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SSRN 2023年

作者： Shen, Kailai Yan, Diqun Hu, Jing Ye, Zhe Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China

Speech quality is a critical consideration for applications such as speech enhancement, coding, transmission, and synthesis. Accurately evaluating the quality of degraded speech without a reference is particularly challenging. As a result, non-intrusive speech quality assessment has been extensively researched due to its efficiency and ease of deployment. This survey comprehensively reviews these studies. Commonly used speech quality assessment databases are reviewed, as they serve as validation sets for objective metrics. Non-intrusive speech quality assessment methods are categorized and reviewed, and the performance of state-of-the-art and classical speech quality methods is compared. The survey presents an overview of classic algorithms and recent advancements in the field of non-intrusive speech quality assessment. © 2023, The Authors. All rights reserved.

关键词： Speech enhancement

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Digital Twin Definition for Detecting Abnormal Perspiration Patterns in High-Stress Professions 23

Digital Twin Definition for Detecting Abnormal Perspiration ...

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23rd RoEduNet Conference: Networking in Education and Research, RoEduNet 2024

作者： Vizitiu, Cristian Turcin, Ioan Dominey, Kevin Alexander Softic, Selver Dinculescu, Adrian-Catalin Bocanet, Vlad I. Dugan, Cosmin Moraru, Sorin-Aurel The Space Applications and Technologies Laboratory Institute of Space Science-Subsidiary of INFLPR Magurele Romania Transilvania University of Brasov Faculty of Electrical Engineering and Computer Science Department of Automatics and Information Technology Brasov Romania CAMPUS 02 University of Applied Sciences Department Automation Technology Graz Austria CAMPUS 02 University of Applied Sciences Department IT and Business Informatics Graz Austria Robotics and Production Management Technical University of Cluj-Napoca Cluj-Napoca Faculty of Industrial Engineering Department of Manufacturing Engineering Romania Bucharest University Emergency Hospital Bucharest Romania

ISBN: (纸本)9798331540388

Perspiration is a physiological response in high-stress situations, that also plays a key role in thermoregulation and stress management. Understanding perspiration patterns is used for assessing physiological responses and optimizing human performance especially in professions such as emergency medical services and military operations, as well as for individuals exposed to Isolated Confined Extreme (ICE) environments such as astronauts and space analog mission participants. This paper defines a Digital Twin (DT) model, that will be integrated with an existing physical sweat simulator module (SSM) prototype. Its goal is to simulate human perspiration under high-stress conditions by integrating environmental, physiological, and psychological data. The system architecture is developed via Model-Based Systems engineering (MBSE). The scope of the designed virtual model is to accurately reflect physical perspiration, detect abnormal perspiration patterns, and generate recommendations regarding stress countermeasures. The goal is to influence the behavioral and/or physiological state of the physical twin, thereby creating a dynamic, responsive system. Machine learning (ML) models will be used to identify patterns in the environmental, physiological, and psychological inputs and predict sweat volume, chemical composition, and regional sweat distributions across the body. ML will also be used to detect abnormal perspiration patterns based on input data. The application of generative AI for creating test data from simulation templates is anticipated to facilitate the comparative analysis of two to three distinct profiles, such as astronauts, soldiers, and paramedics or firefighters, using simulated values. The SSM, with its integrated sensors, will serve the dual purpose of validating the model and providing realistic physical simulation. © 2024 IEEE.

关键词： Physiological models

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Analysis of Waves and Human Interaction Using Beach Webcams

Analysis of Waves and Human Interaction Using Beach Webcams

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OCEANS

作者： Harel Mendelman Tomer Masssas Yair Moshe Signal and Image Processing Laboratory (SIPL) Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Technion – Israel Institute of Technology Haifa Israel

Automatic monitoring of beach video streams is important for improving safety, environmental monitoring, research, and education related to beach activities. This paper introduces a novel approach for monitoring water bodies by analyzing beach video streams. In contrast to earlier works, in the proposed approach we analyze not only the behavior of water bodies, or of humans on beach videos, but also the interactions between them. By integrating human activity and water behavior analysis, the approach provides new insights that are unattainable by analyzing each separately. To accomplish this objective, deep neural networks are utilized to analyze video streams from existing beach webcams. The analysis includes the detection and tracking of people and waves, as well as higher level analysis. We use the task of characterizing surfing conditions as a case study and demonstrate our ability to estimate the values of key parameters that can help determine the quality of a surf spot at a given time.

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Fast Crop Pest Detection Using Lightweight Feature Extraction and Knowledge Distillation

Fast Crop Pest Detection Using Lightweight Feature Extractio...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ze Yang Xianliang Jiang Guang Jin Junkai Huang Jie Bai Dingxin Yu Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China

Pest detection is critical for achieving effective pest control. However, the current deep learning-based pest detection algorithm is unsuitable for deployment on resource-limited edge devices due to its extensive computation and long inference time. Although lightweight models have been widely used for practical detection, their insufficient feature extraction capability leads to a decline in detection accuracy. This paper proposes a fast algorithm for crop pest detection based on lightweight feature extraction and knowledge distillation. Firstly, we introduce partial convolution and propose a lightweight feature extraction module, C3Faster, which reduces the model's computation and speeds up model inference while ensuring effective feature extraction. Secondly, we use knowledge distillation to improve the model's detection accuracy by using teacher networks to assist in training. Finally, we created a dataset, CropPest6, consisting of six crop pest categories and conducted experiments. The experimental results demonstrate that our method reduces the detection time, number of parameters, and computation by 17%, 38%, and 44%, respectively, compared to the baseline model. Furthermore, our method achieves 93.9% Precision, 93.6% Recall, and 97.5% mean Average Precision (mAP), demonstrating its practical suitability for fast crop pest detection.

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Improving the operational stability of perovskite solar cells with cesium-doped graphene oxide interlayer

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Journal of Energy Chemistry 2024年第9期96卷 483-490页

作者： Masaud Almalki Katerina Anagnostou Konstantinos Rogdakis Felix T.Eickemeyer Mostafa Othman Minas M.Stylianakis Dimitris Tsikritzis Anwar Q.Alanazi Nikolaos Tzoganakis Lukas Pfeifer Rita Therisod Xiaoliang Mo Christian M.Wolff Aïcha Hessler-Wyser Shaik M.Zakeeruddin Hong Zhang Emmanuel Kymakis Michael Grätzel Laboratory of Photonics and Interfaces École Polytechnique Fédérale de Lausanne(EPFL)Lausanne 1015Switzerland Department of Electrical and Computer Engineering Hellenic Mediterranean UniversityHeraklionCrete 71410Greece Institute of Electrical and Microengineering(IEM)Photovoltaics and Thin-Film Electronics Laboratory(PV-Lab) École Polytechnique Fédérale de Lausanne(EPFL)Neuchâtel 2000Switzerland Department of Nursing Faculty of Health SciencesHellenic Mediterranean UniversityCrete 71410Greece Centre Interdisciplinaire de Microscopie Electronique École Polytechnique Fédérale de Lausanne(EPFL)Lausanne 1015Switzerland Future Energy Technology Institute King Abdulaziz City for Science and Technology(KACST)P.O.Box 6086Riyadh 11442Saudi Arabia State Key Laboratory of Photovoltaic Science and Technology Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and PerceptionInstitute of OptoelectronicsDepartment of Materials ScienceFudan UniversityShanghai 200433China

Perovskite solar cells(PSCs)have made great advances in terms of power conversion efficiency(PCE),yet their subpar stability continues to hinder their *** interface between the perovskite layer and the charge-carrier transporting layers plays a crucial role in undermining the stability of *** this work,we propose a strategy to stabilize high-performance PSCs with PCE over 23%by introducing a cesium-doped graphene oxide(GO-Cs)as an interlayer between the perovskite and hole-transporting *** GO-Cs treated PSCs exhibit excellent operational stability with a projected T80(the time where the device PCE reduces to 80%of its initial value)of 2143 h of operation at the maximum powering point under one sun illumination.

关键词： Perovskite solar cells Doped graphene oxide Graphene related material Long-term operational stability

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Multibit Attention Fusion for Gaze Estimation Using 12-Bit RAW Data From CMOS Sensors

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Tian, Kaiwen Wang, Han Wu, Zhipeng Lyu, Yongxiang Vai, Mang I. Liu, Yu Tianjin University School of Microelectronics Tianjin300072 China University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology State Key Laboratory of Analog and Mixed-Signal VLSI China

In recent years, deep learning-based gaze estimation techniques using eye images have made significant progress. However, balancing prediction accuracy and computational complexity remains a challenge. In this article, we propose using the RAW data from a CMOS image sensor (CIS) as input, thereby avoiding the high computational demands of the traditional image signal processor (ISP) (which converts RAW to RGB). We designed a special measuring scheme to collect and annotate a 12-bit RAW dataset. Additionally, we introduce the Multibit Attention Fusion Gaze Estimation Network (MAFN) to accurately perform gaze estimation using 12-bit RAW data. Our MAFN effectively extracts features of varying granularities through attention fusion techniques across different bit depths. We also employ axial sensitivity spatial attention, which is more sensitive to the horizontal and vertical positions of the pupil in gaze angle prediction. Furthermore, we propose bit rolling channelization and neural network-friendly quantization (NNFQ) for lossless input of 12-bit data and low-bit data generation, respectively. Experimental results validate the effectiveness of MAFN and demonstrate the superiority of our method, showing that it can accurately measure gaze direction using 12-bit RAW data. © 1963-2012 IEEE.

关键词： Image coding

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Thick Film Interdigitated Electrode Graphite and Carbon Nanotube on Flexible Substrate for Hydrogen Gas Detection

Thick Film Interdigitated Electrode Graphite and Carbon Nano...

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2023 IEEE International Conference on Sensors and Nanotechnology, SENNANO 2023

作者： Abu Bakar, Azlinda Nizar Hamidon, Mohd Yaacob, Mohd Hanif Paiman, Suriati Jaafar, Haslina Wan Jusoh, Wan Nursheila Universiti Putra Malaysia Funtional Device Laboratory Institute of Nanoscience and Nanotechnology Selangor Serdang Malaysia Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication Systems Selangor Serdang Malaysia Universiti Putra Malaysia Faculty of Science Department of Physics Selangor Serdang Malaysia Universiti Putra Malaysia Faculty of Engineering Department of Electrical and Electronic Selangor Serdang Malaysia Universiti Kuala Lumpur Section of Aerospace Malaysians Institute of Aviation Technology Selangor Dengkil Malaysia

ISBN: (纸本)9798350333312

The comparison on the performance of interdigitated electrode (IDE) graphite and carbon nanotube (CNT) using titanium dioxide-multiwall carbon nanotube (TiO2-MWCNT) composite as sensing materials to detect hydrogen gas is presented in this research work. Screen printing technique has been selected to deposited IDE graphite and CNT conductive pastes as the electrode thick film on a flexible substrate, which is continued by firing at 120°C each. TiO2-MWCNT paste was printed as a sensing layer on top of IDE, followed by firing at 350°C. FESEM was used to characterize the morphological analysis of the IDE film. The operating temperature was varied at 27°C, 50°C and 100°C and concentration varied from 100-1000 ppm for gas hydrogen. The gas sensor demonstrated as n-type gas characteristics where the response of the gas sensor produced increased current from the results. The results purpose when the sensor was exposed to hydrogen gas. Therefore, the ability of IDE graphite and CNT as an electrode to harvest the electron from sensing materials were successfully proven. The IDE CNT showed better response compare to IDE graphite, but both electrodes were able to sense 100 - 1000 ppm at the operating temperature of 27°C, 50°C and 100°C. The optimal of operating temperature for IDE graphite is 50°C and IDE CNT is at room temperature for the hydrogen gas detections in this research work. © 2023 IEEE.

关键词： Thick films

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Compressed image quality assessment using stacking

arXiv

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arXiv 2024年

作者： Farhad Hosseini-Benvidi, S. Motamednia, Hossein Mansouri, Azadeh Raei, Mohammadreza Mahmoudi-Aznaveh, Ahmad Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Cyber Research Institute Shahid Beheshti University Tehran Iran

It is well-known that there is no universal metric for image quality evaluation. In this case, distortion-specific metrics can be more reliable. The artifact imposed by image compression can be considered as a combination of various distortions. Depending on the image context, this combination can be different. As a result, Generalization can be regarded as the major challenge in compressed image quality assessment. In this approach, stacking is employed to provide a reliable method. Both semantic and low-level information are employed in the presented IQA to predict the human visual system. Moreover, the results of the Full-Reference (FR) and No-Reference (NR) models are aggregated to improve the proposed Full-Reference method for compressed image quality evaluation. The accuracy of the quality benchmark of the clic2024 perceptual image challenge was achieved 79.6%, which illustrates the effectiveness of the proposed fusion-based approach. © 2024, CC BY.

关键词： Image compression

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Reversing BLE Remote Control Protocols of Dual Channel LED Lamps with CCT and Illumination Control

Reversing BLE Remote Control Protocols of Dual Channel LED L...

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Automatics and Informatics (ICAI), International Conference

作者： Rostislav Kandilarov Yordan Yordanov Department of Machine Science Machine Elements Engineering Graphics and Physics Faculty of Transport University of Ruse “Angel Kanchev” Ruse Bulgaria Computer Systems and Technologies Faculty of Electrical Engeneering Electronics and Automation University of Ruse “Angel Kanchev” Ruse Bulgaria

In this paper various BLE protocols for communication between remote or Android applications and intelligent dual channel (cool and warm white) LED lamps were reverse engineered. Three protocols were fully decoded and several partially. Replay attack is tested and confirmed working. Demo C++ library was developed and a proof-of-concept firmware for esp32 based development board was used to verify the decoding and the attacks applicability.

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Effective Classification for Logs of Routers Based on MLP Neural Networks 15

Effective Classification for Logs of Routers Based on MLP Ne...

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15th International Conference on Knowledge and Systems engineering, KSE 2023

作者： Nguyen-Tat, Bao-Thien Khanh Nguyen, Duy University of Information Technology Faculty of Information Science and Engineering Ho Chi Minh Viet Nam Vietnam National University Ho Chi Minh Viet Nam Institute for Computational Science and Artificial Intelligence Van Lang University Laboratory for Computational Physics Ho Chi Minh Viet Nam School of Technology Van Lang University Faculty of Mechanical - Electrical and Computer Engineering Ho Chi Minh Viet Nam

ISBN: (纸本)9798350329742

This article discusses the classification of abnormal log messages from Juniper routers. Abnormal log messages reflect the unusual activities of these routers. Classifying abnormal log messages into different categories helps monitoring personnel quickly identify errors and take timely measures to protect the devices. We extract features from abnormal logs and use a Multilayer Perceptron (MLP) neural network model for classification. The MLP neural network model requires knowledge of the log data from Juniper routers so that the trained network can analyze and validate that knowledge. We collected log data from Juniper routers operating on SCTV's core network system, identified abnormal log messages, and classified them based on our experience and knowledge. © 2023 IEEE.

关键词： Multilayer neural networks

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