Narula Institute of Technology

About: Narula Institute of Technology is a based out in . It is known for research contribution in the topics: Quantum dot cellular automaton & Cognitive radio. The organization has 288 authors who have published 490 publications receiving 2258 citations. The organization is also known as: NiT.

Brain Activation in Chronic Nonspecific Low Back Pain : A Systematic review and ALE Meta-analysis

Abstract: Pain, a protective mechanism turns into a pathologic response when it becomes chronic. Recent evidences are pointing towards neuroplastic brain changes as the primary factor for the persisting pain in chronic nonspecific low back pain (cLBP). To summarise the previous fMRI studies, a coordinate-based ALE meta-analysis of resting functional brain imaging studies is carried out to identify the clusters activated in the brain in cLBP. Literature survey: ‘PubMed’, ‘Scopus’ and ‘Sleuth’ were searched for studies with resting functional whole-brain imaging in cLBP. Till October 2020; 258, 238, and 7 studies were found respectively after search. The activity pattern was documented in ‘without stimulation’ and ‘with stimulation’ groups. The risk of bias was assessed by Joanna Briggs Institute critical appraisal checklist for analytical cross-section studies. Total seven (224 cLBP patients, 110 activation foci) and six studies (106 cLBP patients, 66 activation foci) were selected among 277 studies for metanalysis in the ‘without stimulation’ and ‘with stimulation’ group respectively. In the ‘without stimulation’ group 8 statistically significant clusters were found. The clusters are distributed in the prefrontal cortex, primary somatosensory cortex, and primary motor cortex, anterior cingulate cortex, insular cortex, putamen, claustrum, amygdala, and associated white matters in both hemispheres. On the other group, 3 statistically significant clusters were found in the frontal cortex, Parietal cortex, and Insula. In the ‘with stimulation’ group, significant lateralization was observed and most of the clusters were in the right hemisphere. The white matter involvement was more in the ‘with stimulation’ group (78.62% Vs 38.21%). The statistically significant clusters found in this study indicate a probable imbalance in GABAergic modulation of brain circuit and dysfunction in descending pain modulation system. This disparity in pain neuro-matrix is the source of spontaneous and persisting pain in cLBP.

Effect of Cognitive Task on the Central Nervous System

Abstract: Electroencephalography (EEG) signal analysis has received great acknowledgment in the domain of biomedical signal processing for the interpretation of human brain activities. There is a close bonding between the EEG signal and human brain activities. In the human brain, millions of neurons interact with one other and as a result, we obtain electrical signals by placing the electrodes on the scalp in a non-invasive way. The human behavior (polite, rude, whimsical, etc.), mood (happy, sad, anger, depressed, etc.), sensory states (movement of the eye, lip, hand, etc.), cognitive task ability (understanding, thinking, problem-solving, implementation, debugging, recalling) can be monitored, interpreted and analyzed with the exploitation of EEG signals. Moreover, to detect neurological diseases and for treatment purposes, EEG signals are countless boons in the field of biomedical signals. The central nervous system is responsible for controlling human behavior, mood, cognitive task motor, and imaginary task to some extent. To find evidence, we have focused on the effects of cognitive tasks on the central nervous system. Due to the non-linearity and non-stationarity nature of the EEG signals, we have investigated the signals using non-linear tools like the Surrogate data test and phase space plot. Moreover, we have explored the topological scalp map view to obtain the visual effects of the scalp.

FPGA-Based Efficient Implementation of CBNS Computational Circuits: A Modular Approach

Abstract: Complex binary number system (CBNS) finds extensive applications in the faster computation of various digital signal processing (DSP) algorithms. In this paper, an attempt has been undertaken to develop various computational circuits based on CBNS for implementation in Spartan XC3S700A FPGA platform. The circuits have been designed following a modular approach. The designed modules involve simple logic gates leading ultimately to efficient implementation on FPGA. The codes for the modules have been developed using verilog hardware description language (HDL). Structural-level designs of nibble size CBNS adder, multiplier, and subtractor have been exclusively accomplished involving these modules. In the design of multiplier and subtractor, a new concept of sub-block has been introduced to efficiently utilize the limited input capability of the designed modules. The proposed design involves less hardware complexity, silicon area, and path delay compared to existing works. Simulation results and performance metrics for all the three CBNS circuits have been included.

Prediction of Blended Fuel Characteristics Through Regression Modelling

Abstract: Blended fuel attracted considerable attention for the environmental sustainability, mitigation of scarcity of non-renewable fuels and enhancement of property modification for the last few decades. Jatropha Curcas oil (JCO), a non-edible vegetable oil, can be utilized for the preparation of non-conventional alternative energy sources like biodiesel which may be blended with diesel fuel for better environmental sustainability. Initially, biodiesel is prepared from JCO with methanol through transesterification reaction maintaining optimized reaction parameters in the presence of biocatalyst. After that mathematical relationships between fuel properties and blended fuel have been established through regression analysis method for the prediction of fuel properties like density, kinematic viscosity, cloud point and flash point. The blended samples are prepared ranging from 10 to 60% (B10 to B60) for biodiesel-diesel fuel. From the experimental results, graph of each fuel property has been plotted and mathematical equation of each fuel property for biodiesel-diesel blends are approximated with their respective coefficient of determination (R2). The results of estimation show that blended fuel properties have linear relationships regarding density, kinematic viscosity, cloud point and flash point. The equations identified for the properties of blended fuels are prerequisites as input data research findings. From the estimation of mathematical regression equation based on experimental findings, prediction can be done for any fuel properties for any ratios of biodiesel-diesel blends. So mathematical understanding contributes a better pathway for finding out the properties of blended fuels which may help to reduce the scarcity of conventional fuels.