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18th IEEE India Council International Conference, INDICON 2021

作者： Chandni, M. Govind, D. Center for Computational Engineering and Networking Tamilnadu Amritanagar Coimbatore 641112 India Department of Computer Science Engineering Andhra Pradesh Greenfields Vaddeswaram Guntur 522501 India

ISBN: (纸本)9781665441759

Epochs are impulse like discontinuities in speech that represent the instants of glottal closure during phonation. The accurate estimation of epochs is essential for epoch synchronous speech processing. There are many algorithms proposed in the literature for the accurate estimation of epochs from speech signals. However majority of those algorithms show degradation during the epoch extraction from telephonic speech signals. The restricted bandwidth (300 Hz-3.4 kHz) of the telephone channel is the reason for degradation in epoch estimation performance. Therefore, the objective of the present work is to propose a wavelet synchrosqueezed transform (WSST) based signal preprocessing stage to improve the performance of zero frequency filtering (ZFF) which is a well known epoch estimation method. Improved time-frequency resolution of WSST reinforce the strength of impulses around the fundamental components to estimate accurate epoch locations. A multiscale product of WSST modified speech and the original speech is computed further enhance the impulse like discontinuities. Effectiveness of the proposed method is confirmed by the epoch estimation performance analysis of phonetically balanced CMU-Arctic database with electroglottogram recordings. The telephone quality in the database is simulated using G.191 software tools. © 2021 IEEE.

关键词： Wavelet transforms

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Deep quantile regression based wind generation and demand forecasts 11th

Deep quantile regression based wind generation and demand fo...

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11th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2019, and 11th World Congress on Nature and Biologically Inspired Computing, NaBIC 2019

作者： Kirthika, N. Ramachandran, K.I. Kottayil, Sasi K. Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Center for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9783030493448

The widespread attention in the growth of clean energy for electricity production necessitates an accurate and reliable generation and demand forecasts. However, the decision-making process in electric power industry involves more uncertainty due to the transition towards distributed energy systems, which are not addressed in the conventional point forecasts. This paper proposes a probabilistic method termed as Deep Quantile Regression (DQR) for the construction of prediction intervals (PIs) that can potentially quantify uncertainty in the point forecasts of wind power generation and demand. The effectiveness of DQR is examined using the low and high seasonal wind and demand datasets. PIs with various confidence levels of 99%, 95% and 90% are estimated by constructing the appropriate quantiles using the proposed DQR method. The quantitative comparison of the quality in all the estimated PIs using the proposed method proves to outperform the other state-of-the-art methods. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Uncertainty analysis

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Driver's Face Detection in Poor Illumination for ADAS Applications 5

Driver's Face Detection in Poor Illumination for ADAS Applic...

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5th International Conference on Computer, Communication, and Signal Processing, ICCCSP 2021

作者： Srivastava, Saksham Adarsh, S. Nair, Binoy B Ramachandran, K I Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communication Engineering Coimbatore India Amrita Vishwa Vidyapeetham Center for Computational Engineering and Networking Amrita School of Engineering Coimbatore India

ISBN: (纸本)9781665432771

The detection of the driver's face is an important part of the active safety system for advanced driver assistance systems (ADAS) and this task becomes challenging and complex when normal visual cameras are used in poor illumination. This paper incorporates the adaptive attenuation quantification retinex (AAQR) method to improve the details of low and poorly illuminated images and videos with the Viola-Jones face detection algorithm. This paper also makes a comparative study of the implementation of the proposed solution and analyzes its performance on three different datasets: EBDD, 3MDAD IR2, and our self-generated validation dataset. The results have shown that when AAQR is combined with Viola-Jones, the face detection has been improved in the range of 11.18% to 24.21% for different illumination categories of EBDD, 16.71% for 3MDAD IR2 (night), and 46.79% for validation datasets. © 2021 IEEE.

关键词： Face recognition

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Optimizing low-cost uav aerial image mosaicing for crop growth monitoring

Optimizing low-cost uav aerial image mosaicing for crop grow...

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American Society for Photogrammetry and Remote Sensing, ASPRS 2021 Annual Conference

作者： Bupathy, Pranavan Sivanpillai, Ramesh Sajithvariyar, V.V. Sowmya, V. Center for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham TN Coimbatore641112 India Wyoming GIS Center University of Wyoming LaramieWY82072 United States

High spatial resolution images acquired with drones can provide useful information to farmers for devising suitable management practices and increase crop yield. Data collected as individual frames or images have to be mosaiced using pattern recognition and matching process. Most flight missions collect hundreds of photos with high overlap and side overlap in order to generate mosaic without data gaps or distortion. These frames are aligned using the location information associated with each image. The same features are identified in multiple frames for generating the mosaic. In this process, it is common to use all or most of the images which requires a lot of resources. Uploading and processing hundreds of images could take several hours to days. Many farmers and crop consultants in developing countries may not have the necessary resources to upload hundreds of images. This study assessed the optimal number of images required to generate an image mosaic for a crop field without any data gaps or distortion. Images were collected at two different heights and directions. First, the mosaic was generated using all (100%) frames followed by subsets containing 90%, through 50% of images. Results obtained will assist us to plan the settings in future flight missions for acquiring optimal number of images required for generating image mosaic. © 2021 International Society for Photogrammetry and Remote Sensing. All rights reserved.

关键词： Developing countries

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Deep Learning Based Early Diagnostic System for Metastatic Breast Cancer Mutation Using MATLAB

Deep Learning Based Early Diagnostic System for Metastatic B...

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： A. G. Anushya P. K. Krishnan Namboori Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Amrita School of Artificial Intelligences Coimbatore Amrita Vishwa Vidyapeetham India Computational Chemistry Group Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Center for Computational Engineering and Networking Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

In this work, we proposed a deep learning- based diagnostic system leveraging convolutional neural networks (CNNs) to analyse histopathological images for early detection of metastatic breast cancer with their gene mutation and expression level. The dataset utilized in this study comprises images obtained from the Genomic Data Commons (GDC) portal, accompanied by pertinent patient information including case-id, gender, primary site, primary diagnosis, race, disease type, ethnicity, gene mutation and expression level. A simple CNN architecture was employed for its ability to autonomously learn discriminative features from input images. The model yielded accuracy of 96%. Additionally, we have implemented a streamlined retrieval mechanism enabling seamless access to specific dataset indices along with corresponding image samples and associated information, facilitating clinical interpretation. The proposed system demonstrates promising results in the early diagnosis of metastatic breast cancer, offering potential utility in clinical settings for improved patient outcomes.

关键词： Recurrent neural networks Accuracy computational modeling Computer architecture Transformers Mathematical models Breast cancer

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An Informal Introduction to Semirings for GraphBLAS using Matlab

An Informal Introduction to Semirings for GraphBLAS using Ma...

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Intelligent Technologies (CONIT), International Conference on

作者： Aadharsh Aadhithya A Anirudh Edpuganti Vinith Rejathalal Sachin Kumar S Soman K. P Prabhaharan Poornachandran Center for Computational Engineering and Networking (CEN) Amrita Vishwa Vidyapeetham Coimbatore India Centre for Internet Studies and Artificial Intelligence Amrita Vishwa Vidyapeetham Amritapuri Kerala India

This paper presents an informal introduction to semirings and their application in GraphBLAS using Matlab. GraphBLAS is a powerful and versatile tool for working with graph algorithms and linear algebra, and its use in education is also discussed. The paper simplifies the mathematical complexity typically associated with semirings and provides an accessible entry point for those who are new to the field of GraphBLAS. The use of Matlab as a tool for implementing GraphBLAS is also highlighted, making it user-friendly and easy to understand. Overall, this paper serves as a useful starting point for anyone interested in learning more about GraphBLAS and its potential applications.

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Cost-sensitive long short-term memory for imbalanced dga family categorization 1st

Cost-sensitive long short-term memory for imbalanced dga fam...

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1st International Conference on Advances in Electrical and Computer Technologies, ICAECT 2019

作者： Mohammed Harun Babu, R. Vinayakumar, R. Soman, K.P. Amrita School of Engineering Center for Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9789811555572

Domain generation algorithm (DGA) is the foundation of malware families because of the very fact that DGA generates huge variety of pseudorandom domain names to associate to a command and control (C2C) infrastructures. This paper focuses in handling classification of class in a imbalanced data. Cost-sensitive long short-term memory (CS-LSTM) approach is proposed which helps in understanding the importance of each class. Detecting malicious domain names (DMD 2018) data set is used. Optimal parameters are set to deep learning architectures using hyper-parameter approach. Experiments on CS-LSTM performed well compared to other deep learning architectures. Using this approach, 74.3% accuracy is obtained, varies 4.6% from the top scored system in DMD-2018. © Springer Nature Singapore Pte Ltd 2020.

关键词： Long short-term memory

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Deep Learning Framework for Fingerspelling System using CNN 9

Deep Learning Framework for Fingerspelling System using CNN

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9th IEEE International Conference on Communication and Signal Processing, ICCSP 2020

作者： Raghavachari, Chakravartula Shanmugha Sundaram, G.A. Center for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India SIERS Research Laboratory Center for Computational Engineering and Networking Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781728149882

Sign language is used for communication among the hearing-challenged-cum-speech-challenged community. It is the language that uses facial expressions, movements of hands and other body parts. Indian Sign language (ISL) is commonly used sign language, in India. These days, online interpreters are available for translating the sign language to common language and vice versa. But it requires an expert who can translate in both ways and also they charge. Thus, the communication between the hearing-challenged-cum-speech-challenged community and the rest has become difficult and costly. This causes isolation of the hearing-challenged-cum-speech-challenged community from the rest of society. Hence, this work focuses on bridging the communication gap between the two. One important aspect of sign language is finger spelling. Through finger spelling, a word that lacks a particular sign in sign language can be expressed by spelling each alphabet of that word. Here, a vision based finger spelling system using Convolutional Neural Networks (CNN) is created. The proposed CNN model performs as par with the InceptionV3 and better than ResNet and VGG16 models. © 2020 IEEE.

关键词： Audition

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Noise Reduction of ECG using Chebyshev filter and Classification using Machine Learning Algorithms

Noise Reduction of ECG using Chebyshev filter and Classifica...

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International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： M. Bhanu Prakash Sowmya V Gopalakrishnan E A Soman K P Center for Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9781728185293

ISBN: (纸本)9781728185309

Cardiac disease detection is a tedious process. Classification of electrocardiogram (ECG) signals plays an important role in the diagnosis of heart diseases. The most important factor that limits the detection of cardiac disease is the rare availability of instances of the abnormal condition collected using ECG sensors. And if the signals contain noise, then the classification might become a challenging task. In this work, we address the problem of cardiac disease detection when the dataset has less number of noisy ECG sensor signals. Here, Chebyshev Type II filter and Chebyshev function, which is termed as Chebfun, are used. The Chebyshev filter is used for high-frequency noise removal and Chebfun is used to approximate the signal with its coefficients. These coefficients known as Chebfun coefficients are used as the features. These features are used for classification. In the proposed work, machine learning algorithms, like SVM, logistic regression, decision tree, and AdaBoost, are used for classifying the features extracted from Chebfun.

关键词： Noise reduction Cardiac disease Filtering algorithms Chebyshev approximation Electrocardiography Feature extraction Task analysis

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Interindividual Age-Independent Differences in Human CX43 Impact Ventricular Arrhythmic Risk

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Research 2024年第3期6卷 417-432页

作者： Laura García-Mendívil María Pérez-Zabalza Antoni Oliver-Gelabert JoséMaría Vallejo-Gil Javier Fañanás-Mastral Manuel Vázquez-Sancho Javier AndréBellido-Morales Alexánder Sebastián Vaca-Núñez Carlos Ballester-Cuenca Emiliano Diez Laura Ordovás Esther Pueyo Biomedical Signal Interpretation and Computational Simulation group(BSICoS) Aragón Institute of Engineering ResearchUniversity of ZaragozaZaragoza 50018Spain BSICoS Instituto de Investigación Sanitaria Aragón(IISA)Zaragoza 50018Spain Centro Universitario de la Defensa(CUD) Zaragoza 50090Spain. Department of Cardiovascular Surgery University Hospital Miguel ServetZaragoza 50009Spain. Institute of Experimental Medicine and Biology of Cuyo(IMBECU) CONICETMendoza 5500Argentina. Fundación Agencia Aragonesa para la Investigación y el Desarrollo(ARAID) Zaragoza 50018Spain. Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine(CIBER-BBN)Zaragoza 50018Spain.

Connexin 43 (CX43) is one of the major components of gap junctions, the structures responsible for the intercellular communication and transmission of the electrical impulse in the left ventricle. There is limited information on the histological changes of CX43 with age and their effect on electrophysiology, especially in humans. Here, we analyzed left ventricular biopsies from living donors starting at midlife to characterize age-related CX43 remodeling. We assessed its quantity, degree of lateralization, and spatial heterogeneity together with fibrotic deposition. We observed no significant age-related remodeling of CX43. Only spatial heterogeneity increased slightly with age, and this increase was better explained by biological age than by chronological age. Importantly, we found that CX43 features varied considerably among individuals in our population with no relevant relationship to age or fibrosis content, in contrast to animal species. We used our experimental results to feed computational models of human ventricular electrophysiology and to assess the effects of interindividual differences in specific features of CX43 and fibrosis on conduction velocity, action potential duration, and arrhythmogenicity. We found that larger amounts of fibrosis were associated with the highest arrhythmic risk, with this risk being increased when fibrosis deposition was combined with a reduction in CX43 amount and/or with an increase in CX43 spatial heterogeneity. These mechanisms underlying high arrhythmic risk in some individuals were not associated with age in our study population. In conclusion, our data rule out CX43 remodeling as an age-related arrhythmic substrate in the population beyond midlife, but highlight its potential as a proarrhythmic factor at the individual level, especially when combined with increased fibrosis.

关键词： CX43 rhythm Independent

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