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A novel image descriptor based on dot-diffused block truncation coding with bit probability

Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 14841-14858页

作者： Riyono, Dwi Sun, Chi-Chia Guo, Jing-Ming Wong, KokSheik Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Electrical Engineering National Formosa University Yunlin Huiwei Taiwan Faculty of Computer Science and Information Technology Monash University-Malaysia Campus Kuala Lumpur Malaysia

With the advent of mobile smart devices and ubiquitous network connections, digital images can now be conveniently captured, edited, and shared online worldwide. The ever-increasing number of pictures poses a technically challenging, yet unavoidable problem of efficiently and accurately finding a desired image. In this work, we develop a new image retrieval system based on an image compression technique, namely dot-diffused block truncation coding (DDBTC) with bit probability. Specifically, the color feature derived from color distribution and the bitmap feature (BF) derived from both edges and textures jointly describe the image. The degree of similarity between two images is then measured by their respective color and BFs by using the modified Canberra distance metric. Experimental results show that the proposed feature descriptor achieves superior image retrieval performance as compared to the former DDBTC image retrieval feature and conventional non-DDBTC based features. Additional experiments on image classification verify that the proposed feature descriptor outperforms conventional image classification methods. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Color

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Adversarial multi-image steganography via texture evaluation and multi-scale image enhancement

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Multimedia Tools and Applications 2025年第9期84卷 5793-5823页

作者： Li, Fengyong Li, Longwei Zeng, Yishu Yu, Jiang Qin, Chuan College of Computer Science and Technology Shanghai University of Electric Power Shanghai201306 China School of Information and Computer Shanghai Business School Shanghai201400 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200092 China

Multi-image steganography refers to a data-hiding scheme where a user tries to hide confidential messages within multiple images. Different from the traditional steganography which only requires the security of an individual image, multi-image steganography considers an overall security for a batch of images. However, existing multi-image steganography all faces a nontrivial problem: how to optimally allocate payload into multiple images to guarantee the security of batch images. To address this problem, this paper proposes an adversarial multi-image steganographic scheme. A multi-scale texture evaluation mechanism is firstly calculated to determine the embeddable cover images. Subsequently, a series of multi-scale filters are introduced to enhance the image content, which can be used to guide the optimal payload assignment of each image. Furthermore, an adversarial embedding mechanism is designed by dynamically adjusting the random gradient mapping of batch images, and finally achieving secure multi-image steganography. Our proposed scheme can optimize the overall steganographic security performance of multiple images, while ensuring the anti-steganalysis capability of a single image. Extensive experiments demonstrate that our scheme can achieve superior performance for multi-image steganography over different large-scale image sets, and outperforms state-of-the art schemes in terms of both single-image and multi-image anti-steganalysis capability. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Embeddings

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Hybrid Deep Learning Based Attack Detection for Imbalanced Data Classification

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Intelligent Automation & Soft Computing 2023年第1期35卷 297-320页

作者： Rasha Almarshdi Laila Nassef Etimad Fadel Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science Faculty of Computer Science and EngineeringUniversity of HailHailSaudi Arabia

Internet of Things(IoT)is the most widespread and fastest growing technology *** to the increasing of IoT devices connected to the Internet,the IoT is the most technology under security *** IoT devices are not designed with security because they are resource constrained ***,having an accurate IoT security system to detect security attacks is *** Detection Systems(IDSs)using machine learning and deep learning techniques can detect security attacks *** paper develops an IDS architecture based on Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)deep learning *** implement our model on the UNSW-NB15 dataset which is a new network intrusion dataset that cate-gorizes the network traffic into normal and attacks *** this work,interpolation data preprocessing is used to compute the missing ***,the imbalanced data problem is solved using a synthetic data generation *** experiments have been implemented to compare the performance results of the proposed model(CNN+LSTM)with a basic model(CNN only)using both balanced and imbalanced ***,with some state-of-the-art machine learning classifiers(Decision Tree(DT)and Random Forest(RF))using both balanced and imbalanced *** results proved the impact of the balancing *** proposed hybrid model with the balance technique can classify the traffic into normal class and attack class with reasonable accuracy(92.10%)compared with the basic CNN model(89.90%)and the machine learning(DT 88.57%and RF 90.85%)***,comparing the proposed model results with the most related works shows that the proposed model gives good results compared with the related works that used the balance techniques.

关键词： IoT IDS deep learning machine learning CNN LSTM

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TSCompiler: efficient compilation framework for dynamic-shape models

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science China(information sciences) 2024年第10期67卷 67-84页

作者： Xiang LUO Chen ZHANG Chenbo GENG Yanzhi YI Jiahui HU Renwei ZHANG Zhen ZHANG Gianpietro CONSOLARO Fan YANG Tun LU Ning GU Li SHANG School of Computer Science Fudan University School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University School of Computer Science and Technology Harbin Institute of Technology Huawei Technologies Co. Ltd. Huawei Paris Research Center School of Microelectronics Fudan University

Today's deep learning models face an increasing demand to handle dynamic shape tensors and computation whose shape information remains unknown at compile time and varies in a nearly infinite range at runtime. This shape dynamism brings tremendous challenges for existing compilation pipelines designed for static models which optimize tensor programs relying on exact shape values. This paper presents TSCompiler, an end-to-end compilation framework for dynamic shape models. TSCompiler first proposes a symbolic shape propagation algorithm to recover symbolic shape information at compile time to enable subsequent optimizations. TSCompiler then partitions the shape-annotated computation graph into multiple subgraphs and fine-tunes the backbone operators from the subgraph within a hardware-aligned search space to find a collection of high-performance schedules. TSCompiler can propagate the explored backbone schedule to other fusion groups within the same subgraph to generate a set of parameterized tensor programs for fused cases based on dependence analysis. At runtime, TSCompiler utilizes an occupancy-targeted cost model to select from pre-compiled tensor programs for varied tensor shapes. Extensive evaluations show that TSCompiler can achieve state-of-the-art speedups for dynamic shape models. For example, we can improve kernel efficiency by up to 3.97× on NVIDIA RTX3090, and 10.30× on NVIDIA A100 and achieve up to five orders of magnitude speedups on end-to-end latency.

关键词： machine learning tensor compilers dynamic shape operator fusion code generation auto-tuning

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Transforming Sustainable Aquaculture: Synergizing Fuzzy Systems and Deep Learning Innovations

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International Journal of Fuzzy Systems 2024年第8期26卷 2536-2552页

作者： Haobijam, Basanta Huang, Yo-Ping Chang, Yue-Shan Chang, Tsun-Wei Department of Electrical Engineering National Taipei University New Taipei City23741 Taiwan Department of Electrical Engineering National Penghu University of Science and Technology Penghu88046 Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Computer Science and Information Engineering National Taipei University New Taipei City23741 Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung41349 Taiwan Department of Computer Science and Information Engineering HungKuo Delin University of Technology New Taipei City236302 Taiwan

Pisciculture encounters an array of intricate challenges that span disease management, preservation of water quality, prevention of genetic hybridization, ensuring the integrity of net systems, sourcing sustainable aquatic feed, and comprehending fish growth and reproductive dynamics. Addressing these multifaceted challenges necessitates a comprehensive research approach. This study employs an innovative synergy of fuzzy logic and deep learning techniques, resulting in a robust strategy to tackle these obstacles effectively. Fuzzy logic excels in assessing stressed fish conditions by handling inherent uncertainties. Simultaneously, YOLOv7 with fuzzy color enhancement (YOLOv7FCE) is used to detect damaged fish nets, thereby mitigating losses and upholding the integrity of the net infrastructure. This approach also leverages YOLOv7FCE for identifying Cobia fish within shoals, streamlining the identification process. Subsequently, DeepLabv3 is implemented to meticulously segment the recognized Cobia fish, facilitating precise measurements of their physical attributes. This comprehensive methodology yields profound insights into growth patterns and feeding tendencies within the confined aquatic environment. By embracing this approach, the research presents a versatile and adaptive framework that not only enhances our comprehension of piscine dynamics but also holds the potential to revolutionize the aquaculture industry. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2024.

关键词： Fish

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Neural Network-Based Fixed-Time Tracking Control for Input-Quantized Nonlinear Systems With Actuator Faults

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IEEE Transactions on Neural Networks and Learning Systems 2024年第3期35卷 3978-3988页

作者： Sun, Wei Wu, Jing Su, Shun-Feng Zhao, Xudong Liaocheng University School of Mathematics Science Liaocheng252000 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116024 China

This study reports a fixed-time tracking control problem for strict-feedback nonlinear systems with quantized inputs and actuator faults where the total number of faults is allowed to be infinite. By taking advantage of radial basis function neural networks (RBFNNs), unknown nonlinear function terms in the system dynamic model can be effectively approached. In addition, based on the sector property of quantization nonlinearities and the structure of the actuator fault model, novel adaptive estimations and innovative auxiliary design signals are constructed to compensate for the influence caused by actuator faults and quantized inputs properly in the fixed-time convergence settings. Then, rigorous theoretical analysis manifests that the proposed control scheme can make the output tracking error converge to a small neighborhood of the origin within a fixed time, and the upper bound of the setting time not only does not depend on initial states of the system but also can be preassigned by selecting parameters appropriately. Meanwhile, all the signals in the closed-loop system remain bounded. Finally, a numerical example and a practical example of a single-link manipulator are presented to demonstrate the effectiveness of the proposed control algorithm. © 2012 IEEE.

关键词： Navigation

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Asynchronous Energy-to-peak Control for 2D Roesser-type Markov Jump Systems

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第11期51卷 1761-1768页

作者： Chen, Qing Zhang, Yu Han, Chengyi Chen, Ling Zhou, Jianping The School of Computer Science and Technology Anhui University of Technology Ma’anshan243032 China The School of Electrical and Information Engineering Wanjiang University of Technology Ma’anshan243032 China

This paper studies asynchronous energy-to-peak control for 2D Roesser-type Markov jump systems (RTMJSs). Given the practical challenge of obtaining the system state, output-feedback is utilized for closing the control loop instead of state feedback. The asynchronous behavior between the controlled plant and the controller is represented by a hidden Markov model. A sufficient condition for the stochastic stability and energy-to-peak disturbance attenuation (EPDA) of the closed-loop RTMJS is established using a Lyapunov function and Schur’s complement. Under fixed conditional probability, a scheme for asynchronous output-feedback controller design is proposed, employing variable replacement and matrix transformation to cope with the nonlinear terms. The present work is then expanded to the uncertain conditional probability scenario and a related controller design method is developed. Finally, the effectiveness of the proposed design methods is demonstrated through a thermal process. © (2024), (International Association of Engineers). All rights reserved.

关键词： Lyapunov functions

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Exploring the Usage of Mobile Phones in the Assessment Process: A Case Study 47

Exploring the Usage of Mobile Phones in the Assessment Proce...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Kovačević, Željko Bubnic, Bostjan Kosar, Tomaž Zagreb University Of Applied Sciences Department Of Computer Science And Information Technology Zagreb Croatia University Of Maribor Faculty Of Electrical Engineering And Computer Science Maribor Slovenia

ISBN: (纸本)9798350382495

technology is changing how students learn in the 21st century significantly. Integrating mobile devices in teaching, learning, and assessment processes has emerged as an important strategy for improving teaching methodologies and students' learning. However, adopting mobile devices presents several challenges, including understanding the students' performance, impact on time, and efficiency in the assessment process. This study explores this area with an empirical study of using mobile devices and compares the results to those using traditional desktop computers. In the educational environment, we investigated students' effective use while interacting with mobile and desktop versions of the Second Introductory programming summative assessment. Multinational/multi-institutional research involved a comparative analysis between two student groups undertaking identical tasks on mobile phones and desktop computers. Our results suggest that the students' results are not affected significantly by using the mobile version of the assessment application. These results highlight the usability of mobile devices as a flexible and effective tool for various educational applications, strengthening their role in the modern learning environment. © 2024 IEEE.

关键词： Object oriented programming

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Enhanced MADM Strategy with Heptapartitioned Neutrosophic Distance Metrics

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Neutrosophic Sets and Systems 2025年 78卷 74-96页

作者： Eassa, Mohamed Myvizhi, M. Elkholy, Mohamed Abdelhafeez, Ahmed Elbehiery, Hussam Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematics KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Faculty of Computer Science& Engineering Alamein International University Egypt

In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Penta-Partitioned Neutrosophic Distance Measures under particular conditional criteria, the suggested technique ranks the options. Additionally, we explore the fundamental characteristics of HNSDM and provide several illustrative theorems. The Heptapartitioned Neutrosophic Set Distance Measures are formulated based on the Hepta Neutrosophic Set. Lastly, a practical numerical example is provided to illustrate the efficacy of the suggested methodology. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioned Neutrosophic set Heptapartitioned Neutrosophic set and MADM strategy Heptapartitioned set Neutrosophic sine distance measure

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Multi-Channel Multi-Frame Integration Detection Method for Weak Target with Coherent MIMO Radar

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IEEE Transactions on Aerospace and Electronic Systems 2025年第3期61卷 6518-6536页

作者： Gao, Longji Li, Xiaolong Wang, Mingxing Sun, Zhi Cui, Guolong Yeo, Tat Soon University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China National University of Singapore Department of Electrical and Computer Engineering Singapore119077 Singapore

Coherent multiple-input multiple-output (MIMO) radar could significantly improve the weak moving target detection ability by accumulating multi-channel and multi-frame echo signal. However, due to the target motion and the geometric configuration differences of each node relative to the target, the multi-channel multi-frame accumulation of target echoes by coherent MIMO radar faces several difficulties, i.e., range migration (RM) and Doppler frequency migration (DFM) issues within each channel, spatial variations of target signal envelope and phase across different channels, time variations of target signal envelope and phase across different frames. To tackle these problems, two contributions are made in this paper. Firstly, by applying the generalized Radon Fourier transform (GRFT) to correct the RM/DFM and obtain the signal integration within each channel, we propose a multi-channel accumulation algorithm to compensate the spatial variations of target signal and realize the target energy accumulation across different channels, which takes full advantage of the GRFT output characteristics and the radar system topological structure. Secondly, a multi-frame accumulation algorithm is developed to compensate the time variations of target signal and achieve the further integration of target energy distributed in different frames. We also provide explicit expressions and analysis for performance metrics, including the output response, SNR improvement performance. The proposed method's effectiveness is confirmed through simulation experiments. © 1965-2011 IEEE.

关键词： Topology

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