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arXiv 2024年

作者： Xiao, Youshen Liao, Sheng Tian, Xuanyang Zhang, Fan Dong, Xinlong Jiang, Yunhui Chen, Xiyu Sun, Ruixi Zhang, Yuyao Gao, Fei The School of Information Science and Technology ShanghaiTech University Shanghai201210 China The School of Information Science and Technology Shanghai Engineering Research Center of Intelligent Vision and Imaging ShanghaiTech University Shanghai201210 China The School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China The Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China

Acoustic-Resolution Photoacoustic Microscopy (AR-PAM) has shown potential in subcutaneous vascular imaging. However, the spatial resolution of its images is limited by the system’s Point Spread Function (PSF). To improve resolution, various deconvolution-based methods can be employed. Traditional deconvolution methods, such as Richardson-Lucy deconvolution and model-based deconvolution, use the PSF as prior knowledge to enhance spatial resolution. However, accurately measuring the system’s PSF is challenging, leading to the commonly used blind deconvolution methods, which often suffer from inaccurate deconvolution. Another challenge of AR-PAM is the long scanning time. To speed up the image acquisition, down-sampling can be used to reduce scanning time. Subsequently, interpolation methods are applied to recover the high-resolution image from down-sampled image;however, traditional interpolation methods are insufficient for high-fidelity image recovery, especially under high under-sampling conditions. In this work, we propose a method based on Implicit Neural Representations (INR) to address both of these issues: unknown PSF and under-sampled image recovery. By using INR, we learn a continuous mapping from spatial positions to initial acoustic pressure, effectively compensating for the discretization of the image space and enhancing the resolution of AR-PAM. Specifically, we set the PSF as learnable parameter to address the issue of inaccurate PSF measurement. We qualitatively and quantitatively evaluated the proposed method on simulated vascular data, demonstrating superior performance in metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) compared to traditional methods. Additionally, we showed qualitative improvements on leaf vein data and in vivo mouse brain microvasculature. © 2024, CC BY.

关键词： Photoacoustic microscopy

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Identification of Two Distinct Antiferromagnetic Phases in the Au-Al-Gd Quasicrystal Approximant

arXiv

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arXiv 2025年

作者： Labib, Farid Sugimoto, Takanori Takakura, Hiroyuki Ishikawa, Asuka Tamura, Ryuji Research Institute of Science and Technology Tokyo University of Science Tokyo125-8585 Japan Center for Quantum Information and Quantum Biology Osaka University Osaka Toyonaka560-8531 Japan Advanced Science Research Center Japan Atomic Energy Agency Ibaraki Tokai319-1195 Japan Hyogo Kobe650-0047 Japan Division of Applied Physics Faculty of Engineering Hokkaido University Sapporo060-8628 Japan Department of Materials Science and Technology Tokyo University of Science Tokyo125-8585 Japan

The structural and physical properties of two single-grain Au-Al-Gd 1/1 approximant crystals (ACs) with analyzed compositions Au73.67Al12.24Gd14.09 and Au74.67Al11.35Gd13.98 were thoroughly investigated. The two variants are iso-structural (cubic, space group: Im3̅), undergoing sharp antiferromagnetic (AFM) transitions at TN = 9.6 K and TN = 8.3 K, respectively. Specific heat measurements in both samples evidenced sharp jump at TN, associated with the AFM transition, accompanied by broad Schottky-type anomalies at lower temperatures. Strikingly, the two 1/1 ACs exhibited markedly different responses to applied magnetic fields: one displayed a pronounced metamagnetic anomaly while the other did not. In this regard, variational calculations with adiabatic transformation of the magnetic field in realistic structural models identified the existence of two possible AFM phases: the cuboc phase with a metamagnetic anomaly and the cluster Néel phase without it, thereby clearly revealing the possible magnetic structures of the present AFM variants. These findings have led to the establishment of a comprehensive magnetic phase diagram for Heisenberg-type 1/1 ACs. This work not only advances our understanding of magnetic phase transitions in these complex systems but also suggests the existence of a broader spectrum of unexplored magnetic states in Tsai-type materials. Copyright © 2025, The Authors. All rights reserved.

关键词： Metamagnetism

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New attribute-based encryption schemes with anonymous authentication and time limitation in fog computing

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Concurrency and Computation: Practice and Experience 2023年第21期35卷

作者： Hafizpour, Hadis Shiri, Mohammad Ebrahim Rahmani, Amir Masoud Department of Computer Engineering Borujerd Branch Islamic Azad University Borujerd Iran Department of Mathematics and Computer Sciences Amir Kabir University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan

Fog computing is a suitable platform for the Internet of Things (IoT). However, it faces threats that pose security and privacy challenges. This article proposes two new, faster, and more secure schemes based on attribute-based encryption (ABE) address some security concerns. The new schemes are Cipher text-policy-ABE-anonymous authentication (CP-ABE-AA) and key-policy-ABE-anonymous authentication (KP-ABE-AA), providing more secure communications and anonymous recognition between servers and users employing registration center (RC). Users and servers register with RC and receive time parameter t and aliases to build an access tree. During the valid time, t a more secure transfer is established. After each t has expired, the servers and users obtain new aliases. RC provides anonymous authentication, and t determines the key's validity period. For simulation, a multi-paradigm programming language, Rust, was installed on Ubuntu 20. Compared to five existing schemes, the simulation results indicated that, on average, CP-ABE-AA has a 5.48% reduction time for key generation but an increased time of 11.28% in encryption and a reduction time of 11.33% in decryption. Also, compared to two other schemes, KP-ABE-AA produced an average time increase of 17.31% for key generation, but encryption and decryption times were reduced by 17.31% and 9.57%. © 2023 John Wiley & Sons, Ltd.

关键词： Fog computing

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Scalable Network-Assisted Full-Duplex Cell-Free Massive MIMO With Limited Fronthaul Capacity

Scalable Network-Assisted Full-Duplex Cell-Free Massive MIMO...

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IEEE Conference on Vehicular Technology (VTC)

作者： Koushi Okui Kengo Ando Giuseppe Abreu Koji Ishibashi Advanced Wireless & Communication Research Center (AWCC) The University of Electro-Communications Tokyo Japan School of Computer Science and Engineering Constructor University Campus Ring 1 Bremen Germany

This paper proposes a scalable network-assisted full-duplex (NAFD) cell-free massive multiple-input multiple-output (CF-mMIMO) system that achieves high spectral efficiency (SE) while reducing the fronthaul load by forming clusters of access points (APs) exclusively for uplink and downlink based on user equipment (UE) requirements, respectively. Specifically, we propose AP clustering techniques based on convex optimization and Hungarian algorithm, along with downlink transmit power control suitable for proposed AP clustering. Numerical results demonstrate that our proposed approach achieves higher SE than conventional scalable TDD CF-mMIMO, NAFD CF-mMIMO and small cell with dynamic time division duplex (TDD), under fronthaul capacity limitations.

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Cache-It: A Distributed Architecture for Proactive Edge Caching in Heterogeneous Iot Scenarios

SSRN

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SSRN 2023年

作者： Zyrianoff, Ivan Gigli, Lorenzo Montori, Federico Sciullo, Luca Kamienski, Carlos Di Felice, Marco Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems "Ercole De Castro" University of Bologna Italy SP Santo André Brazil

The Cloud-to-Things (C2T) continuum combines the proximity of edge infrastructure to the devices with cloud resources to optimize data processing and response time in the Internet of Things (IoT). Proactive edge caching is a potential solution for meeting latency constraints in C2T environments, enabling efficient data processing and storage while reducing redundant computation and cost. However, while 5G/6G infrastructural aspects and caching strategies are extensively studied, no frameworks facilitate the design and deployment of caching strategies or addresses IoT unique requirements. This paper proposes CACHE-IT, a proactive edge caching framework that decouples the caching strategy algorithm from the underlying architecture, enabling customization based on application-specific requirements. Through extensive simulations, we analyzed the impact of different configurations on system metrics and verified that the CACHE-IT positively impacts the system latency and hit rate. By implementing a scenario-specific caching strategy, we illustrate the CACHE-IT deployment in a real-world Structure Health Monitoring (SHM) system. The evaluation demonstrates that it CACHE-IT custom strategy outperforms traditional caching mechanisms. © 2023, The Authors. All rights reserved.

关键词： Internet of things

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Challenges and Limitations in Speech Recognition Technology:A Critical Review of Speech Signal Processing Algorithms,Tools and Systems

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computer Modeling in engineering & sciences 2023年第5期135卷 1053-1089页

作者： Sneha Basak Himanshi Agrawal Shreya Jena Shilpa Gite Mrinal Bachute Biswajeet Pradhan Mazen Assiri Computer Science and Information Technology Department Symbiosis Institute of TechnologySymbiosis International(Deemed University)Pune412115India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International(Deemed University)Pune412115India Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering and Information TechnologyUniversity of Technology SydneyNew South Wales2007Australia Center of Excellence for Climate Change Research King Abdulaziz UniversityJeddah21589Saudi Arabia Earth Observation Centre Institute of Climate ChangeUniversity Kebangsaan MalaysiaBangiSelangor43600Malaysia

Speech recognition systems have become a unique human-computer interaction(HCI)*** is one of the most naturally developed human abilities;speech signal processing opens up a transparent and hand-free computation *** paper aims to present a retrospective yet modern approach to the world of speech recognition *** development journey of ASR(Automatic Speech Recognition)has seen quite a few milestones and breakthrough technologies that have been highlighted in this paper.A step-by-step rundown of the fundamental stages in developing speech recognition systems has been presented,along with a brief discussion of various modern-day developments and applications in this *** review paper aims to summarize and provide a beginning point for those starting in the vast field of speech signal *** speech recognition has a vast potential in various industries like telecommunication,emotion recognition,healthcare,etc.,this review would be helpful to researchers who aim at exploring more applications that society can quickly adopt in future years of evolution.

关键词： Speech recognition automatic speech recognition(ASR) mel-frequency cepstral coefficients(MFCC) hidden Markov model(HMM) artificial neural network(ANN)

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An Explainable AutoML-Driven Meta-Learning Scheme for Intrusion Prevention in Zero-Touch Networks Within Carbon Intelligent IIoT

引用

IEEE Internet of Things Journal 2025年

作者： Islam, Anik Karimipour, Hadis Gadekallu, Thippa Reddy University of Calgary Schulich School of Engineering Deptartment of Electrical and Software Engneering CalgaryT2N1N4 Canada Zhejiang A and F University The College of Mathematics and Computer Science Hangzhou311300 China Lovely Professional University Division of Research and Development Phagwara India Chitkara University Center of Research Impact and Outcome Punjab Rajpura140401 India

Carbon Intelligent Industrial Internet of Things (IIoT) systems are critical for achieving sustainable industrial automation but face challenges such as scalability, operational complexity, and security vulnerabilities. Zero-Touch Networks (ZTN), with their autonomous management capabilities, offer solutions to operational challenges but remain vulnerable to sophisticated cyber intrusions due to their high level of autonomy and interconnectedness. While Artificial Intelligence (AI), especially Deep Learning (DL), shows potential in intrusion detection, current approaches often encounter obstacles such as insufficient datasets, challenges in automated data preprocessing, and a lack of transparency. This paper introduces an AutoML-enabled Meta Learning-based Intrusion Prevention Scheme designed specifically for ZTN within Carbon Intelligent IIoT. The proposed framework integrates AutoML and meta-learning to streamline data preprocessing and improve model adaptability in dynamic and evolving threat environments. To ensure transparency, an Integrated Gradient-based Explainable AI (XAI) mechanism is employed, offering insights into the impact of individual features on model predictions, thereby addressing concerns related to trust and accountability in industrial applications. Experimental evaluations demonstrate the framework’s effectiveness in enhancing intrusion prevention, bolstering security, and improving transparency for ZTN in carbon intelligent IIoT, providing a comprehensive solution to prevailing challenges. © 2014 IEEE.

关键词： Network intrusion

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Experimental evidence of crystal-field, Zeeman-splitting, and spin-phonon excitations in the quantum supersolid Na2BaCo(PO4)2

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Physical Review B 2025年第15期111卷 155129-155129页

作者： Ghulam Hussain Jianbo Zhang Man Zhang Lalit Yadav Yang Ding Changcheng Zheng Sara Haravifard Xiawa Wang Division of Natural and Applied Sciences Duke Kunshan University Kunshan Jiangsu 215316 China Center for High-Pressure Science and Technology Advanced Research Beijing 100094 China Department of Physics Duke University Durham North Carolina 27708 USA Department of Mechanical Engineering and Materials Science Duke University Durham North Carolina 27708 USA

Drawing inspiration from the recent breakthroughs in the Na2BaCo(PO4)2 quantum magnet, renowned for its spin supersolidity phase and its potential for solid-state cooling applications, our study delves into the interplay among lattice, spin, and orbital degrees of freedom within this compound. Using temperature-, field-, and pressure-dependent Raman scattering techniques, we present experimental evidence revealing crystal-electric-field (CEF) excitations, alongside the interplay of CEF-phonon interactions. We performed density functional theory calculations for the phonon frequencies and compared them with the experimentally observed modes. In addition, our experiments elucidated electronic transitions from j1/2 to j3/2 and from j1/2 to j5/2, with energy levels closely aligned with theoretical predictions based on point-charge models. Moreover, the application of a magnetic field and pressure revealed Zeeman splittings characterized by Landé-g factors as well as the CEF-phonon resonances. The anomalous shift in the coupled peak at low temperatures originated from the hybridization of CEF and phonon excitations due to their close energy proximity and shared symmetry. These findings constitute a significant step towards unraveling the fundamental properties of this exotic quantum material for future research in fundamental physics or engineering application.

关键词： Degrees of freedom (mechanics)

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Visual Explanations of ResNet 101 for Blister Package Classification

Visual Explanations of ResNet 101 for Blister Package Classi...

引用

2022 research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

作者： Hnoohom, Narit Maitrichit, Nagorn Wongpatikaseree, Konlakorn Yuenyong, Sumeth Mekruksavanich, Sakorn Jitpattanakul, Anuchit Faculty of Engineering Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Nakorn Pathom Thailand University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Faculty of Applied Science King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

This paper presents an efficient approach to generate visual explanations from a ResNet 101 model for identification of medication. The blister package dataset is used to train a deep learning model built on the PyTorch framework's ResNet 101 pre-trained model. Visual inspections and a quantitative localization benchmark demonstrate that the model approach correctly identifies the critical components of blister packs for medicine identification. A Gradient-weighted Class Activation Mapping (Grad-CAM) method is used to extract the feature map, and then the attention mechanism is utilized to extract the high-level attention maps, that emphasizes the part of the image that is significant to the target class, which can be considered as a visual representation. The experimental results indicated that the Grad-CAM achieved the better visualization and interpretation of ResNet 101 in blister package classification. © 2022 IEEE.

关键词： Deep learning

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A Deep Learning-based Model for Human Activity Recognition using Biosensors embedded into a Smart Knee Bandage 9th

A Deep Learning-based Model for Human Activity Recognition u...

引用

9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

Today, biosensors are effectively utilized in illness detection, prevention, rehabilitation, patient medical monitoring, and personal health promotion. The advancement of biosensing technology offers rigorous methods for measuring an individual's motor function, which contributes significantly to restoring motor function. An smart knee bandage was recently designed to analyze and decode diverse biosignals recorded by knee bandage-integrated biosensors. This research aims to examine human activity recognition (HAR) using biosignals gathered by a smart knee bandage. To accomplish this, a deep residual neural network was established for biosensor-based HAR. For performance assessment, we utilized a significant public biosensor dataset known as the CSL-SHARE dataset, which gathers electromyography (EMG), electrogoniometer (EGM), and inertial measurement unit (IMU) signals of individuals completing 22 simplex and complicated everyday tasks. Five-fold cross-validation was used to train and assess deep learning models. According to our findings, integrating features derived from the EMG, EGM, and IMU signal produced the maximum accuracy of 91.60% and the highest F1-score of 92.13%. © 2022 The Authors. Published by Elsevier B.V.

关键词： Biosensors

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