Pub Date : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673599
S. Sreejith, R. Rajan
An important aspect of Indian Classical music (ICM) is rāga, which serves as a melodic framework for both traditions of classical music. In this work, we propose a CNN-based sliding window analysis on Mel-spectrogram for rāga recognition in Carnatic music. The important contribution of the work is that the proposed method neither requires pitch ex-traction nor metadata for the estimation of rāga. CNN learns the representation of rāga from the patterns in the Mel-spectrogram during training through a sliding-window analysis. We train and test the network on sliced-Mel-spectrogram of the original audio while the final decision is made on the audio as a whole. The performance is evaluated on 10 rāga s from the CompMusic dataset and the Ramanarunachalam carnatic youtube collection. Out of the two proposed schemes, aggregation-based VGG16 model reports a macro F1 measure of 0.61, which is comparable to the result obtained for the base-line sequence classification model.
{"title":"Rāga Recognition in Indian Carnatic Music Using Transfer Learning","authors":"S. Sreejith, R. Rajan","doi":"10.1109/ICMSS53060.2021.9673599","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673599","url":null,"abstract":"An important aspect of Indian Classical music (ICM) is rāga, which serves as a melodic framework for both traditions of classical music. In this work, we propose a CNN-based sliding window analysis on Mel-spectrogram for rāga recognition in Carnatic music. The important contribution of the work is that the proposed method neither requires pitch ex-traction nor metadata for the estimation of rāga. CNN learns the representation of rāga from the patterns in the Mel-spectrogram during training through a sliding-window analysis. We train and test the network on sliced-Mel-spectrogram of the original audio while the final decision is made on the audio as a whole. The performance is evaluated on 10 rāga s from the CompMusic dataset and the Ramanarunachalam carnatic youtube collection. Out of the two proposed schemes, aggregation-based VGG16 model reports a macro F1 measure of 0.61, which is comparable to the result obtained for the base-line sequence classification model.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132800547","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 : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673603
Jayant Gambhir, Vaishali Patil
This paper gives a brief review of the techniques available for speech authentication based on edit detection and speaker verification related to application in forensics. Many past research includes the traditional methods like ENF, DFT, STFT based analysis for speech authentication while some advanced emerging trends like SVM, Ensemble learning and Convolution Neural network have been employed for speech authentication in different application. Similarly, for speaker verification, features such as LPC, MFCC, i-vector, x-vector and techniques like Neural Network, Deep learning etc have been researched. This paper summaries the various experimental results and their merits and demerits from the research papers surveyed. This review is expected to guide towards making an unbiased decision in choosing most promising method for authentication of Speaker and Speech data in forensic application.
{"title":"A Review On Speech Authentication And Speaker Verification Methods","authors":"Jayant Gambhir, Vaishali Patil","doi":"10.1109/ICMSS53060.2021.9673603","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673603","url":null,"abstract":"This paper gives a brief review of the techniques available for speech authentication based on edit detection and speaker verification related to application in forensics. Many past research includes the traditional methods like ENF, DFT, STFT based analysis for speech authentication while some advanced emerging trends like SVM, Ensemble learning and Convolution Neural network have been employed for speech authentication in different application. Similarly, for speaker verification, features such as LPC, MFCC, i-vector, x-vector and techniques like Neural Network, Deep learning etc have been researched. This paper summaries the various experimental results and their merits and demerits from the research papers surveyed. This review is expected to guide towards making an unbiased decision in choosing most promising method for authentication of Speaker and Speech data in forensic application.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134403082","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 : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673607
P. Jisna, T. Jarin, P. Praveen
Cloud Computing is a favored choice of any IT organization in the current context since that provides flexibility and pay-per-use service to the users. Moreover, due to its open and inclusive architecture which is accessible to attackers. Security and privacy are a big roadblock to its success. For any IT organization, intrusion detection systems are essential to the detection and endurance of effective detection system against attacker aggressive attacks. To recognize minor occurrences and become significant breaches, a fully managed intrusion detection system is required. The most prevalent approach for intrusion detection on the cloud is the Intrusion Detection System (IDS). This research introduces a cloud-based deep learning-LSTM IDS model and evaluates it to a hybrid Stacked Contractive Auto Encoder (SCAE) + Support Vector Machine (SVM) IDS model. Deep learning algorithms like basic machine learning can be built to conduct attack detection and classification simultaneously. Also examine the detection methodologies used by certain existing intrusion detection systems. On two well-known Intrusion Detection datasets (KDD Cup 99 and NSL-KDD), our strategy outperforms current methods in terms of accurate detection.
在当前环境中，云计算是任何IT组织的首选，因为它为用户提供了灵活性和按使用付费的服务。此外，由于其开放和包容的架构，攻击者可以访问。安全和隐私是其成功的一大障碍。对于任何IT组织来说，入侵检测系统对于有效检测系统抵御攻击者的侵略性攻击至关重要。为了识别轻微的入侵事件并将其演变为重大的入侵，需要一个全面管理的入侵检测系统。云上最流行的入侵检测方法是入侵检测系统(IDS)。本研究引入了一种基于云的深度学习- lstm IDS模型，并将其评估为堆叠收缩自动编码器(SCAE) +支持向量机(SVM)混合IDS模型。像基础机器学习这样的深度学习算法可以同时进行攻击检测和分类。同时研究某些现有入侵检测系统所使用的检测方法。在两个著名的入侵检测数据集(KDD Cup 99和NSL-KDD)上，我们的策略在准确检测方面优于当前的方法。
{"title":"Advanced Intrusion Detection Using Deep Learning-LSTM Network On Cloud Environment","authors":"P. Jisna, T. Jarin, P. Praveen","doi":"10.1109/ICMSS53060.2021.9673607","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673607","url":null,"abstract":"Cloud Computing is a favored choice of any IT organization in the current context since that provides flexibility and pay-per-use service to the users. Moreover, due to its open and inclusive architecture which is accessible to attackers. Security and privacy are a big roadblock to its success. For any IT organization, intrusion detection systems are essential to the detection and endurance of effective detection system against attacker aggressive attacks. To recognize minor occurrences and become significant breaches, a fully managed intrusion detection system is required. The most prevalent approach for intrusion detection on the cloud is the Intrusion Detection System (IDS). This research introduces a cloud-based deep learning-LSTM IDS model and evaluates it to a hybrid Stacked Contractive Auto Encoder (SCAE) + Support Vector Machine (SVM) IDS model. Deep learning algorithms like basic machine learning can be built to conduct attack detection and classification simultaneously. Also examine the detection methodologies used by certain existing intrusion detection systems. On two well-known Intrusion Detection datasets (KDD Cup 99 and NSL-KDD), our strategy outperforms current methods in terms of accurate detection.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133655423","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 : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673640
Raghuttam K Kulkarni, Deepali Koppad
Design re-usability and Verification re-usability are two major aspects which helps in reducing the time required for the design to reach the market by satisfying the necessary design requirements. Intellectual property (IP's) involved in the design must be re-usable as a golden-reference model. Re-using the pre-silicon validation environment over various design domains saves the time required for the design to reach the market furthermore and helps in improving the overall Design-Verification Standards Verification environment re-use for different applications with different interface is done by developing a wrapper around the design. Increase in the design complexity has led to the development of more robust and reusable verification environment like UVM and OVM methodologies. This paper focuses on carrying a high-quality Design-Verification process by using UVM methodology and perform organized effort in doing considerable amount of work in Design-Verification domain by considering a switch interfaced with APB protocol.
{"title":"High Quality Design-Verification of APB based Switch Interface","authors":"Raghuttam K Kulkarni, Deepali Koppad","doi":"10.1109/ICMSS53060.2021.9673640","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673640","url":null,"abstract":"Design re-usability and Verification re-usability are two major aspects which helps in reducing the time required for the design to reach the market by satisfying the necessary design requirements. Intellectual property (IP's) involved in the design must be re-usable as a golden-reference model. Re-using the pre-silicon validation environment over various design domains saves the time required for the design to reach the market furthermore and helps in improving the overall Design-Verification Standards Verification environment re-use for different applications with different interface is done by developing a wrapper around the design. Increase in the design complexity has led to the development of more robust and reusable verification environment like UVM and OVM methodologies. This paper focuses on carrying a high-quality Design-Verification process by using UVM methodology and perform organized effort in doing considerable amount of work in Design-Verification domain by considering a switch interfaced with APB protocol.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121835562","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 : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673636
A. A. Aadhithya, J. Akshaya, K. G. Devi, B. Nithesh, G. J. Lal, K. P. Soman
Quantum Dot Cellular Automata (QCA) technology is one of the emerging next-generation nano-scale technologies, to subdue the limitations of existing CMOS technologies. As researchers continue to work hard to find an alternative to CMOS technology, QCA provides a solution for a faster computer with a smaller size and low power consumption. This paper implements an operation-rich “HACK ALU”, capable of performing many operations. Hack AL U is a versatile design that implements 64 op-erations with just 6 control bits along with novel implementations of adders and multiplexers. This ALU has been designed using a bottom-up approach by beginning the design by constructing the basic gates and progressing to the construction of adders, and multiplexers. The scope of the research is to provide an efficient ALU design that can be integrated in the CPU design. The present work has been implemented and tested on the QCA designer software. Experimental evaluation shows that logical and arithmetic operations are consistent in the proposed design.
量子点元胞自动机(QCA)技术是克服现有CMOS技术局限性的新一代纳米级技术之一。随着研究人员继续努力寻找CMOS技术的替代品，QCA为更快的计算机提供了一个更小尺寸和低功耗的解决方案。本文实现了一个操作丰富的“HACK ALU”，能够执行多种操作。Hack AL U是一个通用的设计，实现64个操作，只有6个控制位，以及新颖的加法器和多路复用器实现。该ALU采用自下而上的方法设计，从构建基本门开始设计，然后进行加法器和多路复用器的构建。研究的范围是提供一种可以集成在CPU设计中的高效ALU设计。本工作已在QCA设计软件上进行了实现和测试。实验评估表明，所提出的设计逻辑运算和算术运算是一致的。
{"title":"Implementation of Hack ALU using Quantum Dot Cellular Automata","authors":"A. A. Aadhithya, J. Akshaya, K. G. Devi, B. Nithesh, G. J. Lal, K. P. Soman","doi":"10.1109/ICMSS53060.2021.9673636","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673636","url":null,"abstract":"Quantum Dot Cellular Automata (QCA) technology is one of the emerging next-generation nano-scale technologies, to subdue the limitations of existing CMOS technologies. As researchers continue to work hard to find an alternative to CMOS technology, QCA provides a solution for a faster computer with a smaller size and low power consumption. This paper implements an operation-rich “HACK ALU”, capable of performing many operations. Hack AL U is a versatile design that implements 64 op-erations with just 6 control bits along with novel implementations of adders and multiplexers. This ALU has been designed using a bottom-up approach by beginning the design by constructing the basic gates and progressing to the construction of adders, and multiplexers. The scope of the research is to provide an efficient ALU design that can be integrated in the CPU design. The present work has been implemented and tested on the QCA designer software. Experimental evaluation shows that logical and arithmetic operations are consistent in the proposed design.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123443373","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 : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673634
Nakul S Pramod, L. Sajitha, Swathy Mohanlal, K. Thameem, S. M. Anzar
Parkinson's disease (PD) is a degenerative disorder of the human central nervous system that causes tremors and affects movement. Symptoms usually appear gradually over time. Researchers are seeking biomarkers for Parkinson's disease in the hopes of allowing for earlier detection and more tailored treatments to slow the disease's progression. Existing methods of diagnosis include Blood tests, MRI scans, and PET scans. However, these are highly time and resource-consuming. PD also shows an amble change in the voice patterns of a person. Hence, acoustic analysis of voice signals can indicate the progression of PD. This can be analysed using a trained classifier model, which provides an easy diagnosis of the disease. This paper analyses the performance of AI-ML models viz- Linear regression, Support Vector Machine (SVM), K-Nearest Neighbourhood (KNN), Ran-dom Forest, and XG Boost for the detection of Parkinson's disease using vocal feature sets. Experimental evaluations show that the Random Forest model produced an impressive accuracy of 100%. The classification algorithms' accuracy, precision, recall, F1-score, and Mathews Correlation Coefficient (MCC) are all examined. The Random Forest classifiers are 100% accurate, with an accuracy of 1.000, recall of 1.000, F1-score of 1.000, and MCC of 1.000. Implementing dimensionality reduction using the Eigen approach (Principal Component Analysis) and the dataset combination are the critical reasons for the reported high accuracy. The potential of this methodology is prominent as it can be used to diagnose various other diseases, such as asthma, cancer, and Alzheimer's disease.
{"title":"Detection of Parkinson's Disease Using Vocal Features: An Eigen Approach","authors":"Nakul S Pramod, L. Sajitha, Swathy Mohanlal, K. Thameem, S. M. Anzar","doi":"10.1109/ICMSS53060.2021.9673634","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673634","url":null,"abstract":"Parkinson's disease (PD) is a degenerative disorder of the human central nervous system that causes tremors and affects movement. Symptoms usually appear gradually over time. Researchers are seeking biomarkers for Parkinson's disease in the hopes of allowing for earlier detection and more tailored treatments to slow the disease's progression. Existing methods of diagnosis include Blood tests, MRI scans, and PET scans. However, these are highly time and resource-consuming. PD also shows an amble change in the voice patterns of a person. Hence, acoustic analysis of voice signals can indicate the progression of PD. This can be analysed using a trained classifier model, which provides an easy diagnosis of the disease. This paper analyses the performance of AI-ML models viz- Linear regression, Support Vector Machine (SVM), K-Nearest Neighbourhood (KNN), Ran-dom Forest, and XG Boost for the detection of Parkinson's disease using vocal feature sets. Experimental evaluations show that the Random Forest model produced an impressive accuracy of 100%. The classification algorithms' accuracy, precision, recall, F1-score, and Mathews Correlation Coefficient (MCC) are all examined. The Random Forest classifiers are 100% accurate, with an accuracy of 1.000, recall of 1.000, F1-score of 1.000, and MCC of 1.000. Implementing dimensionality reduction using the Eigen approach (Principal Component Analysis) and the dataset combination are the critical reasons for the reported high accuracy. The potential of this methodology is prominent as it can be used to diagnose various other diseases, such as asthma, cancer, and Alzheimer's disease.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125531900","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 : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673629
Greeshma Unnikrishnan, A. George, L. Mary
An audio utterance can be identified as being spoken in a particular language by using automatic language identification (LID). Each language has its own phoneme set. Hence combination of these phonemes governed by phonotactics will help in distinguishing languages. In this work, we propose an automatic language identification system utilizing features derived from non-uniform speech regions to represent phonotac-tic differences among 4 Indian languages, namely Malayalam, Marathi, Assamese, and Kannada. For this, broad phoneme labels, namely approximant (A), closure (C), fricatives (F), nasals (N), plosive/stop (P), voiced stop (B), vowels (V), and silence (S) are obtained automatically by a broad phoneme classifier (BPC). It is a DNN-based classifier which uses hand-crafted features and Mel-frequency cepstral coefficients (MFCC). In order to automatically segment speech to smaller regions, first it is chopped at every silence regions using the labels obtained from BPC. Later, it is split again at the end of each vowel. Hence, small non-uniform regions are obtained which contain phoneme combinations that may be specific to the language of the utterance. From each region, only a fixed number of frames containing certain combination of phonemes are selected. A DNN classifier is trained using 13-dimensional MFCC features of 12 fixed frames of non-uniform regions for performing LID. An average accuracy of 97.03% is obtained for test utterances of 10 sec duration belonging to 4 languages.
{"title":"Non-uniform Region Based Features for Automatic Language Identification","authors":"Greeshma Unnikrishnan, A. George, L. Mary","doi":"10.1109/ICMSS53060.2021.9673629","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673629","url":null,"abstract":"An audio utterance can be identified as being spoken in a particular language by using automatic language identification (LID). Each language has its own phoneme set. Hence combination of these phonemes governed by phonotactics will help in distinguishing languages. In this work, we propose an automatic language identification system utilizing features derived from non-uniform speech regions to represent phonotac-tic differences among 4 Indian languages, namely Malayalam, Marathi, Assamese, and Kannada. For this, broad phoneme labels, namely approximant (A), closure (C), fricatives (F), nasals (N), plosive/stop (P), voiced stop (B), vowels (V), and silence (S) are obtained automatically by a broad phoneme classifier (BPC). It is a DNN-based classifier which uses hand-crafted features and Mel-frequency cepstral coefficients (MFCC). In order to automatically segment speech to smaller regions, first it is chopped at every silence regions using the labels obtained from BPC. Later, it is split again at the end of each vowel. Hence, small non-uniform regions are obtained which contain phoneme combinations that may be specific to the language of the utterance. From each region, only a fixed number of frames containing certain combination of phonemes are selected. A DNN classifier is trained using 13-dimensional MFCC features of 12 fixed frames of non-uniform regions for performing LID. An average accuracy of 97.03% is obtained for test utterances of 10 sec duration belonging to 4 languages.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114376805","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 : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673625
Zahira Tabassum, B.S. Chandrashekhar Shastry
Enhanced use of renewabIes in the power system will not only affect the energy source, but also the nature of how electricity grids are run. Smarter grids will not only be more efficient and make the transition to a more sustainable future easier, but they will also have the potential to alter the institutional connections that exist between generators, customers, and transmission and distribution corporations. India is one of the world's fastest-growing energy consumers, and it is working hard to boost renewable energy production. This research looks into the renewable energy scenario of Karnataka State. Karnataka is among the top five states of India in renewable energy generation. The research highlights the Karnataka government's strategies for increasing renewable energy's inclusion in the energy mix, as well as the obstacles and potential solutions for increasing renewable energy deployment across the state are presented. This research can be used as a guide for policymakers and researchers in other states and countries who seek to increase the use of renewable energy.
{"title":"Renewable Energy Generation-Solar and Wind, Challenges and Policies adopted in Karnataka, India","authors":"Zahira Tabassum, B.S. Chandrashekhar Shastry","doi":"10.1109/ICMSS53060.2021.9673625","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673625","url":null,"abstract":"Enhanced use of renewabIes in the power system will not only affect the energy source, but also the nature of how electricity grids are run. Smarter grids will not only be more efficient and make the transition to a more sustainable future easier, but they will also have the potential to alter the institutional connections that exist between generators, customers, and transmission and distribution corporations. India is one of the world's fastest-growing energy consumers, and it is working hard to boost renewable energy production. This research looks into the renewable energy scenario of Karnataka State. Karnataka is among the top five states of India in renewable energy generation. The research highlights the Karnataka government's strategies for increasing renewable energy's inclusion in the energy mix, as well as the obstacles and potential solutions for increasing renewable energy deployment across the state are presented. This research can be used as a guide for policymakers and researchers in other states and countries who seek to increase the use of renewable energy.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117184110","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 : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673617
C. Junu Jahana, M. Sinith, PP Lalu
Human beings can tell the direction of sound by utilising both their ears. They may instinctively determine the di-rection of sound by combining the somewhat varied impulses that arrive at our ears. Similarly, an array of microphones connected to a computer may be used to create a sound localisation system. The basic idea behind utilising microphone arrays to estimate Direction Of Arrival (DOA) is to leverage phase information in signals picked up by spatially separated sensors (microphones). The acoustic signals arrive to the microphones with temporal delays when they are spatially distant. These time-delays are determined by the signal's DOA for a known array geometry. The audio signal is recorded using a miniDSP UMA-16 microphone array with plug and play USB audio connection. For linear arrays, the angle between the array's orientation and the sound source is calculated here. Given that the sound signal arrives at each microphone at various times, corresponding to different propagation paths, it's reasonable to infer that the recorded signals in each microphone have a Time Difference Of Arrival (TDOA), which is an important factor in microphone array processing. With the aid of the UMA16 microphone array, more visible and accurate sound source localization was achievable at lower sound power intensities, which was considered to be a significant innovation in the field of sound source localization. In addition, the DOA was calculated using an SVM classifier, which can categorise audio signals in coarse as left, right or front, and the performance metrics including accuracy, specificity and sensitivity are analysed.
人类可以用两只耳朵来辨别声音的方向。它们可能本能地通过结合到达我们耳朵的不同脉冲来确定声音的方向。类似地，连接到计算机上的麦克风阵列可用于创建声音定位系统。利用麦克风阵列估计到达方向(DOA)的基本思想是利用空间分离传感器(麦克风)拾取的信号中的相位信息。当空间距离较远时，声信号到达麦克风时具有时间延迟。这些时间延迟是由已知阵列几何形状的信号的DOA决定的。音频信号记录使用miniDSP UMA-16麦克风阵列与即插即用USB音频连接。对于线性阵列，此处计算阵列方向与声源之间的夹角。考虑到声音信号到达每个麦克风的时间不同，对应不同的传播路径，可以合理推断每个麦克风中记录的信号都有一个到达时差(Time Difference Of Arrival, TDOA)，这是麦克风阵列处理中的一个重要因素。借助UMA16麦克风阵列，可以在较低的声功率强度下实现更清晰、更精确的声源定位，这被认为是声源定位领域的重大创新。此外，利用支持向量机分类器对粗音频信号进行左、右、前三种分类，并对其精度、特异性和灵敏度等性能指标进行了分析。
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Pub Date : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673627
Amani Theramban, Renjith V. Ravi
In image processing, quantum mechanics has wide applications. This paper proposes a lower-complexity encryption scheme based on Novel Enhanced Quantum Representation (NEQR) and bit-level adjacent exchange operations. The chaotic henon map and quantum coding were used to create this work. The image is further diffused by XORing key streams from the henon map. This scheme further undergoes a scrambling procedure using the key generated from SHA-256. The encryption procedure done step by step is applied separately on three channels R, G, and B, of a color image. Finally, performance analysis and simulation results confirm that the encryption scheme is robust and secure.
{"title":"Colour Image Encryption Algorithm With Quantum Coding","authors":"Amani Theramban, Renjith V. Ravi","doi":"10.1109/ICMSS53060.2021.9673627","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673627","url":null,"abstract":"In image processing, quantum mechanics has wide applications. This paper proposes a lower-complexity encryption scheme based on Novel Enhanced Quantum Representation (NEQR) and bit-level adjacent exchange operations. The chaotic henon map and quantum coding were used to create this work. The image is further diffused by XORing key streams from the henon map. This scheme further undergoes a scrambling procedure using the key generated from SHA-256. The encryption procedure done step by step is applied separately on three channels R, G, and B, of a color image. Finally, performance analysis and simulation results confirm that the encryption scheme is robust and secure.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129481938","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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