Master’s in Electrical Engineering concentrating on wireless communications and Internet of Things
with expertise in Software Defined Radios (SDRs)
About Me
My self Dhruval Potla I'm passionate about electrical engineering and have been fortunate enough to work on some exciting projects. Recently, I've been working as a Research Lead for the Chip-less RFID development team at the University at Buffalo's WINGS Lab. I helped design and develop an SDR-based Chip-less RFID reader using USRPs X310 and B210, resulting in a 40% increase in tag reading accuracy and efficiency. I also developed an Out-of-Tree block in GNU Radio with embedded Python for dynamic center frequency adjustment at the interrogator, demonstrating my initiative and self-taught skills in Python and linux while improving system accuracy and efficiency. I'm proud to say that I led four progress report and presentation sessions with sponsors and beneficiaries, ensuring effective communication and alignment throughout the project lifecycle.
Before that, I worked as a Research Assistant for the same Chip-less RFID development team at WINGS Lab. During my time there, I improved tag performance by 50% through careful selection of optimal thermal transfer printed tag design from over 200 different design combinations. I also designed and built a makeshift anechoic chamber using cardboard and sponges to achieve a 40% reduction in interference, improving data accuracy and reliability through resourcefulness and thorough testing. I optimized designs through simulation using CST Studio Suite, resulting in improved performance and reduced cost of physical prototyping. I even investigated the relationship between design constraints and thickness of aluminum-based ink using electron microscopy and vector Network Analyzer contributing to the development of optimized tag design.
Outside of work, I've been involved in some cool projects as well. I've conducted MATLAB simulations to implement and analyze digital modulation schemes, such as PAM, QAM, and PSK, and compare their Bit Error Rate (BER) and Symbol Error Rate (SER) curves for optimum receiver detection. I've also designed a 22 dB parallel line 3-section coupler with specific design constraints, including a coupling of 20 dB, directivity of 17 dB, and insertion loss of 1.5 dB at the design frequency, improving circuit performance and increasing productivity. I've even developed skills in deep learning, image processing, and Python while working on a Fingerprint Image Restoration and Classification project, where I improved gender classification accuracy from 50% to 95% for blurry fingerprint images using CNN and TensorFlow Keras.
In terms of education, I'm currently pursuing a Masters in Electrical Engineering from SUNY University at Buffalo, Buffalo, and completed my B.Tech in Electronics and Communication Engineering from SRM University, Chennai, India.
Apart from my technical skills, I'm proud to say that I've also been involved in some extracurricular activities. I've tutored over 100 secondary and higher secondary students in Sciences over the span of six years, demonstrating my excellent communication and soft skills. I've captained a National level field hockey team which was awarded 2nd position at All-India hockey meet and secured 2nd position at pin-level rifle shooting competition, demonstrating my leadership and athletic ability. I've also volunteered in one of the world’s largest distribution of sanitary pads among underprivileged organized by Blooming Beacon NGO, showcasing my social responsibility and community involvement.
Signal processing has a rich history of use in various fields, including modification, prediction, transformation, and numerical analysis. With the rise of computers, however, it has become evident that signal processing algorithms have the potential to do much more. As a result, modern mathematicians have created a variety of digital signal processing algorithms, including the Fast Fourier Transform, Z-Transform, Discrete Fourier Transform, and others. These algorithms are now widely used in a range of computing applications, enabling advanced analysis and processing of digital signals. In this reflection, I will discuss my experience studying the course Digital Signal Processing and the insights I gained into the principles and practical applications of these algorithms.
When I first considered taking the Digital Signal Processing course, I was hesitant because I had already studied a similar course during my bachelor's studies. However, after speaking with Professor Dr. Aldy T. Fam, I discovered that the course had been updated to include modern applications and was structured in a way that would offer a more in-depth understanding of the topics. In particular, I was excited about the opportunity to complete a guided project in the field, which would provide valuable experience for my future job applications. Ultimately, I decided to enrol in the course and have found the experience to be both challenging and rewarding.
The initial portion of the Digital Signal Processing course focused on revising the fundamental concepts of Signals and Systems. We covered both continuous and discrete time signals and their properties, as well as various signal spaces such as Banach Spaces and Hilbert Spaces for both continuous and discrete time signals. By reviewing these fundamental concepts, we were able to establish a solid foundation for understanding the more advanced topics that were to follow. Additionally, this revision helped me refresh my knowledge of the basics and clarified some of the concepts that I had previously found challenging.
One of the most fascinating aspects of the Digital Signal Processing course for me was our in-depth study of the Fourier Transform. This algorithm is, in my opinion, one of the most ingenious of the 20th century. It has proven to be an indispensable tool in many areas of modern technology, including wireless communication and GPS, and its insights have revolutionized the field of signal processing. As we delved into the Fourier Transform, I found myself captivated by its ability to exchange the view between the time and frequency domains. Watching Dr. Fam teach this concept was like witnessing a magic trick - it opened my mind to new ways of thinking and seeing the world. Through this experience, I learned that a complex problem can become much clearer when viewed from a different perspective. Overall, this was one of the most influential classes of my life, and I am grateful for the opportunity to have studied this ground-breaking algorithm in such depth.
Fig. 1 The Fourier Transform of some basic Signals
After our study of the Fourier Transform, we delved into the topic of filter design. We learned about important concepts such as causality, frequency-selective filters, finite impulse response (FIR) filters, and bandpass filters. Through this portion of the course, we explored the practical applications of different filter types and how they can be used in real-world scenarios. In addition to these fundamental topics, we also covered more advanced concepts such as the Continuous Wavelet Transform (CWT), Multiresolution Analysis, and the Two-Dimensional Cases of various signal processing techniques. While some of these advanced topics were challenging to grasp, I found that the guidance of Dr. Fam and my classmates made them more accessible. In particular, the Multiresolution Analysis opened my eyes to new ways of understanding and processing signals, and the Two-Dimensional Cases added a whole new level of complexity to my understanding of signal processing. Overall, I found this portion of the course to be intellectually stimulating and highly relevant to modern signal processing applications.
One of the most surprising and exciting technologies I learned from DSP is the use of error correction in radio transmission. The history of transmitting speech signals in digital form dates back to the 1960s with the development of pulse code modulation (PCM) technology. This technology utilized an analog-to-digital converter with a sampling frequency of 8kHz and a 16-bit quantizer to produce a digital signal with a bit rate of 128kbps. However, transmitting digital signals with higher bit rates was not efficient. In response, several technologies were developed to reduce the bit rate of speech signals without compromising quality, such as sub-band coding in 1977 and Code-Excited Linear Prediction (CELP) in 1982. CELP is still widely used today due to its efficient use of available bandwidth and high-quality output. Through the study of these technologies, I gained a greater understanding of how digital signal processing is used in real-world applications to improve the efficiency and quality of information transmission.
One of the highlights of this course was the academic project that challenged me to apply DSP concepts to real-world problems. I developed a Fingerprint Image Restoration and Classification program that estimated the point spread function for blurry fingerprint images and used blur modelling and deconvolution techniques in the frequency domain for image restoration. I then employed Convolutional Neural Networks for gender-based classification, achieving an impressive 98% accuracy. This project showcased the versatility of DSP concepts and their practical applications beyond their traditional domains. It was a truly rewarding experience that demonstrated the immense potential of DSP in solving complex problems in diverse fields.
Fig. 2 Blurry finger print image recovered using point spread function
In conclusion, DSP is undoubtedly a challenging course, but with the help of Dr. Fam's excellent teaching style and well-structured course material, I found it to be a fascinating and rewarding learning experience. From revising the basics of signals and systems to diving deep into advanced topics such as Fourier-Transform and Multiresolution Analysis, this course provided me with a solid foundation and a comprehensive understanding of the subject. I also had the opportunity to apply the concepts I learned by developing a Fingerprint Image Restoration and classification program, which not only demonstrated the practical applications of DSP but also helped me improve my programming skills. Overall, I highly recommend this course to anyone pursuing a career in engineering, as the concepts learned in DSP are widely used across various fields, and the skills acquired will undoubtedly be beneficial in the future.
As an electrical engineer with a passion for exploring the frontiers of technology, I was intrigued by the RF/Microwave Circuits course offered by Prof. Uttam Singisetti. I was immediately drawn in by the exciting and comprehensive syllabus, which promised to provide a deep dive into the world of high-frequency circuits. As an engineer with a strong theoretical background but little practical experience, I was eager to gain hands-on experience with industry-standard simulation tools like ADS and HFSS.
The course exceeded my expectations, covering a broad range of topics such as transmission line theory, impedance matching, waveguides and connectors, and filter design. The course started with an in-depth review of transmission line theory, which served as an excellent refresher for me. We then moved on to Smith charts, which were essential for understanding impedance matching. The course covered various impedance matching techniques, including lumped elements, stubs, single section and multi-section l/4 matching networks, binomial broadband matching, and Chebyshev broadband matching.
The second half of the course was focused on waveguides and connectors, which was an area that I found particularly fascinating. We learned about various types of waveguides and their properties, including rectangular waveguides and their wall loss, circular waveguides, coaxial cables, stripline, microstrip, and coplanar waveguide. We also explored the concept of scattering matrices and their properties, including the use of S-parameters to characterize microwave components. We then went on to study power dividers, branch line couplers, and coupled line couplers, which are essential building blocks for designing high-performance RF and microwave circuits.
Throughout the course, we had the opportunity to apply our theoretical knowledge to practical assignments that required us to use industry-standard simulation tools. These assignments helped me to develop my skills in designing and simulating microwave circuits, which I know will be invaluable in my future career. One of the highlights of the course was the lab sessions, which provided us with hands-on experience in designing and testing RF and microwave circuits. The lab sessions were a great complement to the theoretical knowledge we gained in the course and helped us to develop important practical skills.
The final project of this course was undoubtedly one of the most challenging but rewarding experiences. Our task was to design a 20±1 dB parallel line 3-section coupler with a center frequency of 3 GHz and a bandwidth of 4 GHz between 1 GHz and 5 GHz. The project required us to meet specific specifications, such as the coupling at 3 GHz at 20±1 dB, directivity at design frequency 15±2 dB, and insertion loss at design frequency of 1.5 dB maximum in ADS.
If you have a passion for RF and mm-wave IC design, I highly recommend taking the RF/Microwave Circuits course. Through a combination of theoretical concepts, practical assignments, and hands-on lab sessions, this course provides a comprehensive understanding of high-frequency circuit design. I found the course to be invaluable in equipping me with the skills and knowledge necessary for a successful career in this field. In fact, it was a core course for me and has solidified my plans to pursue a career in RF and mm-wave IC design.
As a Bachelor's degree holder in communication engineering, I enrolled in the Master's program in Digital Communication at the University at Buffalo to further my knowledge in this field. The variety of courses offered caught my attention, including the EE620 Multiple-Input-Multiple-output(MIMO) wireless communication course. Through this course, I gained a deeper understanding of MIMO, its benefits, how it works, and different techniques. My goal was to enhance my career profile, as I aspire to work in wireless communication. In this reflection, I will discuss my experience in the course, challenges faced, and the impact on my career goals.
MIMO technology is a game changer in wireless communication, utilizing multiple antennas at both the transmitter and receiver ends to create multiple spatial channels for data transmission. This allows for faster data rates and improved reliability compared to traditional wireless communication systems. MIMO has become an essential technology for various wireless communication applications, especially in urban environments with poor signal quality due to interference and noise.
During the first few classes of the course, we extensively discussed the history of wireless communication, from the pioneering work of Marconi in 1901 to the modern-day cellular systems such as 5G. We also examined the evolution of wireless local area networks (WLAN) and the various IEEE 802.xx standards that define them. Specifically, we looked at the technical details of the IEEE 802.11 family of WLAN standards, including 802.11a/b/g/n/ac/ax, and discussed their respective features and capabilities. Additionally, we explored the different generations of cellular systems, starting from 1G all the way up to 5G, and the advancements made in each generation. This foundational understanding of the history and evolution of wireless communication provided us with a contextual framework that was essential in understanding the principles of MIMO wireless communication.
In the following classes, we dolve deeper into wireless communication concepts and reviewed the basics of Single-Input-Single-Output (SISO) communication systems. We also represented various systems mathematically, including the channel model, which is crucial for MIMO system design. The motivation for MIMO systems was discussed in detail, including the need for increased spectral efficiency and higher data rates. With multiple antennas, a MIMO system can combat noise and fading by using spatial diversity techniques. The capacity of a MIMO system increases linearly with the minimum number of transmitter and receiver antennas, which is a key advantage of this technology. We also learned about different types of MIMO systems, such as Spatial Multiplexing (SM) and Space-Time Coding (STC), which use different techniques to achieve high capacity and reliability.
Through the course, I learned about the three distinct phenomena that affect radio signal propagation: path loss, shadowing, and multipath fading. Multipath fading, in particular, was a topic of interest, as it involves the signal taking multiple paths to reach the receiver, causing interference. The course discussed several fading models and their impact on MIMO systems in real-world scenarios. The understanding of fading is crucial in designing and optimizing wireless communication systems, ensuring reliable and robust communication, even in challenging environments.
Fig. 1 Illustrating Multipath fading
One of the most challenging topics we covered in the course was Space-Time coding and Modulation for MIMO communication systems. We explored the motivation behind Space-Time coding, which is to improve the reliability and capacity of MIMO communication systems by exploiting the spatial diversity of multiple antennas. We then discussed ST-coded MIMO channels and the criteria for designing effective Space-Time codes. We delved into various types of codes, including Cyclic codes and ST codes for orthogonal and block orthogonal systems, as well as Diagonal algebraic ST codes. We also discussed the advantages and limitations of different code design approaches and the trade-offs involved in selecting the appropriate code for a particular MIMO system. While this was a challenging topic, I found it very rewarding to learn about the intricacies of Space-Time coding and its practical applications in wireless communication systems.
During the final part of the course, we delved into important determination criteria in MIMO-OFDM systems for broadband wireless communication. We examined the mathematical models and motivations behind these systems, including space-frequency coding and the maximum achievable diversity. Specifically, we explored the concepts of channel estimation, channel equalization, and optimal resource allocation for MIMO-OFDM systems. Additionally, we analyzed different space-frequency coding schemes, such as Alamouti coding and space-time block coding, and their performance under different conditions. This part of the course was particularly interesting to me, as it provided a comprehensive understanding of how MIMO-OFDM systems can overcome various wireless communication challenges and achieve high data rates in broadband wireless networks.
As part of the course, we completed a final project that involved simulating various systems in MATLAB. One of the tasks was to implement and analyze different digital modulation schemes such as PAM, QAM, and PSK to optimize receiver detection. We then compared the bit error rate (BER) and symbol error rate (SER) curves of these schemes. Additionally, we implemented a MIMO wireless system using Jake’s Fading Simulator to compare system performances (SER vs SNR curves) and determine the achieved diversity of the system. These projects not only improved my MATLAB skills but also added an additional skill-set to my professional profile, reflecting in my CV.
Fig. 2 A graph showing the results of the simulation in one of assignments
I strongly recommend this course to anyone interested in pursuing a career in Communications. While the course was mathematically intensive and covered a wide range of topics, it provided valuable insights into the workings of modern communication systems. The concepts covered in the course are essential for cutting-edge technologies that are shaping the future of communication, enabling faster and more reliable transmission of information. Overall, the course was challenging, but it gave me a deeper understanding of the field and equipped me with the knowledge and skills necessary to pursue advanced research in wireless communication systems.
IoT is changing the world by connecting devices, collecting and exchanging data, and making processes smarter and more efficient. It's transforming industries such as healthcare, agriculture, and transportation, and enhancing our daily lives such as smart homes and cities. With its potential to revolutionize every aspect of our lives, learning about IoT is essential to stay ahead in this dynamic field.
When I first attended the class for this course , Professor Dr. Guan warned us about the challenging nature of the course, and I felt like dropping it. But I spoke with my seniors and Dr. Krishnamoorthy, the teaching assistant, who convinced me to give it a shot. They promised me that the lab projects and final projects would be extremely beneficial for my career development. Even though I was already juggling a heavy course load, I decided to take on this course. And I must say, it has been a great decision! I have been enjoying the class and the projects, and I don't regret my decision at all.
In the first few classes of, we delved into the definition of IoT, explored its various use cases, and identified the business opportunities it presents. The introduction was very well-structured and provided a solid foundation for the rest of the course. I appreciated how the professor broke down complex concepts into easy-to-understand components, which made it easier for me to grasp the material.
In the subsequent classes , we doved deeper into data acquisition and local data processing. One of the concepts that particularly fascinated me was software-defined radio. The way it enables radio systems to be configured and reconfigured using software rather than hardware is truly remarkable. I was so intrigued that I decided to learn more about it, and eventually became a research assistant in a project involving software-defined radio. The knowledge and skills that I gained in the course helped me to contribute to the project effectively, and I am grateful for the opportunity to explore this fascinating field.
Throughout the course, we were assigned several paper readings, and were required to present our findings to the class. I had the opportunity to present on three papers, including LMAC: Efficient Carrier-Sense Multiple Access for LoRa, RFlens: Metasurface-Enabled Beamforming for IoT Communication and Sensing, and UAV Network and IoT in the Sky for Future Smart Cities. Preparing for and giving these presentations not only helped me to better understand the concepts covered in the papers, but also enhanced my communication and presentation skills. I appreciated the opportunity to engage with my peers and to learn from their presentations as well.
The course offered various lab sessions that gave us practical experience in applying the concepts learned in class. In the first lab, we set up the hardware and software components required for the rest of the semester, which helped us become familiar with the tools we needed for the lab assignments and final project. In the second lab, we learned about data acquisition systems commonly found in daily applications and the associated design guidelines and mistakes. This prepared us for the final project. In the third lab, we were introduced to IoT communication technologies and used LoRaWAN to send data gathered from sensors to the cloud. These lab sessions equipped us with valuable hands-on experience, which helped us better understand the concepts and prepare us for real-world IoT applications.
The final group project was one of the most challenging yet rewarding parts of the course. Our team decided to build a real-time water quality monitoring and warning system for remote reservoirs, which involved integrating various sensors with both Arduino and Raspberry Pi. We faced numerous technical challenges along the way, including difficulties with configuring the sensors to accurately measure the water quality, and communication issues between the sensors and the microcontrollers. Despite these obstacles, we were determined to build a functional system that could accurately monitor and report the water quality in real-time. We worked long hours, sometimes late into the night, testing and troubleshooting the system. We collaborated closely as a team, sharing ideas and expertise to overcome the technical difficulties we faced. In the end, we successfully integrated all the components and deployed the system in a remote reservoir. The dashboard we created on ThingSpeak IoT platform provided real-time monitoring of water quality and sent warning alerts if the water quality dropped below a certain threshold. The experience of building something from scratch and overcoming technical challenges was immensely valuable and provided a sense of accomplishment.
Fig.1 Picture showing the setup of our final project
Overall, I found this course to be the most comprehensive and informative one that I have ever taken. Despite the challenges and the amount of effort required, I am grateful that I took this course because of the immense amount of knowledge and skills that I have gained. In fact, it has significantly increased my marketability in the field of embedded systems and IoT, and has become a valuable asset to my professional profile.
Therefore, I strongly recommend this course to anyone who is interested in pursuing a career in this field. The course content and hands-on experiences are the shining parts of my resume, and I believe that they will serve as a solid foundation for anyone who is looking to excel in this field.
Job : Research assistant Company: SUNY RF Duration: May 2022 to Jan 2023 SUNY Research Foundation (RF) is the largest comprehensive university-connected research foundation in the country. It provides essential administrative services that enable State University of New York (SUNY) faculty to focus their efforts on educating students and performing life-changing research across a wide range of disciplines including Artificial Intelligence, Clean Energy, Biotechnology, Longevity, Substance Addiction, Nextgen Quantum Computing, Environmental Health, and Resiliency. The RF works with the academic and business leadership of SUNY campuses to facilitate research and discovery by administering sponsored projects and delivering intellectual property and technology transfer services that fuel innovation and move ideas and inventions to the marketplace. The RF is a private non-profit education corporation that is tax-exempt under Internal Revenue Code (IRC) Section 501(c) (3).
Responsibilities As RA:
As a research assistant, I was responsible for conducting extensive testing of RFID tag designs using thermal transfer printing techniques, addressing printing issues, creating an Anechoic chamber to reduce interference, optimizing tag designs through simulation, investigating the relationship between design constraints and ink thickness, designing and developing an SDR-based Chip-less RFID reader, teaching myself embedded Python, and leading progress report and presentation sessions with sponsors and beneficiaries. These responsibilities required me to have technical expertise in RFID technology and associated software tools, problem-solving skills, communication skills, independence, and familiarity with industry-standard equipment.
My learnings :
During my time as a research assistant, I was able to improve my technical skills in RFID technology, simulation software, electron microscopy, and GNU Radio. Additionally, I developed my problem-solving skills through identifying and addressing printing issues, reducing interference, and optimizing tag designs. I also improved my communication skills by leading progress report and presentation sessions with sponsors and beneficiaries, and effectively conveying complex technical information. I gained independence and self-direction, as demonstrated by my self-teaching of embedded Python and development of an Out-of-Tree block in GNU Radio. Finally, I gained familiarity with industry-standard equipment, which may be useful in future projects or employment opportunities.
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