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检索条件"任意字段=International Conference on Data Compression, Communications and Processing"

共 25987 条记录，以下是21-30 订阅

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Semantic-Based Motion Detection Method for Unmanned Aerial Vehicle data Transmission 59

Semantic-Based Motion Detection Method for Unmanned Aerial V...

引用

59th Annual IEEE international conference on communications (IEEE ICC)

作者： Wen, Yihan Liu, Feixiang Cao, Qi Niu, Guanchong Xidian Univ Guangzhou Inst Technol Guangzhou Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China Xidian Univ Sch Telecommun Engn Xian Peoples R China

ISBN: (纸本)9781728190549

Unmanned Aerial Vehicles (UAVs) are crucial for wireless network transmissions, particularly in challenging environments of regular inspection. However, transmitting high-resolution video data from UAVs poses challenges due to limited resources and significant data volumes. Traditional video compression methods, removing redundant information with a single frame, suffer from quality loss as compression rates increase. To address these issues, we propose a novel framework, namely Semantic-based Motion Detection compression (SMDC) to perform the video compression with high-quality resolution. The proposed framework incorporates Generative Diffusion Change Detection (TransC-GD-CD), a robust semantic-based change detection method, to accurately detect motion between adjacent video frames. Specifically, frames with slight motion are eliminated, thereby reducing network bandwidth requirements for the UAV inspection. Furthermore, a neural network-based interpolation technique is integrated to restore the information loss and ensure smooth playback. Experimental results show that SMDC outperforms traditional compression methods based on H.264, achieving higher video quality at matched bitrates. The exceptional performance of SMDC promises its potential as an effective solution for high-resolution video transmission in scenarios with limited bandwidth.

关键词： Motion Detection data Transmission UAV

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1st international conference on Advances in Artificial Intelligence and Machine Learning in Big data processing, AAIMB 2023

1st International Conference on Advances in Artificial Intel...

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1st international conference on Advances in Artificial Intelligence and Machine Learning in Big data processing, AAIMB 2023

ISBN: (纸本)9783031730672

The proceedings contain 51 papers. The special focus in this conference is on Advances in Artificial Intelligence and Machine Learning in Big data processing. The topics include: Topological Navigation of Path Planning Using a Hybrid Architecture in Wheeled Mobile Robot;abnormal Behaviour Detection in Surveillance Videos;ISAApp – Image Based Smart Attendance Application;a Self-learning Ai-Based Information Leak Protection System;enhancing Abnormal Object Detection in Camera-Based Systems Through Computer Vision and Deep Learning Techniques;Detection and Classification of Brain Tumor in Magnetic Resonance Images Using CNN;diagnosis of Parkinson’s Disease Using Machine Learning and Deep Learning Techniques;a Survey on Deep Learning Based Human Activity Recognition System;a Deep Learning Approach for Non - invasive Body Mass Index Calculation;Early-Stage Detection of Alzheimer’s Disease Using MRI Scans with Deep Learning;penguin Search Optimization with Deep Learning Based Cybersecurity Malware Spectrogram Image Classification;Detection and Classification of Skin Disease Using CNN;estimation of Above Ground Biomass Using Machine Learning and Deep Learning Algorithms: A Review;URL Phishing Detection Using Deep Learning and Machine Learning Techniques;enhanced Disease Recognition and Classification in Black Gram Plant Leaves Using Deep Learning;Ensemble Deep Learning Approach for Identification of DDOS Attack;ROCLT: Enhanced Text Classifier for Sentiment on Imbalanced Multiclass Tweet data Using Hybrid Deep Learning Techniques;computer Vision to Animal Footprint Classification Based on Deep Learning Model;Speech Emotion Recognition Using CNN Classifier Based on Deep Learning Model;face Detection and Recognition for Criminal Identification System Using Deep Learning.

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1st international conference on Advances in Artificial Intelligence and Machine Learning in Big data processing, AAIMB 2023

1st International Conference on Advances in Artificial Intel...

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1st international conference on Advances in Artificial Intelligence and Machine Learning in Big data processing, AAIMB 2023

ISBN: (纸本)9783031730641

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LOSSY compression OF ADJACENCY MATRICES BY GRAPH FILTER BANKS 49

LOSSY COMPRESSION OF ADJACENCY MATRICES BY GRAPH FILTER BANK...

引用

49th IEEE international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yanagiya, Kenta Hara, Junya Higashi, Hiroshi Tanaka, Yuichi Ortega, Antonio Osaka Univ Osaka Japan Univ Southern Calif Los Angeles CA USA

ISBN: (纸本)9798350344868;9798350344851

This paper proposes a compression framework for adjacency matrices of weighted graphs based on graph filter banks. Adjacency matrices are widely used mathematical representations of graphs and are used in various applications in signal processing, machine learning, and data mining. In many problems of interest, these adjacency matrices can be large, so efficient compression methods are crucial. In this paper, we propose a lossy compression of weighted adjacency matrices, where the binary adjacency information is encoded losslessly (so the topological information of the graph is preserved) while the edge weights are compressed lossily. For the edge weight compression, the target graph is converted into a line graph, whose nodes correspond to the edges of the original graph, and where the original edge weights are regarded as a graph signal on the line graph. We then transform the edge weights on the line graph with a graph filter bank for sparse representation. Experiments on synthetic data validate the effectiveness of the proposed method by comparing it with existing lossy matrix compression methods.

关键词： Graph signal processing Graph Filter Banks Matrix compression Line Graph

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Communication Performance Evaluation Using compression processing for IoT Systems in Mobile Environments 48

Communication Performance Evaluation Using Compression Proce...

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48th Annual IEEE international Computers, Software, and Applications conference (COMPSAC) - Digital Development for a Better Future

作者： Ito, Chisa Takefusa, Atsuko Nakada, Hidemoto Oguchi, Masato Ochanomizu Univ Bunkyo Ku 2-1-1 Otsuka Tokyo 1128610 Japan Natl Inst Informat Chiyoda Ku 2-1-2 Hitotsubashi Tokyo 1018430 Japan Juntendo Univ Bunkyo Ku 2-1-1 Hongo Tokyo 1138421 Japan

ISBN: (纸本)9798350376975;9798350376968

Various sensor data from Internet of Things (IoT) devices are expected to be routinely collected, analyzed, and utilized in the cloud. However, the required communication throughput and latency for various services must be maintained when collecting IoT data in mobile environments. IoT communication involves a large amount of small-scale streaming data, necessitating efficient transmission methods tailored to the communication environment. In this study, we investigate the effectiveness of compression processing by varying the publish/subscribe data characteristics and data compression ratios to improve performance. In experiments, the performance of MQTT communication over SINETStream is investigated, varying parameters such as data size, compression algorithm, data characteristics, and data compression ratio. The results show that performance is improved for highly compressible data, and the performance difference becomes more pronounced as data size increases. There is no correlation between compression time and data compression ratio, and the impact of compression time on overall execution time is slight, thereby confirming that selecting appropriate algorithms for each data characteristic and applying compression processing according to the data size effectively improves performance.

关键词： Mobile IoT Performance evaluation data compression LTE 5G

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Fed2Com: Towards Efficient compression in Federated Learning

Fed2Com: Towards Efficient Compression in Federated Learning

引用

international conference on Computing, Networking and communications (ICNC)

作者： Zhang, Yu Lin, Wei Chen, Sisi Song, Qingyu Lu, Jiaxun Shao, Yunfeng Yu, Bei Xu, Hong Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Huawei Technol Noahs Ark Lab Shenzhen Peoples R China

ISBN: (纸本)9798350371000;9798350370997

Federated learning (FL) is a distributed machine learning system that enables multiple clients to collaboratively train a machine learning model without sacrificing data privacy. In the last few years, various biased compression techniques have been proposed to alleviate the communication bottleneck in FL. However, these approaches rely on an ideal setting where all clients participate and continuously send their local errors to the cloud server. In this paper, we design a communication-efficient algorithmic framework called Fed2Com for FL with non-i.i.d datasets. In particular, Fed2Com has a two-level structure: At the client side, it leverages unbiased compression methods, e.g., rand-k sparsification, to compress the upload communication, avoiding leaving errors at the client. Then on the server side, Fed2Com applies biased compressors, e.g., top-k sparsification, with error correction to compress the download communication while stabilizing the training process. Fed2Com can achieve high compression ratio while maintaining robust performance against data heterogeneity. We conduct extensive experiments on MNIST, CIFAR10, Sentiment140 and PersonaChat datasets, and the evaluation results reveal the effectiveness of Fed2Com.

关键词： Federated learning Communication compression Non-i.i.d data

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An Efficient Hardware Accelerator For Lossless data compression 5

An Efficient Hardware Accelerator For Lossless Data Compress...

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5th international conference on communications, Signal processing, and their Applications (ICCSPA)

作者： Mahmoud, Adel Farid, Samuel Maged, Mark Mohamed, Othman Karam, Reham Salah, Khaled El-Kharashi, M. Watheq Ain Shams Univ Dept Comp & Syst Fac Engn Cairo Egypt Siemens Digital Ind Software Fremont CA 94538 USA

ISBN: (纸本)9781665482370

data compression is a trending field that is used in data storage and data transmission systems. Lossy compression means that data cannot be completely retrieved while in lossless compression the compressed data must be reconstructed exactly. Lossless data compression is used in compressing binary files, telemetry data and high-fidelity medical and scientific images where details are crucial. There is no generic compression algorithm that gives best compression ratio on all data pattern. In this paper, we propose a hybrid lossless hardware architecture that compresses most of data patterns such as repeated data, Gaussian distribution data and images. A profiling-before-compressing and then choosing the right compression hardware is proposed. The proposed design is a highly parallelized architecture that can compress/decompress 64 bytes/cycle with minor overhead. Moreover, it provides high compression ratio on small block sizes as well as large ones.

关键词： Lossless data compression Lossless Image compression Hardware Accelerator compression Algorithms Rice-Golomb Predictive Coding XORing

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ELECTROENCEPHALOGRAM SENSOR data compression USING AN ASYMMETRICAL SPARSE AUTOENCODER WITH A DISCRETE COSINE TRANSFORM LAYER 49

ELECTROENCEPHALOGRAM SENSOR DATA COMPRESSION USING AN ASYMME...

引用

49th IEEE international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zhu, Xin Pan, Hongyi Rong, Shuaiang Cetin, Ahmet Enis Univ Illinois Dept Elect & Comp Engn Chicago IL 60680 USA Northwestern Univ Machine & Hybrid Intelligence Lab Evanston IL 60208 USA

ISBN: (纸本)9798350344868;9798350344851

Electroencephalogram (EEG) data compression is necessary for wireless recording applications to reduce the amount of data that needs to be transmitted. In this paper, an asymmetrical sparse autoencoder with a discrete cosine transform (DCT) layer is proposed to compress EEG signals. The encoder module of the autoencoder has a combination of a fully connected linear layer and the DCT layer to reduce redundant data using hard-thresholding nonlinearity. Furthermore, the DCT layer includes trainable hard-thresholding parameters and scaling layers to give emphasis or de-emphasis on individual DCT coefficients. Finally, the one-by-one convolutional layer generates the latent space. The sparsity penalty-based cost function is employed to keep the feature map as sparse as possible in the latent space. The latent space data is transmitted to the receiver. The decoder module of the autoencoder is designed using the inverse DCT and two fully connected linear layers to improve the accuracy of data reconstruction. In comparison to other state-of-the-art methods, the proposed method significantly improves the average quality score in various data compression experiments.

关键词： EEG signal sensor data compression asymmetrical sparse autoencoder discrete cosine transform transform domain layer

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A JOINT data compression AND TIME-DELAY ESTIMATION METHOD FOR DISTRIBUTED SYSTEMS VIA EXTREMUM ENCODING 49

A JOINT DATA COMPRESSION AND TIME-DELAY ESTIMATION METHOD FO...

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49th IEEE international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Weiss, Amir Kochman, Yuval Wornell, Gregory W. Bar Ilan Univ Fac Engn Ramat Gan Israel Hebrew Univ Jerusalem Sch Comp Sci & Engn Jerusalem Israel MIT Res Lab Elect 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9798350344868;9798350344851

Motivated by the proliferation of mobile devices, we consider a basic form of the ubiquitous problem of time-delay estimation (TDE), but with communication constraints between two non co-located sensors. In this setting, when joint processing of the received signals is not possible, a compression technique that is tailored to TDE is desirable. For our basic TDE formulation, we develop such a joint compression-estimation strategy based on the notion of what we term "extremum encoding", whereby we send the index of the maximum of a finite-length time-series from one sensor to another. Subsequent joint processing of the encoded message with locally observed data gives rise to our proposed time-delay "maximum-index"-based estimator. We derive an exponentially tight upper bound on its error probability, establishing its consistency with respect to the number of transmitted bits. We further validate our analysis via simulations, and comment on potential extensions and generalizations of the basic methodology.

关键词： Time-delay estimation data compression distributed estimation compression for estimation max-index estimator

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Change a Bit to save Bytes: compression for Floating Point Time-Series data

Change a Bit to save Bytes: Compression for Floating Point T...

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IEEE international conference on communications (IEEE ICC)

作者： Taurone, Francesco Lucani, Daniel E. Feher, Marcell Zhang, Qi Aarhus Univ Dept Elect & Comp Engn DIGIT Aarhus Denmark

ISBN: (纸本)9781538674628

The number of IoT devices is expected to continue its dramatic growth in the coming years and, with it, a growth in the amount of data to be transmitted, processed and stored. compression techniques that support analytics directly on the compressed data could pave the way for systems to scale efficiently to these growing demands. This paper proposes two novel methods for preprocessing a stream of floating point data to improve the compression capabilities of various IoT data compressors. In particular, these techniques are shown to be helpful with recent compressors that allow for random access and analytics while maintaining good compression. Our techniques improve compression with reductions up to 80% when allowing for at most 1% of recovery error.

关键词： data compression preprocessing IoT floating point generalized deduplication

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