2021 44th International Conference on Telecommunications and Signal Processing (TSP)最新文献_第9页

Conductive Printing of RFID Tag and Chip Contacting Methods for High Volume Additive Production RFID标签的导电印刷及大批量增材生产的芯片接触方法

2021 44th International Conference on Telecommunications and Signal Processing (TSP) Pub Date : 2021-07-26 DOI: 10.1109/TSP52935.2021.9522666

M. Kerndl, P. Steffan

引用次数: 0

Comparative Study for Tuberculosis Detection by Using Deep Learning 基于深度学习的肺结核检测比较研究

2021 44th International Conference on Telecommunications and Signal Processing (TSP) Pub Date : 2021-07-26 DOI: 10.1109/TSP52935.2021.9522634

B. Karaca, S. Guney, B. Dengiz, A. Ağıldere

{"title":"Comparative Study for Tuberculosis Detection by Using Deep Learning","authors":"B. Karaca, S. Guney, B. Dengiz, A. Ağıldere","doi":"10.1109/TSP52935.2021.9522634","DOIUrl":"https://doi.org/10.1109/TSP52935.2021.9522634","url":null,"abstract":"Tuberculosis (TB) is an infectious disease which becomes a significant health problem worldwide. Many people have been affected by this disease owing to deficiency of treatment and late or inaccuracy of diagnosis. Therefore, accurate and early diagnosis is the very major solution to checking and preventing the disease. A chest x-ray is a main diagnostic tool used to diagnose tuberculosis. This diagnostic method is limited by the availability of radiologists and the experience and skills of radiologists in reading x-rays. To overcome such a challenge, a computer-aided diagnosis (CAD) system is supposed for the radiologist to interpret chest x-ray images easily. In this study, a CAD system based upon transfer learning is developed for TB detection using Montgomery Country chest x-ray images. We used the VGG16, VGG19, DenseNet121, MobileNet, and InceptionV3 pre-trained CNN models to extract features automatically and used the Support Vector Machine (SVM) classifier to the detection of tuberculosis. Furthermore, data augmentation techniques were applied to boost the performance results. The proposed method performed the highest accuracy of 98.9% and area under the curve (AUC) of 1.00, respectively, with the DenseNet121 on augmented images.","PeriodicalId":243595,"journal":{"name":"2021 44th International Conference on Telecommunications and Signal Processing (TSP)","volume":"283 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117342523","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: 3

Learning Scanning Regime For Electronic Support Receivers by Nonnegative Matrix Factorization 基于非负矩阵分解的电子支援接收机扫描体制学习

2021 44th International Conference on Telecommunications and Signal Processing (TSP) Pub Date : 2021-07-26 DOI: 10.1109/TSP52935.2021.9522658

Ismail Gül, I. Erer

引用次数: 1

Advanced Memory Efficient Outlier Detection Approach for Streaming Data using Swarm Optimization 基于群优化的流数据高级内存高效离群点检测方法

2021 44th International Conference on Telecommunications and Signal Processing (TSP) Pub Date : 2021-07-26 DOI: 10.1109/TSP52935.2021.9522667

Ankita Karale, Milena Lazarova, P. Koleva, V. Poulkov

引用次数: 0

Identification of Multilinear Forms with the Tensorial Kalman Filter 用张量卡尔曼滤波辨识多线性形式

2021 44th International Conference on Telecommunications and Signal Processing (TSP) Pub Date : 2021-07-26 DOI: 10.1109/TSP52935.2021.9522645

Laura-Maria Dogariu, C. Paleologu, J. Benesty, S. Ciochină

引用次数: 1

Cough Sound Classification Based on Similarity Metrics 基于相似度度量的咳嗽声分类

2021 44th International Conference on Telecommunications and Signal Processing (TSP) Pub Date : 2021-07-26 DOI: 10.1109/TSP52935.2021.9522595

N. Petrellis, G. Adam

{"title":"Cough Sound Classification Based on Similarity Metrics","authors":"N. Petrellis, G. Adam","doi":"10.1109/TSP52935.2021.9522595","DOIUrl":"https://doi.org/10.1109/TSP52935.2021.9522595","url":null,"abstract":"Cough and respiratory sound processing can assist in the early diagnosis of infections such as Covid-19. Even asymptomatic Covid-19 patients can be diagnosed early enough if appropriate speech modeling and signal-processing is applied. Covid-19 affects various speech subsystems that are involved in respiration, phonation and articulation. Based on a symptom tracking platform that was recently presented by the authors (Coronario), we focus on the sound processing subsystem that is capable of classifying cough or respiratory sounds in multiple categories. Specifically, we attempt to classify a cough sound file in one of the following 5 categories: male dry or productive, female dry or productive and child’s cough. The classification is performed using Pearson Correlation Similarity, in frequency domain. Several alternative methods that employ averaging and Principal Component Analysis have been tested to estimate their recall and precision/accuracy metrics. The average precision/accuracy achieved is about 75% and 88%, respectively. The sound processing platform used is extensible allowing researches to experiment with several different classification methods applied on the anonymized data exchanged during symptom tracking.","PeriodicalId":243595,"journal":{"name":"2021 44th International Conference on Telecommunications and Signal Processing (TSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121909743","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: 3

A consolidated view of loss functions for supervised deep learning-based speech enhancement 基于监督深度学习的语音增强损失函数的综合视图

2021 44th International Conference on Telecommunications and Signal Processing (TSP) Pub Date : 2020-09-25 DOI: 10.1109/TSP52935.2021.9522648

Sebastian Braun, I. Tashev

引用次数: 46