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American Control Conference (ACC)

作者： Sabbir Ahmed Erfan Nozari Department of Electrical and Computer Engineering University of California Riverside Departments of Mechanical Engineering Electrical and Computer Engineering and Bioengineering University of California Riverside

Application of low pass filters (LPFs) to remove noise components is a widely used methodology for processing signals acquired from diverse systems. LPFs are also intrinsic components of many natural and man-made systems, both intended and epiphenomenal, from electromechanical systems to traffic networks and the brain. Across all cases, the effects of LPFs are often studied in a pure filtering sense, such as temporal smoothing and removing high-pass noise components, causing delays and phase distortions, or limiting bandwidths. In this work, we instead show that low-pass filtering and the temporal averaging that underlies it can also have a major and fundamental impact on the linearity of the dynamics. We show using rigorous analysis that across a wide range of stochastic nonlinear systems, temporal averaging dampens nonlinearities and leads to more and more linear dynamics with stronger temporal averaging (lower LPF cutoff frequency), leading asymptotically to a completely linear system as the width of the window over which temporal averaging occurs tends to infinity (LPF cutoff frequency tends to zero). Our results have major implications in a wide range of application areas, including the study of the nervous system whereby LPFs are biologically and algorithmically abundant and a growing body of empirical evidence has found linear models as capable as nonlinear ones in describing neuronal time series.

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Image denoising using diffractive optical processors

Image denoising using diffractive optical processors

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Isil, Çagatay Gan, Tianyi Ardic, F. Onuralp Mentesoglu, Koray Digani, Jagrit Karaca, Huseyin Chen, Hanlong Li, Jingxi Mengu, Deniz Jarrahi, Mona Aksit, Kaan Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States University College London Department of Computer Science London United Kingdom

We report an image denoising analog processor composed of passive diffractive layers engineered through deep learning to filter out various types of noise from input images, instantly projecting denoised images at the... 详细信息

ISBN: (纸本)9781957171395

关键词： Image denoising

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Automatic Nuclei Segmentation of Label-free Chromatin-sensitive Partial Wave Spectroscopic Microscopy using Convolution Neural Network with Transformer

Automatic Nuclei Segmentation of Label-free Chromatin-sensit...

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Label-Free Biomedical Imaging and Sensing, LBIS 2025

作者： Alom, Md Shahin Daneshkhah, Ali Acosta, Nicolas Liwag, Emily Pujadas Kuo, Tiffany Ye, Rachel Pritchard, Joshua A. Bhattarai, Narayan Backman, Vadim Gaire, Sunil Kumar Department of Electrical and Computer Engineering North Carolina Agricultural and Technical State University GreensboroNC27411 United States Department of Biomedical Engineering Northwestern University EvanstonIL60208 United States Department of Chemical Biological and Bioengineering North Carolina Agricultural and Technical State University GreensboroNC27411 United States

ISBN: (纸本)9781510684102

Chromatin-sensitive Partial Wave Spectroscopic (csPWS) microscopy is a label-free spectroscopic optical nanosensing technique used to study the dynamic chromatin packing structure in the cell nucleus. Chromatin, composed of DNA wrapped around histones to form nucleosomes, organizes into supranucleosomal disordered chromatin chains, which assemble into packing domains (PDs). Dysregulation of these chromatin PDs is a common factor in the transcriptional phenotypic plasticity by which cancer cells change their state without changing their genes. csPWS microscopy quantifies the relationship between the genomic physical scales of these nanoscale chromatin PDs, enabling the evaluation of cancer-promoting markers in the nucleus for cancer screening or treatment care. Nucleus segmentation is a crucial task to ensure accurate analysis of chromatin PDs and spatial analysis, such as assessment of peripheral regions and nucleus shape. Traditional manual nuclei segmentation is labor-intensive, time-consuming, and prone to user bias, leading to errors due to over or under-segmentation. We present a novel hybrid convolutional neural networks-transformer-based approach for automatic cell nuclei segmentation with enhanced accuracy and speed for csPWS microscopy. Our method learns both local and global features from the input images for precise cell nuclei segmentation. We trained our model with several hundred csPWS images (>15,000 nuclei) from HCT116 and U2OS cell lines. With weighted-focal and dice loss, our model achieved median IoU and DSC scores of 0.77 and 0.89 for HCT116 cells and 0.75 and 0.85 for U2OS cells. This automated, rapid, and accurate segmentation streamlines csPWS microscopy data analysis, significantly aiding chromatin research for cancer diagnosis and treatment. Our deep-learning technique can also be leveraged by other microscopy techniques requiring segmentation, broadening its potential across various optical imaging modalities in biomedical research. © 2025

关键词： Cell culture

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Efficient Galvanic Body-Coupled Powering for Wireless Implanted Neurostimulators

Efficient Galvanic Body-Coupled Powering for Wireless Implan...

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IEEE Biomedical Circuits and Systems (BIOCAS)

作者： Asif Iftekhar Omi Emma Farina Anyu Jiang Adam Khalifa Shriya Srinivasan Baibhab Chatterjee Department of Electrical and Computer Engineering University of Florida Gainesville USA Department of Bioengineering Harvard University Boston USA

ISBN: (数字)9798350354959

ISBN: (纸本)9798350354966

Body-coupled powering (BCP) is an innovative wireless power transfer (WPT) technique, recently explored for its potential to deliver power to cutting-edge biomedical implants such as nerve/muscle stimulators. This paper demonstrates the efficient technique of designing WPT systems embedding BCP via galvanic coupling (G-BCP). The G-BCP configuration utilizes two metal circular rings surrounding the body area of interest as the transmitter (TX) electrodes required for galvanic (differential) excitation and a wireless implant as the receiver (RX) equipped with two electrodes for differential power reception accordingly. By focusing on the unique advantages of this approach—such as enhanced targeting accuracy, improved power transfer efficiency (PTE), and favorable tissue penetration characteristics, G-BCP emerges as a superior alternative to traditional WPT methods. A comprehensive analysis is conducted to obtain the optimized device parameters while simultaneously allowing flexible placement of implants at different depths and alignments. To substantiate the proposed design concept, a prototype was simulated in Ansys HFSS, employing a multi-layered tissue medium of 10 mm radius and targeting the sciatic nerve of a rat. Impressively, this prototype achieves $\gt 20 \%$ PTE at 1.25 GHz, with the implant (radius of RX electrodes $=1 \mathrm{~mm}$) located 2 mm deep inside the tissue model having complex load impedance of $R_{\text {load}}=1000 \Omega$ and $C_{\text {load}}=5 p F$. Therefore, the G-BCP-based wirelessly powered microdevices are envisaged to be a key enabler in neural recording and stimulation, specifically for the peripheral nervous system, enhancing therapeutic outcomes and patient experiences.

关键词： Wireless communication Electrodes Transmitters Circuits Prototypes Implants Wireless power transfer Recording Standards Resilience

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Digital Phantoms for Quantitative Ultrasound and Photoacoustic Characterization of Thrombosis

Digital Phantoms for Quantitative Ultrasound and Photoacoust...

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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Momina Masood Sullivan Lauderdale Alycen Wiacek Department of Electrical and Computer Engineering Oakland University Rochester MI Department of Bioengineering Oakland University Rochester MI

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Venous thrombosis, or blood clot formation in veins, poses significant health risks, including pulmonary embolism if untreated. Ultrasound imaging is commonly used for diagnostic purposes due to its portability and safety. However, B-mode ultrasound alone is limited in its ability to quantitatively assess blood clot composition, which is crucial for effective treatment planning. Photoacoustic imaging, combining optical and acoustic methods, provides high molecular contrast and detailed tissue information based on the optical absorption properties of different tissues. In this study, we combined quantitative ultrasound and photoacoustic imaging to optimize thrombus characterization for improved diagnosis. We created a digital phantom representing a 3D heterogeneous blood vessel model, incorporating both optical and mechanical properties of blood clots and surrounding tissues by using the Monte Carlo and k-Wave toolboxes. We varied clot properties such as clot composition, size, and depth during the simulations. Quantitative parameters, including lag-one coherence (LOC), signal amplitude, spectral slope, and midband fit, were computed to assess their predictive relationships with collagen content in the clots. Results showed that photoacoustic signal amplitude strongly correlated with collagen content (R 2 = 0.94), significantly outperforming the ultrasound signal amplitude (R 2 = 0.38). The other quantitative parameters, such as spectral slope and midband fit, also demonstrated strong correlations with tissue microstructure in photoacoustic signals, with R 2 > 0.7. These results demonstrate the potential of a combined imaging approach as a non-invasive method for the quantitative monitoring of thrombosis, particularly in resource-limited settings.

关键词： Ultrasonic imaging Three-dimensional displays Photoacoustic imaging Computational modeling Phantoms Coagulation Optical imaging Feature extraction Acoustics Thrombosis

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A touch-based, cryptographic, smart medication dispensing system 26

A touch-based, cryptographic, smart medication dispensing sy...

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26th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2022

作者： Zhu, Jialun Lin, Shuyu Yu, Wenzhuo Emaminejad, Sam Department of Electrical and Computer Engineering University of California Los Angeles United States Department of Bioengineering University of California Los Angeles United States

ISBN: (纸本)9781733419048

Monitoring the circulating drug levels is critical for the evaluation of medication adherence and prevention of abuse. However, current methods rely on cumbersome sample collection and analysis procedures, are prone to tampering, and are incapable of addressing privacy challenges pertaining to the protection of such sensitive data. To this end, we present a non-invasive and cryptographic drug monitoring modality that allows for acquiring and encrypting the drug level with one-touch and in a sample-to-answer manner. In this approach, we leveraged the fact that many circulating drug molecules partition onto the fingertip surface (primarily via natural perspiration) and in correlation to their circulating levels. To harvest and measure the drug molecule's epidermal flux in-situ, we specifically engineered a thin-hydrogel-coated electrochemical sensor ("TH-sensor";here, adapted to target acetaminophen, a widely used analgesic medication). By integrating the TH-sensor with a biometric unit and robotic arm, we demonstrated a touch-based smart medication dispensing system that bio-authenticates the user (based on their drug level and identity) prior to drug dispensing, verifies the medication intake, while preserving the user's health data privacy via in-situ encryption (utilizing the user's minutiae pattern as a biometric key). © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

关键词： Hydrogels

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A touch-based alcohol intake detection system for in-vehicle bio-authentication and DUI prevention 26

A touch-based alcohol intake detection system for in-vehicle...

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26th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2022

ISBN: (纸本)9781733419048

Alcohol abuse and driving under the influence (DUI) claim > 10,000 lives each year in the United States alone. To manage and prevent such personal/societal harmful outcomes, rapid, reliable, and accessible alcohol monitoring is desired. To this end, we present an in-vehicle bio-authentication system that can simultaneously detect the driver's alcohol/physiological state and identity with a single touch. The core of the system is a thin hydrogel-coated enzymatic sensor (ethanol TH-sensor), which can sample and sense the circulating ethanol molecules that partition onto the fingertip. By deploying this system in human subject studies, we illustrated its potential utility for preventing driving under influence and promoting road safety. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

关键词： Hydrogels

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Cluster-Then-Label Strategy for Sleep Detection U sing Electroencephalogram (EEG) 2

Cluster-Then-Label Strategy for Sleep Detection U sing Elect...

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2nd IEEE International Conference on Data Science and computer Application, ICDSCA 2022

作者： Guo, Yifan Mao, Helen X. Yin, Jijun Mao, Zhi-Hong Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States North Allegheny Senior High School WexfordPA United States Department of Electrical and Computer Engineering Department of Bioengineering University of Pittsburgh PittsburghPA United States

ISBN: (数字)9781665472005

ISBN: (纸本)9781665472005

Data deficiency has long been a major obstacle for many areas of machine learning. Manually labeling data could be utterly time-consuming and expensive, especially in medical fields including the sleep research. To address this challenge, we propose a cluster-then-label strategy, which inserts clustering algorithms into traditional supervised learning pipelines. In this paper, we demonstrate the cluster-then-label strategy for sleep detection using electroencephalogram (EEG) data and show that our proposed strategy can boost the classifier's performance on previously unseen subjects. We also developed a method based on nonlinear transformations that can reshape the feature distributions to resemble normal distributions with which the cluster-then-label algorithm becomes more efficient and robust. © 2022 IEEE.

关键词： Electroencephalography

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Filament-free Bulk RRAM with High Endurance and Long Retention for Neuromorphic Few-Shot Learning On-Chip

Filament-free Bulk RRAM with High Endurance and Long Retenti...

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2024 IEEE International Electron Devices Meeting, IEDM 2024

作者： Kumar, Ashwani Zhou, Yucheng Potladurthy, Sai Praneeth Kim, Jeong-Hoon Xu, Weihong Ponzina, Flavio Kim, Seonghyun Cubukcu, Ertugrul Rosing, Tajana Cauwenberghs, Gert Kuzum, Duygu Department of Electrical and Computer Engineering University of California San DiegoCA United States Department of Bioengineering University of California San DiegoCA United States Department of Computer Science University of California San DiegoCA United States Department of NanoEngineering University of California San DiegoCA United States SK Hynix Korea Republic of

ISBN: (纸本)9798350365429

Bulk switching RRAM devices have emerged to address nonidealities of filamentary RRAM for AI at the edge. However, long retention and high endurance needed for continual on-chip learning have yet to be demonstrated. Here, we report a filament-free and multi-level bulk RRAM (b-RRAM) technology with long retention and high endurance. The oxygen vacancy gradient across the TiOx switching layer and Al2O3 tunnel barrier are designed to suppress filament formation and achieve multi-level switching through modulation of oxygen vacancy distribution. Al-Ti-O oxygen barrier enables reliable switching with high uniformity, retention and endurance. We develop a compact model that captures both DC and pulse switching. MΩ-level resistance and switching current nonlinearity allow highly accurate read/write operations and matrix vector multiplications (MVM) in selectorless b-RRAM crossbars. A neuro-inspired few-shot learning (FSL) algorithm based on dendritic computation and behavioral time plasticity (BTSP) is mapped onto b-RRAM crossbar arrays. Weights are continuously updated on-chip to learn shortest navigation to reward within a few trials in a 2D maze. We also present FSL of large-size images with hyperdimensional (HD) computing using our b-RRAM model. System-level simulations with 64-level b-RRAM arrays show an increase in accuracy by 5.9 % and 5.4 % for a 5-way 10-shot learning of CIFAR-100 and CUB-200 datasets as compared to the state-of-the-art FSL-HD. © 2024 IEEE.

关键词： RRAM

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On the Non-Existence of Immersions for Systems with Multiple Omega-Limit Sets

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IFAC-PapersOnLine 2023年第2期56卷 60-64页

作者： Zexiang Liu Necmiye Ozay Eduardo D. Sontag Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109 USA Department of Electrical and Computer Engineering and Department of Bioengineering Northeastern University Boston MA 02115 USA

Linear immersions (or Koopman eigenmappings) of a nonlinear system have wide applications in prediction and control. In this work, we study the existence of one-to-one linear immersions for nonlinear systems with multiple omega-limit sets. For this class of systems, existing work shows that a discontinuous one-to-one linear immersion may exist, but it is unclear if a continuous one-to-one linear immersion exists. Under mild conditions, we prove that systems with multiple omega-limit sets cannot admit a continuous one-to-one immersion to a class of systems including linear systems. Multiple examples are studied to verify our results.

关键词： Nonlinear Systems Immersion Koopman Operator Equivalent Systems

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