Pranay Yadav

University of Cambridge

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Richard N. Henson

University of Cambridge

Prayag Tiwari

Halmstad University

K Parandham Gowd

A Khare

Annamalai University

Mukesh Prasad

University of Technology Sydney

Gia Nhu Nguyen

Duy Tan University

Munesh Sangwan

Dr. Sarvepalli Radhakrishnan Rajasthan Ayurved University

Gopal Panda

Dr. Sarvepalli Radhakrishnan Rajasthan Ayurved University

Jagendra Singh

Bennett University

Ritu Shrivastava

Sangath

Cooperative Institutions

Maulana Azad National Institute of Technology

Jadavpur University

Rajiv Gandhi Technical University

University of Engineering & Management

Indian Institute of Technology Guwahati

St. Xavier's College (Autonomous)

St Xavier’s College

Maulana Abul Kalam Azad University of Technology, West Bengal

East Asia School of Theology

University of Cambridge

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Color impulse noise removal by modified alpha trimmed median mean filter for FVIN

Pranay Yadav Parool Singh

In this reserch article presents a novel method for the enhancement of color images, when images are corrupted by color impulse noise. According to planned algorithm the color noisy pixels are substituted by novel trimmed mean median value color images. Firstly, the color image (RGB) is sub-split up into three sections, i.e. Red (R), Green (G) and Blue (B) color pixel matrices, then all three matrices are checked for noisy pixels. In our proposed work divided in two parts. In first part is detection of noisy pixels and the second part is removal of noisy with details preservation like edges. When pixels values, are present in between 0's and 255's, it implies that they pixel are noise free pixels. Apply this scenario in whole color image pixels for the detection of color impulse noise. Second stage is the removal of noise. In this stage whole image is divided into a small 3×3 particular window and apply Unsymmetric condition in a small 3×3 window with the combination of mean median filter. Different color images are tested via proposed method. The experimental result shows better Peak Signal to Noise Ratio (PSNR) value, Mean Square Error (MSE), Root Mean Square Error (RMSE) and with better visual and human sensing. This method yields a better output for color impulse noise as compare to the other filters.

Impulse noise

RGB color model

Salt-and-pepper noise

Peak signal-to-noise ratio

Root mean square

10.1109/iccic.2014.7238369

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Citations (19)

Performance Evaluation of FIR based Active Band Pass Filter for SAR Applications

CiiT international journal of wireless communication (2012)

Pranay Yadav K Parandham Gowd A Khare

Technologies have advanced rapidly in the design of filters to enhance effectiveness of Signal to Noise Ratio for mobile communications including SAR applications. In this research work Butter worth active band pass filter for 2 to 6 GHz was designed using XILINX and MATLAB software’s. This was optimized, analyzed and evaluated keeping the sampling frequency at 48 GHz and Kiser window for 0.5 Beta. Further to verify the simulated results hardware circuitry is also designed based on SPARTAN-3 FPGA –development kit. Asb) part of this research work 7 -9 KHz Butter worth active band pass filter was designed using active components which was put into circuit, tested by passing a sinusoidal test signal along with noise and the filtered output signals are presented. Based on these experimental results conclusions have been drawn for 2 to 6 GHz filter for SAR applications. It is observed that when signals are received with heavy noise the coefficients of filters are selected in such way that the noise content can be minimized to great extent.

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Face processing M/EEG data for Dynamic Causal Modelling

Figshare (2022)

Pranay Yadav Richard N. Henson

This dataset consists of BIDS-formatted fMRI data from Wakeman & Henson (2015) that have been processed for DCM analysis. Raw fMRI data from 15 subjects (subject 10 excluded due to fewer runs) have been processed with the analysis pipeline presented in Henson et al (2019). The processed data have been reparametrized, their runs concatenated and re-fit to all the smoothed, normalised fMRI volumes. Individual subjects' VOI time courses have been extracted for 4 regions: Left OFA, Right OFA, Left FFA and Right FFA. The file 'derivatives_fmri_dcm_ready_VOIs_concatenated.zip' consists of dcm-ready data for all 15 subjects. Each subject's data consists of 2 groups of files (9 total): SPM data file: SPM.mat VOI file: a pair of data and mask files each for four regions, for a total of 8 files Supplied filelist.txt contains the full filelist and folder hierarchy inside the compressed zip archive. This dataset was prepared for demonstration of group-level estimation and inference of DCMs from fMRI data. The raw data can be found here: https://openneuro.org/datasets/ds000117 Scripts used to produce this dataset, as well as as scripts demonstrating group DCM analysis can be found here: https://github.com/pranaysy/cognestic22_multimodal_dcm For more information or queries, please get in touch, or open an issue on the GitHub repository linked above.

10.6084/m9.figshare.21333996.v1

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A Square Shape MIMO Patch Array with Square Slit on Rectangular Parasitic Element For 4G/LTE

Munesh Sangwan Gopal Panda Pranay Yadav

In the current era mobile device as well as other communication devices are require smaller and compact day by day. In the senior mobile patch are also required compact design. In mobile handset require MIMO patch as long with minimum mutual coupling. For minimization of mutual coupling in last decade there are different type of parasitic element presented. In this research work proposed a square shape MIMO patch with square slit and Meander-Line Resonators [17] for decoupling in MIMO antenna. The proposed modified parametric element consist of Slotted Meander-Line Resonators with square cut on rectangular copper in between MIMO patch. The proposed MIMO antenna with modified parasitic element display better result in subject of mutual coupling (M.C.) decrease and ECC.

Square (algebra)

Patch antenna

Decoupling (probability)

10.1109/icatme50232.2021.9732737

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Citations (3)

Fusion based En-FEC Transfer Learning Approach for Automobile Parts Recognition System

2021 IEEE Symposium Series on Computational Intelligence (SSCI) (2018)

Mukesh Prasad Shantanu Rajora Deepak Gupta Yousef‐Awwad Daraghmi Eman Daraghmi

The artificially supervised classification of real world entities have gained a phenomenal significance in recent year of computational advancements. An intelligent classification model focuses on rendering accurate outcomes vide the implicated paradigms with respect to the subjected data employed to train the classifier. This paper proposes a novel deep learning approach to classify the various parts of any operational engine such as crank shafts, rock-arms, distributer, air duct, assecorybelt etc. deployed in automobiles. The proposed architecture distinctively utilizes convolution neural networks for this typical classification problem and altogether constructs a robust transfer learning paradigm to render the correct class label against the validation and test images as the conclusive result of the classification. The proposed methodology poses in such a way that it can qualitatively classify and henceforth give the corresponding class label of the machinery/engine part under consideration. This computationally intelligent architecture requires the user to feed the image of the engine part to the model in order to achieve the requisite responses of classification. The main contribution of the proposed method is the development of a robust algorithm that can exhibit pronounced results without training the entire ConvNet architecture from scratch, thereby enabling the proposed paradigm to be deployable in application instances wherein limited labeled training data is available.

Transfer of learning

Contextual image classification

10.1109/ssci.2018.8628789

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Citations (7)

Color image noise removal by modified adaptive threshold median filter for RVIN

Pranay Yadav

In the field of digital image processing impulse noise removal from color images is one of the most challenging tasks for researchers. A Modified Adaptive Threshold Median Filter (MATMF) is presented for impulse noise removal for both low and high-density noise levels. In this paper, we have proposed filter for color image random valued impulse noise reduction. For color images, impulse noise removal there are two main stages, firstly, the detection of the impulse noises on the basis of maximum and minimum value of pixels in a small window. In the second stage removal of noise on the basics median calculation. In the filtering stage, the noise-free pixels remain unchanged in all RGB frames, windows and noisy pixels are restored using a median filter. Experimental results show that proposed method is superior to the conventional methods in Peak Signal Noise Ratio ^[8] (PSNR) values and Mean Square Error (MSE) of the image performs, with and without noise compared. The mathematical analysis describes that the analysis of the noisy pixels and use of noise-free pixels for the de-noising purpose provide much better results and provides better visual quality of de-noised image and provide good quality in human persecution.

Impulse noise

Salt-and-pepper noise

Dark-frame subtraction

Image noise

RGB color model

10.1109/edcav.2015.7060562

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Citations (25)

Face processing M/EEG data for Dynamic Causal Modelling

Figshare (2022)

Pranay Yadav Richard N. Henson

This dataset consists of BIDS-formatted M/EEG data from Wakeman & Henson (2015) that have been processed for DCM analysis. Multimodal data from EEG, MEG magnetometers and MEG planar gradiometers from all 16 subjects have been processed with the analysis pipeline presented in Henson et al (2019). Individual subjects' T1 images were used for creating head models. Forward models (leadfields) for all subjects are included in the dataset.
The file 'derivatives_dcm_ready_with_gainmat_all_subjects.zip' consists of dcm-ready data for all 16 subjects, while the file 'derivatives_dcm_ready_with_gainmat_single_subjects.zip' consists of dcm-ready data for a single subject (sub-01).
Each subject's data consists of 3 files: SPM sidecar data file: maMceffdspmeeg_sub-XX_ses-meg_task-facerecognition_run-01_proc-sss_meg.dat SPM data header file: maMceffdspmeeg_sub-XX_ses-meg_task-facerecognition_run-01_proc-sss_meg.mat Gain matrix (forward model): SPMgainmatrix_maMceffdspmeeg_sub-XX_ses-meg_task-facerecognition_run-01_proc-sss_meg_1.mat *XX here indicates zero-padded subject number (01, 02, ...). Supplied filelist.txt contains the full filelist and folder hierarchy inside the compressed zip archive.
This dataset was prepared for demonstration of group-level estimation and inference of DCMs from M/EEG data. The raw data can be found here: https://openneuro.org/datasets/ds000117
Scripts used to produce this dataset from the raw data, as well as as scripts demonstrating group DCM analysis can be found here: https://github.com/pranaysy/DCM-MEEG-Demo
For more information or queries, please get in touch, or open an issue on the GitHub repository linked above.

10.6084/m9.figshare.21130297

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Design of Low Frequency Band Pass Filters for SAR applications

Pranay Yadav A Khare K Parandham Gowd

Technologies have advanced rapidly in the field of digital signal processing due to advances made in high speed, low cost digital integrated chips. These technologies have further stimulated ever increasing use of signal representation in digital form for purposes of transmission, measurement, control and storage. Design of digital filters especially adaptive or semi adaptive is the necessity of the hour for SAR applications. The aim of this research work is to design and evaluate the performance of 380-400 KHz Bartlett, Blackman and Chebyshev digital semi adaptive filters. XILINX and MATLAB software are used for designing of these filters. As part of practical research work these designs were translated using FPGA hardware SPARTAN-3E kit. Blackman and Chebyshev filters designed using software and hardware were tested by a sinusoidal test signal of 381 KHz along with noise and the filtered output signals are presented. These designs were optimized, analyzed, compared and evaluated keeping the sampling frequency at 5 MHz for 64 orders.

Digital delay line

SIGNAL (programming language)

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Issues in gender diversity and equality in the UK

Amel Bennaceur Antonio Cano Lilia Georgieva Mariam Kiran M. Salama

There has been a significant increase in the number of initiatives to raise awareness of diversity-related challenges in technology worldwide within the past decade. Multiple organizations now emphasize a need for a close to 50%-50% male to female workforce distribution. Example of proposed activities include introducing quotas for women on board positions, promoting equal opportunities for employment in STEM (Science, Technology, Engineering, Mathematics) jobs and creating a woman-friendly work environment. However, despite these efforts, the growth of number of women working in STEM is still slow.

Gender Diversity

Women in science

Science and engineering

10.1145/3195570.3195571

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Citations (7)

IMPACT OF CLOUD TECHNOLOGY ON LEARNING ENVIRONMENTS IN HIGHER EDUCATION

REDSHINE Publication eBooks (2020)

Pranay Yadav

10.25215/9358793864.26

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