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OAM-based diffractive all-optical classification

Advanced Photonics 2024年第1期6卷 6-7页

作者： Md Sadman Sakib Rahman Aydogan Ozcan University of California Electrical and Computer Engineering DepartmentLos AngelesCaliforniaUnited States University of California Bioengineering DepartmentLos AngelesCaliforniaUnited States University of California California NanoSystems Institute(CNSI)Los AngelesCaliforniaUnited States

Object classification is an important aspect of machine *** practices in object classification entail the digitization of object information followed by the application of digital algorithms such as deep neural *** execution of digital neural networks is power-consuming,and the throughput is *** existing von Neumann digital computing paradigm is also less suited for the implementation of highly parallel neural network architectures.^(1)

关键词： consuming digit neural

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Multi-objective optimization of electric automated bus trajectories based on the ε-constraint method

Multi-objective optimization of electric automated bus traje...

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Mediterranean Conference on Control and Automation (MED)

作者： C. Pasquale S. Sacone S. Siri A. Ferrara Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Italy

This paper deals with electric automated buses that have to follow a given route in inter-urban roads including stops, with a given timetable. Some stops are provided with a charging infrastructure allowing to charge the batteries while others are not. In order to control these buses, it is necessary to account for the traffic conditions along the road and to minimize two objectives, respectively related to the minimization of the deviations from the timetable and the minimization of the energy lack, at the end of the bus route, with respect to a desired final energy level. To address this problem and to investigate the conflicting nature of these objectives, two multi-objective methods based on the $\varepsilon$-constraint approach are applied in this paper, allowing to find different sets of efficient solutions for the problem. The results obtained in a real case study show that the two objective are in conflict, and compromise solutions can be found using the methods proposed in this paper.

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UMAP for Dimensionality Reduction in Sleep Stage Classification Using EEG Data

UMAP for Dimensionality Reduction in Sleep Stage Classificat...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yangfan Deng Hamad Albidah Haoliang Cheng Ahmed Dallal Jijun Yin Zhi-Hong Mao Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA USA Department of Computer Science University of Pittsburgh Pittsburgh PA USA Department of Bioengineering University of Pittsburgh Pittsburgh PA USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Sleep is vital for wellness, and electroencephalography (EEG) serves as an instrumental tool in the study of sleep. Sleep is classified into four stages: stages N1-N3, and rapid eye movement (REM). To acquire effective and robust EEG features for sleep detection and analysis, we explore the dimensionality reduction effects of Uniform Manifold Approximation and Projection (UMAP) on various features of the EEG signals. Compared with traditional band power analysis, UMAP demonstrated higher accuracy for sleep stage classification and better reliability. Using UMAP, we observed an average of 11% increase in accuracy and an average of 20% increase in Macro-F1 Score on the same dataset. Particularly, in the wakefulness stage, Macro-F1 Score increased by 23%. Moreover, the 2D visual analysis revealed the outstanding ability of UMAP to cluster EEG signals after significant dimensionality reduction of the data.

关键词： Dimensionality reduction Visualization Accuracy Sleep Power system harmonics Harmonic analysis Feature extraction Electroencephalography Robustness Rapid eye movement sleep

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Title: Device for detection of activity-dependent changes in neural spheroids at MHz and GHz frequencies

arXiv

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arXiv 2024年

作者： Omidi, Saeed Fabi, Gianluca Wang, Xiaopeng Hwang, James C.M. Berdichevsky, Yevgeny Department of Bioengineering Lehigh University Bethlehem United States Department of Material Science and Engineering Cornell University Ithaca United States Department of Electrical and Computer Engineering Bethlehem United States

Intracellular processes triggered by neural activity include changes in ionic concentrations, protein release, and synaptic vesicle cycling. These processes play significant roles in neurological disorders. The beneficial effects of brain stimulation may also be mediated through intracellular changes. There is a lack of label-free techniques for monitoring activity-dependent intracellular changes. Electromagnetic (EM) waves at frequencies larger than 1x106 Hz (1 MHz) were previously used to probe intracellular contents of cells, as cell membrane becomes "invisible" at this frequency range. EM waves interact with membranes of intracellular organelles, proteins, and water in the MHz – GHz range. In this work, we developed a device for probing the interaction between active neurons’ intracellular contents and EM waves. The device used an array of grounded coplanar waveguides (GCPWs) to deliver EM waves to a three-dimensional (3D) spheroid of rat cortical neurons. Neural activity was evoked using optogenetics, with synchronous detection of propagation of EM waves. Broadband measurements were conducted in the MHz-GHz range to track changes in transmission coefficients. Neuronal activity was found to reversibly alter EM wave transmission. Pharmacological suppression of neuronal activity abolished changes in transmission. Time constants of changes in transmission were in the seconds – tens of seconds range, suggesting the presence of relatively slow, activity-dependent intracellular processes. This study provides the first evidence that EM transmission through neuronal tissue is activity-dependent in MHz – GHz range. Device developed in this work may find future applications in studies of the mechanisms of neurological disorders and the development of new therapies. © 2024, CC BY-NC-ND.

关键词： Neurons

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Decentralized Control of Intercity Electric Automated Buses via Time-Varying Objective Prioritization

Decentralized Control of Intercity Electric Automated Buses ...

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IEEE Conference on Decision and Control

This paper considers electric automated buses traveling in inter-urban roads and following a given route including stops, that must be reached according to a given timetable. Some of these stops are provided with a charging infrastructure allowing to charge the bus batteries. The paper proposes a decentralized control scheme for determining the optimal speed profiles, the dwell and charging times of the buses, by taking into account the traffic conditions along the road through a suitable traffic flow prediction model. Two objectives are considered contemporarily: the minimization of the deviations from the timetable and the minimization of the energy lack at the end of the bus route. To attain both these conflicting objectives, a lexicographic approach is adopted to design the controller which considers that, depending on the system state, the priority of the two objectives can change. Accordingly, the proposed control scheme changes the objective prioritization in real time and switches between two different lexicographic-based optimal control solutions. Some tests are discussed in the paper to show the effectiveness of the proposed control scheme.

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A Hardware Accelerator for Quantile Estimation of Network Packet Attributes

A Hardware Accelerator for Quantile Estimation of Network Pa...

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Euromicro Symposium on Digital System Design

作者： Carolina Gallardo-Pavesi Yaime Fernandez Javier E. Soto Cecilia Hernández Miguel Figueroa Department of Electrical Engineering Department of Computer Science Universidad de Concepcion Concepción Chile Center of Biotechnology and Bioengineering (CeBiB) Advanced Center for Electrical and Electronics Engineering (AC3E)

ISBN: (数字)9798350380385

ISBN: (纸本)9798350380392

Measuring statistical properties of network traffic can improve our understanding of traffic distribution and help us detect short and long-term anomalies. However, computing the exact value of these properties requires significant storage and computation, which limits their application in high-speed networks. Hardware accelerators provide the computational power to process a large sequence of network packets with high throughput and low latency, but their performance is ultimately limited by the amount of on-chip memory available on the device. Consequently, researchers have proposed sketch-based algorithms to estimate properties of a data stream with sub linear memory and theoretical estimation error bounds. In this paper, we present a streaming algorithm and hardware accelerator for quantile estimation, which is based on the architecture of the KLL sketch. Implemented on an AMD Virtex XCU55 UltraScale+ FPGA, the accelerator operates at a clock frequency of 356 MHz, thereby achieving a minimum line rate of 182 Gbps and a maximum estimation latency of 4.33 µs. When processing a set of 10 real traffic traces of up to 123 million packets, the accelerator estimates 1000 packet-size quantiles per trace with a median error of 0.39% or less, and a maximum error of 1.3% or less across all traces.

关键词： Performance evaluation Estimation error Memory management Telecommunication traffic Logic gates Throughput System-on-chip Low latency communication Hardware acceleration Field programmable gate arrays

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HO-FMN: Hyperparameter Optimization for Fast Minimum-Norm Attacks

arXiv

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arXiv 2024年

作者： Mura, Raffaele Floris, Giuseppe Scionis, Luca Piras, Giorgio Pintor, Maura Demontis, Ambra Giacinto, Giorgio Biggio, Battista Roli, Fabio University of Cagliari Dept. of Electrical and Electronic Engineering Cagliari Italy Sapienza University of Rome Department of Computer Engineering Rome Italy University of Genoa Department of Computer Science Bioengineering Robotics and Systems Engineering Genoa Italy

Gradient-based attacks are a primary tool to evaluate robustness of machine-learning models. However, many attacks tend to provide overly-optimistic evaluations as they use fixed loss functions, optimizers, step-size schedulers, and default hyperparameters. In this work, we tackle these limitations by proposing a parametric variation of the well-known fast minimum-norm attack algorithm, whose loss, optimizer, step-size scheduler, and hyperparameters can be dynamically adjusted. We re-evaluate 12 robust models, showing that our attack finds smaller adversarial perturbations without requiring any additional tuning. This also enables reporting adversarial robustness as a function of the perturbation budget, providing a more complete evaluation than that offered by fixed-budget attacks, while remaining efficient. © 2024, CC BY.

关键词： Machine learning

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Asemi-Supervised Deep Learning Approach to Automate the Identification of Fetal Behavioral States in Fetal Heart Rate Tracings

Asemi-Supervised Deep Learning Approach to Automate the Iden...

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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Edoardo Spairani Giulio Steyde Luca Subitoni Giovanni Magenes Maria G. Signorini Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Department of Electronics Information and Bioengineering (DEIB) Politecnico di Milano Milano Italy

ISBN: (数字)9798350307993

ISBN: (纸本)9798350308006

computerized Cardiotocography (cCTG) facilitates a thorough and objective examination of the Fetal Heart Rate (FHR), providing valuable insights into the fetal condition and its well-being. A crucial aspect within this context pertains to the automatic identification of periods of fetal activity and quiescence, which are associated with different FHR patterns. The accurate discrimination of these patterns holds the potential to improve the interpretability and diagnostic capabilities of FHR quantitative analysis. Indeed, disruptions in the cycling between active and quiet periods are associated with the development of pathological conditions. This study introduces a deep learning based methodology for the identification of fetal behavioral heart rate patterns. Specifically, the implemented deep neural network (DNN) adopts a 1D encoder-decoder architecture, which is trained to recognize and automatically segment the FHR recordings into active and quiet periods. The proposed framework includes a semi-supervised training process, based on two steps: a) DNN pre-training based on pseudo-labels generated by a Hidden Markov Model (HMM), b) DNN fine-tuning integrating the annotations of an expert Ob-Gyn clinician. The trained DNN exhibits promising results: Balanced Accuracy of 88.37%, Macro F1-Score of 87.87% and Matthews Correlation Coefficient (MCC) of 75.80% on a distinct hold-out test set, encompassing 45 FHR traces annotated by an expert Ob-Gyn clinician.

关键词： Deep learning Training Pathology Accuracy Fetal heart rate Statistical analysis Hidden Markov models

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Properties of Immersions for Systems with Multiple Limi Sets with Implications to Learning Koopman Embeddings

arXiv

引用

arXiv 2023年

作者： Liu, Zexiang Ozay, Necmiye Sontag, Eduardo D. Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Department of Electrical and Computer Engineering Department of Bioengineering Northeastern University BostonMA02115 United States

Linear immersions (such as Koopman eigenfunctions) of a nonlinear system have wide applications in prediction and control. In this work, we study the properties of linear immersions for nonlinear systems with multiple omega-limit sets. While previous research has indicated the possibility of discontinuous one-to-one linear immersions for such systems, it has been unclear whether continuous one-to-one linear immersions are attainable. Under mild conditions, we prove that any continuous immersion to a class of systems including finite-dimensional linear systems collapses all the omega-limit sets, and thus cannot be one-to-one. Furthermore, we show that this property is also shared by approximate linear immersions learned from data as sample size increases and sampling interval decreases. Multiple examples are studied to illustrate our results. Copyright © 2023, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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A Dual-Track Framework for Enhancing Age-Related Macular Degeneration Diagnosis Through Embedding and Vision Classification

A Dual-Track Framework for Enhancing Age-Related Macular Deg...

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IEEE International Symposium on Biomedical Imaging

作者： Hossam Magdy Balaha Ahmed Aboudessouki Ali Mahmoud Mohammed Ghazal Ayman El-Baz Bioengineering Department J.B. Speed School of Engineering University of Louisville Louisville KY USA Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi UAE

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Age-related macular degeneration (AMD) is a leading cause of vision loss in older adults, making timely and accurate diagnosis crucial for effective treatment. This study suggests a new framework for AMD diagnosis that integrates embedding extraction and direct vision classification to improve diagnostic performance. We employ a dual-track methodology, utilizing advanced pretrained models for embedding extraction (utilizing Virchow2) alongside a vision-based approach (utilizing Nomic) to classify fundus images into distinct AMD categories: (1) normal, (2) intermediate, (3) geographic atrophy, and (4) wet AMD. Our experiments achieved the best results, with an accuracy of 98.03%, an F1 score of 98.05%, and a precision of 98.08%. By experimentally investigating the applicability of pretrained embedding models originally designed for histopathological patches to fundus images, we bridge a gap in multimodal image analysis, potentially paving the way for improved diagnostic tools in ophthalmology. The experimental results demonstrate the potential of combining embedding extraction and direct vision classification to improve the accuracy of AMD detection, addressing the urgent need for automated diagnostic systems in the clinical environment.

关键词： Macular degeneration Bridges Atrophy Analytical models Accuracy Image analysis Ophthalmology Older adults Biomedical imaging

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