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Modified Kelvin-Voigt fractional derivative model for viscoelasticity measurement in optical coherence elastography

Chinese Optics Letters 2025年第1期23卷 93-101页

作者： Chenming Yang Zhongliang Li Nan Nan Teng Liu Yaoli Luo Xiangzhao Wang Advanced Light Source and System R&D Center Department of Advanced Optical and Microelectronic EquipmentShanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027China

Optical coherence elastography(OCE)can quantitatively obtain the viscoelasticity of tissues using rheological models and is widely applied to the clinical diagnosis of ***,commonly used rheological models in OCE do not account for the distinctive dependence of high-frequency storage and loss moduli on frequency in tissues,which results in the rheological models failing to accurately measure the viscoelastic properties of *** this paper,a modified Kelvin-Voigt fractional derivative model is presented based on the power-law behavior of soft tissues and the dependence of high-frequency complex shear modulus on frequency in living *** the rheometer and OCE tests,the modified model can provide the prediction of the power-law relationship between the low-frequency shear viscosity and frequency;compared with the Kelvin-Voigt and Kelvin-Voigt fractional derivative models,the modified model has a higher goodness-of-fit(accuracy>96%)for the high-frequency storage moduli of gelatin ***,the proposed model can reduce the root mean square error of fit by approximately 83%for the high-frequency(1–128 kHz)storage modulus of the polydimethylsiloxane phantoms obtained from publicly available ***,the modified model accurately predicts the mechanical properties of biomimetic materials over a wide frequency range,with the potential to more accurately reflect pathological changes in tissues.

关键词： optical coherence tomography optical coherence elastography viscoelasticity rheological model

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Type II Solar Radio Burst Segmentation and Detection using Multi-Model Deep Learning Networks

Type II Solar Radio Burst Segmentation and Detection using M...

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Irish Signals and Systems Conference (ISSC)

作者： Jeremiah Scully Ronan Flynn Peter Gallagher Mark Daly Department of Computer and Software Engineering Technological University of the Shannon Athlone Ireland Astronomy & Astrophysics Section Dublin Institute of Advanced Studies Dublin Ireland

Type II Solar Radio Bursts (SRBs) are the result of particle acceleration by shock waves in the solar corona and interplanetary medium. The shocks are created by solar eruptions involving coronal mass ejections traveling at super-alfvenic speeds. The automatic detection, classification, and segmentation of such radio bursts is a challenge in solar radio physics due to their heterogeneous form. Large data rates produced by cutting-edge radio telescopes like the LOw-Frequency ARray (LOFAR) have made SRB detection and classification more feasible in recent years. In this study, we use a Generative Adversarial Network (GAN) to simulate Type II SRBs and then use this simulated data as a training set for a Mask R-CNN to detect and segment Type II SRBs automatically. Using this multi-model approach, we can accurately detect and segment Type II SRBs with a mean Average Precision (mAP) score of 78.90%.

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Early Retinal Degeneration Detection with Deep Faster CNNs

Early Retinal Degeneration Detection with Deep Faster CNNs

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International Conference on Advances in Information Technology (ICAIT)

作者： K. Priyadharshini Sindhusaranya Balraj JV Rama Kumar Deepak A. Vidhate Thilakavathy. P P. Vanathi Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Sona College of Technonolgy Salem India Computer Science and Engineering SRKR Engineering College Bhimavaram Andhra Pradesh Department of Information Technology Dr. Vithalrao Vikhe Patil College of Engineering Ahmednagar Maharashtra Computer science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai Department of Information Technology Adhiyamaan College of Engineering Hosur

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

The issue of retinogenesis probably constitutes a major public health threat which gives the reason to carry out its early detection for intervention purposes to be efficient. Throughout this investigation, we suggest a new technique which uses the faster CNNs for early detection of retina degradation. We set up a diverse data collection where OCT(optical coherence tomography), fundus photography and angiography modality were included. This collection covered the various stages of degeneration. The dataset was prepared for instance with resizing, normalizing, and data augmentation were used to improve share quality and diversity. Deeper and faster networks that were developed and trained using the backpropagation technique performed well and further optimized using Adam, with high accuracy of 94 in the test dataset. 7%. The strength of our results can be seen through evaluation metrics in detail like confusion matrices and classification reports. The model can distinguish various cases of retinal degeneration accurately which proves to have high precision and recall for mild and normal degeneration. Interpolation analysis of the model using Grad-CAM and occlusion sensitivity, giving the clue about the model's decision making process, setting a precedent for clinical interpretation. The enhanced cross-validated model on an independent dataset also arrays the model in a representational where it will be able to generalize in a diverse population and in different imaging conditions. The systematic approach which proved to be more accurate and resilient than the other techniques was the one which was compared to the mentioned ones. In conclusion, our study shows the possibility for application of the Deep Faster CNNs in identifying the early signs of retinal degeneration; the tool therefore becomes an important tool for enhancing patient outcomes and prevention of vision loss in the world at large.

关键词： Training Analytical models Technological innovation Accuracy Systematics Coherence Tomography Retina Data models Diseases

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Chaotic Waveform-based Signal Design for Noncoherent SWIPT Receivers

arXiv

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arXiv 2023年

作者： Mukherjee, Priyadarshi Psomas, Constantinos Krikidis, Ioannis Advanced Computing & Microelectronics Unit Indian Statistical Institute Kolkata India Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus

This paper proposes a chaotic waveform-based multi-antenna receiver design for simultaneous wireless information and power transfer (SWIPT). Particularly, we present a differential chaos shift keying (DCSK)-based SWIPT multiantenna receiver architecture, where each antenna switches between information transfer (IT) and energy harvesting (EH) modes depending on the receiver’s requirements. We take into account a generalized frequency-selective Nakagami-m fading model as well as the nonlinearities of the EH process to derive closed-form analytical expressions for the associated bit error rate (BER) and the harvested direct current (DC), respectively. We show that, both depend on the parameters of the transmitted waveform and the number of receiver antennas being utilized in the IT and EH mode. We investigate a trade-off in terms of the BER and energy transfer by introducing a novel achievable ‘success rate - harvested energy’ region. Moreover, we demonstrate that energy and information transfer are two conflicting tasks and hence, a single waveform cannot be simultaneously optimal for both IT and EH. Accordingly, we propose appropriate transmit waveform designs based on the application specific requirements of acceptable BER or harvested DC or both. Numerical results demonstrate the importance of chaotic waveform-based signal design and its impact on the proposed receiver architecture. © 2023, CC BY-NC-SA.

关键词： Energy harvesting

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SLAMSpoof: Practical LiDAR Spoofing Attacks on Localization Systems Guided by Scan Matching Vulnerability Analysis

arXiv

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arXiv 2025年

作者： Nagata, Rokuto Koide, Kenji Hayakawa, Yuki Suzuki, Ryo Ikeda, Kazuma Sako, Ozora Chen, Qi Alfred Sato, Takami Yoshioka, Kentaro Department of Electronics and Electrical Engineering Keio University Japan Department of Information Technology and Human Factors the National Institute of Advanced Industrial Science and Technology Japan Department of Computer Science University of California Irvine United States

Accurate localization is essential for enabling modern full self-driving services. These services heavily rely on map-based traffic information to reduce uncertainties in recognizing lane shapes, traffic light locations, and traffic signs. Achieving this level of reliance on map information requires centimeter-level localization accuracy, which is currently only achievable with LiDAR sensors. However, LiDAR is known to be vulnerable to spoofing attacks that emit malicious lasers against LiDAR to overwrite its measurements. Once localization is compromised, the attack could lead the victim off roads or make them ignore traffic lights. Motivated by these serious safety implications, we design SLAMSpoof, the first practical LiDAR spoofing attack on localization systems for self-driving to assess the actual attack significance on autonomous vehicles. SLAMSpoof can effectively find the effective attack location based on our scan matching vulnerability score (SMVS), a point-wise metric representing the potential vulnerability to spoofing attacks. To evaluate the effectiveness of the attack, we conduct real-world experiments on ground vehicles and confirm its high capability in real-world scenarios, inducing position errors of ≥4.2 meters (more than typical lane width) for all 3 popular LiDAR-based localization algorithms. We finally discuss the potential countermeasures of this attack. Code is available at https://***/Keio-CSG/slamspoof © 2025, CC BY.

关键词： Traffic signs

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RCM-Fusion: Radar-Camera Multi-Level Fusion for 3D Object Detection

RCM-Fusion: Radar-Camera Multi-Level Fusion for 3D Object De...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jisong Kim Minjae Seong Geonho Bang Dongsuk Kum Jun Won Choi Department of Electrical Engineering Hanyang University Seoul South Korea Department of Artificial Intelligence Hanyang University Seoul South Korea Graduate School of Mobility Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea Department of Electrical and Computer Engineering College of Liberal Studies Seoul National University Seoul Korea

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

While LiDAR sensors have been successfully applied to 3D object detection, the affordability of radar and camera sensors has led to a growing interest in fusing radars and cameras for 3D object detection. However, previous radar-camera fusion models could not fully utilize the potential of radar information. In this paper, we propose Radar-Camera Multi-level fusion (RCM-Fusion), which attempts to fuse both modalities at feature and instance levels. For feature-level fusion, we propose a Radar Guided BEV Encoder which transforms camera features into precise BEV representations using the guidance of radar Bird’s-Eye-View (BEV) features and combines the radar and camera BEV features. For instance-level fusion, we propose a Radar Grid Point Refinement module that reduces localization error by accounting for the characteristics of the radar point clouds. The experiments on the public nuScenes dataset demonstrate that our proposed RCM-Fusion achieves state-of-the-art performances among single frame-based radar-camera fusion methods in the nuScenes 3D object detection benchmark. The code will be made publicly available.

关键词： Three-dimensional displays Robot vision systems Radar detection Radar Object detection Transforms Cameras

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The Food Preservation using a Solar Dryer Monitoring System 4

The Food Preservation using a Solar Dryer Monitoring System

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4th IEEE International Conference on Smart Technologies in Computing, electrical and Electronics, ICSTCEE 2023

作者： Sugumaran, S. Sivasankaran, V. Vijayakumar, S. Nithya, A. Chitra, M.G. Department Of Ece Sreenivasa Institute Of Technology And Management Studies Andhra Pradesh Chittoor India School Of Electrical And Electronics Engineering Vit Bhopal University M.P Sehore India Department Of Ece Sreeenivasa Institute Of Technology And Management Studies Andhra Pradesh Chittoor India Department Of Ece Mrk Institute Of Technology Tamil Nadu Kattumannarkoil India Department Of Computer Science Mmes Women's Arts And Science College Ranipet Dist. Tamilnadu Melvisharam India

ISBN: (纸本)9798350384628

High-moisture foods like mango, chili, meat etc., are not able to use long days because they are easily decomposed due to changing atmospheres. So the primitive peoples followed different methods to preserve the food and used it for a long time without poisoning. The sun dryer is the usual method to dry the food, but it will take more than one day. The food drying process is done with fossil fuels, but the increasing population is increasing the cost of the fuels. So, the food drying process needs an alternative method to dry the food. In this paper, the food is dried and preserved by solar energy. The overall processes are continuously monitored and preservation of the food by Arduino microcontroller with different sensors such as moisture sensor, LDR, Temperature sensor and cooling fans. The result of this work is better than other existing work and dried food at a low cost. © 2023 IEEE.

关键词： Fossil fuels

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Terahertz plasmonic resonances in coplanar graphene nanoribbon structures

arXiv

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arXiv 2024年

作者： Ryzhii, V. Tang, C. Otsuji, T. Ryzhii, M. Shur, M.S. Research Institute of Electrical Communication Tohoku University Sendai980-8577 Japan Frontier Research Institute for Interdisciplinary Studies Tohoku University Sendai980-8577 Japan Department of Computer Science and Engineering University of Aizu Aizu-Wakamatsu965-8580 Japan Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

We analyze plasmonic oscillations in the coplanar graphene nanoribbon (GNR) structures induced by the applied terahertz (THz) signals and calculate the GNR impedance. The plasmonic oscillations in the GNR structures are associated with the electron and hole inductances and the lateral inter-GNR capacitance. A relatively low inter-GNR capacitance enables the resonant excitation of the THz plasmonic oscillations in the GNR structures with long GNRs. The GNR structures under consideration can be used in different THz devices as the resonant structures incorporated in THz detectors, THz sources using resonant-tunneling diodes, photomixers, and surface acoustic wave sensors. © 2024, CC BY.

关键词： Graphene

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Advancement of Artificial Intelligent and Its Impact on Medicinal Treatment and Pharmaceutical Industries 9

Advancement of Artificial Intelligent and Its Impact on Medi...

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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Jenifer, A. Ahmed, Syed Imran Sangeetha, G. Chakkaravarthy, A. Prabhu Kumar, Ashok Bakhare, Ruhi Rmk Engineerinh College Department of Electrical And Electronics Engineering Tamil Nadu Kavaraipettai601206 India Mj College Department of Electronics And Communication Engineering Telangana Hyderabad500053 India R.M.K College of Engineering And Technology Department of Artificial Intelligence And Data Science Tamil Nadu Thiruvallur India Srm Institute of Science And Technology Department of Networking And Communications Tamil Nadu Kattankulathur603203 India Banasthali Vidyapith Department of Computer Science Rajasthan 304022 India Dr. Ambedkar Institute of Management Studies And Research Department of Cse Deekshbhoomi Maharashtra 440010 India

ISBN: (纸本)9798350365092

In an unanticipatedly short period, a patient may seek advice from a computer prior to seeing a doctor, thus establishing the' standard' medical procedure of the future. Thanks to developments in artificial intelligence (AI), the days of misdiagnosing illnesses and treating their symptoms instead of their underlying causes may soon be over. It's possible that something like this will occur soon. To accommodate a complete 3D representation of an organ, imagine how much space would need to be cleared from your laptop or how many years' worth of blood pressure records would need to be eliminated. The image would require both of these things to be made possible. More uses of artificial intelligence and high performance data-driven medicine are made possible by the growing body of data generated in clinics and preserved in electronic medical records as a result of routine testing and medical imaging. These applications have led to the development of methods used by researchers and medical practitioners to address clinical concerns throughout time, and this trend is anticipated to continue. © 2024 IEEE.

关键词： Artificial intelligence

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Communication-Efficient Federated Learning in Drone-Assisted IoT Networks: Path Planning and Enhanced Knowledge Distillation Techniques

Communication-Efficient Federated Learning in Drone-Assisted...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Gad Gad Aya Farrag Zubair Md Fadlullah Mostafa M. Fouda Department of Computer Science Lakehead University Thunder Bay Canada Department of Computer Science Western University London Canada Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA

As 5G and beyond networks continue to proliferate, intelligent monitoring systems are becoming increasingly prevalent. However, geographically isolated regions with sparse populations still face difficulties in accessing these technologies due to infrastructure deployment challenges. Additionally, the high cost and unreliability of satellite Internet services make them less appealing. This paper studies the challenges of drone-aided networks and presents a communication-efficient Federated Learning (FL) system on a drone-aided Internet of Things (IoT) network to facilitate health analysis in rural areas over LoRa wireless links. The proposed approach consists of two primary components. Firstly, optimizing the drone’s trajectory is theoretically formulated as a modified version of the Traveling Salesman Problem (TSP), with the Self-Organizing Map (SOM) algorithm employed for effective route planning. Secondly, the Knowledge Distillation (KD)-based FL algorithm is utilized to reduce communication overhead by leveraging soft labels. The quality of drone routes generated by the SOM is evaluated on multi-scale maps with pre-determined optimal paths. The experiments reveal SOM’s ability to accurately represent node topologies and yield cost-effective Hamiltonian cycles. The KD-based FL proves to be more efficient in terms of communication than FedAvg as the former exchanges soft labels while the latter exchanges model weights, thus reducing drone waiting time and battery consumption. We showcase the performance of our KD-based FL algorithm using Human Activity Recognition (HAR) datasets, illustrating a communication-efficient alternative for distributed learning, offering competitive performance leveraging a shared dataset for knowledge transfer among IoT devices.

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