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7th International Conference on electronics, Communication and Aerospace Technology, ICECA 2023

作者： Kiruba Sandou, D. Sunad Kumara, A.N. Choudhary, Binod Kumar Gurpur, Shashikala Sarishma Natrayan, L. Sivaramkumar, M. Manakula Vinayagar Institute of Technology Department of Robotics and Automation Pondicherry605107 India BGS Institute of Technology Department of Computer Science and Engineering Adichunchanagiri University Karnataka Mandya571448 India ARKA Jain University Department of Electrical and Electronics Engineering Jharkhand Jamshedpur832108 India Maharashtra Pune411014 India Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun India Saveetha School of Engineering SIMATS Department of Mechanical Engineering Tamilnadu Chennai602105 India Karpagam Academy of Higher Education Department of EEE Tamiinadu Coimbatore India

ISBN: (纸本)9798350340600

Achieving precise trajectory tracking while minimizing errors, reducing settling time, and curbing overshoot presents a significant challenge in the domain of robot-manipulator control. In response, this paper proposes a novel controller design for robot manipulators, incorporating two distinct approaches: a conventional fuzzy controller and an advanced Neuro-Fuzzy controller. These controllers are tailored to elevate trajectory tracking performance by simultaneously optimizing time characteristics in robot manipulator responses. To assess their efficacy, a range of test scenarios is devised, encompassing different trajectory types, including linear and nonlinear paths. Upon comparative analysis, the results highlight an intriguing contrast: while the fuzzy controller exhibits higher maximum overshoot and longer settling times in the robot's response, the Neuro-Fuzzy controller consistently demonstrates superior performance. The comprehensive error analysis such as Integral Square Error (ISE) and Integral Absolute Error (IAE) further underscores the capability of the Neuro-Fuzzy-based robot manipulator in delivering trajectory tracking precision. This study contributes to the advancement of trajectory control techniques for robot manipulators, paving the way for enhanced automation and performance across various applications. © 2023 IEEE.

关键词： Controllers

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Automated Land Cover Classification in Urban Environments with Deep Learning-Based Semantic Segmentation

Automated Land Cover Classification in Urban Environments wi...

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Recent Advances in Science and engineering Technology (ICRASET), International Conference on

作者： S Sheela S Jyothi A P Latha H.J Ganesh M Komala C Naveen Kumar S Mallikarjunaswamy N Sharmila Department of Computer Science and Engineering Global Academy of Technology Bengaluru India Department of Information Science and Engineering Dayananda Sagar College of Engineering Bengaluru India Department of Information Science and Engineering Global Academy of Technology Bengaluru India Dept of Electronics and Communication Engineering SJB Institute of Technology Bengaluru India Dept of Electronics and Communication Engineering JSS Academy of Technical Eduction Bengaluru India Dept of Electronics and Electrical Engineering JSS Science and Technology Universit Mysuru India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

Partisan symbolism has become a vital asset for various applications, including land cover classification, urban planning, disaster management, and environmental monitoring. In this study, a deep learning approach is proposed for the semantic segmentation of satellite images. The proposed model leverages a multi-level U-Net architecture, which has shown effectiveness in various image segmentation tasks. The dataset used in this study comprises satellite images with corresponding ground truth masks labeling different land cover classes, such as buildings, roads, vegetation, water bodies, and unlabeled regions. The data is preprocessed, and the ground truth masks are encoded into categorical labels to facilitate model training. The model is trained using a combination of pixel-wise crossentropy loss and the Jaccard coefficient as evaluation metrics to optimize performance. Test results demonstrate the effectiveness of the proposed approach in accurately segmenting satellite images, achieving high accuracy and Jaccard coefficient values on both training and validation datasets. Additionally, a comprehensive analysis of the model’s performance is provided, discussing potential applications and future directions for satellite image segmentation tasks. The trained model is saved for further deployment and integration into satellite image processing pipelines, offering valuable insights for various remote sensing applications.

关键词： Measurement Training Accuracy Semantic segmentation Roads Urban planning Land surface Vegetation mapping Satellite images Environmental monitoring

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Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmark

arXiv

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arXiv 2024年

作者： Pazho, Armin Danesh Yao, Shanle Noghre, Ghazal Alinezhad Ardabili, Babak Rahimi Katariya, Vinit Tabkhi, Hamed Department of Electrical and Computer Engineering University of North Carolina Charlotte United States Public Policy Program University of North Carolina Charlotte United States

Human-centric Video Anomaly Detection (VAD) aims to identify human behaviors that deviate from normal. At its core, human-centric VAD faces substantial challenges, such as the complexity of diverse human behaviors, the rarity of anomalies, and ethical constraints. These challenges limit access to high-quality datasets and highlight the need for a dataset and framework supporting continual learning. Moving towards adaptive human-centric VAD, we introduce the HuVAD (Human-centric privacy-enhanced Video Anomaly Detection) dataset and a novel Unsupervised Continual Anomaly Learning (UCAL) framework. UCAL enables incremental learning, allowing models to adapt over time, bridging traditional training and real-world deployment. HuVAD prioritizes privacy by providing de-identified annotations and includes seven indoor/outdoor scenes, offering over 5× more pose-annotated frames than previous datasets. Our standard and continual benchmarks, utilize a comprehensive set of metrics, demonstrating that UCAL-enhanced models achieve superior performance in 82.14% of cases, setting a new state-of-the-art (SOTA). The dataset can be accessed at https://***/TeCSAR-UNCC/HuVAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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IoT-Integrated Convolutional Neural Network for Accurate Vehicle Acoustic Signal Analysis

IoT-Integrated Convolutional Neural Network for Accurate Veh...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： P. Sasikala K Subha Sharmini Venkatesan Sorakka Ponnappan K. R. Kavitha K. Gunasekaran C. Srinivasan Department of Mathematics Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (Deemed to be University) Salem India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Mechanical Engineering Sathyabama Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Sona College of Technology Salem India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

This paper examines the use of IoT-integrated Convolutional Neural Networks (CNNs) for precise analysis of vehicle audio signals. The context highlights the growing intricacy of vehicle systems and the need for advanced monitoring methods. Acoustic signals generated by different vehicle components may provide significant insights about vehicle health; nevertheless, traditional analysis techniques are often ineffective. The Internet of Things (IoT) technology allows real-time data collection from sensors installed on vehicles, which is then analyzed using CNNs to identify certain patterns and abnormalities in auditory signals. The approaches used include using IoT sensors to record various vehicle noises and utilizing CNNs for feature extraction and classification, providing great defect detection precision. The results indicate the system's capacity to detect engine faults, braking deficiencies, and other mechanical concerns early, enabling quick treatments. It presents a novel methodology for vehicle monitoring, enhancing predictive maintenance, safety, and performance, with prospects for future developments in autonomous diagnostics. The proposed system improved vehicle acoustic signal processing using IoT and CNN models, achieving high accuracy of 99% in anomaly detection, preventive maintenance, and real-time traffic monitoring.

关键词： Training Accuracy Noise Real-time systems Acoustics Sensor systems Sensors Convolutional neural networks Internet of Things Intelligent sensors

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Text2Scene: Text-driven Indoor Scene Stylization with Part-Aware Details

Text2Scene: Text-driven Indoor Scene Stylization with Part-A...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Inwoo Hwang Hyeonwoo Kim Young Min Kim Department of Electrical and Computer Engineering Seoul National University Interdisciplinary Program in Artificial Intelligence and INMC Seoul National University

We propose Text2Scene, a method to automatically create realistic textures for virtual scenes composed of multiple objects. Guided by a reference image and text descriptions, our pipeline adds detailed texture on labeled 3D geometries in the room such that the generated colors respect the hierarchical structure or semantic parts that are often composed of similar materials. Instead of applying flat stylization on the entire scene at a single step, we obtain weak semantic cues from geometric segmentation, which are further clarified by assigning initial colors to segmented parts. Then we add texture details for individual objects such that their projections on image space exhibit feature embedding aligned with the embedding of the input. The decomposition makes the entire pipeline tractable to a moderate amount of computation resources and memory. As our framework utilizes the existing resources of image and text embedding, it does not require dedicated datasets with high-quality textures designed by skillful artists. To the best of our knowledge, it is the first practical and scalable approach that can create detailed and realistic textures of the desired style that maintain structural context for scenes with multiple objects.

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IoT-based ELF Magnetic Flux Density Meter

IoT-based ELF Magnetic Flux Density Meter

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International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO)

作者： Manolis G. Tampouratzis George A. Adamidis Demosthenes Vouyioukas Traianos Yioultsis Dimitrios Stratakis Department of Information and Communication Systems Engineering University of the Aegean Karlovasi Greece Department of Electronics Engineering Hellenic Mediterranean University (HMU) Chania Greece Department of Electrical and Computer Engineering (ECE) Aristotle University of Thessaloniki (AUTH) Thessaloniki Greece Department of Electrical and Computer Engineering (ECE) Hellenic Mediterranean University (HMU) Heraklion Greece

This study presents an IoT-based ELF magnetic field meter that can measure magnetic flux density from 100nT. The proposed device has a flat response across the frequency range from 40Hz to 10 KHz, detecting and measuring most magnetic field sources. The proposed meter can measure fields from the power supply network (usually 50 or 60Hz) and harmonic frequencies extending several times above the base frequency in the operating frequency band. The proposed circuit of the magnetic flux density (magnetic field) meter is quite simple for implementation and the measured field can be displayed on any mobile device with Wi-Fi connectivity. A WeMos D1 Arduino board with an embedded ESP-8266 module is responsible for data transferring from the sensor to the cloud as a complete IoT solution, supported by the Blynk application via Android and iOS operating systems or web interface.

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Federated learning: Overview, strategies, applications, tools and future directions

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Heliyon 2024年第19期10卷 e38137页

作者： Yurdem, Betul Kuzlu, Murat Gullu, Mehmet Kemal Catak, Ferhat Ozgur Tabassum, Maliha Department of Electrical and Electronics Engineering Izmir Bakircay University Izmir Turkey Batten College of Engineering and Technology Old Dominion University Norfolk VA United States Department of Electrical Engineering and Computer Science University of Stavanger Rogaland Norway

Federated learning (FL) is a distributed machine learning process, which allows multiple nodes to work together to train a shared model without exchanging raw data. It offers several key advantages, such as data privacy, security, efficiency, and scalability, by keeping data local and only exchanging model updates through the communication network. This review paper provides a comprehensive overview of federated learning, including its principles, strategies, applications, and tools along with opportunities, challenges, and future research directions. The findings of this paper emphasize that federated learning strategies can significantly help overcome privacy and confidentiality concerns, particularly for high-risk applications. © 2024 The Author(s)

关键词： Data privacy Distributed machine learning Federated learning

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LVDT Read-Out Circuit Based on Modified Wien Bridge Oscillator

LVDT Read-Out Circuit Based on Modified Wien Bridge Oscillat...

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International Conference on electrical engineering/electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Krit Angkeaw Preecha Thongdit Department of Instrumentation and Electronics Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

This paper presents the LVDT Read-Out Circuit based on the Modified Wien Bridge Oscillator. The proposed read-out circuit converts inductance from LVDT to frequency output and does not need compensation circuits for primary to secondary phase shifts. The basic structure of the read-out circuit consists of a modified Wien bridge oscillator, a Level shift circuit, a comparator circuit, and a frequency counter (Atmega328p). The ratio metric method is used for linearity. The experiments show that it effectively enhances the LVDT linearity range, with linearity errors below 20%.

关键词： Power measurement Linearity Bridge circuits Position measurement Frequency measurement Telecommunications Pressure measurement

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PhageBox: An Open Source Digital Microfluidic Extension with Applications for Phage Discovery

TechRxiv

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TechRxiv 2023年

作者： Albin, Dreycey Buecherl, Lukas Kochavi, Eitan Niehaus, Elise Novack, Sasha Uragoda, Shenali Myers, Chris J. Alistar, Mirela Department of Computer Science CU Boulder BoulderCO United States Biomedical Engineering Program CU Boulder United States Department of Electrical Computer and Energy Engineering and Biomedical Engineering Program CU Boulder United States ATLAS Institute The Department of Computer Science CU Boulder BoulderCO United States

Objective: Recent advancements demonstrate the significant role of digital microfluidics in automating laboratory work with DNA and on-site viral testing. However, since commercially available instruments are limited to droplet manipulation, our work addresses the need for accelerated integration of other components, such as temperature control, that can expand the application domain. Methods: We developed PhageBox— an accessible device that can be used as a biochip extension. At hardware level, PhageBox integrates temperature and electromagnetic control modules. At software level, PhageBox is controlled by embedded software containing a unique model for bio-protocol programming, and a graphical user interface for visual device feedback and operation. Results: To evaluate PhageBox’s efficacy for biomedical applications, we performed functional testing. Similarly, we validated the temperature control using thermography, obtaining a range of ±0.2◦C. The electromagnets produced a magnetic force of 15 milliTesla, demonstrating precise immobilization of magnetic beads. We show the potential of PhageBox for bacteriophage research through three initial protocols: a universal framework for PCR, T7 bacteriophage restriction enzyme digestion, and concentrating X174 RF genomic DNA. Conclusion: Our work presents an open-source hardware and software extension for digital microfluidics devices. This extension integrates temperature and electromagnetic modules, demonstrating efficacy in biomedical applications and potential for bacteriophage research. Significance: We developed PhageBox to be accessible: the components are off-the-shelf at a low cost (≤$200), and the hardware designs and software code are open-source. With the long aim of ensuring reproducibility and accelerating collaboration, we also provide a DIY-build document. (GitHub: https://***/Dreycey/PhageBox) © 2023, CC BY-SA.

关键词： Digital microfluidics

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Carrier-Based Continuous PWM for Compensating the Effect of Unbalanced and Oscillating Neutral-Point Voltage in T-type Inverter

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IEEE Transactions on Transportation Electrification 2025年

作者： Ghaderloo, Reza Asrar Shen, Yidi Chaudhry, Hassan Habib Singhabahu, Chanaka Khaligh, Alireza University of Maryland Maryland Power Electronics Laboratory Department of Electrical and Computer Engineering College ParkMD20742 United States

The three-level T-type inverter incorporates two cascaded DC-link capacitors that share a common coupling node (CCN) known as neutral-point. During the operation of T-type inverter, the CCN voltage oscillates at triple the line frequency. Furthermore, in some applications like dual-input photovoltaic systems, there may be a DC voltage imbalance across DC-link capacitors, resulting in an unbalanced CCN voltage. The presence of an unbalanced or oscillating CCN voltage in T-type inverter affects the quality of output current and voltage waveform by introducing low-frequency harmonics. To address this issue, this paper proposes a novel modulation method to compensate the effects of unbalanced and oscillating CCN voltage, enabling the generation of non-distorted phase voltage and current. By enhancing the output waveforms quality, the proposed method can result in reduction of the DC-link capacitor size and improvement of power density in the T-type inverter. In this method, the modulation signal is adjusted using simple mathematical calculations based on the sensed CCN and DC-link voltage, which imposes negligible processing time on the micro-controller. This method is applicable across the entire range of operation, regardless of load power factor or modulation index. Furthermore, the proposed method preserves the high-quality and sinusoidal shape of output current waveform even under dynamic disturbances in the CCN and DC-link voltage. To provide further insight, the normalized increase in total semiconductor loss due to the implementation of the proposed method is also evaluated. Finally, the analytical and experimental results are conducted to validate the effectiveness of the proposed compensating method. © 2015 IEEE.

关键词： Harmonic analysis

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