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Device Processing Challenges for Miniaturized Sensing Systems Targeting Biological Fluids

Biomedical Materials and Devices 2023年第1期1卷 82-98页

作者： Stoukatch, S. Dupont, F. Redouté, J.-M. Microsys Lab Department of Electrical Engineering and Computer Science Liege University Seraing Belgium

This article presents a review of device processing technologies used in the fabrication of biomedical systems, and highlights the requirements of advanced manufacturing technology. We focus on biomedical systems that perform diagnostics of fluidic specimens, with analytes that are in the liquid phase. In the introduction, we define biomedical systems as well as their versatile applications and the essential current trends. The paper gives an overview of the most important biomolecules that typically must be detected or analyzed in several applications. The paper is structured as follows. First, the conventional architecture and construction of a biosensing system is introduced. We provide an overview of the most common biosensing methods that are currently used for the detection of biomolecules and its analysis. We present an overview of reported biochips, and explain the technology of biofunctionalization and detection principles, including their corresponding advantages and disadvantages. Next, we introduce microfluidics as a method for delivery of the specimen to the biochip sensing area. A special focus lies on material requirements and on manufacturing technology for fabricating microfluidic systems, both for niche and mass-scale production segments. We formulate requirements and constraints for integrating the biochips and microfluidic systems. The possible impacts of the conventional microassembly techniques and processing methods on the entire biomedical system and its specific parts are also described. On that basis, we explain the need for alternative microassembly technologies to enable the integration of biochips and microfluidic systems into fully functional systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of

关键词： Alternative microassembly techniques Biological analysis Biomedical system Biosensors

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DeRL: Coupling Decomposition in Action Space for Reinforcement Learning Task

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IEEE Transactions on Emerging Topics in Computational Intelligence 2024年第1期8卷 1030-1043页

作者： He, Ziming Li, Jingchen Wu, Fan Shi, Haobin Hwang, Kao-Shing Northwestern Polytechnical University School of Computer Science Xi'an710072 China National Sun Yat-sen University Department of Electrical Engineering Kaohsiung80424 Taiwan

This paper is concerned with complex reinforcement learning tasks whose observations are difficult to characterize as appropriate inputs for policy mapping. The representation learning technique is leveraged to extract the features from the observations for optimal action generation. In literature, the action vector, which consists of actions in each dimension, is usually learned from the full features. However, we find empirically that different actions may be only highly related to small part of the features and weakly depend on the rest. Therefore, this unified learning strategy may lead to performance degradation, and a separate learning method is motivated. In this paper, we propose a novel method called Decoupled Reinforcement Learning (DeRL) that decomposes action space by replacing the policy network with decoupled sub-policy group. To cater to all types of tasks where agents. actions in different dimensions can be either weakly correlated or strongly correlated, the Bidirectional Recurrent Neural Network (Bi-RNN) is added as an essential component to capture further shared features for generating more accurate action. In this framework, the decoupled policy network maintains joint representations required by the decision of all actions in different dimensions while decreasing preference in the learning process. In addition, we give a theoretical analysis of DeRL from the perspective of information theory, which shows the difference in information loss between DeRL and others. The performance of the proposed method has been verified by contrastive experiments on 12 tasks, including Mujoco, Atari, and other popular environments. © 2017 IEEE.

关键词： Reinforcement learning

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SMT-based Layout Synthesis for Silicon-based Quantum Computing with Crossbar Architecture 24

SMT-based Layout Synthesis for Silicon-based Quantum Computi...

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43rd International Conference on computer-Aided Design, ICCAD 2024

作者： Huang, Sheng-Tan Jiang, Ying-Jie Fang, Shao-Yun Cheng, Chung-Kuan Department of Electrical Engineering National Taiwan University of Science and Technology Taiwan Department of Computer Science and Engineering University of California San Diego United States

ISBN: (纸本)9798400710773

With the announcement of the most advanced silicon spin quantum-bit (qubit) chip by Intel, silicon-based quantum circuit manufacturing technology has shown the superior potential to realize quantum computing to other technologies because of the mature semiconductor manufacturing technologies and the compatibility with electronics. Due to the bottleneck in scalability caused by interconnections in silicon-based quantum circuits, crossbar architectures serve as one of the most promising solutions for circuitry implementation. This paper proposes the first satisfiability modulo theories (SMT) formulation to optimally solve the layout synthesis problem by simultaneously performing scheduling, mapping, and routing for silicon-based quantum systems with a specific crossbar architecture. The experiments demonstrate that the proposed method can effectively generate layout synthesis results with minimal usage of swap and shuttle gates within the shortest circuit depths, and the solutions greatly outperform those derived from a state-of-the-art work. © 2024 Copyright is held by the owner/author(s).

关键词： Qubits

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Agreements of Nonlinear Opinion Dynamics in Switching Social Networks

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IEEE Transactions on Automatic Control 2024年第6期69卷 4174-4181页

作者： Lee, Ti-Chung Su, Youfeng National Sun Yat-sen University Department of Electrical Engineering Kaohsiung80424 Taiwan Fuzhou University College of Computer and Data Science Fuzhou350116 China

This article formulates and analyzes agreements of the nonlinear opinion dynamics in social networks according to switching interactions, where the agents' susceptibilities depend on current states. These switching interactions are formulated by switching directed graphs with some mild joint connectivity, and depicted by nonlinear switching model. An extended LaSalle invariance principle is established for analyzing the agreements of this switching model as well as three specialized scenarios, where the limiting equations formed by the weak∗ convergence are used to study the steady-state behavior so that the stability analysis is substantially simplified in the sense that the 'max - min' Lyapunov function may be assumed to be constant along any bounded forward complete solutions of limiting equations. Examples and simulations are used to verify the obtained results. © 1963-2012 IEEE.

关键词： Social networking (online)

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GAN-Augmented Breast Cancer Classification with Histogram-Based Gradient Boosting 6

GAN-Augmented Breast Cancer Classification with Histogram-Ba...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Kayikci, Safak Khoshgoftaar, Taghi Department of Electrical Engineering and Computer Science Florida Atlantic University Boca Raton United States

ISBN: (纸本)9798350394634

The main method for screening for breast cancer is mammography, which aims to lower the risk of breast cancer death by early detection. Deep learning techniques have been found to show good predictive results with many medical image datasets. Even so, extensive data augmentation approaches are necessary for accurate computer-assisted diagnosis due to the scarcity of annotated medical images. After being trained on photos, Generative Adversarial Networks (GANs) can produce new images that resemble real images and have a lot of genuine features. The realism and representativeness of the generated features can vary depending on the factors like quality of training data, training parameters, evaluation metrics, and application domain. We used a Histogram-Based Gradient Booster classifier to improve the traditional deep learning methods and encourage the use of GANs for data augmentation. When compared to the baseline, the results demonstrate that GANs increased recall without lowering precision and enhanced classification performance on the malignant class without sacrificing performance on the benign class. © 2024 IEEE.

关键词： Generative adversarial networks

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Fast Distributed Memory Parallel Algorithms for Finding Connected Components in Large Graphs

Fast Distributed Memory Parallel Algorithms for Finding Conn...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Khan, Maleq Boddu, Sharon Texas A&m University-Kingsville Department of Electrical Engineering and Computer Science United States

ISBN: (纸本)9798350362480

Finding the connected components in a graph is a fundamental problem in graph theory and network science. A connected component in a graph is a maximal set of vertices such that there is a path between any two vertices in the component. In a serial setting, connected components can be efficiently computed by running a breadth-first search (BFS). However, BFS-based algorithms do not lead to good parallelization. At the early stage, parallel algorithms for the connected component problem were developed for the PRAM model and shared-memory systems. With the availability of big data and big graphs and the development of a new area called network science, there is a renewed interest in developing parallel algorithms for this problem. In recent years, a number of distributed-memory parallel algorithms have been developed. These algorithms are based on mainly Shiloach and Vishkin's PRAM algorithm, called the SV algorithm, or some variants of SV. In this paper, we present a PRAM algorithm, called SELP, which is elegant and significantly simpler than the SV algorithm. Based on SELP, we develop a distributed-memory parallel algorithm, called DSELP. In addition, we incorporate some novel techniques to minimize communications among the processors. Experimental results show that our algorithms outperform the recent state-of-the-art distributed-memory algorithms significantly on a wide variety of graphs, and scale very well to a large number of processors. © 2024 IEEE.

关键词： Consensus algorithm

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A Robust End-to-End CNN Architecture for Efficient COVID-19 Prediction form X-ray Images with Imbalanced Data

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Informatica (Slovenia) 2023年第7期47卷 115-126页

作者： Oraibi, Zakariya A. Albasri, Safaa Department of Computer Science College of Education for Pure Sciences Unviersity of Basrah Basrah Iraq Electrical Engineering Department College of Engineering Mustansiriyah University Baghdad Iraq

The world witnessed big changes in 2019 when a new virus called coronavirus affected the lives of hundreds of millions of individuals and led to huge disruptions in healthcare systems. Early prediction of this virus was a top priority to limit its damage and save countless lives. Many advanced artificial intelligence technologies like deep learning have used chest X-ray images for this task. In this paper, a new CNN architecture is introduced to classify chest X-ray images. The new model is applied on a 256 × 256 × 3 input image and consists of six convolutional blocks. In addition, we improve the performance of our model by adding regularization techniques, including batch normalization and dropout. We tested our model using an imbalanced COVID-19 dataset of 5000 COVID and Non-COVID images. Four metrics were used to test the new model: sensitivity, specificity, precision, and F1 score. In experiments, we achieved a sensitivity rate of 97%, a specificity rate of 99.32%, a precision rate of 99.90%, and F1 score of 97.73% despite being provided with fewer training images. In conclusion, we proposed a light deep learning model capable of achieving high prediction accuracy that outperformed the best deep learning methods in terms of specificity and achieved high sensitivity result. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Coronavirus

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Intelligent predictions of Covid disease based on lung CT images using machine learning strategy

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Materials Today: Proceedings 2023年 80卷 3744-3750页

作者： Prabha, B. Kaur, Sandeep Singh, Jaspreet Nandankar, Praful Kumar Jain, Sanjiv Pallathadka, Harikumar Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram Guntur India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Department of Computer Science and Engineering Adesh Institute of Technology Chandigarh Campus India Department of Electrical Engineering Government College of Engineering Nagpur India Medi-Caps University India Manipur International University Manipur India

Covid or Corona Virus, a term ruling the world from past two years and causes a huge destruction in all countries. One of the most important Covid disease identification method is Lung based Computed Tomography (CT) image scanning, in which it provides an effective disease identification means in clear manner. However, this Lung CT image based disease detection principles are complex to health care representatives and doctors to predict the Covid disease accurately. Several manual errors and medical flaws are raised day-by-day, so that a new systematic methodology is required to identify the Covid disease effectively with respect to machine learning principles. The machine learning principles are most popular to identify the respective disease efficiently as well as classify the disease in accurate manner without any time consumption. The infected portions of the chest are identified accurately and report to the respective person without any delay. In this paper, a new machine learning strategy is introduced called Hybrid Disease Detection Principle (HDDP), in which it is derived from the two classical machine learning algorithms called Convolutional Neural Network (CNN) and the AdaBoost Classifier. Both these algorithms are integrated together to produce a new strategy called HDDP, in which it process the lung CT image based on the machine learning factors such as pre-processing, feature extraction and classification. Based on these effective image processing strategies the proposed algorithm handles the CT images to predict the Covid disease and report to the respective user with proper accuracy ratio. This paper intends to provide effcient disease predictions as well as provide a sufficient support to medical people and patients in fine manner to assist them with modern classification algorithms. © 2021

关键词： Machine learning

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HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach 31

HPC Application Parameter Autotuning on Edge Devices: A Band...

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31st Annual IEEE International Conference on High Performance Computing, Data, and Analytics, HiPC 2024

作者： Hossain, Abrar Badawy, Abdel-Hameed A. Islam, Mohammad A. Patki, Tapasya Ahmed, Kishwar Department of Electrical Engineering and Computer Science The University of Toledo United States Department of Electrical and Computer Engineering New Mexico State University United States Department of Computer Science and Engineering The University of Texas Arlington United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States

ISBN: (纸本)9798331509095

The growing necessity for enhanced processing capabilities in edge devices with limited resources has led us to develop effective methods for improving high-performance computing (HPC) applications. In this paper, we introduce LASP (Lightweight Autotuning of Scientific Application Parameters), a novel strategy designed to address the parameter search space challenge in edge devices. Our strategy employs a multi-armed bandit (MAB) technique focused on online exploration and exploitation. Notably, LASP takes a dynamic approach, adapting seamlessly to changing environments. We tested LASP with four HPC applications: Lulesh, Kripke, Clomp, and Hypre. Its lightweight nature makes it particularly well-suited for resource-constrained edge devices. By employing the MAB framework to efficiently navigate the search space, we achieved significant performance improvements while adhering to the stringent computational limits of edge devices. Our experimental results demonstrate the effectiveness of LASP in optimizing parameter search on edge devices. © 2024 IEEE.

关键词： Edge computing

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Zener Diode Controlled GaN FET Gate Drive Protection Scheme 11

Zener Diode Controlled GaN FET Gate Drive Protection Scheme

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11th IEEE Workshop on Wide Bandgap Power Devices and Applications, WiPDA 2024

作者： Zahid, Yousef McCann, Roy University of Arkansas Fayetteville Electrical Engineering and Computer Science Department FayettevilleAR United States

ISBN: (纸本)9798350375602

Innovation in GaN semiconductors has improved aspect of power capability, switching speed and size. However, their short-circuit withstand time is low compared to SiC and IGBT switches. It would require a response time of less than 400 η {s} to reduce short-circuit damage to the device. This paper explores a quick turn-off gate drive circuit by implementing a zener diode control for the V{g s} of the GaN transistor. An additional soft turn-off subcircuit is added to discharge the gate capacitance in a short-circuit or load change event. Circuit operation is described in detail and simulation plots verify the hypothesis. Comparison for response time between different circuit topologies is also illustrated. Index Terms-Zener diode, Short-circuit fault protection, GaN-FETs, Gate driver, short-circuit withstand time, response time. © 2024 IEEE.

关键词： Gallium nitride

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