IET Bhaddal

Rūpnagar, India

IET Bhaddal

Rūpnagar, India
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Kaushal M.,IET Bhaddal | Singh T.,IET Bhaddal | Kumar A.,Nanjing Forestry University
International Journal of Applied Engineering Research | Year: 2012

Mobile Phone usage has been rapidly spread globally and to provide proper coverage (signal strength), numbers of cell towers are also increasing worldwide generating a public concern as to whether frequent utilization of such devices is unsafe. Effects of Mobile Tower Radiations are seen in many countries. So, in this paper, we have discussed Effects of Mobile Tower Radiations and case studies of different countries to address the issue. © Research India Publications.

Khosla A.,National Institute of Technology Jalandhar | Jha R.,IET Bhaddal
Journal of Medical Engineering and Technology | Year: 2014

This study aims to develop a Steady State Visual Evoked Potential (SSVEP)-based Brain Computer Interface (BCI) system to control a wheelchair, with improving accuracy as the major goal. The developed wheelchair can move in forward, backward, left, right and stop positions. Four different flickering frequencies in the low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using LabVIEW. Four colours (green, red, blue and violet) were included in the study to investigate the colour influence in SSVEPs. The Electroencephalogram (EEG) signals recorded from the occipital region were first segmented into 1s windows and features were extracted by using Fast Fourier Transform (FFT). Three different classifiers, two based on Artificial Neural Network (ANN) and one based on Support Vector Machine (SVM), were compared to yield better accuracy. Twenty subjects participated in the experiment and the accuracy was calculated by considering the number of correct detections produced while performing a pre-defined movement sequence. SSVEP with violet colour showed higher performance than green and red. The One-Against-All (OAA) based multi-class SVM classifier showed better accuracy than the ANN classifiers. The classification accuracy over 20 subjects varies between 75-100%, while information transfer rates (ITR) varies from 12.13-27 bpm for BCI wheelchair control with SSVEPs elicited by violet colour stimuli and classified using OAA-SVM. © 2014 Informa UK Ltd. All rights reserved: reproduction in whole or part not permitted.

Deep G.,IET Bhaddal | Kaur L.,Punjabi University | Gupta S.,UIET
Procedia Computer Science | Year: 2016

This paper focuses on the comparison of two new proposed pattern descriptors i.e., local mesh ternary pattern (LMeTerP) and directional local ternary quantized extrema pattern (DLTerQEP) for biomedical image indexing and retrieval. The standard local binary patterns (LBP) and local ternary patterns (LTP) encode the gray scale relationship between the center pixel and its surrounding neighbors in two dimensional (2D) local region of an image whereas the former descriptor encodes the gray scale relationship among the neighbors for a given center pixel with three selected directions of mess patterns which is generated from 2D image and later descriptor encodes the spatial relation between any pair of neighbors in a local region along the given directions (i.e., 0°, 45°, 90° and 135°) for a given center pixel in an image. The novelty of the proposed descriptors is that they use ternary patterns from images to encode more spatial structure information which lead to better retrieval. The experimental results demonstrate the superiority of the new techniques in terms of average retrieval precision (ARP) and average retrieval rate (ARR) over state-of-the-art feature extraction techniques (like LBP, LTP, LQEP, LMeP etc.) on three different types of benchmark biomedical databases. © 2016 The Authors. Published by Elsevier B.V.

Garg N.,Punjab Technical University | Garg N.,Amity UniversityUttar Pradesh | Singla S.,IET Bhaddal | Jangra S.,HCTM Kaithal
Procedia Computer Science | Year: 2016

Big Data, the new buzz word in the industry, is data that exceeds the processing and analytic capacity of conventional database systems within the time necessary to make them useful. With multiple data stores in abundant formats, billions of rows of data with hundreds of millions of data combinations and the urgent need of making best possible decisions, the challenge is big and the solution bigger, Big Data. Comes with it, new advances in computing technology together with its high performance analytics for simpler and faster processing of only relevant data to enable timely and accurate insights using data mining and predictive analytics, text mining, forecasting and optimization on complex data to continuously drive innovation and make the best possible decisions. While Big Data provides solutions to complex business problems like analyzing larger volumes of data than was previously possible to drive more precise answers, analyzing data in motion to capture opportunities that were previously lost, it poses bigger challenges in testing these scenarios. Testing such highly volatile data, which is more often than not unstructured generated from myriad sources such as web logs, radio frequency Id (RFID), sensors embedded in devices, GPS systems etc. and mostly clustered data for its accuracy, high availability, security requires specialization. One of the most challenging things for a tester is to keep pace with changing dynamics of the industry. While on most aspects of testing, the tester need not know the technical details behind the scene however this is where testing Big Data Technology is so different. A tester not only needs to be strong on testing fundamentals but also has to be equally aware of minute details in the architecture of the database designs to analyze several performance bottlenecks and other issues. Like in the example quoted above on In-Memory databases, a tester would need to know how the operating systems allocate and de-allocate memory and understand how much memory is being used at any given time. So, concluding, as the data-analytics Industry evolves further we would see the IT Testing Services getting closely aligned with the Database Engineering and the industry would need more skilled testing professional in this domain to grab the new opportunities. © 2016 The Authors. Published by Elsevier B.V.

Devi V.,IET Bhaddal
International Journal of Modern Physics E | Year: 2015

In this paper, two parameter single-term energy formula EJ = aJb is used to study the energy spin relationship within the ground bands of even-even Mg-Zr nuclei. The formula works better for the γ-soft nuclei as well as vibrational nuclei. We also compared it with other two-parameter formulas: Ejiri, ab, pq and soft rotor formula (SRF). We also study the symmetry of the nuclei in the framework of interacting boson model (IBM-1). The IBM-1 was employed to determine the most appropriate Hamiltonian, the Hamiltonian of the IBM-1 and O(6) symmetry calculation, for the study of these isotopes. We have also calculated energy levels and B(E2) values for number of transitions in these 76-78Se and 76-78Kr isotopes and there is a good agreement between the presented results and the previous experimental data. © 2015 World Scientific Publishing Company.

Singla R.K.,National Institute of Technology Jalandhar | Khosla A.,National Institute of Technology Jalandhar | Jha R.,IET Bhaddal
International Journal of Biomedical Engineering and Technology | Year: 2014

One of the key points of Brain Computer Interface (BCI) system is how different Electroencephalogram (EEG) features are abstracted and distinguished. Therefore, EEG signal processing method is the focus of BCI. In this study, four different flickering frequencies in low-frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using LabVIEW. Four stimuli colours, green, red, blue and violet, were used in this study to investigate the influence of colour on SSVEPs. The EEG signals recorded from the occipital region were segmented into 1 second windows and features were extracted by using wavelet transform. Support Vector Machines (SVMs) are used for classification. In colour comparison, SSVEP with the violet colour showed higher accuracy than that with other stimuli. Copyright © 2014 Inderscience Enterprises Ltd.

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