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IEEE International Conference on Electro-Information Technology

作者： Diego Rodriguez Garcia Alvaro Aparicio Serna David Sancho Crespo Xinrui Yu Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

Although the surveillance industry is growing due to increased concerns for public safety, traditional surveillance systems can be ineffective due to fixed positions, limited coverage, and the need for human operation. This paper proposes an edge computing surveillance system that can detect, track, and identify human presence using AI software. The system includes a video camera integrated into a structure that allows pan and tilt movements and a processing unit that performs all computations locally. The system can operate in two different modes, manual and automatic, and it can be controlled from a user-friendly web application. The manual mode allows the user to control the camera remotely, adjusting the position with buttons and handling additional functionalities like zoom. On the other hand, in automatic mode, the system detects people and identifies if they are an unknown subject or not. Identifications are recorded in an online log and the system takes pictures of unknown people to recognize recurrent intruders.

关键词： Tracking Surveillance Face recognition Streaming media Cameras Software Real-time systems

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Autonomous Patrol and Threat Detection Through Integrated Mapping and computer Vision

Autonomous Patrol and Threat Detection Through Integrated Ma...

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IEEE International Conference on Electro-Information Technology

作者： Robert Soler Alae Moudni Gabriel Roskowski Xinrui Yu Mikhail Gormov Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A.

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

This paper presents the creation of an innovative autonomous security robot designed to perform security functions with efficiency and reliability. The robot boasts mapping capabilities, which it utilizes to facilitate autonomous patrol in designated areas. Its primary operations involve the use of computer vision to detect violence, identify weapons and dangerous items, and recognize individuals. Critical incidents are met with an immediate alarm and the subsequent transmission of data to a central security server, which then generates comprehensive reports displayed through a web application for security personnel. The application itself features remote control of the robot, incident report management, status updates, and incident analytics. The robot demonstrates substantial real-world application potential, particularly in crowded environments where it could outperform conventional surveillance. The project combines concepts of engineering, computer science, and cybersecurity, functioning per design but with considerable potential for future refinement and expansion, embodying the concept of an evolving technological solution.

关键词： computer science computer vision Technological innovation Surveillance Weapons Reliability engineering Threat assessment

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Learning Cross-Modal Audiovisual Representations with Ladder Networks for Emotion Recognition

Learning Cross-Modal Audiovisual Representations with Ladder...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Lucas Goncalves Carlos Busso Department of Electrical and Computer Engineering Multimodal Signal Processing (MSP) Laboratory The University of Texas at Dallas Richardson TX USA

Representation learning is a challenging, but essential task in audiovisual learning. A key challenge is to generate strong cross-modal representations while still capturing discriminative information contained in unimodal features. Properly capturing this information is important to increase accuracy and robustness in audiovisual tasks. Focusing on emotion recognition, this study proposes novel cross-modal ladder networks to capture modality-specific information while building strong cross-modal representations. Our method utilizes representations from a backbone network to implement unsupervised auxiliary tasks to reconstruct intermediate layer representations across the acoustic and visual networks. The skip connections between the cross-modal encoder and decoder provide powerful modality-specific and multimodal representations for emotion recognition. Our model on the CREMA-D corpus achieves high performance with precision, recall, and F1 scores over 80% on a six-class problem.

关键词： Representation learning Emotion recognition Visualization Focusing Speech recognition signal processing Acoustics

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Time reversal and Inverse filter for Blind Deconvolution of Ultrasonic Communication Through Solid Channel

Time reversal and Inverse filter for Blind Deconvolution of ...

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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Jafar Saniie Xin Huang Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A.

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Ultrasonic waves provide an effective means of transmitting information through solid media, such as metal pipes and bars. However, the complex geometry of these materials causes reverberations that degrade the quality of communication. In this paper, we propose denoising approaches using time reversal and inverse filtering methods for blind deconvolution. These methods enable significant improvements in signal-to-noise ratio (SNR), with the inverse filter demonstrating superior performance in both simulated and real-world scenarios. Our results show that this approach provides a robust solution for improving ultrasonic communication in complex solid channels.

关键词： Geometry Deconvolution Filtering Noise reduction Metals Media Solids Reverberation Frequency control signal to noise ratio

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Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge Regression

Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Manel Marweni Majdi Mansouri Mansour Hajji Mohamed Faouzi Mimouni Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine Tunisia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The modern energy landscape is undergoing a seismic change from traditional, finite energy sources and toward cleaner, renewable alternatives. The restrictions faced by traditional sources, which are not only finite but increasingly shrink in the face of burgeoning global energy demands driven by population increase and industrial expansion, are driving this change. Although promising, renewable energy poses complications, particularly the reliance on climatic conditions. An important aspect of addressing these difficulties is effective energy management within distribution systems, which includes forecasting and optimization phases. This research focuses on forecasting using an advanced machine learning (ML) approach. Accurately forecasting renewable energy generation over time is critical for improving energy management. This technique is evaluated using a variety of performance indicators, including Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R 2 ). Empirical studies support the method's usefulness, demonstrating noteworthy performance with low error rates.

关键词： Photovoltaic systems Renewable energy sources Technological innovation Accuracy Machine learning Predictive models Forecasting Kernel Energy management Optimization

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Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data

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Intelligent Medicine 2024年第3期4卷 170-176页

作者： Bijan Roudini Boshra Khajehpiri Hamid Abrishami Moghaddam Mohamad Forouzanfar Department of Systems Engineering School of Advanced TechnologyUniversity of QuebecMontrealCanada Machine Vision and Medical Image Processing(MVMIP)Laboratory Faculty of Electrical and Computer EngineeringK.N.Toosi University of TechnologyTehranIran Research Center of the Montreal University Institute of Geriatrics MontrealCanada

Background Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new *** learning(ML)models have shown satisfactory performance in short-term mortality prediction in patients with heart disease,whereas their utility in long-term predictions is *** study aimed to investigate the performance of tree-based ML models on long-term mortality prediction and effect of two recently introduced biomarkers on long-term *** This study used publicly available data from the Collaboration Center of Health Information Appli-cation at the Ministry of Health and Welfare,Taiwan,*** collected data were from patients admitted to the cardiac care unit for acute myocardial infarction(AMI)between November 2003 and September *** collected and analyzed mortality data up to December *** records were used to gather demo-graphic and clinical data,including age,gender,body mass index,percutaneous coronary intervention status,and comorbidities such as hypertension,dyslipidemia,ST-segment elevation myocardial infarction,and non-ST-segment elevation myocardial *** the data,collected from 139 patients with AMI,from medical and demographic records as well as two recently introduced biomarkers,brachial pre-ejection period(bPEP)and brachial ejection time(bET),we investigated the performance of advanced ensemble tree-based ML algorithms(random forest,AdaBoost,and XGBoost)to predict all-cause mortality within 14 years.A nested cross-validation was performed to evaluate and compare the performance of our developed models precisely with that of the conventional logistic regression(LR)as the baseline *** The developed ML models achieved significantly better performance compared to the baseline LR(C-Statistic,0.80 for random forest,0.79 for AdaBoost,and 0.78 for XGBoost,vs.0.77 for LR)(PRF<0.001,PAdaBoost<0.001,a

关键词： Electrocardiogram Predication Mortality prediction Machine learning

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Extended Version of "New Theory and Faster Computations for Subspace-Based Sensitivity Map Estimation in Multichannel MRI"

arXiv

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arXiv 2023年

作者： Lobos, Rodrigo A. Chan, Chin-Cheng Haldar, Justin P. The Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States

This is an unabridged version of a journal manuscript that has been submitted for publication [1]. (Due to length restrictions, we were forced to remove substantial amounts of content from the version that was submitted to the journal, including more detailed theoretical explanations, additional figures, and a more comprehensive bibliography. This content remains intact in this version of the document). Sensitivity map estimation is important in many multichannel MRI applications. Subspace-based sensitivity map estimation methods like ESPIRiT are popular and perform well, though can be computationally expensive and their theoretical principles can be nontrivial to understand. In the first part of this work, we present a novel theoretical derivation of subspace-based sensitivity map estimation based on a linear-predictability/structured low-rank modeling perspective. This results in an estimation approach that is equivalent to ESPIRiT, but with distinct theory that may be more intuitive for some readers. In the second part of this work, we propose and evaluate a set of computational acceleration approaches (collectively known as PISCO) that can enable substantial improvements in computation time (up to ∼100× in the examples we show) and memory for subspace-based sensitivity map estimation. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Constrained Energy Minimization (CEM) for Hyperspectral Target Detection: Theory and Generalizations

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IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷

作者： Chang, Chein-I. Information and Technology College Dalian116026 China University of Maryland Baltimore County Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering BaltimoreMD21250 United States National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan

Target detection is a fundamental task of hyperspectral imaging where constrained energy minimization (CEM) has been widely used for subpixel target detection techniques. Due to its effectiveness, CEM has been generalized to various versions, such as kernel CEM (KCEM), kernel target-constrained interference-minimized filter (KTCIMF), ensemble cascaded CEM (ECEM), and hierarchical CEM (HCEM). Unfortunately, these generalizations overlooked the key design rationale behind CEM. This article revisits CEM for hyperspectral target detection (HTD) and proves how and why it works mathematically. Specifically, several new CEM generalizations are derived and particularly noteworthy. By including spatial information in an iterative process, KCEM, ECEM, and HCEM can be generalized to iterative KCEM (IKCEM), iterative KTCIMF (IKTCIMF), iterative ECEM (IECEM), and iterative HCEM (IHCEM). Also, by utilizing an iterative random training sampling (IRTS) to generate the desired target signature to be detected, these algorithms are further generalized to IRTS KCEM (IRTS-KCEM), IRTS ECEM (IRTS-ECEM), and IRTS HCEM (IRTS-HCEM). A comprehensive analysis along with comparative study on these generalizations is conducted through extensive experiments to demonstrate the effectiveness of IKCEM, IHCEM, and IECEM. © 1980-2012 IEEE.

关键词： Iterative methods

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An Efficient Algorithm for Spatial-Spectral Partial Volume Compartment Mapping with Applications to Multicomponent Diffusion and Relaxation MRI

arXiv

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arXiv 2024年

作者： Liu, Yunsong Mandal, Debdut Liao, Congyu Setsompop, Kawin Haldar, Justin P. Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Departments of Radiology and Electrical Engineering Stanford University StanfordCA94305 United States

We introduce a new algorithm to solve a regularized spatial-spectral image estimation problem. Our approach is based on the linearized alternating directions method of multipliers (LADMM), which is a variation of the popular ADMM algorithm. Although LADMM has existed for some time, it has not been very widely used in the computational imaging literature. This is in part because there are many possible ways of mapping LADMM to a specific optimization problem, and it is nontrivial to find a computationally efficient implementation out of the many competing alternatives. We believe that our proposed implementation represents the first application of LADMM to the type of optimization problem considered in this work (involving a linear-mixture forward model, spatial regularization, and nonnegativity constraints). We evaluate our algorithm in a variety of multiparametric MRI partial volume mapping scenarios (diffusion-relaxation, relaxation-relaxation, relaxometry, and fingerprinting), where we consistently observe substantial (∼3×-50×) speed improvements. We expect this to reduce barriers to using spatially-regularized partial volume compartment mapping methods. Further, the considerable improvements we observed also suggest the potential value of considering LADMM for a broader set of computational imaging problems. Copyright © 2024, The Authors. All rights reserved.

关键词： Mapping

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Bayesian Rule Ontologies For XAI Classification and Regression

Bayesian Rule Ontologies For XAI Classification and Regressi...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Akash Kumar Panda Bart Kosko Department of Electrical and Computer Engineering Signal and Image Processing Institute University of Southern California Los Angeles California

A random foam defines a modular rule-based ontology after sampling from a neural or other input-output system. A random foam combines several rule-based systems and averages the systems. It gives a Bayesian posterior over the subsystems. It also gives separate Bayesian posteriors over the rules of each subsystem. The shape of the rules controls how well the random-foam ontology performs in classification and regression. We found that a heterogenous ontology that mixes different rule shapes can perform better than a homogenous ontology based on a single Gaussian or other rule type. Random foams are also universal function approximators. So they can train on a neural black box and act as its explainable proxy system. We prove this uniform approximation theorem for the important case of bump-function random foams with throughput combination. Random foams also measure their output’s uncertainty through the conditional variance. Bump function rules performed better than Cauchy rules at classification while Cauchy rules performed better at regression. Gaussian rules performed best in both classification and regression. A homogeneous Gaussian random foam that trained on a 96.7% accurate neural classifier was itself 95.96% accurate on the MNIST data set. A heterogeneous random foam with two-thirds Gaussian rules and one-third Laplacian rules did better than did the all-Gaussian foam ontology.

关键词： Uncertainty Laplace equations Shape Measurement uncertainty Machine learning Ontologies Aerospace electronics

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