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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Yanzhou Al-Zogbi, Lidia Liu, Jiawei Shepard, Lauren Ghazi, Ahmed Tokuda, Junichi Leonard, Simon Krieger, Axel Iordachita, Iulian Johns Hopkins University Department of Mechanical Engineering The Laboratory for Computational Sensing and Robotics BaltimoreMD United States Johns Hopkins University Laboratory of Computational Sensing and Robotics BaltimoreMD United States Johns Hopkins University Brady Urological Institute BaltimoreMD United States Harvard Medical School Department of Radiology Brigham and Women's Hospital BostonMA United States Johns Hopkins University Laboratory for Computational Sensing and Robotics Department of Computer Science BaltimoreMD United States

ISBN: (纸本)9798350371499

Prostate cancer diagnosis continues to encounter challenges, often due to imprecise needle placement in standard biopsies. Several control strategies have been developed to compensate for needle tip prediction inaccuracies, however none were compared against each other, and it is unclear whether any of them can be safely and universally applied in clinical settings. This paper compares the performance of two resolved-rate controllers, derived from a mechanics-based and a data-driven approach, for bevel-tip needle control using needle shape manipulation through a template. We demonstrate for a simulated 12-core biopsy procedure under model parameter uncertainty that the mechanics-based controller can better reach desired targets when only the final goal configuration is presented even with uncertainty on model parameters estimation, and that providing a feasible needle path is crucial in ensuring safe surgical outcomes when either controller is used for needle shape manipulation. © 2024 IEEE.

关键词： Biopsy

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Cyber Deception System based on Monte Carlo Simulation in the Mobile Edge Computing (MEC)

Cyber Deception System based on Monte Carlo Simulation in th...

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2023 IEEE International Conference on computer, Information, and Telecommunication Systems, CITS 2023

作者： Kabdjou, Joelle Tagne, Elie Fute Rawat, Danda B. Acosta, Jaime Kamhoua, Charles University of Dschang Department of Mathematics and Computer Science Dschang Cameroon University of Buea Department of Computer Engineering Dschang Buea Cameroon Howard University Department of Electrical Engineering and Computer Science Washington United States Network Security Branch Devcom Army Research Laboratory Adelphi United States

ISBN: (纸本)9798350336092

The frequency of malicious activities by cyber attackers is on the rise, posing a significant challenge to counter cyber attacks in the Mobile Edge Computing (MEC) environment. The vulnerability of wireless networks, which serve as the primary communication media for MEC servers and IoT applications, is due to their open nature of communication. This work proposes a MEC architecture that utilizes Network Function Virtualization (NFV) and Service Function Chain (SFC) to combat cyber adversaries. Additionally, we evaluate the performance of a cyber deception system, which leverages a software-defined networking (SDN) controller and decision- maker to continuously monitor incoming MEC traffic and direct cyber adversaries to a deception MEC server. The deception MEC server collects information about cyber adversaries' intent, capabilities, and potential damage to the system, thus securing legitimate users from cyber attacks. The proposed approach was evaluated using Monte Carlo simulations, and numerous results were obtained which demonstrate the effectiveness of cyber deception in MEC. © 2023 IEEE.

关键词： Network function virtualization

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A Bicriterion Concentration Inequality and Prophet Inequalities for k-Fold Matroid Unions 16

A Bicriterion Concentration Inequality and Prophet Inequalit...

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16th Innovations in Theoretical computer science Conference, ITCS 2025

作者： Alon, Noga Gravin, Nick Pollner, Tristan Rubinstein, Aviad Wang, Hongao Weinberg, S. Matthew Zhang, Qianfan Department of Mathematics Princeton University NJ United States Schools of Mathematics and Computer Science Tel Aviv University Tel Aviv Israel Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics China Department of Management Science and Engineering Stanford University CA United States Department of Computer Science Stanford University CA United States Department of Computer Science Purdue University West LafayetteIN United States Department of Computer Science Princeton University NJ United States

ISBN: (纸本)9783959773614

We investigate prophet inequalities with competitive ratios approaching 1, seeking to generalize k-uniform matroids. We first show that large girth does not suffice: for all k, there exists a matroid of girth ≥ k and a prophet inequality instance on that matroid whose optimal competitive ratio is12. Next, we show k-fold matroid unions do suffice: we provide a prophet inequality with competitive ratio (Formula presented) for any k-fold matroid union. Our prophet inequality follows from an online contention resolution scheme. The key technical ingredient in our online contention resolution scheme is a novel bicriterion concentration inequality for arbitrary monotone 1-Lipschitz functions over independent items which may be of independent interest. Applied to our particular setting, our bicriterion concentration inequality yields "Chernoff-strength" concentration for a 1-Lipschitz function that is not (approximately) self-bounding. © Noga Alon, Nick Gravin, Tristan Pollner, Aviad Rubinstein, Hongao Wang, S. Matthew Weinberg, and Qianfan Zhang.

关键词： Choquet integral

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Tonic and Event-Related Phasic Transcutaneous Auricular Vagus Nerve Stimulation Alters Pupil Responses in the Change-Detection Task

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Neuromodulation 2025年第4期28卷 652-662页

作者： Chen, Jingxin Ke, Yufeng Ni, Guangjian Liu, Shuang Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin International Joint Research Centre for Neural Engineering Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin University Tianjin China Department of Biomedical Engineering College of Precision Instrument and Optoelectronics Engineering Tianjin University Tianjin China Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin China

Background: Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a potential modulator of cognitive behavior that activates the locus coeruleus-noradrenaline (LC-NA) system. Previous studies explored both phasic and tonic taVNS by investigating their impact on LC-NA markers such as pupil dilation and heart rate variability (HRV). Objective: Inconsistencies persist in the identification of reliable markers for assessing the effects of taVNS on noradrenergic activity. Furthermore, it remains unclear whether the effects of taVNS extend beyond pure vagal nerve responses, particularly in specific cognitive domains such as working memory. In the present study, we investigated the effects of taVNS on working memory capacity and LC-NA markers using a change-detection task. Materials and Methods: Twenty-two healthy, right-handed university students participated in a sham-controlled, randomized cross-over experiment with four sessions. We applied two types of phasic and event-related stimulation (Pre-event and Event-synchronous), tonic stimulation (Pre-task), and sham stimulation across different sessions. Pupil size and electrocardiogram data were recorded during the tasks. Results: taVNS did not significantly modulate behavioral performance on the change-detection task, specifically working memory capacity. However, both tonic and event-related phasic taVNS significantly influenced the pupillary response during the task. In addition, the Pre-task condition of the taVNS affected the low-frequency parameter of HRV. Conclusions: Our findings suggest that tonic and event-related phasic taVNS may modulate noradrenergic activity, as evidenced by pupil responses and HRV changes during the change-detection task. This study provides new evidence regarding the impact of taVNS on cognitive tasks, thus supporting the development of noninvasive neuromodulation interventions. © 2025 International Neuromodulation Society

关键词： Heart rate variability (HRV) LC-NA pupil response pupillometry taVNS

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Image Generation of Tomato Leaf Disease Identification Based on Small-ACGAN

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computers, Materials & Continua 2023年第7期76卷 175-194页

作者： Huaxin Zhou Ziying Fang Yilin Wang Mengjun Tong School of Mathematics and Computer Science Zhejiang A&F UniversityHangzhou311300China Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research Hangzhou311300China School of Humanities and Law Zhejiang A&F UniversityHangzhou311300China Department of Computer Science and Engineering Lehigh UniversityPA18015USA

Plant diseases have become a challenging threat in the agricultural *** learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases ***,deep learning entails extensive data for training,and it may be challenging to collect plant *** though plant datasets can be collected,they may be uneven in *** a result,the problem of classification model overfitting *** study targets this issue and proposes an auxiliary classifier GAN(small-ACGAN)model based on a small number of datasets to extend the available ***,after comparing various attention mechanisms,this paper chose to add the lightweight Coordinate Attention(CA)to the generator module of Auxiliary Classifier GANs(ACGAN)to improve the image ***,a gradient penalty mechanism was added to the loss function to improve the training stability of the *** show that the proposed method can best improve the recognition accuracy of the classifier with the doubled *** AlexNet,the accuracy was increased by 11.2%.In addition,small-ACGAN outperformed the other three GANs used in the ***,the experimental accuracy,precision,recall,and F1 scores of the five convolutional neural network(CNN)classifiers on the enhanced dataset improved by an average of 3.74%,3.48%,3.74%,and 3.80%compared to the original ***,the accuracy of MobileNetV3 reached 97.9%,which fully demonstrated the feasibility of this *** general experimental results indicate that the method proposed in this paper provides a new dataset expansion method for effectively improving the identification accuracy and can play an essential role in expanding the dataset of the sparse number of plant diseases.

关键词： Deep learning ACGAN CA gradient penalty tomato diseases identification

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FlashMix: Fast Map-Free LiDAR Localization via Feature Mixing and Contrastive-Constrained Accelerated Training

FlashMix: Fast Map-Free LiDAR Localization via Feature Mixin...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Raktim Gautam Goswami Naman Patel Prashanth Krishnamurthy Farshad Khorrami Department of Electrical and Computer Engineering Control/Robotics Research Laboratory (CRRL) NYU Tandon School of Engineering Brooklyn NY USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Map-free LiDAR localization systems accurately localize within known environments by predicting sensor position and orientation directly from raw point clouds, eliminating the need for large maps and descriptors. However, their long training times hinder rapid adaptation to new environments. To address this, we propose FlashMix, which uses a frozen, scene-agnostic backbone to extract local point descriptors, aggregated with an MLP mixer to predict sensor pose. A buffer of local descriptors is used to accelerate training by orders of magnitude, combined with metric learning or contrastive loss regularization of aggregated descriptors to improve performance and convergence. We evaluate FlashMix on various LiDAR localization benchmarks, examining different regularizations and aggregators, and demonstrating its effectiveness for rapid and accurate LiDAR localization in real-world scenarios. The code is available at https://***/raktimgg/FlashMix.

关键词： Location awareness Training Measurement Point cloud compression Laser radar Accuracy Pose estimation Benchmark testing Mixers Convergence

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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AI-Powered Drone to Address Smart City Security Issues 37th

AI-Powered Drone to Address Smart City Security Issues

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37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Salama, Ramiz Al-Turjman, Fadi Culmone, Rosario Department of Computer Engineering Research Center for AI and IoT AI and Robotics Institute Near East University Nicosia Mersin 10 Nicosia Turkey Artificial Intelligence Engineering Department AI and Robotics Institute Near East University Mersin 10 Nicosia Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin 10 Kyrenia Turkey Computer Science Division School of Science and Technology University of Camerino Camerino62032 Italy

ISBN: (纸本)9783031286933

The idea of a dazzling metropolis has drawn interest from all across the world. New innovations like blockchain, IoT, artificial intelligence, robots, and many other things were added to it. Security is one of the top issues for people living in big cities, and everyone wants to feel completely secure when traveling around every single day. In this research, we will look into how CEOs in affluent cities use and value robots, especially in terms of security. To understand the robot security the board stream, some approaches and intricacies are used. Following that is a discussion of issues with urban security and the application of artificial intelligence to drones as a management tool. The use of cutting-edge technology, such as blockchain, to support smart urban community management is covered in the last part. The smart city idea and all of its benefits for local inspection are supported by robotic use. The idea of thriving urban areas is spreading around the world and is crucial in the context of developing economies. We examined how to leverage cutting-edge technologies in this project to make it feasible. Artificial intelligence, robotics breakthroughs, and blockchain technologies all have significant effects. This research highlights their significance to analysts and how they consider them when assessing prospects. This project will be very beneficial for analysts and experts in a relevant subject. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Drones

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Optimizing Medical Imaging: High-Performance Hardware for Image Processing, Machine Learning 24

Optimizing Medical Imaging: High-Performance Hardware for Im...

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7th International Conference on Digital Medicine and Image Processing, DMIP 2024

作者： Kalyan Chakravarthy, N.S. Balapanur, Mouli Chandra Pandian, Arun Nambi Pulikanti, Jyothi Prasad, D. Syed Masood, Jafar Ali Ibrahim High-Performance Computing Research Laboratory QIS College of Engineering and Technology Andhra Pradesh Ongole India Department of Internet of Things School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798400709586

The demand for high-performance hardware solutions for machine learning tasks is growing as medical imaging evolves. In this paper, we will focus on the latest hardware advanced technologies: GPUs, TPUs and FPGAs that can be used for image processing optimization in medical imaging such as brain tumor segmentation or chest X-ray classification. We test these devices on the BraTS and ChestX-ray14 datasets to show how running models quicker increases diagnostic performance. The study offers a comprehensive performance and energy efficiency evaluation of all the considered hardware solutions when training deep learning models for medical imaging, providing clinical guidelines to choose appropriate new-generation accelerators before engaging in expensive leasing contracts. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： GPUS

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A 2.2-3.6 GHz CMOS Reconfigurable Fourth-Order Bandpass Filter With Compact Size and High Selectivity Using Transformer-Type Resonators

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IEEE Transactions on Microwave Theory and Techniques 2023年第1期71卷 218-229页

作者： Li, Kun Liu, Bin Chi, Pei-Ling Wang, Yong Yang, Tao University of Electronic Science and Technology of China School of Electronic Science and Engineering Chengdu Sichuan610054 China National Chiao Tung University Department of Electrical and Computer Engineering Hsinchu300 Taiwan Nanhu Laboratory Research Center of Advanced Rf Chips and Systems Jiaxing Zhejiang314000 China

A compact reconfigurable CMOS bandpass filter (BPF) with high selectivity is proposed using transformer-type resonators in this article. The transformer-type resonator is constructed by a transformer loaded with parallel varactors for frequency tuning, and two varactors are then added across primary wind and secondary wind for coupling tuning. With the transformer-type structure, one resonator is formed inside another resonator, thus fully utilizing the chip area and resulting in a miniaturized structure. Magnetic and electrical couplings are formed within the transformer-type resonator and facilitate the bandwidth (BW) tuning. The quality factor (Q) of each resonator is improved by cross-coupled negative resistance structure. Meanwhile, two pairs of transformer-type resonators are placed side-by-side and naturally form a quadruplet structure;as a result, two transmission zeros (TZs) are introduced at each side of the passband thus significantly improving the selectivity and stopband rejection of the BPF. The proposed tunable on-chip BPF is manufactured by a commercial 55 nm bulk CMOS technology with a core area of 1.48 × 0.54 mm2. The filter center frequency (CF) can be tuned from 2.2 to 3.6 GHz, while the 3 dB fractional BW (FBW) can be tuned from 14% to 33%. The shape factor of the proposed filter is 1.4-1.9. © 1963-2012 IEEE.

关键词： Varactors

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