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8th international conference on Information and Communication technology for Intelligent systems, ICTIS 2024

8th International Conference on Information and Communicatio...

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8th international conference on Information and Communication technology for Intelligent systems, ICTIS 2024

ISBN: (纸本)9789819758098

The proceedings contain 65 papers. The special focus in this conference is on Information and Communication technology for Intelligent systems. The topics include: A Malmquist Index for Measuring Productivity and Efficiency of ICTs on Literacy Rate Versus Life Expectancy;importance of Cybersecurity Education to Reduce Risks in Academic Institutions;learn with Love Application to Support the Development of Reading Skills in People with Down Syndrome;boosting Accuracy and Reducing Time in Laser Shooting Practices: Video Processing Solutions for Impact Evaluation;differential Performance of Satellite Navigation in Urban and Rural Andes of Ecuador;IP to Satellite Adaptation Level for a Bent Pipe Network Using DVB-S2/RCS2 Standards;performance Analysis for image Rendering on Different Operating systems;Classification of Motor imagery Tasks Using EEG signal Analysis and Linear Discriminant Analysis;unveiling Mortality Patterns in Ecuador: A K-Modes Clustering Approach to Categorical Data Analysis;FFAD: A Novel Metric for Assessing Generated Time Series Data Utilizing Fourier Transform and Auto-Encoder;deTroll—Leveraging Graph Neural Networks with Attention Mechanism to Detect State-Sponsored Trolls;cancer Ease: You Are Not Alone!;experimental Development of Scripts for Data Network Automation, Using the Python Programming Language and Open-Source Tools;a Novel Two-Stage Classification Architecture Integrating Machine Learning and Artificial Immune System for Intrusion Detection on Balanced Dataset;identification and Functional Annotation of Potential Biomarkers Associated with Thalassemia Using Machine Learning-based Knowledge Discovery;Artificial Intelligence in Colorectal Cancer Diagnosis: "SYNERGY-NET" in Campania FESR-POR (European Fund of Regional Development—Regional Operative Program) Research Project;A New Optimization of Agricultural Greenhouse’s PAYLOAD Applying Light Weighted, Green Intelligent, and Secured Solutions based on Visual Watermarking of Objective an

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8th international conference on Information and Communication technology for Intelligent systems, ICTIS 2024

8th International Conference on Information and Communicatio...

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8th international conference on Information and Communication technology for Intelligent systems, ICTIS 2024

ISBN: (纸本)9789819766741

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PTMNet: Pixel-Text Matching Network for Zero-Shot Anomaly Detection 9

PTMNet: Pixel-Text Matching Network for Zero-Shot Anomaly De...

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9th international conference on Big Data and Information Analytics, BigDIA 2023

作者： Deng, Huilin Guo, Yanming Xu, Zhenyi Kang, Yu University of Science and Technology of China Information Science and Technology College Hefei China Anhui University of Technology Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma'Anshan China National University of Defense Technology Systems Engineering College Changsha China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9798350330076

Visual anomaly detection is crucial for automating industrial quality inspection. However, prior research has primarily involved training custom models for specific scenarios, limiting their flexibility and scalability. Zero-shot anomaly detection, based on the vision-language model, leverages robust natural language supervision to enable defect detection in new categories without the need for dedicated training. Recently CLIP exhibits impressive transferability on zero-shot classification tasks. However, CLIP's effectiveness in zero-shot anomaly detection is limited due to the lack of defect-related knowledge and the complexity of extending image-text pair matching to per-pixel prediction tasks. In this study, we propose a novel framework for independently learning text representations for normal and anomalous cases and enhance modal fusion to capture pixel-text associations within the text-image joint space. Specifically, we transform image-text matching into pixel-text matching to generate pixel-text score maps. These score maps are further refined using a transformer fusion module, effectively strengthening modal fusion and improving the utilization of pretrained knowledge. Additionally, to enhance the learning of visual representations, we introduce an extra linear layer following the image encoder. Extensive experiments demonstrate the superior performance of our methods. © 2023 IEEE.

关键词： Pixels

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8th international conference on Information and Communication technology for Intelligent systems, ICTIS 2024

8th International Conference on Information and Communicatio...

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8th international conference on Information and Communication technology for Intelligent systems, ICTIS 2024

ISBN: (纸本)9789819766833

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8th international conference on Information and Communication technology for Intelligent systems, ICTIS 2024

8th International Conference on Information and Communicatio...

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8th international conference on Information and Communication technology for Intelligent systems, ICTIS 2024

ISBN: (纸本)9789819766772

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8th international conference on Information and Communication technology for Intelligent systems, ICTIS 2024

8th International Conference on Information and Communicatio...

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8th international conference on Information and Communication technology for Intelligent systems, ICTIS 2024

ISBN: (纸本)9789819757985

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8th international conference on Information and Communication technology for Intelligent systems, ICTIS 2024

8th International Conference on Information and Communicatio...

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8th international conference on Information and Communication technology for Intelligent systems, ICTIS 2024

ISBN: (纸本)9789819766802

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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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Construction of Power-related Safety Fault Section of Distribution Network Combining internet of Things and image Recognition Algorithm

Construction of Power-related Safety Fault Section of Distri...

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2023 international conference on Big Data Mining and Information Processing, BDMIP 2023

作者： Lu, Weizhi Li, Qiang Song, Ningxi Mei, Lin State Grid Henan Electric Power Information & Telecommunication Company Henan Zhengzhou523000 China

ISBN: (纸本)9798400709166

With the continuous development and expansion of power system, the security problems related to electricity in distribution network are increasingly prominent. To enhance the precision and efficiency of fault detection within a distribution network, this study introduces a methodology for constructing power-related safety fault sections in the distribution network. The proposed approach integrates the internet of Things (IoT) and an image recognition algorithm. Furthermore, an online section location system for identifying single-phase grounding faults in resonant grounding systems is developed by leveraging IoT technology. The system primarily comprises zero-sequence voltage sensing nodes, zero-sequence current sensing nodes, a communication network, and a monitoring center server. This paper introduces a novel method for locating electrical safety faults in distribution networks based on image recognition. The transient process's generator physical quantities are computed through short-term numerical integration. Subsequently, the data for each generator's physical quantities are transformed into fixed-size images, serving as inputs for a Convolutional Neural Network (CNN). Experimental results demonstrate that this method achieves superior accuracy and efficiency in fault detection, thereby offering crucial support for the secure operation of distribution networks. The results of this study have the most practical value and application prospect, which provides sufficient technical support for strengthening the maintenance and management of power system. © 2023 ACM.

关键词： Electric power distribution

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A Novel signal Detection Method based on Multivariate Test Statistics for Ambient Backscatter Communications

A Novel Signal Detection Method Based on Multivariate Test S...

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IEEE/CIC international conference on Communications in China (ICCC)

作者： Wang, Jing Ren, Pinyi Xu, Dongyang Lu, Lei Xi An Jiao Tong Univ Shaanxi Smart Networks & Ubiquitous Access Res Ct Sch Informat & Commun Engn Xian Peoples R China

ISBN: (数字)9781665484800

ISBN: (纸本)9781665484800

Ambient backscatter technology is an important technology of the internet of Things (IoT), which can use ambient radio frequency (RF) sources to communicate between passive devices. However, most studies on the signal detection problem of ambient backscatter communication are based on the fact that tags have two backscatter states, reflective and non-reflective. In this paper, we present a signal detection scheme for ambient backscatter systems which the tag can backscatter in three states. First, we design a maximum a posteriori (MAP) detector to detect the multiple symbols sent by the tag using the continuously received signals. Then we propose a coding scheme that reduces the bit error rate (BER) and improves the system throughput. Furthermore, we derive expressions of detection threshold and BER, and give the system throughput under different coding schemes. At last, experimental simulations are conducted to prove our study.

关键词： ambient backscatter coding scheme signal detection bit error rate (BER)

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