Dr Ramesh Vaddi, Associate Professor & Head of the Department of Electronics and Communication Engineering, along with his PhD Scholar Mr A Vinod Kumar has developed a new system for real-time and accurate detection and classification of manufacturing defects in Printed Circuit Boards (PCBs). This groundbreaking invention has been filed and published with Application Number: 202441021739 in the Patent Office Journal.

Abstract

This study presents a new system for real-time detection and classification of defects in Printed Circuit Boards (PCBs), which are critical in electronic products and systems. It employs an efficient model with pre-trained weights to detect defects for enhanced quality control. The model is initially trained and fine-tuned on a computer and then deployed on a compact computing board. For real-time imaging, a high-definition USB camera is connected to the system, allowing direct defect identification without the need for external devices. The output is shown on a monitor, with the PCB image featuring clearly labelled boxes to indicate the type and location of defects. This method offers a streamlined approach to defect classification, helping to improve the quality control process in electronics manufacturing.

Explanation of the Research in Layperson’s Terms

This research focuses on finding defects in PCBs, which are essential for most electronic devices like computers and phones. The system uses a powerful computer model to quickly identify any defects in real time. The model is trained on a regular computer to recognise normal PCBs and various defects. Once ready, it is transferred to a small, efficient computer board. A camera captures images of the PCBs, and the system analyses these images to identify defects. The results are displayed on a screen, clearly marking where the defects are and what types they are. This helps companies quickly and accurately detect defects in their electronics manufacturing process, saving time, reducing waste, and improving product quality

Practical Implementation/Social Implications of the Research

The practical implementation of this research involves deploying a system for real-time detection and classification of defects in PCBs, essential components in nearly all electronic devices. Using advanced deep learning techniques, the system can quickly identify manufacturing defects early in the production process. This leads to significant improvements in quality control, reduced waste, and lower production costs. By improving quality control in electronics manufacturing, the system helps reduce electronic waste, a significant environmental concern. Early detection of defects also decreases the chances of faulty electronic products reaching consumers, enhancing safety and reducing the need for product recalls. The system’s efficiency and accuracy could lead to more reliable electronics, fostering greater consumer trust in electronic products. This, in turn, encourages companies to invest in higher-quality manufacturing processes, ultimately leading to a more sustainable and responsible electronics industry.

Collaborations

To develop this system, the research team first trained a computer model to recognise defects in PCBs. The training involved feeding the model a large dataset of PCB images, some with defects and some without. The model learned to identify common defects by analysing these examples. Once trained, the model was implemented in a real-time setting and integrated with equipment to inspect PCBs during production. The system used a camera to capture images of each PCB and applied the trained model to analyse these images for defects. Running in real-time, the system could immediately detect issues and alert the manufacturing team, allowing them to correct problems on the spot. This approach improved product quality, reduced the chances of defective electronics reaching consumers, sped up the quality control process, and reduced waste, making the manufacturing process more efficient.

Future Research Plans

The research team has outlined several future plans to enhance and expand their defect detection system for PCBs:

• Model Optimization: Refining the machine learning model to improve accuracy and speed, experimenting with different architectures and training techniques to boost performance.
• Expanded Defect Library: Gathering a more extensive dataset of PCB defects to enable the model to identify a wider range of issues, making the system more versatile for various manufacturing environments.
• Real-World Testing: Testing the system in a broader range of manufacturing settings to ensure robustness and adaptability, understanding performance in diverse scenarios, and fine-tuning for optimal results.
• Integration with Manufacturing Systems: Aiming to integrate the system with other manufacturing processes and technologies for seamless communication between defect detection and other quality control systems, enhancing overall workflow.
• Automation and Robotics: Exploring the use of automation and robotics to streamline the defect detection process, potentially leading to a more automated manufacturing line with reduced human intervention and errors.
• Collaboration and Partnerships: Planning to collaborate with more industry partners and academic institutions to accelerate research and development, gaining valuable insights and resources to advance the system.

In a remarkable stride for the field of wireless communication, Prof. Rupesh Kumar from the Department of Electronics and Communication Engineering has authored a pivotal book that promises to redefine our understanding of radar and RF systems. The book, entitled “Radar and RF Front End System Designs for Wireless Systems,” is the latest gem in the prestigious “Advances in Wireless Technologies and Telecommunication (AWTT)” series.

With his profound expertise, Prof. Kumar navigates through the complexities of designing state-of-the-art front-end systems, offering readers a treasure trove of knowledge that bridges theory and practical application. This book is set to become an essential read for aspiring engineers and seasoned professionals alike, enriching the academic and industry landscape with its innovative approach.
Join us in celebrating Prof. Kumar’s exceptional contribution to the world of electronics and communication engineering. Dive into the depths of this masterful work and emerge with insights that could shape the future of wireless systems.

About the Book:
Radar and RF Front End System Designs for Wireless Systems delves into the intricate world of wireless technologies, particularly focusing on radar and RF front-end systems. The advent of wireless communication has ushered in a new era of connectivity, revolutionising various sectors including healthcare, smart IoT systems, and sensing applications. In this context, the role of RF front-end systems, with their reconfigurable capabilities, has become increasingly vital. The impetus behind this book stems from the remarkable surge in innovation witnessed in RF front-end systems. Researchers and practitioners alike have contributed a plethora of new configurations and design architectures, paving the way for unprecedented advancements in wireless systems. Our aim with this publication is to provide a platform for researchers to explore both theoretical insights and practical applications, thereby facilitating the dissemination of the latest trends and developments in the field. The contents of this book cover a wide spectrum of topics, ranging from RF frontend antenna systems to the impact of artificial intelligence and machine learning in system design. With contributions from experts in academia and industry, readers can expect a comprehensive exploration of radar and antenna system design, modeling, and measurement techniques. We envision this book serving as a valuable resource for students, researchers, scientists, and industry professionals seeking to deepen their understanding of RF front-end antenna and radar system designs. Whether it’s exploring reconfigurable antenna systems for 5G/6G networks, or delving into radar modelling and signal processing techniques, this book offers insights that are both timely and relevant.

Co-author of the Book:

Shilpa Mehta, a co-author of the book, holds a PhD from Auckland University of Technology, New Zealand. Presently, she serves as a Teaching Assistant at AUT, Auckland. Shilpa received the Summer Doctoral Research Scholarship for her PhD work. Her research spans across projects such as Radio Frequency Integrated Circuits, RF front ends, Optimization, Internet of Things, Wireless Communication, Artificial Intelligence, Healthcare, Radars, Software-defined Radios, and Smart Cities.

For the Book Chapter Publication, Click the Link

In a significant advancement for sustainable energy technology, the Indian Patent Office Journal has officially granted a patent for the “Mini magnetically levitated wind turbine system for power generation.” This groundbreaking invention, bearing Application Number: 202241051560, is the brainchild of Dr Goutam Rana, Assistant Professor in the Department of Electronics and Communication Engineering.

Dr Rana, along with his dedicated team of B. Tech ECE students—Mr Vybagula Sai Vamsi, Mr Moparthi Teja, Mr Indrakanty Satwik, and Mr Pidikiti Venkata Abhinash have developed a system that promises to revolutionise how we harness wind energy. The turbine’s miniaturised and magnetically levitated design allows for efficient power generation with minimal mechanical friction, leading to a longer lifespan and reduced maintenance costs.
The team’s innovation aligns with global efforts to transition to renewable energy sources and showcases the potential of academic research in contributing to real-world challenges. The patent grant not only recognises the technical ingenuity of the invention but also underscores the collaborative spirit of the students and faculty at the institution.

Abstract:

Due to the increasing demand and supply gap, in the electrical energy system, wind energy is coming out as an alternative form of clean-with zero-carbon footprint renewable energy sources for power generation. The same is true for hydrocarbon-based fuels whose resources are limited and the contribution of vehicular pollution is also raising concerns in every-degrading the air quality index (AQI) of Indian cities. The electric and hybrid vehicles thus emerging fast as an alternative but often being hindered by the unavailability of proper charging infrastructures on roads.

The current invention is aimed to enable the use of wind turbines for harnessing wind energy and utilize the same to charge batteries of electrical vehicles or hybrid vehicles. The major challenges that have prevented the use so far are mainly two viz. low air flow and larger air drag. To address low airflow in normal road conditions in congested city alleys, we demonstrated the use of magnetically levitated Vertical-axis turbines instead of conventional ball-baring-based Horizontal-axis wind turbines. To reduce the air drag the use of vertical axis magnetically levitated wind turbines is a good option since the air drag experienced in the blade unit is not exactly in contact with the car body.

To improve on the drag further we have introduced an array of mini turbine units instead of one big unit which helps in distributing the total drag over a large area and since air can pass easily through small units overall drag experienced will be small. Also, to keep the levitation small, the rotating unit is made lighter with 3D printing perforated PLA material.
The rest of the operation of the system is similar to any wind turbine system i.e. with the help of permanent magnet and coil arrangement we will convert the wind energy (rotor movement) into electrical energy (e.m.f.). Only here instead of one single source, we will generate multiple small sources of induced electrical energy which can then be coupled together and used for charging the battery.

Since the invention uses magnetic levitation, friction is minimal. This helps the rotor to become independent of natural wind flow and use the movement of the vehicle to generate the required torque for the rotor movement. Our invention can be installed in the rooftop space of any vehicle and since it is divided into an array of smaller units, allows the optimum use of available space of the used vehicle. Overall cost and weight are also very minimal. Here, the most practical use case can be the widespread E-rickshaws in India. The choice of the use case is based on the fact of their large presence, longer run hours, and limited speed for the runs.

Explanation of the Invention in Layperson’s Terms:
The invention will act as a source of energy and can be used to charge batteries of electric vehicles or hybrid vehicles. The device converts wind energy to electrical energy through mini wind turbine arrays which can be placed on top of the rooftop of the vehicles. The rotor is kept suspended from the stator unit using magnets to eliminate friction. This helps to operate the device without the presence of strong natural wind, it utilizes the movement of the vehicle to generate the necessary rotation. The mini turbine arrays and magnetic suspension help to reduce the effect of wind drag (extra wind replaced by the turbine unit).

Practical Implementation or the Social Implications Associated
The invention is intended to solve a few on-road challenges of Electric Vehicles (EVs). The current invention is:
1. Low cost and one-time investment with zero maintenance charge for an EV
2. The proposed device can act as a secondary power source for the vehicle
3. The proposed device will convert wind energy, thus completely environment-friendly, and with comes with absolutely zero carbon footprint
4. The proposed device can add some extra mileage to the current battery storage as much as it runs.
5. With other renewable sources together a hybrid vehicle can be built which is free of fossil fuel completely.

Future Research Plans:

The current invention is in just proof of concept stage. We are currently working on the following
1. to improve the overall efficacy of the device such that each unit can harness wind energy to its optimal potential. With this, we will try to ensure that the battery gets charged completely (or at least a significant percentage) during each run during the day.
2. We are also working on the numerical study to calculate the actual wind drag with a more optimal design so that we can estimate how many units a certain vehicle will require and what should be the optimal placement scheme to utilize the maximum wind effect.

In a significant academic achievement, Dr Anirban Ghosh, Assistant Professor from the Department of Electronics and Communication Engineering along with Mr Naga Srinivasarao Chilamkurthy, PhD Scholar, and Mr Shaik Abdul Hakeem, an undergraduate student, have made a remarkable contribution to the field of communication engineering. Their paper, titled “Optimal Routing Protocol in LPWAN Using SWC: A Novel Reinforcement Learning Framework,” has been published in the esteemed IEEE Sensors Journal, with an impressive impact factor of 4.3.

This publication marks a milestone for the university and highlights the innovative research being conducted by its faculty and students. The paper delves into the development of an optimal routing protocol for Low-Power Wide-Area Network (LPWAN) using State-Wise Communication (SWC), employing a novel reinforcement learning framework to enhance network efficiency and performance.
This work will pave the way for advancements in LPWAN technologies, which are crucial for the Internet of Things (IoT) ecosystem. The university community celebrates this achievement and looks forward to the positive impact it will have on technology and society.

Abstract:
Low Power Wide Area Network (LPWAN) has emerged as a dominating communication technology that offers low-power and wide coverage for the Internet of Things (IoT) applications. However, the direct data transmission approach has a limited network lifetime. Even multi-hop data transmission experiences several difficulties including high data latency, poor bandwidth utilization, and reduced data throughput. To overcome these challenges, in this paper, a recent breakthrough in social networks known as Small-World Characteristics (SWC) is incorporated into LPWANs.

In particular, in this work, Small-World LPWANs (SW-LPWANs) are developed by using the Reinforcement Learning (RL) technique and using different node centrality measures like degree, betweenness, and closeness centrality. Further, the performance of the developed SW-LPWANs is evaluated in terms of energy efficiency (alive/dead devices, and network residual energy) and Quality-of-Service (average data latency, data throughput, and bandwidth utilization), and is compared with that of conventional multi-hop LPWAN. Finally, to validate the simulation results, similar analyses are performed on the real-field LPWAN testbed.

The obtained simulation results confirm that SW-LPWAN developed by the RL method performs better than other techniques, with 11% more alive devices, 5.5% higher residual energy, 2.4% improved data throughput, and 14% efficient bandwidth utilization compared to the next best method. A similar trend is observed with real-field LPWAN testbed data also.

Explanation of the Research in Layperson’s Terms

Social networks primarily revolve around establishing human connections, whereas LPWANs are designed for connecting IoT devices that have limited battery-driven power. In this context, the smart devices must communicate in an IoT setting to conserve the limited energy available to them. To achieve this, the concept at the core of social networking also known as small world characteristic is incorporated into LPWAN using the Q-learning technique.

Practical Implementation or the Social Implications of the Research

IoT applications such as remote healthcare, smart environmental monitoring, asset tracking, and smart traffic systems require low transmission delay and high network lifetime. The proposed research helps in achieving the above parameters.

Collaborations
Dr Om Jee Pandey, Assistant professor Department of Electronics Engineering, Indian Institute of Technology, (BHU), Varanasi. e-mail: omjee.ece@iitbhu.ac.in

Dr Linga Reddy Cenkeramaddi, Professor, Department of Information and Communication Technology, University of Agder, Norway. e-mail:linga.cenkeramaddi@uia.no

Future Research Plan
In the next phase of research, we will be interested in investigating how the energy efficiency and other quality of service of smart devices in an IoT setting can be improved if they are partially or completely mobile.

Link to the Article

Dr M Durga Prakash, Assistant Professor in the Department of Electronics and Communication Engineering, and his PhD scholar, Ms U Gowthami, have published a research paper titled “Performance Improvement of Spacer-engineered N-type Tree Shaped NSFET towards Advanced Technology nodes” in the Q1 journal, IEEE Access. The paper has an impact factor of 3.9 and will pave the way for significant advancements in the field.

Here’s an abstract of their research paper

Abstract:

Scaling gate lengths deep is most reliable with tree-shaped Nanosheet FETS (NSFET). This paper uses TCAD simulations to study the 12nm gate length (LG) n-type Tree-shaped NSFET with a stack of high-k dielectric (HfO2) and (SiO2) spacers. The Tree-shaped NFET device features high on-current (ION) and low off-current (IOFF) with T(NS) = 5 nm, W(NS) = 25 nm, WIB=5nm, and HIB = 25 nm. Comparison of single- and dual-k spacer 3D devices and DC properties are shown. Because fringing fields with spacer dielectric prolong the effective gate length, the dual-k device has the highest ION / IOFF ratio, 109, compared to 107. This research also examines where work function, inter bridge height, breadth, gate lengths, temperature, and analog/RF and DC metrics affect the device. The suggested device has good electrical properties at 12 nm LG, with DIBL = 23 mV/V, SS = 62 mV/dec, and switching ratio (ION / IOFF) = 109. The device’s performance proves Moore’s law applies to lower technological nodes, enabling scalability.

The link to the article- https://ieeexplore.ieee.org/document/10499264 DOI: 10.1109/ACCESS.2024.3388504