(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

This paper presents a design of a four-port, dual-band uniform circular array (UCA) to generate three orthogonal Orbital Angular Momentum (OAM) modes (OAM mode 0, +1 and-1) at two different frequency 2.45 GHz (ISM band/WLAN) and 5.4 GHz (WLAN). The proposed antenna is designed with the help of four 50-Ohm microstrip line radiator (MLR) having an annular slot on the ground plane. The effect of variation of the physical design parameters (like the length of 50-Ohm MLR, the radius of annular slot and annular slot width) on reflection coefficient and the radiation pattern is shown and discussed. © 2020 IEEE.

Electrical Engineering

World University root node

2020 7th International Conference on Signal Processing and Integrated Networks, SPIN 2020

Dual-band triple mode OAM generation using annular slot microstrip radiator

In this study, the extraction of carrot seed oil is carried out using supercritical CO2. Screening design was applied on operating parameters such as pressure, temperature, solvent flow rate, particle size and co-solvent to study the contribution of each parameter on extraction yield. Central composite design is employed on reduced parameters with operating ranges: 20–40 MPa (pressure), 50–70 °C (temperature), 5–15 g/min (solvent flow rate) and 0–10 wt\% (co-solvent). Extraction yield is varied from 2.8 to 12.9 wt\% of carrot seed. Experimental data are fitted well in the least-square regression model where optimized conditions are found as 40 MPa, 70 °C, 8.53 g/min and 5.87 wt\% corresponding to the maximum yield of 13.5 wt\%. Analysis of oil reveals the presence of saturated and unsaturated fatty acids. Principle fatty acid of carrot seed oil, i.e., oleic acid, is also optimized to maximize its extraction. The industrial-scale economic assessment is performed to study the feasibility of the supercritical fluid extraction of carrot seed oil. © 2019, Islamic Azad University (IAU).

Chemical Engineering

International Journal of Environmental Science and Technology

Supercritical CO2 extraction of carrot seed oil: screening, optimization and economic analysis

In this paper, a compact multiband CPW-fed monopole antenna is proposed. The designed antenna has a simple structure, which consists of a rectangular radiating patch with two symmetrically structured meandered line slots on both sides and a CPW-fed 50 Ω microstrip line. The designed antenna occupies only 23 × 23 mm2 size on a FR-4 substrate sheet which makes it a good candidate for wireless mobile communications. Triple-band functionality is realized by making slots on patch to form a meandered strip on both sides of the rectangular radiating patch. These triple bands are suitable for Bluetooth, WiMAX, and WLAN applications, which are operating at 2.4 GHz, 3.5 GHz, and 5.5 GHz, respectively. The fabricated and characterized antenna prototype is having a good omnidirectional and directional radiation pattern in both H-plane and E-plane for the specified bands, respectively. © 2019, Shiraz University.

Iranian Journal of Science and Technology - Transactions of Electrical Engineering

Compact Triple-Band CPW-Fed Monopole Antenna for Bluetooth/WiMAX/WLAN Applications

The use of ultrasound to enhance the regeneration of zeolite 13X for efficient utilization of thermal energy was investigated as a substitute to conventional heating methods. The effects of ultrasonic power and frequency on the desorption of water from zeolite 13X were analyzed to optimize the desorption efficiency. To determine and justify the effectiveness of incorporating ultrasound from an energy-savings point of view, an approach of constant overall input power of 20 or 25 W was adopted. To measure the extent of the effectiveness of using ultrasound, the ultrasonic-power-to-total power ratios of 0.2, 0.25, 0.4 and 0.5 were investigated and the results compared with those of no-ultrasound (heat only) at the same total power. To analyze the effect of ultrasonic frequency, identical experiments were performed at three nominal ultrasonic frequencies of ~28, 40 and 80 kHz. The experimental results showed that using ultrasound enhances the regeneration of zeolite 13X at all the aforementioned power ratios and frequencies without increasing the total input power. With regard to energy consumption, the highest energy-savings power ratio (0.25) resulted in a 24\% reduction in required input energy and with an increase in ultrasonic power, i.e. an increase in acoustic-to-total power ratio, the effectiveness of applying ultrasound decreased drastically. At a power ratio of 0.2, the time required for regeneration was reduced by 23.8\% compared to the heat-only process under the same experimental conditions. In terms of ultrasonic frequency, lower frequencies resulted in higher efficiency and energy savings, and it was concluded that the effect of ultrasonic radiation becomes more significant at lower ultrasonic frequencies. The observed inverse proportionality between the frequency and ultrasound-assisted desorption enhancement suggests that acoustic dissipation is not a significant mechanism to enhance mass transfer, but rather other mechanisms must be considered. © 2020 Elsevier B.V.

Mechanical Engineering

Fulltext

Ultrasonics Sonochemistry

Ultrasound-assisted regeneration of zeolite/water adsorption pair

In spite of the recent advancements of deep learning based techniques, automatic photo aesthetic assessment still remains a challenging computer vision task. Existing approaches used to focus on providing a single aesthetic score or category ("good" or "bad") of photograph, rather than quantifying "goodness" or "badness". The existing algorithms often ignore the importance of different attributes contributing to the artistic quality of the photograph. To obtain the human-interpretability of aesthetic score of photo, we advocate learning the aesthetic attributes alongwith the prediction of the general aesthetic score. We propose a multi-task deep CNN, that collectively learns aesthetic attributes alongwith a general aesthetic score for the photograph. To understand the mathematical representation of the attributes in the proposed model, a visualization technique is proposed using back propagation of gradients. These visualization of attributes correspond to the location of objects in the images in order to find out which part of an image "triggers" the classification outcome, thus providing the insights about the model's understanding of these attributes. This paper proposes an aesthetic feature vector based on the relative foreground position of the object in the image. The proposed aesthetic features outperform the state-of-art methods especially for Rule of Thirds attribute. © 2020 The Institution of Engineering and Technology.

Computer Science and Engineering

IET Image Processing

Measuring photography aesthetics with deep CNNs

Human Activity Recognition in RGB-D videos has been an active research topic during the last decade. However, only a few efforts have been made, for recognizing human activity in RGB-D videos where several performers are performing simultaneously. In this paper we introduce such a challenging dataset with several performers performing the activities simultaniously. We present a novel method for recognizing human activities performed simultaniously in the same videos. The proposed method aims in capturing the motion information of the whole video by producing a dynamic image corresponding to the input video. We use two parallel ResNet-101 architectures to produce the dynamic images for the RGB video and depth video separately. The dynamic images contain only the motion information of the whole frame, which is the main cue for analyzing the motion of the performer during action. Hence, dynamic images help recognizing human action by concentrating only on the motion information appeared on the frame. We send the two dynamic images through a fully connected layer for classification of activity. The proposed dynamic image reduces the complexity of the recognition process by extracting a sparse matrix from a video, while preserving the motion information required for activity recognition, and produces comparable results with respect to the state-of-the-art. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Multimedia Tools and Applications

Human activity recognition in RGB-D videos by dynamic images

Stochastic gradient descent (SGD) is one of the core techniques behind the success of deep neural networks. The gradient provides information on the direction in which a function has the steepest rate of change. The main problem with basic SGD is to change by equal-sized steps for all parameters, irrespective of the gradient behavior. Hence, an efficient way of deep network optimization is to have adaptive step sizes for each parameter. Recently, several attempts have been made to improve gradient descent methods such as AdaGrad, AdaDelta, RMSProp, and adaptive moment estimation (Adam). These methods rely on the square roots of exponential moving averages of squared past gradients. Thus, these methods do not take advantage of local change in gradients. In this article, a novel optimizer is proposed based on the difference between the present and the immediate past gradient (i.e., diffGrad). In the proposed diffGrad optimization technique, the step size is adjusted for each parameter in such a way that it should have a larger step size for faster gradient changing parameters and a lower step size for lower gradient changing parameters. The convergence analysis is done using the regret bound approach of the online learning framework. In this article, thorough analysis is made over three synthetic complex nonconvex functions. The image categorization experiments are also conducted over the CIFAR10 and CIFAR100 data sets to observe the performance of diffGrad with respect to the state-of-the-art optimizers such as SGDM, AdaGrad, AdaDelta, RMSProp, AMSGrad, and Adam. The residual unit (ResNet)-based convolutional neural network (CNN) architecture is used in the experiments. The experiments show that diffGrad outperforms other optimizers. Also, we show that diffGrad performs uniformly well for training CNN using different activation functions. The source code is made publicly available at https://github.com/shivram1987/diffGrad. © 2012 IEEE.

IEEE Transactions on Neural Networks and Learning Systems

DiffGrad: An Optimization Method for Convolutional Neural Networks

In the present work, we devised a facile, and environmental imprinting and hot-water treatments for developing a durable self-cleaning aluminum surface. The micro and nanostructured surfaces were composed of pillars and ellipsoidal flakes, respectively. The fluorination (vapor deposition) of surfaces imparted superhydrophobicity (θs &gt; 150°), with high adhesion observed for micro-patterned surface, while nano and hierarchical surfaces exhibited low hysteresis and tilting angles (θ &lt; 5°). The nanostructured (ΔE=-0.3J/m) surface has shown the prevalence of stable Cassie state compared to hierarchical (ΔE=-0.07J/m) counterpart. The nanostructured surface has also exhibited higher durability under static and dynamic conditions than the hierarchical counterpart. © 2020 Elsevier B.V.

Materials Letters

Sustainable approach for the development of durable superhydrophobic metallic surfaces

Present communication is an attempt to explore the best PV technology suitable for PVT solar drying system through environmental and economic feasibility. Experimental setup for one of the PV technology (c-Si) has been taken for experimental validation with theoretical model. Further, the environmental feasibility and environ economic evaluation for drying system having roof with various PV namely, c-Si, p-Si or m-Si, a-Si, CdTe, and CIGS have been evaluated through energy matrices (EPBT, EPF, and LCCE), CO2 emission, net CO2 mitigation and carbon credit earned which is based on energy and exergy output of the system on yearly basis. The economic analysis has been carried out with experimental data taken on c-Si PVT drying system by considering grapes as a crop. Cogeneration efficiency has been calculated for different PV technologies and found to be highest for c-Si PV technology that is, 61.71\%, 77.98\%, and 12.25\% based on energy (electrical and thermal) gain, equivalent (overall) energy gain, and exergy gain, respectively. LCCE on energy basis reveals that for the five-year system life, c-Si and CIGS have equal efficiency but after five years, c-Si is best. Cogeneration efficiency of system on the basis of energy (electrical + thermal) gain, equivalent (overall) energy gain and overall exergy gain found to be maximum with c-Si PV technology that is, 61.71\%, 77.98\%, and 12.25\%, respectively. © 2020 American Institute of Chemical Engineers

Environmental Progress and Sustainable Energy

Environmental and economic sustainability of PVT drying system: A heat transfer approach

The human attention span is continuously decreasing, and the amount of time a person wants to spend on reading is declining at an alarming rate. Therefore, it is imperative to provide a quick glance of important news by generating a concise summary of the prominent news article, along with the most intuitive headline in line with the summary. When humans produce summaries of documents, they not only extract phrases and concatenate them but also produce new grammatical phrases or sentences that coincide with each other and capture the most significant information of the original article. Humans have an incredible ability to create abstractions; however, automatic summarization is a challenging problem. This paper aims to develop an end-to-end methodology that can generate brief summaries and crisp headlines that can capture the attention of readers and convey a significant amount of relevant information. In this paper, we propose a novel methodology known as SHEG, which is designed as a hybrid model. It works by integrating both extractive and abstractive mechanisms using a pipelined approach to produce a concise summary, which is then used for headline generation. Experiments were performed on publicly available datasets, viz. CNN/Daily Mail, Gigaword, and NEWSROOM. The results obtained validate our approach and demonstrate that the proposed SHEG model is effectively producing a concise summary as well as a captivating and fitting headline. © 2020, Springer-Verlag London Ltd., part of Springer Nature.