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Utilization of resting-state electroencephalography spectral power in convolutional neural networks for classification of primary progressive aphasia

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NeuroImage: Reports 2025年第1期5卷 100242-100242页

作者： Quinn, Christina Craik, Alex Tessmer, Rachel Henry, Maya L. Dial, Heather Department of Communication Sciences and Disorders University of Houston 3871 Holman St Houston 77204 TX United States Department of Electrical and Computer Engineering University of Houston 4226 Martin Luther King Boulevard Houston 77204 TX United States Geriatric Research Education and Clinical Center VA Pittsburgh Healthcare System University Drive C Pittsburgh 15240 PA United States Department of Speech Language and Hearing Sciences The University of Texas at Austin 2504a Whitis Ave a1100 Austin 78712 TX United States Department of Neurology The University of Texas at Austin Dell Medical School 1501 Red River St Austin 78712 TX United States

We investigated relative power spectral density (PSD) in primary progressive aphasia (PPA) in delta, theta, alpha, and beta frequency bands in eyes open and closed resting-state electroencephalography (EEG). Our aims were to assess whether discernible differences could be observed between each PPA variant and to determine the utility of PSD for PPA classification when used as input to a convolutional neural network (CNN). Findings in the current study were similar to previous studies in logopenic PPA, with a significant increase in relative PSD in delta and theta bands and a significant reduction in the beta band (consistent with oscillatory slowing). We did not observe a significant increase in power for lower frequency bands or a reduction of power in higher frequency bands for semantic or nonfluent PPA, in contrast to what has been previously reported. In semantic PPA, evidence pointed to oscillatory speeding, not the slowing that was previously reported in a single-case study. In nonfluent PPA, spectral power fell between logopenic and semantic PPA, suggesting there is oscillatory slowing but to a lesser extent than logopenic PPA. The CNN was relatively successful in distinguishing PPA from healthy controls (F1 = 0.851). The CNN did not perform as well on four-way classification (lvPPA, svPPA, nfvPPA, controls;F1 = 0.586) but was significantly above chance. These results are promising and suggest that resting-state EEG may prove useful as a biomarker for PPA diagnosis. Potential factors underlying the differences between the findings of the current study and previous work are discussed. © 2025 The Author(s)

关键词： Classification Deep learning Power spectral density Primary progressive aphasia Resting-state electroencephalography

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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Parametric heart activity condensed photoacoustic stimulation from MRI images

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AIP Conference Proceedings 2023年第1期2523卷

作者： T. Senthil Kumar Maram Ashok M. Suresh Anand Karthikeyan Palaniappan 1Department of Electronics and Communication Engineering GRT Institute of Engineering and Technology Tiruttani Tamil Nadu India. 2Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Secunderabad Telangana India. 3Department of Computer Science and Engineering Sri Sairam Engineering College Chennai Tamil Nadu India. 4Department of Centre for System Design Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India.

In this article, we propose a computer research approach that uses only a few tomographic optoacoustic projections to analyze main components for high-frame volumetric heart imagery is seen herein in the non-invasive imaging of a smoking rodent. This approach is beneficial for discerning periodic movements. The training process allows the heart motion information to be effectively compressed and used in a higher frame rate as prior information for image restoration. Image consistency with report book decrease in the data flow is seen to be safeguarded. We prove that the parametric motion can be effectively obtained under such conditions by focusing on moment data from a single tomographic detector. A visualization of perfusing a contrast agent in vivo indicates the possibility of recording intermittent (quasi) events not recorded in the training process. The proposed method can be used to dramatically improve optoacoustic imaging time resolution and help create more accessible and data efficiency systems.

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Pulse compression;designing and generating chirplet signal on FPGA

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AIP Conference Proceedings 2022年第1期2386卷

作者： Mohammed Abdulbasit Alzaki Zainab Sabah Idan 1Department of Communication Techniques Engineering Faculty of Communication system Ministry of Education General Directorates Vocational Educations Al Najaf Iraq 2Department of Computer Techniques Engineering Faculty of Technical Engineering Alkafeel University Najaf Iraq

The electronics and telecommunications industries are constantly growing. The growth of the electronics and telecommunications industries poses new challenges for researchers and industrialists. One of these challenges is to generate the signal with the required bandwidth over a period of time that is used in various fields, especially radar systems. In short, to improve radar range resolution while achieving good SNR, it is necessary to reduce the signal width and increase the peak power. But there are cases such as Phased array radars that use solar cells and millimeter wave radars where the peak power cannot be increased, in which case the pulse compression technique is suggested as a solution. According to a common definition, pulse compression is a process performed on a wide pulse to turn it into a narrow pulse. In this paper, an attempt has been made to implement the signal compression steps in a practical way using FPGA. In order to achieve a signal with the possibility of better detection in the receiver, in this study, in addition to frequency modulation (Chirp signal), using a suitable architecture, amplitude modulation is performed on the generated signal (Chirplet signal). Simply put, if the chirp signal is designed in such a way that in addition to the frequency changes, the signal amplitude also changes over time, we get a model of the signal called the Chirplet. The main advantage of using the Chirplet signal is the reduction of unwanted frequencies entering the system due to the sudden avoidance of increasing and decreasing the initial and final sides of the signal. This ultimately makes it possible to detect the return signal with higher quality and accuracy. The FPGA under review is from the Spartan 6 xc6slx9 family. The simulation results show the production of compressed signal with the desired quality so that the ISLR and PSLR parameters for the final generated signal are obtained −7.5 and −14.5 respectively. In summary, according to the result

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Information Freshness in A Dual Monitoring system

Information Freshness in A Dual Monitoring System

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GLOBECOM 2022 - 2022 IEEE Global communications Conference

作者： Dapeng Deng Zhengchuan Chen Howard H. Yang Nikolaos Pappas Limei Hu Min Wang Yunjian Jia Tony Q.S. Quek School of Microelectronics and Communication Engineering Chongqing University Chongqing China National Mobile Communications Research Laboratory Southeast University Nanjing China Zhejiang University/University of Illinois at Urbana-Champaign Institute Zhejiang University Haining China Department of Computer and Information Science Linkoping University Linkoping Sweden School of Optoelectronics Engineering Chongqing University of Posts and Telecommunications Chongqing China Information System Technology and Design Pillar Singapore University of Technology and Design Singapore

ISBN: (纸本)9781665435413

We study the average age of information (AoI) and peak AoI (PAoI) of a dual-queue status update system that monitors a common stochastic process. We capture the state transition characteristics of the considered system by establishing a Markov chain. Using the state flow graph analysis method, we derive closed-form expressions of the average peak age of information (PAoI) and the average age of information (AoI) for the dual-queue update system. The numerical results show that compared with the single-queue update system, the average PAoI of the dual-queue update system is reduced by 33.5% and the average AoI dropped by 37.5%.

关键词： Closed-form solutions Markov processes Reliability theory Information age Sensor systems Real-time systems Sensors

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ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR Imaging with Adaptive Threshold

arXiv

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arXiv 2022年

作者： Wang, Muhan Zhang, Zhe Qiu, Xiaolan Gao, Silin Wang, Yue Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology China Suzhou Aerospace Information Research Institute Jiangsu Suzhou China Aerospace Information Research Institute Chinese Academy of Sciences China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Electrical and Computer Engineering Department George Mason University FairfaxVA United States

Tomographic SAR technique has attracted remarkable interest for its ability of three-dimensional resolving along the elevation direction via a stack of SAR images collected from different cross-track angles. The emerged compressed sensing (CS)-based algorithms have been introduced into TomoSAR considering its super-resolution ability with limited samples. However, the conventional CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity, and complex parameter fine-tuning. Aiming at efficient TomoSAR imaging, this paper proposes a novel efficient sparse unfolding network based on the analytic learned iterative shrinkage thresholding algorithm (ALISTA) architecture with adaptive threshold, named Adaptive Threshold ALISTA-based Sparse Imaging Network (ATASI-Net). The weight matrix in each layer of ATASI-Net is pre-computed as the solution of an offline optimization problem, leaving only two scalar parameters to be learned from data, which significantly simplifies the training stage. In addition, adaptive threshold is introduced for each azimuth-range pixel, enabling the threshold shrinkage to be not only layer-varied but also element-wise. Moreover, the final learned thresholds can be visualized and combined with the SAR image semantics for mutual feedback. Finally, extensive experiments on simulated and real data are carried out to demonstrate the effectiveness and efficiency of the proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Master equations and quantum trajectories for squeezed wave packets

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Physical Review A 2022年第2期105卷 023721-023721页

作者： Jonathan A. Gross Ben Q. Baragiola T. M. Stace Joshua Combes Institut Quantique Université de Sherbrooke Sherbrooke Quebec Canada J1K 2R1 Center for Quantum Information and Control University of New Mexico Albuquerque New Mexico 87131-0001 USA Yukawa Institute for Theoretical Physics Kyoto University Kitashirakawa Oiwakecho Sakyo-ku Kyoto 606-8502 Japan Centre for Quantum Computation and Communication Technology School of Science RMIT University Melbourne Victoria 3000 Australia ARC Centre for Engineered Quantum System Department of Physics University of Queensland Brisbane Queensland 4072 Australia Department of Electrical Computer and Energy Engineering University of Colorado Boulder Boulder Colorado 80309 USA

The interaction between matter and squeezed light has mostly been treated within the approximation that the field-correlation time is small. Methods for treating squeezed light with more general correlations currently involve explicitly modeling the systems producing the light. We develop a general purpose input-output theory for a particular form of narrowband squeezed light, a squeezed wave-packet mode, that only concerns the statistics of the squeezed field and the shape of the wave packet. This formalism allows us to derive the input-output relations and the master equation. We also consider detecting the scattered field using photon counting and homodyne measurements, which necessitates the derivation of the stochastic master equation. The non-Markovian nature of the field manifests itself in the master equation as a coupled hierarchy of equations. We illustrate these with consequences for the decay and resonance fluorescence of two-level atoms in the presence of such fields.

关键词： Open quantum systems Squeezing of quantum noise Stochastic processes

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Energy Management and Trajectory Optimization for UAV-Enabled Legitimate Monitoring systems

arXiv

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arXiv 2020年

作者： Hu, Shuyan Wu, Qingqing Wang, Xin State Key Laboratory of ASIC and System School of Information Science and Technology Fudan University Shanghai200433 China Department of Electrical and Computer Engineering National University of Singapore Singapore119077 Singapore State Key Laboratory of ASIC and System Shanghai Institute for Advanced Communication and Data Science Department of Communication Science and Engineering Fudan University Shanghai200433 China

Thanks to their quick placement and high flexibility, unmanned aerial vehicles (UAVs) can be very useful in the current and future wireless communication systems. With a growing number of smart devices and infrastructure-free communication networks, it is necessary to legitimately monitor these networks to prevent crimes. In this paper, a novel framework is proposed to exploit the flexibility of the UAV for legitimate monitoring via joint trajectory design and energy management. The system includes a suspicious transmission link with a terrestrial transmitter and a terrestrial receiver, and a UAV to monitor the suspicious link. The UAV can adjust its positions and send jamming signal to the suspicious receiver to ensure successful eavesdropping. Based on this model, we first develop an approach to minimize the overall jamming energy consumption of the UAV. Building on a judicious (re)formulation, an alternating optimization approach is developed to compute a locally optimal solution in polynomial time. Furthermore, we model and include the propulsion power to minimize the overall energy consumption of the UAV. Leveraging the successive convex approximation method, an effective iterative approach is developed to find a feasible solution fulfilling the Karush-Kuhn-Tucker (KKT) conditions. Extensive numerical results are provided to verify the merits of the proposed schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Game-based Learning Approach to Cybersecurity

Game-based Learning Approach to Cybersecurity

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IEEE Education engineering (EDUCON)

作者： Llanos Tobarra Antonio Pérez Trapero Rafael Pastor Antonio Robles-Gómez Roberto Hernández Andrés Duque Jesús Cano Communication and Control System Department Spanish National Distance University (UNED) Madrid Spain Student Computer Science Engineering School Spanish National Distance University (UNED) Madrid Spain

ISBN: (数字)9781728109305

ISBN: (纸本)9781728109312

The inclusion of practical activities to train critical thinking skills is very relevant in the case of engineering subjects. In particular, it becomes a key element in the context of cybersecurity, as our case is. Therefore, this paper analyses a gambling experience in a cybersecurity subject within a computer engineering degree, in order to assess whether the spaced learning methodology contributes to the improvement of learning. The gamification experience is carried out through a Flag Capture Competition (CTF) among the enrolled students in the course. The use of the platform has also been evaluated in an exploratory way.

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Anomaly detection of time series with smoothness-inducing sequential variational auto-encoder

arXiv

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arXiv 2021年

作者： Li, Longyuan Yan, Junchi Wang, Haiyang Jin, Yaohui State Key Lab of Advanced Optical Communication System and Network MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China

Deep generative models have demonstrated their effectiveness in learning latent representation and modeling complex dependencies of time series. In this paper, we present a Smoothness-Inducing Sequential Variational Auto-Encoder (SISVAE) model for robust estimation and anomaly detection of multi-dimensional time series. Our model is based on Variational Auto-Encoder (VAE), and its backbone is fulfilled by a Recurrent Neural Network to capture latent temporal structures of time series for both generative model and inference model. Specifically, our model parameterizes mean and variance for each time-stamp with flexible neural networks, resulting in a non-stationary model that can work without the assumption of constant noise as commonly made by existing Markov models. However, such a flexibility may cause the model fragile to anomalies. To achieve robust density estimation which can also benefit detection tasks, we propose a smoothness-inducing prior over possible estimations. The proposed prior works as a regularizer that places penalty at non-smooth reconstructions. Our model is learned efficiently with a novel stochastic gradient variational Bayes estimator. In particular, we study two decision criteria for anomaly detection: reconstruction probability and reconstruction error. We show the effectiveness of our model on both synthetic datasets and public real-world benchmarks. © 2021, CC BY.

关键词： Recurrent neural networks

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