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2023 IEEE National Aerospace and Electronics Conference, NAECON 2023

作者： Kasongo, Muana Ytterdal, Trond Lee, John Rizkalla, Maher Kumar, Mukesh Iupui Electrical and Computer Engineering Department Indianapolis United States Norwegian Institute of Technology Electronic Systems Department Trondheim Norway Indian Institute of Technology Indore Electrical Engineering Department Indore India

ISBN: (纸本)9798350338782

Memristors are novel electronic devices. Memristor-based crossbar arrays have gained a lot of attention for potential applications in nonvolatile memory, in-memory computing, logic design, neuromorphic computing systems, and neural networks. In this paper, we demonstrate the sneaky path current issue within a 3×3 crossbar array. 1T1M scheme has been used to reduce the leakage current. Integrating nMOS and pMOS devices in series with memristors in the crossbar array has optimized the leakage currents. The results show that nMOS devices reduce the leakage currents by 97.35%, whereas pMOS devices reduce the leakage currents by 92.20%. Thus, nMOS outperform pMOS devices in leakage currents optimization. © 2023 IEEE.

关键词： Memristors

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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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Costmap-based Local Motion Planning using Deep Reinforcement Learning

Costmap-based Local Motion Planning using Deep Reinforcement...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Luís Garrote João Perdiz Urbano J. Nunes Department of Electrical and Computer Engineering and the Institute of Systems and Robotics University of Coimbra Portugal

Local motion planning is an essential component of autonomous robot navigation systems as it involves generating collision-free trajectories for the robot in real-time, given its current position, the map of the environment and a goal. Considering an a priori goal path, computed by a global planner or as the output of a mission planning approach, this paper proposes a Two-Stream Deep Reinforcement Learning strategy for local motion planning that takes as inputs a local costmap representing the robot’s surrounding obstacles and a local costmap representing the nearest goal path. The proposed approach uses a Double Dueling Deep Q-Network and a new reward model to avoid obstacles while trying to maintain the lateral error between the robot and the goal path close to zero. Our approach enables the robot to navigate through complex environments, including cluttered spaces and narrow passages, while avoiding collisions with obstacles. Evaluation of the proposed approach was carried out in an in-house simulation environment, in five scenarios. Double and Double Dueling architectures were evaluated; the presented results show that the proposed strategy can correctly follow the desired goal path and, when needed, avoid obstacles ahead and recover back to following the goal path.

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ZCD Circuit Improvement and Novel Signal Processing Method for GaN-Based CRM PWM Converters

ZCD Circuit Improvement and Novel Signal Processing Method f...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Chen, Xingyu Yu, Gibum Li, Qiang Virginia Polytechnic Institute and State University Center for Power Electronics Systems The Bradley Department of Electrical and Computer Engineering BlacksburgVA United States

ISBN: (纸本)9798350376067

This paper introduces a design guidance for zero current detection (ZCD) circuit in Gallium-Nitride (GaN) device based critical conduction mode (CRM) pulse-width-modulation (PWM) converters to reduce the sensing delay, avoid false trigger and prevent trigger missing. A ZCD signal processing logic is also proposed for CRM PWM converters to limit the maximum switching frequency while keeping a simple closed loop control structure. The proposed ZCD circuit design and ZCD signal processing method are verified in the simulation and on a GaN based CRM buck converter. © 2024 IEEE.

关键词： Pulse width modulation

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Adaptive inter-intradomain alignment network with class-aware sampling strategy for rolling bearing fault diagnosis

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Science China(Technological Sciences) 2023年第10期66卷 2862-2870页

作者： GAO QinHe HUANG Tong ZHAO Ke SHAO HaiDong JIN Bo LIU ZhiHao WANG Dong National Key Discipline Laboratory of Armament Launch Theory&Technology Rocket Force University of EngineeringXi’an 710025China Key Laboratory of Road Construction Technology&Equipment Ministry of Education Chang’an UniversityXi’an 710054China College of Mechanical and Vehicle Engineering Hunan UniversityChangsha 410082China Institute of Systems and Robotics Department of Electrical and Computer EngineeringUniversity of CoimbraCoimbra 3030-290Portugal

Existing unsupervised domain adaptation approaches primarily focus on reducing the data distribution gap between the source and target domains,often neglecting the influence of class information,leading to inaccurate alignment *** by this observation,this paper proposes an adaptive inter-intra-domain discrepancy method to quantify the intra-class and inter-class discrepancies between the source and target ***,an adaptive factor is introduced to dynamically assess their relative *** upon the proposed adaptive inter-intradomain discrepancy approach,we develop an inter-intradomain alignment network with a class-aware sampling strategy(IDAN-CSS)to distill the feature *** classaware sampling strategy,integrated within IDAN-CSS,facilitates more efficient *** multiple transfer diagnosis cases,we comprehensively demonstrate the feasibility and effectiveness of the proposed IDAN-CSS model.

关键词： unsupervised domain adaptation inter-class domain discrepancy intra-class domain discrepancy class-aware sampling strategy

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Symmetric Series-Capacitor Buck in 48V-to-12V Regulated Conversion for High-Performance Server Boards

Symmetric Series-Capacitor Buck in 48V-to-12V Regulated Conv...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Xu, Xinmiao Li, Qiang Virginia Polytechnic Institute and State University Center for Power Electronics Systems Department of Electrical and Computer Engineering BlacksburgVA24061 United States

ISBN: (纸本)9798350376067

This paper presents an extended-conversion-ratio modulation for a two-phase symmetric series-capacitor buck (SSCB) converter. With the proposed scheme, highly efficient and regulated 48V-to-12V conversion can be realized with the SSCB topology. A versatile regulated first stage streamlines power delivery architectures for server boards in data centers, because regulated 12-V buses not only serve loads requiring well-controlled 12-V input but also simplify design and optimization of a 12V-to-1V second stage for processors. Based on this idea, a highly efficient and regulated power architecture is proposed for 48-V server boards. A prototype is built with gallium nitride (GaN) transistors and minimal high-frequency loop inductance, operating with a relatively wide input voltage range from 40 V to 60 V and generating a regulated 12-V output. In 48V-to-12V regulated conversion, the GaN-based prototype achieves 98.88% peak efficiency, 98.11% full-load efficiency (including gating loss) and 787 W/inch2 power density with 80-A output current, which is beyond the performance of state-of-the-art commercially available solutions. © 2024 IEEE.

关键词： Gallium nitride

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8-Bit Precision 6T SRAM Compute-in-Memory Macro Using Global Bitline-Combining Scheme for Edge AI Chips

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IEEE Transactions on Circuits and systems II: Express Briefs 2024年第4期71卷 2304-2308页

作者： Su, Jian-Wei Lu, Pei-Jung Wu, Ping-Chun Chou, Yen-Chi Liu, Ta-Wei Chung, Yen-Lin Hung, Li-Yang Ren, Jin-Sheng Huang, Wei-Hsing Chien, Chih-Han Mei, Peng I. Li, Sih-Han Sheu, Shyh-Shyuan Lo, Wei-Chung Chang, Shih-Chieh Hong, Hao-Chiao Lo, Chung-Chuan Liu, Ren-Shuo Hsieh, Chih-Cheng Tang, Kea-Tiong Chang, Meng-Fan Hsinchu300044 Taiwan The Department of Electrical Engineering National Tsing Hua University Hsinchu300044 Taiwan The Institute of Electronic Engineering National Tsing Hua University Hsinchu300044 Taiwan The Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan The Institute of Systems Neuroscience National Tsing Hua University Hsinchu300044 Taiwan

Efforts to advance the use of analog SRAM compute-in-memory (SRAM-CIM) macros for high-precision multiply-and-accumulate (MAC) operations must deal with issues pertaining to energy efficiency, computing latency (TAC), and area overhead. This brief presents a novel SRAM-CIM structure that utilizes (1) a high input precision computing cell (HIPCC) to perform 8b-MAC operations with high multiplication throughput, and (2) a global bitline-combining (GBL-comb) scheme to improve energy efficiency by reducing the number of analog-to-digital converters (ADCs). A 28nm 384-kb SRAM-CIM macro with 20-bit output precision (near-full precision) was fabricated using a foundry-provided 28nm logic process for MAC operations with 8b-input, 8b-weight, and 16 accumulations. The resulting macro achieved a TAC of 3.6 ns with energy efficiency of 14.97 TOPS/W when applied to 8-bit MAC operations. © 2023 IEEE.

关键词： Static random access storage

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Ultimate Boundedness and Output Convergence of Prioritized Output Tracking Control Under Nonsmooth and Imperfect Feedback Linearization

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IEEE Transactions on Automatic Control 2025年

作者： An, Sang-Ik Lee, Dongheui Park, Gyunghoon University of Seoul School of Electrical and Computer Engineering Korea Republic of Tu Wien Autonomous Systems at Institute of Computer Technology Faculty of Electrical Engineering and Information Technology Austria Institute of Robotics and Mechatronics Germany

The concept of priority has been introduced to the robotic systems in 1980 s as an effort to overcome problems caused by singularity and it has attracted considerable attention from both the robotics and control societies. However, none of the previous works have successfully addressed two fundamental degenerate properties of singularity: nonsmoothness and imperfect inversion. This technical note proposes a prioritized output tracking control method that guarantees the ultimate boundedness and the convergence of higher priority outputs when the input-output feedback linearization becomes nonsmooth and imperfect by singularity. For that purpose, we firstly prioritize the input-output feedback linearization and discuss how two degenerate properties can occur when the system becomes singular. Then, we introduce the differential inclusion to deal with the nonsmooth internal dynamics and establish a condition using the upper Dini derivative for asymptotically stable zero dynamics. Also, we propose a strictly positive realness-based feedback gain design and find a condition related to an M-matrix in order to handle nonlinearities that appear in the imperfect feedback linearization. Finally, we combine these two results and show the ultimate boundedness and the output convergence. Additionally, we provide a motivational example with a planar four-link manipulator to illustrate the effectiveness and limitations of the proposed method. © 1963-2012 IEEE.

关键词： Feedback linearization

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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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Design and Development of a Fixed-Wing Tail-Sitter Tethered VTOL Drone with Custom Autopilot and Robust Control Techniques 16th

Design and Development of a Fixed-Wing Tail-Sitter Tether...

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16th APCA International Conference on Automatic Control and Soft Computing, CONTROLO 2024

作者： Safaee, Ahmad Aguiar, A. Pedro Santos, Filipe Neves Moreira, Antonio Paulo SYSTEC-ARISE Research Center for Systems and Technologies Faculty of Engineering University of Porto Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering Technology and Science CRIIS-Centre for Robotics in Industry and Intelligent Systems Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering and DEEC/FEUP - Department of Electrical and Computer Engineering Faculty of Engineering of the University of Porto Porto4200-465 Portugal

ISBN: (纸本)9783031817236

The need for aerial platforms capable of sustained operation is critical in fields such as surveillance, agricultural monitoring, disaster response, and temporary telecommunication systems. Traditional unmanned aerial vehicles (UAVs) are hampered by limited battery capacity and inefficient energy utilization. This paper introduces an autonomous vertical flight drone tethered to a ground-based platform, which overcomes these limitations. The drone is designed to optimize its position and energy usage by harnessing wind currents, functioning similarly to a kite when conditions permit. This is enabled by a custom-designed autopilot system comprising both hardware and software tailored for this application. We detail the model identification process for three vertical take-off and landing (VTOL) drone systems, employing authentic data that measures lateral positional errors from the tether. Robust mixed sensitivity controllers are developed and implemented. The effectiveness of these controllers is validated through simulations showing their capability to handle uncertainties. Furthermore, the practical application and robust performance of the system are confirmed by a real-world case study, highlighting its potential in real operational environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： VTOL/STOL aircraft

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