Shiv Nadar University

About: Shiv Nadar University is a education organization based out in Dadri, Uttar Pradesh, India. It is known for research contribution in the topics: Population & Graphene. The organization has 1015 authors who have published 1924 publications receiving 18420 citations.

Future perspectives and challenges in hyperspectral remote sensing

Abstract: Remote sensing (RS) technology has rapidly advanced in terms of radiometric, spatial, and spectral resolution. This trend has led to increasing complexity of data types ranging from low to high spatial and spectral resolutions and data dimensionality. In the chapters of this book, the state of the art has been presented, outlining the advantages of hyperspectral imaging (HSI) systems over multispectral data, and key future challenges and research directions with HSI have been illustrated. This chapter provides a perspective on the evolution of hyperspectral RS methods and applications along with challenges and barriers faced during research and innovation activities. The promise of upcoming missions with higher spatial and spectral resolution sensors in orbit in the near future will increase the utility of hyperspectral data in several research domains and will likely increase the number of users of HSI for soils, forestry, agriculture, urban, and cryosphere research. This chapter is intended as a resource to be aware of challenges and the future potential of hyperspectral RS to current and prospective users of high spectral resolution data to extract meaningful information for their research and applications.

Detection and mitigation of fraudulent resource consumption attacks in cloud using deep learning approach

Abstract: Fraudulent Resource Consumption (FRC) attacks can be synthesized by subtly consuming metered resources of the cloud servers over a sustained period of time. The objective of the attacker in such attacks is to exploit the utility pricing model by fraudulently consuming cloud resources. This skillful over-consumption of resources results in a considerable financial burden to the client. These attacks are characterized by low-intensity HTTP requests per hour, akin to requests by legitimate users. Hence, the attack requests differ in intent but not in content, which makes FRC attacks hard to detect. In this paper, we propose P-estimation detection scheme to effectively detect these attacks. This is accomplished by training several deep learning LSTM models based on the web server logs. An estimate of attack percentage is calculated and then used to deploy the appropriate detection model. This technique takes into account the dynamic nature of websites where the popularity of web pages can change with time, by retraining and updating the detection models periodically. To the best of the authors’ knowledge, this technique outperforms all the existing FRC detection techniques with a False Negative Rate (FNR) and False Positive Rate (FPR) of 0.0059% and 0.0% respectively. The proposed technique is able to detect attacks as low as 2% intensity. In addition to the detection scheme, this paper also delivers a mitigation and attribution technique to identify such attackers and block them.

Dynamic Surface evolution and scaling studies on TiO2 thin films by Ti ion implantation

Abstract: The surface dynamics of TiO2 thin films, evolving under the implantation of 50 keV Ti ions, have been investigated. The morphological evolution, as investigated with atomic force microscopy, delineates a surface smoothening by ion implantation.The nanoscale structures at surfaces also undergo a size reduction. Scaling formalism has been applied to understand this temporal and spatial dynamics by estimating the scaling exponents ( α , β and γ ) via Height-height correlation function (HHCF) and power spectral density (PSD) investigations. The roughness exponent α (0.5 α β ) also delineates surface smoothening. Exponent, γ , is observed to increase from ~ 2 at lower ion fluences to 2.5 at the highest fluence. This behavior suggests that diffusion is predominantly controlling the dynamical evolution of the ion irradiated TiO2 surfaces at the highest fluence, similar to that in the bulk case.

Context-Aware Monitoring and Control of Ventilation Rate in Indoor Environments Using Internet of Things

Abstract: As the proportion of time spent by humans in indoor environment increases, it becomes challenging to maintain good air quality for healthy and productive life. The need to develop a context aware, reliable system capable of providing real time information and alerts on indoor air quality is addressed in this article. The proposed Internet-of-Things (IoT) system serves to collect data, predict ventilation states, and provide alerts and recommendations to the end user. A novel method for determination of ventilation states using three indoor pollutants PM2.5, PM10, and CO is proposed. Multilevel logistic regression is first used to define indoor ventilation states using ventilation rate which is calculated with the help of indoor CO2 concentration. $K$ -NN classification technique then predicts indoor ventilation state with the help of three input attributes, PM2.5, PM10, and CO. Context-aware information about indoor environment and current ventilation state is conveyed to the end-user in form of an alert, through a smartphone application. The system is found to determine the poor ventilation state with accuracy, precision, recall and F1 score values of 94.34%, 0.91, 0.88, and 0.89, respectively.

A single benzene fluorescent probe for efficient formaldehyde sensing in living cells using glutathione as an amplifier

Abstract: Formaldehyde (FA), a simple reactive carbonyl molecule, is endogenously produced in the cell at various physiological condition. At elevated level, FA causes severe cell toxicity as well as damage in macromolecules such proteins and DNA. For detecting FA in living cell, we identify a small but effective fluorescent turn on probe comprising single benzene-based orothophenylenediamine compound. Further study reveals that carboxylic group in orothophenylenediamine plays the important role in enhancing fluorescent signal than another electron withdrawing group. It is even interesting to observe the occurrence of fluorescent enhancement in glutathione (GSH) environment which is generally abundant in every cell. Our probe enables to detect FA over other bio-analytes efficiently with limit of detection of 123 nM and 355-fold of enhancement in cellular mimicking conditions. Moreover, this probe could be useful in discriminating cell that has high concentration of FA as well as GSH.