Research News

Wireless Sensor Networks (WSNs) and their derivatives such as Internet of Things (IoT) and the Internet of Industrial Things (IIOT) are no longer confined to traditional applications such as smart homes and transportation. It has already marked its presence in Industrial applications and extended even to wildlife conservation. The impending concerns associated with such wireless networks are their privacy and security. One such menace afflicting wildlife is cyber poaching. Taking this into consideration, Dr Manjula R, Assistant Professor, and her student Mr Tejodbhav Koduru, from the Department of Computer Science and Engineering, have published a paper, “Position-independent and Section-based Source Location Privacy Protection in WSN” in the journal, ‘IEEE Transactions on Industrial Informatics’ having an Impact Factor of 10.215. The article is published in collaboration with Ms Florence Mukamanzi from the University of Rwanda, Rwanda, Africa and Prof Raja Datta from IIT Kharagpur, West Bengal, India.

The sensors collect data about these endangered animals and report it to the central controller which is connected to the Internet. Over the period, the hunters have also evolved and are equipped with smart devices that help them to easily locate the animal with minimal effort. In the simplest form, the attacker or the hunter just eavesdrops on the communication links to know the message’s origin and backtrack to the source of information. Once the source of information i.e., the location is identified then the endangered animal is captured. To overcome such backtracking issues, their work aims at delaying the information disclosure to the attacker through traffic obfuscation.

Although it may not act as an ultimate solution, the research work focuses on contextual privacy, unlike traditional content privacy. The attacker collects only contextual information such as packet rate, traffic intensities, routing paths, time correlations etc., to determine the source of information. The work focuses on mitigating traffic correlation i.e., hop-by-hop backtrack attacks and protecting the assets that are monitored using WSNs. The performance metrics include safety period and network lifetime amongst other metrics. The proposed random-walk-based routing solution achieves an improved safety period and network lifetime compared to the existing schemes. The work was simulated using a custom-designed simulation tool and was validated with the numerical results obtained using mathematical models.

The proposed solutions could be seamlessly used in monitoring endangered animals such as rhinoceros or in military applications to track soldiers. In addition, the routing algorithm could also be used in delaying tolerant networks to improve the efficiency and lifetime of the network, in designing the random trajectories of bio-nano bots for intrabody monitoring etc. Their future research plan includes developing improved source location privacy preservation techniques for terrestrial and underwater wireless sensor networks using the benefits of Artificial Intelligence and Machine Learning. In addition, they also aims at the development of data collection and routing protocols for intrabody nanonetwork operating at tera hertz frequencies— next-generation networks, envisioned networks.

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The Department of Electronics and Communication Engineering is glad to announce that Dr V Udaya Sankar, Assistant Professor has published the patent (App no. 202141056542), ‘A system and method with Matrix enabled Road distress classification with reduced computational complexity and reduced memory requirements’, in collaboration with Dr Siva Sankar Yellampalli and Ms Gayathri Lakshmi Chinthakrindi.

This work has applications related to visual inspection systems. While this research considers road crack detection application, the same can be extended to various applications such as leaf disease prediction, covid prediction etc. This invention provides an alternative approach instead of using traditional machine learning algorithms that has less computational complexity as opposed to deep neural networks that take more complex operations. This method will also lead to further research in matrix-based machine learning applications related to image processing and image classification.

The research team is planning to collaborate with Efftronics Systems Pvt ltd. for PCB defect detection and discussions are initiated with some start-ups for visual inspection applications. Their future research plan is to look deeper into these algorithms in combination with some of the deep neural networks to reduce computational complexity. In addition, Dr Udaya Sankar is also looking forward to establishing his own start-up in the incubation centre soon.

Abstract of the Research

A method for image classification is provided, wherein, the pre-processed gray scale image is first sent to the feature extraction block, and the said feature extraction block considers every image as a matrix and computes the metrics for features, viz., 1) EMD distance which is popularly known as Wassertain distance/Earth movers distance and is computed with respect to block image and 2) Frobenius Norm which is the square root of the sum of the absolute squares of its elements and finally, 3) Condition Number, which measures the ratio of the maximum relative stretching to the maximum relative shrinking that matrix does to any non-zero vectors. This method is preferred over the existing methods due to the drastic reduction in computational complexities and, utilizing lesser memory. Also, with this method and system, the communicational complexities too are significantly reduced and also, and the results yielded are far more significantly accurate.

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The Department of Electrical and Electronics Engineering is proud to announce that the estimable book of Springer Nature, ‘Soft Computing: Theories and Applications’ has featured three publications by Dr Tousif Khan, Assistant Professor. His publications are part of the book series, Lecture Notes in Networks and Systems (LNNS), Volume 425. The book stimulates discussions on various emerging trends, innovations, practices, and applications in the field of soft computing, ranging from data mining, prediction analysis, control systems, image processing, health care, medicine, agriculture analysis, supply chain management and cryptanalysis etc.

The first chapter titled “Design of Fast Battery Charging Circuit for Li-Ion Batteries” was co-authored by Dr Khan along with the final year EEE students; P Manoj Sai, G Nithin Sai, Puja Manohari, and P Gopi Krishna. In this chapter, a battery charging topology has been designed and developed for the fast charging of Li-ion batteries. The charging circuitry comprises a Proportional-Integral-Derivative (PID) controlled DC-DC buck converter system for reducing the charging time in Li-ion batteries. Battery charging time depends on several factors and the charging current is one of the major criteria. In this work, the buck converter is used to attain a high charging current, besides providing the regulated voltage to the battery. Initially, the AC supply obtained from the mains is converted to DC using an AC-DC rectifier. The rectifier output is further fed to the buck converter to increase the output current of the circuit. The buck converter reduces the output voltage and increases through it.

The circuit parameters are designed by considering the commercially available Lithium-ion battery LIR18650 as the load with a capacity of 2600 mAh and a nominal voltage of 3.7 V. The considered battery requires a standard charging current of 0.5 A, however, the circuit is designed to provide the rapid charge current of 1.3 A as the output by using the buck converter. The converter is operated in continuous conduction mode and helps in charging the battery under constant current mode. To avoid interruption to the charging current when there is a simultaneous discharge of the battery, further improvement in the closed-loop control action is made by employing a PID controller. Extensive simulation work has been conducted using the MATLAB/Simulink tool. The results obtained suggest there is a significant reduction in charging time under different conditions compared to the conventional method of battery charging.

In the chapter, “Global Horizontal Solar Irradiance Forecasting Based on Data-Driven and Feature Selection Techniques”, Dr Khan discusses the need for an accurate solar prediction. It has become an essential part of the renewable energy sector with the rapidly expanding infrastructure of the solar energy system. Over the past decade, various machine learning (ML) algorithms have been used for this purpose. Although the prediction of solar irradiance forecasting has been discussed in many studies, the use of meta-heuristic optimization techniques has not been explored to select features for the forecasting model. This study comprises two meta-heuristic optimization techniques such as simulated annealing (SA) and ant colony optimization (ACO) for feature selection. The results show that feature selection based on meta-heuristics gave better results than models without feature selection.

Amongst the two optimization methods, ACO outperformed SA with some exceptions. For SA, the declining order of performance observed is extreme gradient boosting (XGBoost), random forest (RF), multilayer perceptron (MLP), decision tree (DT) and support vector regression (SVR), while for ACO the declining order observed is XGBoost followed by MLP, RF, DT and SVR. This manuscript indicates the potential capability of meta-heuristic techniques for accurate prediction of global horizontal irradiance (GHI) given a wide array of feature variables.

In yet another chapter, “Exhaustive Search Approach to Place PV in Radial Distribution Network for Power Loss Minimization”, co-authored with Dr Shubh Lakshmi, Assistant Professor, and the final year students; P Manoj Sai and M Dhana Sai Baji from the Department of Electrical and Electronics Engineering, an exhaustive search approach to determine the best location and size of PV placement for power loss minimization of radial distribution networks is discussed. In this approach, the network power loss is determined by placing PV in each location, one at a time, and the size of PV in the same location is varied between 10 and 300 kW with an increment of 10 kW.

The combination of location and size of PV which provides the minimum network power loss can be the best location and size of PV for power loss minimization of radial distribution networks. The forward–backward sweep load flow algorithm is used to incorporate the PV model. The 33-bus radial distribution network is used to demonstrate the approach. The simulation results show that the placement of a suitable size of PV in some specific locations significantly reduces the network power loss.

Publishing the latest advancements in Networks and Systems, The LNNS series will serve as an edifying read for all the researchers and scientists across the globe. Volumes published in LNNS give a deep insight into all aspects and subfields of, as well as new challenges in, Networks and Systems. The series encompasses the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them.

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The paper titled “An invasive shrub Lantana camara L. alters the flora and soils in tropical dry deciduous forests of Central India” has been published by Dr Javid Ahmad Dar, Assistant Professor of Environmental Science at SRM University-AP, in “Biotropica” having an impact factor 2.508 (Q1 Journal).

Abstract

The findings of this research reveal how an invasive shrub Lantana camara L. significantly alters the flora and soils in tropical dry deciduous forests of Central India and suggested long-term monitoring studies and proper management strategy.

Practical implementation

The findings would be helpful to forest managers, scientists and policymakers for better understanding, management, and restoration of the invaded landscapes in tropical forest ecosystems.

Collaborations

Prof M. L. Khan, Department of Botany, Dr Harisingh Gour Vishwavidyalaya (A Central University), Sagar, Madhya Pradesh, India.
Prof Raman Sukumar, Centre for Ecological Sciences, Indian Institute of Science (IISc), Bengaluru, India.
Prof Mukund Dev Behara, CORAL, Indian Institute of Technology, Kharagpur, West Bengal, India.
Prof S. M. Sundarapandian, Ecology and Environmental Sciences, Pondicherry University, Puducherry, India.

Future research plans:

Dr Javid Dar’s research plan for the next five years is to bring together several unique aspects of forest ecology which will be focused on carbon dynamics, mortality, microbial diversity and their relationship in shaping the structure and functional aspects of different forest ecosystems in the on-going and future climate change. Another major aspect of the research will be, to focus on ecophysiology and plant functional trait analysis in forest ecosystems as they are vulnerable to climate change and to see the impacts of climate change on diversity, productivity and stand structure in tropical and temperate forest ecosystems.

The paper “A unique bridging facets assembly of gold nanorods for the detection of thiram through SERS” has been published by Prof Ranjit Thapa, Professor of Physics and his PhD student, Ms Anjana Tripathi, in ACS Sustainable Chemistry & Engineering having an Impact Factor of 8.198.

Abstract

The addition of Au NRs (Gold Nanorods) to TRM (Thiram) of higher and lower concentrations, yields side-by-side assembly (SSA) and bridging facets assembly (BFA), respectively, and exhibited excellent hotspots for the ultra-low detection of TRM. Bridging facets of Au NRs, such as (5 12 0) and (5 0 12) planes are mainly responsible for the BFA. This kind of interaction is observed for the first time and not reported elsewhere. The detailed facets of Au NRs, namely side facets, bridging facets, and pyramid facets, were discussed with the 3D model of Au NRs. The computational studies confirm the SSA and BFA for Au NRs with varying concentrations of TRM are well in agreement with the experimental results.

Research in brief

Au NRs were synthesized successfully using the seed-mediated method and characterized by UV-Vis analysis, SEM, TEM, FT-IR, Raman, and XPS analysis. Synthesized Au NRs were employed for the detection of TRM. Upon adding Au NRs to TRM of higher and lower concentrations yields side by side (SSA) and bridging facet assembly (BFA), validated by TEM analysis. This unique BFA was observed for the first time and not reported before to the best of our knowledge. Elemental mapping confirms the good adsorption of TRM over Au NRs, and FT-IR, Raman, SERS, and XPS analysis confirm the adsorption of TRM on Au NRs through Au-S bond. A uniformity study was performed for the TRM-Au NRs sample using 25 random places and obtained an RSD of ≤ 10% for each peak in SERS. This shows TRM is uniformly adsorbed on Au NRs. LOD and EF were achieved at 10 pM and 2.8 ×106, respectively. Hence, Au NRs are considered an excellent substrate for the detection of TRM. The unique assembly of BFA may play a significant role in the research community to further study the facet-dependent interactions of nanostructures. The computational study was performed to know the reason behind SSA and BFA. The density functional theory (DFT) was carried out using the Vienna Ab-initio Simulation Package (VASP). The Perdew-Burke-Ernzerhof (PBE) functional within Generalized Gradient Approximation (GGA) is adopted to treat the exchange-correlation interactions. These studies confirm the formation of a strong bond between Au and S, as well as the SSA and BFA for higher and lower TRM concentrations with Au NRs. The binding energy of TRM in SSA and BFA is -3.81 eV and 3.19 eV respectively. From the theory, it shows that TRM of lower concentration form BFA and higher concentration of TRM, due to high barrier energy for TRM diffusion, Au NRs form SSA. In this respect, we calculated the activation barrier for thiram migration from edge site (BFA) to in between site (SSA). Results indicate that TRM needs 2.40 eV energy to migrate from the edge site to in between site to form side-by-side assembly. Therefore, for diffusion from edge to in between (SSA) site high-energy barrier is required i.e. higher concentration is required for such configuration. Hence, at low concentration, TRM will form bridge facet assembly and due to high barrier energy for TRM diffusion, the side-by-side assembly is possible only at high concentration.

Practical implementation/social implications

Concerns have grown in recent years about the widespread use of the pesticide thiram (TRM), which has been linked to negative effects on local ecosystems. This highlights the critical need for quick and accurate point-of-need pesticide analysis tools for real-time applications. The detection of TRM using gold nanorods (Au NRs) with a limit of detection (LOD) of 10-11 M (10 pM) and an enhancement factor (EF) of 2.8 × 106 along with 6.2% of signal homogeneity (with respect to peak at 1378 cm-1) achieved through surface-enhanced Raman scattering (SERS). The interaction of Au NRs with TRM is sensitive, and ultra-low detection of hazardous TRM through SERS makes an ideal technique for environmental protection, real-time applications, and analysis of one-of-a-kind materials.

Collaborations

Bhavya M. B, Akshaya K. Samal
Institute: Centre for Nano and Material Sciences, Jain University, Jain Global Campus
Ramanagara, Bangalore 562112, India

Proteins are the most vital life forms which maintain close coordination with almost living activities through their biological functions. Nevertheless, in most cases, proteins suffer from low charge (electron) transfer efficiency as they are mainly made of insulating organic molecules. The interdisciplinary research publication, of Dr Sabyasachi Mukhopadhyay and Dr Sabyasachi Chakrabortty from the Department of Physics & Department of Chemistry respectively, along with their PhD scholars: Ms Ashwini Nawade, Mr Kumar Babu Busi and Ms Kunchanapalli Ramya, envisions the molecular-level understanding of the charge transport behaviour of various protein-metal nanocluster hybrid.

The article titled ‘“Improved Charge Transport across Bovine Serum Albumin – Au Nanoclusters’ Hybrid Molecular Junction” was featured in the prestigious Q1 journal ACS Omega (IF: 3.512), published by the ‘American Chemical Society’. They successfully incorporated Gold Nanoclusters inside the protein backbone leading to an increase in their conductivity. This will provide new avenues for the rational design of bioelectronic devices with optimized features. The BSA-Au cluster has been a promising model for bioelectronic functionalities. With an increase in their current carrying capacity, they can be used for many more applications, especially as the interface between tissue and organ in biocompatible devices. The research team is also planning to work with various protein dopants to understand their charge transport mechanism. These studies will help in using the protein for various applications mainly in bioimplants or biosensors for drug testing and diagnostics purposes.

Abstract of the Research

Proteins, a highly complex substance, have been the essential element in the living organism where various applications are envisioned due to their biocompatible nature. Apart from protein’s biological functions, contemporary research mainly focuses on their evolving potential associated with nanoscale electronics. Here, we report one type of chemical doping process in model protein molecules (BSA) to modulate its electrical conductivity by incorporating metal (Gold) nanoclusters on the surface or within it. The as-synthesized Au NCs incorporated inside the BSA (Au 1 to Au 6) were optically well characterized with UV-Vis, time-resolved photoluminescence (TRPL), X-ray photon spectroscopy, and high-resolution transmission electron microscopy techniques. The PL quantum yield for Au 1 is 6.8% whereas Au 6 is 0.03%. In addition, the electrical measurements showed ~10-fold enhancement of conductivity in Au 6 where maximum loading of Au NCs was predicted inside the protein matrix. We observed a dynamic behaviour in the electrical conduction of such protein-nanocluster films, which could have real-time applications in preparing biocompatible electronic devices.

Water pollution continues to be one of the serious concerns facing the country. The ensuing scenario of eutrophication and harmful algal blooms has exacerbated the menace. This demands wholescale water management techniques to segregate the pollutants, retrieve useful nutrients, and treat the water effectively for sustainable use. Dr Karthik Rajendran and his PhD scholar, Mr Sarath Chandra, from the Department of Environment Science have published a paper discussing various nutrient recovery methods and their consequential outcomes. The research was done in collaboration with Dr Deepak Kumar from SUNY College of Environmental Science and Forestry, Syracuse, NY and Dr Richen Lin from Southeast University, Nanjing, China.

The article titled, “Nutrient recovery from wastewater in India: A perspective from mass and energy balance for a sustainable circular economy” was published in Bioresource Technology Reports (Q1 Journal), having an Impact Factor of 4.41. Their research investigates the possibilities of recovering Nitrogen (N) and Phosphorous (P) from wastewater in terms of technology, energy, and economic point of view. Excessive presence of Nitrogen and Phosphorous can result in eutrophication and algal blooming. These nutrients also pose a harmful threat to infrastructure. Nutrient recovery can mitigate these challenges and improve the quality of water.

Phosphorus is one of the limited resources available on earth and a key ingredient in fertilizer production. The recovery process also helps in transforming wastewater into resource pools that can efficiently churn out valuables that hold the key to a sustainable future. This will help reduce the imports of fertilizers and bring down the emissions to half in producing fertilizers. Their findings will also pave the way for making necessary policies to reduce water pollution and recover nutrients. As two-thirds of wastewater remains uncollected, they claim that effective treatment and water management practices can save around 800 crores per annum. Their future research plan also includes the experimental analysis of the nutrient recovery system.

Abstract of the Research

Wastewater (WW) is a potential source to recover N, and P, whereas, in India, it is scarcely explored. In this work, four different nutrient recovery methods were compared from a mass- and energy-balance perspective to understand the overall process flow. From 1000-m3 WW, chemical precipitation yielded 33.8 kg struvite, while micro-algae resulted in 299.1 kg (dry powder). Energy consumption was lowest for the fuel cells at 216.2 kWh/1000 m3, while microalgae used the highest energy at 943.3 kWh/1000 m3. Nonetheless, the cost-saving analysis showed that microalgae (78.6$/1000 m3) as a nutrient recovery choice, had higher savings than any other methods compared. For a country like India, where two-thirds of urban wastewater is untreated, wastewater-biorefinery options such as nutrient recovery hold the key to a sustainable circular economy.

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A paper titled “Study on ferroelectric polarization induced resistive switching characteristics of neodymium-doped bismuth ferrite thin films for random access memory applications” has been published by Dr Pranab Mandal, Assistant Professor of Physics and his PhD student, Ms K N Malleswari in the journal ‘Current Applied Physics’ having an Impact Factor of 2.480.

Doi: https://doi.org/10.1016/j.cap.2022.04.013

Abstract

Resistive random-access memory (ReRAM) devices are based on the resistance switching (RS) effect. Such RS devices have recently attracted significant attention due to their potential application in realizing the next-generation non-volatile memory (NVM) devices. The present work reports on resistive switching (RS) characteristics of Neodymium (Nd)-doped bismuth ferrite (BFO) layers. The Nd (2–10 at%) doped BFO thin film layers were deposited using a spray pyrolysis method. The structural analysis reveals that a higher Nd doping concentration in BFO leads to significant distortion of the prepared Nd: BFO thin films from rhombohedral to tetragonal characteristics. The morphological analysis shows that all the deposited Nd: BFO thin films have regularly arranged grains. The X-ray photoelectron spectroscopy (XPS) analysis reveals that the prepared Nd: BFO thin films have a higher Fe3+/Fe2+ ratio and fewer oxygen vacancy (VO) defects which enrich the ferroelectric characteristics in Nd: BFO layers. The polarization-electric field (P-E) and RS characteristics of the fabricated Nd: BFO-based RS device were examined. It was observed that the Nd (7 at%) doped BFO RS device shows large remnant polarization (P r) of 0.21 μC/cm2 and stable RS characteristics.

Research in brief

Non-volatile resistive random access memory (RRAM) are future generation random access memory device with potential benefits such as high operational speed (nanoseconds read and write time), non-volatility, high endurance scalability and low power consumption [Namnoscale Research Lett., 15, 90, 2020]. Here in this work, we presented the resistive switching characteristics of a multiferroic material namely Nd-doped BiFeO3 material. The device shows stable resistive switching characteristics.

Practical implementation/social implications

Researchers in this field are focusing to overcome challenges of high operation current, lower resistance ratios, and reliability issues [Namnoscale Research Lett., 15, 90, 2020]. While several prototype RRAMs have been developed by other groups, future memory applications would require overcoming the challenges mentioned above.

Collaboration

The work has been conceptualized by Dr Amiruddin at  Crescent Institute of Science and Technology, Chennai; and Dr Pranab Mandal and Ms Malleswari provided inputs on ferroelectric polarization – electric field (P – E) measurement and drafting.

A paper titled “Organic food preferences: A comparison of American and Indian consumers” has been published by Prof Bharadhwaj Sivakumaran, Dean- SEAMS, SRM University-AP, Kirubaharan Boobalan (SSN College of Engineering), and Margaret Susairaj (Great Lakes Institute of Management, Chennai) in the journal Food Quality and Preference having an Impact Factor of 5.6.

This research tests a nomological model predicting organic food attitudes and purchase intentions in USA and India. Data were collected from India (n = 687) and the USA (n = 632) using Amazon M Turk and were analyzed using structural equation modelling and multi-group moderation technique. Results revealed that over and above attitude, subjective norm and perceived behavioural control, response efficacy and self-expressive benefits significantly affect consumers’ attitudes and purchase intentions toward organic food among American and Indian consumers. Findings reveal that response efficacy and attitude matter more in the USA while subjective norms and self-expressive benefits exert a greater influence in India. Therefore, marketers may reinforce belief-related elements while selling organic food products in the USA and societal-related elements while selling in India. Theoretically, this work adds to the Theory of Planned Behavior by adding self-expressive benefits and develops a common model for organic food across samples in USA and India.

Surface-enhanced Raman Spectroscopy (SERS) is a nuanced chemical technique that amplifies the Raman scattering of molecules by utilising plasmonic nanostructured materials. SERS operates as a powerful detection tool that allows for the structural fingerprinting of a molecule. The ultra-high sensitivity and selectivity of the process offer it a vast array of applications in surface and interface chemistry, nanotechnology, biology, biomedicine, food science, environmental analysis and other areas.

Dr J P Raja Pandiyan and his PhD scholar, Ms Arunima Jinachandran from the Department of Chemistry have been keenly involved in exploring the possibilities of SERS technology in food science and other fields. The safety and quality concerns related to food were the primary reasons that impelled them to step into this domain. Their article “Surface-enhanced Raman spectroscopy for food quality and safety monitoring” was published in the book Nanotechnology Applications for Food Safety and Quality Monitoring, published by Elsevier. The article was published in collaboration with Dr Selvaraju Kanagarajan from the Swedish University of Agricultural Sciences.

As an analytical technique, SERS possesses several advantages such as non-destructive, sensitive, and selective. In the chapter, the necessity, and applications of SERS in food science are elaborately discussed. They have also discussed all the possible food contaminants and how to identify them using SERS to ensure food quality. This book will serve as an enlightening read to research groups who are working on Raman, surface-enhanced Raman spectroscopy, analytical chemistry, and food quality analysis.