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Underwater object detection based on enhanced YOLOv4 architecture

Multimedia Tools and Applications 2024年第18期83卷 53759-53783页

作者： Liu, Ching-Hua Lin, Chang Hong Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan

Object detection and image restoration pose significant challenges in deep learning and computer vision. These tasks are widely employed in various applications, and there is an increasing demand for specialized environments where images are prone to blur or noise, which can adversely affect subsequent results. In recent years, significant breakthroughs have been achieved in object detection performance. While some previously proposed methods prioritize high accuracy at the cost of longer inference times, others prioritize speed. Therefore, it is crucial to design an efficient network architecture that maintains both inference speed and high accuracy. This research proposes a network architecture for underwater object detection with an attention mechanism. The proposed approach differentiates itself from other methods by employing a deblurring network as a preprocessing step to restore and enhance the image quality of the underwater dataset. Additionally, in the feature extraction stage of the detection network, channel and spatial feature information are individually enhanced. These adaptive attention features are then integrated into a multi-scale feature fusion. Finally, the cross-stage local method is combined to improve the learning ability of the convolutional neural network while reducing the size of the model. Based on the experimental results, our proposed model architecture achieves leading accuracy and strikes a favorable balance in terms of model size compared to previously proposed methods. Based on our structure, the metrics of AP and AP50 reach 66.8 and 87.6, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Image reconstruction

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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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A Facile Approach To Represent Neural Network With Less Storage Space

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IEEE Transactions on Emerging Topics in Computational Intelligence 2023年第4期7卷 1205-1216页

作者： Huang, Sheng-An Leu, Jenq-Shiou National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei Taiwan

Deep neural networks have been applied to different applications, such as image recognition, data analysis, natural language processing, and so on. Some companies regularly release their mobile applications to app stores. Usually, app stores have a restriction that overweight applications can not be downloaded until the smartphone connects to Wi-Fi. Unfortunately, the size of the application that consists of deep neural networks may be too large. In order to reduce the size of application, an efficient compression approach is highly desired. An efficient encoding for compressed neural networks is a critical point for reducing storage complexity. Most of previous studies focus on the sparse matrix encoding instead of the dense one. The dense matrix encoding methods have not been studied well. Meanwhile, sparse matrix encoding can not attain a high compression rate when encoding a dense matrix. Toward this end, we proposed an alternative compression algorithm that linearizes the weights on each layer and then stores linearized weights with critical information. For a dense matrix, our algorithm can achieve a higher compression rate than one existing sparse matrix encoding. Experiment results indicate that our proposed piecewise linearization scheme can make VGG-16, Resnet152, and Densenet169 achieve at least two times of the compression rates. In other words, neural networks parameters can be stored in 40-50% of the original space. © 2017 IEEE.

关键词： Linearization

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Google Map-based Password Authentication Systems Using Tolerant Distance and Homomorphic Encryption

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhou, Zhili Yang, Ching-Nung Wang, Shaowei Nan, Guoshun Cimato, Stelvio Zheng, Yifeng Wang, Qian Guangzhou University Institute of Artificial Intel-ligence China Guangdong Key Labor-atory of Blockchain Security Guangdong510006 China National Dong Hwa University Department of Computer Science and Information Engineering Hualian97401 Taiwan national engineering research center for mobile network technologies Beijing100876 China Universitá degli studi di Milano Italy The Hong Kong Polytechnic University Department of Electrical and Electronic Engi-neering Hong Kong Wuhan University School of Cyber Science and Engineering Wuhan430072 China

Passwords are widely used for authentication in Internet applications. Recently, users tend to adopt graphical passwords instead of traditional alphanumeric passwords, since it is much easier for humans to remember images than verbal representations. However, the existing graphical password authentication systems generally suffer from three main issues. 1) It is required to remember and perform complicated operations during the registration/login phases, which significantly limits the systems' usability;2) The users' passwords are simply stored as plaintexts in servers, and thus the security is compromised;3) The users need to register/login to each server separately when they are applied in multi-server environment. To address the above issues, we propose a user-friendly and secure Google map-based graphical password (FS-GMGP) system using tolerant distance and homomorphic encryption. By using a homomorphic encryption scheme, each user encrypts his password point and response point selected on Google map, while the servers compute and decrypt the distance between the two encrypted points and then compare the resulting value with a tolerant distance for authentication. Moreover, the FS-GMGP system is extended for multi-server environment. The evaluation results and security analysis show that the FS-GMGP and its extended version achieve desirable usability and security in single-server environment and multi-server environment, respectively © 2025 IEEE.

关键词： Authentication Protocol

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A Robust Key Exchange and Tamper-Resistant Protocol for HAN and NAN Networks in Smart Grids

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IEEE Internet of Things Journal 2025年

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart Grids (SG) rely on Home Area Networks (HAN) and Neighborhood Area Networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the Physical Unclonable Function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol's resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol's effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol's enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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SWRNet: A Deep Learning Approach for Small Surface Water Area Recognition Onboard Satellite

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2023年 16卷 10369-10380页

作者： Bui, Trong-An Lee, Pei-Jun National Taiwan University of Science and Technology Electronic and Computer Engineering Department Taipei106335 Taiwan

This article proposes a deep learning approach for small surface water recognition using multispectral satellite imaging, which reduces the computational complexity by 18.66 times and increases the accuracy of surface water recognition by up to 14.1%. The proposed model uses near infrared combined with RGB spectral imagery to increase the accuracy of surface water recognition. In addition, since surface water only accounts for a small percentage of the remote sensing dataset, thus creating an imbalance problem, a proposed loss function is introduced to combine region-based and distribution-based loss. This article introduces an adaptive factor that automatically adjusts the weighting between distribution- and region-based loss functions. The proposed adaptive factor is determined based on the loss value of the previous training step. The mean intersection over union of surface water between predicted and ground truth regions is recorded as 0.80. © 2008-2012 IEEE.

关键词： Remote sensing

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Monocular 3D Object Detection Utilizing Auxiliary Learning With Deformable Convolution

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IEEE Transactions on Intelligent Transportation Systems 2024年第3期25卷 2424-2436页

作者： Chen, Jiun-Han Shieh, Jeng-Lun Haq, Muhamad Amirul Ruan, Shanq-Jang National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan

In autonomous driving systems, the monocular 3D object detection algorithm is a crucial component. The safety of autonomous vehicles heavily depends on a well-designed detection system. Therefore, developing a robust and efficient 3D object detection algorithm is a major goal for institutes and researchers. Having a 3D sense is essential in autonomous vehicles and robotics, as it allows the system to understand its surroundings and react accordingly. Compared with stereo-based and Lidar-based methods, monocular 3D Object detection is a challenging task as it only utilizes 2D information to generate complex 3D features, making it low-cost, less computationally intensive, and with great potential. However, the performance of monocular methods is impaired due to the lack of depth information. In this paper, we propose a simple, end-to-end, and effective network for monocular 3D object detection without the use of external training data. Our work is inspired by auxiliary learning, in which we use a robust feature extractor as our backbone and multiple regression heads to learn auxiliary knowledge. These auxiliary regression heads will be discarded after training for improved inference efficiency, allowing us to take advantage of auxiliary learning and enabling the model to learn critical information more conceptually. The proposed method achieves 17.28% and 20.10% for the moderate level of the Car category on the KITTI benchmark test set and validation set, respectively, which outperforms the previous monocular 3D object detection approaches. © 2000-2011 IEEE.

关键词： Feature extraction

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Analytical Modeling and Performance Evaluation of Beam-Based Random Access Protocol for 5G Beamforming Networks

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IEEE Internet of Things Journal 2023年第24期10卷 22246-22255页

作者： Lin, Ting-An Cheng, Ray-Guang National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei106335 Taiwan

Access parameters, user equipment (UE) density, system load, and channel conditions significantly impact the performance of random-access (RA) protocols, influencing network capacity, latency, and robustness. In 3GPP standard, the prach-ConfigurationIndex access parameter is used to designate reserved RA channel occasions (ROs) for each beam, enabling UEs to derive beam mapping patterns and select desired beams through a beam-based RA protocol. This article addresses the challenge of bursty arrivals from massive Internet of Things (IoT) devices with sporadic traffic patterns in 5G beamforming networks. We propose an analytical model to evaluate the performance of the beam-based RA protocol for any beam mapping pattern. Extensive computer simulations are conducted to validate the accuracy and effectiveness of our model across various scenarios. The results demonstrate that our model provides valuable insights into the behavior and performance of the beam-based RA protocol and offers a reliable tool to evaluate and optimize beam mapping patterns in practical 5G network deployments. © 2014 IEEE.

关键词： Analytical models

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Parallel Computation of Dominance Scores for Multidimensional Datasets on GPUs

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IEEE Transactions on Parallel and Distributed Systems 2024年第6期35卷 764-776页

作者： Chen, Wei-Mei Tsai, Hsin-Hung Ling, Joon Fong National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei106 Taiwan

The dominance scoring problem in a multidimensional dataset is to return the number of points dominated by a given point, which is a common metric for evaluating the quality of a data point. Dominance scoring is an elementary operator for variations of the skyline operator, including top k dominating and k-skyband queries. This study proposes query processing for dominance scores that operates primarily on the graphics processing unit (GPU) to fully utilize its massive processing resources and restricted memory space while reducing the transfer overhead between the central processing unit (CPU) and GPU. We introduce a heap-based multidimensional data structure with complete and well-balanced characteristics. Using our preprocessed data, we can construct a complete R-tree with the non-overlapping property, ensuring that the bounding boxes of internal nodes of the same level do not overlap, thereby reducing redundant operations. In addition, we propose two algorithms based on depth-first and breadth-first traversals to accumulate the dominance score on GPUs in parallel. Both take full advantage of the GPU's computing resources and memory space supported by the non-overlapping tree structures. Experiments on synthetic and real-world datasets demonstrate that the proposed algorithms implemented on GPUs dramatically improve the efficiency of dominance scoring. © 1990-2012 IEEE.

关键词： Graphics processing unit

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New Radio-Frequency Liquid Permittivity Measurement System Using Filter-Based Microfluidic Sensor

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IEEE Sensors Journal 2023年第12期23卷 12785-12795页

作者： Nguyen, Tuan-Khanh Tseng, Chao-Hsiung National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei106335 Taiwan

A new radio-frequency (RF) liquid permittivity measurement system using a filter-based microfluidic sensor is proposed in this article. Integrating the filter-based sensor with an RF source, a power divider, and a gain/phase detector (GPD) can form a new permittivity measurement system without using a bulky and expensive vector network analyzer (VNA) to acquire measured data. Since two coupled resonators with a strong coupling synthesize the filter-based sensor, it can provide a stronger electric-field concentration in the sensing area and improve the sensor's sensitivity. The 8- μ L water-ethanol mixtures with ethanol volume fractions (EVF) ranging from 10% to 90% in 10% increments are used as the test liquids to evaluate the proposed sensor and system. Compared with the measured results using the commercial dielectric probe kit, the maximum errors of r' and r using the filter-based sensor with a VNA are -4.34% and 13.24%, respectively. At the same time, r' andr errors are 8.28% and 14%, respectively, using the proposed permittivity measurement system. Since the proposed permittivity measurement system can flexibly synthesize the sensing area size, control the electric-field intensity, and get rid of a VNA, it makes it easier to satisfy the requirements of various microfluidic sensing scenarios. © 2001-2012 IEEE.

关键词： Microfluidics

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