2021 IEEE Region 10 Symposium (TENSYMP)最新文献

Supervised Learning-based Sound Source Distance Estimation Using Multivariate Features 基于监督学习的多变量特征声源距离估计

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9551007

Kalamkas Zhagyparova, Ruslan Zhagypar, A. Zollanvari, M. Akhtar

{"title":"Supervised Learning-based Sound Source Distance Estimation Using Multivariate Features","authors":"Kalamkas Zhagyparova, Ruslan Zhagypar, A. Zollanvari, M. Akhtar","doi":"10.1109/TENSYMP52854.2021.9551007","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9551007","url":null,"abstract":"This paper introduces the use of supervised machine learning methods with a combination of several sound source distance-dependent features to tackle the problem of distance-of-arrival (DisOA) estimation. The DisOA estimation is approached as a classification problem, which aims to classify a recorded audio signal into one of the predefined four DisOA classes regardless of the orientation angle. The datasets for both training and testing purposes are simulated by convolving appropriate room impulse responses with anechoic speech signals. The performance of three conventional and efficient classifiers was examined along with various subsets of four extracted features including: 1) Diffuseness (DIFF); 2) Binaural spectral magnitude difference standard deviation (BSMD-STD); 3) Magnitude squared coherence (MSC); and 4) Direct-to-reverberant ratio (DRR). The simulations consider the use of different source signals as well as varying directions-of-arrival and the room sizes. Our empirical results show that the use of a single univariate feature, namely, MSC, along with K-nearest neighbor (KNN) could potentially lead to an accurate DisOA classification rule.","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"27 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":"115759449","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

Deep Learning Approach to Optical Camera Communication Receiver Design 基于深度学习的光学相机通信接收机设计

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550896

Sangshin Park, Hoon Lee

{"title":"Deep Learning Approach to Optical Camera Communication Receiver Design","authors":"Sangshin Park, Hoon Lee","doi":"10.1109/TENSYMP52854.2021.9550896","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9550896","url":null,"abstract":"This paper investigates a deep learning (DL) framework for designing optical camera communication (OCC) systems where a receiver is realized with optical cameras capturing images of transmit LEDs. The optimum decoding strategy is formulated as the maximum a posterior (MAP) estimation with a given received image. Due to the absence of analytical OCC channel models, it is challenging to derive the closed-form MAP detector. To address this issue, we employ a convolutional neural network (CNN) model at the OCC receiver. The proposed CNN approximates the optimum MAP detector that determines the most probable data symbols by observing an image of the OCC transmitter implemented by dot LED matrices. The supervised learning philosophy is adopted to train the CNN with labeled images. We collect training samples in real-measurement scenarios including heterogeneous background noise and distance setups. As a consequent, the proposed CNN-based OCC receiver can be applied to arbitrary OCC scenarios without any channel state information. The effectiveness of our model is examined in the real-world OCC setup with Raspberry Pi cameras. The experimental results demonstrate that the proposed CNN architecture performs better than other DL models.","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"1 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":"116779806","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

An Ultra-thin, Flexible FSS with Simultaneous Stop-Pass EM Filtering 超薄，灵活的FSS与同时停止通EM滤波

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550866

Muhammad Shafqat, Malik Muhammad Asim, Fahad Ahmed, Syed Hussain Ali Bokhari

引用次数: 0

Bidirectional Long Short-term Memory-based Intelligent Auxiliary Diagnosis of Fetal Health 基于双向长短期记忆的胎儿健康智能辅助诊断

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550851

Zhang Yefei, D. Yanjun, Zhang Xiaohong, Shao Lihuan, Zhao Zhidong

引用次数: 3

An Efficient Structural Analysis of SAS and its Application to White-Box Cryptography SAS的有效结构分析及其在白盒密码中的应用

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550967

Hyoung-Kyu Yim, Ju-Sung Kang, Yongjin Yeom

引用次数: 0

Parametrization and Core Temperature Estimation of Lithium-Ion Batteries for Thermal Management 锂离子电池热管理参数化及堆芯温度估算

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550840

J. Jeewandara, Karunadasa Jp, K. Hemapala

引用次数: 1

Utilization of seismic activity infrasound signals to estimate wind in the atmosphere 利用地震活动、次声信号来估计大气中的风

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550988

M. Batubara, Masahiro Yamamoto, Hiroaki Saito

引用次数: 0

Performance Comparison of Quantum Phase Estimation Algorithm with Different Number of Register Qubits on Noisy Quantum Processor 噪声量子处理器上不同寄存器量子比特数的量子相位估计算法性能比较

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550939

Jinyoung Ha, Jun Heo

引用次数: 2

MANET Routing Protocols with Emphasis on Zone Routing Protocol – an Overview 以区域路由协议为重点的MANET路由协议概述

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550879

Eesha Kurode, Naishadh Vora, Sachin P. Patil, V. Attar

引用次数: 7

Information multiple distillation super resolution network based on feedback mechanism 基于反馈机制的信息多重蒸馏超分辨网络

2021 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2021-08-23 DOI: 10.1109/TENSYMP52854.2021.9550997

Yiming Wu, Zeyu Chen, Shiming He, Jin Wang

{"title":"Information multiple distillation super resolution network based on feedback mechanism","authors":"Yiming Wu, Zeyu Chen, Shiming He, Jin Wang","doi":"10.1109/TENSYMP52854.2021.9550997","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9550997","url":null,"abstract":"There are lots of image data in the field of remote sensing, most of which have low-resolution due to the limited image sensor. The super-resolution method can effectively restore the low-resolution image to the high-resolution image. However, the existing super-resolution method has both heavy computing burden and number of parameters, which greatly limits the super resolution method in the mobile terminal. For saving costs, we propose the information multiple distillation network based on feedback mechanism (Feedback-IMDN), which considers the feedback mechanism as the framework to attain lower features through high-level refining. Further, for high-level feature extraction, we use the information multiple distillation blocks (IMDBs) to carry out hierarchical feature extraction with the method of course learning in the case of a small number of parameters. Compared to other state-of-the-art lightweight algorithms, our proposed algorithm can reach convergences more rapidly with fewer parameters, and the performance of the network can be markedly enhanced on the image texture and object contour reconstruction with better peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"15 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":"121652157","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