suggest that encoding TFS by proportional frequency compression results in better speech perception in noise compared to NO-TFS. An important observation of this current study is that the speech recognition was better than the sine wave vocoder for all TFS conditions including frequency compressed 600 Hz TFS. © 2021 Elsevier LtdItem O-SegNet: Robust Encoder and Decoder Architecture for Objects Segmentation from Aerial Imagery Data(Institute of Electrical and Electronics Engineers Inc., 2022) Eerapu, K.K.; Lal, S.; Narasimhadhan, A.V.The segmentation of diversified roads and buildings from high-resolution aerial images is essential for various applications, such as urban planning, disaster assessment, traffic congestion management, and up-to-date road maps. However, a major challenge during object segmentation is the segmentation of small-sized, diverse shaped roads, and buildings in dominant background scenarios. We introduce O-SegNet- the robust encoder and decoder architecture for objects segmentation from high-resolution aerial imagery data to address this challenge. The proposed O-SegNet architecture contains Guided-Attention (GA) blocks in the encoder and decoder to focus on salient features by representing the spatial dependencies between features of different scales. Further, GA blocks guide the successive stages of encoder and decoder by interrelating the pixels of the same class. To emphasize more on relevant context, the attention mechanism is provided between encoder and decoder after aggregating the global context via an 8 Level Pyramid Pooling Network (PPN). The qualitative and quantitative results of the proposed and existing semantic segmentation architectures are evaluated by utilizing the dataset provided by Kaiser et al. Further, we show that the proposed O-SegNet architecture outperforms state-of-the-art techniques by accurately preserving the road connectivity and structure of buildings. © 2017 IEEE.Item An efficient parallel-pipelined intra prediction architecture to support DCT/DST engine of HEVC encoder(Springer Science and Business Media Deutschland GmbH, 2022) Poola, L.; Aparna., P.The complexity of intra prediction in high-efficiency video coding (HEVC) is increased due to the addition of five variable sized prediction units (PUs) and 35 directional predictions. In this work, we propose an efficient parallel-pipelined architecture that can process 8 samples in parallel for every clock cycle. The functional units needed to predict the PU samples work in a pipelined fashion. With this balanced combination of parallel-pipelined structure, we are able to achieve higher throughput with limited hardware resources than existing literature works. The samples are processed row-wise, so that they can be directly transform coded, thus eliminating the need for an intermediate memory buffer of 8 K between the two modules. A compact reconfigurable reference buffer of size 0.8 KB is incorporated to reduce the read-write latency associated with reference samples’ fetching. A dedicated module for arithmetic operations is used in the intra engine that ensures the reuse of multipliers to increase the hardware efficiency. The architecture so designed supports all the PU sizes and directional modes. The proposed design is tested and implemented on a field-programmable gate array (FPGA) platform operating at 150 MHz frequency to achieve 8 samples throughput with a hardware cost of 16.2 K Look-Up Tables (LUTs) and 5.7 K registers to support HD 4 K real-time video encoding applications. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Item Semantic context driven language descriptions of videos using deep neural network(Springer Science and Business Media Deutschland GmbH, 2022) Naik, D.; Jaidhar, C.D.The massive addition of data to the internet in text, images, and videos made computer vision-based tasks challenging in the big data domain. Recent exploration of video data and progress in visual information captioning has been an arduous task in computer vision. Visual captioning is attributable to integrating visual information with natural language descriptions. This paper proposes an encoder-decoder framework with a 2D-Convolutional Neural Network (CNN) model and layered Long Short Term Memory (LSTM) as the encoder and an LSTM model integrated with an attention mechanism working as the decoder with a hybrid loss function. Visual feature vectors extracted from the video frames using a 2D-CNN model capture spatial features. Specifically, the visual feature vectors are fed into the layered LSTM to capture the temporal information. The attention mechanism enables the decoder to perceive and focus on relevant objects and correlate the visual context and language content for producing semantically correct captions. The visual features and GloVe word embeddings are input into the decoder to generate natural semantic descriptions for the videos. The performance of the proposed framework is evaluated on the video captioning benchmark dataset Microsoft Video Description (MSVD) using various well-known evaluation metrics. The experimental findings indicate that the suggested framework outperforms state-of-the-art techniques. Compared to the state-of-the-art research methods, the proposed model significantly increased all measures, B@1, B@2, B@3, B@4, METEOR, and CIDEr, with the score of 78.4, 64.8, 54.2, and 43.7, 32.3, and 70.7, respectively. The progression in all scores indicates a more excellent grasp of the context of the inputs, which results in more accurate caption prediction. © 2022, The Author(s).