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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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Sequential Source Selection Based On Evidential Value of Information 26

Sequential Source Selection Based On Evidential Value of Inf...

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26th International Conference on Information Fusion, FUSION 2023

作者： Kowalski, Pawel Jousselme, Anne-Laure Wickramarathne, Thanuka Nato Sto Centre for Maritime Research and Experimentation La Spezia Italy Cs Group Research Laboratory La Garde France University of Massachusetts Lowell Department of Electrical and Computer Engineering LowellMA United States

ISBN: (纸本)9798890344854

Epistemic decisions about which sources to trust and query are critical for a decision-maker, when the end-goal decisions are to be made using limited resources. Toward this, we previously proposed preliminary extensions to classical measures of Value of Information (VoI) for imprecise belief states represented by belief functions relying on a general observation model. These methods primarily aim to assist a decision-maker toward making rational decisions, by generating necessary metadata about the information that is being considered for the decision-making task (e.g. probability of source reliability, degree of self-confidence expressed by the source). In this paper, we explore the behavior and performance of our previously proposed belief theoretic VoI measures, the Evidential Expecetd Value of Sampled Information (EEVSI), and propose a procedure to sequentially select sources to query. We also consider the case where the information sources are providing contradictory evidence. We leverage a maritime surveillance scenario, where the decision-maker has to make a rational ordered selection of information sources among a set of both physical sensors and human sources, to illustrate the behavior of the proposed method. We compare the proposed policy with a traditional probability-based approach in a simulation environment. We conclude by providing some insights on future research directions to further expand on our proposed new measures. © 2023 International Society of Information Fusion.

关键词： Decision making

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Impact of Temporal Precision on Speech Synthesis Accuracy From Electrocorticographic Brain Signals

Impact of Temporal Precision on Speech Synthesis Accuracy Fr...

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

作者： Rabbani, Qinwan Fifer, Matthew S. Crone, Nathan E. Moro-Velazquez, Laureano Department of Electrical and Computer Engineering Johns Hopkins University Baltimore United States Research and Exploratory Development Department Johns Hopkins University Applied Physics Laboratory Laurel United States Department of Neurology Johns Hopkins Hospital Baltimore United States

ISBN: (纸本)9798350368741

Accurately time-aligned spectral targets are essential for training electrocorticographic (ECoG) brain-computer interfaces (BCIs) intended for real-time speech output. This alignment is particularly challenging with "silent speech, " in which speech occurs without phonation, or in the extreme, without articulation. Crafting suitably precise targets for silent speech is complex and error-prone due to multiple sources of temporal imprecision. We investigated how these temporal inaccuracies impact deep neural network performance in synthesizing speech from a BCI clinical trial participant who retained some speech capability. By simulating silent speech conditions through distortions in known acoustic target timings, we observed significant performance degradations at both phonetic and syllabic timescales. Accuracy-based measures offered a more reliable assessment of intelligibility than traditional metrics like the short-time objective intelligibility index, which may overestimate performance in low-precision contexts. These results underscore the need for advanced alignment techniques with precise phonetic and syllabic guarantees for silent speech BCIs focused on providing immediate output. © 2025 IEEE.

关键词： brain-computer interface deep neural network electrocorticography speech synthesis temporal alignment

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Strategies for Effective EEG Preprocessing

Strategies for Effective EEG Preprocessing

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Learning and Technology Conference (L&T)

作者： Nema Salem Nora Alamoudi Lama Mustafa Fatima Bagais Electrical Research Laboratory College of Engineering Effat University Jeddah Saudi Arabia Department of Electrical and Computer Engineering College of Engineering Effat University Jeddah Saudi Arabia

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

The administration of anesthesia is critical for ensuring the safety of surgical procedures, yet it comes with inherent risks associated with medication mistakes. Monitoring EEG signals during anesthesia is crucial for understanding patient physiology; however, the accuracy of this information heavily relies on effectively eliminating noise. Decreasing noise in EEG signals is vital for accurately interpreting data, ultimately enhancing patient safety and refining anesthesia management. This research concentrates on an advanced preprocessing technique for EEG signals, employing segmentation, detrending, variational mode decomposition (VMD), and noise reduction through Gaussian filtering, moving averages, Independent Component Analysis (ICA), and Discrete Wavelet Transform (DWT). The study utilized EEG data collected during surgeries and applied these preprocessing approaches to reduce noise and artifacts. Our results indicate that employing the Discrete Wavelet Transform (DWT) for denoising is robust and produces superior outcomes compared to other techniques investigated, achieving a high Signal-To-Noise Ratio (SNR) of 43.23 dB.

关键词： Noise reduction Surgery Transforms Anesthesia Wavelet analysis Electroencephalography Discrete wavelet transforms Safety Monitoring Signal to noise ratio

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Generalization-Enhanced Few-Shot Object Detection in Remote Sensing

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5445-5460页

作者： Lin, Hui Li, Nan Yao, Pengjuan Dong, Kexin Guo, Yuhan Hong, Danfeng Zhang, Ying Wen, Congcong China Academy of Electronics and Information Technology Beijing100846 China China Meteorological Administration National Satellite Meteorological Center Innovation Center for FengYun Meteorological Satellite Beijing100081 China Tsinghua University State Key Laboratory of Hydroscience and Engineering Department of Hydraulic Engineering Beijing100084 China Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing100083 China New York University Abu Dhabi Department of Electrical and Computer Engineering Abu Dhabi United Arab Emirates University of Science and Technology of China Anhui China

Object detection is a fundamental task in computer vision that involves accurately locating and classifying objects within images or video frames. In remote sensing, this task is particularly challenging due to the high resolution, multi-scale features, and diverse ground object characteristics inherent in satellite and UAV imagery. These challenges necessitate more advanced approaches for effective object detection in such environments. While deep learning methods have achieved remarkable success in remote sensing object detection, they typically rely on large amounts of labeled data. Acquiring sufficient labeled data, particularly for novel or rare objects, is both challenging and time-consuming in remote sensing scenarios, limiting the generalization capabilities of existing models. To address these challenges, few-shot learning (FSL) has emerged as a promising approach, aiming to enable models to learn new classes from limited labeled examples. Building on this concept, few-shot object detection (FSOD) specifically targets object detection challenges in data-limited conditions. However, the generalization capability of FSOD models, particularly in remote sensing, is often constrained by the complex and diverse characteristics of the objects present in such environments. In this paper, we propose the Generalization-Enhanced Few-Shot Object Detection (GE-FSOD) model to improve the generalization capability in remote sensing FSOD tasks. Our model introduces three key innovations: the Cross-Level Fusion Pyramid Attention Network (CFPAN) for enhanced multi-scale feature representation, the Multi-Stage Refinement Region Proposal Network (MRRPN) for more accurate region proposals, and the Generalized Classification Loss (GCL) for improved classification performance in few-shot scenarios. GE-FSOD demonstrates superior robustness and accuracy in remote sensing FSOD tasks through these enhancements. Extensive experiments on the DIOR and NWPU VHR-10 datasets show that our

关键词： Labeled data

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On a novel tracking differentiator design based on iterative learning in a moving window

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Control Theory and Technology 2023年第1期21卷 46-55页

作者： Xiangyang Li Rafal Madonski Zhiqiang Gao Senping Tian Key Laboratory of Autonomous Systems and Network Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510641China Energy and Electricity Research Center Jinan UniversityZhuhai 519070China Department of Electrical and Computer Science Cleveland State UniversityClevelandOH 44115USA

Differential signals are key in control engineering as they anticipate future behavior of process variables and therefore are critical in formulating control laws such as proportional-integral-derivative(PID).The practical challenge,however,is to extract such signals from noisy measurements and this difficulty is addressed first by *** in the form of linear and nonlinear tracking differentiator(TD).While improvements were made,TD did not completely resolve the conflict between the noise sensitivity and the accuracy and timeliness of the *** two approaches proposed in this paper start with the basic linear TD,but apply iterative learning mechanism to the historical data in a moving window(MW),to form two new iterative learning tracking differentiators(IL-TD):one is a parallel IL-TD using an iterative ladder network structure which is implementable in analog circuits;the other a serial IL-TD which is implementable digitally on any computer *** algorithms are validated in simulations which show that the proposed two IL-TDs have better tracking differentiation and de-noise performance compared to the existing linear TD.

关键词： Tracking differentiator(TD) Iterative learning Iterative learning tracking differentiator(IL-TD) Active disturbance rejection control(ADRC)-Two-dimensional system(2-D system)

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Estimating ECG Intervals from Lead-I Alone: External Validation of Supervised Models

arXiv

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arXiv 2024年

作者： Alam, Ridwan Stultz, Collin M. the Research Laboratory of Electronics the Department of Electrical Engineering Computer Science the Massachusetts Institute of Technology United States

The diagnosis, prognosis, and treatment of a number of cardiovascular disorders rely on ECG interval measurements, including the PR, QRS, and QT intervals. These quantities are measured from the 12-lead ECG, either manually or using automated algorithms, which are readily available in clinical settings. A number of wearable devices, however, can acquire the lead-I ECG in an outpatient setting, thereby raising the potential for out-of-hospital monitoring for disorders that involve clinically significant changes in ECG intervals. In this work, we therefore developed a series of deep learning models for estimating the PR, QRS, and QT intervals using lead-I ECG. From a corpus of 4.2 million ECGs from patients at the Massachusetts General Hospital, we train and validate each of the models. At internal holdout validation, we achieve mean absolute errors (MAE) of 6.3 ms for QRS durations and 11.9 ms for QT intervals, and a MAE of 9.2 ms for estimating PR intervals. Moreover, as a well-defined P-wave does not always exist in ECG tracings – for example, when there is atrial fibrillation – we trained a model that can identify when there is a P-wave, and consequently, a measurable PR interval. We validate our models on three large external healthcare datasets without any finetuning or retraining - 3.2 million ECG from the Brigham and Women’s Hospital, 668 thousand from MIMIC-IV, and 20 thousand from PTB-XL - and achieve similar performance. Also, our models significantly outperform two publicly available baseline algorithms. This work demonstrates that ECG intervals can be tracked from only lead-I ECG using deep learning, and highlights the potential for out-of-hospital applications. © 2024, CC BY-NC-ND.

关键词： Electrocardiography

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Distributed Consensus Multi-Agent System Control of AC Microgrid Under Various Communication Disturbances

Distributed Consensus Multi-Agent System Control of AC Micro...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Ibtissam Kharchouf Mahmoud S. Abdelrahman Hossam M. Hussein S M Sajjad Hossain Rafin Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami USA

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

The resilience of AC microgrids (MGs) in the face of communication disturbances, including variable delays, packet loss, packet corruption (noise), and limited bandwidth, is studied. A Controller Hardware-in-the-Loop (CHIL) platform is used to investigate the ramifications of these disturbances comprehensively. The MG cyber-physical system (CPS) integrates a real-time islanded AC microgrid deployed on RT-LAB, distributed secondary consensus controllers on single-board computers, and a real communication network infrastructure. Through meticulous analysis and experimentation, this research highlights the vulnerabilities of AC MGs to communication disturbances and their detrimental effects on the system resiliency, stability, and efficiency. The experiments emphasize the need for designing robust and adaptive control strategies that can effectively mitigate the effects of communication disruptions and enhance the overall reliability and performance of AC microgrid systems.

关键词： Noise Packet loss Microgrids Bandwidth Cyber-physical systems Stability analysis Real-time systems Delays Resilience Multi-agent systems

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LSTM-Based Digital Twin Modeling for LVDC Microgrid Operation with Pulsed Load

LSTM-Based Digital Twin Modeling for LVDC Microgrid Operatio...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Mahmoud S. Abdelrahman Mustafa Esoofally Hossam Hussein Ibtissam Kharchouf Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami USA

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

Proper control and energy management is crucial for the effective, practical, and reliable operation of DC microgrids. In DC microgrids, the high certainties of renewables and the dynamic behavior of particular loads propose different modeling, operation, and control challenges. With recent advancements in information and communication technologies offering energy solution services relying on data analytics, machine learning (ML), and IoT technologies, the concept of digital twins (DT) has emerged as a feasible and active model to enhance the operation and resilience of microgrids. This paper proposes a long short-term memory (LSTM) neural networkbased strategy to construct a data-driven digital twin (DT) model of DC microgrids. The model is tested and validated with a standalone DC MG containing Renewable Energy Sources (RES), Energy Storage Sources (ESSs), and different load types. The accuracy of the created twin model is tested and evaluated under different scenarios of the microgrid operation, such as a sudden decrease in renewable generation and energizing pulsed load to the MG.

关键词： Renewable energy sources Accuracy Simulation Microgrids Real-time systems Digital twins Reliability Long short term memory Load modeling Resilience

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Neural Network Modeling Bias for Hafnia-based FeFETs 23

Neural Network Modeling Bias for Hafnia-based FeFETs

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Proceedings of the 18th ACM International Symposium on Nanoscale Architectures

作者： Osama Yousuf Imtiaz Hossen Andreu Glasmann Sina Najmaei Gina C. Adam Department of Electrical and Computer Engineering George Washington University USA DEVCOM Army Research Laboratory USA

ISBN: (纸本)9798400703256

Modeling bias – the difference between the test accuracy obtained by a reference network prototype and a simulated model of that prototype – is explored in the context of hafnia-based ferroelectric field effect transistor (FeFET) devices. Device operating conditions are investigated as a parameter for mitigating the impact of device-to-device variability on the underlying network performance. The computational framework includes a physics-based compact model with artificial variance to sample device data and a multivariate Kriging model to create jump table device models; this framework is a fast and efficient technique to model device populations for realistic neural network simulations. Neural network simulations elucidate optimal operating conditions for practical implementation of FeFET neuromorphic circuits. Future work will include experimental verification and performance comparison against other neuromorphic emerging-memory technologies such as resistive random-access memory (ReRAM) in terms of neural network performance.

关键词： Ferroelectric field effect transistor (FeFET) emerging device modeling hardware neural network neuromorphic hardware

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