Papers by VIVEK UPADHYAYA
Kalpa publications in engineering, Oct 23, 2018
Electrocardiogram (ECG) signal is a bio-electrical activity of the heart. It is a common routine ... more Electrocardiogram (ECG) signal is a bio-electrical activity of the heart. It is a common routine and important cardiac diagnostic tool where in electrical signals are measured and recorded to know the functional status of heart, but ECG signal can be distorted with noise as, various artifacts corrupt the original ECG signal and reduces it quality. Therefore, there is a need to remove such artifacts from the original signal and improve its quality for better interpretation. Digital filters are used to remove noise error from the low frequency ECG signal and improve the accuracy the signal. Noise can be any interference due to motion artifacts or due to power equipment that are present where ECG had been taken. Thus, ECG signal processing has become a prevalent and effective tool for research and clinical practices. This paper presents the comparative analysis of FIR and IIR filters and their performances from the ECG signal for proper understanding and display of the ECG signal.
Fusion Rule Optimisation for Energy Efficient Cluster-Based Cooperative Spectrum Sensing
Lecture notes in electrical engineering, 2022
Study and Analysis of 4G-5G Spectrum Mobile Signals on Germination Seed and Further Growth
2022 IEEE Pune Section International Conference (PuneCon)
Compressive Sensing-Based Medical Imaging Techniques to Detect the Type of Pneumonia in Lungs
CRC Press eBooks, Feb 9, 2023
Effect of sensing matrices on quality index parameters for block sparse bayesian learning-based EEG compressive sensing
International Journal of Wavelets, Multiresolution and Information Processing
Due to the ongoing research in the medical domain, we get lot of data for storage and transmissio... more Due to the ongoing research in the medical domain, we get lot of data for storage and transmission purposes. Real-time processing and reduction of medical data are tedious. Hence, an approach is required to compress the data and reconstruct it by using a few samples. We proposed a model with a remote Health Care Unit & Patient for EEG signals in this work. In this model, our prime concern is to reduce the number of samples to reconstruct a compressed EEG signal. So, to reduce the number of samples, we opt for compressive sensing approach. As it is a well-known concept, Compressive Sensing is the framework that mainly depends upon the Sensing matrix for compression and the Basis matrix for representation. By considering this fact, we demonstrate a technique, which is a combination of the Compressive Sensing and BSBL by employing different measurement matrices. Since BSBL has already been mentioned in the literature, we compared the results based on this demonstration with the previou...
Algorithm Based on Stockwell Transform for Processing of Communication Signal to Detect Superimposed Harmonics and Transient Disturbances
2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)
An algorithm based on Stockwell Transform focused on processing of communication signals to detec... more An algorithm based on Stockwell Transform focused on processing of communication signals to detect harmonics and transient disturbances superimposed on the signals is presented in this paper. These disturbances are being superimposed on the signals in the communication channel or at the transmitter or the receiver stations. Investigated transient disturbances include impulsive transient and oscillatory transients. Communication signals incorporating harmonics or transient disturbance are decomposed with the help of Stockwell Transform and S-matrix is derived. A summation of absolute values curve, median curve and maximum absolute values plot are proposed to detect disturbances. These curves are obtained from S-matrix. On comparing these plots of signal having harmonics or transient disturbances with respective curves of pure sinusoidal communication signal, superimposed harmonics or transient disturbance have been detected successfully. Effectiveness of the proposed approach is established using the MATLAB software.
Joint approach based quality assessment scheme for compressed and distorted images
Chaos, Solitons & Fractals
Metal Recovery from E-Waste by Recycling Techniques: A Review
2022 8th International Conference on Smart Structures and Systems (ICSSS)
Waste Electric and Electronic Component (WEEE) is becoming a major issue for the environment and ... more Waste Electric and Electronic Component (WEEE) is becoming a major issue for the environment and the society. The hazardous metal contents available in WEEE can leave harmful impact on the well-being of humans and animals. The existing methods of Electronic Waste (E-waste) treatment are reuse, recycle and remanufacture. Along with these methods incineration and landfilling are also considered as options for Ewaste treatment. The recycling of E-waste helps in waste treatment and in the recovery of valuable metals. This paper gives a systematic review of existing recycling techniques for Ewaste management, their advantages and limitations and valuable metals recovery from E-waste. This article may help in waste utilization and metal recovery from E-waste. This article also reviews the opportunities and challenges faced in the process of metal recovery from E-waste.
Stockwell Transform Based Algorithm for Processing of Digital Communication Signals to Detect Superimposed Noise Disturbances
2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), 2019
This research work presents a method using Stockwell transform which is aimed to process the comm... more This research work presents a method using Stockwell transform which is aimed to process the communication signals to detect noise disturbances superimposed on the signals in the communication channel or at the transmitter or the receiver stations. The communication signals with noise disturbances are simulated with the help of mathematical relations. The communication signals with noise disturbance are decomposed with the help of Stockwell Transform and S-matrix is obtained. A summing of absolute values curve is proposed and calculated by summing of absolute values of each columns of S-matrix and plotted against time. A median curve is also proposed and calculated using median of absolute values of each columns of S- matrix. Proposed maximum absolute values plot is calculated using maximum values of absolute values of each columns of S- matrix. On comparing these plots of signal having noise disturbance with corresponding plots of pure sinusoidal communication signal, superimposed noise disturbances have been detected successfully. Proposed study is performed using the MATLAB software.
Speech Signal Compression and Reconstruction Using Compressive Sensing Approach
Algorithms for Intelligent Systems, 2021
Variation Measurement of SNR and MSE for Musical Instruments Signal Compressed Using Compressive Sensing
Algorithms for Intelligent Systems, 2020
There are various data compression techniques available in the literature. But for proper reconst... more There are various data compression techniques available in the literature. But for proper reconstruction of the original signal we have to follow the Shannon theorem. According to the Shannon theorem the sampling rate must be greater than twice of the highest component of that signal. But as we know that there are various applications present nowadays in which we have required lot of data so due to that this is so much tedious to follow the Shannon’s theorem. The solution of this problem is known as Compressive Sensing. It is the method by using which we can reconstruct the signal by using very few components so the problem associated with the Shannon’s theorem can be resolved. In this paper, our main objective is to show the SNR improvement by using the Compressive Sensing (CS) technique. Sound signals of five music instruments are taken into this analysis. We used l1 reconstruction algorithm for the proper reconstruction of the original signal. Gaussian matrix is used as measurement matrix and the DCT is used as the basis matrix. SNR and MSE are the two crucial parameters which are recorded in this analysis. Effect of Compressive Sensing on the five music instrumental signals are analyzed and shown by using two different plots.
© The Author(s). This is an open-access article distributed under the terms of the Creative Commo... more © The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/), which permits use, distribution, and reproduction in any medium, provided that the Article is properly cited. Abstract—Advancement in medical technology creates some issues related to data transmission as well as storage. In real-time processing, it is too tedious to limit the flow of data as it may reduce the meaningful information too. So, an efficient technique is required to compress the data. This problem arises in Magnetic Resonance Imaging (MRI), Electrocardiogram (ECG), Electroencephalogram (EEG), and other medical signal processing domains. In this paper, we demonstrate Block Sparse Bayesian Learning (BSBL) based compressive sensing technique on an Electroencephalogram (EEG) signal. The efficiency of the algorithm is described using the Mean Square Error (MSE) and Structural Similarity Index Measur...
Compressive Sensing: Methods, Techniques, and Applications
IOP Conference Series: Materials Science and Engineering, 2021
According to the latest research, it is very much clear that in future we require a huge amount o... more According to the latest research, it is very much clear that in future we require a huge amount of data as modern advancement in communication and signal processing generates a large number of bytes some examples are 5G peak data rate about 300 Mb/sec, an image of black hole required several petabytes to store & in medical signal processing huge amount of data required. So, by these examples, we can easily understand the scarcity of storage in near future. To overcome this problem of data scarcity such type of data compression is required in which the information of the signal will not be degraded. A well-known method is Compressive Sensing which can easily tackle this problem of data compression.
International Journal of Online and Biomedical Engineering (iJOE), 2021
Medical Imaging and scanning technologies are used to provide better resolution of body and tissu... more Medical Imaging and scanning technologies are used to provide better resolution of body and tissues. To achieve a better quality Magnetic Resonance (MR) image with a minimum duration of processing time is a tedious task. So our purpose in this paper is to find out a solution that can minimize the reconstruction time of an MRI signal. Compressive sensing can be used to accelerate Magnetic Resonance Image (MRI) acquisition by acquiring fewer data through the under-sampling of k-space, so it can be used to minimize the time. But according to the relaxation time, we can further classify the MRI signal into T1, T2, and Proton Density (PD) weighted images. These weighted images represent different signal intensities for different types of tissues and body parts. It also affects the reconstruction process conducted by using the Compressive Sensing Approach. This study is based on finding out the effect of T1, T2, and Proton Density (PD) weighted images on the reconstruction process as well...
As we know that for data compression generally Shannon – Nyquist theorem taken in to consideratio... more As we know that for data compression generally Shannon – Nyquist theorem taken in to consideration. But a severe problem which is associated with the traditional theory is the storage problem. According to this theorem the sampling rate must be twice the largest frequency component of the signal which we want to reconstruct. Due to this the data which is required to transmit a signal or to store it is too large. So to overcome this problem a new method is proposed, which is known as Compressive sensing. The sampling rate which is required reconstruct the signal is comparatively low in the compressive sensing. The various aspects about the compressive sensing and literature review with some important properties is given below. KeywordsCompressive Sensing(CS),Restricted Isometry Property (RIP) __________________________________________________*****_________________________________________________
Simulation and identification of emotions have attracted much interest from fields like cognitive... more Simulation and identification of emotions have attracted much interest from fields like cognitive science, psychology, and, recently, engineering. Even though a good quantity of investigation has been conducted on behavioral modalities, there are some underresearched aspects such as physiological signals. This research brings forth the ECG signal and introduces a complete study of its psychological characteristics. The very institution of this signal as a biometric property justifies subject-reliant emotion identifiers that record the immediate changeability of the signal from its homeostatic standard level. We are enhancing the implementation of the Emotion Identification through the application of ECG signals in this work. We recommend Ensemble Pragmatic Mode Decomposition or EPMD technique to diminish the operation duration and enhance the categorization rate.
Compressive Sensing: An Efficient Approach for Image Compression and Recovery
Compressive sensing (CS) is a technique that is very popular nowadays for compression and reconst... more Compressive sensing (CS) is a technique that is very popular nowadays for compression and reconstruction. This technique is too efficient than the traditional methods for data compression. As per the Nyquist sampling theorem, for proper reconstruction of a signal, we have to do sampling at double the rate of bandwidth. Therefore, the storage which is required to store the signal is also very large. As a resultant, the cost effectiveness of the system reduces. The compressive sensing technique has the key feature to reduce this sampling rate by using the two parameters: basis and sensing matrices. In order to achieve this, there are two other important properties that are also discussed along with compressive sensing. The name of these properties are restricted isometry property (RIP) and independent and identically distributed (IID) property. For proper reconstruction of a signal, both these properties must be satisfied by the compressive sensing technique. In this paper a novel app...
To Analysis the Effects of Compressive Sensing on Music Signal with variation in Basis Sensing Matrix
2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2018
Compressive Sensing (CS) is a new approach for compression and reconstruction of compressed signa... more Compressive Sensing (CS) is a new approach for compression and reconstruction of compressed signals using very minute observations. These minute observations are also called the number of measurement. The basic benefits of CS are that the number of measurements which are required for proper reconstruction of the compressed signal is very less than the conventional method. If we go through the literature then, we get that for proper reconstruction of signal a theory is given by Shannon. This theory states that the sampling frequency must be higher than twice the highest frequency component in that signal. So the limitation of the conventional method is that it requires so much storage to store and a large bandwidth to transmit the data. Both the things are so much scarce now days, as we know that if we have to required high resolution of signal then the storage which required to store this is also so much high. As there are various parameters in the theory of CS. But the two parameters are so much important than the others. These two parameters are basis and sensing matrices. Various types of other properties like RIP property and IID property also shows a big role in CS theory. By changing the sensing and measurement matrix the SNR value can also be enhanced. In this paper Gaussian matrix is taken as a sensing matrix & DST, DCT considered as the Basis matrices. The combination of basis and sensing matrix enhances the quality & level of compression. As the quality of compression enhanced it enhances the Signal to Noise ratio too. We cannot check the quality by using only one signal so comparison is made using Single Tone, Multi Tone and Vocal Song. 11minimization technique is used for reconstruction of compressed signal
Encryption and Decryption Analysis of Non – Stationary image Using Canonical Transforms with Scrambling Technique
Electrocardiogram (ECG) signal is the signal which consists of the parameters which reflects the ... more Electrocardiogram (ECG) signal is the signal which consists of the parameters which reflects the electrical representation of heart activity. The main components which are shown by the ECG signal have some important attributes of human heart as well as some hidden information of heart. The information which is found from the ECG signal is so much meaningful to derive various vital parameters related to heart. But the ECG signal can easily be affected with the Noise. Noise is the signal which distorts or interfere the actual power level of ECG signal, this can be due to the motion artifacts or due to the power sources which are resided where this ECG had been taken The ECG system which is typically based on computer have some units, the very first unit is used for pre-processing of ECG signal, second unit is used to detect the heart beats, third unit is used for feature extraction & the last one is used for classification. Signal processing unit which is used for ECG signal is much important for both research & clinical experiments. The artifacts which are analyzed due to the motion & shown in the heart beat processing can effectively remove & the ECG signal is cleaned after using LMS, NLMS and Notch processing. This paper comprises of result and analysis of ECG signal processing using NLMS algorithm & shows its important role in the biomedical applications.
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Papers by VIVEK UPADHYAYA