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2024 International Conference on Next Generation Electronics and Photonics, INGEP 2024

作者： Huang, Lanqing Lu, Hanglin Liu, Xingpeng Song, Zhuo Su, Jiande Li, Zhuofeng Song, Shuxiang Liu, Linsheng Guangxi Key Laboratory of Brain-Inspired Computing and Intelligent Chips Key Laboratory of Integrated Circuits and Microsystems Education Department of Guangxi Zhuang Autonomous Region School of Electronic and Information Engineering School of Integrated Circuits Guangxi Normal University Guilin541004 China College of Physics Science and Technology Guangxi Normal University Guilin541004 China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin541004 China

In this work, high-quality CsPbBr3 and CsPbI3 nanowires were successfully grown on Si substrates via a vapor-liquid-solid (VLS) self-catalyzed growth mechanism using chemical vapor deposition (CVD). The nanowires were characterized by various techniques, including Scanning Electron Microscopy (SEM), Photoluminescence (PL), and Raman spectroscopy. The results demonstrate that the perovskite nanowires grown under the VLS mechanism exhibit strong photoluminescence intensity and excellent crystalline quality. Based on these high-quality nanowires, we further fabricated single-nanowire photodetectors. The CsPbBr3 nanowire photodetector exhibited a high photocurrent to dark current ratio of 4.2×103 and an outstanding responsivity of 1338 A/W. Meanwhile, the CsPbI3 nanowire photodetector also showed a photocurrent to dark current ratio of 10.6 and a responsivity of 991 A/W. These results indicate that perovskite nanowires grown by the VLS growth mechanism hold great promise for applications in photodetection. © Published under licence by IOP Publishing Ltd.

关键词： Nanowires

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HFANTrack: A Hierarchical Feature-Aware Network for Visual Tracking 30

HFANTrack: A Hierarchical Feature-Aware Network for Visual T...

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30th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2024

作者： Gu, Fengwei Li, Ao Liu, Tienan Shi, Tao Zhao, Ning Ju, Zhaojie Harbin University of Science and Technology School of Computer Science and Technology Harbin150080 China State Gird Harbin Electric Power Supply Company State Gird Harbin150023 China Shandong Baimeng Information Technology Co. Ltd. Weihai264200 China Bohai University College of Control Science and Engineering Jinzhou121013 China University of Portsmouth School of Computing PortsmouthPO1 3HE United Kingdom

ISBN: (纸本)9798350391916

In the field of visual tracking, it is a significant challenge to accurately capture the dynamic changes of targets in complex scenes. To address this issue, this paper proposes a novel Hierarchical Feature-Aware Network (HFAN) to improve tracking performance. The design of HFAN mainly includes two pivotal components: Feature-Enhanced Unit (FEU) and Hierarchical Feature-Aware Unit (HFAU). FEU enhances the richness and discriminative power of target representations by reinforcing features from the templates and search regions. HFAU establishes comprehensive dependencies among multi-level features to capture the changing characteristics of targets across different spatial hierarchies. Finally, a Siamese tracker called HFANTrack is proposed to improve tracking accuracy and robustness in complex scenarios. Extensive experimental results show that our method achieves competitive tracking performance with a real-time speed of 49.3fps compared to state-of-the-art methods. © 2024 IEEE.

关键词： Complex networks

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Influence of Contextual Information on Bengali-English Forward and Backward Transliteration Using Binary Coding 1

Influence of Contextual Information on Bengali-English Forwa...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Fahad, Md. Tipu, Amrita Das Mandal, Ashis Kumar Chakraborty, Basabi Hajee Mohammad Danesh Science and Technology University Department of Computer Science and Engineering Dinajpur Bangladesh University of Saskatchewan Department of Computer Science Saskatoon Canada Iwate Prefectural University Research and Regional Cooperation Division Takizawa Japan Madanapalle Institute of Technology and Science School of Computing Madanapalle India

ISBN: (纸本)9798350328042

Transliteration of words across different scripts carries importance for both social media platforms and messaging apps. It preserves the phonetic meaning of words across various writing systems. This study evaluates the influence of contextual information within words on the transliteration of Bengali and English language pair. It utilizes the Dakshina and NEWS 2018 datasets and binary encoding for feature representation. With the random forest classifier, the results demonstrate that using neighboring transliteration units as context yields the best performance on both datasets, obtaining the highest accuracy of 81.04%. These findings highlight the vital role of contextual information within words in enhancing the performance of transliteration. © 2023 IEEE.

关键词： Classification (of information)

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Exploring the Power of Deep Learning in Anomaly Detection: A Comprehensive Review and Analysis 12th

Exploring the Power of Deep Learning in Anomaly Detection: A...

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12th International Conference on Soft computing for Problem Solving, SocProS 2023

作者： Kumar, Gaurav Kumar, Ankit Pandy, Saroj Kumar Singh, Kamred Udham Singh, Teekam Kumar, Lalan Choudhury, Tanupriya Department of Computer Engineering and Applications GLA University UP Mathura India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Master of Computer Application GL Bajaj Institute of Technology and Management Greater Noida India Department of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9789819732913

This paper aims to provide a comprehensive overview of existing and state-of-the-art fraud and intrusion detection strategies, specifically focusing on incorporating neural networks. We begin by discussing the fundamental concept of data mining and its significance in detecting anomalies. By leveraging advanced machine learning and neural network techniques, we can effectively uncover subtle patterns and irregularities within complex datasets. We explore how the development of anomalies has enhanced our ability to identify and mitigate various threats, such as malware attacks and unlawful practices. Traditional detection strategies have proven effective, but as deep learning progresses, we uncover new opportunities and insights that can significantly enhance the accuracy and efficiency of anomaly detection systems. Throughout this study, we delve into the most cutting-edge approaches, ranging from broad neural networks to shallower architectures, and their applicability to fraud and intrusion detection. We analyze the strengths and limitations of these strategies, shedding light on their performance characteristics, scalability, and interpretability. By addressing these key issues, we aim to provide a comprehensive summary of the latest fraud and intrusion detection advancements driven by deep learning methodologies. This research contributes to the broader understanding of data mining's potential in uncovering anomalies and bolstering security measures across diverse domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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Owl: Differential-Based Side-Channel Leakage Detection for CUDA Applications

Owl: Differential-Based Side-Channel Leakage Detection for C...

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International Conference on Dependable Systems and Networks (DSN)

作者： Yu Zhao Wenjie Xue Weijie Chen Weizhong Qiang Deqing Zou Hai Jin National Engineering Research Center for Big Data Technology and System Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jinyinhu Laboratory Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350341058

ISBN: (纸本)9798350341065

Over the past decade, various methods for detecting side-channel leakage have been proposed and proven to be effective against CPU side-channel attacks. These methods are valuable in assisting developers to identify and patch side-channel vulnerabilities. Nevertheless, recent research has revealed the feasibility of exploiting side-channel vulnerabilities to steal sensitive information from GPU applications, which are beyond the reach of previous side-channel detection methods. Therefore, in this paper, we conduct an in-depth examination of various GPU features and present Owl, a novel side-channel detection tool targeting CUDA applications on NVIDIA GPUs. Owl is designed to detect and locate side-channel leakage in various types of CUDA applications. When tracking the execution of CUDA applications, we design a hierarchical tracing scheme and extend the A-DCFG (Attributed Dynamic Control Flow Graph) to address the massively parallel execution in CUDA, ensuring Owl's detection scalability. After completing the initial assessment and filtering, we conduct statistical tests on the differences in program traces to determine whether they are indeed caused by input variations, subsequently facilitating the positioning of side-channel leaks. We evaluate Owl's capability to detect side-channel leaks by testing it on Libgpucrypto, PyTorch, and nvJPEG. Meanwhile, we verify that our solution effectively handles a large number of threads. Owl has successfully identified hundreds of leaks within these applications. To the best of our knowledge, we are the first to implement side-channel leakage detection for general CUDA applications.

关键词： Target tracking Statistical analysis Filtering Scalability Graphics processing units Side-channel attacks Feature extraction

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Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

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Intelligent Automation & Soft computing 2024年第3期39卷 417-436页

作者： Muhammad S.Alam Farhan B.Mohamed Ali Selamat Faruk Ahmed AKM B.Hossain Department of Emergent Computing School of ComputingUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia Malaysia-Japan International Institute of Technology(MJIIT) Universiti Teknologi MalaysiaKuala Lumpur54100Malaysia Media and Game Innovation Centre of Excellence(MaGICX) Universiti Teknologi MalaysiaJohor Bahru81310Malaysia Department of Engineering Technology University of MemphisMemphisTN38152USA Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia Department of Information System and Cyber Security College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance *** scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and *** research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image *** annotated image dataset trains the proposed system and predicts the camera pose in *** system mainly improved the localization performance of indoor cameras by more accurately predicting the camera *** also recognizes the scene changes during the sequence and evaluates the effects of these *** system achieved high accuracy and real-time *** scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact ***,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

关键词： Camera pose estimation indoor camera localization real-time localization scene change detection simultaneous localization and mapping(SLAM)

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Location-invariant representations for acoustic scene classification

Location-invariant representations for acoustic scene classi...

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European Signal Processing Conference (EUSIPCO)

作者： Akansha Tyagi Padmanabhan Rajan School of Computing and Electrical Engineering Indian Institute of Technology Mandi

ISBN: (数字)9789082797091

ISBN: (纸本)9781665467995

High intra-class variance is one of the significant challenges in solving the problem of acoustic scene classification. This work identifies the recording location (or city) of an audio sample as a source of intra-class variation. We overcome this variation by utilising multi-view learning, where each recording location is considered as a view. Canonical correlation analysis (CCA) based multi-view algorithms learn a subspace where samples from the same class are brought together, and samples from different classes are moved apart, irrespective of the views. By considering cities as views, and by using several variants of CCA algorithms, we show that intra-class variation can be reduced, and location-invariant representations can be learnt. The proposed method demonstrates an improvement of more than 8% on the DCASE 2018 and 2019 datasets, when compared to not using the view information.

关键词： Training Image analysis Correlation Urban areas Signal processing algorithms Europe Acoustics

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Synchronization of Hyper-Lorenz System Based on DNA Strand Displacement

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2022年第3期19卷 1897-1908页

作者： Zou, Chengye Zhang, Qiang Wei, Xiaopeng School of Mathematics and Statistics of Anyang Normal University Anyang455000 China Dalian University Ministry of Education Key Laboratory of Advanced and Intelligent Computing Dalian116622 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116024 China

Lorenz system is depicted by chemical reaction equations of an ideal formal chemical reaction network, and a series of reversible reactions are added into chemical reaction network in order to construct a cluster of hyper-Lorenz system. DNA as a universal substrate for chemical dynamics can approximate arbitrary dynamical characteristics of ideal formal chemical reaction network through auxiliary DNA strands and displacement reactions. Based on Lyapunov's stableness theory, a novel synchronization strategy is proposed. A 6-dimensional hyper-Lorenz system is taken as examples for simulation and shows that DNA strands displacement reactions can implement the synchronization of ideal formal chemical reaction networks. Numerical simulations indicate that synchronization based on DNA strand displacement is robust to the detection of DNA strand concentration, control of reaction rate, and noise. © 2004-2012 IEEE.

关键词： Substrates

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Adaptive Dual Covariance Steering with Active Parameter Estimation

arXiv

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arXiv 2024年

作者： Knaup, Jacob W. Tsiotras, Panagiotis School of Interactive Computing College of Computing Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA30332-0250 United States School of Aerospace Engineering Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA30332-0150 United States

This work examines the optimal covariance steering problem for systems subject to unknown parameters that enter multiplicatively with the state and control, in addition to additive disturbances. In contrast to existing works, the unknown parameters are modeled as random variables and are estimated online. This work proposes the utilization of recursive least squares estimation for efficient parameter identification. A dual control problem is formulated in which the effect of the planned control policy on the parameter estimates is modeled and optimized for. The parameter estimates are then used to modify the pre-computed control policy online in an adaptive control fashion. Finally, the proposed approach is demonstrated in a vehicle control example with closed-loop parameter identification. © 2024, CC BY.

关键词： Parameter estimation

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VQGAN Text-to-Image Generation Enhancement using CLIP

VQGAN Text-to-Image Generation Enhancement using CLIP

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International Conference on Communication, computing and Internet of Things (IC3IoT)

作者： Banu Priya Prathaban R. Subash A. Ashwini D. Ferlin Deva Shahila R. Prasanna Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Computing Technologies School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Ramapuram Campus Chennai India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Text-to-image synthesis is a computer vision task that involves understanding and converting textual descriptions into corresponding and relevant images. Recently, Generative Adversarial Networks (GANs) and Contrastive Language-Image Pre-training (CLIP) have been employed to accomplish this objective. VQGAN comprises of a generator and a discriminator network that work together in a competitive manner to create realistic images based on input descriptions. In contrast, CLIP is a transformer-based language model that has been pre-trained using a diverse set of text and image data. Integrating the capabilities of both VQGAN and CLIP can lead to notable enhancements in text-to-image generation compared to using either technique individually. This abstract offers a concise overview of the latest progress in this area and emphasizes the potential advantages of leveraging a combination of GANs and CLIP for text-to-image synthesis.

关键词： Industries Computer vision Transformers Generative adversarial networks Generators Data models Telecommunication computing

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