Techno India

About: Techno India is a based out in . It is known for research contribution in the topics: Computer science & Cloud computing. The organization has 4724 authors who have published 4005 publications receiving 34112 citations.

Prediction of cutting process parameters in boring operations using artificial neural networks

Abstract: This study deals with the development of displacement of the tool (amplitude of vibration), cutting temperature and tool wear prediction model for boring process using artificial neural networks (A...

A novel design of a high speed hysteresis-based comparator in 90-nm CMOS technology

Abstract: Analog-to-digital circuit converts an analog signal having continuous-time and continuous-amplitude to a discrete-time and discrete-amplitude digital signal. A comparator is a vital block in any analog-to-digital circuit. The comparators play a crucial part in the analog to digital conversion. This paper puts forth the design of hysteresis comparator in 90-nm CMOS technology. The proposed comparator reduces the occurrence of noisy output and has high speed, in comparison to the conventional comparator. The circuit design and analysis has been done using Cadence.

The Control of Stress Induced Type I Diabetes Mellitus in Humans through the Hepatic Synthesis of Insulin by the Stimulation of Nitric Oxide Production

Abstract: The role of stress induced development of Type-1 diabetes mellitus (T1DM) as opposed to autoimmunity remains obscure. It has been reported that a stress induced protein, identified to be dermcidin isoform 2 (dermcidin) inhibited insulin synthesis in both the pancreatic β cells and the hepatic cells. As dermcidin effect could be neutralized by the increased production of systemic nitric oxide (NO), investigations were carried out to determine the feasibility of controlling stress induced T1DM through the neutralization of dermcidin by systemic increase of NO. To determine the role of plasma dermcidin level in T1DM subjects (n=45), if any, when the plasma dermcidin level were determined, it was found that the protein level was increased in 65% of the participating volunteers. Efforts were made to normalize the plasma glucose level (median=175 mg/dL) in these T1DM subjects by systemic increase of NO by applying a cotton pad containing 0.28mmol sodium nitroprusside on the abdominal skin. It was found that the systemic increase of NO level decreased the blood glucose level of 275 mg/dL (median) to 115 mg/dL (median) in these volunteers within 24 h with concomitant increase of plasma insulin level from 7.5 μunits/dL to 101 μunits/dL at the same time. The increase of plasma insulin level was accompanied by the decrease of dermcidin level of 124.5 nM to 18 nM with increase of NO from 0.43 ± 0.19 nM to 4.1 ± 1.56 nM. The results suggested that the stress induced T1DM by dermcidin could be controlled by the systemic increase of NO which in consequence led to increased synthesis of insulin.

Classification of EMG Signals Using Eigenvalue Decomposition-Based Time-Frequency Representation

Abstract: Electromyogram (EMG) signals are commonly used by doctors to diagnose abnormality of muscles. Manual analysis of EMG signals is a time-consuming and cumbersome task. Hence, this chapter aims to develop an automated method to detect abnormal EMG signals. First, authors have applied the improved eigenvalue decomposition of Hankel matrix and Hilbert transform (IEVDHM-HT) method to obtain the time-frequency (TF) representation of motor unit action potentials (MUAPs) extracted from EMG signals. Then, the obtained TF matrices are used for features extraction. TF matrix has been sliced into several parts and fractional energy in each slice is computed. A percentile-based slicing is applied to obtain discriminating features. Finally, the features are used as an input to the classifiers such as random forest, least-squares support vector machine, and multilayer perceptron to classify the EMG signals namely, normal and ALS, normal and myopathy, and ALS and myopathy, and achieved accuracy of 83%, 80.8%, and 96.7%, respectively. Classification of EMG Signals Using Eigenvalue Decomposition-Based TimeFrequency Representation