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arXiv 2024年

作者： Bravo-Frank, Nicholas Mesyngier, Nicolas Feng, Lei Hong, Jiarong Department of Electrical and Computer Engineering University of Minnesota United States Saint Anthony Falls Laboratory University of Minnesota United States Minnesota Robotics Institute University of Minnesota United States Department of Mechanical Engineering University of Minnesota United States

We developed a digital inline holography (DIH) system integrated with deep learning algorithms for real-time detection of particulate matter (PM) and bacterial contamination in peritoneal dialysis (PD) fluids. The system comprises a microfluidic sample delivery module and a DIH imaging module that captures holograms using a pulsed laser and a digital camera with a 40× objective. Our data processing pipeline enhances holograms, reconstructs images, and employs a YOLOv8nbased deep learning model for particle identification and classification, trained on labeled holograms of generic PD particles, Escherichia coli (E. coli), and Pseudomonas aeruginosa (P. aeruginosa). The system effectively detected and classified generic particles in sterile PD fluids, revealing diverse morphologies predominantly sized 1–5 µm with an average concentration of 61 particles/µL. In PD fluid samples spiked with high concentrations of E. coli and P. aeruginosa, our system achieved high sensitivity (>90%) in detecting and classifying these bacteria at clinically relevant low false positive rates (~0.5%). Further validation against standard colony-forming unit (CFU) methods using PD fluid spiked with bacterial concentrations from approximately 100 to 10,000 bacteria/mL demonstrated a clear one-to-one correspondence between our measurements and CFU counts. Our DIH system provides a rapid, accurate alternative to traditional culture-based methods for assessing bacterial contamination in PD fluids. By enabling real-time sterility monitoring, it can significantly improve patient outcomes in PD treatment, facilitate point-of-care fluid production, reduce logistical challenges, and be extended to quality control in pharmaceutical production. © 2024, CC BY.

关键词： Escherichia coli

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An Augmented Reality Application for Upper-Limb Rehabilitation Supported by a Robotic Arm

An Augmented Reality Application for Upper-Limb Rehabilitati...

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Experiment@ International Conference (exp.at)

作者： José Carlos Rodrigues Maria Teresa Restivo Paulo Menezes LAETA-INEGI Faculty of Engineering University of Porto Porto Portugal LAETA-INEGI University of Porto Porto Portugal A3ES Lisbon Portugal Department of Electrical and Computer Engineering University of Coimbra Institute of Systems and Robotics Coimbra Portugal

ISBN: (数字)9798350314403

ISBN: (纸本)9798350314410

This work looks at the potential use of a robotic arm and augmented reality application, with a head-mounted display, in an upper-limb rehabilitation context. A sample application using a robotic simulator and a cross-sectional study using a real robotic arm are shown. Results from the usability evaluation are positive and encourage further development, but some identified problems must be corrected before further evaluations.

关键词： Head-mounted displays Sociology Medical treatment Collaborative robots Manipulators Real-time systems Usability

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Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach

arXiv

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arXiv 2024年

作者： Yan, Yuling Wainwright, Martin J. Institute for Data Systems and Society Massachusetts Institute of Technology United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology United States Department of Electrical Engineering and Computer Sciences Massachusetts Institute of Technology United States Department of Mathematics Massachusetts Institute of Technology United States

Longitudinal or panel data can be represented as a matrix with rows indexed by units and columns indexed by time. We consider inferential questions associated with the missing data version of panel data induced by staggered adoption. We propose a computationally efficient procedure for estimation, involving only simple matrix algebra and singular value decomposition, and prove non-asymptotic and high-probability bounds on its error in estimating each missing entry. By controlling proximity to a suitably scaled Gaussian variable, we develop and analyze a data-driven procedure for constructing entrywise confidence intervals with pre-specified coverage. Despite its simplicity, our procedure turns out to be instance-optimal: we prove that the width of our confidence intervals match a non-asymptotic instance-wise lower bound derived via a Bayesian Cramér-Rao argument. We illustrate the sharpness of our theoretical characterization on a variety of numerical examples. Our analysis is based on a general inferential toolbox for SVD-based algorithm applied to the matrix denoising model, which might be of independent interest. Copyright © 2024, The Authors. All rights reserved.

关键词： Singular value decomposition

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Neural Transcoding Vision Transformers for EEG-to-fMRI Synthesis

arXiv

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arXiv 2024年

作者： Lanzino, Romeo Fontana, Federico Cinque, Luigi Scarcello, Francesco Maki, Atsuto Sapienza University of Rome Computer Science Department Rome00198 Italy University of Calabria Department of Computer Engineering Modeling Electronics and Systems Rende87030 Italy KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm100 44 Sweden

This paper introduces the Neural Transcoding Vision Transformer (NT-ViT), a generative model designed to estimate high-resolution functional Magnetic Resonance Imaging (fMRI) samples from simultaneous Electroencephalography (EEG) data. A key feature of NT-ViT is its Domain Matching (DM) sub-module which effectively aligns the latent EEG representations with those of fMRI volumes, enhancing the model’s accuracy and reliability. Unlike previous methods that tend to struggle with fidelity and reproducibility of images, NT-ViT addresses these challenges by ensuring methodological integrity and higher-quality reconstructions which we showcase through extensive evaluation on two benchmark datasets;NT-ViT outperforms the current state-of-the-art by a significant margin in both cases, e.g. achieving a 10× reduction in RMSE and a 3.14× increase in SSIM on the Oddball dataset. An ablation study also provides insights into the contribution of each component to the model’s overall effectiveness. This development is critical in offering a new approach to lessen the time and financial constraints typically linked with high-resolution brain imaging, thereby aiding in the swift and precise diagnosis of neurological disorders. Although it is not a replacement for actual fMRI but rather a step towards making such imaging more accessible, we believe that it represents a pivotal advancement in clinical practice and neuroscience research. Code is available at https://***/rom42pla/ntvit. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Optimizing complex event forecasting 22

Optimizing complex event forecasting

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16th ACM International Conference on Distributed and Event-Based systems, DEBS 2022

作者： Stavropoulos, Vasileios Alevizos, Elias Giatrakos, Nikos Artikis, Alexander Institute of Informatics and Telecommunications Ncsr Demokritos Greece School of Electrical and Computer Engineering Technical University of Crete Greece Athena Research Center Institute for Management of Information Systems Greece Department of Maritime Studies University of Piraeus Greece

ISBN: (纸本)9781450393089

In Complex Event Recognition (CER), applications express business rules in the form of patterns and deploy them in a CER Engine which seeks the occurrence of such patterns on incoming streams. This is useful for practical applications which rely on the timely detection of patterns to support critical decisions. One step further, stakeholders want to act proactively, accurately forecasting the occurrence of patterns on raw streams well ahead of time to better schedule their decisions. This calls for making the transition from CER to Complex Event Forecasting (CEF). In CEF, stochastic models of future behavior are embedded into the event processing loop to project into the future the sequence of events that have occurred so far and to estimate the likelihood of the imminent occurrence of more complex patterns. CEF performance engages the stochastic model's training speed and forecast accuracy. In turn, these performance dimensions are affected by few parameters. However, CEF parameter tuning so that optimal CEF performance is achieved is a non-trivial task. This is due to the fact that there is an infinite number of possible parameter combinations, each affecting CEF performance in ways which are hard to predict. In this work, we introduce the first CEF Optimizer that gracefully automates CEF parameter tuning decisions, rapidly cherry picking good CEF configurations. We detail the novel internal architecture of our CEF Optimizer and present an elaborate empirical analysis on two applications that illustrates the effectiveness of our optimization approach. © 2022 ACM.

关键词： Stochastic models

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An Encoder-Sequencer-Decoder Network for Lane Detection to Facilitate Autonomous Driving 23

An Encoder-Sequencer-Decoder Network for Lane Detection to F...

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23rd International Conference on Control, Automation and systems, ICCAS 2023

作者： Hussain, Muhammad Ishfaq Rafique, Muhammad Aasim Ko, Yeongmin Khan, Zafran Olimov, Farrukh Naz, Zubia Kim, Jeongbae Jeon, Moongu School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Korea Republic of College of Computer Sciences and Information Technology King Faisal University Department of Information Systems Al Ahsa31982 Saudi Arabia Monitorapp Threat Intelligence Team Seoul Korea Republic of Pusan National University Pusan Korea Republic of

ISBN: (纸本)9788993215274

Lane detection in all weather conditions is a pressing necessity for autonomous driving. Accurate lane detection ensures the safe operation of autonomous vehicles, enabling advanced driver assistance systems to effectively track and maintain the vehicle within the lanes. Traditional lane detection techniques heavily rely on a single image frame captured by the camera, posing limitations. Moreover, these conventional methods demand a constant stream of pristine images for uninterrupted lane detection, resulting in degraded performance when faced with challenges such as low brightness, shadows, occlusions, and deteriorating environmental conditions. Recognizing that continuous sequence patterns on the road represent lanes, our approach leverages a sequential model to process multiple images for lane detection. In this study, we propose a deep neural network model to extract crucial lane information from a sequence of images. Our model adopts a convolutional neural network in an encoder/decoder architecture and incorporates an extended short-term memory model for sequential feature extraction. We evaluate the performance of our proposed model using the TuSimple and CuLane datasets, showcasing its superiority across various lane detection scenarios. Comparative analysis with state-of-the-art lane detection methods further substantiates our model's effectiveness. © 2023 ICROS.

关键词： Advanced driver assistance systems

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Manipulating the Posterior Support of a Discrete Bayesian Estimator Under Full Sensor Control

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IFAC-PapersOnLine 2023年第2期56卷 252-257页

作者： Daniel Selvaratnam Farhad Farokhi Iman Shames Henrik Sandberg Decision and Control Systems School of Electrical Engineering & Computer Science KTH Royal Institute of Technology 114 28 Stockholm Sweden Department of Electrical and Electronic Engineering The University of Melbourne Parkville VIC 3010 Australia College of Engineering & Computer Science Australian National University Canberra ACT 2601 Australia

The asymptotic implausibility problem is introduced from the perspective of an adversary that seeks to drive the belief of a recursive Bayesian estimator away from a particular set of parameter values. It is assumed that the adversary controls all sensors informing the estimator, and can transmit false measurements stochastically according to a fixed distribution of its choice. First, we outline a method for verifying whether a given distribution solves the problem. We then consider the class of spoofing attacks, and show that the asymptotic implausibility problem has a solution if and only if it can be solved by a spoofing attack. Attention is restricted to finite parameter and observation spaces.

关键词： Network Security Bayesian Estimation Cyber-Physical systems Network systems Resilient Control systems Signal Processing

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Boolean matrix compressed sensing

Boolean matrix compressed sensing

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qiang Soleymani, Mahdi Mahdavifar, Hessam National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Halıcıoğlu Data Science Institute University of California La Jolla San DiegoCA92093 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

ISBN: (纸本)9798350368741

In real-world datasets, leveraging the low-rank and sparsity properties enables developing efficient algorithms across a diverse array of data-related tasks, including compression, compressed sensing, matrix completion, etc. Notably, these two properties often coexist in certain real-world datasets, especially in Boolean datasets and quantized real-valued datasets. To harness the advantages of low-rank and sparsity simultaneously, we adopt a technique inspired by compressed sensing and Boolean matrix completion. Our approach entails compressing a low-rank sparse Boolean matrix by performing inner product operations with a randomly generated Boolean matrix. We then propose a decoding algorithms based on message-passing techniques to recover the original matrix. Our experiments demonstrate superior recovery performance of our proposed algorithms compared to Boolean matrix completion, with equal measurement requirements. © 2025 IEEE.

关键词： Belief Propagation Boolean Matrix Compressed Sensing Matrix Completion

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Distributed Optimization via Gradient Descent with Event-Triggered Zooming over Quantized Communication

arXiv

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arXiv 2023年

作者： Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. The Department of Electrical and Computer Engineering Division of Systems Engineering Boston University BostonMA02215 United States Hong Kong The Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia1678 Cyprus The Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland The Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden

In this paper, we study unconstrained distributed optimization strongly convex problems, in which the exchange of information in the network is captured by a directed graph topology over digital channels that have limited capacity (and hence information should be quantized). Distributed methods in which nodes use quantized communication yield a solution at the proximity of the optimal solution, hence reaching an error floor that depends on the quantization level used;the finer the quantization the lower the error floor. However, it is not possible to determine in advance the optimal quantization level that ensures specific performance guarantees (such as achieving an error floor below a predefined threshold). Choosing a very small quantization level that would guarantee the desired performance, requires information packets of very large size, which is not desirable (could increase the probability of packet losses, increase delays, etc) and often not feasible due to the limited capacity of the channels available. In order to obtain a communication-efficient distributed solution and a sufficiently close proximity to the optimal solution, we propose a quantized distributed optimization algorithm that converges in a finite number of steps and is able to adjust the quantization level accordingly. The proposed solution uses a finite-time distributed optimization protocol to find a solution to the problem for a given quantization level in a finite number of steps and keeps refining the quantization level until the difference in the solution between two successive solutions with different quantization levels is below a certain pre-specified threshold. Therefore, the proposed algorithm progressively refines the quantization level, thus eventually achieving low error floor with a reduced communication burden. The performance gains of the proposed algorithm are demonstrated via illustrative examples. © 2023, CC BY.

关键词： Directed graphs

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State of Charge Estimation for Zinc-Bromine Flow Batteries by Improved Long Short-Term Memory Network and Kalman Filter

State of Charge Estimation for Zinc-Bromine Flow Batteries b...

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Energy Storage and Conversion (ISESC), CPSS & IEEE International Symposium on

作者： Kaiwen Peng Bichen Chen Liguo Wang Denis Sidorov Aliona Dreglea Zongjie Wang School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China School of Electrical Engineering and Automation Northeast Agricultural University Harbin China Melentiev Energy Systems Institute Siberian Branch of the Russian Academy of Sciences Irkutsk Russia Department of Electrical and Computer Engineering University of Connecticut Mansfield USA

ISBN: (数字)9798350380514

ISBN: (纸本)9798350380521

In order to improve the accuracy of estimating the state of charge (SOC) of zinc-bromine flow batteries (ZBFB) in the discharge stage and overcome the problems caused by sudden voltage changes, this paper proposes an algorithm for estimating and correcting the SOC. The core contribution is the proposal of an improved Long Short-Term Memory (LSTM) neural network algorithm, which considers the effect of the circulating pump speed on the battery voltage during the discharge stage, and establishes a neural network model for the battery. The estimation result of the improved LSTM network has a mean square error of 3.322, representing a 12% improvement in estimation accuracy compared to the traditional LSTM network. Additionally, Kalman filtering is employed for error mitigation to further enhance estimation accuracy. After applying the proposed method, the maximum error between the estimated SOC and the actual SOC is 1.91%, and the average error is 0.30%. This indicates that the proposed method improves the accuracy of SOC estimation through enhanced real-time LSTM network estimation and Kalman filtering error cancellation, particularly during voltage sudden change in the discharge of ZBFB.

关键词： Accuracy Filtering Neural networks Estimation Voltage Discharges (electric) Batteries State of charge Kalman filters Long short term memory

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