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5th International IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2023

作者： Mohammed, Shaimaa H. Mohra, Ashraf S. Hassan, Ashraf Y. Elnokrashy, Ahmed F. Benha University Faculty of Engineering Department of Electrical Engineering Benha Benha Egypt Benha University Benha Faculty of Engineering Benha Egypt Nile University School of Information Technology and Computer Science Giza Egypt

ISBN: (纸本)9798350381030

Radio Frequency (RF) energy harvesting has been employed to power wireless devices. Nevertheless, RF energy harvesting encounters restrictions regarding the quantity of power it can harvest depending on signal accessibility. As a result, accurately predicting energy levels becomes crucial for enhancing the performance of energy harvesting circuits. Most research efforts have concentrated on enhancing power harvesting policies or theoretically estimating the energy obtained through RF energy harvesting. Moreover, the existing literature has primarily focused on single-band prediction approaches. This paper presents a multi-band RF energy prediction approach for RF energy harvesting systems. We collect real-time RF energy using software-defined radio technology. The proposed approach leverages Long Short-Term Memory (LSTM) neural networks to accurately predict the mean RF energy in different frequency bands for the next 100 samples, which corresponds to approximately one hour and a half. The research explores the research gap in modeling the radio frequency signal and the need for multi-band prediction techniques. The results demonstrate the effectiveness of the proposed approach in predicting RF energy across different frequency bands, with average accuracies above 98%. © 2023 IEEE.

关键词： Energy harvesting

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Assessing the Impact of AI-Driven Systems on Automated Data science

Assessing the Impact of AI-Driven Systems on Automated Data ...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Sharma, Bhuvnesh JayasudhaS, S. Dhabliya, Dharmesh Acharjya, Kalyan Zaidi, Taskeen Dev, Sudhanshu Vivekananda Global University Department of Mechanical Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350329773

This paper gives an evaluation of current literature assessing the impact of synthetic intelligence (AI) on automatic information science approaches. AI-driven structures have been, and continue to be, integral for numerous aspects of facts science, from statistics preprocessing to the detection of styles. The paper opinions current studies on the effect of AI-driven systems on facts technology, consisting of the usage of automated device learning gear, in addition to paintings on synthetic intelligence mixed with herbal language processing. Additionally, the paper examines the potential implications that the use of automated data technology ought to have on society as a whole. The paper concludes by using presenting counseled directions for future research. © 2023 IEEE.

关键词： Flexible AC transmission systems

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A Cognitive Memory-Augmented Network for Visual Anomaly Detection

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IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1296-1307页

作者： Tian Wang Xing Xu Fumin Shen Yang Yang the Center for Future Multimedia and School of Computer Science and Engineering University of Electronic Science and Technology of China(UESTC)Chengdu 611731China the Center for Future Multimedia and School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731 the Institute of Electronic and Information Engineering of UESTC in Guangdong Dongguan 523808China

With the rapid development of automated visual analysis,visual analysis systems have become a popular research topic in the field of computer vision and automated *** analysis systems can assist humans to detect anomalous events(e.g.,fighting,walking alone on the grass,etc).In general,the existing methods for visual anomaly detection are usually based on an autoencoder architecture,i.e.,reconstructing the current frame or predicting the future ***,the reconstruction error is adopted as the evaluation metric to identify whether an input is abnormal or *** flaws of the existing methods are that abnormal samples can also be reconstructed *** this paper,inspired by the human memory ability,we propose a novel deep neural network(DNN)based model termed cognitive memory-augmented network(CMAN)for the visual anomaly detection *** proposed CMAN model assumes that the visual analysis system imitates humans to remember normal samples and then distinguishes abnormal events from the collected ***,in the proposed CMAN model,we introduce a memory module that is able to simulate the memory capacity of humans and a density estimation network that can learn the data *** reconstruction errors and the novelty scores are used to distinguish abnormal events from *** addition,we develop a two-step scheme to train the proposed model so that the proposed memory module and the density estimation network can cooperate to improve *** experiments evaluated on various popular benchmarks show the superiority and effectiveness of the proposed CMAN model for visual anomaly detection comparing with the state-of-the-arts *** implementation code of our CMAN method can be accessed at https://***/CMANcode/CMAN_pytorch.

关键词： Cognitive computing density estimation memory visual analysis systems visual anomaly detection.

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High-speed all-in-focus 3D imaging technology based on the liquid lens focus scanning 10

High-speed all-in-focus 3D imaging technology based on the l...

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Optoelectronic Imaging and Multimedia technology X 2023

作者： Cheng, Huayu Wang, Lihui Tabata, Satoshi He, Yuan Hu, Yan Liu, Jiang School of Information Science and Engineering Shenyang University of Technology Shenyang110870 China Institute of Semiconductors Guangdong Academy of Sciences Guangzhou510640 China Research Institute for Science and Technology Tokyo University of Science Tokyo1258585 Japan Graduate School of Science and Technology Keio University Yokohama2238522 Japan Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9781510667839

A large open aperture in an optical system can capture high-resolution images but yields a shallow depth of field. To overcome this issue, we propose a method for improving microscopy imaging systems by using a variable-focus liquid lens to achieve 3D focus scanning. Specifically, the focal length of the imaging system was changed by the liquid lens, and a sequence of 12 images was captured in different focal planes. The image scale was adjusted according to the change in focal length, and the phase of the image was corrected by the phase only correction method. Then the in-focus pixels were abstracted by employing the Laplacian operator. Finally, an all-in-focus sharp image was generated, and a depth map was obtained. Additionally, to accelerate the processing speed, the Fast Fourier Transform image processing during phase correction was optimized. Meanwhile, we propose a parallel optimization solution for the original processing flow. © 2023 SPIE.

关键词： Imaging systems

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Clothing Classification Method Based on Convolutional Network and Attention Mechanism

Clothing Classification Method Based on Convolutional Networ...

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2022 International Conference on Advanced Algorithms and Neural Networks, AANN 2022

作者： Liao, Liefa Zhang, Saisai Li, Zhiming Yuan, Pu Yang, Yiguo School of Information Engineering Jiangxi University of Science and Technology Jiangxi Ganzhou341000 China School of Computer Engineering and Science Shanghai University Shanghai 200444 China

ISBN: (纸本)9781510655751

To address the problems that existing garment classification effects are greatly affected by background noise and that network features are not highly expressive. A multi-scale deep network model (MCA-Inception) is proposed based on convolutional networks and attention mechanisms. This network model uses the modified Inception V3 as the backbone network and expands the perceptual field by adding convolutional kernels of different scale sizes to enrich the contextual detail information of the garment content. At the same time, the CBAM attention module is embedded in the improved backbone network to suppress the interference of noisy information such as cluttered background and enhance the representation of adequate feature information. Average classification accuracies of 81.63% and 77.80% were obtained on the publicly available clothing datasets DeepFashion and ACS, respectively. Experimental comparisons with other methods show that the proposed network model performs better in the clothing classification task. © 2022 SPIE.

关键词： Image classification

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PVTree: Realistic and Controllable Palm Vein Generation for Recognition Tasks

arXiv

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arXiv 2025年

作者： Shang, Sheng Zhao, Chenglong Zhang, Ruixin Jin, Jianlong Zhang, Jingyun Guo, Rizen Ding, Shouhong Wu, Yunsheng Zhao, Yang Jia, Wei School of Computer Science and Information Engineering Hefei University of Technology China Tencent Youtu Lab Tencent WeChat Pay Lab33

Palm vein recognition is an emerging biometric technology that offers enhanced security and privacy. However, acquiring sufficient palm vein data for training deep learning-based recognition models is challenging due to the high costs of data collection and privacy protection constraints. This has led to a growing interest in generating pseudo-palm vein data using generative models. Existing methods, however, often produce unrealistic palm vein patterns or struggle with controlling identity and style attributes. To address these issues, we propose a novel palm vein generation framework named PVTree. First, the palm vein identity is defined by a complex and authentic 3D palm vascular tree, created using an improved Constrained Constructive Optimization (CCO) algorithm. Second, palm vein patterns of the same identity are generated by projecting the same 3D vascular tree into 2D images from different views and converting them into realistic images using a generative model. As a result, PVTree satisfies the need for both identity consistency and intra-class diversity. Extensive experiments conducted on several publicly available datasets demonstrate that our proposed palm vein generation method surpasses existing methods and achieves a higher TAR@FAR = 1e − 4 under the 1:1 Open-set protocol. To the best of our knowledge, this is the first time that the performance of a recognition model trained on synthetic palm vein data exceeds that of the recognition model trained on real data, which indicates that palm vein image generation research has a promising future. Copyright © 2025, The Authors. All rights reserved.

关键词： Constrained optimization

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Adaptive Strategies for Scalable and Heterogeneous VANET Network 5

Adaptive Strategies for Scalable and Heterogeneous VANET Net...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Thangam, S. Jha, Priyanshu Hawari, Jawed Sah, Nabin Kumar Jain, Nikunj Jesy Janet Kumari, J.J. Saleem Raja, A. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India College of Computing and Information Sciences University of Technology and Applied Sciences It Department Shinas Oman

ISBN: (纸本)9798350385649

Despite technological advancements, Vehicular Ad-hoc Networks (VANETs) continue to face challenges related to reliability and high-speed mobility. This study focuses on enhancing scalability, improving mobility, and addressing network heterogeneity in the context of data dissemination. Data dissemination involves the distribution or transmission of statistical data to users. Tools like NS3, SUMO utilizing OSM for realistic traffic simulations, MATLAB for Data Analysis and evaluation of various protocols like ADHOC, Dynamic Source Routing (DSR), Optimized Link State Routing Protocol (OLSR), Destination Sequenced Distance Vector Routing (DSDV). The research aimed to refine VANET simulations for effective decision making, as its primary objective data dissemination i.e. distribution or transmission of statistical data to users, forming the central focus of this investigation. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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VoCAPTER: Voting-based Pose Tracking for Category-level Articulated Object via Inter-frame Priors 24

VoCAPTER: Voting-based Pose Tracking for Category-level Arti...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Li Han, Zean Zhong, Yan Yu, Qiaojun Wu, Xingyu Wang, Xue Wang, Rujing Hefei Institute of Physical Science Chinese Academy of Sciences University of Science and Technology of China Astribot Inc AnHui HeFei China Department of Mathematics Chinese University of Hong Kong New Territories Hong Kong School of Mathematical Sciences National Engineering Research Center of Visual Technology Peking University Beijing China Department of Computer Science Shanghai Jiao Tong University ShangHai China The Hong Kong Polytechnic University Hong Kong Hong Kong Hefei Institute of Physical Science Chinese Academy Sciences AnHui HeFei China

ISBN: (纸本)9798400706868

Articulated objects are common in our daily life. However, current category-level articulation pose works mostly focus on predicting 9D poses on statistical point cloud observations. In this paper, we deal with the problem of category-level online robust 9D pose tracking of articulated objects, where we propose VoCAPTER, a novel 3D Voting-based Category-level Articulated object Pose TrackER. Our VoCAPTER efficiently updates poses between adjacent frames by utilizing partial observations from the current frame and the estimated per-part 9D poses from the previous frame. Specifically, by incorporating prior knowledge of continuous motion relationships between frames, we begin by canonicalizing the input point cloud, casting the pose tracking task as an inter-frame pose increment estimation challenge. Subsequently, to obtain a robust pose-tracking algorithm, our main idea is to leverage SE(3)-invariant features during motion. This is achieved through a voting-based articulation tracking algorithm, which identifies keyframes as reference states for accurate pose updating throughout the entire video sequence. We evaluate the performance of VoCAPTER in the synthetic dataset and real-world scenarios, which demonstrates VoCAPTER's generalization ability to diverse and complicated scenes. Through these experiments, we provide evidence of VoCAPTER's superiority and robustness in multi-frame pose tracking of articulated objects. We believe that this work can facilitate the progress of various fields, including robotics, embodied intelligence, and augmented reality. All the codes will be made publicly available. © 2024 ACM.

关键词： Augmented reality

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Learning Deep Tree-based Retriever for Efficient Recommendation: Theory and Method

arXiv

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arXiv 2024年

作者： Liu, Ze Zhang, Jin Feng, Chao Lian, Defu Wang, Jie Chen, Enhong The School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230027 China The School of Artificial Intelligence and Data Science University of Science and Technology of China Anhui Hefei230027 China The State Key Laboratory of Cognitive Intelligence School of Computer Science and Technology School of Artificial Intelligence and Data Science University of Science and Technology of China Anhui Hefei230027 China The Department of Electronic Engineering and Information Science University of Science and Technology of China Anhui Hefei230027 China

With the advancement of deep learning, deep recommendation models have achieved remarkable improvements in recommendation accuracy. However, due to the large number of candidate items in practice and the high cost of preference computation, these methods still suffer from low recommendation efficiency. The recently proposed tree-based deep recommendation models alleviate the problem by directly learning tree structure and representations under the guidance of recommendation objectives. To guarantee the effectiveness of beam search for recommendation accuracy, these models strive to ensure that the tree adheres to the max-heap assumption, where a parent node's preference should be the maximum among its children's preferences. However, they employ a one-versus-all strategy, framing the training task as a series of independent binary classification objectives for each node, which limits their ability to fully satisfy the max-heap assumption. To this end, we propose a Deep Tree-based Retriever (DTR for short) for efficient recommendation. DTR frames the training task as a softmax-based multi-class classification over tree nodes at the same level, enabling explicit horizontal competition and more discriminative top-k selection among them, which mimics the beam search behavior during training. To mitigate the suboptimality induced by the labeling of non-leaf nodes, we propose a rectification method for the loss function, which further aligns with the max-heap assumption in expectation. As the number of tree nodes grows exponentially with the levels, we employ sampled softmax to approximate optimization and thereby enhance efficiency. Furthermore, we propose a tree-based sampling method to reduce the bias inherent in sampled softmax. Theoretical results reveal DTR's generalization capability, and both the rectification method and tree-based sampling contribute to improved generalization. The experiments are conducted on four real-world datasets, validating the effectivenes

关键词： Deep reinforcement learning

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Video Compressed Sensing Via Wavelet Residual Sampling and Dual-Domain Fusion

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IEEE Transactions on Multimedia 2025年

作者： Yin, Zhu Wu, Zhongcheng Shi, Wuzhen Hu, Guyue Lin, Weisi Anhui University School of Internet Hefei230601 China Chinese Academy of Sciences High Magnetic Field Laboratory Hefei Institutes of Physical Science Hefei230031 China University of Science and Technology of China Hefei230026 China Shenzhen University College of Electronics Information Engineering Shenzhen518060 China Anhui University School of Artificial Intelligence Hefei230601 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore

Deep learning-based compressed sensing (CS) technology attracts widespread attention owing to its remarkable reconstruction with only a few sampling measurements and low computational complexity. However, the existing video compressive sampling approaches cannot fully exploit the inherent interframe and intraframe correlations and sparsity of video sequences. To address this limitation, a novel sampling and reconstruction method for video CS (called WRDD) is proposed, which exploits the advantages of wavelet residual sampling and dual-domain fusion ***, in order to capture high-frequency details and achieve efficient and high-quality measurements, we propose a wavelet residual (WR) sampling strategy for the nonkeyframe sampling, which is achieved by the wavelet residuals between nonkeyframes and keyframes. Furthermore, a dual-domain (DD) fusion strategy is proposed, which fully combine intraframe and interframe to improve the reconstruction quality of nonkeyframes both in the pixel domain and multilevel feature domains. Extensive experiments demonstrate that our WRDD surpasses the state-of-the-art video and image CS methods in both subjective and objective evaluations. Besides, it exhibits outstanding antinoise capability and computational efficiency. © 2025 IEEE.

关键词： Video recording

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