Pub Date : 2022-05-24DOI: 10.1109/NCC55593.2022.9806818
H. Dasgupta, P. C. Pandey
A technique is presented for excitation epoch and voicing detection in speech signal using its Hilbert envelope and employing single-pass processing. The excitation epoch detection comprises dynamic range compression for reducing amplitude variability, Hilbert envelope calculation and dynamic peak detection for excitation saliency enhancement, and epoch marking by locating the maximum-sum subarray peaks. The voicing detection is based on thresholding the inter-epoch similarity calculated as the normalized covariance of the adjacent inter-epoch intervals of the Hilbert envelope. The total algorithmic delay is less than 60 ms. The epoch detection and the voicing detection for clean and telephone-quality speech showed a good match with those obtained from the EGG, and the detection performances compared favorably with the earlier techniques.
{"title":"Excitation Epoch and Voicing Detection Using Hilbert Envelope with Single-Pass Processing","authors":"H. Dasgupta, P. C. Pandey","doi":"10.1109/NCC55593.2022.9806818","DOIUrl":"https://doi.org/10.1109/NCC55593.2022.9806818","url":null,"abstract":"A technique is presented for excitation epoch and voicing detection in speech signal using its Hilbert envelope and employing single-pass processing. The excitation epoch detection comprises dynamic range compression for reducing amplitude variability, Hilbert envelope calculation and dynamic peak detection for excitation saliency enhancement, and epoch marking by locating the maximum-sum subarray peaks. The voicing detection is based on thresholding the inter-epoch similarity calculated as the normalized covariance of the adjacent inter-epoch intervals of the Hilbert envelope. The total algorithmic delay is less than 60 ms. The epoch detection and the voicing detection for clean and telephone-quality speech showed a good match with those obtained from the EGG, and the detection performances compared favorably with the earlier techniques.","PeriodicalId":403870,"journal":{"name":"2022 National Conference on Communications (NCC)","volume":"116 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":"121886457","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}
Pub Date : 2022-05-17DOI: 10.1109/NCC55593.2022.9806819
Karthik Gvb, V. Borkar, G. Kasbekar
We consider the problem of scheduling packet transmissions in a wireless network of users while minimizing the energy consumed and the transmission delay. A challenge is that transmissions of users that are close to each other mutually interfere, while users that are far apart can transmit simultaneously without much interference. Each user has a queue of packets that are transmitted on a single channel and mutually non interfering users reuse the spectrum. Using the theory of Whittle index for cost minimizing restless bandits, we design four index-based policies and compare their performance with that of the well-known policies: Slotted ALOHA, maximum weight scheduling, quadratic Lyapunov drift, Cella and Cesa Bianchi algorithm, and two Whittle index based policies from a recently published paper. We make the code used to perform our simulations publicly available, so that it can be used for future work by the research community at large.
{"title":"Scheduling in Wireless Networks using Whittle Index Theory","authors":"Karthik Gvb, V. Borkar, G. Kasbekar","doi":"10.1109/NCC55593.2022.9806819","DOIUrl":"https://doi.org/10.1109/NCC55593.2022.9806819","url":null,"abstract":"We consider the problem of scheduling packet transmissions in a wireless network of users while minimizing the energy consumed and the transmission delay. A challenge is that transmissions of users that are close to each other mutually interfere, while users that are far apart can transmit simultaneously without much interference. Each user has a queue of packets that are transmitted on a single channel and mutually non interfering users reuse the spectrum. Using the theory of Whittle index for cost minimizing restless bandits, we design four index-based policies and compare their performance with that of the well-known policies: Slotted ALOHA, maximum weight scheduling, quadratic Lyapunov drift, Cella and Cesa Bianchi algorithm, and two Whittle index based policies from a recently published paper. We make the code used to perform our simulations publicly available, so that it can be used for future work by the research community at large.","PeriodicalId":403870,"journal":{"name":"2022 National Conference on Communications (NCC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124352759","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}
Pub Date : 2021-10-14DOI: 10.1109/NCC55593.2022.9806726
V. Yajnanarayana
Beyond 5G networks will operate at high frequencies with wide bandwidths. This brings both opportunities and challenges. Opportunities include high throughput connectivity with low latency. However, one of the main challenges in these networks is due to the high path loss at these operating frequencies, which requires network to be deployed densely to provide coverage. Since these cells have small inter-site-distance (ISD), the dwell-time of the UEs in these cells are small, thus supporting mobility in these types of dense networks is a challenge and require frequent beam or cell reassignments. A pro-active mobility management scheme which exploits the historical trajectories can provide better prediction of cells and beams as UEs move in the coverage area. We propose an AI based method using sequence-to-sequence modeling for the estimation of handover cells/beams along with dwell-time using the trajectory information of the UE. Results indicate that for a dense deployment, an accuracy of more than 90 percent can be achieved for handover cell estimation and very low mean absolute error (MAE) for dwell-time.
{"title":"Proactive Mobility Management of UEs Using Sequence-to-Sequence Modeling","authors":"V. Yajnanarayana","doi":"10.1109/NCC55593.2022.9806726","DOIUrl":"https://doi.org/10.1109/NCC55593.2022.9806726","url":null,"abstract":"Beyond 5G networks will operate at high frequencies with wide bandwidths. This brings both opportunities and challenges. Opportunities include high throughput connectivity with low latency. However, one of the main challenges in these networks is due to the high path loss at these operating frequencies, which requires network to be deployed densely to provide coverage. Since these cells have small inter-site-distance (ISD), the dwell-time of the UEs in these cells are small, thus supporting mobility in these types of dense networks is a challenge and require frequent beam or cell reassignments. A pro-active mobility management scheme which exploits the historical trajectories can provide better prediction of cells and beams as UEs move in the coverage area. We propose an AI based method using sequence-to-sequence modeling for the estimation of handover cells/beams along with dwell-time using the trajectory information of the UE. Results indicate that for a dense deployment, an accuracy of more than 90 percent can be achieved for handover cell estimation and very low mean absolute error (MAE) for dwell-time.","PeriodicalId":403870,"journal":{"name":"2022 National Conference on Communications (NCC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126052896","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}
Pub Date : 2021-01-15DOI: 10.1109/NCC55593.2022.9806720
Aalok Gangopadhyay, Prajwal Singh, S. Raman
Automatic plot extraction involves understanding and inferring the data distribution and therefore, extracting individual line plots from an image containing multiple 2D line plots. It is an important problem having many real-world applications. The existing methods for addressing this problem involve a significant amount of human intervention. To minimize this intervention, we propose APEX-Net, a deep learning based framework with novel loss functions for solving the plot extraction problem. Further, we introduce APEX-1M - a new large scale dataset that contains both the plot images and the raw data. We demonstrate the performance of APEX-Net on the APEX-1M test set and show that it obtains impressive accuracy. We also show visual results of our network on unseen plot images and demonstrate that it extracts the shape of the plots to a great extent. Finally, we develop a GUI for plot extraction that can benefit the community at large. The dataset and code will be made publicly available.
{"title":"APEX-Net: Automatic Plot Extraction Network","authors":"Aalok Gangopadhyay, Prajwal Singh, S. Raman","doi":"10.1109/NCC55593.2022.9806720","DOIUrl":"https://doi.org/10.1109/NCC55593.2022.9806720","url":null,"abstract":"Automatic plot extraction involves understanding and inferring the data distribution and therefore, extracting individual line plots from an image containing multiple 2D line plots. It is an important problem having many real-world applications. The existing methods for addressing this problem involve a significant amount of human intervention. To minimize this intervention, we propose APEX-Net, a deep learning based framework with novel loss functions for solving the plot extraction problem. Further, we introduce APEX-1M - a new large scale dataset that contains both the plot images and the raw data. We demonstrate the performance of APEX-Net on the APEX-1M test set and show that it obtains impressive accuracy. We also show visual results of our network on unseen plot images and demonstrate that it extracts the shape of the plots to a great extent. Finally, we develop a GUI for plot extraction that can benefit the community at large. The dataset and code will be made publicly available.","PeriodicalId":403870,"journal":{"name":"2022 National Conference on Communications (NCC)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124033661","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}
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