Indian Institute of Technology Indore

About: Indian Institute of Technology Indore is a education organization based out in Indore, Madhya Pradesh, India. It is known for research contribution in the topics: Fading & Support vector machine. The organization has 1606 authors who have published 4803 publications receiving 66500 citations.

Max-Max User-Relay Selection Scheme in Multiuser and Multirelay Hybrid Satellite-Terrestrial Relay Systems

Abstract: We consider a hybrid satellite-terrestrial relay system that employs a multiantenna satellite to communicate with multiple users via multiple amplify-and-forward relays. We employ a max-max user-relay selection scheme to minimize the outage probability of the system. Considering both variable and fixed gain relaying protocols, we derive exact closed-form expressions of outage probability and their asymptotic behavior over independent and identically distributed fading channels for each hop. Numerical and simulation results validate our analysis and highlight the performance gains of the proposed scheme.

Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease: A Review

Abstract: Alzheimer’s disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer’s. Many novel approaches are proposed by researchers for classification of Alzheimer’s disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer’s is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer’s with possible future directions.

A review of the effect of the composition of biodiesel on NOx emission, oxidative stability and cold flow properties

Abstract: This paper will review and attempt to discover the ideal fatty acid composition of biodiesel which exhibits lower NO x emissions, better oxidative stability and cold flow properties. The physicochemical properties of biodiesel strongly depend on their fatty acid composition. A high percentage of unsaturated fatty acid in biodiesel is correlated with higher NO x emissions, poor oxidative stability and better cold flow properties. The presence of saturated fatty acids (SFA), in particular the long chain type, exhibits good oxidative stability and produces lower NO x emissions. SFA do however demonstrate poor cold flow properties. The polyunsaturated fatty acids (PUFA) exhibit better cold flow properties but produces higher NO x emissions and poorer oxidative stability. The ideal requirements of biodiesel properties impose contradictory conditions on the fatty acid composition of biodiesel. For example, coconut and palm kernel oils which have a high percentage of lauric fatty acid are reported to circumvent all three drawbacks of biodiesel. The monounsaturated fatty acids (MUFA), specifically oleic acids, a major component in almost all biodiesel, display the positive characteristics of both SFA and PUFA. Biodiesel properties can therefore be improved by using various remedial methods including genetic engineering, reformulated biodiesel and additives.