Research News

Persistent dry coughing and abnormally high body temperature are identified as more relevant risk factors associated with the COVID-19 crisis. Besides these, maintaining 6 feet social distancing norm was also recognized as a crucial factor. By taking these three features into consideration, the research group consisting of Dr Kshirasagar Sahoo, Assistant Professor, Department of Computer Science and Engineering at SRM University-AP have designed a smart, reliable and efficient COVID-19 tracking device model to monitor suspected infected people in public places. The device named “Smart COVID-Shield” utilising the IoT technology is equipped with a suspender and a belt to be placed over the clothes of the user which constitutes a cough detect unit, temperature detects unit and distance computing unit. Coughing and temperature patterns can be detected through the PIR sensor of the suspender while a belt with an ultrasonic sensor can be used to track people who violate the 6 feet social distancing norms in a real-time environment. A research paper titled “Smart COVID-shield: An IoT Driven Reliable and Automated Prototype for COVID-19 Symptoms Tracking” is published in Computing Journal as a part of the project.

Abstract of the paper:

IoT technology is revolutionizing healthcare and is transforming it into more personalized healthcare. In the context of the COVID-19 pandemic, IoT’s intervention can help to detect its spread. This research proposes an effective “Smart COVID-Shield” that is capable of automatically detecting prevalent symptoms like fever and coughing along with ensuring social distancing norms are properly followed. It comprises three modules which include Cough Detect Module (CDM) for dry cough detection, Temperature Detect module (TDM) for high-temperature monitoring, and Distance Compute Module (DCM) to track social distancing norm violator. The device comprises a combination of a lightweight fabric suspender worn around the shoulders and a flexible belt wrapped around the waist. The suspender is equipped with a passive infrared (PIR) sensor and temperature sensor to monitor persistent coughing patterns and high body temperature and the ultra-sonic sensor verify 6 feet distance for tracking an individual’s social distancing norms. The developed model is implemented in an aluminium factory to verify its effectiveness. Results obtained were promising and reliable when compared to conventional manual procedures. The model accurately reported when body temperature rises. It outperformed thermal gun as it accurately recorded a mean of only 4.65 candidates with higher body temperature as compared to 8.59% with the thermal gun. A significant reduction of 3.61% on social distance violators was observed. Besides this, the latency delay of 10.32 s was manageable with a participant count of over 800 which makes it scalable.

This is a collaborative work of H. K. Tripathy, S. Mishra from School of Computer Engineering, KIIT Deemed to Be University, Bhubaneswar, Odisha, India and A. Nayyar from Graduate School, Faculty of Information Technology, Duy Tan University, Da Nang 550000, Vietnam.

Early detection of the coronavirus symptoms is one feasible means to restrict the spreading of coronavirus. The IoT enabled “Smart COVID-Shield” is developed and implemented in this study to monitor social distancing violators in crowded places. In future, all other COVID symptoms can be incorporated into the model to make it more effective and real-time. An emergency alert module can also be included as part of the model to create awareness among people. An enhanced security mechanism can be further embedded in the working model to prevent any data compromise and dilution in data availability.

Read the full paper here.

Being a researcher demands commitment, sustained effort, and a high level of inspiration. Ms Lakshmi Bhargavi from 3rd-year Computer Science Engineering has presented a paper titled “Application of distributed back propagation neural network for dynamic real-time bidding” at the 11th International Advanced Computing Conference (IACC-2021), hosted by the University of Malta, Europe. It is a reputed conference indexed in Scopus and DBLP, having H-index 25.

The research is based on the backend of ad placement on websites which involves finances. The process involves finding the best deal between the dealer and the supplier. In the present system, the bid is prefixed, thereby reducing the possibility of optimal budget utilisation. In comparison, Ms Lakshmi’s research uses an ML algorithm, which is dynamic and learns from the previous bids. This research has resulted in 15% lower costs for the suppliers, thereby saving a lot of money and resulting in a better system.

Abstract — Programmatic buying, popularly known as real-time bidding (RTB), is a key ascendancy in online advertising. While data has become essential for targeting and ad performance, data businesses have become difficult to differentiate due to their proliferation, as well as limitations of attribution. This provides an opportunity for Big Data practitioners to leverage this data and use machine learning to improve efficiency and make more profits. In such an opportunity, the research came up with an application of a machine learning algorithm, distributed back propagation neural network, d-bpnn, to predict bid prices in a real-time bidding system. This paper depicts how d-bpnn is used to achieve less eCPM for advertisers while preserving win rate and budget utilisation.

The 11th International Advanced Computing Conference (IACC-2021) was hosted by the University of Malta, Europe, with an H-index of 25. The conference is indexed in Scopus and DBLP and in collaboration with Springer. A few selected papers will be published in SCOPUS/SCI Indexed journals. The presentation was held on 19th December 2021. It was an ideal platform for people to share views and experiences in Futuristic Research Techniques.

Let’s hear from Ms Lakshmi:

My university has been with me in every step taken towards this conference. I would like to thank the mentoring of Dr Priyanka throughout the writing and presentation of the paper. The immense support of SRM AP management, my professors, HOD, Pro VC sir and VC sir made me reach the level to write a paper confidently and show my knowledge to the world.
I feel honoured to present a good paper at a global conference. The experience and connections I made through this conference are priceless. It gave me new insights into several other technical domains. I believe I gave my best at the unique opportunity given to me and hopefully will continue to deliver good work in future too.

SERB-DST projects aim to build up the best systems that would match the best global practices in the area of promotion and funding of basic research. Dr Murali Krishna Enduri, Assistant Professor, Department of Computer Science Engineering at SRM University-AP is yet another faculty member who has obtained a project with a total outlay of ₹18 lacs for a duration of three years. The project is sanctioned under the scheme of Teachers Associateship for Research Excellence (TARE) of SERB-DST, Government of India.

In the duality theory, the dual problem is the problem of checking the duality of a pair of monotone Boolean expressions in disjunctive normal form. Problem: DUAL Input: The complete DNF of two monotone Boolean functions, f and g. Output: If f is dual of g. Whether the problem DUAL admits a polynomial-time algorithm has been one of the challenging open problems in the field of Duality theory of Boolean function for the last 35 years. It is one of the few problems whose polynomial-time solvability is still unknown. So, this problem is important in complexity theory due to its unknown complexity status and it plays a central role in various applications arising in computational logic, data mining, reliability theory, artificial intelligence and game theory etc. The project goal is to solve the dual problem for an interesting class of Boolean functions. Improving the existing complexity results of the DUAL problem for a particular class of Boolean functions is a challenging task.

Few applications of the project are as follows:
Type error diagnosis: Type error diagnosis is the task of generating an explanation for some error. It requires finding all minimal unsatisfiable subsets of a given set of constraints (representing the error) which can be managed via solving the computational variant of Dual in its minimal transversal formulation.

Computational medicine: Optimal vaccination strategies are given a subset of initially infected individuals from a population of individuals and assumptions about disease transmission. The task of computing inclusion minimal vaccination strategies can be solved using the computational variant of Dual in its transversal hypergraph formulation.

The project will be carried out in collaboration with IIT Madras (Dr Jayalal Sarma, Associate Professor, Department of Computer Science & Engineering, Indian Institute of Technology Madras, Chennai, India.)

Nowadays, the energy demand of the present electrical power industry is increasing exponentially, and most of the electricity production depends on fossil fuel resources. A research paper titled “Dynamic Economic and Emission Dispatch with Renewable Energy Integration Under Uncertainties and Demand Side Management”, published by Dr B Lokeshgupta, Assistant Professor, Department of Electrical and Electronics Engineering, SRM University-AP, Andhra Pradesh, answers some of the pertinent questions regarding reducing the environmental pollution level.

Integration of renewable energy resources (RERs) along with demand-side management (DSM) is almost inevitable in the present scenario to meet the growing energy demand with minimum environmental pollution. This work proposes a combined model of dynamic economic and emission dispatch (DEED) and DSM to integrate renewable energy resources (RERs). In this analysis, the DSM load-shifting scheme is incorporated with the DEED problem to obtain the generation side operational benefits as well as the reduction in environmental pollution level. In this study, various smart home appliances and their complex constraints are included in the DSM load shifting process. The variability and stochastic nature of the load demand and RERs such as solar, wind are modelled with Normal, Beta, and Weibull distribution functions, respectively. The proposed model is implemented in both deterministic and stochastic approaches with the help of the non-dominated sorting genetic algorithm (NSGA-II) and the Monte Carlo Simulation (MCS) approach. In the stochastic model, the MCS approach appropriately handles the uncertainties of system load demand and RERs. Four different case studies are carried out in the simulation analysis to show the impacts of RERs and DSM integration on the traditional DEED problem.

Meeting the excessive energy demand with the minimum environmental pollution is a challenging task. The integration of RERs such as wind and solar into the grid is one of the superior solutions for this issue. However, the variability and uncertainty of the RERs bring challenges to the power system operation. Energy management schemes such as demand-side management (DSM) methods can help the power industry address the challenges of RERs integration. That is why the combination of renewable energy integration and DSM is one of the key solutions in the smart grid environment to meet the increased energy demand with the lowest possible energy cost and minimum pollution level. The RERs and DSM combination gives several financial, environmental, and technical benefits to the power industry along with a better system operation.

The dynamic economic and emission dispatch (DEED) is one of the widely adopted tools in the operation and planning of power systems. Both DEED and DSM are the essential tools in the smart grid environment for efficient energy management with the concern of economic and environmental aspects. The DEED’s primary task is to obtain the optimal scheduling of generators with minimum cost and emission for the given load demand. At the same time, the DSM’s primary goal is to improve the optimal values of system objective functions by shifting or managing the controllable loads of consumers. This work introduces a combined stochastic optimisation model of DEED and DSM scheme with the integration of solar and wind energy to show how DSM and RERs bring benefits to a generation company, and also to get better optimal operation cost and emission values simultaneously. The DSM load-shifting scheme is implemented in this study with the help of 10,000 active residential consumers. The effectiveness of the proposed combined model has been tested on a system of six thermal generating units, one wind-powered generator, and one solar-powered generator. The MCS approach and NSGA-II method are used in this paper to solve the proposed stochastic combined DEED and DSM optimisation model.

From the overall analysis, it is recognized that the implementation of the DSM load-shifting scheme along with RERs integration is essential for future smart grids to improve the financial savings of generation companies as well as to reduce the environmental pollution level. The paper is written in collaboration with Dr S Sivasubramani, Associate Professor, Department of Electrical Engineering, Indian Institute of Technology, Patna.

In future, the proposed DSM optimisation can be extended with the inclusion of a neighbourhood power-sharing model in the environment of multiple smart home consumers and prosumers. The proposed DSM model can also be integrated with the distribution network planning and operation problems to enhance the financial and technical benefits of distribution companies.

Imaging various three-dimensional (3D) objects under ultra-darkness is a fascinating process. However, our conventional cameras are not intelligent enough to capture the experience. Dr Inbarasan Muniraj, Assistant Professor in the Department of Electronics and Communications Engineering, is all about changing that.

Dr Inbarasan Muniraj’s project, “Sensing in the dark: An automated off-focused points detection and removal from the photons starved 3D volumetric dataset”, has received a SERB-SRG-DST of Rs. 20.8 lacs for a two-year duration.

Dr Muniraj describes his project as such,

“Assume that there is no external light, e.g., a dark room, when you capture an image using a camera (mobile or DSLR). Often, the captured images will look dark, and it is too difficult tointerpret anything from the picture. However, algorithms have been developed to make use of the low scattered photons from a scene to estimate the equivalent normal intensity image. We use one such technique to generate photons-counted images for a 3D object and perform a 3D image reconstruction. One of the major problems in 3D reconstruction is off-focused points which look blurry and redundant. Therefore, in this project, we aim to employ a deep learning technique to smartly recognise and remove the off-focused points from a reconstructed 3D scene under photons starved conditions.”

Dr Inbarasan Muniraj is the sole investigator of this project. According to him, there are much more extensive social implications associated with this project. To note, this technique can be extended for various applications such as night vision, security, and biomedical imaging etc.

Continue reading →

SRM University-AP is pleased to announce that Dr Anil K. Suresh, Associate Professor in the Department of Biological Sciences, has been awarded a DBT grant for his project entitled “Pilot-scale ultra-efficient fixative sewage dye-degradation by our ‘3D-megacatalyst’ generated using intact eggshell waste”.

About the Project:

Catalysis is widely used in various industrial and pharmaceutical processes to fasters the production of the desired end products. The use of inert matrices or frameworks is emerging as a “supported catalysts” arena with the potential for efficient reuse and recovery of the catalyst. We have recently generated a wide area supported catalyst utilising intact eggshell bio-waste, and the process is autogenic, facile, cost-efficient and entirely biodegradable. This supported megacatalyst can be effortlessly removed from the reaction by hand.

The current DBT-funding through Accelerated Translational Grant for Commercialization (ATGC) program is to support the technological reediness of our project for its commercialisation as a measure of Technology Readiness Level (TRL), an estimation technologies maturity and readiness for its utilisation in the commercial market. By demonstrating proof-of-concept laboratory studies, we are currently at TRL-6, and through this project, we will demonstrate pilot-scale studies for reaching the TRL-9 (Market/Operational level).

The main objectives of the proposal are:

1. Demonstrating large-volume degradations of sewage dyes at 500 L to 1000 Litres volume capacities in custom-built batch reactors.

2. Gram-scale hydrogenation of nitroarenes for the production of ~500 grams of pharmaceutical derivatives.

Social Implications of the project by Dr Anil K. Suresh:

Thousands of litres of harmful textile, paper sewage dyes that are corrosive and toxic to the environment and are unintendedly released can now be degraded into detoxified colourless by-products and water by using our “Au@megacatalyst”.

Pharmaceutically important precursors such as 4-aminophenol, propargyl amines can be produced in milligrams of quantities by using “Au@megacatalyst”, which otherwise are expensive and hard to synthesise. And most importantly, such precursors are currently being procured from China and other countries that can be avoided, and indigenous make-in-India can be conceptualised using our catalyst.

Dr Anil K. Suresh would be the principal investigator of this project, with a total outlay of Rs. 31 lacs over two years.

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Padmaja Buggaveeti, an outstanding student from 3rd-year Computer Science Engineering at SRM University-AP, has won Best Paper (Third Prize) and a cash prize of Rs 10000 /- (Ten thousand rupees only) at the 4th ISEA Virtual International Conference on Security and Privacy 2021. Ms Padmaja, under the mentorship of her guide Dr V M Manikandan, presented a paper titled “A Novel Prediction Error Histogram Shifting-based Reversible Data Hiding Scheme for Medical Image Transmission” at the International Conference on Security and Privacy, sponsored by Information Security and Education Awareness Project Phase-II (ISEA-II) and organised by IIT (ISM) Dhanbad, India from October 27-30, 2021.

Abstract: In this paper, Ms Padmaja proposed a new prediction error histogram shifting-based reversible data hiding scheme that ensures a high embedding rate and lossless image recovery. The pixels in the images are categorised into two different classes: white pixels and black pixels based on a checkerboard pattern. To predict the black pixel value for finding the prediction, they used the average of three pixels out of 4-neighbourhood pixels, which are very close to the central pixel value. The prediction error histogram is considered for further data hiding through the histogram shifting approach. An efficient overflow handling technique is used for this. The proposed algorithms were implemented using Matlab-2020, and the experimental study of the proposed scheme is carried out on the standard medical images and natural images.

International Conference on Security and Privacy is a premier conference focused on information security and privacy. This year’s conference was sponsored by Information security and Education Awareness Project Phase-II (ISEA-II) and organised by IIT (ISM) Dhanbad, India, from October 27-30, 2021.

Prize Details: Best Paper (Third Prize) and a cash prize of Rs. 10000 /- (Ten thousand rupees only).

Let’s hear what Ms Padmaja says about her achievement!

I thank Dr V M Manikandan, Assistant Professor in the Department of Computer Science and Engineering, for his valuable guidance in this research work. Winning the best paper award and cash prize in a reputed conference, ISEA-ISAP, has strikingly boosted my confidence to do research. I am grateful to all the faculty members of the CSE Department for their kind support and encouragement throughout my study at SRM University-AP.

SRM University-AP is pleased to announce that Dr Nimai Mishra, Assistant Professor, Department of Chemistry, SRM University-AP, Andhra Pradesh, along with his research group comprising of students pursuing PhD under him, Mr Rahul Singh, Mr Syed Akhil, and Ms V.G.Vasavi Dutt, has published a research article titled “Shell thickness-dependent photostability studies of green-emitting “Giant” quantum dots” in the journal Nanoscale Advances (The Royal Society of Chemistry) with an impact factor of ~4.533.

About the research:

Highly efficient green-emitting core/shell giant quantum dots have been synthesized through a facile “one-pot” gradient alloy approach. Furthermore, an additional ZnS shell was grown using the “Successive Ionic Layer Adsorption and Reaction” (SILAR) method. Due to the faster reactivity of Cd and Se compared to an analogue of Zn and S precursors it is presumed that CdSe nuclei are initially formed as core and gradient alloy shells simultaneously encapsulate the core in an energy-gradient manner and eventually thick ZnS shells were formed. Using this gradient alloy approach, we have synthesized four different sized green-emitting giant core-shell quantum dots to study their shell thickness-dependent photostability under continuous UV irradiation, and temperature-dependent PL properties of nanocrystals. There was a minimum effect of the UV light exposure on the photostability after a certain thickness of the shell. The QDs diameter of ≥ 8.5 nm shows substantial improvement in photostability compared to QDs with a diameter ≤ 7.12 nm when continuously irradiated under the strong UV light (8 W/cm2, 365 nm) for 48 h. The effect of temperature on the photoluminescence intensities was studied with respect to shell thickness. There were no apparent changes in PL intensities observed for the QDs ≥ 8.5 nm, on the contrary, for example, QDs with < 8.5 nm in diameter (for ~7.12 nm) show a decrease in PL intensity at higher temperatures ̴90°C.

More importantly, these results highlight the synthesized green-emitting gradient alloy QDs with superior optical properties can be used for highly efficient green emitters and are potentially applicable for the fabrication of green LEDs.

Read the full paper: https://pubs.rsc.org/en/content/articlelanding/2021/NA/D1NA00663K

Prof S Mannathan has added SRM University-AP to the list of those research-oriented Universities which try to direct their research to make the world a better place. With this new SERB-DST project of Rs. 44 lacs fund, Dr Mannathan and his team will be able to investigate into developing a strategy to prepare functionalized alkenes in a manner that is pro-environment and industrially economic.

Why functionalized alkenes? Alkene is a hydrocarbon with a C=C bond. It is readily available and a fan favourite among chemists. It reacts favourably with a variety of reactants and is a preference in the synthesis of bioactive compounds. This procedure aids in the preparation of several valuable compounds with a variety of uses from the manufacturing of antidepressants to the treatment of cancer.

What Prof Mannathan says about the project:

“We intend to develop a highly efficient, low-cost, environmentally-friendly strategy to prepare functionalized alkenes in a highly regio- and stereoselective manner. A step- and atom economic reductive coupling strategy will be employed by using a photoredox and a low valent metal dual catalyst to prepare such molecules. This novel approach avoids the use of any external reducing agent and generates the low valent metal species in situ using a photocatalyst.”

Dr Mannathan was granted a total of Rs. 44,11,264 for a period of 3 years. Dr Mannathan believes that with a SERB sanctioned fund of Rs. 44 lacs, he and his team will be able to design a new and revolutionary protocol. The newly designed protocol could be useful in the synthesis of various biologically active molecules and natural products such as aigialomycin D which is a helpful compound in inhibiting protein kinases that are related to cancer pathways.

SRM University-AP could not be more proud to announce that Dr S Mannathan, Head of Department of Chemistry has made it to the top 5% in the list of the Most Cited Authors by the Royal Society of Chemistry. It is inspiring to have a faculty member in our midst whose work has helped and facilitated the research of so many others.

Dr Mannathan obtained his doctorate from National Tsing Hua University, Taiwan. His research interests primarily lie in Metal-catalyzed organic transformation reactions, Multicomponent reactions, and Asymmetric synthesis. His research followed by scientists all over the world leading him to become one of the top 5% authors in terms of citations

In the field of Transition Metal Complexes as Catalysts in Organic Reactions, he particularly leans towards ‘Nickel-and cobalt-catalyzed three-component coupling and reductive coupling reactions’, and ‘Palladium-catalyzed reductive arylation’. Similarly, in Asymmetric Synthesis, he favours research into ‘Asymmetric reductive Heck reaction for the synthesis of chiral indanones’, and ‘Synthesis of bicyclic tertiary alcohols and its related asymmetric version via reductive [3+2] cycloaddition reaction by using chiral cobalt complexes.’

About the top 5% most cited paper:

In this work, he reported the synthesis and application of a Zn-Bp-BTC MOF (Bp – 4,4′-bipyridine; BTC – 1,3,5-benzene tricarboxylic acid; MOF – metal organic framework) as a heterogeneous catalyst for mediating organic reactions. Initial reaction conditions were optimized for the Knoevenagel condensation reaction using Zn-Bp-BTC as a heterogeneous catalyst. Various factors such as the effect of solvent, temperature and catalyst loading were evaluated. Although the reaction proceeded at room temperature using methanol as the solvent, 60 °C offered the best yield in a shorter duration. Under optimized reaction conditions, a wide range of α,β-unsaturated dicyano compounds were prepared from the corresponding carbonyl precursor and malononitrile, the active methylene counterpart. A systematic investigation was also carried out to assess the role of the ligand and metal salt in the Knoevenagel condensation reaction. It was found that the Zn-Bp-BTC MOF catalyzed the reaction efficiently in comparison to its analogue Zn-BTC MOF and precursor Zn(NO 3 ) 2 ·6H 2 O. Finally, catalytic recycling and stability studies showed that the catalyst is able to mediate the reaction for up to five consecutive cycles without undergoing any significant chemical or morphological changes. Further, the catalyst was tested for its efficacy in a multicomponent reaction (MCR). An MCR with the Zn-Bp-BTC MOF as the catalyst afforded good yields and there was no reaction in the absence of the catalyst. Similarly, the catalyst was tested for its efficiency in benzimidazole synthesis.

Dr Mannathan did this research in collaboration with Dr. Kathiresan Murugavel, Scientist, Electro Organic Division, CSIR-Central Electrochemical Research Institute (Govt of India), Karaikudi.