(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

The history, culture, current and future socioeconomic status and environmental sustainability of India and its people are intricately linked to the water resources which are available from dams. These water resources available through dams are one of the main sources available for the usage to industries, livestock, irrigation etc. and there is a critical need to ensure the safety of the water level at these dams against any natural or anthropogenic threats and to develop an effective Water Level Management system using IoT. This paper gives an outline for the development of an information system based on the existing systems with the utilization of some sensors and IoT. This paper also proposes a novel idea of collecting and sharing real-time information about water levels to an authorized central command center through far field communication. The authorized central command center then takes a call whether to release the water by opening dam gates or keep them closed. By doing so, the operation of dams all over the country is centralized and automatized. © 2018 IEEE.

Electrical Engineering

Proceedings - 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages, IoT-SIU 2018

Water Level Monitoring and Management of Dams using IoT

Bimodal composite structures conjugating both high hardness and fracture toughness can effectively counter erosion induced degradation. The present work successfully develops bimodal metal matrix composite claddings using facile and cost-effective microwave technique, exploiting the direct and effective coupling phenomenon of reinforcement with microwave irradiation. For comprehensiveness, unimodal micro-/nano-reinforced clads were also developed. Interestingly, unimodal micro-composite clad showed refined structure compared to nano counterpart which is due to the effective coupling of later with microwave irradiation causing particle dissociation into the matrix. Compared to unimodal claddings, bimodal showed the highest hardness and indentation fracture toughness. In addition to ex-situ reinforcements, intrinsically evolved secondary phases also effectively contributed towards improved mechanical properties. The better performance of the bimodal clad is related to reduced agglomeration and dissociation of the nanoparticles, refined microstructure and constructive interaction between micro and nano-reinforcements. The phenomenal mechanical properties of bimodal composite clad resulted in improved durability compared to unimodal composite claddings under different tribological test conditions. © 2018 Elsevier B.V.

Mechanical Engineering

Civil Engineering

Surface and Coatings Technology

Towards highly durable bimodal composite claddings using microwave processing

The generation of terahertz radiation using photoconductive antennas is becoming very popular. Several experimental and simulation studies have been performed to study the characteristics of the photoconductive antenna (PCA). Although various methods have been proposed to increase the radiated power from it, the radiated power remains very low. In this paper, we present an analytical study of improving the radiated power from a large aperture PCA using an external magnetic field source. The transit time behavior of the carriers is computed using the basic semiconductor carrier dynamics model, including the transient mobilities with the dependencies on the electric field and carrier's concentration. Analytical studies show that substantial enhancement in the radiated field can be achieved when such external magnetic field is applied. Furthermore, the polarity of the radiated field depends on the orientation of the applied magnetic field. The results obtained from analytical calculations exhibit similar behavior as reported in some experimental results. © 2017 IEEE.

World University root node

IEEE Transactions on Microwave Theory and Techniques

Magnetic Field-Assisted Radiation Enhancement from a Large Aperture Photoconductive Antenna

Face recognition in images is an active area of interest among the computer vision researchers. However, recognizing human face in an unconstrained environment, is a relatively less-explored area of research. Multiple face recognition in unconstrained environment is a challenging task, due to the variation of view-point, scale, pose, illumination and expression of the face images. Partial occlusion of faces makes the recognition task even more challenging. The contribution of this paper is two-folds: Introducing a challenging multi-face dataset (i.e., IIITS-MFace Dataset) for face recognition in unconstrained environment and evaluating the performance of state-of-the-art hand-designed and deep learning based face descriptors on the dataset. The proposed IIITS-MFace dataset contains faces with challenges like pose variation, occlusion, mask, spectacle, expressions, change of illumination, etc. We experiment with several state-of-the-art face descriptors, including recent deep learning based face descriptors like VGGFace, and compare with the existing benchmark face datasets. Results of the experiments clearly show that the difficulty level of the proposed dataset is much higher compared to the benchmark datasets. © 2018 IEEE.

Computer Science and Engineering

2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018

A Multi-Face Challenging Dataset for Robust Face Recognition

The attractiveness of a baby face image depends on the perception of the perceiver. However, several recent studies advocate the idea that human perceptual analysis can be approximated by statistical models. We believe that the cuteness of baby faces depends on the low level facial features extracted from different parts (e.g., mouth, eyes, nose) of the faces. In this paper, we introduce a new problem of classifying baby face images based on their cuteness level using supervised learning techniques. The proposed learning model finds the potential of a deep learning technique in measuring the level of cuteness of baby faces. Since no datasets are available to validate the proposed technique, we construct a dataset of images of baby faces, downloaded from the internet. The dataset consists of several challenges like different view-point, orientation, lighting condition, contrast and background. We annotate the data using some well-known statistical tools inherited from Reliability theory. The experiments are conducted with some well-known image features like Speeded Up Robust Feature (SURF), Histogram of Oriented Gradient (HOG), Convolutional Neural Network (CNN) on Gradient and CNN on Laplacian, and the results are presented and discussed. © 2017, Springer Science+Business Media, LLC.

Multimedia Tools and Applications

Measuring level of cuteness of baby images: a supervised learning scheme

We propose a unified method for recognizing human action and human related events in a realistic video. We use an efficient pipeline of (a) a 3D representation of the Improved Dense Trajectory Feature (DTF) and (b) Fisher Vector (FV). Further, a novel descriptor is proposed, capable of representing human actions and human related events based on the FV representation of the input video. The proposed unified descriptor is a 168-dimensional vector obtained from each video sequence by statistically analyzing the motion patterns of the 3D joint locations of the human body. The proposed descriptor is trained using binary Support Vector Machine (SVM) for recognizing human actions or human related events. We evaluate the proposed approach on two challenging action recognition datasets: UCF sports and CMU Mocap datasets. In addition to the two action recognition dataset, the proposed approach is tested on the Hollywood2 event recognition dataset. On all the benchmark datasets for both action and event recognition, the proposed approach has shown its efficacy compared to the state-of-the-art techniques. © 2017, Springer Science+Business Media, LLC.

Human action and event recognition using a novel descriptor based on improved dense trajectories

Analyzing the lifecycle of topics, that are present in user-generated text content, has emerged as a mainstream topic of social network research. The literature presently identifies topics on Twitter, a prominent online social network, as either individual hashtags, or a burst of keywords within a short span of time, or as latent concept spaces obtained from sophisticated text analysis mechanisms, such as Latent Dirichlet Allocation (LDA). The first and second approaches fail to recognize that topics do not restrict themselves to individual hashtags and are likely to span across (semantically related) keywords, while the third does not capture the user’s intended topics expressed via hashtags. In the current paper, we propose a novel methodology that addresses these shortcomings. We jointly utilize the temporal concurrency of the hashtags contained in given tweets and the latent concept space addressed by the tweet content, to identify groups of hashtags representing concept space—a “topic”—addressed by many tweets. A given topic, thus, is represented by a different set of representative hashtags at different times; the usage rate of the different hashtags change such that some hashtags gain prominence over others over time. Unlike the literature, where lifecycle analysis of one topic typically comprises of analyzing one hashtag, we analyze and characterize the lifecycle of a topic as a combination of multiple semantically and temporally related hashtags. We derive novel insights about lifecyle of topics: the inception and continuity of the topics over time (expressed over different hashtags), and how topics morph over hashtags, from one set of hashtags to another, before eventually dying down. © Springer International Publishing AG 2018.

Studies in Computational Intelligence

A semantic continuity based analysis of topic lifecycle on social networks

Background: Topic lifecycle analysis on social networks aims to analyze and track how topics are born from user-generated content, and how they evolve. Twitter researchers have no agreed-upon definition of topics; topics on Twitter are typically derived in the form of (a) frequently used hashtags, or (b) keywords showing sudden trends of large occurrence in a short span of time (“bursty keywords”), or (c) concepts latent within the tweets that are grouped using variations of semantic clustering techniques. Methods: In the current paper, we jointly model the hashtags present and the semantic concepts embedded in the content, which in turn helps us identify hashtag groups that define a “topic”—a concept space—that are used by a large number of tweets. Results: We observe that different hashtags belonging to a given cluster are more prominent compared to the others, at different times. We further observe that the participation and influence levels of the different users play important roles in determining which hashtag would be more prominent than the others at given times. We thus observe topics to often morph from one to the other (via morphing of dominant hashtags representing the same semantic concept space), rather than becoming extinct outright, which is a novel insight about topic lifecycles. We further present novel observations about the role of users in determining the lifecycle of discussion topics on Twitter. Conclusions: We infer that topic lifecycles are governed by user interests, and not by user influence, which is a key observation made by our work. © 2018, The Author(s).

Fulltext

Computational Social Networks

Assessing the role of participants in evolution of topic lifecycles on social networks

Homophily, the phenomenon of similar people getting connected to and being socially familiar with each other, is well-known on online social networks. Detection of user stance towards given topics, on online social networks, specifically Twitter, has emerged as a mainstream research topic. The current work provides insights into the impact of topic-specific stance similarity and social familiarity on social interaction dynamics. This is a novel and yet fundamental problem in social networks research, that has so far remained unexplored in the literature. Specifically, we address two key aspects. One, we investigate whether the smoothness (politeness) level of conversations between user pairs, relate with overall stance similarity (spanning across topics). Two, we examine the impact on interaction smoothness (politeness) with respect to social familiarity and topical stance-similarity. We propose a novel approach based on word embedding, to compare across users and across topics. We analyze the relationship between topical stance similarity, social familiarity and interaction politeness of users, with respect to specific familiarities between user pairs as well as social communities. © Springer International Publishing AG 2018.

Assessing the effects of social familiarity and stance similarity in interaction dynamics

Supercritical fluid extraction (SFE) process is the sustainable green process for the extraction. Mathematical modeling of SFE process is carried out using mass transfer resistances, which vary with the types of solute. In this paper, the effect of matrices such as leaves, flower concrete, flower bud, herb plant, shrub seed and vegetable matter is studied on extraction yield through different models. These models are solved using COMSOL Multiphysics 5.2 solver and results are validated with that of literature. Experimental data of each type of solute matrix are fitted in various models and best suited model is predicted. © 2017 Taylor & Francis.