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Experimental Evaluation of OTFS for Underwater Acoustic Communications 100

Experimental Evaluation of OTFS for Underwater Acoustic Comm...

引用

100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Xue, Yukang Zhu, Xiyuan Khan, Uzma Zheng, Y. Rosa Lehigh University Dept. of Electrical & Computer Engineering BethlehemPA United States

ISBN: (纸本)9798331517786

This paper applies the Orthogonal Time-Frequency Space (OTFS) modulation to underwater mobile acoustic communications where the communication channel suffers from severe multipath and Doppler effects simultaneously. Field experiments have been conducted with acoustic transmission at a center frequency of 115 kHz and a data rate of 11.5 kilosymbols per second. We propose a new Turbo Decision Feedback Equalizer and Decoder (TDFED) for the receiver and the experimental results demonstrate the superior performance of the proposed TDFED algorithm over existing MMSE equalizers. © 2024 IEEE.

关键词： Decision feedback equalizers

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Cybersecurity for Industrial IoT: A Review on Vulnerabilities and Attacks 8

Cybersecurity for Industrial IoT: A Review on Vulnerabilitie...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Kumari, Reetu Kumar, Sunil Amandeep Dutta, Kamlesh Kumari, Aparna Guru Jambheshwar University of Science and Technology Dept. of Computer Science & Engineering Haryana Hisar India Guru Jambheshwar University of Science and Technology Dept. of AI & Data Science Haryana Hisar India National Institute of Technology Hamirpur Dept. of Computer Science & Engineering Himachal Pardesh India Institute of Technology Nirma University Dept. of Computer Science & Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798331520762

Internet has evolved from a network of connecting people to a network of connecting things, leading to a more complex and sophisticated network of Industrial Things, known as Industrial IoT (IIoT) today. This evolution has revolutionized the traditional Industrial system into real-time smarter systems that enhanced overall efficiency and productivity. Despite its benefits, the interconnected nature of IIoT, expanded the attack surface, exposing the systems to new crucial and complex cybersecurity threats. Several real time attacks on various industries as mentioned in this paper and the increasing cybersecurity cost over the years, clearly states the need of robust security solutions. To deeply understand the cybersecurity aspect, we provided a comprehensive study of attacks and vulnerabilities in this paper. In this paper, we did a indept. study of various IIoT related severe attacks and aspects related to these attacks, like vulnerabilities, impacts, potential solutions etc. We also provided future research directions to help the research community, to work upon and develop more robust and efficient cybersecurity solutions. © 2025 IEEE.

关键词： attacks cybersecurity etc IIoT Industrial IoT IoT vulnerabilities

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Image Segmentation based Approach for Skin Disease Detection and Classification using Machine Learning Algorithms 2

Image Segmentation based Approach for Skin Disease Detection...

引用

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Sany, Nazmul Hasan Chandra Shill, Pintu Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350317558

Skin disorders are ubiquitous, stemming from causes including fungus, germs, allergies, and viruses. Although laser and photonics medical technologies have revolutionised rapid and precise diagnosis, their exorbitant prices restrict accessibility. To address this, this paper describes an image processing strategy for dermatological screening and procedure that comprises obtaining digital photographs of damaged skin patches and applying image processing to detect the disease type. Moreover, when conducted manually by medical specialists, identifying skin illnesses is time-intensive and subjective. As a consequence, both patients and physicians want automated skin disease prediction, which makes the treatment plan faster. This cost-effective approach depends on a basic setup: a camera and a computer. Operating on colour photos, a solution combines resizing for feature extraction using a pertained convolutional neural network, followed by classification using a multiclass SVM. The technology efficiently recognises four separate skin illnesses with an astonishing 90.8% accuracy, giving users complete findings spanning disease kind, extent, and severity. © 2024 IEEE.

关键词： Decision Tree Image Processing K-NN Machine Learning Skin Disorder SVM

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Vehicle Movement Status Network on Drone-Perspective View with Adaptive Adversarial Learning 50

Vehicle Movement Status Network on Drone-Perspective View wi...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Lee, Youlkyeong Choi, Jehwan Jo, Kanghyun Dept. of Electrical Electronic and Computer Engineering University of Ulsan Ulsan Korea Republic of

ISBN: (纸本)9781665464543

The rapidly developing autonomous driving field now needs a more secure transportation system through information between multiple mobility. Deep learning that can judge traffic conditions by convergence of various sensor data and in particular, research on the convolutional neural network using computer vision are being actively conducted. In addition, recognizing many objects at once in a large area through drone images and understanding the movement of the object is used as safe traffic assistance information. In this study, an image classification study is conducted to determine the status of the vehicle on the road through drone flight image data. The goal is to build a new image classification model robust to the proposed image classification network by applying the weighted adversarial learning method. Weight adversarial learning is a method of securing robust performance in image classification of various statuses while disturbing the model by forcibly reflecting the slope value in reverse when updating the network through the reverse gradient layer. In the experiment, model performance is evaluated through the collected drone flight data set. © 2024 IEEE.

关键词： Drones

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Enhancing 3D Modeling Quality Through Orthorectified Image Accumulation

Enhancing 3D Modeling Quality Through Orthorectified Image A...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kwag, Cheolsu Kim, Euntaek Lee, Yongsuk Jung, Seungmin Hwang, Sung Soo Ryu, Hwangryol Moon, Youngmin Handong Global University Dept. of Computer Science and Electrical Engineering Pohang Korea Republic of School of Computer Science and Electrical Engineering Handong Global University Pohang Korea Republic of Research Institute of Industrial Science and Technology Smart Engineering Research Laboratory Pohang Korea Republic of

ISBN: (纸本)9798331510756

This study introduces a novel methodology designed to facilitate the capture of comprehensive image datasets, crucial for accurate 3D modeling of expansive indoor spaces. Leveraging orthophotos generated from panoramic camera data and visualizing image coverage density through heatmaps, our approach effectively identifies under-captured and unvisited areas. This enables targeted image acquisition, minimizes redundant reshoots, and enhances the overall quality and fidelity of 3D models. © 2025 IEEE.

关键词： Digital elevation model

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Body Posture Correction and Hand Gesture Detection Using Federated Learning and Mediapipe 2

Body Posture Correction and Hand Gesture Detection Using Fed...

引用

2nd International Conference for Innovation in Technology, INOCON 2023

作者： Singhal, Rijul Modi, Hardik Srihari, S. Gandhi, Advit Prakash, C.O. Eswaran, Sivaraman PES University Dept. of Computer Science and Engineering Bengaluru India Curtin University Malasyia Dept. of Electrical and Computer Engineering Sarawak Malasyia Miri98009 Malaysia

ISBN: (纸本)9798350320923

In today's post-covid culture, where everyone works from home, there is a huge possibility of serious long-term health problems. A lot of people have started taking up exercises at home and if done incorrectly, they can have major negative effects. Another one of the main contributors to these health issues is bad sitting posture, which is only exacerbated when working for hours on end. Hand gesture detection has many useful applications in elderly healthcare, automating actions and gesture-based presentations and games. To help users with these actions, our paper proposes pinpointing the points of the error to the user in real-time and in a lightweight manner for yoga posture correction. The incorrect positions shall be shown in real-time on top of the user's video feed to help them correct it properly. The user shall be told about when they are sitting in a bad position, and the overall bad posture time will also be shown for the session, which will provide the required information to the user. To further help users in a useful manner, our paper looks to augment the hand gesture detection feature with federated learning and personalization to avoid the common pitfall of privacy concerns, while still allowing users to customize their experience. The proposed library for the implementation of these tasks is the MediaPipe library. This library is one of the key components that makes the features lightweight and easy to use. The aforementioned library also looks to implement the features in real time with no lag while keeping the resource requirements as low as possible. © 2023 IEEE.

关键词： Palmprint recognition

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A Pulse Width Modulation-Based Spike Generator to Eliminate Timing Errors in Spiking Neural Networks

A Pulse Width Modulation-Based Spike Generator to Eliminate ...

引用

2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Jo, Jungkook Sim, Keonhee Sim, Sangwoong Jun, Jaehoon Inha University Dept. of electrical and computer engineering and program in semiconductor convergence Incheon Korea Republic of Inha University Dept. of electrical engineering Incheon Korea Republic of

ISBN: (纸本)9798331510756

This paper proposes a novel spike generator for processing in memory (PIM) technology. Most of the electronics today utilize a von Neumann architecture. The von Neumann architecture suffers from the separation of memory and processor. This architecture delays data transfer between memory and processor. To overcome the issues, we utilize the spiking neural network (SNN) that combines memory and processor. SNN can be classified into voltage-based, current-based, and time-based architectures, each with its own pros and cons. Time-based SNN suffers from timing issues. To address the timing sensitivity of time-based neuron SNN, this paper proposes a PWM-based SNN. The PWM-based SNN utilizes pulse width-based logic to overcome timing sensitivity. © 2025 IEEE.

关键词： Pulse width modulation

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Cat-CNN: Human Eye Cataract Detection from Color Fundus Photograph with Deep CNN with Optimized Cascaded Network

Cat-CNN: Human Eye Cataract Detection from Color Fundus Phot...

引用

2024 IEEE International Conference on Signal Processing, Information, Communication and Systems, SPICSCON 2024

作者： Islam, Md Sariful Bappy, Md Tusher Ahmad Shawon, Jubayer Ahmed Hasan, Mehedi Rahman, Wahidur Akter, Lija Azad, Mir Mohammad Dept. of Computer Science and Engineering Uttara University Uttara Dhaka Bangladesh Dept. of ICE Bangladesh Army University of Engineering and Technology Natore Bangladesh Dept. of Computer Science and Engineering Inpendent Researcher Dhaka Bangladesh

ISBN: (纸本)9798331510213

Cataracts are clouding of the lens in the eye, leading to loss of vision that can progress to blindness if not treated. This paper proposed a new method for automatic cataract detection using color fundus images and deep learning methods. A dataset of 1,105 color fundus images labeled by expert ophthalmologists was used in this process. We used seven pre-trained CNNs (DenseNet121, EfficientNetB0, MobileNetV2, InceptionV3, Xception, ResNet50, VGG16, and VGG19) for feature extraction before reducing the extracted features using PCA. We used the following combination of machine learning classifiers: SVC, RF, Decision Tree, Gaussian Naive Bayes, XGBoost, K-Nearest Neighbors, and Logistic Regression. For evaluating the models’ performance, we used accuracy, precision, recall, F1-score, and computational efficiency. For all metrics, MobileNetV2 with Random Forest achieved perfect scores: 100% accuracy, precision, recall, and F1-score, with an average processing time of 669 ms ± 28.8 ms. Thus, it can be applied in real-time applications. EfficientNetB0 with SVC gave an average accuracy of 87.33%, with the rest of the precision and recall metrics above 86%. Then, ResNet50, VGG16, and VGG19 followed with high accuracies between the range of 89.64% to 90.50%. It systemizes the proper choice of architectures of CNNs and classifiers, making the system both accurate and computationally efficient. Future work will include augmentation of the dataset, real-time support in the clinical setting, and advanced techniques for image preprocessing, generative adversarial networks. In addition, the development of an automated annotation tool, improvement of explainable AI, will further improve the deployment of robust AI systems in early diagnosis of cataracts, enhancing the outcome for patients. © 2024 IEEE.

关键词： Cataract detection CNN color fundus photography deep learning feature extraction image classification machine learning ophthalmology PCA

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Real Time Modeling and Control Algorithm of a Grid-Connected Battery Energy Storage System

Real Time Modeling and Control Algorithm of a Grid-Connected...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Karimi, Sepehr Nazaralizadeh, Solmaz Srivastava, Anurag Salem, Aboubakr Famouri, Parviz West Virginia University Lane Department of Computer Science and Electrical Engineering Morgantown United States WVU-IT Electrical and Computer Engineering Dept. Beckley United States PCS Eaton PittsburghPA United States

ISBN: (纸本)9798331541125

This work develops real time model and associate control for a grid-tied battery energy storage system (BESS), based on the real industrial system specifications, 362kW/1499kWh lithium-ion BESS. An active and reactive power controller based on the dq reference frame is designed for the grid-tied inverter. To ensure system performance, an LCL filter with passive damping is also developed and analyzed. The system is tested using the Controller Hardware-in-the-Loop (CHIL) platform: Typhoon. The results demonstrate the controller's effective reference tracking across varying load conditions, with the filter design achieving a 3.3% output current THD, emphasizing the inverter's optimized performance. © 2025 IEEE.

关键词： Battery storage

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PSMGD: Periodic Stochastic Multi-Gradient Descent for Fast Multi-Objective Optimization 39

PSMGD: Periodic Stochastic Multi-Gradient Descent for Fast M...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Mingjing Ju, Peizhong Liu, Jia Yang, Haibo Dept. of Computing and Information Sciences Ph.D. Rochester Institute of Technology United States Dept. of Computer Science University of Kentucky United States Dept. of Electrical and Computer Engineering The Ohio State University United States

ISBN: (纸本)157735897X

Multi-objective optimization (MOO) lies at the core of many machine learning (ML) applications that involve multiple, potentially conflicting objectives. Despite the long history of MOO, recent years have witnessed a surge in interest within the ML community in the development of gradient manipulation algorithms for MOO, thanks to the availability of gradient information in many ML problems. However, existing gradient manipulation methods for MOO often suffer from long training times, primarily due to the need for computing dynamic weights by solving an additional optimization problem to determine a common descent direction that can decrease all objectives simultaneously. To address this challenge, we propose a new and efficient algorithm called Periodic Stochastic Multi-Gradient Descent (PSMGD) to accelerate MOO. PSMGD is motivated by the key observation that dynamic weights across objectives exhibit small changes under minor updates over short intervals during the optimization process. Consequently, our PSMGD algorithm is designed to periodically compute these dynamic weights and utilizes them repeatedly, thereby effectively reducing the computational overload. Theoretically, we prove that PSMGD can achieve state-of-the-art convergence rates for strongly-convex, general convex, and non-convex functions. Additionally, we introduce a new computational complexity measure, termed backpropagation complexity, and demonstrate that PSMGD could achieve an objective-independent backpropagation complexity. Through extensive experiments, we verify that PSMGD can provide comparable or superior performance to state-of-the-art MOO algorithms while significantly reducing training time. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Multiobjective optimization

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