2022 IEEE International Conference on Signal Processing and Communications (SPCOM)最新文献_第2页

Robustifying GNN Via Weighted Laplacian 基于加权拉普拉斯算子的GNN鲁棒化

2022 IEEE International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840814

Bharat Runwal, Vivek, Sandeep Kumar

{"title":"Robustifying GNN Via Weighted Laplacian","authors":"Bharat Runwal, Vivek, Sandeep Kumar","doi":"10.1109/SPCOM55316.2022.9840814","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840814","url":null,"abstract":"Graph neural network (GNN) is achieving remarkable performances in a variety of application domains. However, GNN is vulnerable to noise and adversarial attacks in input data. Making GNN robust against noises and adversarial attacks is an important problem. The existing defense methods for GNNs are computationally demanding, are not scalable, and are architecture dependent. In this paper, we propose a generic framework for robustifying GNN known as Weighted Laplacian GNN (RWLGNN). The method combines Weighted Graph Laplacian learning with the GNN implementation. The proposed method benefits from the positive semi-definiteness property of Laplacian matrix, feature smoothness, and latent features via formulating a unified optimization framework, which ensures the adversarial/noisy edges are discarded and connections in the graph are appropriately weighted. For demonstration, the experiments are conducted with Graph convolutional neural network(GCNN) architecture, however, the proposed framework is easily amenable to any existing GNN architecture. The simulation results with benchmark dataset establish the efficacy of the proposed method over the state-of-the-art methods, both in accuracy and computational efficiency.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130810975","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

BVRIQE: A Completely Blind No Reference Virtual Reality Image Quality Evaluator BVRIQE:一个完全盲无参考的虚拟现实图像质量评估器

2022 IEEE International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840855

A. Poreddy, Balasubramanyam Appina

{"title":"BVRIQE: A Completely Blind No Reference Virtual Reality Image Quality Evaluator","authors":"A. Poreddy, Balasubramanyam Appina","doi":"10.1109/SPCOM55316.2022.9840855","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840855","url":null,"abstract":"In this paper, we develop a framework to assess the perceptual quality of Virtual Reality (VR) images by studying the joint dependencies between luminance and disparity pairs using Bivariate Generalized Gaussian Distribution (BGGD) model. We compute model parameters ($alpha, beta$) of BGGD at multi-scale and multi-orient steerable pyramid decomposition of the cube map projection (CMP) faces of both left and right views of a VR image. We learn Multivariate Gaussian (MVG) model parameters from BGGD features of CMP faces of pristine images as a reference quality representative. We compute Mahalanobis distance between pristine MVG model parameters and distorted image BGGD features to estimate the joint luminance and disparity quality score of a CMP face of a test VR image. We generate an inner map from saliency and phase congruency maps of CMP faces of both left and right views of a VR image. We compute entropy scores of the inner map to pool the joint luminance and disparity quality score of a VR image. Further, we apply IL-NIQE model on CMP faces to derive the overall spatial quality score of a test VR image. Finally, we pool the spatial IL-NIQE score and CMP face level quality score to estimate the overall quality score of a test VR image. The proposed model, dubbed Blind Virtual Reality Image Quality Evaluator (BVRIQE) delivered a consistent performance across all distortion types of the LIVE 3D VR IQA dataset.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132273832","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

Navigational Aid for Open-Ended Surveillance, by Fusing Estimated Depth and Scene Segmentation Maps, Using RGB Images of Indoor Scenes 基于RGB室内场景图像融合估计深度和场景分割图的开放式监视导航辅助

2022 IEEE International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840820

Binoy Saha, Neha Shah, Sukhendu Das

{"title":"Navigational Aid for Open-Ended Surveillance, by Fusing Estimated Depth and Scene Segmentation Maps, Using RGB Images of Indoor Scenes","authors":"Binoy Saha, Neha Shah, Sukhendu Das","doi":"10.1109/SPCOM55316.2022.9840820","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840820","url":null,"abstract":"Open-ended surveillance task for a robot in an unspecified environment using only an RGB camera, has not been addressed at length in literature. This is unlike the popular scenario of path planning where both the target and environments are often known. We focus on the task of a robot which needs to estimate a realistic depiction of the surrounding 3D environment, including the location of obstacles and free space to navigate in the scene within the view field. In this paper, we propose an unsupervised algorithm to iteratively compute an optimal direction for maximal unhindered movement in the scene. This task is challenging when presented with only a single RGB view of the scene, without the use of any online depth sensor. Our process combines cues from two deep-learning processes - semantic segmentation and depth map estimation, to automatically decide plausible robot movement paths while avoiding hindrance posed by objects in the scene. We make assumptions of the use of a low-end RGB USB camera, pre-set camera view direction (angle) and field of view, incremental movement of the robot in the view field, and iterative analysis of the scene, all catering to any open-ended (target-free) surveillance/patrolling applications. Inverse perspective geometry has been used to map the optimal direction estimated in the view field, to that on the floor of the scene for navigation. Results of evaluation using a dataset of videos of scenes captured from indoor (office, labs, meeting/class-rooms, corridors, lounge) environments, reveal the success of the proposed approach.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115132479","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

Classification of Cold and Non-Cold Speech Using Vowel-Like Region Segments 基于类元音区域分段的冷语和非冷语分类

2022 IEEE International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840775

Pankaj Warule, S. Mishra, S. Deb

{"title":"Classification of Cold and Non-Cold Speech Using Vowel-Like Region Segments","authors":"Pankaj Warule, S. Mishra, S. Deb","doi":"10.1109/SPCOM55316.2022.9840775","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840775","url":null,"abstract":"This work uses vowel-like region segments of speech to classify cold and non-cold speech signals. As various articulators are affected by the common cold, speech produced during the common cold gets affected. These changes in a speech during common cold can be used to classify cold and non-cold speech. Vowel-like region (VLR) in speech includes vowels, semi-vowels, and diphthongs phonemes. Vowel-like regions are the dominant part of the speech signal. Hence, we have considered only vowel-like regions for cold and non-cold speech classification. The VLRs are identified by locating the VLR onset point (VLROP) and end point (VLREP). The Hilbert envelope and zero frequency filtering methods are used for detection of VLROPs and VLREPs. Mel frequency cepstral coefficients (MFCCs) feature are extracted from VLRs, and the performance of these features are evaluated using a deep neural network. Features extracted from VLRs give comparable results to features extracted from complete active speech (CAS) signal. Compared to the CAS technique, the number of frames that needs to be processed utilizing VLRs is significantly less.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"159 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128902608","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: 9

On safe sequential optimization using posterior sampling 基于后验抽样的安全序贯优化

2022 IEEE International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840822

Pratik Kar, V. Sukumaran, S. Sumitra

引用次数: 0

Indic Visual Question Answering 印度视觉问答

2022 IEEE International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840835

A. Chandrasekar, Amey Shimpi, D. Naik

引用次数: 1

Joint Optimization of Network Lifetime and SNR in UAV-Assisted Edge Networks 无人机辅助边缘网络中网络寿命和信噪比的联合优化

2022 IEEE International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840843

Shraddha Tripathi, O. Pandey, R. Hegde

{"title":"Joint Optimization of Network Lifetime and SNR in UAV-Assisted Edge Networks","authors":"Shraddha Tripathi, O. Pandey, R. Hegde","doi":"10.1109/SPCOM55316.2022.9840843","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840843","url":null,"abstract":"One of the critical challenges of unmanned aerial vehicle (UAV)-assisted edge networks is to prolong the network lifetime with improved quality-of-service (QoS) at the sensor nodes (SNs). UAVs are typically resource-constrained with limited energy and communication capacity. Collaborative beamforming (CB) in a single antenna-mounted UAV network is an effective way of addressing the aforementioned challenges. Particularly, in this work, the optimal number of UAVs and their locations are obtained for CB, resulting in maximized network lifetime and improved signal-to-noise ratio (SNR). To meet the objective of maximizing network lifetime and SNR, antenna array gain is maximized by computing optimum spacing within the array. In this context, an optimization problem is formulated to jointly optimize the network lifetime and SNR. The proposed method minimizes the UAVs positioning error over time while forming the array. The method considers UAVs mobility parameters, the optimal number of collaborating UAVs, and UAVs power consumption as constraints. Finally, extensive simulation results show the effectiveness of the proposed method in terms of better network coverage, the minimum number of UAVs required, and maximum SNR compared to existing schemes.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126522113","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

Opinion Dynamics in the Presence of Bots 机器人存在下的意见动态

2022 IEEE International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840793

Ashish Shukla, Neeraja Sahasrabudhe, Sharayu Moharir

引用次数: 1

Optimal temporal filtering for COW-QKD COW-QKD的最优时域滤波

2022 IEEE International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840768

G. Shaw, Shyam Sridharan, A. Prabhakar

引用次数: 3

Performance Analysis of Interference Limited Hybrid FSO/RF Systems 限制干扰的FSO/RF混合系统性能分析

2022 IEEE International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840786

S. Kirubakaran, M. D. Selvaraj

引用次数: 0