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International Conference on Green Computing Communication and Electrical engineering (ICGCCEE)

作者： R. Anil R. Danymol Harsha Gawande R. Gandhiraj Centre for Excellence in Computational Engineering and Networking Amrita VishwaVidyapeetham Coimbatore India Department of Electronics and Communication Engineering Amrita VishwaVidyapeetham Coimbatore India

This paper gives an insight about how to create classifier plug-ins (signal processing blocks) using hard-code input for GNU Radio Companion (GRC). GNU Radio Companion is an open source Visual programming language for any real time signal processing applications. At present there is no classifier block available inside this GRC tool. Here we are introducing a low cost classifier which utilizes the basic machine learning algorithms:linear regression and logistic regression. The creation of classifier plug-ins in an open source software enables easy manipulation of real time classification problems during the transmission and reception of signals in Software Defined Radios. So this workdescribes the development of signal processing block that can be done by changing the Python code and C++ codes of the `gr-modtool' package. It is highly cost effective and with great potential since GNU Radio software is open source and free.

关键词： Logistics Linear regression Signal processing Real-time systems Software radio Software Signal processing algorithms

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Audio Data Authentication with PMU Data and EWT

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Procedia Technology 2015年 21卷 596-603页

作者： Premjith B. Neethu Mohan Prabaharan Poornachandran Soman K.P. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetha Coimbatore – 641112 India Amrita Center for Cyber Security Systems and Networks Amrita Vishwa Vidyapeetham Kollam

Digital forensics has become a flourishing research area. Electrical Network Frequency (ENF) plays an important role in assessing the authenticity of a digital recording such as audio. ENF criterion is a tool for extracting the embedded power line frequency from the recording. A cross correlation between a reference PMU data and extracted ENF signal can be done in order to determine the authenticity of an audio signal. In this paper, Empirical Wavelet Transform (EWT) is used for extracting the ENF from an audio signal. EWT decomposes signal into N modes. Hilbert Transform is used to compute the instantaneous frequency and amplitude of the extracted mode corresponding to ENF. EWT method is not able to capture the weak harmonics in a signal. This problem is resolved by fixing the frequency domain boundaries of each mode.

关键词： Digital Forensics Electrical Network Frequency Empirical Wavelet Transform Hilbert Transform

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Variance Based Offline Power Disturbance Signal Classification Using Support Vector Machine and Random Kitchen Sink

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Procedia Technology 2015年 21卷 163-170页

作者： Aneesh C. Hisham P.M. Sachin Kumar S. Maya P. K.P. Soman Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore – 641 112 India Dept of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham Coimbatore – 641 112 India

In this paper, five classes of different power quality disturbances such as swell, sag, harmonics, sag with harmonics and swell with harmonics are synthesized using MATLAB/SIMULINK software which is further decomposed into 8 intrinsic mode functions using variational mode decomposition (VMD). VMD is an adaptive signal processing method that decomposes the signal in to several intrinsic mode functions (IMF) or components. The variance calculated from each of the mode is taken as feature representation. It is found that sines and cosines of variance vector of eight different IMF candidates of a signal acts as feature vector that can accurately extract salient and unique nature of the power disturbances. The classification is performed using Support Vector Machines (SVM) and Random Kitchen Sink (RKS) algorithm. The classification results in a highest accuracy of 94.44% for RKS method when compared to SVM.

关键词： Variational mode decomposition Random Kitchen Sink Power system Support vector machine variance

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Application and Analysis of Smart Meter Data along with RTL SDR and GNU Radio

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Procedia Technology 2015年 21卷 317-325页

作者： Aswathi M. Gandhiraj R. Soman K.P. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore- 641 112 India Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Coimbatore- 641 112 India

Introduction of smart meter offered fine grained information about the energy consumption of customers. To categorize the appliances consuming the energy, the Electromagnetic Interferences emitted by them are measured using Realtek Software Defined Radio. To avoid the frequency range mismatch of the interferences and the input frequency range of Realtek Software Defined Radio, an up converter is used. The interferences can be processed by using a GNU radio. The time stamped signals are stored as DAT file format. Disaggregation of appliances consuming the power is attained by comparing the results with that of smart meter results. To achieve this task smart meters are employed to calculate the total energy consumption. Smart meter data will also be time stamped. The data collected by the smart meter is processed by Raspberry Pi and is stored in memory. Both the GNU radio data and smart meter data are compared to conclude final results. This algorithm is superior to the existing one as it helps to disintegrate the multiple appliances consuming power at the same time.

关键词： Smart meter GNU radio RTLSDR antenna.

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IoT and WSN integration for Data Acquisition and Supervisory Control

IoT and WSN integration for Data Acquisition and Supervisory...

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2022 IEEE World Conference on Applied Intelligence and Computing, AIC 2022

作者： Shah, Sanjeev Kumar Joshi, Kapil Khantwal, Sandeep Bisht, Yashwant Singh Chander, Harish Gupta, Ashulekha Uttaranchal University Uttaranchal Institute of Technology Dehradun India Tula's Institute Dehradun India Amrita Vishwa Vidyapeetham Centre for Excellence in Computational Engineering and Networking Tamil Nadu Coimbatore India Graphic Era University Dept. of Management Studies Dehradun India

ISBN: (数字)9781665479882

ISBN: (纸本)9781509050017

There is an increased use of WSN (Wireless Sensor Networks) in our daily lives with WSN finding application in different areas like maintaining health, better quality of life scenarios, production monitoring in industries, traffic control and various other fields. WSNs have a scope for being incorporated in IoT (Internet of Things). IoT is beneficial foe Web based applications having specific requirements of storage and computation. This paper gives a flexible and extensible architecture to integrate WSN and IoT. REST based internet services as used as a layer interoperating as an application layer which has a possibility of being integrated directly into the other domains of application to remotely monitor smart homes, VAN (Vehicular area networks) or healthcare services. © 2022 IEEE.

关键词： Data acquisition

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An experimental study on application of Orthogonal Matching Pursuit algorithm for image denoising

An experimental study on application of Orthogonal Matching ...

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International Multi-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s)

作者： M Suchithra P Sukanya Pinchu Prabha O K Sikha V Sowmya K P Soman Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu IN Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

Signal or image reconstruction has now become a common task in many applications. According to linear algebra perspective, the number of measurements made or the number of samples taken for reconstruction must be greater than or equal to the dimension of signal or image. Also reconstruction follows the Shanon's sampling theorem which is based on the Nyquist sampling rate. The reconstruction of a signal or image using the principle of compressed sensing is an exception which makes use of only few number of samples which is below the sampling limit. Compressive sensing also known as sparse recovery aims to provide a better data acquisition and reduces computational complexities that occur while solving problems. The main goal of this paper is to provide clear and easy way to understand one of the compressed sensing greedy algorithm called Orthogonal Matching Pursuit (OMP). The OMP algorithm involves the concept of overcomplete dictionary that is formulated based on different thresholding methods. The proposed method gives the simplified approach for image denoising by using OMP only. The experiment is performed on few standard image data set simulated with different types of noises such as Gaussian noise, salt and pepper noise, exponential noise and Poisson noise. The performance of the proposed method is evaluated based on the image quality metric, Peak Signal-to-Noise Ratio (PSNR).

关键词： Matching pursuit algorithms Noise Dictionaries Vectors Image reconstruction Sparse matrices Compressed sensing

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GNU Radio Based Control System

GNU Radio Based Control System

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International Conference on Advances in Computing and Communications (ICACC)

作者： Anand R. Gintu Xavier Hariharan V. Neethu Prasannan Rakesh Peter Soman K.P. Centre For Excellence in Comput. Eng. and Networking Amrita Vishwa Vidyapeetham Coimbatore-641112India Amrita Vishwa Vidyapeetham Coimbatore India Centre For Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

This paper is an attempt to reveal the untapped immense power of the GNU Radio - an open source software, in control and monitoring systems, both real time applications and class-room demonstrations. GNU Radio has already gained wide acceptance and glory in communication and signal processing. As a novel attempt to bring the controlling capability of GNU Radio to limelight, an experiment for temperature control, out of its innumerable applications, and the possibility of a revolution by this free open source software is predicted and explained through this paper. This paper describes the hardware and software requirements for the temperature control experiment with SBHS (Single Board Heater System) using GNU Radio and describes the recommended classroom demonstration.

关键词： Heating Software Real time systems Monitoring Software radio

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Prediction of Coronary Artery Disease by Adapting Hybrid Approach of Machine Learning Methods 3

Prediction of Coronary Artery Disease by Adapting Hybrid App...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Parthasarthy, Vijay Pallavi, L. Anandaram, Harishchander Praveen, M. Arun, S. Krishnamoorthy, R. University of Petroleum and Energy Studies Chemical Engineering - Energy Cluster School of Engineering Dehradun India B v Raju Institute of Technology Department of Cse India Amrita Vishwa Vidyapeetham Centre for Excellence in Computational Engineering and Networking India Saveetha Engineering College Department of Ece Chennai India Jerusalem College of Engineering Department of Ece Chennai India Chennai Institute of Technology Centre for Nonlinear Systems Chennai India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Recently, there have been many implementations of AI and its application tactics in the field of medicine, and it is commonly mentioned as a substantial rich data. One of the major causes of death from one end of the world to the other is coronary artery disease, which can be prevented with early diagnosis. The goal of this work is to use reliable clinical data to predict coronary course infection. Expecting Coronary Artery Disease (CAD) is a very challenging and challenging undertaking in the clinical profession. One of the virtuosi in the clinical field is the early forecast, especially in the cardiovascular region.. The earlier studies on the creation of the early forecast model encouraged an understanding of the new approaches to find the variation in clinical imaging. An eating plan graph prepared by the concerned doctor following early anticipation might satisfy the cardiovascular counteraction. Our exam paper includes a forecast based on a suggested computation created using a pooling region bend AI technology. This data-based ID is a crucial element for accurate expectation. Despite the weak pixels around it, this extensive methodology has a respectable impact on deciding variety in clinical images. With the help of vein halting and vein plaque, this pooling region development in our AI calculation is storing contracting veins and tissues. The new flexible picture-based grouping strategies are presented in this investigation piece, which also contrasts the current characterization techniques with anticipated CAD previous for a higher exact worth. This suggested method uses any prior cardiac ailment as evidence to draw a conclusion. In our suggested calculation, the decision-production of grouped yield yields more precise results. © 2022 IEEE.

关键词： Electrocardiograms

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Apache Spark a Big Data Analytics Platform for Smart Grid

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Procedia Technology 2015年 21卷 171-178页

作者： Shyam R. Bharathi Ganesh H.B. Sachin Kumar S. Prabaharan Poornachandran Soman K.P. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetha Coimbatore – 641112 India Amrita Center for Cyber Security Systems and Networks Amrita Vishwa Vidyapeetham Kollam India

Smart grid is a complete automation system, where large pool of sensors is embedded in the existing power grids system for controlling and monitoring it by utilizing modern information technologies. The data collected from these sensors are huge and have all the characteristics to be called as Big Data. The Smart-grid can be made more intelligent by processing and deriving new information from these data in real time. This paper presents Apache spark as a unified cluster computing platform which is suitable for storing and performing Big Data analytics on smart grid data for applications like automatic demand response and real time pricing.

关键词： Smart Grid Data Analytics Big Data Apache Spark

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Real-Time Human Action Recognition with Multimodal Dataset: A Study Review 5th

Real-Time Human Action Recognition with Multimodal Dataset: ...

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5th International Conference on Recent Innovations in Computing , ICRIC 2022

作者： Joshi, Kapil Rastogi, Ritesh Joshi, Pooja Anandaram, Harishchander Gupta, Ashulekha Mohialden, Yasmin Makki Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of IT Noida Institute of Engineering and Technology Greater Noida India Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India Dehradun India Computer Science Department Mustansiriyah University Baghdad Iraq

ISBN: (纸本)9789819906000

Due to difficulties including a cluttered background, partial occlusion, and variations on dimensions, angle, illumination, or look, identifying human endeavors using video clips or still images is a challenging process. A numerous mechanism for recognizing activity is necessary for numerous applications, such as robotics, human–computer interaction, and video surveillance for characterizing human behavior. We outline a classification of human endeavor approaches and go through their benefits and drawbacks. In specifically, we classify categorization of human activity approaches into the two broad categories based on whether or not they make use of information from several modalities. This study covered a depth motion map-based approach to human recognizing an action. A motion map in depth created by adding up to the fullest differences with respect to the two following projections maps for each projection view over the course of the entire depth video series. The suggested approach is demonstrated to be computationally effective, enabling real-time operation. Results of the recognition using the dataset for Microsoft Research Action3D show that our method outperforms other methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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