(SoE) School of Engineering

<p>Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.</p>

Shiv Nadar University

Scratchpad memory (SPM) is considered a useful component in the memory hierarchy, solely or along with caches, for meeting the power and energy constraints as performance ceases to be the sole criteria for processor design. Although the efficiency of SPM is well known, its use has been restricted owing to difficulties in programmability. Real applications usually have regions that are amenable to exploitation by either SPM or cache and hence can benefit if the two are used in conjunction. Dynamically adjusting the local memory resources to suit application demand can significantly improve the efficiency of the overall system. In this article, we propose a compiler technique to map application data objects to the SPM-cache and also partition the local memory between the SPM and cache depending on the dynamic requirement of the application. First, we introduce a novel graph-based structure to tackle data allocation in an application. Second, we use this to present a data allocation heuristic to map program objects for a fixed-size SPM-cache hybrid system that targets whole program optimization. We finally extend this formulation to adapt the SPM and cache sizes, as well as the data allocation as per the requirement of different application regions. We study the applicability of the technique on various workloads targeted at both SPM-only and hardware reconfigurable memory systems, observing an average of 18% energy-delay improvement over state-of-the-art techniques. © 2016 ACM.

World University root node

ACM Transactions on Design Automation of Electronic Systems

Partitioning and data mapping in reconfigurable cache and scratchpad memory-based architectures

In present study, study has taken attempt to show the analysis of N-number of photovoltaic thermal (PVT)-compound parabolic concentrator (CPC) water collectors connected in series water collectors under constant collection temperature mode for the point of user's demand unlike constant flow rate mode. The performance has been examined for thermal energy, thermal exergy and overall thermal energy of partially covered (N) number of Photovoltaic thermal-compound parabolic concentrator collectors connected in series has been analyzed. Here, the collector of present system is partially covered with 50% of PV module. The thermal modelling has been developed with basic energy balance equation of each component of proposed system. The outlet at Nth of partially covered Photovoltaic thermal (PVT)-compound parabolic concentrator (CPC) collectors has been obtained. The expression of analytical temperature dependent electrical efficiency of proposed system has been derived. The analysis has been carried out for clear day condition in month of January at New Delhi, India. The maximum thermal energy has been found 18.14 kWh per day and electrical energy has been found 1.30 kWh per day with constant outlet collection temperature at 67 °C. © 2016 IEEE.

Mechanical Engineering

International Conference on Emerging Trends in Electrical, Electronics and Sustainable Energy Systems, ICETEESES 2016

Energy analysis of partially covered number (N) of photovoltaic thermal-compound parabolic concentrator collectors connected in series at constant collection temperature mode

Existing systems deliver high accuracy and F1-scores for detecting paraphrase and semantic similarity on traditional clean-text corpus. For instance, on the clean-text Microsoft Paraphrase benchmark database, the existing systems attain an accuracy as high as 0.8596. However, existing systems for detecting paraphrases and semantic similarity on user-generated short-text content on microblogs such as Twitter, comprising of noisy and ad hoc short-text, needs significant research attention. In this paper, we propose a machine learning based approach towards this. We propose a set of features that, although well-known in the NLP literature for solving other problems, have not been explored for detecting paraphrase or semantic similarity, on noisy user-generated short-text data such as Twitter. We apply support vector machine (SVM) based learning. We use the benchmark Twitter paraphrase data, released as a part of SemEval 2015, for experiments. Our system delivers a paraphrase detection F1-score of 0.717 and semantic similarity detection Fl-score of 0.741, thereby significantly outperforming the existing systems, that deliver F1-scores of 0.696 and 0.724 for the two problems respectively. Our features also allow us to obtain a rank among the top-10, when trained on the Microsoft Paraphrase corpus and tested on the corresponding test data, thereby empirically establishing our approach as ubiquitous across the different paraphrase detection databases. © 1963-2018 ACL.

Computer Science and Engineering

COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers

A paraphrase and semantic similarity detection system for user generated short-text content on microblogs

Energy efficiency have always been a priority while designing wireless sensor networks. Introduction of mobile agent technology in wireless sensor networks for collaborative signal and information processing has provided the new scope for efficient processing and aggregation of data. Mobile agent based distributed computing paradigm offers numerous benefits over the existing and commonly used client/server computing paradigm in wireless sensor networks. Mobile agent performs the task of data processing and data aggregation at the node level rather than at the sink, thus, eliminating the redundant network overhead. One of the most important issues in mobile agent based paradigm is planning of an itinerary for agent traversal. In this paper, we have proposed a dynamic mobile agent based data aggregation approach that takes into consideration energy efficiency, network lifetime, end to end delay and aggregation ration while making the decision for migration of agent in multihop sensor network. As our approach focuses on finding the most informative route by traversing comparatively less number of nodes consequently mobile agent takes less time to return to processing element, thus, exhibiting lower delay. © 2016, Springer Science+Business Media New York.

Wireless Personal Communications

Energy Efficient Data Aggregation in Mobile Agent Based Wireless Sensor Network

Pedestrian fatalities from 2001 to 2009 in Delhi, India show that pedestrians have the largest share in total road fatalities. Though, facilities like pedestrian underpasses and overpasses are provided to cross the road at several locations, all too often pedestrians do not use them. This study was designed to analyze the pedestrians’ perceptions for underutilization of pedestrian facilities. Five hundred pedestrians were interviewed at seven different locations based on available pedestrian facilities such as zebra crossing, pedestrian underpasses and overpasses. Analysis of usage of these facilities showed that usage of underpasses and zebra crossing is less by female respondents. To know the safety and convenience perception of pedestrians with respect to a specific facility, ordinal logit model was used in this study. Results showed that convenience perception is statistically significant (at 95% CI) for the use of zebra crossings. The conclusions drawn from this study can be used to improve the state of pedestrian facilities in Delhi for prioritization of the facilities based on stated preferences. © 2016 Elsevier Ltd

Civil Engineering

Transportation Research Part F: Traffic Psychology and Behaviour

Pedestrians perceptions for utilization of pedestrian facilities – Delhi, India

Traffic crash fatalities from 2006 to 09 in Delhi, India show that pedestrians have the largest share in total road fatalities. Perception of risk provides important information to identify potential crash risk. Thus, the main objective of this paper is to explore the pedestrian perception of risk in getting involved in a traffic crash in different locations and the role of built environment features in risk perception of pedestrians. Locations having a high number of pedestrian fatal crashes were identified using GIS map of Delhi. Pedestrian perceptions of risk at forty-five actual crash sites were collected through questionnaire survey. Risk perception of pedestrians of built environment features were analyzed based on pedestrian demographic characteristics. Ordered logit model was used to examine the influence of these variables on risk perception of respondent's neighborhood and of the location where the survey was conducted. Seventy percent respondents perceive the neighborhoods where they reside having higher risk than the actual crash locations. Several factors such as gender, number of lanes, sidewalk width, sidewalk maintenance, traffic speed and traffic volume were significantly associated with perceived risk. Survey sites were further categorized into four groups i.e. foot of flyover, four-way junctions below flyover, midblocks and intersections to find the impact of characteristics of locations on pedestrians' risk perception of different locations. Four-way junctions below flyover were found to have higher perceived risk compared to other locations. © 2016 Elsevier Ltd.

Safety Science

Pedestrians risk perception of traffic crash and built environment features - Delhi, India

This paper presents complete simulation, modeling and control of threephase grid connected solar Photo Voltaic (PV) module including evaluation of various power quality issues. All the steps involved in complete simulation of three-phase grid connected PV module are presented and discussed in detail. Perturb and Observe (P&O) method has been used for Maximum Power Point Tracking (MPPT). In the proposed model DC bus voltage control, harmonic mitigation and power factor control are discussed as power quality issues. The simulation results are shown in the graphical waveforms and are performed in MATLAB using SIMULINK environment and PSB toolboxes. Copyright © 2016 Institute of Advanced Engineering and Science. All rights reserved.

Electrical Engineering

International Journal of Electrical and Computer Engineering

Evaluation of power quality issues in grid connected PV systems

In order to meet stringent environmental, safety and performance requirements from respective regulatory bodies, various technology-based industries are promoting the use of advanced carbon nanotube (CNT) reinforced lightweight and high strength polymer nanocomposites (PNCs) as a substitute to conventional materials both in structural and non-structural applications. The superior mechanical properties of PNCs made up of CNTs or bundles of CNTs can be attributed to the interfacial interaction between the CNTs and matrix, CNT's morphologies and to their uniform dispersion in the matrix. In PNCs, CNTs physically bond with polymeric matrix at a level where the assumption of continuum level interactions is not applicable. Modeling and prediction of mechanical response and failure behavior of CNTs and their composites becomes a complex task and is dealt with the help of up-scale modeling strategies involving multiple spatial and temporal scales in hierarchical or concurrent manner. Firstly, the article offers an insight into various modeling techniques in studying the mechanical response of CNTs; namely, equivalent continuum approach, quasi-continuum approach and molecular dynamics (MD) simulation. In the subsequent steps, these approaches are combined with analytical and numerical micromechanics models in a multiscale framework to predict the average macroscopic response of PNCs. The review also discusses the implementation aspects of these computational approaches, their current status and associated challenges with a future outlook.

Progress in Aerospace Sciences

Modeling of carbon nanotubes and carbon nanotube-polymer composites

This paper introduces two novel motion based features for recognizing human facial expressions. The proposed motion features are applied for recognizing facial expressions from a video sequence. The proposed bag-of-words based scheme represents each frame of a video sequence as a vector depicting local motion patterns during a facial expression. The local motion patterns are captured by an efficient derivation from optical flow. Motion features are clustered and stored as words in a dictionary. We further generate a reduced dictionary by ranking the words based on some ambiguity measure. We prune out the ambiguous words and continue with key words in the reduced dictionary. The ambiguity measure is given by applying a graph-based technique, where each word is represented as a node in the graph. Ambiguity measures are obtained by modelling the frequency of occurrence of the word during the expression. We form expression descriptors for each expression from the reduced dictionary, by applying an efficient kernel. The training of the expression descriptors are made following an adaptive learning technique. We tested the proposed approach with standard dataset. The proposed approach shows better accuracy compared to the state-of-the-art. © 2016 ACM.

ACM International Conference Proceeding Series

Recognizing facial expressions using novel motion based features

Language usage behavior of users evolves over time, as they interact on social media such as Twitter. We study the evolution of language usage behavior of individuals, across topics, on microblogs. We propose Man-O-Meter, a framework to model such evolution. We model the evolution using a combination of three dimensions: (a) time, (b) content (topics) and (c) influence flow over social relationships. We assert the goodness of our approach, by predicting ranks of experts, with respect to their influence in their respective expertise category, using the change in language used in time. We apply our framework on 2, 273 influential microbloggers on Twitter, across 62 categories, spanning over 10 domains. Our work is applicable in predicting activity and influence, interest evolution, job change and community change expected to happen to a user, in future. © Springer International Publishing Switzerland 2016.