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Realtime and Integrated Framework for LiDAR-based Object Tracking

Journal of Institute of Control, Robotics and Systems 2025年第3期31卷 196-205页

作者： Lee, Gyuseok Kim, Kana Lee, Jejun Kim, Hakil Department of Electrical and Computer Engineering Inha University Korea Republic of

This study proposes a real-time integrated framework for LiDAR-based object tracking in autonomous driving environments. Advancements in LiDAR sensors are increasing point cloud data collection, leading to a demand for reliable real-time processing methods. The proposed framework applies voxelization and ground removal techniques to reduce computational load and integrates clustering and deep learning-based object recognition to ensure stability. Combining the point cloud data from LiDAR and the IMU data corrects distortions and refines real-time object movement, enabling accurate tracking in dynamic environments. This framework supports a maximum detection range of 100 m, with a computation time of 52 ms, a positional error of 1.06 m, a heading error of 3.79°, a relative velocity error of 1.46 m/s, and an average tracking frame count of 101, thereby improving object recognition accuracy and tracking performance while fulfilling real-time processing requirements. © ICROS 2025.

关键词： 3D object detection autonomous driving LiDAR object tracking

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Impact of Memory Motors on an IGBT-Based Inverter Efficiency

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IEEE Transactions on Industry Applications 2025年第3期61卷 4214-4225页

作者： Aljehaimi, Akrem M. Abdel-Mageed, Bassam Samy Pillay, Pragasen Department of Electrical and Computer Engineering Concordia University Montreal Canada

This paper investigates the impact of hybrid memory motors on an IGBT-based inverter in the field-weakening region. Hybrid variable-flux motors have been introduced to overcome the limitations of conventional IPMSMs in traction applications. Different memory motor topologies have been introduced and compared in the literature. Yet, their impact on the drive inverter has not been reported. An existing prototyped ten-horsepower series-hybrid variable motor has been used as a case study in this paper. In the field weakening region, comparing the operation with partially demagnetized magnets to fully magnetized magnets, it is found that not only does the motor current decrease, but the motor power factor also improves. This unique finding of improved power factor operation positively impacts the inverter conduction loss. This has significant practical implications, suggesting that hybrid memory motors can lead to more efficient traction inverters. An air-cooled IGBT-based three-phase 750 VDC, 30 ARMS, is used in this study. In the simulation, the switch characteristics from the datasheet have been used for loss calculations. Also, the inverter power loss and efficiency have been measured at different speeds, loads, and magnetization states. A two to three percent improvement in inverter efficiency was noted in the high-speed region beyond two per unit speed. © 2025 IEEE.

关键词： Permanent magnets

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Video captioning using transformer-based GAN

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Multimedia Tools and Applications 2025年第10期84卷 7091-7113页

作者： Babavalian, Mohammad Reza Kiani, Kourosh Electrical and Computer Engineering Department Semnan University Semnan Iran

Video captioning is the process of automatically generating natural language descriptions of video content. Historically, most video captioning methods have relied on extending Sequence-to-Sequence (Seq2Seq) models. However, such approaches possess limitations due to the sequential nature of the captions, which leads to less accurate captions. To address this limitation, this paper introduces a novel end-to-end architecture for video captioning that combines conditional Wasserstein Generative Adversarial Networks (cWGAN) with a transformer model. The proposed architecture consists of two modules: feature extraction and caption generation. The feature extraction module aims to obtain an encoded feature vector representing the video contents, while the caption generation module generates human-readable captions from encoded feature vector. To the best of our knowledge, this is the first architecture for generative video captioning that integrates a transformer model with GAN. The results of the proposed model based on the BLEU-4, METEOR, ROUGE-L, and CIDEr metrics, on two datasets, MSVD (BLEU-4 = 61.2, METEOR = 41.6) and MSR-VTT (BLEU-4 = 61.2, METEOR = 31.1), compared to state-of-the-art approaches, demonstrate the effectiveness of the transformer with generative model in generating accurate and human-readable captions. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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MCVL: Multi-directional Camera-based Visual Localization Resistant to Seasonal and Illumination Changes

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Journal of Institute of Control, Robotics and Systems 2025年第2期31卷 153-159页

作者： Mun, Giyoung Kim, Hakil Department of Electrical and Computer Engineering Inha University Korea Republic of

The global navigation satellite system-based technology has inherent limitations due to its reliance on radio signals. In contrast, visual localization operates independently of radio communication, presenting a viable solution to overcome these limitations. However, it is susceptible to seasonal and illumination variations, highlighting the need for research to address these challenges. Therefore, this paper proposes the multi-directional camera-based visual localization, which is robust against seasonal and illumination changes. The proposed method combines image from multiple directions and extracts global deep learning features. Subsequently, local deep learning features are extracted to preserve the characteristics of each combined image, allowing for the identification of geographically similar images. This approach utilize multi-directional cameras, enabling resilient performance under various constraints. Moreover, it demonstrates an improvement of 7.56% in recall rate at 1-meter threshold compared to existing methods. © ICROS 2025.

关键词： Global positioning system

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A novel data-driven algorithm for object detection, tracking, distance estimation, and size measurement in stereo vision systems

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Multimedia Tools and Applications 2025年第12期84卷 11041-11061页

作者： Taromi, Amirhossein Dadashzadeh Klidbary, Sajad Haghzad Department of Engineering University of Zanjan Zanjan Iran Faculty of Engineering Department of Electrical and Computer Engineering University of Zanjan Zanjan Iran

Distance and size estimation of objects of interests is an inevitable task for many navigation and obstacle avoidance algorithms mainly used in autonomus and robotic systems. Stereo vision systems, inspired by human visual perception, can infer depth from images as a cheap and accessible solution. On one hand, accurately calibrating cameras is a challenging task and the main source of error in current stereo vision based distance and size estimation algorithms. On the other hand, considering the recent advancements in Deep Learning, alongside the fact that human eyes do not need calibration but human brain can estimate the distance and size of objects fairly accurate was the main motivation behind this study. The proposed algorithm uses YOLOv8 as the object detector, and an MLP to learn the relation between distance, size, and disparity from collected data in a stereo vision system. In our experiments, conducted at distances ranging from 50 to 200 centimeters with calibrated and uncalibrated cameras, our proposed algorithm showcased accurate performance in both scenarios. It achieved distance measurements with an accuracy of up to 99.99% in select cases and maintained the mean accuracy of 98.15% for distance, 92.87% for width, and 93.92% for height estimations. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Object detection

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A Density-Based Fuzzy Clustering Algorithm Using Multi-Representatives Points Per Cluster Based on a New Distance Measure Using KNN Algorithm

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International Journal of engineering, Transactions B: Applications 2025年第12期38卷 2865-2876页

作者： Klidbary, S. Haghzad Javadian, M. Faculty of Engineering Department of Electrical and Computer Engineering University of Zanjan Zanjan Iran School of Electrical Engineering Shahid Beheshti University Tehran Iran

In analyzing phenomena around us, clustering is among the most commonly used techniques in machine learning for comparing, and categorizing them into different groups based on intrinsic features. One of the main challenges facing clustering algorithms is selecting a suitable representative for each cluster. Existing algorithms often choose a single representative, which can lead to suboptimal performance on many datasets (especially asymmetric datasets). This process is completely dependent on the type of internal distribution of the clusters, and that single point may not be a suitable representative for that cluster. The proposed algorithm for dealing with datasets, inspired by the fuzzy ALM method and avoiding complex formulas, and calculations, initially breaks the system down into simpler (two-dimensional) systems. After spreading ink drops, by finding the vertical Narrow path and the horizontal narrow path, it selects a set of points as the representation of each cluster. The proposed algorithm, unlike many conventional algorithms, provides a representative set for each cluster and also enhances the algorithm's performance in dealing with datasets that have an asymmetric structure by introducing a new distance measure based on the KNN method and utilizing the set of prime numbers. The Accuracy, F1-Score, and AMI achieved when working with many low-dimensional, and high-dimensional datasets has been higher compared to algorithms such as FUALM, HiDUALM, K-Means, DBSCAN, DENCLUE and IRFLLRR and in some cases, the achieved accuracy has been equal to 100 percent. ©2025 The author(s)

关键词： K-means clustering

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An active learning framework for adversarial training of deep neural networks

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Neural Computing and Applications 2025年第9期37卷 6849-6876页

作者： Ghosh, Susmita Chatterjee, Abhiroop Fiondella, Lance Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Electrical and Computer Engineering University of Massachusetts Dartmouth United States

This article introduces a novel approach to bolster the robustness of Deep Neural Network (DNN) models against adversarial attacks named "Targeted Adversarial Resilience Learning (TARL)". The initial evaluation of a baseline DNN model reveals a significant accuracy decline when subjected to adversarial examples generated through techniques like FGSM, PGD, Carlini Wagner, and DeepFool attacks. To address this vulnerability, the article proposes an active learning framework, wherein the model iteratively identifies and learns from the most uncertain and misclassified instances. The key components of this approach include uncertainty estimation score in predicting the class of the input sample, selecting challenging samples based on this uncertainty score, labeling these challenging examples and augmenting them into the training set, and thereafter retraining the model with the expanded training set. The iterative active learning process, governed by parameters such as the number of iterations and batch size, demonstrates the potential to systematically enhance the resilience of DNN against adversarial threats. The proposed methodology has been investigated on several popular datasets such as the SARS-CoV-2 CT scan, MNIST, CIFAR-10, and Caltech-101, and demonstrated to be effective. Experiments illustrate that the learning framework improves the adversarial accuracies from 17.4% to 98.71% for the SARS-CoV-2 dataset, from 8.4% to 99.89% for the MNIST dataset, 1.6% to 78.84% for the CIFAR-10, and 12% to 92.92% for Caltech-101. Further, comparative analysis with several state-of-the-art methods suggests that the proposed framework offers superior defense against various attack methods and offers promising defensive mechanisms to deep neural networks. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Active learning

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Impact of Different Integration Times on Distributions of Rain Rates for Predictions of Rain Attenuation

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Progress in Electromagnetics Research C 2025年 154卷 229-238页

作者： Hassan, Mohammad Rofiqul Rafiqul, Islam Md. Habaebi, Mohamed Hadi Suriza, Ahmad Zabidi Badron, Khairayu Budalal, Asma Ali Mahfuz, Md. Mahmudul Hasan Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia Jalan Gombak Kuala Lumpur53100 Malaysia College of Electrical and Electronics Technology Benghazi Libya Department of Electrical and Computer Engineering Concordia University Canada

All wireless communication systems are moving towards higher and higher frequencies day by day which are severely attenuated by rains in outdoor environment. To design a reliable RF system, an accurate prediction method of rain attenuation is established and used globally based on local rain intensity measurement. Required rain intensity used for attenuation prediction is generally measured at a point with 1-min integration time or converted from higher integration time to 1-min. Recent measurements of rain intensity with a 10-second integration time indicate that intensity is not uniform over a 1-minute duration. Consequently, the statistics of rain intensity distribution and attenuation predictions are influenced by measurements with integration times shorter than 1 minute. It has been established that an integration time of 0.01% provides the optimal fit for actual rain rate data. This paper presents the rain intensity distributions from data measured with 2-min, 1-min, 30-sec, 20-sec, and 10-sec integration times, and it has impact on rain rate distributions as well as rain attenuation predictions. © 2025, Electromagnetics Academy. All rights reserved.

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Transparent OLED displays for selective bidirectional viewing using ZnO/Yb:Ag cathode with highly smooth and low-barrier surface

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Light(Science & Applications) 2025年第3期14卷 673-683页

作者： Eun-young Choi Sung-Cheon Kang Kanghoon Kim Su-Hyeon Lee Jeong-Beom Kim Jang-Kun Song Department of Electrical and Computer Engineering Sungkyunkwan UniversitySuwon-siRepublicof Korea Department of Semiconductor and Display Engineering Sungkyunkwan UniversitySuwon-siRepublicof Korea

Transparent organic light-emitting diode(TrOLED)displays represent cutting-edge technology posed to significantly enhance user *** study addresses two pivotal challenges in TrOLED ***,we focus on the innovation of transparent cathodes,a fundamental component in TrOLEDs,by introducing a ZnO/Yb:Ag *** cathode employs a combination of seed layer and metal doping techniques to achieve a highly uniform surface morphology and a low surface energy *** optimized Yb:Ag cathode on ZnO,with a mere thickness of 15 nm,exhibits remarkable properties:an extremely low surface roughness of 0.52 nm,sheet resistance of 11.6Ω γ^(-1),an optical transmittance of 86.7%at 510 nm,and tunable work function(here,optimized to be 3.86 eV),ensuring superior electron injection ***,we propose a novel TrOLED pixel structure that features selective bidirectional viewing,allowing different types of information to be selectively displayed on each side while preserving overall transparency and minimizing pixel *** design innovation distinguishes itself from conventional TrOLEDs that display images on only one *** bidirectional TrOLED design not only enhances openness and esthetic appeal but also holds promise for diverse applications across various user environments.

关键词： transparent OLED displays transparent cathodesa troled displays significantly enhance user experiencethis selective bidirectional viewing ZNO Yb Ag cathode low barrier surface highly smooth surface

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Stability Enhancement of Grid Connected Power System Using Type-II Neurofuzzy Wavelet Control

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International Journal of Fuzzy Systems 2025年 1-26页

作者： Dilshad, Saad Badar, Rabiah Haider, Aun Abas, Naeem Department of Electrical Engineering University of Management and Technology Lahore Sialkot Campus Sialkot Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad Pakistan Department of Electrical and Computer Engineering International Islamic University Islamabad Islamabad Pakistan Department of Electrical Engineering University of Gujrat Hafiz Hayat Campus Gujrat Pakistan

Conventional methods like Automatic Voltage Regulators (AVRs) and Power System Stabilizers (PSSs) fall short of effectively damping Low-Frequency Oscillations (LFOs), necessitating the exploration of advanced solutions. This paper addresses the challenge of power system stability by utilizing advanced soft computing techniques in Supplementary Damping Control (SDC) for Static Synchronous Compensator (STATCOM) to mitigate low frequency oscillations in multi-machine power system. The proposed Type-II NeuroFuzzy Wavelet Controls (NFWC) employ Gaussian Type-II membership functions, with uncertainties in mean and standard deviation parameters, in the antecedent part and Wavelet Neural Networks (WNNs) in the consequent part. A multi-machine power system with STATCOM is considered for the performance evaluation of the proposed controllers. The proposed Type-II NFWC-1 and Type-II NFWC-2 outperform Adaptive NeuroFuzzy TSK Control (ANFTSKC) in terms of convergence speed and damping performance. Type-II NFWC-2 exhibits and maintains its superior performance over Type-II NFWC-1 and ANFTSK, highlighting the effectiveness of incorporating additional uncertainty in both mean and standard deviation parameters. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2025.

关键词： Frequency stability

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