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21st International Symposium on VLSI Design and Test (VDAT)

作者： Singh, Jatindeep Mohapatra, Satyajit Mohapatra, Nihar Ranjan Indian Inst Technol Dept Elect Engn Gandhinagar 382355 India

ISBN: (纸本)9789811074707;9789811074691

The 64b/66b technique conventionally is suited for low BER fiber optic channels, but can be extended for higher BER channels by including proper error correcting code and preamble. A modified 64b/66b line encoding technique for the design of high speed SERDES is proposed. Unlike earlier 8b/10b technology, run-length is no more guaranteed but is statistically bound. Generated polynomials are statistically tested in MATLAB prior VHDL implementation. Optimal selection of primitive polynomial limits run length to 11 and provides sub-optimal data security. Proposed 64/66b encoding technique reduces overhead by 15.8% (at 6.3% CRC) with respect to conventional 8b/10b, while is also suited for high BER channels like wireless and free space. A performance optimum between security, run-length, ISI and DC equalization, this scheme finds potential application in space camera electronics, 5G technology and other IOT applications like driverless cars that require to handle large volumes of real time data with sufficient security on high BER wireless channels.

关键词： Serializer-Deserializer (SERDES) Inter-Symbol Interference (ISI) Cyclic redundancy check (CRC) encoding technique Bit error rate (BER)

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Dec :: Patents :: 4475212 Frequency-Independent Self-Clocking encoding technique and Apparatus for Digital Communications Sep81

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2016年

Dec :: Patents :: 4475212 Frequency-Independent Self-Clocking encoding technique and Apparatus for Digital Communications Sep81 by published by

关键词： apparatus bit bit cell communications data data signal decoded nrz digital encoded encoded signal encoding encoding technique frequency-independent leading leading edge negative pulse nrz output positive pulse pulse pulses second pulse second type signal

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Extractive Summarization of Telugu Text Using Modified Text Rank and Maximum Marginal Relevance

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2023年第9期22卷 1-18页

作者： Babu, Anand G. L. Badugu, Srinivasu Anurag Univ Dept Informat Technol Hyderabad 500088 Telangana India Stanley Coll Engn & Technol Women Dept CSE Chapel Rd Hyderabad 500001 Telangana India

With the rapid growth of digital content, there is a need for an automatic text summarizer to provide short text from a long text document. Many research works have been presented for extractive text summarization (ETS). This article mainly focuses on the graph-based ETS approach for multiple Telugu text documents. A modified Text-Rank algorithm is employed with the noun and verb count of each sentence in the text as the initial score of each node. To get the optimal features, a novel feature selection algorithm called improved Flamingo Search Algorithm is proposed in this article. Though graph-based ETS is an important approach, the generated summaries are redundant. To reduce the redundancy in the generated summary, maximum marginal relevance is combined with the modified Text-Rank. Different word-embedding techniques such as Fast-Text, Word2vec, TF-IDF, and one-hot encoding are utilized to experiment with the proposed approach. The performance of the proposed text summarization approach is evaluated with BLEU and ROUGE in terms of F-measure, precision, and recall.

关键词： Deep learning Telugu text summarizer improved flamingo search algorithm encoding technique

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Brain regions supporting retrieval of words drawn at encoding: fMRI evidence for multimodal reactivation

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MEMORY & COGNITION 2025年第1期53卷 282-298页

作者： Roberts, Brady R. T. Meade, Melissa E. Fernandes, Myra A. Univ Waterloo Dept Psychol 200 Univ Ave West Waterloo ON N2L 3G1 Canada Huron Coll Western Univ Dept Psychol London ON Canada

Memory for words that are drawn or sketched by the participant, rather than written, during encoding is typically superior. While this drawing benefit has been reliably demonstrated in recent years, there has yet to be an investigation of its neural basis. Here, we asked participants to either create drawings, repeatedly write, or list physical characteristics depicting each target word during encoding. Participants then completed a recognition memory test for target words while undergoing functional magnetic resonance imaging (fMRI). Behavioural results showed memory was significantly higher for words drawn than written, replicating the typical drawing effect. Memory for words whose physical characteristics were listed at encoding was also higher than for those written repeatedly, but lower than for those drawn. Voxel-wise analyses of fMRI data revealed two distributed sets of brain regions more active for items drawn relative to written, the left angular gyrus (BA 39) and bilateral frontal (BA 10) regions, suggesting integration and self-referential processing during retrieval of drawn words. Brain-behaviour correlation analyses showed that the size of one's memory benefit for words drawn relative to written at encoding was positively correlated with activation in brain regions linked to visual representation and imagery (BA 17 and cuneus) and motor planning (premotor and supplementary motor areas;BA 6). This study suggests that drawing benefits memory by coactivating multiple sensory traces. Target words drawn during encoding are subsequently remembered by re-engaging visual, motoric, and semantic representations.

关键词： Drawing effect fMRI encoding technique Memory Neuroimaging

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Optimized Reversible Cellular Automata Based Clustering 29th

Optimized Reversible Cellular Automata Based Clustering

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29th IFIP WG 1.5 International Workshop on Cellular Automata and Discrete Complex Systems (AUTOMATA)

作者： Manoranjan, Viswonathan Rao, Ganta Sneha Vaidhianathan, Subramanian Vishnumangalam Bhattacharjee, Kamalika Natl Inst Technol Dept Comp Sci & Engn Tiruchirappalli 620015 Tamil Nadu India

ISBN: (纸本)9783031422492;9783031422508

The research optimizes reversible cellular automata based clustering technique for any high dimensional dataset. The reversible rules are characterized using the cycle structure properties of each rule to identify effective rules for clustering. This essentially reduces the rule search space for a given neighborhood size. A novel encoding technique (BiNCE encoding) that encodes any dataset into binary form without significant data loss is also introduced for our algorithm. Finally, the algorithm and implementation is transformed into a package which is applicable on various datasets, split sizes and cluster sizes for ease of accessibility and reproducibility. While compared against the state-of-the-art methods using benchmark clustering metrics, it is shown that our algorithm is at par or beating the scores for certain datasets and settings.

关键词： Reversible Cellular Automata (RCAs) Clustering Cycle encoding technique Silhouette Score Intra-cluster distance Package

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Fast and efficient lossless encoder in image compression with low computation and low memory

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IET IMAGE PROCESSING 2021年第11期15卷 2494-2507页

作者： Karthikeyan, N. Saravanakumar, N. M. Sivakumar, M. Anna Univ Dept CSE Sri Krishna Coll Engn & Technol Coimbatore Tamil Nadu India Anna Univ Dept CSE M Kumarasamy Coll Engn Karur India

This research focuses on frequency-based lossless new encoding technique with low computation and low memory scheme. This encoding technique doesn't require, any table similar to Huffman and Golomb Rice encoder, and doesn't take high computation time like an arithmetic encoder. For better comparative results, nearly 200 standard images were tested with the proposed encoder and their results were compared with the standard encoders. Based on the analysis of results reported in the experimental part, the proposed scheme in Discrete Cosine Transformation (DCT) achieves a high Compression Ratio (CR) of 97.68 %, Bits Per Pixel (BPP) of 0.19, Peak Signal To Noise Ratio (PSNR) of 27.62 dB for pepper image, CR of 98.65%, BPP of 0.11 and PSNR of 29.24 dB for house image, CR of 98.58 %, BPP of 0.11, PSNR of 30.59 dB for pepper image, CR of 98.37%, BPP of 0.13, PSNR of 31.37dB for house image in Discrete Wavelet Transformation (DWT) than compared to other encoders. It consumes less computation time to attain superior CR with a better quality of reconstructed picture than DCT based Huffman, Golomb- Rice (GR) coder and Low Complexity, Low memory Entropy Coder (LLEC), DWT based Arithmetic, GR code, LLEC.

关键词： superior CR Golomb- Rice coder discrete cosine transformation discrete cosine transforms frequency-based lossless data compression arithmetic encoder high compression ratio high computation time Computer vision and image processing techniques Golomb Rice encoder entropy codes Image and video coding noise figure 29.24 dB image coding noise figure 31.37 dB encoding technique Integral transforms low computation DWT based Arithmetic BPP fast encoder discrete wavelet transforms noise figure 30.59 dB house image low memory scheme image compression Low memory Entropy Coder table similar discrete wavelet transformation noise figure 27.62 dB Huffman codes efficient lossless encoder standard encoders Huffman PSNR pepper image

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Protein Sequence Classification Through Deep Learning and encoding Strategies

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Procedia Computer Science 2024年 238卷 876-881页

作者： Farzana Tasnim Sultana Umme Habiba Tanjim Mahmud Lutfun Nahar Mohammad Shahadat Hossain Karl Andersson International Islamic University Chittagong Bangladesh Bangladesh University of Engineering & Technology Bangladesh Rangamati Science and Technology University Rangamati 4500 Bangladesh University of Chittagong Bangladesh Lulea University of Technology Skelleftea Sweden

Protein sequence classification is vital for understanding protein functionalities, aiding in the inference of novel protein functions. Machine learning and deep learning algorithms have revolutionized this field, offering insights into specific protein classes and functions. This study employs Natural Language Processing (NLP) techniques, including Integer and Blosum encoding, for efficient classification. SVM with count vectorizer achieves the highest accuracy of 92%, while Integer encoding with CNN surpasses NLP embedding techniques by 4%. The goal is to develop an automated system for predicting protein functionality based on sequence classification, contributing to advancements in proteomics and computational biology.

关键词： Protein sequence Natural Language Processing Deep learning Machine Learning Feature Extraction encoding technique Protein Sequence Classification

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A survey of encoding techniques for reducing data-movement energy

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JOURNAL OF SYSTEMS ARCHITECTURE 2019年 97卷 373-396页

作者： Mittal, Sparsh Nag, Subhrajit IIT Hyderabad Dept Comp Sci & Engn Hyderabad Telangana India

In modern processors, data-movement consumes two orders of magnitude higher energy than a floating-point operation and hence, data-movement is becoming the primary bottleneck in scaling the performance of modern processors within the fixed power budget. Intelligent data-encoding techniques hold the promise of reducing the data-movement energy. In this paper, we present a survey of encoding techniques for reducing data-movement energy. By classifying the works on key metrics, we bring out their similarities and differences. This paper is expected to be useful for computer architects, processor designers and researchers in the area of interconnect and memory system design.

关键词： Data movement encoding technique Energy saving Sparse code Limited weight coding Value locality Value prediction

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Online Evolving Spiking Neural Networks for Incremental Air Pollution Prediction

Online Evolving Spiking Neural Networks for Incremental Air ...

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Maciaq, Piotr S. Kryszkiewicz, Marzena Bembenik, Robert Warsaw Univ Technol Inst Comp Sci Nowowiejska 15-19 PL-00665 Warsaw Poland

ISBN: (纸本)9781728169262

In this article, we offer a novel model, Online evolving Spiking Neural Network for Incremental Prediction (OeSNN-IP), of forecasting from data streams, which we employ to air pollution prediction. OeSNN-IP makes predictions of pollution values and learns from a given number of recent values in streams of pollution and weather data rather than only from the most recent value from each data stream. In the proposed prediction method, older stream values have less influence on predicted values than newer ones. Moreover, we contribute to the theory of evolving spiking neural networks by offering a new fast and effective technique for encoding input values into order values of input neurons and weights of synapses linking input and output neurons. Also, we formulated the tight upper bound on the Euclidean distance between vectors of synapses weights of an output neuron and of a candidate output neuron, which simplifies the selection of a similarity threshold used in the learning phase of OeSNN-IP when a candidate output neuron is compared with output neurons. The experiments conducted on Warsaw-Ursynow pollution data show highly competitive results of the OeSNN-IP prediction as compared to the results obtained using state-of-the-art methods and algorithms.

关键词： evolving spiking neural networks incremental air pollution prediction online learning data stream encoding technique

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Deep activity recognition on imaging sensor data

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ELECTRONICS LETTERS 2019年第17期55卷 928-930页

作者： Setiawan, Feri Yahya, Bernardo Nugroho Lee, Seok-Lyong Hankuk Univ Foreign Studies Dept Ind & Management Engn Yongin 17025 South Korea

Inspired by the recent success of deep learning (DL) approaches in computer vision domain, this Letter proposes a framework to encode the sensor data into an image representation for the activity recognition task. The signal from sensors is encoded based on the Gramian Angular Field. The encoding technique increases the dimension of the data, captures a local temporal relationship in terms of temporal correlation between time intervals on the geometric interpretation, and can be easily applied to the pre-trained DL architecture. The proposed framework is examined with respect to six popular sensor-based activity recognition datasets. Using the authors' framework, the results show that their approach outperforms most of the state-of-the-art approaches.

关键词： encoding technique Image sensors image representation recent success Gramian Angular Field computer vision popular sensor-based activity recognition datasets image recognition image motion analysis deep learning sensor data authors Knowledge engineering techniques activity recognition task Computer vision and image processing techniques Other topics in statistics Optical, image and video signal processing learning (artificial intelligence) computer vision domain state-of-the-art approaches temporal correlation image sensors deep activity recognition local temporal relationship Image recognition

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