2020 National Conference on Communications (NCC)最新文献_第8页

On the Latency in Vehicular Control using Video Streaming over Wi-Fi 基于Wi-Fi的视频流车辆控制延迟研究

2020 National Conference on Communications (NCC) Pub Date : 2020-02-01 DOI: 10.1109/NCC48643.2020.9056067

Pratik Sharma, Devam Awasare, Bishal Jaiswal, S. Mohan, N. Abinaya, Ishan S. Darwhekar, A. Svr, B. Amrutur, Aditya Gopalan, Parimal Parag, Himanshu Tyagi

引用次数: 1

Self Attentive Context dependent Speaker Embedding for Speaker Verification 基于上下文的自关注说话人嵌入方法

2020 National Conference on Communications (NCC) Pub Date : 2020-02-01 DOI: 10.1109/NCC48643.2020.9056043

Sreekanth Sankala, B. M. Rafi, S. Kodukula

{"title":"Self Attentive Context dependent Speaker Embedding for Speaker Verification","authors":"Sreekanth Sankala, B. M. Rafi, S. Kodukula","doi":"10.1109/NCC48643.2020.9056043","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056043","url":null,"abstract":"In the recent past, Deep neural networks became the most successful approach to extract the speaker embeddings. Among the existing methods, the x-vector system, that extracts a fixed dimensional representation from varying length speech signal, became the most successful approach. Later the performance of the x-vector system improved by explicitly modeling the phonological variations in it i.e, c-vector. Although the c-vector framework utilizes the phonological variations in the speaker embedding extraction process, it is giving equal attention to all the frames using the stats pooling layer. Motivated by the subjective analysis of the importance of nasals, vowels, and semivowels for speaker recognition, we extend the work of the c-vector system by including a multi-head self-attention mechanism. In comparison with the earlier subjective analysis on the importance of different phonetic units for speaker recognition, we also analyzed the attentions learnt by the network using TIMIT data. To examine the effectiveness of the proposed approach, we evaluate the performance of the proposed system on the NIST SRE10 database and get a relative improvement of 18.19 % with respect to the c-vector system on the short-duration case.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121869011","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 Two-Way Optimization Framework for Clustering of Images using Weighted Tensor Nuclear Norm Approximation 基于加权张量核范数逼近的图像聚类双向优化框架

2020 National Conference on Communications (NCC) Pub Date : 2020-02-01 DOI: 10.1109/NCC48643.2020.9055997

A. Johnson, Jobin Francis, Baburaj Madathil, S. N. George

引用次数: 4

Throughput of an Energy-Harvesting UAV Assisted Cognitive Radio Network 能量收集无人机辅助认知无线电网络的吞吐量

2020 National Conference on Communications (NCC) Pub Date : 2020-02-01 DOI: 10.1109/NCC48643.2020.9056090

A. Bhowmick, Sanjay Dhar Roy, S. Kundu

引用次数: 2

Language Specific Information from LP Residual Signal Using Linear Sub Band Filters 语言特定信息从LP残余信号使用线性子带滤波器

2020 National Conference on Communications (NCC) Pub Date : 2020-02-01 DOI: 10.1109/NCC48643.2020.9056005

Soma Siddhartha, Jagabandhu Mishra, S. Prasanna

引用次数: 3

Secrecy at Physical Layer in NOMA with Cooperative Jamming 协同干扰下NOMA物理层保密

2020 National Conference on Communications (NCC) Pub Date : 2020-02-01 DOI: 10.1109/NCC48643.2020.9056050

Shashibhushan Sharma, Sanjay Dhar Roy, S. Kundu

{"title":"Secrecy at Physical Layer in NOMA with Cooperative Jamming","authors":"Shashibhushan Sharma, Sanjay Dhar Roy, S. Kundu","doi":"10.1109/NCC48643.2020.9056050","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056050","url":null,"abstract":"We have proposed and analyzed secrecy performance of a network architecture which contains a source, two destinations, a friendly jammer (FJ) and an eavesdropper (Eve). To jam the Eve in order to secure the confidential information of both users, the FJ sends a jamming signal while the source sends the combined information signal using non-orthogonal multiple access (NOMA) technique to both the users. A known jamming is considered which is known by both the users. Both the users subtract the known jamming from the received signal and then decode the information signal as per the NOMA technique. We analyzed the physical layer security of the network in terms of the secrecy outage probability (SOP) where the two users have different target secrecy rates. A closed form expression of the SOP is developed. We have analyzed the SOP under the impact of several numerical parameters such as: source transmit signal to noise ratio (SNR), FJ transmit SNR, fraction of source transmit power, and different target secrecy rates. For a fixed transmit SNR of the FJ, we obtain an optimal value of the source transmit SNR at which SOP is minimized. These analyzed results are also supported by the MATLAB based simulation results.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":" 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133120598","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: 8

Classification of Autism in Young Children by Phase Angle Clustering in Magnetoencephalogram Signals 脑磁图信号相位角聚类对幼儿自闭症的分类

2020 National Conference on Communications (NCC) Pub Date : 2020-02-01 DOI: 10.1109/NCC48643.2020.9056022

Kasturi Barik, Katsumi Watanabe, J. Bhattacharya, G. Saha

{"title":"Classification of Autism in Young Children by Phase Angle Clustering in Magnetoencephalogram Signals","authors":"Kasturi Barik, Katsumi Watanabe, J. Bhattacharya, G. Saha","doi":"10.1109/NCC48643.2020.9056022","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056022","url":null,"abstract":"Autism spectrum disorder (ASD) is a complex neu- rodevelopmental condition that appears in early childhood or infancy, causing delays or impairments in social interaction and restricted range of interests of a child. In this work, our goal is to classify autistic children from typically developing children using a machine learning framework. Here, we have used magnetoencephalography (MEG) signals of thirty age and gender matched children from each group. We perform a spectral domain analysis in which the features are extracted from both power and phase of large-scale neural oscillations. In this work, we propose a novel phase angle clustering (PAC) based feature and have compared its performance with commonly used power spectral density (PSD) based feature. It is observed that with Artificial Neural Network (ANN) classifier, PAC yields better classification accuracy (88.20±3.87%) than the PSD feature (82.13±2.11%). To investigate laterality of brain activity, we evaluate the classification performance of each feature type over all channels as well as over individual hemispheres. Using machine learning framework it is found that the discriminating PSD features are mostly from high gamma band i.e. 50–100 Hz frequency oscillations and the PSD features are dominant in right hemisphere. These findings are in line with studies carried before in other framework. However, PAC based feature in our study shows that the whole brain contains important attributes of autism. The discriminating PAC features are mostly from theta band (i.e. 4–8 Hz frequency oscillations) that signifies memory formation and navigation. In this study, it is found that impaired theta oscillations correlate with autistic symptoms. Overall, our findings show the potential of such signal processing and classification based study to aid the clinicians in diagnosis of ASD.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114503041","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: 6

Performance Analysis of a Li-Fi System under Ambient Light Conditions 环境光条件下Li-Fi系统的性能分析

2020 National Conference on Communications (NCC) Pub Date : 2020-02-01 DOI: 10.1109/NCC48643.2020.9056061

Jayashree Pradhan, K. Kiran, S. Das

引用次数: 5

Uniformly Most Powerful CFAR Test for Pareto-Target Detection in Pareto Distributed Clutter Pareto分布杂波中Pareto目标检测的一致最强大CFAR检验

2020 National Conference on Communications (NCC) Pub Date : 2020-02-01 DOI: 10.1109/NCC48643.2020.9056098

J. Gali, P. Ray, G. Das

{"title":"Uniformly Most Powerful CFAR Test for Pareto-Target Detection in Pareto Distributed Clutter","authors":"J. Gali, P. Ray, G. Das","doi":"10.1109/NCC48643.2020.9056098","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056098","url":null,"abstract":"In the Radar context, Pareto distribution has been validated for both sea clutter and aircraft target under specific scenarios. Primarily, after the sea clutter is modeled as Pareto, some heuristic constant-false-alarm-rate (CFAR) processors appeared in the literature with the same form of adaptive thresholding that is derived for conventional exponential vs. exponential hypothesis testing (i.e., for detecting Swerling-I target in exponentially distributed clutter). Statistical procedures obtained under such idealistic assumptions cease to be optimal when applied to newer models esp. heavy tail distributions like Pareto. So, even to accommodate a wide range of application scenarios, in addition to Pareto modeled aircraft detection, we solve for heavy tail in Pareto vs. Pareto distributed lots from the composite hypothesis testing framework. Here, we derive the uniformly most powerful (UMP) test that complies CFAR property with-respect to the tail-index, using the least favorable density (lfd) concept. We further validate this CFAR property from extensive simulation results, and attribute that the geometric mean (GM)-CFAR is the optimal test in UMP sense.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129561295","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

RLS-Based Adaptive Time-Varying RCS Estimation and Imaging in MIMO Radar Systems MIMO雷达系统中基于rls的自适应时变RCS估计与成像

2020 National Conference on Communications (NCC) Pub Date : 2020-02-01 DOI: 10.1109/NCC48643.2020.9056049

Archana Rawat, Saumya Dwivedi, Suraj Srivastava, A. Jagannatham

引用次数: 1