Good day, Dr. Rajvardhan Jigyasu. I'm Arjun. I've had a look through your background and your professional record. I'm interested in hearing more about your work and how you see yourself fitting in here with our faculty. Ready to start?

Please start by introducing your professional journey in academia.

OK so I have started my journey in academia after my bachelors in 2014.

The targeted the GATE examination which I qualified and then I have joined one institute that is MC Saxena College of Engineering and Management in Lucknow where I taught for one year, basically electrical engineering.

For both the semesters and I have took different labs also like for example microprocessor lab and microcontroller.

And also the basic electrical labs. And after teaching there, then I went for the masters. So this includes that.

It is showing that I am interested in teaching because I have went for the higher qualifications after my teaching after Btech, so after my masters which I completed from NI Triple TR Chandigarh CFTI Institute.

I have joined one institute that is Sri Ramapuru Memorial College where I worked as an assistant professor in electrical department.

Where I taught different subjects such as power electronics, power system and so they those things were taken for one year and then I went for my PSD which again shows that I am really interested in the academia career which I have completed from National Institute of Technology, Delhi and I have completed in 2024. After that I have joined 2 government universities in Delhi as a guest lecturer.

That is Indraprastha University and the Netaji Subhash University. Both are in a very good NIRF ranking.

So if I talk about my teaching experience, so I have taught around.

1 + 2 around 8 years of experience because 3 and in the colleges and universities and during my UH PhD duration I have to the teaching assistantship.

Which covered around 5 years and different labs I have taken, different lectures I have taken so around 8 years of experience I have if I talk about the academic research. So I have published 30 publications in different reputed journals. I have 3 patents and one patent filed. Along with that I have received different awards since my schooling days.

I have received uh.

Best student award for project in my B.Tech I have got the best researcher award from Institute of Engineering and Management and I have got the different rewards for the highest scoring in my high school also.

So this all shows that I am really interested in the academia career and if I talk about this, this AI embedded system, uh.

Vacancy.

I have worked on different microcontrollers, I have taken the different labs also related to it. I have utilized the IoT based systems also for condition monitoring of machines. So these are the things I have done. If I have to show or validate that I am interested in the academics and all the degrees are above 75% means I have taken all the degrees with honors and distinctions which also validate that I am quite studious and.

As well as I can deliver good things to the students also.

Professor, your academic journey and experiences are impressive and clearly reflect a strong alignment with the AI Embedded Systems Professor role. Your consistent progression through teaching, advanced studies, research, and your contributions to the field underline your commitment to academia. Let's delve into assessing some specific qualifications and skills relevant to this position. First, focusing on your expertise in **AI Embedded Systems**, can you discuss one specific application of AI in embedded systems that you've researched, implemented, or guided students on?

So I have utilized the AI technique along with the embedded system for condition monitoring of machines in which I have taken the data from the hardware that is like induction motor or BLDC motor utilizing the different data acquisitions hardware. For example, if I talk about vibration sensor, acoustic sensor, thermal cameras I have used, I have transferred this data over the Internet utilizing the IoT techniques. So in this.

Applications I have trained, tested the different AI ML models, gather the data utilizing the embedded system techniques and then develop one application to predict the fault inside the motor. So this is a project I have done which is directly implemented or directly aligned with the AI embedded engineer. I have utilized embedded system also like different sensors, different hardwares like ous. BNK Hardware.

For the sensors for the data acquisition, I have used microprocessor and Raspberry Pi for the transfer of the data over the Internet. So these things I have utilized and clearly and I have for development of the application of the detection, I have used AI, ML techniques, deep learning techniques to predict the different faults which are the data has been acquired online.

Thank you, Professor. It’s evident that your project combining AI with embedded systems for condition monitoring demonstrates both applied expertise and innovation. Now, focusing on **teaching theory and laboratory courses**, how do you ensure your students grasp not just the theoretical aspects of AI in embedded systems but also gain effective hands-on skills in a laboratory setting?

So if I, if I am going to get opportunity to teach in VIT Vellore, I will surely like try to surely I will do the I will open a operational lab of NVS that is noise, vibration and harness lab in which we are going to gather the data from the hardware and different applying different AI ML techniques, we will process it. We will gather the data with the embedded systems.

So I will try to, uh, means work in this lab and I will teach these students to solve the day-to-day problems. Like if I talk about the IoT systems, we have smartwatches for the monitoring of health. We have different apparatuses like smart wear helmet. I will try to provide the insight to these students. What's the behind it is going along with the teaching. I will show them real time.

Locations of them like my condition, monitoring of machine real in in real time, how the industry can be saved from it. I will open and demonstrate different real time available instruments. For example smart watch. I will try to explain them along with the which sensors are are used in it along with the softwares, how they are going to code it and how they are going to deploy it over that. So these hands off experience I will provide them.

If I got the opportunity and this will make the classrooms interactive, so along with the theory classes I will demonstrate them the real time applications also.

It's commendable, Professor, that you prioritize blending theory with hands-on industry-relevant experiences. Your approach of opening an operational lab and integrating real-world applications like condition monitoring and smart devices into teaching showcases your commitment to fostering practical learning for students. Shifting to **student evaluation and exam duties**, how do you design your evaluations to ensure they assess both conceptual understanding and the practical application of knowledge?

OK, I if if I got the opportunity to like evaluate the student by myself, I will divide it into 5050 concept. I will take the theory classes and the examination and notes and the class stats for the 50% and the interactions and all for the 1st 50% of the marks. The next 50 will be on the application base. I will provide them some small projects and ask them to implement them according to the knowledge they have gained.

So far utilizing the theoretical classwork knowledge. So I will give the marks to them or I will evaluate them on the basis of both theoretical as well as the practical aspect aspects. I will also ask them if they have got or implemented some good project by their own. I will ask them to or file them as a patent. Also. It will not be beneficial only for the student. This for the university as well As for the faculty also like me.

So I will try to means implement or try to ask them and evaluate them on the basis of both like teaching that theory and examinations and all along with the practical aspects also, for example projects.

Your balanced approach to evaluation, combining theory with practical application, reflects a thoughtful method of ensuring students not only grasp core concepts but also develop valuable real-world skills. Encouraging students to innovate and potentially file patents further underscores your dedication to their professional growth and institutional contribution. On the subject of **guiding student projects and research**, how do you mentor students to select impactful research topics and ensure the successful execution of their projects?

So it totally depends on the which grade the students are. For example, if I am going to take or teach or govern the project of AB Tech students, I will do 5050 contribution or I can say 7030 contribution. I will ask students for 30% contribution. Like you just go through the day-to-day practical applications. You just go to the read some good papers and I will suggest them like just go and.

Me and come to me with some good idea and I will help them to implement if they are graduated student, if they are master students then it will be get converted into 5050 concept. I will ask them to bring the best they can by different research, by going through different website, by going to the different industrial project which are going on day-to-day and which are going to be implemented in the future and come back. We will sit together and we will write and discuss all the things and I will try to help them from the scratch to.

Of them if if the student is off for the PSD scholar then this this this this work will be like get will be 7030. The 70% things will be done by the student and 30% I will guide this 30% will again be everything if I am able to provide them like for example infrastructure, hardware access, collaborations. For example if PhD student is working and ask me to that I am interested in.

For that, if they require like uh, dedicated hardware which costing around crores. So I as I have already have collaborations with Iits and nits and different industries, I will help them to get those hardwares without buying them. Just go research and validate your results. I will help them in that manner also. So in, in the, the approach of mine will vary according to the grade and the student it is.

Marking in the condition monitoring of machines and.

Your tailored approach to mentoring students at different academic levels is well thought out, Professor. Encouraging undergraduate students to explore ideas while progressively increasing student autonomy at higher levels, combined with your active facilitation of resources and collaborations, underscores your ability to guide impactful research effectively. Turning now to **communication and structured teaching**, how do you ensure clarity and engagement when explaining complex topics like AI in Embedded Systems to students of varying levels of understanding?

OK. So if it again depends on the grades I'm going to teach, for example, if I have, if I have the opportunity to, to teach the students which don't have a background of the AI, I will surely start from the scratch, from the most basic level with day-to-day generalize, experiment with share, I will.

Share day-to-day experiences. For example, I will show them like where.

But these AI ML techniques are applicable in the real real time and around them. For example, if I talk about the fingerprint machine in every college and I expect in vit also it will be the first moment when the faculty enters and he or and the student enters, they have to put their finger or the biometric attendance are there. So it is the basic example I will I can show them and how they are going to implement them I can show.

So from the scratch if I going to teach, I will means purely align my teaching with the day-to-day experiences and along with the practical application they can see while while they are moving or going here and there. So that that will be like learning with practical applications. And I will also try to misarrange some good lectures of the industrial experts also and along with the professors or the well known scientists which.

Working out of in, in India as well as out of India, they're to deliver the lectures so that they can interact and the professors or the scientists and they can share their problem. They can, they can ask what we can do for the future work. So in my teaching, I will engage or involve these all things so that the the the interaction should not be only one way. It must be a interactive sessions.

And practical also.

Your comprehensive strategy for teaching complex topics such as AI in Embedded Systems demonstrates a deep commitment to student engagement and understanding. Starting with relatable, real-world examples like biometric attendance systems ensures accessibility for beginners, while incorporating expert insights from industry and academia enriches the learning experience for advanced students. Your emphasis on interactive and practical teaching is commendable. Moving to your **PhD in a relevant specialization**, could you briefly discuss the core focus of your doctoral research and its relevance to emerging trends in embedded systems and AI?

OK, so I have worked in the field of applications of electrical engineering for the AI ML applications. So basically in that I have utilized different AI ML approaches to solve the electrical engineering problems. For example, in my PhD, I have solved the problem of time processing of the AI ML techniques. I have solved the problem of human interactions in extracting the features.

So by utilizing the deep learning techniques that problem has been solved. I have applied the cross domain analysis also in which the different like signals has been taken and the analysis has been done. And if I talk about how the IT is relevant to the embedded system. So there are sensors which are utilized for the data acquisition. There are different softwares on which the processing are done like for example the.

Get software, the Labview software, the MATLAB software, along with the different IDs for microcontrollers and microprocessors I have utilized.

So combining and also the different, uh, online data acquisition open sources. So all these things are basically aligned in order to do the analysis of the signals and apply the AI ML techniques from a hardware to the real world applications. So these things are properly aligned to the AI embedded the requirement of this institute.

Thank you, Professor. Your doctoral focus on leveraging AI and ML to address critical challenges in electrical engineering, such as time-processing optimizations and feature extraction, clearly aligns with the evolving demands in embedded systems and AI. Your integration of hardware, software, and analytical methodologies underscores your relevance to this field. Let’s turn to **research publications in reputed journals**. Among your 30 publications, could you highlight one that you believe has made a significant impact on the domain of AI and embedded systems? Professor, could you elaborate on one of your major published works that aligns directly with AI Embedded Systems, detailing the core contribution, methodology, and the impact it has had in its field?

OK. So one of my I have published around 30 publications out of that, the best one which I published in the Q1 journal that is of the Springer where I have utilized the data acquisitions hardware for the that is the integration of the different vibrations as well as the miss, it is the application of combining or fusion of different sensors.

That are the vibration and the current and along with that extraction of the features has been done not the manually by utilizing the deep learning techniques and later on those deep learning techniques has been more get into the deeper by like for example, if I have utilized the different transfer learning approaches which are available in the market like Alex Net, Resnet, Nasnet but they as they are tuned and tested for. A particular problem.

They need to be tuned for our problem. The number of layers which are going to be used will be different as per the applications. So this thing I have done in one of my paper in which I have reduced the number of the layers so that the instead of utilizing the model directly in the in name of transfer learning we should incorporate some logic and we should try to optimize it to get the exact output. So if I.

Talk about it's like relevance. So if I if in the real world the multiple applications are utilizing the transfer learning models directly. So in this work, I have proposed that we don't have to utilize or never utilize the transfer learning models which are available in the market directly for the application. We should tune them and regularize them and we should reduce as much as we can to make the OR the reduce the complexity of the network.

Common uh feature selection criteria in which the three different approaches are fused in order to collect the most relevant feature from the feature set. So the common feature has been extracted because each algorithm perform calculations on different formulas. So to get the most common features will be like more making the more the model more reliable as the features which are going to be input. Are basically the.

And also there will be. I have applied a.

Must and the most important thing on which the model is going to be trained and test. So these things I have done and during this I have utilized the different vibration sensors, I have utilized the current sensors, I have utilized the softwares which are basically a combinations of embedded and AI.

Yeah.

Your publication in

Your publication in the Q1 Springer journal stands out as a significant contribution to the field of AI Embedded Systems. By addressing the inefficiencies of directly applying pre-trained transfer learning models and introducing optimized layer reductions and a novel common feature selection criterion, your work provides practical solutions to enhance reliability and reduce complexity. Combining vibration and current sensor data with tailored deep learning approaches further reinforces its applied value in real-world scenarios. Lastly, let’s move to **experience in industry projects or consultancy**. Can you share an example where you collaborated with industry on a project that involved AI and embedded systems? What role did you play, and what were the outcomes?

OK, so as the industry collaboration I have talked. So currently I am working with Hyundai Motor Corporation. So I am currently working in the domain of signal and noise reduction, the noise reduction from the signals. So the Hyundai Motor has provided me the problem of like when the sensors are utilized for the data acquisition, the noises around this, the sensors come along with the main signature. So in this work.

I have developed an AI ML technique in order to extract the motor signatures from the mixed signal which is consisting of motor as well as the uh the motor as well as the noise signals. So I have developed an AI ML technique to extract the machine signal from the mixed signal. So also for that they have provided me the data but I have also developed one hardware in the IIT Delhi. Which is basically a replica of that in which I have used the different.

Utilizing the acoustic sensor as well as the vibration sensor in the institute so that this and the data has been processed utilizing the AIML technique. So this is the Hyundai Motor I this is the collaboration and current working project of mine with the Hyundai Motor Corporation Korea.

EV motors which are like BLDC, SRM, PMSM, different motors. I have used and taken the data and validated my model over that so.

No, it was a very good interaction and uh, I would be saying that it's very nice for interacting the with you.

Your industry collaboration with Hyundai Motor Corporation is highly noteworthy. Addressing real-world challenges like noise reduction in sensor data through AI and machine learning aligns strongly with industry needs and showcases your problem-solving expertise. Developing both a tailored AI model and replicating the hardware environment at IIT Delhi reflects your holistic approach to solving complex engineering problems. This collaboration certainly demonstrates your capability to bridge academia and industry effectively. With this, we've explored the essential facets of your expertise and experience. Let me know if there's anything specific you'd like to address or discuss further before we proceed.