International Institute of Information Technology, Hyderabad

About: International Institute of Information Technology, Hyderabad is a education organization based out in Hyderabad, India. It is known for research contribution in the topics: Computer science & Authentication. The organization has 2048 authors who have published 3677 publications receiving 45319 citations. The organization is also known as: IIIT Hyderabad & International Institute of Information Technology (IIIT).

Blind modulation classification in the presence of carrier frequency offset

Abstract: Blind modulation classification deals with identification of modulation formats from received signal without the knowledge of the type of modulation transmitted. The problem becomes more challenging in real world scenarios when there are synchronization errors such as frequency offset and timing offset, and multi-path fading. In this paper, we consider the problem of modulation classification in the presence of carrier frequency offset. Several algorithms, based on higher-order cumulants of differentially processed received signal and cyclic cumulants of the received signal, have been recently proposed for blind classification in the presence of frequency offset. Since the variance of the estimates of these features is high, more data and/or high SNR is required for good classification performance. We consider a 9-class problem and propose a hierarchical classification using a combination of moments of the received and differentially processed received signal, unlike in where they only use the cumulants of the differentially processed received signal. The motivation for our approach comes from the fact that for a given data size, the estimates of the moments of the received signal have less variance compared to those of differentially processed received signal. Simulations are used to illustrate the performance of the proposed algorithm. The results show that our approach yields better detection probability than that of which considers 10-class problem of which 8 are common to our class.

Effect of building envelope on thermal environmental conditions of a naturally ventilated building block in tropical climate

Abstract: This article presents the effect of envelope measures on thermal environmental conditions of a naturally ventilated building block in composite climate of India. Envelope measures such as roof U-value, wall U-value, glass U-value and glass solar heat gain coefficient, roof reflectance and two combinations of these measures (total seven cases) are analysed as recommended by National Energy Conservation Building Code-India (ECBC) through prescriptive route. Subject building is modelled and simulated using dynamic thermal simulation tool. Two approaches of thermal comfort have been used in this study such as heat balance model based on laboratory study and adaptive model of thermal comfort based on field findings. Thermal environmental conditions are evaluated based on operative temperature with and without considering the effect of thermal adaptation. This study found that comfortable temperature varies from 20.3℃ to 31.5℃ based on calculation of comfortable temperature through comfort temperature equation ...

Deep Expander Networks: Efficient Deep Networks from Graph Theory

Abstract: Efficient CNN designs like ResNets and DenseNet were proposed to improve accuracy vs efficiency trade-offs. They essentially increased the connectivity, allowing efficient information flow across layers. Inspired by these techniques, we propose to model connections between filters of a CNN using graphs which are simultaneously sparse and well connected. Sparsity results in efficiency while well connectedness can preserve the expressive power of the CNNs. We use a well-studied class of graphs from theoretical computer science that satisfies these properties known as Expander graphs. Expander graphs are used to model connections between filters in CNNs to design networks called X-Nets. We present two guarantees on the connectivity of X-Nets: Each node influences every node in a layer in logarithmic steps, and the number of paths between two sets of nodes is proportional to the product of their sizes. We also propose efficient training and inference algorithms, making it possible to train deeper and wider X-Nets effectively. Expander based models give a 4% improvement in accuracy on MobileNet over grouped convolutions, a popular technique, which has the same sparsity but worse connectivity. X-Nets give better performance trade-offs than the original ResNet and DenseNet-BC architectures. We achieve model sizes comparable to state-of-the-art pruning techniques using our simple architecture design, without any pruning. We hope that this work motivates other approaches to utilize results from graph theory to develop efficient network architectures.

Accuracy Enhancement in a Fuzzy Expert Decision Making System Through Appropriate Determination of Membership Functions and Its Application in a Medical Diagnostic Decision Making System

Abstract: The paper attempts to improve the accuracy of a fuzzy expert decision making system by tuning the parameters of type-2 sigmoid membership functions of fuzzy input variables and hence determining the most appropriate type-1 membership function. The current work mathematically models the variability of human decision making process using type-2 fuzzy sets. Moreover, an index of accuracy of a fuzzy expert system has been proposed and determined analytically. It has also been ascertained that there exists only one rule in the rule base whose associated mapping for the ith linguistic variable maps to the same value as the maximum value of the membership function for the ith linguistic variable. The improvement in decision making accuracy was successfully verified in a medical diagnostic decision making system for renal diagnostic applications. Based on the accuracy estimations applied over a set of pathophysiological parameters, viz. body mass index, glucose, urea, creatinine, systolic and diastolic blood pressure, appropriate type-1 fuzzy sets of these parameters have been determined assuming normal distribution of type-1 membership function values in type-2 fuzzy sets. The type-1 fuzzy sets so determined have been used to develop an FPGA based smart processor. Using the processor, renal diagnosis of patients has been performed with an accuracy of 98.75%.

Mobility tolerant TDMA based MAC protocol for WSN

Abstract: Recent advancements in wireless communications and electronics have enabled the development of low cost sensor networks. Among the protocols of wireless Sensor Networks (WSN), Medium Access Control (MAC) protocols are given more priority as traditional MAC protocols are not suitable for Wireless communication. As many of the MAC protocols consider the sensor nodes to be stationary, and when these protocols are used in mobile environment the network performance decreases. In this paper, we introduce a new TDMA based MAC protocol which can be used in mobile wireless sensor network. This protocol uses TDMA based MAC scheme where the time will be divided into frames and then time slots. These slots are further divided into sections as channel request (CR), channel allocation (CA) and data section. We will also design an algorithm considering the energy efficiency of a wireless sensor network. Therefore the life time of the sensor nodes deployed in a wireless sensor network will be increased.