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International conference on Inventive Computing and Informatics (ICICI)

作者： Suye Li Jinan Vocational College Jinan China

ISBN: (数字)9798350373295

ISBN: (纸本)9798350373301

This study explores advanced techniques for security testing of sensor device chips, critical components in embedded systems and the Internet of Things (IoT). The study emphasizes comprehensive considerations including power management, communication protocol compatibility, and software/hardware interface stability. A review of existing literature highlights significant contributions to improving chip security, multi-factor authentication, data encryption, and intelligent security mechanisms. The proposed methodology focuses on improving hardware security, implementing multi-factor authentication, data encryption, and integrating intelligent security mechanisms for future sensor device chips. Detailed test procedures for sensor device chips are presented, including light source testing, position sensor chip testing, and monitoring system testing. The results contribute to the ongoing development of secure and efficient sensor device chips that are essential for various IoT applications.

关键词： Multi-factor authentication Protocols Embedded systems Reviews Power system management Hardware security Stability analysis

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2022 International conference on Electromagnetics in advanced applications, ICEAA 2022

2022 International Conference on Electromagnetics in Advance...

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23rd International conference on Electromagnetics in advanced applications, ICEAA 2022

ISBN: (纸本)9781665481113

The proceedings contain 145 papers. The topics discussed include: efficient broadband modeling of microwave devices with machine learning and analytical extension of eigenvalues;influence of antenna systems with metamaterial screens composition and geometric parameters on mutual coupling between antennas;wave-particle interactions in earth outer radiation zone;phaseless bi-spherical near-field measurement;spherical near field to far field transformation algorithm for offset-mounted antennas on metallic ground;a joint magnetoelectric sensor and human head model for biomagnetic simulation;a novel time-domain asymptotic solution for a backward transient scattered electric field by a coated metal cylinder;machine-learning for optimization of electrodes and waveforms for electroporation;the tunable single-/narrow-band terahertz metamaterial absorber through photoconductivity;and radiofrequency wave absorption in a graded-index GNP-treated cancer cell substrate inserted in a waveguide.

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Enhanced Feline Facial Recognition: Advancing Cat Face Detection with YOLOv8 and TensorRT 4

Enhanced Feline Facial Recognition: Advancing Cat Face Detec...

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4th International conference on Computer Vision and Pattern Analysis, ICCPA 2024

作者： Wang, Teng University College Durham University DurhamDH1 3LE United Kingdom

ISBN: (纸本)9781510682528

In the field of computer vision, the ability to accurately detect and recognise animal features in various environments is an area of growing interest and application. This study presents an advanced cat face detection system utilising the You Only Look Once Version 8 (YOLOv8) architecture, enhanced with TensorRT optimisation for real-time processing. The approach involves a comprehensive data augmentation process to improve detection accuracy across diverse cat breeds and environmental conditions. Performance evaluation is based on quantifiable metrics;the optimised model achieves a notable reduction in inference time from 50.1ms to 0.9ms and a decrease in GPU power usage from 77 watts to 63 watts, without compromising accuracy. The accelerated processing speed and reduced power requirements make the system highly suitable for real-time applications, such as pet monitoring systems or behaviour analysis tools, where rapid and accurate detection is paramount. The research highlights the potential of deep learning algorithms in precise animal feature recognition and contributes to the field of computer vision by addressing challenges in small, diverse object detection. © 2024 SPIE.

关键词： Object detection Data modeling Performance modeling Education and training Facial recognition systems Animals Computer vision technology Deep learning

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12th International conference on Data Science, Technology and applications, DATA 2023

12th International Conference on Data Science, Technology an...

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12th International conference on Data Science, Technology and applications, DATA 2023

ISBN: (纸本)9789897586644

The proceedings contain 74 papers. The special focus in this conference is on Data Science, Technology and applications. The topics include: Temporal Multidimensional Model for Evolving Graph-Based Data Warehouses;predicting Academic Performance of Low-Income Students in Public Ecuadorian Online Universities: An Educational Data Mining Approach;identifying High-Quality Training Data for Misinformation Detection∗;hawkes Processes on Social and Mass Media: A Causal Study of the #BlackLivesMatter Movement in the Summer of 2020;Conv-LSTM for Real Time Monitoring of the Mineral Grades in the Flotation Froth;disease Prediction with Heterogeneous Graph of Electronic Health Records and Toxicogenomics Data;heterogeneous Ensemble Learning for Modelling Species Distribution: A Case Study of Redstarts Habitat Suitability;a Proactive Approach for the Sustainable Management of Water Distribution systems;Rigor in Applied Data Science Research Based on DSR: A Literature Review;Extracting Frequent Gradual Patterns Based on SAT;An advanced BERT LayerSum Model for Sentiment Classification of COVID-19 Tweets;a Comparison Study for Disaster Tweet Classification Using Deep Learning Models;Prediction of QT Prolongation in advanced Breast Cancer Patients Using Survival Modelling Algorithms;anomaly Detection of Medical IoT Traffic Using Machine Learning;Semantic, Technical and Legal Interoperability of European Company Open Data in Practice: The STIRData Approach;analyzing Cyber-Physical systems in Cars: A Case Study;structuring the End of the Data Life Cycle;fundus Unimodal and Late Fusion Multimodal Diabetic Retinopathy Grading;a Visual Analysis of Hazardous Events in Contract Risk Management;pump and Dump Cryptocurrency Detection Using Social Media;Embedding-Enhanced Similarity Metrics for Next POI Recommendation.

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Two-Dimensional Black Phosphorus: Preparation, Properties, and Environmental applications in Water Treatment 9

Two-Dimensional Black Phosphorus: Preparation, Properties, a...

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9th International Technical conference on Frontiers of Hydraulic and Civil Engineering Technology, HCET 2024

作者： Hussain, Ahlam K. Hussain, Rana K. Kadhom, Mohammed Yousif, Emad Department of Environmental Science College of Energy and Environmental Science Alkarkh University of Science Baghdad10081 Iraq Department of Environmental Health College of Energy and Environmental Science Alkarkh University of Science Baghdad10081 Iraq Department of Chemistry College of Science Al-Nahrain University Baghdad10071 Iraq

ISBN: (纸本)9781643685595

This work examines the diverse properties and environmental applications of two-dimensional black phosphorus (BP), a promising nanomaterial noted for its remarkable mechanical, thermal, electrical, and optical characteristics. We elucidate multiple preparation methods, encompassing both top-down and bottom-up approaches, emphasizing the importance of synthesis techniques in augmenting BP's functionality. The distinctive puckered crystal architecture of BP enables the formation of heterostructures that display unique properties, essential for applications in water treatment and sensing. Our research also highlights the significant anisotropic optical, mechanical, and electrical characteristics of BP, rendering it a compelling candidate for electrochemical energy storage and water purification applications. For the water treatment applications, we examine the effectiveness of BP in membrane-based processes, where its integration into membranes improves pollutant rejection and antibacterial characteristics. Furthermore, we introduce advanced point-of-use disinfection systems employing BP nanosheets, showcasing their efficacy in eradicating microbial contaminants. Other applications include BP's utilization in sensing technologies, specifically its capacity to identify hazardous gases and organic pollutants via improved adsorption mechanisms. © 2024 The Authors.

关键词： Disinfection

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Design and Property Analysis of an Anti resonant Hollow Core Fiber in Fiber-enhanced Raman Spectroscopy systems 13

Design and Property Analysis of an Anti resonant Hollow Core...

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advanced sensor systems and applications xIII 2023

作者： Du, Cheng Yang, Minghong Li, Wei Guo, Donglai Nie, Qilu Qian, Feng National Engineering Laboratory for Fiber Optical Sensing Technology Wuhan University of Technology Wuhan430070 China Fiberhome Telecommunication Technologies Co. Ltd Wuhan430074 China

ISBN: (纸本)9781510667914

The article presents a unique design for a hollow-core anti-resonant fiber, which utilizes a single-layer semi-circular antiresonant unit positioned around the central core. The simulation analysis reveals this design can achieve transmission over a wide bandwidth of 500-1300nm, while exhibiting relatively low losses at wavelengths near 700-800nm and 900-1100nm. The anti-resonant fiber with this structure boasts strong wavelength limitation capability for light transmission, along with high transmission stability in a low-loss mode. Based on the simulation analysis results of the changes in loss characteristics, the hollow core anti-resonant fiber we designed has a mode field diameter of approximately 30 microns, and the wall thickness of the core anti-resonant structure is approximately 0.4 microns. By optimizing the process, such as through preform shaping and wire tension adjustment, a 5km long span with high uniformity was achieved for the hollow core anti-resonant fiber, and corresponding tests were carried out. The test results indicate that the total loss value of the hollow anti-resonant fiber, prepared under optimized process conditions, can be managed at a reduced level, with an attenuation of less than 0.1dB/m at 1064nm. Due to the outstanding pure single mode characteristics of optical fibers, there is no obvious mode coupling between the optical signal and the quartz cladding, neither the inner quartz wall in the transmission channel. In devices utilizing fiber enhanced Raman spectroscopy (FERS), this particular fiber can achieve highly sensitive sensing and precise analysis of Raman spectroscopy. Due to its low attenuation characteristics, the system achieves excellent sensing performance in fiber-enhanced Raman spectroscopy systems, enabling precise detection of a 450ppm CO2 concentration. Based on simulation analysis and testing, this article confirms the benefits of this novel fiber in fiber-enhanced Raman spectroscopy systems, presenting a fresh op

关键词： Raman spectroscopy

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Convolutional Neural Network Classification of Vulnerable Road Users based on Micro-Doppler Signatures using an Automotive Radar 25

Convolutional Neural Network Classification of Vulnerable Ro...

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IEEE 25th International conference on Intelligent Transportation systems (ITSC)

作者： Wachtel, Diogo Edler, Johannes Schroder, Sabine Queiroz, Samuel Huber, Werner TH Ingolstadt Ctr Automot Res Integrated Safety Syst & Measurem Inst Automated Driving CARISSMA IAD Ingolstadt Germany Friedrich Alexander Univ Erlangen Nurnberg FAU Erlangen Germany Inst Tecnol Aeronaut Sao Jose Dos Campos Brazil

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

advanced driver assistance systems (ADAS) are growing in quantity and complexity, requiring even more sensor effectiveness and computational resources. Even though cameras and LiDAR sensors can provide a steady solution in ideal conditions, they are unreliable for adverse weather conditions, such as rain and dense fog. Therefore, radar technology is used for those circumstances is an option to be used for those circumstances. However, its classification accuracy must be enhanced to compete with the other sensor types. This work presents a solution to enhance radar classification quality, adapting a Convolutional Neural Network (CNN) structure to the input of micro-Doppler images of vulnerable road users (VRUs), such as pedestrians, cyclists, and e-scooter drivers. Moreover, two CNNs were trained, each for a particular region of interest (ROIs), one in the near field of the radar until 15m, and the second one up to 25m. The proposed solution presents a promising overall accuracy for its first trial. Nevertheless, it must be further extended to more complex scenes, including more participants, obstacles, and weather conditions.

关键词： radar applications convolutional neural networks object classification

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A Synthesis Methodology for Intelligent Memory Interfaces in Accelerator systems 25

A Synthesis Methodology for Intelligent Memory Interfaces in...

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30th Asia and South Pacific Design Automation conference, ASP-DAC 2025

作者： Limaye, Ankur Bohm Agostini, Nicolas Barone, Claudio Castellana, Vito Giovanni Fiorito, Michele Ferrandi, Fabrizio Marquez, Andres Tumeo, Antonino Pacific Northwest National Laboratory RichlandWA United States Politecnico di Milano Milano Milano Italy

ISBN: (纸本)9798400706356

Domain-specific systems improve the performance of specific applications compared to general-purpose processing systems by deploying custom hardware accelerators. These hardware accelerators are generated using high-level synthesis (HLS) tools. The HLS tools enable a comprehensive design space exploration, optimizing the accelerators' compute performance. However, they often ignore the challenges of implementing the accelerators in a system-on-chip, particularly how they access memory. Our work introduces a buffering system design that improves accelerators' memory accesses by intelligently employing burst transactions to prefetch useful data from external memory to on-chip local buffers. Our design is dynamic, parametric, and transparent to the accelerators generated by HLS tools. We derive the buffering system parameters using appropriate compiler-based analysis passes and memory channel latency constraints. The proposed buffering system design results in, on average, 8.8× performance improvements while lowering memory channel utilization by 53.2% for a set of PolyBench kernels. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： System-on-chip

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Rational Design of Robust and Efficient Natural Leather-Based Ionic Thermoelectric Detectors for Energy-Autonomous and Anti-scalding

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advanced FIBER MATERIALS 2025年第3期7卷 864-881页

作者： Guan, xiaoyu Zheng, Sai Han, Qingxin Wang, xuechuan Yang, Zuhan Zhang, Bingyuan Zhu, Yanxia Li, Dongping An, Meng Fan, Haojun Shaanxi Univ Sci & Technol Inst Biomass & Funct Mat Coll Bioresources Chem & Mat Engn Xian 710021 Shaanxi Peoples R China Univ Tokyo Dept Mech Engn 7-3-1 Hongo Bunkyo Tokyo 1138656 Japan Sichuan Univ Minist Educ Key Lab Leather Chem & Engn Chengdu 610065 Sichuan Peoples R China

Compared with those traditional initiating devices of anti-scalding systems, ionic thermoelectric sensors with energy-autonomous performance show higher reliability. However, the current ionic thermoelectric materials (i-TEs) suffer from complex nano-/micro-channel design, high production costs, environmentally unfriendly, weak mechanical properties, as well as the low moving speed of ions. Herein, the functional leather collagen fibers-bearing natural channels are employed as the polymer matrixes, while the trisodium citrate (SC) organic acid salt exhibits the function of cationic moving self-enhancement as the primary mobile ions for signaling. Including numerous and suitable nano-/micro-channels together with fast-moving cations, the leather-based i-TEs (LITE), LITE-SC0.75 M, possess excellent thermoelectric properties, achieving a Seebeck coefficient of 6.23 mV/K, a figure of merit of 0.084, and an energy conversion efficiency of 2.12%. Combined with its excellent thermal stability, mechanical performance, flexibility, durability, low cost, and outstanding capabilities for low-grade heat harvesting and thermal sensing, the LITE-SC0.75 M detector bearing long service life would show great promise in automatic anti-scalding alarm suitable for multiple scenarios and extreme environments. Therefore, the present work aims to design an efficient, robust, and energy-autonomous leather collagen fibers-based thermoelectric detector to address the limitation of current anti-scalding alarm technology as well as drive advancements in the nano-energy and its effective conversion *** AbstractThe robust leather collagen fibers-based ionic thermoelectric (i-TEs) detectors with numerous nano-/micro-channels and fast-moving cations are successfully constructed, which demonstrate great potential for automatic anti-scalding applications in various scenarios and extreme environments

关键词： Energy conversion Ionic thermoelectricity Functional leather collagen fibers Flexible sensor Anti-scalding alarm

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Applicability of Machine Learning to Improve Mastitis Prediction in Livestock

Applicability of Machine Learning to Improve Mastitis Predic...

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2024 IEEE International conference on Industrial Engineering and Engineering Management, IEEM 2024

作者： Shiotsu, H. Martono, N.P. Ohwada, H. Dept. of Industrial Systems and Engineering Tokyo University of Science Noda Japan

ISBN: (纸本)9798350386097

In recent years, the automation of dairy management using artificial intelligence has been sought after, with mastitis detection being one such application. Mastitis is an inflammatory response that occurs in dairy cows, leading to economic losses such as decreased milk production. Therefore, early detection is desirable. Currently, raw milk analysis devices using lactate dehydrogenase (LDH), a biomarker, are widely used for early mastitis detection. However, the use of sensor systems often results in false positives. It is common practice to refer to detection results from the past few days for the final infection judgment, which relies on the farmer’s experience, leaving room for improvement. This study aims to combine raw milk analysis devices with machine learning techniques to detect mastitis more accurately without relying on the farmer’s experience. We constructed three machine learning detection models, achieving a maximum recall of 0.89, precision of 0.81. Furthermore, the infection prediction approach proposed in this study is widely applicable and can achieve more advanced predictions when combined with related research. © 2024 IEEE.

关键词： Adversarial machine learning

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