2020 IEEE REGION 10 CONFERENCE (TENCON)最新文献

Age Progression using Generative Adversarial Networks 使用生成对抗网络的年龄进展

2020 IEEE REGION 10 CONFERENCE (TENCON) Pub Date : 2020-11-16 DOI: 10.1109/TENCON50793.2020.9293764

Pallavi Madhukar, Rachana Chetan, Supriya Prasad, Mohamed Shayan, B. N. Krupa

引用次数: 0

Comparative Evaluation of Assessment Activities in an Introductory Occupational Safety and Health Course 职业安全与健康导论课程评鉴活动之比较评估

2020 IEEE REGION 10 CONFERENCE (TENCON) Pub Date : 2020-11-16 DOI: 10.1109/TENCON50793.2020.9293851

Erwin B. Daculan

{"title":"Comparative Evaluation of Assessment Activities in an Introductory Occupational Safety and Health Course","authors":"Erwin B. Daculan","doi":"10.1109/TENCON50793.2020.9293851","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293851","url":null,"abstract":"This paper emerged during the process of continuous quality improvement of an introductory course on occupational safety and health. Did the students correctly associate an assessment activity to the right course outcome? The paper compared the evaluation of the students and the facilitator on the different activities used to assess the demonstration of abilities/outcomes attributed with the course. There were two activities per quarter of the semester. Each activity had been scheduled to demonstrate specific outcome indicators by the facilitator and is described in detail. An evaluation questionnaire was provided at the end of the semester to ascertain whether the students assessed the same outcome indicator as what course the set for each activity. The contention was to harmonize what outcomes the students believed they are exhibiting and what outcomes the course had intended for such activity. The gathered data from the evaluation questionnaire were tabulated and then compared with the original intended outcomes by the facilitator. Interpretation of the data was also forwarded and the path for continuous quality improvement was drawn.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"224 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127188294","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

Improved Viseme Recognition using Generative Adversarial Networks 基于生成对抗网络的改进Viseme识别

2020 IEEE REGION 10 CONFERENCE (TENCON) Pub Date : 2020-11-16 DOI: 10.1109/TENCON50793.2020.9293784

Jayanth Shreekumar, Ganesh K Shet, Vijay P N, Preethi S J, Niranjana Krupa

引用次数: 1

Transmit Power Minimization in Intelligent Reflecting Surfaces-Aided Uplink Communications 智能反射面辅助上行通信中的发射功率最小化

2020 IEEE REGION 10 CONFERENCE (TENCON) Pub Date : 2020-11-16 DOI: 10.1109/TENCON50793.2020.9293833

Jiao Wu, B. Shim

引用次数: 1

Density Based Clustering Methods for Road Traffic Estimation 基于密度的道路交通估计聚类方法

2020 IEEE REGION 10 CONFERENCE (TENCON) Pub Date : 2020-11-16 DOI: 10.1109/TENCON50793.2020.9293790

N. JagadishD., Lakshman Mahto, Arun Chauhan

引用次数: 1

A Development of Medication Assist Device Based on Multi-Object Recognition 基于多目标识别的药物辅助装置的研制

2020 IEEE REGION 10 CONFERENCE (TENCON) Pub Date : 2020-11-16 DOI: 10.1109/TENCON50793.2020.9293874

Yu-Sheng Lin, Chia-Ching Tsai, Kai-Ming Chang, Pao-Chin Shih, Ching-Lan Cheng

引用次数: 1

Development of a High Efficiency DC-DC Converter Using Hysteretic Control for Hydroelectric Energy Harvester in a Wireless Sensor Network 基于滞后控制的无线传感器网络水能采集器高效DC-DC变换器的研制

2020 IEEE REGION 10 CONFERENCE (TENCON) Pub Date : 2020-11-16 DOI: 10.1109/TENCON50793.2020.9293894

Gennylyn Canacan, John Thimotee Llanto, Eala Eireen Moredo, Adonis S. Santos, Francis A. Malabanan, J. Tabing, Sherryl M. Gevana

{"title":"Development of a High Efficiency DC-DC Converter Using Hysteretic Control for Hydroelectric Energy Harvester in a Wireless Sensor Network","authors":"Gennylyn Canacan, John Thimotee Llanto, Eala Eireen Moredo, Adonis S. Santos, Francis A. Malabanan, J. Tabing, Sherryl M. Gevana","doi":"10.1109/TENCON50793.2020.9293894","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293894","url":null,"abstract":"Efficiency is a requirement when it comes to utilizing a wireless sensor network (WSN) where hydrokinetic energy harvesting through turbine is involved. Thus, not only WSN needs an efficient supply but also the sensors and the storage unit which are powered up by an energy harvesting module, a turbine DC generator. Turbine DC generator produces a low voltage and low voltage means low power. To produce high efficiency output despite the low power it produces, a DC-DC converter is one of the preliminaries. DC-DC converter regulates its input coming from the turbine DC generator and produces a more stable power supply. However, blocks that the DC-DC converter supplies have different voltage requirement. Therefore, the researchers will develop two DC-DC converter topology which are the Buck Converter and the Boost Converter. On the contrary, turbine DC generator produces varying DC supply to the boost converter inducing noises and reducing the efficiency needed. Therefore, to achieve a highly efficient output the device needs to be low noise. To prevent noise from affecting the efficiency of the device, the researchers will use a technique called Hysteretic Control (HC) of DC-DC converter. This research intended to design a high efficiency Direct Current-to-Direct Current Converter for hydroelectric energy harvester in wireless sensor network. Using an Electronic Design Automation (EDA) tool, Synopsys, and ensuing the full custom analog design is practiced, the researchers develop a DC-DC converter that will provide an efficiency of 80% - 95% by reducing the noise by using a switching DC-DC converter.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116226244","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

TRETA - A Novel Heuristic Based Efficient Task Scheduling Algorithm in Cloud Environment 一种新的基于启发式的云环境下高效任务调度算法

2020 IEEE REGION 10 CONFERENCE (TENCON) Pub Date : 2020-11-16 DOI: 10.1109/TENCON50793.2020.9293787

K. Jayasena, K. M. P. Bandaranayake, B. Kumara

{"title":"TRETA - A Novel Heuristic Based Efficient Task Scheduling Algorithm in Cloud Environment","authors":"K. Jayasena, K. M. P. Bandaranayake, B. Kumara","doi":"10.1109/TENCON50793.2020.9293787","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293787","url":null,"abstract":"Cloud computing is a computing platform that allows users to access various kinds of computing services over the internet. Cloud provides on-demand, scalable and highly available resources on pay-per-usage subscriptions. Cloud is an optimum solution for executing a large number of different size tasks as for the computing capability it offers. Task scheduling is one of the major open challenges that need to be addressed. The Task scheduling problem in the cloud is known to be an NP- complete problem. Hence heuristics can be used to get an optimal solution. There have been many heuristics proposed for the task scheduling problem in the cloud. None of them has considered the total execution time of the virtual machine as a factor for finding a better schedule. In this paper, we proposed a new task scheduling algorithm named Total Resource Execution Time Aware Algorithm (TRETA) which takes into account the total execution time of computing resources in obtaining an optimal schedule. The algorithm is compared with Min-Min, Min-Max, FCFS, and MCT heuristics for Makespan, Degree of Imbalance and System Throughput. The proposed algorithm shows a significant amount of improvement in Makespan compared to other heuristics. The algorithm also outperforms other heuristics with respect to System Throughput and Degree of Imbalance which results in better workload distribution among the cloud resources.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121897787","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

Water Level Detection from CCTV Cameras using a Deep Learning Approach 利用深度学习方法从闭路电视摄像机中检测水位

2020 IEEE REGION 10 CONFERENCE (TENCON) Pub Date : 2020-11-16 DOI: 10.1109/TENCON50793.2020.9293865

Punyanuch Borwarnginn, J. Haga, Worapan Kusakunniran

引用次数: 1

Cells Detection and Segmentation in ER-IHC Stained Breast Histopathology Images ER-IHC染色乳腺组织病理学图像的细胞检测与分割

2020 IEEE REGION 10 CONFERENCE (TENCON) Pub Date : 2020-11-16 DOI: 10.1109/TENCON50793.2020.9293726

Mohammad F. Jamaluddin, M. F. A. Fauzi, F. S. Abas, Jenny T. H. Lee, S. Y. Khor, K. Teoh, L. Looi

{"title":"Cells Detection and Segmentation in ER-IHC Stained Breast Histopathology Images","authors":"Mohammad F. Jamaluddin, M. F. A. Fauzi, F. S. Abas, Jenny T. H. Lee, S. Y. Khor, K. Teoh, L. Looi","doi":"10.1109/TENCON50793.2020.9293726","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293726","url":null,"abstract":"In this paper, we present our recent work on cells detection and segmentation in estrogen receptor immunohistochemistry (ER-IHC)-stained breast carcinoma images. The proposed cell detection and segmentation is very useful in the predictive scoring of hormone receptor status in ER-IHC stained whole-slide images, which helps pathologists to decide whether a patient should be offered hormonal therapy or other treatments. The proposed method is based on deep convolutional neural network, followed by watershed-based post-processing. The cell detection results are compared and evaluated objectively against the ground truth provided by our collaborating pathologists. The cell segmentation results, on the other hand, are evaluated visually by overlaying the computer segmented boundaries on the ER-IHC images for comparison. The automated cell detection algorithm recorded precision and recall rates of 95% and 91% respectively. The very promising performances for both the detection and segmentation paves the way for an automated system for hormone receptor scoring in ER-IHC stained whole-slide breast carcinoma images.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128744609","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