(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

In this work, we investigated the performance of single input single output (SISO) downlink channel considering 5G new radio (NR). A number of parameters such as different modulation schemes, channel coding with varying code rates, scalable numerology μ and 3GPP channel models have been considered for evaluation. In addition, the minimum mean square error-interference rejection combining (MMSE-IRC) technique for interference mitigation and bit error rate (BER) performance is analyzed and presented. We also compared the sum-rate performance of LTE and 5G NR. It is observed that MMSE-IRC receiver successfully mitigates the interferences compared to only MMSE based receiver. Simulation results also show performance improvement over various parameters like sum-rate, interference mitigation and BER compared to prior technologies i.e. 4G-LTE, WiMAX, etc. © 2020 IEEE.

World University root node

Electrical Engineering

2020 IEEE 3rd 5G World Forum, 5GWF 2020 - Conference Proceedings

Performance of Downlink SISO NR System using MMSE-IRC Receiver

Life Sciences

(SoNS) School of Natural Sciences

(SME) School of Management and Entrepreneurship

Methods in Molecular Biology

Preface

To characterize the polycyclic aromatic hydrocarbons (PAHs) in PM2.5 (particles with an aerodynamic diameter ≤ 2.5 µm) in Delhi, the national capital of India and one of the most polluted megacities in the world, we conducted a comprehensive field campaign at six sampling sites in different areas during winter and summer. Both the PM2.5 and PAH concentrations exhibited seasonal variations, with higher values during winter (356 ± 136 µg m–3 and 75.1 ± 50.2 ng m–3 for the PM2.5 and PAHs, respectively) than summer (268 ± 94 µg m–3 and 10.4 ± 8.5 ng m–3, respectively). Additionally, the maximum winter concentrations were found in the urban industrial-cum-residential area (430 ± 104 µg m–3 and 124.5 ± 70.7 ng m–3 for the PM2.5 and PAHs, respectively). Among the PAHs, benzo[ghi]perylene displayed the highest ambient concentration (14.3 ± 7.4 ng m–3 during winter), followed by indeno[1,2,3-cd]pyrene (13.1 ± 7.3 ng m–3 during winter), at the majority of the sampling sites. Additionally, based on the benzo[a]pyrene-equivalent concentrations at the six sites, we estimated the PAH-associated incremental lifetime cancer risk (ILCR) in the entire study area to be 423 per 1 million persons, which exceeds the World Health Organization (WHO) limit. Source apportionment performed with the Chemical Mass Balance Model version 8.2 (CMB8.2) revealed that emissions from vehicles, municipal waste burning and biomass burning contributed 62\%, 15\% and 11\% to the total PAH mass, respectively. Our results indicate that PM2.5-bound PAHs in Delhi will continue to pose serious health risks without collective initiatives for pollution control from scientific, policy-making and regulatory bodies. ©The Author(s).

Civil Engineering

Fulltext

Aerosol and Air Quality Research

Spatio-seasonal concentrations, source apportionment and assessment of associated human health risks of pm2.5-bound polycyclic aromatic hydrocarbons in delhi, india

This Letter proposes an automated method for the detection and suppression of muscle artefacts (MAs) in the single-channel electroencephalogram (EEG) signal based on variational mode decomposition (VMD) and zero crossings count threshold criterion without the use of reference electromyogram (EMG). The proposed method involves three major steps: decomposition of the input EEG signal into two modes using VMD; detection of MAs based on zero crossings count thresholding in the second mode; retention of the first mode as MAs-free EEG signal only after detection of MAs in the second mode. The authors evaluate the robustness of the proposed method on a variety of EEG and EMG signals taken from publicly available databases, including Mendeley database, epileptic Bonn database and EEG during mental arithmetic tasks database (EEGMAT). Evaluation results using different objective performance metrics depict the superiority of the proposed method as compared to existing methods while preserving the clinical features of the reconstructed EEG signal. © 2020 Institution of Engineering and Technology. All rights reserved.

Healthcare Technology Letters

Effective automated method for detection and suppression of muscle artefacts from single-channel EEG signal

Opportunistic maintenance approaches deal with performing group preventive maintenance (PM) on the other units in a series system due to the intervention of any scheduled PM of a component. Simultaneous maintenance actions show better economic performance due to the direct reduction of downtime costs and production losses. However, it is uneconomical to perform maintenance on all units simultaneously. To address this issue, various simulation and optimization approaches including Markov chains, genetic algorithm etc. have been applied in order to achieve optimum solutions in group maintenance models. However, most of these strategies suffer from intractability as the problem size increases. In the present paper, we develop an efficient opportunistic grouping methodology for the multi-unit series system while considering imperfect preventive maintenance. The aim is to obtain an optimum PM interval and grouping of units to minimize the expected total system maintenance cost per unit time during the mission. A recently developed meta-heuristic named ‘Jaya algorithm’ is applied to optimize the objective function. The effectiveness of the proposed approach is examined with three maintenance models: single unit model, mono-group model and the proposed opportunistic group model. Results reveal that the proposed group maintenance model results in 19\% cost savings as compared to the mono-group model and 71\% compared to the single component maintenance model. © 2019, Operational Research Society of India.

Mechanical Engineering

OPSEARCH

An opportunistic group maintenance model for the multi-unit series system employing Jaya algorithm

Jaya algorithm has recently been proposed, which is simple and efficient meta-heuristic optimization technique and has received a great attention in the world of optimization. It has been successfully applied to some thermal, design and manufacturing associated optimization problems. This paper aims to analyze the performance of Jaya algorithm for permutation flow-shop scheduling problem which is a well-known NP-hard optimization problem. The objective is to minimize the makespan. First, to make Jaya algorithm adaptive to the problem, a random priority is allocated to each job in a permutation sequence. Second, a job priority vector is converted into job permutation vector by means of Largest Order Value (LOV) rule. An exhaustive comparative study along with statistical analysis is performed by comparing the results with public benchmarks and other competitive heuristics. The key feature of Jaya algorithm of simultaneous movement towards the best solution and going away from the worst solution enables it to avoid being trapped in the local optima. Furthermore, the uniqueness of Jaya algorithm compared with any other evolutionary based optimization technique is that it is totally independent of specific parameters. This substantially reduces the computation effort and numerical complexity. Computational results reveal that Jaya algorithm is efficient in most cases and has considerable potential for permutation flow-shop scheduling problems. © 2020 by the authors; licensee Growing Science, Canada.

International Journal of Industrial Engineering Computations

A discrete jaya algorithm for permutation flow-shop scheduling problem

Stem cell research has come into prominence because of its applications in assisted reproductive technology and the treatment of deadly diseases. In teleost fishes, spermatogonial stem cells have been effectively used to produce surrogate gametes and progeny through germ cell transplantation technique. The present study is the first report of an innovative application of stem cell therapy in fish species for revitalising the reproductive competence of senescent individuals. Senescent male goldfish, Carassius auratus, approximately 10 years of age were procured from a fish-breeding farm and were reared locally in the lab for an additional two years. The senescence of the individuals was then evaluated and confirmed using histological analysis, gonadal index assessment, and germ-cell specific vasa gene expression. Analyses revealed absence of spermatogonial cells and other germ cells in the testes of the senescent fish (n = 5). Spermatogonial cells from sexually immature C. auratus male donor were isolated using discontinuous percoll gradients, labelled with the fluorescent dye PKH-26, and transplanted into the gonads of senescent C. auratus males through urogenital papilla. Six months after the transplant, spermatozoa were collected through applying gentle manual pressure on the abdomen and were observed under a microscope. All C. auratus males with the transplant had produced spermatozoa from the transplanted cells. This was confirmed by the retention of PKH-26 in the spermatozoa and diagnostic SSR locus. Gravid C. auratus females were artificially inseminated with the spermatozoa of those senescent males and natural spawning was allowed. As a result viable progeny were produced. These observation suggests that the reproductive competence of senescent male fishes can be revitalised through spermatogonial stem cell therapy to produce functional gametes. © 2020 Majhi et al.

Computer Science and Engineering

PeerJ

Resumption of donor-origin spermatogenesis in senescent goldfish Carassius auratus (Linnaeus, 1758) following spermatogonial cell therapy

Biomedical image retrieval is a challenging problem due to the varying contrast and size of structures in the images. The approaches for biomedical image retrieval generally rely on the feature descriptors to characterize the images. The feature descriptor of query image is compared with the descriptors of images from the database, to find the best matches. Several hand-crafted feature descriptors have been proposed so far for biomedical image retrieval by exploiting the local relationship of neighboring image pixels. It is observed in the literature that the local bit-plane decoded features are well suited for this retrieval task. Moreover, in recent past, it is also observed that the convolutional neural network-based features such as AlexNet, Vgg16, GoogleNet and ResNet perform well in many computer vision-related tasks. Motivated by the success of the deep learning-based approaches, this paper proposes a local bit-plane decoding-based AlexNet descriptor (LBpDAD) for biomedical image retrieval. The proposed LBpDAD is computed by max-fusing the ReLU operated feature maps of pre-trained AlexNet at a particular layer, obtained from the original and local bit-plane decoded images. The proposed approach is also compared with Vgg16, GoogleNet and ResNet models. The experiments on the proposed method over three benchmark biomedical databases of different modalities such as MRI, CT and microscopic show the efficacy of the proposed descriptor. © 2019, Springer-Verlag London Ltd., part of Springer Nature.

Neural Computing and Applications

Local bit-plane decoded convolutional neural network features for biomedical image retrieval

This paper proposes a novel technique for single image dehazing using adaptive nearest neighbor regularization to obtain a haze-free transmission map and then approximating the additional airlight component present in the hazy image. The proposed method relies on the intensity distribution in the small image patches of the image, exhibited from the Y channel of the YCbCr representation of the image, in order to preserve the texture information of the image. We substitute the commonly used soft matting technique in assessing the refined transmission map for haze removal, by adaptive nearest neighbor classifier. We assume that the actual color of the haze-free pixels in the image is approximated by a set of discrete colors. We discover the haze-free pixels using the Nearest-Neighbor (NN) regularization. Finally, unlike the state-of-the-art methods, we approximate the additional airlight present in the image patch and eliminate that to clear the haze, instead of estimating the transmission of the medium. The proposed nearest neighbor regularization technique automatically changes the patch size, which helps in dealing with the high depth region (e.g., sky region) of the image. We experimented on standard synthetic and real hazy image datasets and observed that, the proposed method outperforms the state-of-the-art, especially for images with sky regions. © 2020 Elsevier B.V.

Neurocomputing

Single image dehazing by approximating and eliminating the additional airlight component

In spite of the recent advancements in the field of deep learning based techniques for facial expression recognition, the efficiency of the state-of-the-art recognition methods in the wild scenarios, remains a challenge. The main reason behind the less efforts made for handling wild scenarios is two-folds: very less and varying levels of cues available to identify the distinguishable patterns of features (spatial and temporal) and non-availability of a big dataset to train a deep learning model. Recently, a huge dataset called AffectNet is introduced in the literature providing enough base to apply a deep learning model to train. This paper proposes an efficient combination of hand crafted and deep learning features for facial expression recognition in the wild. We use facial landmark points as hand-crafted features and XceptionNet for the deep learned features. We experiment with XceptionNet and Densenet propose the use of XceptionNet as it performs better compared to DenseNet, when applied on wild scenarios. The proposed fusion of the hand-crafted and XceptionNet features outperforms the state-of-the-art methods for facial expression recognition in the wild. © 2020 Elsevier B.V.