2022 National Conference on Communications (NCC)最新文献

Performance study of Neural Structured Learning using Riemannian Features for BCI Classification 基于黎曼特征的脑机接口分类神经结构学习性能研究

2022 National Conference on Communications (NCC) Pub Date : 2022-05-24 DOI: 10.1109/NCC55593.2022.9806736

Vinay Gupta, J. Meenakshinathan, T. Reddy, L. Behera

{"title":"Performance study of Neural Structured Learning using Riemannian Features for BCI Classification","authors":"Vinay Gupta, J. Meenakshinathan, T. Reddy, L. Behera","doi":"10.1109/NCC55593.2022.9806736","DOIUrl":"https://doi.org/10.1109/NCC55593.2022.9806736","url":null,"abstract":"Riemannian Geometry-based features have been among the most promising electroencephalography(EEG) classification methods in recent years. However, these features can be classified using many machine learning(ML) algorithms. When compared against the standard methods, deep learning-based approaches are successful in classification accuracy and transfer learning. In this paper, we attempt to study Neural structured learning(NSL) to develop robust and regularized neural network models that preserve the similarity structure of the input EEG signals for a more reliable Brain-Computer Interface(BCI) classification. In this study, we have used the state-of-the-art Euclidean Tangent Space features projected from the Riemannian Covariance features of EEG to train the standard feedforward neural nets while incorporating the NSL module. It creates a similarity graph among the input samples and minimizes a graph regularization loss to maintain the neighbor structure. The proposed approach is evaluated on the standard 4-class Dataset 2a from BCI competition 2008. The results show that the proposed model improves accuracy compared to the base model without graph regularization. Surprisingly, it requires very few training samples to achieve almost state-of-the-art accuracy for some subjects using a mere two hidden layered neural network.","PeriodicalId":403870,"journal":{"name":"2022 National Conference on Communications (NCC)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124618061","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

Analysis of Smart Grid Wide Area Network for Three Hop Mixed PLC/RF/FSO Channel 三跳PLC/RF/FSO混合信道的智能电网广域网分析

2022 National Conference on Communications (NCC) Pub Date : 2022-05-24 DOI: 10.1109/NCC55593.2022.9806792

Ashish Kumar Padhan, H. Sahu, P. R. Sahu, S. Samantaray

引用次数: 1

Centralized and Distributed Reconfigurable Intelligent Surfaces Assisted NOMA 集中式和分布式可重构智能表面辅助NOMA

2022 National Conference on Communications (NCC) Pub Date : 2022-05-24 DOI: 10.1109/NCC55593.2022.9806789

M. Kumar, S. Sharma, K. Deka, M. Thottappan

{"title":"Centralized and Distributed Reconfigurable Intelligent Surfaces Assisted NOMA","authors":"M. Kumar, S. Sharma, K. Deka, M. Thottappan","doi":"10.1109/NCC55593.2022.9806789","DOIUrl":"https://doi.org/10.1109/NCC55593.2022.9806789","url":null,"abstract":"Reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) are promising technologies for next-generation wireless networks. RIS can reconfigure wire-less channels through passive reflecting elements, and NOMA enhances spectral efficiency (SE) and connectivity. In this paper, a base station (BS) transmits superimposed precoded symbols to near and far users via two different RIS deployment strategies. Initially, a single RIS is deployed at the BS and consists of N passive reflecting elements, referred to as centralized deployment of RIS-assisted NOMA (CDR-NOMA). On the other hand, two RISs having N/2 elements are kept at users and referred to as distributed deployment of RIS-assisted NOMA (DDR-NOMA). We have optimized the phase shift at RIS using the semidefinite relaxation (SDR) technique to maximize the received signal-to-noise ratio (SNR). Simulation results show that the bit error rate (BER) of the CDR-NOMA system is superior to the DDR-NOMA and a conventional RIS-assisted NOMA system. Further, the sum-rate of the proposed CDR-NOMA and DDR-NOMA is calculated and it is better than the orthogonal multiple access (OMA). Furthermore, impact of transmitting antennas and reflecting surfaces are studied on the sum-rate and BER performance in the CDR-NOMA and DDR-NOMA.","PeriodicalId":403870,"journal":{"name":"2022 National Conference on Communications (NCC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114847773","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

Digital Predistortion for mm-Wave MIMO Phased Arrays 毫米波MIMO相控阵的数字预失真

2022 National Conference on Communications (NCC) Pub Date : 2022-05-24 DOI: 10.1109/NCC55593.2022.9806716

Varsha Balakumar, R. Ganti

引用次数: 0

UAV Altitude Optimization for Efficient Energy Harvesting in IoT Networks 物联网网络中高效能量收集的无人机高度优化

2022 National Conference on Communications (NCC) Pub Date : 2022-05-24 DOI: 10.1109/NCC55593.2022.9806741

Aditya Singh, Surender Redhu, R. Hegde

{"title":"UAV Altitude Optimization for Efficient Energy Harvesting in IoT Networks","authors":"Aditya Singh, Surender Redhu, R. Hegde","doi":"10.1109/NCC55593.2022.9806741","DOIUrl":"https://doi.org/10.1109/NCC55593.2022.9806741","url":null,"abstract":"Energy harvesting plays a crucial role in improving the operational lifetime of an IoT network. Moreover, the source and receiver separation dominates the amount of energy har-vested in rechargeable IoT networks. In recent, Unmanned Aerial Vehicles (UAVs) have been utilized as RF energy transmitters for energy harvesting IoT networks. In this work, a method is proposed for optimizing the altitude of UAV s for energy-efficient charging of the IoT nodes. The proposed method maximizes the energy usage efficiency of the UAV over the IoT network subject to altitude and energy harvesting constraints. The proposed maximization problem is first transformed into an equivalent convex optimization problem using the First-Order Taylor Series Approximation. A heuristic algorithm based on the sequential convex programming approach is employed to obtain the optimal UAV altitude above the IoT network. The accuracy of the obtained results is evaluated analytically by computing the global optimum of the optimization problem via monotonic transformations. The results motivate the usage of UAV-aided energy harvesting in self-sustaining IoT networks.","PeriodicalId":403870,"journal":{"name":"2022 National Conference on Communications (NCC)","volume":"61 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133038648","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

Emission Time Estimation with Rectangular Input Concentration in Molecular Communication Systems 基于矩形输入浓度的分子通信系统发射时间估计

2022 National Conference on Communications (NCC) Pub Date : 2022-05-24 DOI: 10.1109/NCC55593.2022.9806777

Ajit Kumar, Sudhir Kumar

引用次数: 1

Importance of excitation source and sequence learning towards spoken language identification task 激励源和序列学习对口语识别任务的重要性

2022 National Conference on Communications (NCC) Pub Date : 2022-05-24 DOI: 10.1109/NCC55593.2022.9806768

Jagabandhu Mishra, Soma Siddhartha, S. Prasanna

{"title":"Importance of excitation source and sequence learning towards spoken language identification task","authors":"Jagabandhu Mishra, Soma Siddhartha, S. Prasanna","doi":"10.1109/NCC55593.2022.9806768","DOIUrl":"https://doi.org/10.1109/NCC55593.2022.9806768","url":null,"abstract":"Spoken LID systems generally capture the long term temporal dynamic information present in the speech signal. To achieve that, sequence modeling techniques are used after the feature extraction process. But, the performance of the spoken LID system degrades in cross channel and noisy scenarios. From the literature, we can observe the benefit of excitation source information in noisy and cross-channel scenarios. Besides that, excitation features are also used as complementary evidence in spoken LID systems with spectral features. Motivated from this, an excitation based feature called integrated residual linear frequency cepstral coefficient (IRLFCC) has been proposed in this work. This work also provides a comparison between various deep learning based sequence modeling architectures towards capturing spoken language specific information. The experiments are performed using OLR2020 dataset. From the experiments, it can be observed that in the cross channel scenario, the proposed best system provides a relative improvement of 70.5% and 57.2% over the baseline in terms of $EER_{avg}$ and $C_{avg}$ respectively. Similarly, in the noisy scenario, the proposed best system provides a relative improvement of 37.8% and 45 % over the baseline system.","PeriodicalId":403870,"journal":{"name":"2022 National Conference on Communications (NCC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115512775","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-Based Facial Emotion Recognition for Driver Healthcare 基于深度学习的驾驶员面部情感识别

2022 National Conference on Communications (NCC) Pub Date : 2022-05-24 DOI: 10.1109/NCC55593.2022.9806751

G. K. Sahoo, S. Das, Poonam Singh

引用次数: 3

Performance Analysis of a Relay-Assisted D2D Underlay Cellular Network 中继辅助D2D底层蜂窝网络的性能分析

2022 National Conference on Communications (NCC) Pub Date : 2022-05-24 DOI: 10.1109/NCC55593.2022.9806796

Mahari B. Tsegay, Kalpana Dhaka, R. Bhattacharjee

引用次数: 0

A Fast and Efficient No-Reference Video Quality Assessment Algorithm Using Video Action Recognition Features 基于视频动作识别特征的快速高效无参考视频质量评估算法

2022 National Conference on Communications (NCC) Pub Date : 2022-05-24 DOI: 10.1109/NCC55593.2022.9806466

N. Suresh, Pavan Manesh Mylavarapu, Naga Sailaja Mahankali, Sumohana S. Channappayya

{"title":"A Fast and Efficient No-Reference Video Quality Assessment Algorithm Using Video Action Recognition Features","authors":"N. Suresh, Pavan Manesh Mylavarapu, Naga Sailaja Mahankali, Sumohana S. Channappayya","doi":"10.1109/NCC55593.2022.9806466","DOIUrl":"https://doi.org/10.1109/NCC55593.2022.9806466","url":null,"abstract":"This work addresses the problem of efficient noreference video quality assessment (NR-VQA). The motivation for this work is that even the best and fastest VQA algorithms do not achieve real-time performance. The speed of quality evaluation is impeded primarily by the spatio-temporal feature extraction stage. This impediment is common to both traditional as well as deep learning models. To address this issue, we explore the efficacy of features used in the action recognition problem for NR- VQA. Specifically, we leverage the efficiency offered by Gate Shift Module (GSM) in extracting spatio-temporal features. A simple yet effective improvement to the GSM model is proposed by adding the self-attention module. We first show that GSM features are indeed effective for NR-VQA. We then demonstrate a speed-up that is orders of magnitude faster than the current state-of-the-art VQA algorithms, albeit at the cost of overall performance. We evaluate the efficacy of our algorithm on both Standard Dynamic Range (SDR) and High Dynamic Range (HDR) datasets like KoNViD-1K, LIVE VQC, HDR.","PeriodicalId":403870,"journal":{"name":"2022 National Conference on Communications (NCC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127896084","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