2021 IEEE Region 10 Symposium (TENSYMP)最新文献_第9页

Detection and Diagnosis of Breast Cancer Using Deep Learning 使用深度学习的乳腺癌检测和诊断

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550975

Mohammad Ashik Alahe, M. Maniruzzaman

{"title":"Detection and Diagnosis of Breast Cancer Using Deep Learning","authors":"Mohammad Ashik Alahe, M. Maniruzzaman","doi":"10.1109/TENSYMP52854.2021.9550975","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9550975","url":null,"abstract":"Breast Cancer (BC) is a cancerous growth that is a result of uncontrolled cell division in the mammary tissues, usually in the ducts and in the lobules. BC is the most dominant fast-growing cancer and one of the leading cause of cancer mortality in women. BC incidents are increasing swiftly every year around the world especially in developing countries due to grown life expectancy and assumption of western culture. The conventional process of detecting BC involves a clinical expert who observed the medical images of affected breast tissues and looks for structural changes, irregularities in cell forms, ordination of cells in the tissue and determining the stage of the cancer. As conventional interpretation is often time consuming, expensive and error prone; computer-aided detection (CAD) technique is used as an alternative to provide a more accurate, automatic, fast and reproducible procedure to detect BC. This research presents a fully automatic process of BC detection. Two well know filter such as Gaussian Blur (GB) and Detail Enhanced (DE) filter has been used here for the preprocessing purpose. Convolutional Neural Network (CNN) classifier has been used here for classification. The proposed model is performed on an openly accessible dataset named Breast Histopathology Image dataset and the outcome exhibits the sharpness of our proposed model. The obtained accuracy is 87.49%, 88.46% and 88.10% in Case-I, Case-II and Case-III, respectively.","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"228 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":"115230379","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

Integration of Network science approaches and Data Science tools in the Internet of Things based Technologies 基于物联网技术的网络科学方法和数据科学工具的集成

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550992

Farhan Amin, W. Lee, Abdul Mateen, S. Hwang

引用次数: 4

Proliferative Diabetic Retinopathy Classification from Retinal Fundus Images Using Fractal Analysis 基于分形分析的增殖性糖尿病视网膜病变视网膜眼底图像分类

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550926

Gusna Naufal Taris, A. Handayani, T. Mengko, B. R. Hermanto

{"title":"Proliferative Diabetic Retinopathy Classification from Retinal Fundus Images Using Fractal Analysis","authors":"Gusna Naufal Taris, A. Handayani, T. Mengko, B. R. Hermanto","doi":"10.1109/TENSYMP52854.2021.9550926","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9550926","url":null,"abstract":"Diabetic retinopathy is a complication of diabetes mellitus that affects the retinal tissue in the eye This disease is one of the leading causes of blindness in the world. Proliferative diabetic retinopathy is the most dangerous type of diabetic retinopathy (PDR). PDR is characterized by the development of neovascularization. Many studies have been conducted to identify PDR automatically. In this study, the authors used a retinal blood vascular structure approach to detect neovascularization on images. This strategy is implemented using fractal analysis. The wavelet transform segmentation method with 2D-Gabor wavelet was used in this study to provide optimal fractal feature values for classifying PDR. The maximum red lesions probability feature was also used in this study to detect PDR symptoms other than neovascularization. The most significant feature is the fractal analysis's shanon entropy in combination with the maximum red lesion probability, which yielded AUC values of 0.9335, with a sensitivity of 93.38 percent and a specificity of 81.17 percent. This method produces test results that show that as image resolution decreases, PDR classification remains stable, whereas PDR classification degrades with poor image quality.","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"189 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":"123529999","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

Dense-depth-net: a spatial-temporal approach on depth completion task 深度补全任务的一种时空方法

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550990

Tri-Hai Nguyen, Myungsik Yoo

{"title":"Dense-depth-net: a spatial-temporal approach on depth completion task","authors":"Tri-Hai Nguyen, Myungsik Yoo","doi":"10.1109/TENSYMP52854.2021.9550990","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9550990","url":null,"abstract":"Depth completion is essential functionality in the perception system of an autonomous vehicle. With various convolution neural networks (CNN), scene geometric representation has been studied extensively under supervised learning or self-supervised learning. This paper utilizes recurrent neural networks (RNNs) to investigate temporal information from camera video sequences, which can help mitigate the mismatch between two consecutive data frames. Our paper proposed an architecture consisting of two sequence processing: the spatial exploitation stage built from a two-branches network and the temporal exploitation stage, a novel convolutional LSTM (ConvLSTM). Furthermore, we take the ability of long short-term memory (LSTM)-based RNNs to estimate a one-step depth map as an additional role of the representations of objects not only in a data frame but also in its temporal neighborhood. Moreover, the proposed ConvLSTM network demonstrated to have the option to make depth forecasts for future or occluded parts of an image frame. We evaluate the performance of the proposed architecture on the KITTI dataset and achieve the result proving to improve accuracy via a supervised-learning.","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"70 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":"124957503","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

Design of a Low Power Bfloat16 Pipelined MAC Unit for Deep Neural Network Applications 面向深度神经网络的低功耗Bfloat16流水线MAC单元设计

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550912

Ankita Tiwari, G. Trivedi, P. Guha

{"title":"Design of a Low Power Bfloat16 Pipelined MAC Unit for Deep Neural Network Applications","authors":"Ankita Tiwari, G. Trivedi, P. Guha","doi":"10.1109/TENSYMP52854.2021.9550912","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9550912","url":null,"abstract":"Evolution of artificial intelligence (AI) and advances in semiconductor technology has enabled us to design many complex systems ranging from IoT based applications to high performance compute engines. AI incorporates various application driven machine learning algorithms, in which floating point numbers are employed for the training of neural network models. However, few simpler number systems, such as fixed-point and integers, are employed in inference due to their smaller bit-width, which reduce area and power consumption at the cost of accuracy due to quantization. The usage of floating point MAC improves the accuracy, but it results in a larger area and more power consumption. In this paper, an area and power efficient pipelined Bfloat16 MAC is proposed aiming performance improvement of neural network applications. The proposed unit is able to handle overflow, underflow, and normalization efficiently. Additionally, computational accuracy of MAC is improved by increasing mantissa bit-width and by eliminating normalization in the intermediate stages. The proposed non-pipelined MAC utilizes 18.61% less resources as compared to similar architectures. The area and power of the proposed 16-bit nonpipelined Bfloat16 MAC is reduced by 5.21% and 32%, respectively, at 200 MHz as compared to another 16-bit nonpipelined Bfloat16 MAC reported in [26]. The area and power of our proposed MAC is improved by 38.6% and 93% at 200 MHz, and 7.1% and 11.52% at 01 GHz, when it is compared with a 16-bit pipelined posit MAC and a pipelined Bfloat16 MAC reported in [27], respectively.","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"59 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":"128743525","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: 5

Analysis of public-key cryptography using a 3-regular graph with a perfect dominating set 使用具有完美支配集的3正则图分析公钥密码

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550868

Sujin Kwon, Ju-Sung Kang, Yongjin Yeom

{"title":"Analysis of public-key cryptography using a 3-regular graph with a perfect dominating set","authors":"Sujin Kwon, Ju-Sung Kang, Yongjin Yeom","doi":"10.1109/TENSYMP52854.2021.9550868","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9550868","url":null,"abstract":"Research on post-quantum cryptography (PQC) to improve the security against quantum computers has been actively conducted. In 2020, NIST announced the final PQC candidates whose design rationales rely on NP-hard or NP-complete problems. It is believed that cryptography based on NP-hard problem might be secure against attacks using quantum computers. N. Koblitz introduced the concept of public-key cryptography using a 3-regular graph with a perfect dominating set in the 1990s. The proposed cryptosystem is based on NP-complete problem to find a perfect dominating set in the given graph. Later, S. Yoon proposed a variant scheme using a perfect minus dominating function. However, their works have not received much attention since these schemes produce huge ciphertexts and are hard to implement efficiently. Also, the security parameters such as key size and plaintext-ciphertext size have not been proposed yet. We conduct security and performance analysis of their schemes and discuss the practical range of security parameters. As an application, the scheme with one-wayness property can be used as an encoding method in the white-box cryptography (WBC).","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"24 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":"124487947","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

Parallel Processing Architecture of Intuitive Digital Image Indices Based on Open Sources 基于开源的直观数字图像索引并行处理体系

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550885

HyungTae Kim, Dong-Wook Lee

引用次数: 0

Performance analysis of metal-coated fiber Bragg gratings for strain sensing at high temperatures 金属涂层光纤布拉格光栅高温应变传感性能分析

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550881

M. Vimal, Archana Thrikkaikuth Chalackal, Srijith Kanakambaran

引用次数: 0

Weather Parameters and Heat Index Prediction Applying Deep Neural Networks 应用深度神经网络预测天气参数和热指数

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550852

Kazi Fahim Lateef, Joy Paul, Zerin Jahan

{"title":"Weather Parameters and Heat Index Prediction Applying Deep Neural Networks","authors":"Kazi Fahim Lateef, Joy Paul, Zerin Jahan","doi":"10.1109/TENSYMP52854.2021.9550852","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9550852","url":null,"abstract":"The way through which change of weather parameters is measured can be called as weather forecasting. Weather forecast plays important role in predicting natural calamities, agricultural sectors, some industrial sectors, etc. Heat index is a vital part of weather which depends on some weather parameters. Human body temperature, evaporation, etc. depends on heat index. Previously, several researchers worked on this issue to determine whether it rains or not for two to three days. Some of them worked just to determine the effect of change of heat index in different regions. A number of works were based on machine learning algorithms and most of them had either low accuracy or high error rate. But in this paper, we proposed a combined deep learning model. It can predict five most important weather parameters up to 30 days with very low error rate by using single cell long short term memory (LSTM). Along with this, the output from LSTM was further used to predict heat index using artificial neural network (ANN) which also gave a very high accuracy. The dataset provided to the model was pre-processed properly by using Gaussian filter, Median filter and scaling. It increased the sensitivity and performance of the model. We got the mean absolute percentage error rate (MAPE) ranging from 0.02%-8.53% with LSTM model and 94.68% of accuracy from ANN model. Further judging parameters of ANN are precision (94.57%), recall (94.57%), f1 score (94.57%), loss (12.75%). The other error rates of LSTM model are MSE (1%-16%), RMSE (10.72%-39.44%), R2 (99%-100%), MAE (8.17%-34.59%).","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"22 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":"128223050","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

Analysis of Supercapacitors as an Energy Source – A Proof-of-Concept Study for Power Optimisation Circuits 超级电容器作为能源的分析——功率优化电路的概念验证研究

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550823

Nur Aqilah Bte Muhammed Hashim, No-Dong Kim

引用次数: 0