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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-Resolution

arXiv

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arXiv 2024年

作者： Jiang, Aiwen Wei, Zhi Peng, Long Liu, Feiqiang Li, Wenbo Wang, Mingwen School of Computer and Information Engineering Jiangxi Normal University Nanchang China School of Digital Industry Jiangxi Normal University Shangrao China Institute of Advanced Technology University of Science and Technology of China Hefei China Noah’s Ark Lab Huawei Technologies Ltd. Shenzhen China

Image super-resolution pursuits reconstructing high-fidelity high-resolution counterpart for low-resolution (LR) image. In recent years, diffusion-based models have garnered significant attention due to their capabilities with rich prior knowledge. The success of diffusion models based on general text prompts has validated the effectiveness of textual control in the field of text2image. However, given the severe degradation commonly presented in low-resolution images, coupled with the randomness characteristics of diffusion models, current models struggle to adequately discern semantic and degradation information within severely degraded images. This often leads to obstacles such as semantic loss, visual artifacts, and visual hallucinations, which pose substantial challenges for practical use. To address these challenges, this paper proposes to leverage degradation-aligned language prompt for accurate, fine-grained, and high-fidelity image restoration. Complementary priors including semantic content descriptions and degradation prompts are explored. Specifically, on one hand, image-restoration prompt alignment decoder is proposed to automatically discern the degradation degree of LR images, thereby generating beneficial degradation priors for image restoration. On the other hand, much richly tailored descriptions from pretrained multimodal large language model elicit high-level semantic priors closely aligned with human perception, ensuring fidelity control for image restoration. Comprehensive comparisons with state-of-the-art methods have been done on several popular synthetic and real-world benchmark datasets. The quantitative and qualitative analysis have demonstrated that the proposed method achieves a new state-of-the-art perceptual quality level, especially in real-world cases based on reference-free metrics. Related source codes and pre-trained parameters will be public in https://***/puppy210/DaLPSR. © 2024, CC BY.

关键词： Restoration

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Assessing Quantum Supremacy: Evaluating Its Potential to Revolutionize Cryptographic Practices and Strengthen Cybersecurity Frameworks Against Quantum Attacks

Assessing Quantum Supremacy: Evaluating Its Potential to Rev...

引用

Emerging Technologies and Innovation for Sustainability (EmergIN), International Conference on

作者： Gurpreet Singh Walia R. Saranya Uma Maheswari S Vijay Vasanth Aroulanandam Hemalatha R J Jerin Jose Lead enterprise security and networking architect NetApp Inc. Department of Information Technology Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Networking and Communications Department School of Computing SRM Institute of Science & Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.D. Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Sasi Institute of Technology & Engineering West Godavari Andra Pradesh India

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

The advent of quantum supremacy, in which quantum computers achieve superior performance compared to classical systems on certain tasks, will have far-reaching consequences for cybersecurity and encryption. Current cybersecurity frameworks rely on traditional cryptographic methods like RSA and ECC, which are in danger of being rendered obsolete by the advent of quantum computing. Researchers in this study investigate the pressing need for post-quantum cryptography (PQC) solutions that are resistant to quantum attacks and the ways in which quantum supremacy may transform cryptographic procedures. Examining quantum algorithms that can crack classical encryption protocols, such as Shor's and Grover's, the study brings attention to the weaknesses in the present cybersecurity system. The article also discusses new PQC methods that aim to protect data in a future where quantum technology is widely used. We conclude that current cryptographic systems could be compromised by quantum supremacy, but that cybersecurity on a global scale could be protected by the early adoption of cryptographic solutions that are resistant to quantum attacks. With the advent of quantum computing on the horizon, this study highlights the paramount significance of moving to post-quantum solutions to protect sensitive data.

关键词： computers Resistance Quantum computing Quantum advantage Quantum algorithm Transforms Elliptic curve cryptography Encryption Cryptography computer security

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Artificial Driving and Spectral Graph Theory for Automated Plant Leaf Disease Classification

Artificial Driving and Spectral Graph Theory for Automated P...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Chhavi Bajpai Manish Gaur J Roselin Suganthi Vinayagam S S. Kaliappan Manoj Senthil K Department of Computer Science & Engineering Centre for Advanced Studies Lucknow India Department of Computer Science & Engineering Institute of Engineering and Technology Lucknow India Department of ECE K. Ramakrishnan College of Engineering Trichirapalli India Department of Bioscience Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Research and Development Lovely Professional University Jalandhar India Department of Electronics and Communication Engineering Kongu Engineering College Erode India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Having a certified plant pathologist visually inspect the diseased leaves is now the gold standard for disease diagnosis in plants. This is because symptoms are present in the vast majority of plant illnesses. Because manual disease diagnosis is slow and results are highly dependent on the skill of the pathologist, this is an ideal domain for computer-aided diagnostic *** one of the suggested methods are preprocessing, step two is feature extraction, and step three is training the model. Pictures are acquired, then processed, enhanced, resized, segmented, and finally converted from RGB to HSI as part of the preprocessing phase. The topic of this study is the extraction of numerous features for the purpose of leaf disease categorization. Hexagons of colors are retrieved for classification purposes. The model was trained using Spectral Graph Theory. On average, this state-of-the-art technique achieves a higher accuracy rate of 92.48% than CNN and SVM.

关键词： Support vector machines Training Accuracy Computational modeling Crops Color Feature extraction Graph theory Medical diagnosis Diseases

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A Novel Platform to Control Biofouling in Pearl Oysters Cultivation

A Novel Platform to Control Biofouling in Pearl Oysters Cult...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Van-Nhan Tran Quan-Dung Pham Tan-Sang Ha Yue Him Wong Sai-Kit Yeung Division of Integrative Systems and Design School of Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong SAR Department of Computer Science and Engineering School of Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong SAR Institute for Advanced Study Shenzhen University Shenzhen Guangdong China

This paper presents a simple yet effective design of a platform to automate the task of shellfish aquaculture, specifically pearl oysters. Compared to traditional methods, our platform can eliminate the tedious task of cleaning the pearl oysters due to fouling. Inspired by the low and high tide characteristics of the intertidal zone, our platform employs an air-water displacement mechanism to periodically float pearl oysters above the water's surface, exposing fouling organisms to air and sunlight. While pearl oysters have developed the ability to stay alive during low tide, these fouling organisms cannot survive after prolonged exposure, thus preventing them from developing. Additionally, the platform provides an alternative approach to grow not only pearl oysters but also various types of shellfish, consequently benefiting the aquaculture industry. We introduce the design of the platform and provide a comprehensive analysis. We also demonstrate the practical deployment of the platform for cultivating pearl oysters.

关键词：

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Lightator: An Optical Near-Sensor Accelerator with Compressive Acquisition Enabling Versatile Image Processing

arXiv

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arXiv 2024年

作者： Morsali, Mehrdad Reidy, Brendan Najafi, Deniz Tabrizchi, Sepehr Imani, Mohsen Nikdast, Mahdi Roohi, Arman Zand, Ramtin Angizi, Shaahin Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ United States Department of Computer Science and Engineering University of South Carolina ColumbiaSC United States School of Computing University of Nebraska-Lincoln LincolnNE United States Department of Computer Science University of California Irvine IrvineCA United States Department of Electrical and Computer Engineering Colorado State University Fort CollinsCO United States

This paper proposes a high-performance and energy-efficient optical near-sensor accelerator for vision applications, called Lightator. Harnessing the promising efficiency offered by photonic devices, Lightator features innovative compressive acquisition of input frames and fine-grained convolution operations for low-power and versatile image processing at the edge for the first time. This will substantially diminish the energy consumption and latency of conversion, transmission, and processing within the established cloud-centric architecture as well as recently designed edge accelerators. Our device-to-architecture simulation results show that with favorable accuracy, Lightator achieves 84.4 Kilo FPS/W and reduces power consumption by a factor of ∼24× and 73× on average compared with existing photonic accelerators and GPU baseline. © 2024, CC BY-NC-SA.

关键词： Photonic devices

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SpatioTemporal Learning for Human Pose Estimation in Sparsely-Labeled Videos

arXiv

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arXiv 2025年

作者： Jiao, Yingying Wang, Zhigang Wu, Sifan Fan, Shaojing Liu, Zhenguang Xu, Zhuoyue Wu, Zheqi College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Zhejiang Gongshang University China School of Computing National University of Singapore Singapore The State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China

Human pose estimation in videos remains a challenge, largely due to the reliance on extensive manual annotation of large datasets, which is expensive and labor-intensive. Furthermore, existing approaches often struggle to capture long-range temporal dependencies and overlook the complementary relationship between temporal pose heatmaps and visual features. To address these limitations, we introduce STDPose, a novel framework that enhances human pose estimation by learning spatiotemporal dynamics in sparsely-labeled videos. STDPose incorporates two key innovations: 1) A novel Dynamic-Aware Mask to capture long-range motion context, allowing for a nuanced understanding of pose changes. 2) A system for encoding and aggregating spatiotemporal representations and motion dynamics to effectively model spatiotemporal relationships, improving the accuracy and robustness of pose estimation. STDPose establishes a new performance benchmark for both video pose propagation (i.e., propagating pose annotations from labeled frames to unlabeled frames) and pose estimation tasks, across three large-scale evaluation datasets. Additionally, utilizing pseudo-labels generated by pose propagation, STDPose achieves competitive performance with only 26.7% labeled data. Copyright © 2025, The Authors. All rights reserved.

关键词： Large datasets

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Machine Learning Applications in Traumatic Brain Injury: A Spotlight on Mild TBI

arXiv

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arXiv 2024年

作者： Ellethy, Hanem Chandra, Shekhar S. Vegh, Viktor School of Electrical Engineering and Computer Science University of Queensland QLD Australia The Centre for Advanced Imaging Australian Institute for Bioengineering and Nanotechnology University of Queensland QLD Australia ARC Training Centre for Innovation in Biomedical Imaging Technology QLD Australia

Traumatic Brain Injury (TBI) poses a significant global public health challenge, contributing to high morbidity and mortality rates and placing a substantial economic burden on healthcare systems worldwide. The diagnosis of TBI relies on clinical information along with Computed Tomography (CT) scans. Addressing the multifaceted challenges posed by TBI has seen the development of innovative, data driven approaches, for this complex condition. Particularly noteworthy is the prevalence of mild TBI (mTBI), which constitutes the majority of TBI cases where conventional methods often fall short. As such, we review the state-ofthe-art Machine Learning (ML) techniques applied to clinical information and CT scans in TBI, with a particular focus on mTBI. We categorize ML applications based on their data sources, and there is a spectrum of ML techniques used to date. Most of these techniques have primarily focused on diagnosis, with relatively few attempts at predicting the prognosis. This review may serve as a source of inspiration for future research studies aimed at improving the diagnosis of TBI using data driven approaches and standard diagnostic data . © 2024, CC BY-NC-ND.

关键词： Brain

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ConceptX: A Framework for Latent Concept Analysis

arXiv

引用

arXiv 2022年

作者： Alam, Firoj Dalvi, Fahim Durrani, Nadir Sajjad, Hassan Khan, Abdul Rafae Xu, Jia Qatar Computing Research Institute HBKU Research Complex Qatar Faculty of Computer Science Dalhousie University Canada School of Engineering and Science Steven Institute of Technology United States

The opacity of deep neural networks remains a challenge in deploying solutions where explanation is as important as precision. We present ConceptX, a human-in-the-loop framework for interpreting and annotating latent representational space in pre-trained Language Models (pLMs). We use an unsupervised method to discover concepts learned in these models and enable a graphical interface for humans to generate explanations for the concepts. To facilitate the process, we provide auto-annotations of the concepts (based on traditional linguistic ontologies). Such annotations enable development of a linguistic resource that directly represents latent concepts learned within deep NLP models. These include not just traditional linguistic concepts, but also task-specific or sensitive concepts (words grouped based on gender or religious connotation) that helps the annotators to mark bias in the model. The framework consists of two parts (i) concept discovery1 and (ii) annotation platform.2,3 Copyright © 2022, The Authors. All rights reserved.

关键词： Linguistics

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An energy efficient selection of cluster head and disease prediction in IoT based smart agriculture using a hybrid artificial neural network model

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Measurement: Sensors 2024年 32卷

作者： Ara, Tabassum Bhagappa Ambareen, Javeria Venkatesan, S. Geetha, M. Bhuvanesh, A. Department of Artificial Intelligence and Machine Learning HKBK College of Engineering Karnataka Bangalore India Department of Computer Science and Engineering Brindavan College of Engineering Karnataka Bangalore India School of Computing and Information Technology REVA University Karnataka Bangalore India Department of Computer Science and Engineering Ramco Institute of Technology Rajapalayam Tamil Nadu India Department of Electrical and Electronics Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India

The advent of new technologies paved the way for the proliferating productivity of agriculture and farming activities in a cost effective way. Internet of Things is the one among them and used for automatic smart farming applications. Hence to overcome the issues faced in the wide agriculture sector most of the farmers are now changed to IoT based applications and in the meantime many scientists also used artificial intelligence techniques to predict the diseases of the crops from the data collected using the IoT sensors in the agriculture farming. The sensors gather data from crops and fused with the local unit and forwarded to the cluster heads of network. To achieve the disease free crops and therein improve the productivity we have proposed a novel energy efficient IoT based smart farming approach, in which we have utilized K-means algorithm for the cluster formation and Adaptive Mud Ring optimization algorithm (AMR) for CH and energy efficient optimal path. The collected data via the CH are then stored in the cloud storage. Subsequently, the data are accessed via the proposed Hybrid Artificial Neural Network (HANN) to predict the diseases. The Artificial Neural Network (ANN) is hybridized using the Google Net in order to extract the exact features to predict the diseases. Performance validation is effectuated with Network Simulator-2 (NS-2) software and the results are compared with the state-of-art works. Several parameters such as energy efficiency, delay, network lifetime, accuracy and etc, are used for analyzing and our approach surpasses all the other approaches. © 2024 The Authors

关键词： Neural networks

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