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arXiv 2024年

作者： Li, Menglu Ahmadiadli, Yasaman Zhang, Xiao-Ping Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Canada

The availability of smart devices leads to an exponential increase in multimedia content. However, the rapid advancements in deep learning have given rise to sophisticated algorithms capable of manipulating or creating multimedia fake content, known as Deepfake. Audio Deepfakes pose a significant threat by producing highly realistic voices, thus facilitating the spread of misinformation. To address this issue, numerous audio antispoofing detection challenges have been organized to foster the development of anti-spoofing countermeasures. This survey paper presents a comprehensive review of every component within the detection pipeline, including algorithm architectures, optimization techniques, application generalizability, evaluation metrics, performance comparisons, available datasets, and open-source availability. For each aspect, we conduct a systematic evaluation of the recent advancements, along with discussions on existing challenges. Additionally, we also explore emerging research topics on audio anti-spoofing, including partial spoofing detection, cross-dataset evaluation, and adversarial attack defence, while proposing some promising research directions for future work. This survey paper not only identifies the current state-of-the-art to establish strong baselines for future experiments but also guides future researchers on a clear path for understanding and enhancing the audio anti-spoofing detection mechanisms. Copyright © 2024, The Authors. All rights reserved.

关键词： Speech synthesis

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A large field‑of‑view,single‑cell‑resolution two‑and three‑photon microscope for deep and wide imaging

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eLight 2024年第1期4卷 26-39页

作者： Aaron T.Mok Tianyu Wang Shitong Zhao Kristine E.Kolkman Danni Wu Dimitre G.Ouzounov Changwoo Seo Chunyan Wu Joseph R.Fetcho Chris Xu School of Applied and Engineering Physics Cornell UniversityIthacaNYUSA Meining School of Biomedical Engineering Cornell UniversityIthacaNYUSA Department of Electrical and Computer Engineering Boston UniversityBostonMAUSA Department of Neurobiology and Behavior Cornell UniversityIthacaNYUSA Harvard T.H.Chan School of Public Health Harvard UniversityCambridgeMAUSA Department of Molecular&Cellular Biology Harvard UniversityCambridgeMAUSA McGovern Institute for Brain Research at MIT CambridgeMAUSA

In vivo imaging of large-scale neuronal activity plays a pivotal role in unraveling the function of the brain’s *** microscopy,a powerful tool for deep-tissue imaging,has received sustained interest in advancing its speed,field of view and imaging ***,to avoid thermal damage in scattering biological tissue,field of view decreases exponentially as imaging depth *** present a suite of innovations to optimize three-photon microscopy for large field-of-view imaging at depths unreachable by two-photon *** techniques enable us to image neuronal activities of transgenic animals expressing protein calcium sensors in a~3.5-mm diameter field-of-view with single-cell resolution in the deepest cortical layer of mouse *** further demonstrate simultaneous large field-of-view two-photon and three-photon imaging,subcortical imaging in the mouse brain,and whole-brain imaging in adult *** demonstrated techniques can be integrated into typical multiphoton microscopes to enlarge field of view for system-level neural circuit research.

关键词： Three-photon microscopy Two-photon microscopy Large field of view Brain imaging DEEPscope

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Cramér-Rao Bounds for the Simultaneous Estimation of Power System Electromechanical Modes and Forced Oscillations

Cramér-Rao Bounds for the Simultaneous Estimation of Power ...

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IEEE General Meeting Power& Energy Society

作者： Luke Dosiek Department of Electrical Computer & Biomedical Engineering Union College Schenectady NY

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

In this paper, the Cramér-Rao Bounds (CRB) for the simultaneous estimation of power system electromechanical modes and forced oscillations (FO) are derived. Two cases are considered; in the first case only the steady-state response to the FO is present in the measured system output used by estimation algorithms. In the second, the startup transient of the FO is present in addition to the steady-state response. The CRBs are analyzed numerically to explore sensitivities to FO frequency, signal-to-noise ratio (SNR) and observation window length. It is demonstrated that 1) the CRB of FO parameters is not affected by the presence of the transient response, 2) the CRB of the system modes is not affected by the presence of an FO in steady-state and 3) the CRB of the system modes can be drastically reduced by the presence of a FO startup transient.

关键词： Meters Transient response Sensitivity Estimation Frequency estimation Steady-state Power systems Transient analysis Oscillators Signal to noise ratio

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SecAP: Security Analysis of the Artificial Pancreas 4

SecAP: Security Analysis of the Artificial Pancreas

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Walenda, Melanie Lektorov, Alexander Saxena, Sahaj Bhatia, Sajal Kumar, Navin School of Computer Science and Engineering Sacred Heart University FairfieldCT United States Thapar Institute of Engineering and Technology Electrical and Instrumentation Engineering Department Punjab Patiala India Indian Institute of Technology Ropar Department of Biomedical Engineering Punjab Roop Nagar India

ISBN: (纸本)9798350306118

The artificial pancreas system is a recent development to assist patients with type 1 diabetes controlling their blood glucose levels. It involves several medical devices that use wireless protocols for communication and thus are vulnerable to cyber attacks. This paper focuses on replay and denial-of-service attacks because of their commonality and potentially significant impact on the patient's health. These attacks were modeled and implemented on the GlucoSym Simulator, a program used to develop safer and more accurate AP control algorithms. The effects of each attack on the patient's glucose and insulin dose levels were observed and recorded. Observations of events of hyperglycemia and hypoglycemia over the course of the simulation were used to identify the ideal time for an attacker to cause the most harm to the patient. Finally, a detection method for these attacks is proposed to alert the AP user that corrective actions need to be taken in the event of an attack. © 2023 IEEE.

关键词： Insulin

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Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Images 5

Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Im...

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5th International Conference on engineering Technology and its Applications, IICETA 2022

作者： Alslatie, Mohammad Alquran, Hiam Mustafa, Wan Azani Abu-Qasmieh, Isam Alqudah, Ali Mohammad Alkhayyat, Ahmed The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman Jordan Yarmouk University Biomedical Systems and Medical Informatics Engineering Irbid Jordan Arau Faculty of Electrical Engineering Technology Perlis Malaysia University of Manitoba Biomedical Engineering Program Department of Electrical and Computer Engineering Canada The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9781665472159

The state of the art of artificial intelligence (AI) for various medical imaging applications leads to enhanced accuracy, analysis, visualization, and interpretation of chest Xray (CXR) images for diagnosis. Many diseases are diagnosed based on CXR images. In this paper, two types of abnormalities are diagnosed based on AI techniques. The two classes are atelectasis and cardiomegaly. The acquired images are segmented to localize the chest region and then enhanced using gray-level transformation methods. The enhanced images are passed to two pretrained convolutional neural networks (CNNs): shuffle and mobile net. The transfer learning approach is utilized in this stage. The automated features are extracted from the last fully connected layer. Each CNN deserves to have the two most representative features for the two classes. These four features are passed to support the vector machine classifier. The training accuracy reached 100% and the test accuracy was 96.7%. The proposed method can be extended to be a milestone in the classification of all heart-lung diseases that can be diagnosed using chest X-ray images. © 2022 IEEE.

关键词： Convolutional neural networks

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Reachability Analysis of Nonlinear Discrete-Time Systems Using Polyhedral Relaxations and Constrained Zonotopes 63

Reachability Analysis of Nonlinear Discrete-Time Systems Usi...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Rego, Brenner S. Raffo, Guilherme V. Terra, Marco H. Scott, Joseph K. Georgia Institute of Technology Department of Chemical and Biomolecular Engineering 311 Ferst Dr. AtlantaGA30318 United States University of Säo Paulo Department of Electrical and Computer Engineering SP Säo Carlos13566-590 Brazil Federal University of Minas Gerais Department of Electronics Engineering and the Graduate Program in Electrical Engineering MG Belo Horizonte31270-901 Brazil

ISBN: (纸本)9798350316339

This paper presents a novel algorithm for reachability analysis of nonlinear discrete-time systems. The proposed method combines constrained zonotopes (CZs) with polyhedral relaxations of factorable representations of nonlinear functions to propagate CZs through nonlinear functions, which is normally done using conservative linearization techniques. The new propagation method provides better approximations than those resulting from linearization procedures, leading to significant improvements in the computation of reachable sets in comparison to other CZ methods from the literature. Numerical examples highlight the advantages of the proposed algorithm. © 2024 IEEE.

关键词： Numerical methods

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IoT based Smart U-Turn Vehicle Accident Prevention System 2

IoT based Smart U-Turn Vehicle Accident Prevention System

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Pradeepkumar, G. Praveen Santhoshkumar, G. Rohith Bhat, C. Jeyalakshmi, M. Muthukumar, T. Kumar, Neelam Sanjeev Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India Nandha Engineering College Department of Electronics and Communication Engineering Tamilnadu Erode India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India Ssm Institute of Engineering and Technology Department of Electronics and Communication Engineering Dindigul India Kongunadu College of Engineering and Technology Department of Electrical and Electronics Engineering India Saveetha School of Engineering Simats Department of Biomedical Engineering Chennai India

ISBN: (纸本)9781665491990

Unintentional deaths occur at a very high rate in developing countries. Curved roads have significantly more fatalities than straight roads. This occurs mainly on U-turns, hairnin turns, and narrow mountain roads. Drivers in this position cannot see the vehicle approaching from the opposite direction. As a result, thousands of people are killed in car accidents every year. The best way to avoid further accidents is to alert the car driver approaching from the side. Place the ultrasonic range detection sensor on one side of the road before the bend and the light indicator system on the opposite side after the bend. When a vehicle approaches from afar, an ultrasonic sensor on one side of the road sends a signal to the other side of the road via a light system. In response to a warning, the driver may stop the car until the other vehicle has passed. A buzzer will also be used to warn the driver of the car that is approaching. © 2023 IEEE.

关键词： Ultrasonic applications

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Big Data Infrastructures Using Apache Storm for Real-Time Data Processing

Big Data Infrastructures Using Apache Storm for Real-Time Da...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Ranganathan, Chitra Sabapathy Sampathrajan, Rajeshkumar Reddy, P. L. Kishan Kumar Parkavi, S. Meenakshi, B. Sujatha, S. Mphasis Corporation Tamil Nadu Chennai India McKinsey & Company Fort WorthTX United States Department of Computer Science and Engineering Andhra Pradesh Guntur India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331527495

Apache Storm-based Big Data infrastructures strive to provide a scalable and fault-tolerant platform that will transform real-time data processing. The goal is to use Apache Storm's features to handle and analyze massive amounts of data in real-time, guaranteeing correct insights that are delivered promptly. The objective is to provide a solid foundation that can manage data moving at high speeds from many sources, allowing for analytics in real-time and continuous computing. The goal is to maximize throughput while reducing delay via architectural optimization and efficient data flow methods. The goal is to improve Apache Storm's capabilities and make sure data processes smoothly across dispersed systems by combining it with other big data technologies. The end goal is a robust system that can provide analytics on data in real-time, which will aid in decision-making across several industries, including banking, telecoms, and social media. Efficiency, scalability, and dependability in managing massive data streams are key considerations. The data flow topology dataset for 5 distinct components and 5 streams shows throughput values ranging from 2500 to 4100 tuples per second. Similarly, the fault tolerance and reliability dataset for 5 distinct nodes and 5 different intervals shows throughput values ranging from 700 to 1050 tuples per second. All these results are derived from real-time sensor data used for traffic monitoring. © 2025 IEEE.

关键词： Large datasets

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Real-Time Object Detection Performance of YOLOv8 Models for Self-Driving Cars in a Mixed Traffic Environment 2

Real-Time Object Detection Performance of YOLOv8 Models for ...

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2nd International Conference on computer System, Information Technology, and electrical engineering, COSITE 2023

作者： Afdhal, Afdhal Saddami, Khairun Sugiarto, Sugiarto Fuadi, Zahrul Nasaruddin, Nasaruddin Doctoral Program School of Engineering Universitas Syiah Kuala Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Indonesia Department of Civil Engineering Universitas Syiah Kuala Indonesia Department of Mechanical dan Industrial Engineering Universitas Syiah Kuala Indonesia Indonesia

ISBN: (纸本)9798350343069

Self-driving cars have gained significant attention in recent years. Real-time object detection is a critical component of their perception system. One of the main challenges in developing safe and efficient self-driving cars lies in accurately and in real-time detecting objects in diverse and complex traffic environments. This paper presents the performance evaluation of real-time object detection of YOLOv8 models, a state-of-the-art deep learning framework for self-driving cars in mixed traffic environments. The objective is to assess the extent of YOLOv8's object detection capabilities can be used for self-driving car within complex realworld traffic scenarios. The experimental results show that the accuracy of YOLOv8 during normal daylight scenarios ranges from 0.60 ~ 0.80. In contrast, the accuracy values obtained in night scenarios fall between 0.15 ~ 0.25. Similarly, the FMeasure of YOLOv8 models under daylight conditions ranges from 0.75 ~ 0.87. Conversely, the F-Measure in night conditions falls between 0.27 ~ 0.46. Based on our thorough evaluation results, YOLOv8 has demonstrated its robustness in detecting objects. However, the algorithm requires improvement to effectively handle the challenges of self-driving cars' object detection systems in mixed traffic, such as diverse object classifications, small-scale objects, fast-moving objects, blur, glare, and low-light illumination, particularly in nighttime scenarios. © 2023 IEEE.

关键词： Object detection

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Hybrid Blockchain and IoT Driven Framework for Supply Chain Optimization by Enhancing Transparency, Efficiency and Cost Reduction 3

Hybrid Blockchain and IoT Driven Framework for Supply Chain ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Pradeep Kumar, S. Basha, C.H. Hussaian Prasath, C. Arun Amirthayogam, G. Lathif, Syed Ismail Abdul Leelavathi, R. Rajalakshmi Engineering College Computer Science Design Department Thandalam Chennai602105 India Sr University Department of Electrical and Electronics Engineering Warangal506371 India Mahendra Engineering College Department of Ece Namakkal637503 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600062 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India Velalar College of Engineering and Technology Department of Biomedical Engineering Erode638012 India

ISBN: (纸本)9798331509675

Blockchain technology represents a modern supply chain management revolution which fixes operational challenges while providing enhanced transparency at reduced costs. The research develops HB-SCOF (Hybrid Blockchain-Enabled Supply Chain Optimization Framework) which combines blockchain self-verification databases with automatic smart contracts to maintain continuous supply chain observation and evaluate data credibility. The framework integrates tamper-proof transaction tracking mechanisms with unified supply chain network connectivity for securing data transmission as a trust delivery method through unbreakable shared databases. The retail-industry case study demonstrated a 25% decrease in expenses stemming from agency elimination alongside enhanced flow management and better stock distribution. The system demonstrates its effectiveness through assessments of key performance indicators including transaction speed and data accuracy together with business cost efficiency. This part looks at implementation methods through training stakeholders in addition to transforming organizational policies which produce successful outcomes. Blockchain technology labs verify its capability to enhance supply chain management efficiency while showing realistic deployment methods. © 2025 IEEE.

关键词： Smart contract

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