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共 366 条记录，以下是11-20 订阅

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Low Voltage CMOS Class AB Current Mirror/Precision Rectifier 66

Low Voltage CMOS Class AB Current Mirror/Precision Rectifier

引用

2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Paul, Anindita Ramirez-Angulo, Jaime Gangineni, Manaswini Huerta-Chua, Jesus Morehead State University Department of Computer Science & Electronics Morehead United States Instituto Technologico Superior de Poza Rica Electronic Engineering Department Poza Rica Mexico Nmsu Klipsch Sch. of Elect. & Computer Engineering Las Cruces United States

ISBN: (纸本)9798350302103

A low-voltage CMOS class AB current mirror/precision rectifier is introduced that can handle positive and negative current signals much larger than the bias current. It is based on a current switch and two current mirrors. Simulations in 180nm CMOS technology with a 1v supply and a 1μA bias current show that the circuit can handle 200μA peak-to-peak bidirectional current signals with high accuracy. © 2023 IEEE.

关键词： analog integrated circuits class AB amplifiers current mirrors flipped voltage follower

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Cross-Subject EEG Emotion Recognition Using Domain Adaptive Few-Shot Learning Networks

Cross-Subject EEG Emotion Recognition Using Domain Adaptive ...

引用

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Ning, Run Philip Chen, C.L. Zhang, Tong Sch. of Computer Science Engineering South China University of Technology Guangzhou China Sch. of Computer Science Engineering South China University of Technology Pazhou Lab Guangzhou China

ISBN: (纸本)9781665401265

Due to the individual differences and nonstationary of EEG signals, it is difficult to classify EEG emotions with traditional machine methods, which assume that the training and testing set come from the same data distribution, but this assumption is usually not true in the EEG field, therefore the accuracy of emotion recognition is very poor. In this paper, a Single-Source Domain Adaptive Few-Shot Learning Networks (SDA-FSL) was proposed for cross-subject EEG emotion recognition. This is the first time that domain adaptation method with few-shot learning has been used in the field of EEG emotion recognition. A CBAM-based feature mapping module was designed to extract the common features of the two domains, and the domain adaptation module was used to align the data distribution of two domains. In addition, Prototypical Networks with instance-attention mechanism is introduced to preserve domain-specific information. The proposed method was evaluated on DEAP and SEED datasets in within-dataset and cross-dataset experiments under various N-way k-shot settings. Experimental results show that the performance of SDA-FSL outperforms other comparison methods and has superior generalization performance on cross-dataset experiments. © 2021 IEEE.

关键词： Emotion Recognition

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Design and Implementation of a Memory Protection Mechanism Adhering to the Specifications of AUTOSAR OS SC3 8

Design and Implementation of a Memory Protection Mechanism A...

引用

8th International Conference on Communication, Image and Signal Processing, CCISP 2023

作者： Miao, Zhiwei Xu, Shuanghao Liu, Jianpei Wang, Zhiguo Wen, Bin Sch. of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing China Beijing Siener Electronics Tech. Development Co. Ltd. Product Development Department Beijing China

ISBN: (纸本)9798350305838

As the dynamics of intelligentization, connectivity, electrification, and collaborative processes within the automotive sector persist in their rapid evolution, there has been an escalating focus on the matter of functional safety within vehicular operating systems. Within the context of this evolving landscape, the present sch.larly work offers an exposition on the conception and realization of a memory protection mechanism in adherence to the AUTOSAR OS SC3 standard. In response to the limitations inherent in traditional memory protection mechanisms, necessitating manual configuration of address ranges for the determination of protection intervals, this novel mechanism introduces an approach for the dynamic acquisition of addresses and the establishment of protection range parameters. The practical execution and empirical assessment of this mechanism transpired on the Infineon AURIX TC275 microcontroller. The outcomes of the testing procedures elucidate that this approach not only fulfills the requisites of memory protection but also augments code portability and development efficiency. © 2023 IEEE.

关键词： Automotive industry

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Enhancing Energy Efficiency: Statistical Analysis of RRAM Operations using ZnO-based Approach

Enhancing Energy Efficiency: Statistical Analysis of RRAM Op...

引用

IEEE International Conference on Sensors and Nanotechnology

作者： Usman Isyaku Bature Illani Mohd Nawi Mohd Haris Md Khir Saeed S. Ba Hashwan Furqan Zahoor Mohammad Obaidur Rahman Electrical and Electronic Engineering Universiti Teknologi Petronas Seri Iskandar Malaysia Sch. of Computer Science and Engineering Nanyang Technological University Singapore

RRAM has garnered significant attention from various perspectives; however, certain barriers hinder its widespread adoption. These challenges revolve around reliability and power operation, making their resolution crucial. This work highlights the effects of operating at high voltages (>10V), which also affect other memory technologies, including Flash memory. The study introduces an insightful approach involving voltage flow analysis coupled with exploring the fundamental thermal energy flow in ZnO-based RRAM stacks operating within 1.2V. The model adopts a two-dimensional arrangement in COMSOL Multiphysics and further employs MATLAB (LiveLinkTM). The research delves into the dynamic resistance switching process, elucidating the role of joule heating effects, field-driven ion transport, and the temperature influence when the operating voltage exceeds 1.2V, as depicted in various plots. This investigation advances our comprehension of the complex dynamics involved in RRAM operation and demonstrates methods to enhance device performance, improve reliability, and ensure long-term functionality in practical applications.

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BCNN: A Bantamweight Convolutional Neural Network for P300 Detection

BCNN: A Bantamweight Convolutional Neural Network for P300 D...

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2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023

作者： Liu, M. Wang, S. Beyette, F.R. University of Georgia School of Electrical and Computer Engineering AthensGA United States Hebei Normal Univ. of Sci. & Tech Sch. of Math. and Info. Sci. & Tech Hebei Qinhuangdao China Yanshan University School of Information Science and Engineering Hebei Qinhuangdao China

ISBN: (纸本)9798350341256

As one kind of Event-Related Potentials (ERPs), P300 plays an important role in studying neural activities and cognitive processes, and lays the foundation for P300 speller - the Brain computer Interface (BCI) working by detecting P300. However, due to P300's high subject variability and low Signal Noise Ratio (SNR), it's always a challenge to do P300 detection. Convolutional Neural Networks (CNNs) have been widely recognized as effective methods for P300 detection, leading to the development of increasingly complex CNN architectures for P300 detection in recent years. However, this paper takes a different approach by proposing the bantamweight CNN (BCNN), the simplest and fast-training CNN for P300 detection. BCNN comprises only one convolutional filter and a total of 141 parameters. Surprisingly, it achieves state-of-the-art performance after just 2 epochs of training, making it an exceptionally lightweight and fast CNN for P300 detection. BCNN not only provides new ideas for CNN design, but also effectively address resource and time constrained situations. © 2023 IEEE.

关键词： Brain computer interface

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Enhancing C-V2X Vehicle Platooning: A Dual-Strategy of Time Synchronisation and Partition-Based sch.duling

Enhancing C-V2X Vehicle Platooning: A Dual-Strategy of Time ...

引用

IEEE International Workshop on Measurements and Networking Proceedings (M&N)

作者： Hossein Khalilnasl Salvatore Dello Iacono Paolo Ferrari Alessandra Flammini Brian McCarthy Emiliano Sisinni Dept. of Information Engineering University of Brescia Brescia Italy Sch. of Computer Science and Inf. Technology University College Cork Cork Ireland

ISBN: (数字)9798350370539

ISBN: (纸本)9798350370546

There is clear evidence indicating that the standardized Sensing-Based Semi-Persistent sch.duling sch.me in Out-of-coverage possesses specific vulnerabilities. This unsatisfactory performance lies in the absence of coordination in channel sch.duling and transmission time. Specifically, the determination to re-assign a resource after the reservation period expires is currently based on a random selection process, without consideration of the actual interference encountered by the utilized resources. In this study, a novel sch.me is proposed to markedly improve the random selection method for vehicle positioning.

关键词： Interference Sensors Synchronization Standards

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Exploration of TPU Architectures for the Optimized Transformer in Drainage Crossing Detection

Exploration of TPU Architectures for the Optimized Transform...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Nazeri, Amirhossein Godwin, Denys W. Maria Panteleaki, Aikaterini Anagnostopoulos, Iraklis Edidem, Michael I. Li, Ruopu Shu, Tong Clemson University Dept. of Automotive Engineering ClemsonSC United States Clark University Graduate School of Geography WorcesterMA United States Southern Illinois University Sch. of Electr. Comp. and Biomed. Engr. CarbondaleIL United States Southern Illinois University Sch. of Earth Systems and Sustain. CarbondaleIL United States University of North Texas Dept. of Computer Science and Engr. DentonTX United States

ISBN: (纸本)9798350362480

Understanding hydrologic connectivity within landscapes is crucial for managing environmental challenges. Despite advancements in high-resolution Digital Elevation Models (DEMs) derived from Light Detection and Ranging (LiDAR) technology, accurately delineating hydrologic connectivity remains challenging due to disruptions caused by virtual flow barriers, such as roads and bridges. This study addresses this issue by enhancing the detection performance and reducing the latency of Transformer models for image detection of drainage crossings. We retrained a Detection Transformer (DETR) with a specialized recipe to improve culvert detection performance. Owing to the high susceptibility of LiDAR-based DEMs to measurement noise and varying data modalities, we conducted extensive data preprocessing to ensure DETR compatibility with the culvert dataset. Ablation studies on input size indicate that the model performs optimally with 800×800 pixel inputs, demonstrating its adaptability to new data modalities. Additionally, we employed Tensor Processing Units (TPUs) to decrease the model's latency. We developed a novel strategy to optimize TPU architecture, utilizing genetic algorithms to expedite the discovery of optimal TPU configurations for detection deployment. Our model surpasses the performance of previous models on the same task. This work not only addresses the computational complexities of deploying advanced object detection in environmental contexts but also significantly contributes to the precise and efficient monitoring of hydrologic connectivity. © 2024 IEEE.

关键词： Culverts

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Link Quality-Aware Geographic Predictive Routing for V2V Network Based on GPSR

Link Quality-Aware Geographic Predictive Routing for V2V Net...

引用

International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Michiko Harayama Masahiro Mishioka Dept. Electrical Electronic and Computer Engineering Faculty of Engineering Gifu University Dept. Intelligence Science and Engineering Grad. Sch. Natural Science and Technology Gifu University

In recent years, advances in automotive IoT technology have not only improved automated driving and driving convenience but have also led to a diminishing number of traffic accidents. Vehicle Ad-hoc Networks (VANETs) have attracted much attention because automotive IoT is based on wireless communications. Ad-hoc routing protocols that apply the characteristics of vehicle-to-vehicle (V2V) networks are needed. One of them is Greedy Perimeter Stateless Forwarding (GPSR), a routing protocol using geographic information. It uses Global Positioning System (GPS) information for routing. However, GPSR may increase the packet drop rate due to radio attenuation and fading. Therefore, C. Li et al. proposed to select the next hop by evaluating the link quality. However, due to the large moving speed of the node, the next hop may be out of the radio propagation range at the communication timing. Therefore, in this study, we propose a new routing method that combines the future location prediction proposed by Z.S. Houssaini et al. The effectiveness of the method is demonstrated by simulations using ns-3 with traffic data acquired by SUMO.

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Glucose Sensing Utilizing Complex-Valued Neural Networks 23

Glucose Sensing Utilizing Complex-Valued Neural Networks

引用

4th International Conference on Computing, Networks and Internet of Things, CNIOT 2023

作者： Lv, Yiqi Meng, Xiang Luo, Yong Pei, Yan The Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University China and Jt. International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai China Doctoral Program Grad. Sch. of Comp. Science and Engineering Graduate Department of Computer and Information Systems University of Aizu Aizuwakamatsu Japan Computer Science Division University of Aizu Aizuwakamatsu Japan

ISBN: (纸本)9798400700705

This paper proposes a Complex-Valued Neural Network (CVNN) for glucose sensing in milli-meter wave (mmWave). Based on the propagation characteristics of millimeter wave in glucose medium, we obtain the S21 parameter of glucose with the concentration range of 0-300mg/dL in the 60-80 GHz frequency band by High Frequency Structure Simulator (HFSS) simulations. Then we combine the sensing model with a neural network to detect and predict the glucose concentration relying on the learning ability of the neural network. In the prediction of the concentration of unknown samples, the absolute error between the predicted value and the true value is within 5mg/dL, which confirms the ability of the proposed CVNN model. © 2023 ACM.

关键词： Millimeter waves

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Genetic Programming-Based Multi-Object Matching for Mussel Floats in Mussel Farm Images

Genetic Programming-Based Multi-Object Matching for Mussel F...

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International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Dylon Zeng Ying Bi Ivy Liu Bing Xue Ross Vennell Mengjie Zhang Sch. of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Electrical and Information Engineering Zhengzhou University Zhengzhou China Coastal and Freshwater Group Cawthron Institute Nelson New Zealand

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

The demand for Artificial Intelligence (AI)-powered solutions in New Zealand's aquaculture sector, particularly in mussel farms, is fast growing due to the need for enhanced productivity, reduced carbon footprint, and a safer worksite. An effective crop monitoring system that relies on high-resolution images from boat-mounted cameras requires an effective multi-object matching module. This paper presents a new Genetic Programming (GP)-based multi-object matching approach for mussel floats in farm images, addressing site-specific challenges such as a large number of tracklets, near-identical appearances, and low-overlapping scenes. Current object matching methods, including appearance models, motion models, and transformer models, struggle under these conditions. Evaluated on mussel farm images from the Marlborough Sounds of New Zealand, the proposed GP method demonstrates significantly higher matching accuracy, achieving 96.4% compared to 39.6% by a state-of-the-art transformer model. Our GP method achieves accurate multi-object matching in both slow and fast-moving scenarios, outperforming the state-of-the-art particularly in the latter.

关键词： Productivity Accuracy Tracking Genetic programming Crops Transformers Cameras Object recognition Monitoring Carbon footprint

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