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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71-80 订阅

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Adaptive Per-File Lossless compression of Floating-Point data

Adaptive Per-File Lossless Compression of Floating-Point Dat...

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1st international conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Rodriguez, Andrew Azami, Noushin Burtscher, Martin Texas State Univ Dept Comp Sci San Marcos TX 78666 USA

ISBN: (纸本)9798350364613;9798350364606

The large amount of floating-point data generated by scientific applications makes data compression essential for 110 performance and efficient storage. However, floating-point data is difficult to compress losslessly, and most compression algorithms are only effective on some files. In this paper, we study the benefit of compressing each file with a potentially different algorithm. For this purpose, we created AdaptiveFC, which is based on a tool that can chain data transformations together to generate millions of compression algorithms. AdaptiveFC uses a genetic algorittm to quickly identify an effective compressor in this vast search space for a given file. A comparison of AdaptiveFC to 15 leading lossless CPU compressors on 77 files from 6 datasets in the STAIDench suite shows that per-file compression yields higher compression ratios on average than any individual algorithm.

关键词： data compression floating-point data genetic algorithm CPU processing

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Reliable ML-Based Image processing and compression for an Onboard Imaging Pipeline 15

Reliable ML-Based Image Processing and Compression for an On...

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15th Space Computing conference

作者： Garrett, Tyler Wildenstein, Diego Wang, Eileen Gretok, Evan George, Alan Univ Pittsburgh NSF Ctr Space High Performance & Resilient Comp S Pittsburgh PA 15260 USA

ISBN: (纸本)9798350384543;9798350384536

Sensing and onboard-processing capabilities of next-generation spacecraft continue to evolve. Enabled by advances in avionic systems, large amounts of data can be collected and stored on orbit. Nevertheless, loss of signal, communication delays, and limited downlink rates remain a bottleneck for delivering data to ground stations or between satellites. This research investigates a multistage image-processing pipeline and demonstrates rapid collection, detection, and transmission of data using the Space Test Program - Houston 7 - Configurable and Autonomous Sensor processing Research (STP-H7-CASPR) experiment aboard the international Space Station as a case study. Machine-learning (ML) models are leveraged to perform intelligent processing and compression of data prior to downlink to maximize available bandwidth. Moreover, to ensure accuracy and preserve data integrity, a fault-tolerant ML framework is employed to increase pipeline reliability. This pipeline fuses the fault-tolerant Resilient TensorFlow framework with ML-based tile classification and the CNNJPEG compression algorithm. This research shows that the imaging pipeline is able to alleviate the impact of limited communication bandwidth by using reliable, autonomous data processing and compression techniques to achieve reduced transfer sizes of essential data. The results highlight the benefits provided by resilient classification and compression including minimized storage use and reduced downlink time. Furthermore, the findings of this research are used to assess the feasibility of such a system for future space missions. The combination of these approaches enables the system to achieve up to a 98.67% reduction in data size and downlink time as well as the capacity to capture imagery up to 75.19x longer time period for a given storage size, respectively, while maintaining reconstruction quality and data integrity.

关键词： data compression Fault-Tolerant Computing Resilient Computing Image processing Image Classification Machine Learning Deep Learning Onboard processing

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Medical Self-Reporting With Adversarial data Injection Modeled via Game Theory 6

Medical Self-Reporting With Adversarial Data Injection Model...

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6th international conference on communications Signal processing and their Applications

作者： Badia, Leonardo Bonagura, Valeria Pascucci, Federica Vadori, Valentina Grisan, Enrico Univ Padua Padua Italy Univ Roma Tre Rome Italy London South Bank Univ London England

ISBN: (纸本)9798350384826;9798350384819

We present a game theoretic analysis of a personal e-health system, where a user reports self-measured data to a collection center. Our focus lies on addressing the challenge of potential mistakes in the reported data, a common issue for untrained users in e-health scenarios. The system alternates between the states of correct or erroneous data about the user being available at the collection center. Our goal function is related to age of incorrect information, a measure of the staleness of the information content. It linearly increases as time spent in the erroneous state elapses further. In this scenario, we introduce an additional malicious agent that injects erroneous measurements with the objective of exacerbating the staleness of information. This leads to an adversarial game between the user of interest and the malicious agent, the equilibrium of which we discuss. We derive closed-form expressions based on the system parameters, providing insights into the parametric ranges where the impact of the adversary is most menacing.

关键词： Personalized medicine Medical self-reporting False data injection Age of incorrect information Game theory

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Modified Unitary data Storage Method

Modified Unitary Data Storage Method

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2025 Systems of Signals Generating and processing in the Field of on Board communications, SOSG 2025

作者： Terekhov, Alexey N. Korchagina, Alla S. Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (纸本)9798331532635

The article is intended to consider a method of data storage based on the use of a modified relative unary coding system. During the research, it is planned to develop a data storage algorithm using relative unary code. It is planned to identify the disadvantages and advantages of the proposed algorithm. Suggest a method for the utilitarian application of the developed algorithm for relative information storage. Provide formulas for the utilitarian application of the developed algorithm for relative information storage. To test the functionality of the model, create software that implements the developed algorithm for relative information storage. Show that the information sequences used in the method are mutually orthogonal and belong to the orthonormal Euclidean space. Establish additional ways to reduce information redundancy that increase the compression ratio of the unary data encoding method. Adapt the data compression method by encoding the lengths of the series for use in the developed algorithm for relative information storage. Evaluate the efficiency of data storage in the relative storage algorithm. © 2025 IEEE.

关键词： Storage efficiency

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Intelligent Computing for Cybersocial Space 12

Intelligent Computing for Cybersocial Space

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12th IEEE international Black Sea conference on communications and Networking (BlackSeaCom)

作者： Hahanov, Vladimir Litvinova, Eugenia Davitadze, Zaza Chumachenko, Svetlana Devadze, David Abashidze, Marina Kharkov Natl Univ Radioelect Design Automat Dept Kharkov Ukraine Batumi Shota Rustaveli State Univ Dept Comp Sci Batumi Georgia Batumi Maritime Acad Maritime Fac Batumi Georgia

ISBN: (纸本)9798350351866;9798350351859

Intelligent computing architectures are proposed for managing cyber social processes based on monitoring and analysis of big data. It is proposed to process big data as truth table addresses to solve problems of identification, clustering, and classification of patterns of social and production processes. A family of automata is proposed for analyzing big data such as addresses. The purpose of this investigation is to enhance the big data economy by analyzing data as truth table addresses to identify patterns of production functionality based on the similarity-difference metric. The truth table is considered as a smart form of explicit data structures that have a useful constant - the standard order of addresses. For processing big data, it is necessary to structure their using a truth table. The representation of data as addresses is associated with the unitary coding of patterns by binary vectors on the found universe of primitive data. The mechanism is focused on non-processor data processing based on read-write transactions using in-memory computing technology with significant savings in time and energy. The truth table metric for big data processing is parallelism, technological simplicity, and linear computational complexity. The tradeoff for these benefits is the exponential memory redundancy of storing explicit structured data.

关键词： Intelligent Computing Cloud Fog and Edge Computing Big data Computing In-Memory Computing Cyber Social Computing Hadoop Map-Reduce Technique Big data as Addresses Truth Table Logical Vector Similarity-Differences data Equivalence Universe Of Primitives Patterns As Binary Vectors

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Federated Learning Model for Contextual Sensitive data Quality Applications: Healthcare Use Case 31

Federated Learning Model for Contextual Sensitive Data Quali...

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31st IEEE conference on Signal processing and communications Applications (SIU)

作者： Jeon, Kyung-Chan Han, Geun-Seok Han, Chae-Yun Chong, Ilyoung SmartCore Seoul South Korea Hankuk Univ Foreign Studies Seoul South Korea

ISBN: (纸本)9798350343557

This paper proposes the novel approach of federated learning model with applying contextual data quality indexes to fit business goal. And this presents a federated learning model that applies a data quality management index that adapts to the purpose of business application of healthcare application with contextual data characteristics. The WoO based ontology data model was used to analyze the quality of contextual healthcare data.

关键词： Federated Laearning data Quality Web of Objects Ontology

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Exploring the Role of data Augmentation and FDLP Features on E2E Children's ASR Under Low Resource Conditions 15

Exploring the Role of Data Augmentation and FDLP Features on...

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15th international conference on Signal processing and communications (SPCOM)

作者： Ankita Shambhavi Pushp, Shivesh Shahnawazuddin, S. NIT Patna Dept ECE Patna Bihar India IIT Patna Dept CSE Patna Bihar India CU Dept CSE Mohali India

ISBN: (纸本)9798350350463;9798350350456

The work presented in this paper aims at enhancing the performance of end-to-end (E2E) speech recognition task for children's data under low resource conditions. Since training a robust E2E automatic speech recognition (ASR) system requires a huge amount of data, extremely poor recognition performances are observed when a limited amount of children's speech is used. To overcome the problems arising from data scarcity, we have resorted to several out-of-domain and in-domain data augmentation techniques. For out-of-domain data augmentation, we have explicitly modified adults' speech to render it acoustically similar to children's speech before pooling into training. On the other hand, in the case of in-domain data augmentation, we have slightly modified the pitch and duration of children's speech in order to create more data capturing greater diversity. In addition to that, we have also explored the efficacy of frequency domain linear prediction (FDLP) technique for front-end speech parameterization. The application of linear prediction on the frequency domain representation of speech signal helps to effectively capture the temporal envelope during front-end feature extraction. In our experimental explorations, the use of proposed data augmentation in combination of FDLP features has shown a relative improvement in character error rate by 92.9% over the baseline ASR system.

关键词： Low resource children's ASR data augmentation FDLP features end-to-end ASR

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IoT-based Compressive Sensing for Real-World Infrastructural Monitoring Application

IoT-based Compressive Sensing for Real-World Infrastructural...

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IEEE international conference on communications (IEEE ICC)

作者： Zerbino, Matteo Bisio, Igor Garibotto, Chiara Lavagetto, Fabio Sciarrone, Andrea Univ Genoa DITEN Dept Genoa Italy

ISBN: (纸本)9781538674628

Compressive Sensing (CS) is a sampling technique that challenges the traditional sampling scheme introduced by the Whittaker-Shannon theorem. Under certain conditions, a signal can be sampled at rates lower than the Nyquist rate, introducing a different kind of approach to signal handling, both in the acquisition and in the reconstruction phases. CS relies on the property of sparsity, the idea that a signal possesses an amount of information which is smaller than the amount of data required to store it. This paper employs the CS approach to inertial signals sensed by innovative IoT devices by showing applications of real-world infrastructure monitoring. Numerical results show that our approach is able to efficiently estimate the infrastructures modal frequencies with an innovative inertial IoT prototype by achieving a compression level around 20 times below the Nyquist rate.

关键词： Compressive Sensing (CS) Infrastructural Monitoring IoT node Inertial Signals Signal processing

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Making METOC data portable with video codecs 27

Making METOC data portable with video codecs

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27th international conference on Information Fusion (FUSION)

作者： Gommers, Daan Strik, Dennis van Leijen, Vincent COMMIT JIVC KIXS Datalab Utrecht Netherlands

ISBN: (纸本)9798350371420;9781737749769

Numerical Weather Predictions and Ocean forecasts (METOC) are typically high quality datasets with a large file size. Some users have limited bandwidth available and require much smaller file size that can be shared by email or satellite communications. compression of the datasets can be a solution for these use cases. In previous research, we experimented with lossy compression to find the right balance between compression factor, information loss and speed of operations. In this work, we expand on this by evaluating the use of common video codecs to make METOC data portable. Such codes are maintained for a very large user community and offer decompression in near-real time of small compressed datasets while preserving most of the important information.

关键词： meteorology oceanography compression

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Anomalous Node Detection in Socially-Aware Mobile IoT Networks Using Q-Learning 15

Anomalous Node Detection in Socially-Aware Mobile IoT Networ...

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15th international conference on Signal processing and communications (SPCOM)

作者： Tripathi, Shraddha Hegde, Rajesh M. Sandhan, Tushar Indian Inst Technol Kanpur Dept Elect Engn Kanpur Uttar Pradesh India Indian Inst Technol Dharwad Dept Elect Engn Dharwad Karnataka India

ISBN: (纸本)9798350350463;9798350350456

Rapid growth in demand for Internet of Things (IoT) applications has driven advancements in network technologies. Mobile IoT applications, including tracking, intelligent wearables, and autonomous vehicles, necessitate high data throughput, privacy, low-latency data transfer, and prolonged network lifetime. In every IoT application, many sensor devices collect data from physical objects and send the acquired information to the access point (AP). A secure connection between IoT nodes and the AP is crucial for optimal data routing, transmission reliability, and network lifetime in IoT networks. Additionally, it is essential to detect and remove anomalous nodes to protect the network from unwanted intrusion. This work proposes a socially-aware anomalous node detection and data routing strategy, representing the mobile IoT network as a dynamic graph. A novel social relationship index (SRI)-factor is computed based on current and predicted future node encounters to identify and remove the imposture nodes. The network graph is then dynamically updated. Moreover, an optimization problem is formulated to maximize the network lifetime with respect to data transmission latency, optimum routing path, and energy budget. The proposed optimization problem is solved using the Q-learning framework. Q-learning-based routing algorithm adapts to the changes of the dynamic graph by continuously updating the Q-values on the basis of feedback. Finally, numerical results demonstrate a significant performance improvement compared to existing methods.

关键词： Social IoT networks anomalous node detection graph signal processing routing network lifetime Q-learning

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