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EventLFM:event camera integrated Fourier light field microscopy for ultrafast 3D imaging

Light(Science & Applications) 2024年第7期13卷 1401-1415页

作者： Ruipeng Guo Qianwan Yang Andrew S.Chang Guorong Hu Joseph Greene Christopher V.Gabel Sixian You Lei Tian Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Physiology and Biophysics Boston UniversityBostonMA 02215USA. Neurophotonics Center Boston UniversityBostonMA 02215USA. Research Laboratory of Electronics(RLE)in the Department of Electrical Science and Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA. Department of Biomedical Engineering Boston UniversityBostonMA 02215USA

Ultrafast 3D imaging is indispensable for visualizing complex and dynamic biological *** scanning-based techniques necessitate an inherent trade-off between acquisition speed and space-bandwidth product(SBP).Emerging single-shot 3D wide-field techniques offer a promising alternative but are bottlenecked by the synchronous readout constraints of conventional CMOS systems,thus restricting data throughput to maintain high SBP at limited frame *** address this,we introduce EventLFM,a straightforward and cost-effective system that overcomes these challenges by integrating an event camera with Fourier light field microscopy(LFM),a state-of-theart single-shot 3D wide-field imaging *** event camera operates on a novel asynchronous readout architecture,thereby bypassing the frame rate limitations inherent to conventional CMOS *** further develop a simple and robust event-driven LFM reconstruction algorithm that can reliably reconstruct 3D dynamics from the unique spatiotemporal measurements captured by *** results demonstrate that EventLFM can robustly reconstruct fast-moving and rapidly blinking 3D fluorescent samples at kHz frame ***,we highlight EventLFM’s capability for imaging of blinking neuronal signals in scattering mouse brain tissues and 3D tracking of GFP-labeled neurons in freely moving *** believe that the combined ultrafast speed and large 3D SBP offered by EventLFM may open up new possibilities across many biomedical applications.

关键词： field linking overcome

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Spectral Learning of Shared Dynamics Between Generalized-Linear Processes 38

Spectral Learning of Shared Dynamics Between Generalized-Lin...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Oganesian, Lucine L. Sani, Omid G. Shanechi, Maryam M. Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Ming Hsieh Department of Electrical and Computer Engineering Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los AngelesCA United States

Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usage of GLDMs is to model a single data source, certain applications require jointly modeling two generalized-linear time-series sources while also dissociating their shared and private dynamics. Most existing GLDM variants and their associated learning algorithms do not support this capability. Here we address this challenge by developing a multi-step analytical subspace identification algorithm for learning a GLDM that explicitly models shared vs. private dynamics within two generalized-linear time-series. In simulations, we demonstrate our algorithm's ability to dissociate and model the dynamics within two time-series sources while being agnostic to their respective observation distributions. In neural data, we consider two specific applications of our algorithm for modeling discrete population spiking activity with respect to a secondary time-series. In both synthetic and real data, GLDMs learned with our algorithm more accurately decoded one time-series from the other using lower-dimensional latent states, as compared to models identified using existing GLDM learning algorithms. © 2024 Neural information processing systems foundation. All rights reserved.

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Factors Affecting the Performance of FastICA Algorithm for Decomposition of High-Density Surface Electromyogram 9th

Factors Affecting the Performance of FastICA Algorithm for D...

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9th Latin American Congress on biomedical engineering, CLAIB 2022 and 28th Brazilian Congress on biomedical engineering, CBEB 2022

作者： Castilhos, Mateus Augusto Schneider Elias, Leonardo Abdala Department of Electronics and Biomedical Engineering School of Electrical and Computer Engineering University of Campinas SP Campinas Brazil Neural Engineering Research Laboratory Center for Biomedical Engineering University of Campinas SP Campinas Brazil

ISBN: (纸本)9783031494031

The motor unit (MU) physiology and the neural control of human movement can be studied via surface electromyogram (sEMG) decomposition techniques, which estimate the discharge times of MUs during a given contraction. The recent decomposition methods are based on the blind source separation (BSS) methods, and some approaches such as independent component analysis (ICA) can be applied to solve the problem. This study aims to investigate the influence of several parameters of the fast independent component analysis (FastICA) algorithm in the decomposition process of high-density sEMG. The extension factor, the number of iterations, and the method of initialization of the separation vector were explored for three evaluation metrics, namely, the execution time of decomposition, the absolute number of unique decomposed MUs, and the ratio of the number of unique MUs to the total number of extracted MUs. The number of iterations increased the execution time of decomposition and the absolute number of unique decomposed MUs. Besides, the method of initialization of the separation vector had little influence on the execution time of decomposition, an optimal value can be achieved for the absolute number of unique decomposed MUs. Moreover, the repeated convergence of the FastICA algorithm to the same source was mainly affected by the number of iterations. The study has reinforced the importance of evaluating the parameter combinations to achieve a better FastICA decomposition performance based on the chosen evaluation metric. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Blind source separation

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Controlling Biomolecular Timekeeping via Regulated Gene Product Degradation 63

Controlling Biomolecular Timekeeping via Regulated Gene Prod...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Rezaee, Sayeh Nieto, Cesar Singh, Abhyudai University of Delaware Department of Electrical and Computer Engineering NewarkDE United States University of Delaware Biomedical Engineering Mathematical Sciences Center of Bioinformatic and Computational Biology Department of Electrical and Computer Engineering NewarkDE United States

ISBN: (纸本)9798350316339

Numerous biological timing mechanisms are associated with the duration required for key regulatory molecules to reach critical threshold levels. Recent studies have mainly explored how random fluctuations affect the timing of events as molecules accumulate to reach a high threshold. This study examines the complementary scenario: decrease in protein levels until they reach a low critical threshold. We aim to study the timing precision resulting from different degradation pathways and their inherent stochastic nature, especially under low-copy number conditions. More specifically, we explore how integer-valued protein molecular counts decrease stochastically following Michaelis-Menten kinetics. This approach encompasses both zero-order (where the net degradation rate is independent of protein levels) and first-order (where the rate is proportional to protein levels) decay processes. Our results show that, while zero-order decay can be sluggish in terms of mean timing, it provides the highest precision in timing with the lowest noise in threshold-crossing time. Conversely, upon considering randomness in the initial protein levels, first-order decay demonstrates better precision compared to zero-order. Interestingly, in the presence of multiple noise sources, timing stochasticity can be minimized when Michaelis-Menten decay kinetics operate under sub-saturation conditions. This characterization of timing driven by decreasing copy numbers has implications for improving the precision of biological clocks and understanding the timing of cell-cycle events. © 2024 IEEE.

关键词： Stochastic systems

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Weak and Extremely Low Frequency Magnetic Fields Alter the Growth Rates of HT-1080 Fibrosarcoma

Weak and Extremely Low Frequency Magnetic Fields Alter the G...

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2025 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2025

作者： Dang, Nhat Keller, Jason Bajtos, Marek Gurhan, Hakki Barnes, Frank Department of Electrical Computer and Energy Engineering College of Engineering and Applied Science University of Colorado Boulder BoulderCO United States Department of Electromagnetics and Biomedical Engineering Faculty of Electrical Engineering and Information Technology University of Zilina Zilina Slovakia

ISBN: (纸本)9781946815200

This study presents experimental results showing that weak, extremely low frequency electromagnetic (EM) fields influence the growth rates of HT-1080 fibrosarcoma cells in vitro and offers a theoretical explanation for these effects. The results demonstrate that weak magnetic fields both increase and decrease the growth rates of fibrosarcoma cells by altering the mitochondrial superoxide (O2-) and calcium ions (Ca2+) concentrations in living cells. Over a 4-day exposure period, fibrosarcoma growth rates were modulated in the presence of low-level alternating current (AC) magnetic fields at a 10 μT amplitude at 13.5 Hz to 30 Hz frequency, superimposed on a 45 μT static background field. Effects due to changes in amplitude from 10 to 30 μT at selected frequencies are also investigated with the same superimposed static field. © 2025 USNC-URSI.

关键词： Electromagnetic field effects

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PWM-Based Hardware Authentication for Smart Inverters: Detecting Counterfeit Inverter through Transient Response Analysis

PWM-Based Hardware Authentication for Smart Inverters: Detec...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Hingu, Chanakya Fu, Xingang Mishan, Ramkrishna Kim, Taesic University of Nevada Reno Department of Electrical and Biomedical Engineering Reno United States University of Missouri-Columbia Department of Electrical Engineering and Computer Science Columbia United States

ISBN: (纸本)9798350376067

Amidst the growing complexity of inverter-based power systems, the security of smart inverters is becoming increasingly critical, particularly for protecting power electronics components from counterfeit threats. Typical methods of PCB hardware authentication utilize a signal injection to the PCB through a hardware interface such as JTAG, which might be impractical in smart inverters. This paper presents a cost-effective hardware authentication method using Pulse Width Modulation (PWM) and transient response to detect counterfeit solar inverters. By conducting a detailed analysis and comparing the transient responses from both inverters, this work reveals significant differences that can serve as analog signatures for authentication. The effectiveness of this validation method is demonstrated through an extensive statistical analysis, providing a solid basis for implementing this strategy as a safeguard against counterfeit penetration in power systems. Additionally, this method shows a potential tool for industry practitioners and researchers to verify, detect, and distinguish between counterfeit and genuine printed circuit boards, enhancing the security posture of power systems infrastructure. © 2024 IEEE.

关键词： Transient analysis

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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2024 Prognostics and System Health Management Conference, PHM 2024

作者： Cai, Congrui Lu, Zhonghai School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Embedded Systems Master Program Sweden School of Electrical Engineering and Computer Science Department of Electrical Engineering KTH Royal Institute of Technology Sweden

ISBN: (纸本)9798350360585

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms. © 2024 IEEE.

关键词： Long short-term memory

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Detection of Mycobacterium Tuberculosis Using Real-time Detection Transformer 25

Detection of Mycobacterium Tuberculosis Using Real-time Dete...

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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Umam, Khairul Purnama, I. Ketut Eddy Rachmadi, Reza Fuad Wulandari, Sari Ayu Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Department of Computer Engineering Surabaya Indonesia Universitas Dian Nuswantoro Department of Biomedical Engineering Semarang Indonesia

ISBN: (纸本)9798350378573

Tuberculosis is a severe disease caused by Mycobacterium tuberculosis (MT). In handling it, the sputum examination method was used because it is relatively cheap and easy. The importance of MT detection in medical personnel analysis. We propose detection of Mycobacterium tuberculosis (MT) with a 1-stage detection object using two models: Detection Transformer (DETR) and Real-time Detection Transformer (RT-DETR) with different backbone variations to determine the performance of both models and conduct experiments using other models, such as yolov5l and Faster-RCNN, to compare their evaluation results with those of the DETR and RT-DETR models. In this study, MT detection using DETR with the Backbone ResNet 101 variation obtained AP:50 = 0.681 and AP:50:95 = 0.276. The RT-DETR model with HGNetv2l backbone variations yielded AP:50 = 0.837 and AP:50:95 = 0.147. The Yolov5l model produced AP:50 = 0.75 and AP:50:95 = 0.326 values. while the Faster-RCNN model produced AP:50 = 0.696 and AP:50:95 = 0.30. The results of RT-DETR with the HGNetv2-l backbone demonstrated good performance in the detection of Mycobacterium tuberculosis. © 2024 IEEE.

关键词： detection detection transformer mycobacterium tuberculosis transformer tuberculosis

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A Cyclic Vernier Digital-to-Time Converter for Time-Mode Successive Approximation TDC 66

A Cyclic Vernier Digital-to-Time Converter for Time-Mode Suc...

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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Lee, Daniel Junehee Yuan, Fei Zhou, Yushi Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering TorontoON Canada Lakehead University Department of Electrical and Computer Engineering Thunder BayON Canada

ISBN: (纸本)9798350302103

This paper presents an 8-bit cyclic Vernier digital-to-time converter (DTC) for time-mode successive approximation register time-to-digital converters (SAR TDCs). The DTC offers a low degree of mismatch-induced nonlinearity hence a small lower bound of the dynamic range, a virtually unlimited upper bound of the dynamic range, and a small silicon area. The principle, operation, design, and calibration of the DTC are presented. The DTC is designed in TSMC 130 nm 1.2 V technology and analyzed using Spectre with BSIM3v3 device models. Simulation results show that the DTC achieves a dynamic range of 2.31 591.4 ps, a DNL of 0.37 LSB, an INL of 0.66 LSB, and consumes 1.49 mW. © 2023 IEEE.

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Exploring Biological Features of Electroencephalography Signals for Explanation-Guided Learning

Exploring Biological Features of Electroencephalography Sign...

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2024 International Conference on Electronics, Information, and Communication, ICEIC 2024

作者： Joo, Hyosung Kim, Dongseok Trang, Le Thi Quan, Luong Do Anh Woo, Jihwan University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan Korea Republic of University of Ulsan Department of Biomedical Engineering Ulsan Korea Republic of

ISBN: (纸本)9798350371888

Explanation-guided learning (EGL) has gained prominence for improving both the explainability and performance of deep neural networks by integrating additional supervision signals based on the elucidation of the model. However, its applicability in analyzing electroencephalography (EEG) signals remains underexplored. This study attempted to identify and evaluate biological features for EGL-based EEG analysis using compact convolutional neural networks. The identified features included anatomical location, dipolarity, relative peak power, and event-related desynchronization of the cortical source. The feasibility of the features was evaluated with performance prediction tests, which predict model performance based on the interpreted features. All features except dipolarity possessed a significant relationship with model performance. These findings indicate the possibility of utilizing these features as biological supervision signals in future EGL-based EEG analysis. © 2024 IEEE.

关键词： Electroencephalography

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