2021 IEEE Region 10 Symposium (TENSYMP)最新文献_第6页

Application of Taylor Tapering to a Non-linear and Irregular Microstrip Array with Minimum Power Division Ratios 泰勒渐变在最小功率分比非线性不规则微带阵列中的应用

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550765

Haider Ali, K. Esselle

引用次数: 2

Accelerating Deep Neural Networks Using FPGAs and ZYNQ 利用fpga和ZYNQ加速深度神经网络

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550853

H. Lee, Jae Wook Jeon

{"title":"Accelerating Deep Neural Networks Using FPGAs and ZYNQ","authors":"H. Lee, Jae Wook Jeon","doi":"10.1109/TENSYMP52854.2021.9550853","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9550853","url":null,"abstract":"This article aims at implementing a Deep Neural Network (DNN) using Field Programmable Gate Arrays (FPGAs) for real time deep learning inference in embedded systems. In now days DNNs are widely used where high accuracy is required. However, due to the structural complexity, deep learning models are highly computationally intensive. To improve the system performance, optimization techniques such as weight quantization and pruning are commonly adopted. Another approach to improve the system performance is by applying heterogeneous architectures. Processor with Graphics Processing Unit (GPU) architectures are commonly used for deep learning training and inference acceleration. However, GPUs are expensive and consume much power that not a perfect solution for embedded systems. In this paper, we implemented a deep neural network on a Zynq SoC which is a heterogenous system integrated of ARM processor and FPGA. We trained the model with MNIST database, quantized the model’s 32-bit floating point weights and bias into integer and implemented model to inference in FPGA. As a result, we deployed a network on an embedded system while maintaining inference accuracy and accelerated the system performance with using less resources.","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"68 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131673752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Input Power Range Extension Using Duty-Cycling and Granularity Reduction in a 5.8GHz RF Energy Harvester Dynamic Matching Circuit 5.8GHz射频能量采集器动态匹配电路中基于占空比和粒度减小的输入功率范围扩展

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550954

Patrice Abbie D. Legaspi, L. Alarcón

引用次数: 0

Impacts of climate change on human health and agriculture in recent years 近年来气候变化对人类健康和农业的影响

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550876

A. Mohanty

引用次数: 7

Integral Framework for UAV: Navigational and Wireless Connectivity Aspects 无人机的整体框架:导航和无线连接方面

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550847

Sravanti Kanchi, R. V. Prasad

引用次数: 0

IMU based Smart Safety Hook for Fall Prevention at Construction Sites 基于IMU的建筑工地防坠落智能安全钩

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550944

Muhammad Khan, Rabia Khalid, Sharjeel Anjum, Numan Khan, Chansik Park

引用次数: 8

Home-based tele-rehabilitation system provides task-oriented training courses based on the stages of stroke 基于家庭的远程康复系统提供基于中风阶段的任务导向培训课程

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550884

Chih-Chun Lin, Yu-Chen Lin, H. Hsu, Yu-Sheng Lin, F. Su, L. Kuo

引用次数: 3

Implementation of a 32-Channel Low-cost Low Current Measuring Circuit using Analog Devices ADA4530-1 on Automated Test Equipment for Parallel Leakage Current Test on Semiconductor Switch Test Platform Conversion 基于模拟器件ADA4530-1的32通道低成本低电流测量电路在半导体开关测试平台转换并联漏电流测试自动化设备上的实现

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550815

Cyrus Peter M. Lim, Ericson D. Dimaunahan

引用次数: 0

Event Identification for Phase-sensitive OTDR based on Boosting Ensemble Learning 基于增强集成学习的相敏OTDR事件识别

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550964

A. Yan, Lijun Wan, Mengshi Wu

{"title":"Event Identification for Phase-sensitive OTDR based on Boosting Ensemble Learning","authors":"A. Yan, Lijun Wan, Mengshi Wu","doi":"10.1109/TENSYMP52854.2021.9550964","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9550964","url":null,"abstract":"The distributed optical fiber sensor based on phase-sensitive optical time-domain reflectometer (φ-OTDR), of which has the advantages of simple structure, low energy consumption and strong anti-electromagnetic interference capability, is widely used in dynamic sensing fields such as perimeter security. However, in practical applications, due to the extremely high sensitivity of φ-OTDR, most of the received signals are useless natural sounds caused by environmental noise and human activities, which not only limits the efficiency of the system, but also causes false alarms. In order to effectively identify and timely respond to detected disturbance signals, this paper proposed an ensemble learning algorithm for optical fiber disturbance signal detection and recognition, which combined with wavelet packet decomposition (WPD) and adaptive Boosting (Adaboost) algorithm. This paper obtained optical fiber disturbance signals of 4 kinds of intrusion/interference events through field measurement, combined with three classic performance indicators of pattern recognition to verify the performance of the proposed algorithm. Experimental results show that the total classification accuracy is high to 97.78%.","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117058204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of Virescens Fresh Fruit Bunch Ripeness Prediction using LiDAR for Smart Agriculture 智能农业中利用激光雷达预测鲜果束成熟度的研究进展

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550981

Abdul Dzuljalal Ikram bin Mat Seri, Mohd Sallehin bin Mohd Kassim, Siti Rahmah binti Abdul Rahman, Aznida Abu Bakar Sajak

{"title":"Development of Virescens Fresh Fruit Bunch Ripeness Prediction using LiDAR for Smart Agriculture","authors":"Abdul Dzuljalal Ikram bin Mat Seri, Mohd Sallehin bin Mohd Kassim, Siti Rahmah binti Abdul Rahman, Aznida Abu Bakar Sajak","doi":"10.1109/TENSYMP52854.2021.9550981","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9550981","url":null,"abstract":"Smart Agriculture is a part of Humanitarian Technology. Oil palm fruit is one of the leading agricultural product exports by Malaysia. At present, the general methods used to determine the ripeness of oil palm fresh fruit bunch are using human vision, computer vision and laser-based imaging techniques. This research aims to design and build a scanning system based on a LiDAR sensor and servo motors and obtain point cloud data from oil palm fresh fruit bunch (FFB). The proposed project consists of LiDAR Lite V3, Arduino UNO and two servo motors as its main component. LiDAR sensor is used to collect the intensity value that reflects from the Virescens oil palm FFB, and the data collected are saved in a CSV file for further analysis. The methodology used in this research is the Iterative Waterfall model. This model supports redesign if there are any improvements needed in this project, and the phase can be looped back to the previous iteration if the process faces any errors. The system proposed works successfully to produce point clouds from oil palm fresh fruit bunch, and it is found that ripe oil palm fruit has a lower mean intensity value than unripe oil palm fresh fruit bunch.","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115416227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1