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29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Gholami, Mohammadreza Faramarzi, Mahshad Pakravan, Mansooreh Alipour, Nima Department of Electrical and Computer Engineering Tarbiat Modares Univeristy Tehran Iran Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy

ISBN: (纸本)9798331523114

This study introduces a novel pipeline for feature extraction from Functional Magnetic Resonance Imaging (fMRI) data, focusing on age-related trends in node embeddings derived from causal brain graphs during naturalistic stimuli (movie observation). Directed Acyclic Graphs (DAGs) constructed using the Non-combinatorial Optimization via Trace Exponential and Augmented Lagrangian for Structure learning (NOTEARS) algorithm modeled causal relationships among 25 brain regions of interest (ROIs). Graph embeddings were generated using the DeepWalk algorithm to capture low-dimensional representations of graph structures. Unlike previous studies analyzing functional brain graphs with traditional graph metrics (e.g., degree, centrality, clustering coefficient), our approach employs modern techniques to uncover patterns in effective brain graphs. The results revealed decreasing feature similarity with age in regions associated with Theory of Mind (ToM), Pain, Face and Place Processing, reflecting age-related neural changes. Conversely, the Left Retrosplenial Cortex (lRSC) demonstrated increasing similarity, suggesting enhanced maturation and efficiency in spatial memory functions. Regions like the Left Fusiform Face Area (lFFA) and Right Transverse Occipital Sulcus (rTOS) showed no significant trends, emphasizing the role of functional relevance in brain responses to naturalistic stimuli. The novelty of our research lies in analyzing localized patterns in causal brain graphs using embedding techniques like DeepWalk, moving beyond traditional metrics. For the first time, features were extracted from fMRI data to study node-level similarities across age groups during naturalistic stimuli. By combining effective graph construction with localized embedding methods, this pipeline advances age-related brain connectivity research, demonstrating the consistent impact of movie stimuli on brain dynamics and the potential of graph-based methods to reveal age-related neural connectivity c

关键词： Graph embeddings

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Remarks on the Gradient Training of Linear Neural Network Based Feedback for the LQR Problem 63

Remarks on the Gradient Training of Linear Neural Network Ba...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： De Oliveira, Arthur Castello B. Siami, Milad Sontag, Eduardo D. Northeastern University Department of Electrical & Computer Engineering BostonMA02115 United States Northeastern University Department of Bioengineering BostonMA02115 United States

ISBN: (纸本)9798350316339

Motivated by the growing use of Artificial intelligence (AI) tools in control design, this paper takes steps toward bridging results from Direct Gradient methods for the Linear Quadratic Regulator (LQR), and neural networks. More specifically, it looks into the case where one wants to find a Linear Feed-Forward Neural Network (LFFNN) feedback that minimizes the LQR cost. This work develops gradient formulas that can be used to implement the training of such networks and derives an important conservation law of the system. This conservation law is then leveraged to prove the global convergence of solutions and invariance of the set of stabilizing networks under the training dynamics. These theoretical results are followed by an extensive analysis of the simplest version of the problem (the 'scalar case') and by numerical evidence of faster convergence of the training of general LFFNNs when compared to traditional direct gradient methods. © 2024 IEEE.

关键词： Gradient methods

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All-optical image classification through unknown random diffusers using a single-pixel diffractive network

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Light(Science & Applications) 2023年第4期12卷 570-584页

作者： Bijie Bai Yuhang Li Yi Luo Xurong Li Ege Cetintas Mona Jarrrahi Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCalifornia 90095USA Bioengineering Department University of CaliforniaLos AngelesCalifornia 90095USA California Nano Systems Institute(CNSI) University of CaliforniaLos AngelesCalifornia 90095USA

Classification of an object behind a random and unknown scattering medium sets a challenging task for computational imaging and machine vision *** deep learning-based approaches demonstrated the classification of objects using diffuser-distorted patterns collected by an image *** methods demand relatively large-scale computing using deep neural networks running on digital ***,we present an all-optical processor to directly classify unknown objects through unknown,random phase diffusers using broadband illumination detected with a single pixel.A set of transmissive diffractive layers,optimized using deep learning,forms a physical network that all-optically maps the spatial information of an input object behind a random diffuser into the power spectrum of the output light detected through a single pixel at the output plane of the diffractive *** numerically demonstrated the accuracy of this framework using broadband radiation to classify unknown handwritten digits through random new diffusers,never used during the training phase,and achieved a blind testing accuracy of 87.74±1.12%.We also experimentally validated our single-pixel broadband diffractive network by classifying handwritten digits"0"and"1"through a random diffuser using terahertz waves and a 3D-printed diffractive *** single-pixel all-optical object classification system through random diffusers is based on passive diffractive layers that process broadband input light and can operate at any part of the electromagnetic spectrum by simply scaling the diffractive features proportional to the wavelength range of *** results have various potential applications in,e.g.,biomedical imaging,security,robotics,and autonomous driving.

关键词： network processor random

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Estimating and Analyzing Neural Information flow using Signal Processing on Graphs 48

Estimating and Analyzing Neural Information flow using Signa...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Schwock, Felix Bloch, Julien Atlas, Les Abadi, Shima Yazdan-Shahmorad, Azadeh University of Washington Department of Electrical and Computer Engineering Seattle United States University of Washington Department of Bioengineering Seattle United States

ISBN: (纸本)9781728163277

Correlating neural communication in brain networks with behavior and cognition can provide fundamental insights into the functionality of both healthy and diseased brains. We demonstrate how communication in the brain can be estimated from recorded neural activity using concepts from graph signal processing. The communication is modeled as a flow signals on the edges of a graph and naturally arises from a graph diffusion process. We apply the diffusion model to micro-electrocorticography (ECoG) recordings from sensorimo-tor cortex of two non-human primates to estimate the neural communication flow during excitatory optogenetics. Comparisons with a baseline model demonstrate that adding the neural flow can improve ECoG predictions. Finally, we demonstrate how the neural flow can be decomposed into a gradient and rotational component and show that the gradient component depends on the location of stimulation. This technique, for the first time, offers the opportunity to study neural communication on an unprecedented spatiotemporal scale. © 2023 IEEE.

关键词： Brain

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computer Vision-Based Hydroponic Lettuce Contour and Area Recognition System

Computer Vision-Based Hydroponic Lettuce Contour and Area Re...

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2023 International Conference on electrical engineering and Informatics, ICEEI 2023

作者： Pranowo, Samuel Raymond Sukra, Zener Lie Asy'ari, Muhammad Zacky Paryasto, Marisa Binus Aso School of Engineering Bina Nusantara University Automotive and Robotics Program Computer Engineering Department Jakarta Indonesia Telkom University Computer Engineering Program Electrical Engineering Department Bandung Indonesia

ISBN: (纸本)9798350381290

Agriculture is a vital industry for the people of Indonesia, but there are several obstacles, including limited space in urban areas and inefficient conventional sorting and harvesting methods. Using computer vision to choose hydroponic lettuce that is ready to be harvested and a robotic arm to bring the selected lettuce yields, the produced system aims to solve this problem. The system's computer vision algorithms include color space conversion, intensity transformation, and an algorithm for following borders. This project's research methodology combines a quantitative approach with an experimental approach, which consists of conducting several trials on the built system. Several trials demonstrated that the computer vision software was able to accomplish the specified objectives. The average success rate of the developed computer vision system is 93%, while the average success rate of the robot arm is 85%. © 2023 IEEE.

关键词： Lettuce

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A Low-Noise Capacitively-Coupled Chopper Instrumentation Amplifier with SAR-Assisted Low Current Ripple Reduction Technique 21

A Low-Noise Capacitively-Coupled Chopper Instrumentation Amp...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Jo, Jungkook Jun, Jaehoon Inha University Department of Electrical and Computer Engineering Korea Republic of Inha University Program in Semiconductor and Device Korea Republic of

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377088

This paper presents a novel design technique for reducing ripple in various sensor read-out integrated circuits (IC) by calibrating the amplifier offset through current control in bias resistors. Traditional methods for reducing ripple often involve complex amplifiers consuming static power. In contrast, the proposed ripple reduction technique employs successive-approximation register (SAR) logic to control the current flowing through the bias resistor, thus minimizing static power consumption. Experimental results obtained through simulations in a 28 nm CMOS process demonstrate significant ripple reduction to 8.94 μVpp from 2 mVpp. This approach is suitable for low-power design, particularly in wearable device applications, and can potentially extend to other amplifier configurations utilizing chopping technique. © 2024 IEEE.

关键词： Chopper amplifiers

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Real Time Chest X-Ray Interpretation Applying Vision Transformer for Tuberculosis Detection

Real Time Chest X-Ray Interpretation Applying Vision Transfo...

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2024 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Irhamsyah, Muhammad Roslidar Yunidar Al Lail Qadrillah, M. Nasaruddin Arnia, Fitri Munadi, Khairul Universitas Syiah Kuala Post Graduate Program in Engineering Banda Aceh2311 Indonesia Faculty of Engineering Department of Electrical and Computer Engineering 2311 Indonesia

ISBN: (纸本)9798350368802

Tuberculosis (TB) is an infectious disease that can attack the lungs and other organs. In the diagnosis of TB, the interpretation of X-ray radiological images requires special skills from medical specialists. However, the lack of experience of medical personnel and focus on X-ray examinations can be a challenge for radiologists because it has the potential to cause diagnostic errors in image interpretation. Therefore, we proposed a web-based application of real-time chest X-ray interpreter by integrating the deep learning model of Vision Transformer (ViT) to interpret chest X-ray images. The inference model was trained and tested on 652 normal and 652 tuberculosis X-ray images. The data preprocessing of Contrast Adaptive Histogram Equalization (CLAHE) was implemented to increase chest X-ray image contrast as well as inference model performance. The proposed real time system can inteprete the tested chest x-ray images with an accuracy of 95% and with simulation duration time of 4.7 seconds. © 2024 IEEE.

关键词： Diagnosis

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High-Throughput Independent Component Analysis Processor for Full Duplex Systems

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IEEE Journal on Selected Areas in Communications 2023年第9期41卷 2821-2832页

作者： Cheng, Jen-Hao Chang, Tien-Min Shen, Chung-An Fouda, Mohammed E. Eltawil, Ahmed M. National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan University of California at Irvine Electrical Engineering and Computer Science Department IrvineCA92697 United States King Abdullah University of Science and Technology Electrical and Computer Engineering Program Thuwal23955 Saudi Arabia

This paper presents the algorithm and very-large-scale integration (VLSI) architecture of a high-throughput and highly efficient independent component analysis (ICA) processor for self-interference cancellation (SIC) in in-band full-duplex (IBFD) systems. This is the first VLSI architecture reported in the literature based on the state-of-the-art entropy bound minimization (EBM) approach. A novel ICA algorithm is presented in this paper with momentum gradient descent optimization. Simulation results show that the number of iterations for the proposed algorithm is significantly reduced compared to the conventional ICA algorithms. Furthermore, a novel early-distribution estimation scheme is proposed in the designed ICA processor to compute multiple distribution functions with low latency and low complexity. The processing flow and the efficiency for the hardware utilization are specifically designed so that the processing speed is maximized with minimum employment of hardware components. The proposed ICA processor is designed and implemented based on the application-specific-integrated circuit (ASIC) flow. The post-layout estimations show that compared with the conventional EBM-based scheme, the proposed design improves the throughput and efficiency by 30x. In addition, compared to prior designs shown in the literature, the proposed ICA processor also demonstrates a significant enhancement in terms of throughput and efficiency. © 1983-2012 IEEE.

关键词： VLSI circuits

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Imaging Snapshot Spectrometers enabled with 2-Photon Polymerization based Additive Manufacturing

Imaging Snapshot Spectrometers enabled with 2-Photon Polymer...

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2024 Imaging Systems and Applications, ISA 2024 - Part of Optica Imaging Congress

作者： Tkaczyk, Tomasz S. Lu, Jiawei Cao, Haimu Department of Bioengineering Rice University HoustonTX77005 United States Electrical and Computer Engineering Rice University HoustonTX77005 United States

The work presents examples of two imaging spectrometers enabled with 2-Photon Polymerization 3D printing. The presented spectrometers are field integral devices and image reorganization is achieved with high density waveguide array (waveguide spectrometer) or multifaceted, multi-angle mirror array respectively (Image Mapping Spectrometer - IMS). Fabricated components allow void spaces between image points dedicated for spectral information. High precision and repeatability of the fabricated components simplifies system calibration, improved image quality allows and more compact systems implementations. © 2024 The Author(s).

关键词： Spectrometers

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Experimental measurement and numerical modeling of deformation behavior of breast cancer cells passing through constricted microfluidic channels

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Microsystems & Nanoengineering 2024年第1期10卷 129-147页

作者： Pouyan Keshavarz Motamed Hesam Abouali Mahla Poudineh Nima Maftoon Department of Systems Design Engineering University of WaterlooWaterlooON N2L 3G1Canada Center for Bioengineering and Biotechnology University of WaterlooWaterlooON N2L 3G1Canada Department of Electrical and Computer Engineering University of WaterlooWaterlooON N2L 3G1Canada

During the multistep process of metastasis,cancer cells encounter various mechanical forces which make them deform *** accurate in-silico models,capable of simulating the interactions between the mechanical forces and highly deformable cancer cells,can pave the way for the development of novel diagnostic and predictive methods for metastatic ***-network models of cancer cell,empowered by our recently proposed identification approach,promises a versatile numerical tool for developing experimentally validated models that can simulate complex interactions at cellular *** this numerical tool,we presented spring-network models of breast cancer cells that can accurately replicate the experimental data of deformation behavior of the cells flowing in a fluidic domain and passing narrow constrictions comparable to ***,using high-speed imaging,we experimentally studied the deformability of breast cancer cell lines with varying metastatic potential(MCF-7(less invasive),SKBR-3(medium-high invasive),and MDA-MB-231(highly invasive)in terms of their entry time to a constricted microfluidic *** observed that MDA-MB-231,that has the highest metastatic potential,is the most deformable cell among the ***,by focusing on this cell line,experimental measurements were expanded to two more constricted microchannel *** experimental deformability data in three constricted microchannel sizes for various cell sizes,enabled accurate identification of the unknown parameters of the spring-network model of the breast cancer cell line(MDA-MB-231).Our results show that the identifed parameters depend on the cell size,suggesting the need for a systematic procedure for identifying the size-dependent parameters of spring-network models of *** the numerical results show,the presented cell models can simulate the entry process of the cell into constricted channels with very good agreements with the measured experimen

关键词： strict passing numerical

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