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检索条件"任意字段=IEEE Data Compression Conference"

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ECG compression Techniques of Spiht Decoder for Mobile Health Applications

ECG Compression Techniques of Spiht Decoder for Mobile Healt...

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2023 ieee International conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Aruna, V.B.K.L. Chitra, E. Padmaja, M. Srm Institute of Science and Technology Ece Depatment Chennai India V R Siddhartha Engineering College Ece Department Vijayawada India

ISBN: (纸本)9798350398465

The demand for remote health applications is increasing as e-health monitoring devices improve. For remote health applications, e-health monitoring devices are becoming more and more popular. Cardiovascular diseases (CVDs) were recently thought to be the most common chronic illnesses among patients. Therefore, it is necessary to have more diagnosis and medical therapy and also requires transformation of information from cardiac patients to distant hospitals. There are some challenges according to the devices to take over like excess energy consumption, excessive power consumption, data protection, storage space as well as transmission rate. In addition to that, the Real-time processing of electrocardiogram (ECG) data is required, and the captured data must have high accuracy and lossless compression. A SPIHT decoder with enhanced capabilities of Set Partitioning in Hierarchical Tree has been designed for signal processing. The transmission of the ECG signal on a wireless channel is seriously impacted by numerous disturbances because of the inadequate channel in medical applications. Input database can be taken using the database named as MIT-BIH and was developed on Xilinx platform and MATLAB. SPIHT decoder results can be estimated by different algorithms and also measured the parameters like to demonstrate its effectiveness, it is compared to existing designs and measured for the compression Ratio (CR), Signal to Noise Ratio (SNR), Percentage Root Mean Square Difference (PRD), and Root Mean Square Error (RMSE). Many ECG recordings from the Arrhythmia database are used in the tests for this lossy compression/decompression technique. The collected results show how the suggested method can increase compression ratio while preserving high signal quality. © 2023 ieee.

关键词： Electrocardiograms

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A Proficient Test data compression and Decompression System for Enhanced Test Competence in SOC Testing 6

A Proficient Test Data Compression and Decompression System ...

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6th International conference on Inventive Computation Technologies, ICICT 2023

作者： Jhancy Mabel, D.J. Caroline Viola Stella Mary, M. Dept of ECE Kamaraj Polytechnic College Pazhavilai Kanyakumari India Dept of IT Francis Xavier Engineering College Tirunelveli India

ISBN: (纸本)9798350398496

BIST is a systematic methodology capable of addressing many of the issues encountered when testing systems-on-chip. However, larger registers are required to handle the large amount of test information produced in each clock cycle, which has a significant impact on overall circuit performance. Huge data volume generally requires not only more memory but also a longer testing time. The proposed design develops a test compression method thatemploys both an efficient dictionary and creating and capturing value collection to dramatically reduce testing high memory requirements. data compression reduces test data quantity without affecting overall system performance. This data compression method is applied to the test patterns generated by the BIST technique, and the compressed data is then applied to the module being tested. Following that, a simple processor is designed. The concept of Null Conventional Logic is commonly used in the testing of the basic processing units in the processor designed. © 2023 ieee.

关键词： Application specific integrated circuits

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Model-free adaptive cluster consensus control for nonlinear multi-agent systems under DoS attack 12

Model-free adaptive cluster consensus control for nonlinear ...

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ieee 12th data Driven Control and Learning Systems conference (DDCLS)

作者： Li, Yuhan Bu, Xuhui Guo, Jinli Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo 454003 Henan Peoples R China

ISBN: (纸本)9798350321050

Aiming at the cluster consensus problem for a class of unknown discrete time nonlinear multi-agent systems with denial of service(DoS) attacks, a model-free adaptive control algorithm is proposed. It is assumed that the system has a fixed topology and only part of the agents in each cluster can receive the leader information. Firstly, a dynamic linearization method is used to construct a data relationship model for multi agent systems, and the periodic DoS attack model conforms to Bernoulli distribution is constructed by limiting the corresponding attack time and frequency. A data-driven cluster tracking control protocol is designed by combining the cluster consensus error under the coupling effect among agents. Then, the sufficient conditions to ensure the convergence of tracking error under the sense of mathematical expectation are given by the method of compression mapping, and theoretically analyzes the convergence of the algorithm. Finally, simulation results verify the effectiveness of the proposed algorithm.

关键词： Model-free adaptive control Multi-agent system Cluster consensus control DoS attacks data-driven control

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Why Model-Based Lossy compression is Great for Wind Turbine Analytics 40

Why Model-Based Lossy Compression is Great for Wind Turbine ...

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40th ieee International conference on data Engineering, ICDE 2024

作者： Jensen, Soren Kejser Thomsen, Christian Pedersen, Torben Bach Muñiz-Cuza, Carlos Enrique Abduvakhobov, Abduvoris Aalborg University Department of Computer Science Aalborg Denmark

ISBN: (纸本)9798350317152

Modern wind turbines are equipped with wired high-quality sensors that produce high-frequency sensor data in the form of time series as shown in Figure 1 a. From working with multiple different practitioners, we have learned that relatively few but very long high-quality time series are produced. The time series are either univariate, i.e., have one value per timestamp, or multivariate, i.e., have multiple values per timestamp. Further, they are either regular, i.e., have a fixed time interval between consecutive data points, or irregular. Despite these differences, the volume and velocity of the time series that are being produced are generally major challenges. For example, if the sensors are sampled at 100Hz, a single park of 100 wind turbines generates more than 11 PiB of data each year [1]. The sensor data is collected by weak edge devices and then transferred to powerful cloud servers over a relatively slow connection as shown in Figure 2. However, it is infeasible to transfer and store the raw time series due to their volume and velocity. Renewable energy system installations use low-end commodity PCs on the edge, e.g., 4 CPU cores, 4 GiB RAM, and an HDD [1]. In addition, the bandwidth between the edge and the cloud can be as low as 0.5-5 Mbit/s [1]. Thus, practitioners use simple aggregates, e.g., 10-minute averages, which remove valuable outliers and fluctuations as shown in Figure 1b. To remedy this, practitioners want to use lossy compression with a per-value error bound (E) to collect more high-frequency time series and thus improve their analytics. © 2024 ieee.

关键词： Time series

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Deep Learning on Home Drone: Searching for the Optimal Architecture

Deep Learning on Home Drone: Searching for the Optimal Archi...

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ieee International conference on Robotics and Automation (ICRA)

作者： Maalour, Alaa Gurfinkerl, Yotam Diker, Barak Gal, Oren Rus, Daniela Feldman, Dan Univ Haifa Dept Comp Sci Robot & Big Data Labs Haifa Israel MIT CSAIL Cambridge MA 02139 USA

ISBN: (纸本)9798350323658

We suggest the first system that runs real-time semantic segmentation via deep learning on the weak microcomputer Raspberry Pi Zero v2 (whose price was $15) attached to a toy drone. In particular, since the Raspberry Pi weighs less than 16 grams, and its size is half of a credit card, we could easily attach it to the common commercial DJI Tello toy drone (<$100. <90 grams, 98 x 92.5x 41 mm). The result is an autonomous drone (no laptop nor human in the loop) that can detect and classify objects in real-time from a video stream of an onboard monocular RGB camera (no GPS or LIDAR sensors). The companion videos demonstrate how this Tello drone scans the lab for people (e.g. for the use of firefighters or security forces) and for an empty parking slot outside the lab. Existing deep learning solutions are either much too slow for real-time computation on such IoT devices, or provide results of impractical quality. Our main challenge was to design a system that takes the best of all worlds among numerous combinations of networks, deep learning platforms/frameworks, compression techniques, and compression ratios. To this end, we provide an efficient searching algorithm that aims to find the optimal combination which results in the best tradeoff between the network running time and its accuracy/performance.

关键词： Drones

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Using Asymmetric Attention Mechanism Access Class Barring with Gated Recurrent Unit for an End-to-end Image compression 15

Using Asymmetric Attention Mechanism Access Class Barring wi...

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15th ieee International conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Samonte, Mary Jane C. Liu, Suping Li, Yanan School of Information Technology Mapúa University Makati City Philippines

ISBN: (纸本)9798350381177

The exponential expansion of the Internet has resulted in an overwhelming abundance of multimedia data. However, image and video compression technology faces challenges such as insufficient feature extraction, inaccurate bit rate estimation, and quantization errors. Within this study, we introduce optimization methods to address these issues. The AT-ACB module is introduced to enhance compact feature extraction using an Asymmetrical Convolution Block and hybrid attention module. Besides, the parallel conditional context module captures global relevance and reduces spatial redundancy. Then, the Tiki-Taka training method mitigates quantizer mismatch. This paper mainly researches the end-to-end image compression optimization scheme built upon deep learning. Finally, the experimental results demonstrate that the proposed optimization method achieves BD-Rate savings of 3.95%, 0.97%, and 3.18% on the Kodak dataset, resulting in a total BD-Rate saving of 6.89%. © 2023 ieee.

关键词： Image compression

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Adaptive Arithmetic Coding using Neural Network 14

Adaptive Arithmetic Coding using Neural Network

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14th International conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kuchhal, Utkarsh Vyapak, Ved Panda, Jeebananda Delhi Technological University New Delhi India

ISBN: (纸本)9798350335095

In today's data-driven world, industries across various sectors are grappling with the enormous influx of data being generated. The sheer volume of data poses significant challenges in terms of infrastructure costs, data transfer speeds, and storage requirements. As a result, many industries are reevaluating their approach to cloud-based storage solutions. One crucial solution to mitigate the issues associated with excessive storage usage, data integrity, transfer efficiency, and improved streaming speeds is lossless data compression. Numerous compressors currently available employ prediction-based compression techniques and utilize learned models to address these challenges *** tackle this problem, our research focuses on exploring and developing strategies for compressing sequential data using neural network predictors. Neural networks are well-known as universal function approximators capable of learning complex mappings, and they have demonstrated impressive performance in prediction tasks. We leverage this capability by combining an arithmetic coder with recurrent neural network predictions, enabling us to achieve lossless compression for a wide range of datasets including synthetic, text, and genomic data [1]. Through a series of experiments using both simulated and actual text and genomic datasets, we evaluate the effectiveness of our approach. The results and observations from these experiments provide valuable insights for future research and advancements in the field of lossless data compression. © 2023 ieee.

关键词： data compression

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LGCM: A Communication-Efficient Scheme for Federated Learning in Edge Devices 19

LGCM: A Communication-Efficient Scheme for Federated Learnin...

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19th ieee-India-Council International conference (INDICON)

作者： Saadat, Nafas Gul Thahir, Sameer Mohamed Kumar, Santhosh G. Jereesh, A. S. Cochin Univ Sci & Technol Dept Comp Sci Cyber Phys Syst Lab Cochin Kerala India

ISBN: (纸本)9781665473507

For a couple of recent years, fields such as Computer Vision, NLP, and Speech Recognition have been revolutionized by Machine Learning (ML). This success can be attributed to Big data. The Process of Collecting this large amount of data has often been done in privacy-invasive ways. Traditional ML approaches require collecting the training data in a common store to train a model and deploy it. Federated Learning (FL), is a recent technique that allows machine learning models to be trained without needing to submit collected data to the server. Instead of sharing the data with the client, the model parameters will be sent to the clients to collaboratively train the model. The data used to train the global model never leaves the devices, strictly enforcing privacy. Only the clients and the server share model learning parameters such as Weights, Biases. While this approach respects privacy and is flexible, it comes at a cost of additional challenges like communication cost and model bias due to data heterogeneity that needs to be tackled. To improve FL's efficiency in terms of communication overhead and accuracy, In this work, we propose a scheme called Learned Gradient compression Method (LGCM). In this scheme accuracy declination which causes by non-iid(non-independent and identically distributed) data and communication overhead is improved. To improve the accuracy declination caused by non-iid data, clients with a lower level of non-iid data have been selected for training the ML Model. To improve communication efficiency we employ compression of weight updates by using Autoencoder(AE). The proposed scheme has been applied in a typical ML task to study the performance and effectiveness of the scheme and has been compared with baseline algorithms. We have used a CNN model to classify handwritten digits using the MNIST dataset. The achieved accuracy of 96% outperforms the baseline FedAvg algorithm. the compression The ratio achieved using AE is approx. 5000x.

关键词： Federated Learning Communication Overhead Gradient compression Autoencoder Non-IID data Deep Learning CNN

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6th International conference on Intelligent Computing in data Sciences, ICDS 2024

6th International Conference on Intelligent Computing in Dat...

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6th International conference on Intelligent Computing in data Sciences, ICDS 2024

ISBN: (纸本)9798350351200

The proceedings contain 115 papers. The topics discussed include: MEDIFICS: model calling enhanced VLM for medical VQA;N-gram based HASSANIYA dialect classification;a comprehensive review on machine learning advancements for plant disease detection and classification;utilizing deep learning and machine learning methods to forecast market performance;optimizing video compression quality using AI-boosted HEVC;performance of prophet in stock-price forecasting: comparison with ARIMA and MLP networks;abstractive biomedical long document summarization through zero-shot prompting;comprehensive and comparative analysis between transfer learning and custom built VGG and CNN-SVM models for wildfire detection;and resume ranker: AI-based skill analysis and skill matching system.

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Optimized Deep Learning Approach for Image compression Using Compressed Sensing Technique with Convolutional Autoencoders 2

Optimized Deep Learning Approach for Image Compression Using...

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2nd ieee International conference on data Science and Network Security, ICDSNS 2024

作者： Puttegowda, Kiran Veeraprathap, V. Sequeira, Sharmila Shanthi Aruna, B. Kirankumarhumse Sunil Kumar, D.S. Sudheesh, K.V. Vidyavardhaka College of Engineering Department of Ece Karnataka Mysuru India Atme College of Engineering Department of Ece Karnataka Mysuru India Nitte Meenakshi Institute of Technology Department of Cse Karnataka Bengaluru India Bgs Institute of Technology Adichunchanagiri University Department of Ece Karnataka Mandya India Anatta Innovations Bgs B-School Bengaluru India

ISBN: (纸本)9798350373110

Image compression is crucial for efficient storage and transmission of visual data. Traditional methods like JPEG use transform coding, which may result in loss of fine details. Compressed sensing (CS) offers an alternative by exploiting signal sparsity, allowing for significant data reduction without substantial loss of information. Compressed sensing (CS) has proven to be an important technique in signal acquisition, especially in contexts in which sensor quality or the maximum possible duration of the measurement is limited. In this paper, deep learning techniques are used to improve compressive sensing in the context of image acquisition. In a existing approaches deployed stacked denoising autoencoders capable of reconstructing images considerably faster than conventional iterative methods. Apart from reviewing this approach, a possible extension using convolutional autoencoders inspired by the popular VGGnet architecture is discussed. Instead of learning models from scratch, a simple yet effective way for adapting available filters used in ImageNet classification is presented. By reformulation of the autoencoder structure in terms of a fully convolutional network can be adapted to arbitrarily large images for efficient learning of the measurement matrix and sparsity basis. Finally, experimental results indicate that the suggested approach outperforms current state-of-the-art methods across several metrics: reconstruction accuracy, time efficiency, and noise reduction capability. Particularly noteworthy is its exceptional reconstruction accuracy even with a limited number of measurements. © 2024 ieee.

关键词： Noise abatement

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