In the patent titled “A System and a Method For Real-Time Pneumonia Diagnosis On a Resource- Constrained Hardware Platform,” authored by Prof. Siva Sankar Yellampalli from the Department of ECE and his research scholars – Mr Rahul Gowtham Poola and P L Lahari, a novel diagnostic solution is presented to enhance pneumonia detection in low-resource settings. With Application No: 202441084727, this research explores the integration of advanced deep learning techniques with a compact microcontroller-based system, providing an innovative approach to improve healthcare accessibility and prompt medical intervention.

Abstract:

The research focuses on the development of an innovative system for real-time pneumonia diagnosis leveraging advanced deep learning techniques integrated with edge computing technology. The proposed solution employs the MAX78000 microcontroller, a resource-constrained hardware platform, to deploy a sophisticated neural network model capable of analyzing chest X-ray images. The invention addresses the pressing need for accessible, cost-effective, and efficient diagnostic tools in under-resourced and remote environments. The system encompasses a complete diagnostic pipeline, including image acquisition via an onboard parallel camera module, real-time image processing, and display of results on a 3.5″ touch-enabled TFT screen. The deep learning model, optimized for the constraints of the MAX78000, performs real-time classification of chest X-ray images into either normal or pneumonia-affected categories. By operating entirely on-device, the system eliminates the need for high-power servers or internet connectivity, thereby reducing latency and dependency on external infrastructure. This research emphasizes portability, energy efficiency, and low-cost deployment, making the solution highly suitable for primary healthcare facilities, rural clinics, mobile health units, and disaster-response scenarios. With the ability to deliver immediate, accurate diagnoses, the device significantly enhances clinical decision-making and enables timely medical intervention. Additionally, the scalable and adaptable design of the system opens possibilities for broader medical imaging applications, extending its utility beyond pneumonia diagnostics. Experimental results showcase the performance of the neural network model, demonstrating prediction accuracies ranging between 66% and 97% for different test cases on the MAX78000 microcontroller. These findings underline the potential of the proposed system as a transformative tool for advancing point-of-care diagnostics in low-resource settings.

Explanation in Layperson’s terms.

The research presents a compact, affordable device that helps doctors quickly detect pneumonia by analyzing chest X-ray images in real-time. It uses advanced artificial intelligence (AI) technology, called deep learning, to examine the X-rays and determine whether a patient has pneumonia or not. What makes this device special is that it works entirely on a small, low-power microcontroller called the MAX78000, instead of needing powerful computers or internet access. The process begins when the device captures a chest X-ray image using its built-in camera. Then, the AI model, which has been trained to recognize patterns associated with pneumonia, analyzes the image. The results are displayed instantly on a small screen, allowing healthcare providers to make quick decisions. This real-time diagnosis can be life-saving, especially in emergency or rural settings where access to advanced medical equipment or high-speed internet is limited. Technically, this system combines AI and edge computing, meaning all the heavy processing happens directly on the device rather than in remote servers. This design keeps costs low, ensures patient data privacy, and makes the device highly portable and energy-efficient. The technology can work even in places with unreliable electricity, making it ideal for use in mobile health units, rural clinics, or disaster zones. Additionally, the invention can be adapted for diagnosing other diseases, showcasing its versatility in improving healthcare globally.

Practical Implementation

This research can be practically implemented as a compact, standalone device for diagnosing pneumonia in healthcare settings where access to advanced medical equipment is limited. It works as follows:

• Deployment in Rural Clinics and Mobile Health Units: The device can be used in clinics in remote or underserved areas where large X-ray machines and advanced computing resources are unavailable. It provides on-the-spot diagnosis.
• Point-of-Care Diagnostics: The portability and integration of image acquisition, AI-based processing, and display into a single unit make it ideal for bedside use in hospitals or during emergency care.
• Disaster Response: Its low-power and internet-free design make it a critical tool in disaster zones, refugee camps, or any setting where power and connectivity are unreliable.
• Telemedicine Integration: The device can complement telemedicine by providing accurate diagnostic results to remote doctors, helping bridge the gap between frontline healthcare workers and specialists.

Social Implications

Improved Access to Healthcare: By making pneumonia diagnosis accessible in rural and underserved regions, this device can drastically reduce the gap in healthcare services between urban and remote areas. It empowers healthcare providers in low-resource settings to deliver timely diagnoses.

• Affordability: The use of a low-cost microcontroller ensures that the device is affordable for governments and healthcare organizations, particularly in developing countries. This can enhance healthcare access for low-income populations.
• Reduced Mortality Rates: Pneumonia is a leading cause of death in children under five and elderly individuals, especially in low-income countries. This device’s ability to provide real-time, accurate diagnosis allows for earlier intervention and treatment, potentially saving countless lives.
• Privacy and Security: Since all data is processed locally on the device, it ensures patient privacy by eliminating the need to transfer sensitive medical data to cloud servers, addressing concerns about data security.
• Scalability: The underlying technology can be adapted to diagnose other diseases, creating a broader impact on global health. For example, similar systems could be used for tuberculosis, COVID-19, or other respiratory conditions, further enhancing healthcare infrastructure.

By addressing critical gaps in diagnostic capabilities and ensuring accessibility and affordability, this research has the potential to transform healthcare delivery and improve quality of life, especially in marginalized communities.

Collaborations:

Rahul Gowtham Poola, Ph.D Scholar, Dept of ECE, SRM University-AP

P.L. Lahari, Ph.D Scholar, Dept of ECE, SRM University-AP

Prof. Siva Sankar Yellampalli, Professor of Practice, Dept of ECE, SRM University-AP

The research team from the Department of Electronics and Communication Engineering has published a paper titled “RadiomixNet: Integrating Radiomics and Feature Extraction for Advanced Pneumonia Diagnosis” in the journal IEEE Access with an impact factor of 3.4. Prof. Siva Sankar Yellampalli, Professor of Practice, and Mr Rahul Gowtham, PhD Scholar, have worked on RadiomixNet, a smart computer-assisted system designed to help doctors diagnose pneumonia more accurately using chest X-ray images.

Abstract

The research presents RadiomixNet, a pneumonia diagnosis framework integrating radiomics-based feature extraction with advanced classification techniques. Chest X-ray images are pre-processed using denoising, resizing, and enhancement methods to ensure uniformity and high image quality. Radiomics features are extracted using Gray Level Co-Occurrence Matrix (GLCM), Gray Level Size Zone Matrix (GLSZM), Gray Level Run Length Matrix (GLRLM), and Gray Level Dependence Matrix (GLDM). Power Spectral Density (PSD) analysis using Burg, Yule Walker, and Welch techniques enhances the understanding of frequency characteristics within the radiomics feature matrices. To classify pneumonia cases, machine learning classifiers such as Bernoulli Naïve Bayes, Random Subspace Boost, Quadratic Discriminant, and Gradient Boosting are employed. Among these, Gradient Boosting demonstrated superior performance, achieving a Cohen’s Kappa of 0.93, MCC of 0.88, Youden’s Index of 0.82, and a Log Loss of 0.27. The proposed methodology enhances diagnostic accuracy, reduces variability in pneumonia detection, and provides a structured approach to feature-based pneumonia classification.

Explanation of the Research in Layperson’s Terms

Traditional diagnosis relies on a doctor visually examining the X-ray, which can sometimes lead to misinterpretations. RadiomixNet improves this process by using advanced image processing and artificial intelligence (AI) techniques.

1. Preprocessing the X-rays – Before analysis, we clean the images by removing noise (unwanted distortions), adjusting brightness, and resizing them to a standard format. This ensures all images are high quality and uniform.
2. Generating More Training Data – Since AI models need a large amount of data to learn effectively, we use Generative Adversarial Networks (GANs) to create additional synthetic X-ray images. This helps balance the dataset and improve the model’s ability to detect pneumonia accurately.
3. Extracting Hidden Patterns – The system breaks down X-ray images into tiny texture and shape details using advanced techniques like GLCM, GLSZM, GLRLM, and GLDM. These methods capture the structure of the lungs and highlight patterns that indicate pneumonia.
4. Analysing Frequency Components – Similar to how an audio equalizer separates different sound frequencies, we analyze the X-ray’s frequency components using techniques like Burg PSD, Yule Walker PSD, and Welch PSD. This helps uncover hidden details in the images that may not be visible to the human eye.
5. Making the Final Diagnosis – After extracting these detailed features, we use AI models to classify the images as “pneumonia” or “healthy.” We tested different models, including Naïve Bayes, Random Subspace Boost, Quadratic Discriminant, and Gradient Boosting. Among them, Gradient Boosting performed the best, making the most accurate predictions.
6. Evaluating Accuracy – To ensure the system is reliable, we used various accuracy-checking methods such as Cohen’s Kappa, Matthews Correlation Coefficient (MCC), Sensitivity, Specificity, Log Loss, and Brier Score.

Practical Implementation/Social Implications of the Research

Practical Implementation:

RadiomixNet has the potential to be integrated into real-world healthcare systems to assist in pneumonia diagnosis. Its implementation can take place in various ways:

1. Hospital Integration – RadiomixNet can be deployed in hospitals as a decision-support tool for radiologists. By analysing chest X-rays in real time, it can provide secondary validation, reducing diagnostic errors and improving accuracy in pneumonia detection.
2. Telemedicine and Remote Diagnosis – The system can be integrated into telemedicine platforms, allowing doctors in rural or under-resourced areas to diagnose pneumonia remotely. Patients can upload their X-ray images, and RadiomixNet can assist in providing a preliminary diagnosis.
3. Medical Imaging Centers – Radiology centers can incorporate RadiomixNet into their existing Picture Archiving and Communication Systems (PACS) to enhance diagnostic efficiency, reduce the workload of radiologists, and provide automated analysis.
4. Edge Computing in Low-Resource Settings – Unlike deep learning models that require expensive GPUs, RadiomixNet is optimized for standard computing hardware. This makes it feasible for implementation in clinics and hospitals that lack high-end computational resources.
5. Clinical Trials and Further Validation – Pilot studies in hospitals can validate RadiomixNet’s accuracy and reliability before widespread deployment. The system can be fine-tuned based on real-world patient data to improve its performance across diverse populations.

Social Implications:

1. Early and Accurate Diagnosis – By improving pneumonia detection, RadiomixNet can enable earlier treatment, reducing complications and mortality rates, especially in high-risk populations such as children, the elderly, and immunocompromised individuals.
2. Reducing Radiologist Workload – With increasing patient loads, radiologists often face diagnostic fatigue. RadiomixNet can act as an assistant, helping them focus on complex cases while automating routine pneumonia detection.
3. Bridging the Healthcare Gap – In developing countries where expert radiologists are scarce, RadiomixNet can assist general practitioners and healthcare workers in diagnosing pneumonia without requiring extensive radiology expertise.
4. Affordable and Scalable Solution – Since the system does not require expensive hardware, it can be implemented in low-resource settings, making advanced pneumonia detection accessible to a broader population.
5. Pandemic Preparedness – Pneumonia is a major complication of respiratory infections like COVID-19. RadiomixNet can be adapted to detect pneumonia-related lung infections, aiding in large-scale screening during outbreaks.

By integrating RadiomixNet into healthcare systems, we can enhance diagnostic accuracy, improve patient outcomes, and make pneumonia diagnosis more accessible and efficient globally.

Read more

RadiomixNet Implementation Framework

The Department of Electronics and Communication Engineering is proud to announce that Dr Durga Prakash M and his scholar Prasanthi Ms Prasanthi Lingala have their invention titled “An Organic Thin-Film Transistors (OTFTs) with Steep Subthreshold and Ultra-Low Temperature Solution Processing for Label-Free Biosensing” published in the Indian Patent Office Journal with the Application Number: 202541000088. Their research focus on developing an Organic Thin-Film Transistor (OTFT) that is able to work as a biosensor in detecting diseases or for real-time health monitoring.

Abstract

Organic Thin-Film Transistor (OTFT): The name “organic thin-film transistor” (OTFT) refers to a type of transistor that employs organic semiconductor materials in its active layer rather than the more traditional inorganic materials such as silicon. Optical thin-film transistors (OTFTs) are distinguished by their adaptability, low fabrication cost, and optimal applicability for electronic devices that are lightweight and portable. Considering their high sensitivity to changes in the surrounding environment and their compatibility with functionalised layers for the detection of biomolecules, these transistors find widespread application in the field of biosensors.

Explanation of the Research in Layperson’s Terms

Imagine a flexible electronic switch that can be bent, stretched, and used in lightweight devices—this is what an Organic Thin-Film Transistor (OTFT) does! Unlike traditional transistors made from rigid silicon, OTFTs use special organic materials, making them more adaptable for wearable sensors, flexible displays, and medical devices.

The research focuses on how these transistors can be used as biosensors, meaning they can detect tiny changes in the environment, like the presence of certain chemicals or biomolecules. This is important for medical testing, where OTFTs could help develop low-cost, highly sensitive diagnostic tools—imagine a simple patch that can detect diseases from sweat or a flexible sensor for real-time health monitoring! By improving how OTFTs interact with biological substances, the team aims to make them more accurate, efficient, and reliable for next-generation healthcare and wearable technology.

Fig.: Schematic structure of DNTT based OTFT

Faculty duo from the Department of Electronics and Communication Engineering, Dr Anirban Ghosh and Dr Sunil Chinnadurai, along with their research cohort, Phanindra Rayapudi Venkata, Baswala Srujana, Gadde Saranya, and Abburi Sowgandhi (B.Tech. ECE students) have published their patent titled “A System and Method for Breast Cancer Diagnosis” (Application number: 202441088356). Their cutting-edge research presents a system to help diagnose breast cancer more accurately and efficiently using advanced image analysis techniques.

Abstract

The present disclosure discloses a system for diagnosing breast cancer that utilizes topological data analysis to transform mammogram images into meaningful diagnostic insights. It includes a data preprocessing module for image standardization and enhancement and a feature extraction module to create histograms for topological analysis. The topological data analysis module converts these histograms into Persistent Homology Diagrams (PHDs) representing topological features. An Earth Mover’s Distance (EMD) matrix is generated by a similarity metric module to compare PHDs. Representative PHDs are identified using a representative selection module, enabling accurate classification by the classification module. The system’s performance is assessed through various metrics by a performance analysis module, and a web service module provides an intuitive interface for users to upload images and receive diagnostic results. This approach enhances breast cancer detection by focusing on persistent topological features, offering improved precision and interpretability.

Figure 1. Conversion of mammograms into PHDs

Explanation of the Research in Layperson’s Terms

Here’s how the system works in simple terms:

1. Preparing the Images: Mammogram images are cleaned and adjusted to ensure they’re clear and easy to analyse. The focus is on areas that might show signs of cancer.

2. Extracting Patterns: The system looks for patterns in the images that could indicate healthy or unhealthy tissue. It turns these patterns into a visual map that represents the shape and structure of the tissue.

3. Analysing Shapes: The system uses math to study how these shapes appear and disappear as the image details change. The most persistent shapes (important ones) are kept, and random noise is ignored.

4. Comparing Images: A tool measures how similar or different these patterns are between images. This helps the system group them into healthy or cancerous categories.

5. Making a Decision: The system compares a new mammogram to its library of known patterns to decide whether it’s likely healthy or shows signs of cancer.

6. Easy to Use: Doctors can upload an image to a web-based tool and quickly get results, complete with visual explanations.

This system helps doctors by making the diagnosis process faster, more reliable, and easier to understand, which can lead to earlier and better treatment for breast cancer.

Figure 2. Flow diagram of normal method

Figure 3. Flow diagram of hybrid method

Practical Implementation/Social Implications of the Research

This research enhances breast cancer detection by enabling earlier, more accurate diagnoses and improving survival rates. Its web-based tool ensures access to advanced diagnostics in remote and underserved areas, reducing disparities in healthcare. Supporting radiologists with objective insights minimizes errors and workload, especially in resource-limited settings. Patients benefit from faster, clearer results, leading to timely and cost-effective treatment. Additionally, the innovative methods could inspire advancements in diagnosing other diseases, driving broader medical progress and improving global health outcomes.

Future Research Plans

Future research could expand this system to detect other diseases like lung or liver cancer, improve diagnostic accuracy by reducing false results, and integrate multimodal data for comprehensive analysis. Incorporating patient-specific information for personalized risk assessments, creating self-learning models, and optimizing computational efficiency could enhance its adaptability. Large-scale global trials and user-friendly interfaces would ensure effective implementation across diverse populations and healthcare systems, making the technology more versatile, accessible, and impactful.

Dr Saswat Kumar Ram and Dr Banee Bandana Das from the Department of Electronics and Communication Engineering have developed an intelligent safety system to prevent road accidents caused by drowsiness and alcohol consumption. Their patented research, A System and Method for Vehicles to Prevent Road Accidents During the Inebriated State of the Driver (Indian Patent No. 202441037564), uses simple sensors and microcontrollers to provide real-time safety feedback, offering an affordable and effective solution for enhancing vehicle safety.

A Brief Abstract:

This research offers the best solution to improve security measures in the control system by integrating simple sleep and odour sensors. The goal is to create an intelligent safety management system. The devices used are microcontroller, motor drivers, motors, MQ-3 (alcohol sensor), IR sensor (drowsiness detection), buzzer, and lights, these are the main components used in this project for hardware. The embedded-C language is used for the Arduino IDE. The system is designed to provide safety feedback in a variety of applications. MQ-3 can be used to detect and identify environmental odours in people, whether drunken. Our aim is to design product that can be inexpensive and easy to integrate into existing control systems, making it suitable for many applications such as automobiles, workplaces, and homes. This research is a good step towards improving the security of the control system by using the ability of simple and inexpensive equipment for instant monitoring and response.

Explanation in Layperson’s Terms:

The proposed safety control system effectively detects drowsiness and alcohol consumption, utilizing simple sensors MQ-3 and IR sensor to enhance road safety. By integrating these sensors with Microcontroller, the system triggers real-time alerts to prevent accidents. Upon detecting alcohol, the car automatically slows down, minimizing speed and activating LED backlights to indicate the driver’s inebriated state. Similarly, when drowsiness is detected, the system reduces car speed, emits a buzzing sound, and activates lights, signaling the driver’s drowsy condition. This comprehensive approach addresses both drowsiness and alcohol consumption, two major contributors to road accidents. The system’s simplicity, affordability, and ease of integration make it a promising solution for enhancing vehicle safety.

Practical Implementation

The current invention addresses the generic problems that cause road accidents and tries to control the speed of the vehicle and play buzzer to ensure the safety of the passengers and driver. To best of authors’ knowledge, th parameter detection i.e. alcohol and drowsiness detection.

The present invention can be used in smart city and smart villages applications for making the road safe, driver and passengers safe, and few application areas are:

• Smart City and Smart Village: This technique and system can reduce road accidents caused due to alcohol consumption and drowsiness.
• Automobile Industry: The system can be easily integrated with the existing control system in vehicles to ensure safety.

Future Research Plans:

To develop more secure and reliable safety mechanism for safe driving.

According to the picture, the person wearing the glasses is the driver, and he’s not sleepy.

Here driver is in drowsy state so that Parking lights on, speed reduces and buzzer is ON.

Research Scholars Chetan Mylapilli, Jethin Sai Chilukuri, Rohith Kumar Akula, Sana Fathima, and Assistant Professor Dr Anirban Ghosh from the Department of Electronics and Communication Engineering at SRM University-AP have co-authored an innovative paper titled “A System and Method for Detecting Density-Based Intelligent Parallel Traffic.” This pioneering research delves into the development of an intelligent traffic control system that dynamically adjusts traffic signals based on real-time vehicle density analysis, their research with the patent no- 202241044904 represents a significant step in integrating technology with transportation efficiency.

Abstract

This work presents an intelligent traffic control system that addresses the gaps in the current state-of-the-art by using a novel hardware-software integration. The system evaluates traffic density in each lane direction and dynamically adjusts traffic lights using a computational algorithm to significantly reduce waiting times at junctions. It also ensures safe pedestrian movement and enables parallel traffic flows. A Raspberry Pi serves as the system’s control unit, utilizing video processing to determine traffic density, while LEDs simulate the traffic lights. The system integrates various hardware and software components, including Raspberry Pi, LEDs, relay modules, VNC software, and sample traffic videos, to provide an efficient solution to the traffic management problem.

Explanation of Research in Layperson’s Terms

The current system uses a Raspberry Pi to control traffic lights based on real-time video of cars at intersections. It detects how many vehicles are in each lane and adjusts the lights to reduce waiting time. Pedestrian safety is managed by ensuring safe crossing times. LED lights simulate the traffic signals, and the system allows smoother traffic flow by handling vehicles moving in parallel. However, it can’t yet control turning vehicles or prioritize emergency vehicles.

Practical Implementation of the Research

The intelligent traffic control system significantly reduces congestion by dynamically adjusting traffic signals, leading to shorter wait times and smoother commutes. It helps lower pollution and fuel consumption by minimizing idle time at junctions, contributing to better air quality and conservation of resources. Pedestrian safety is improved through designated crossing times, reducing accidents. The system also supports economic growth by cutting time wasted in traffic, enhancing productivity.

Future Research Plans

Future research will focus on adding control for turning traffic and distinguishing between different vehicle types to enable emergency vehicle priority. To improve real-time video processing, the system will transition from Raspberry Pi to more efficient hardware like FPGAs or GPUs. Machine learning will be explored for better vehicle detection and traffic signal optimization. Integration with V2X communication will enhance traffic management, and real-world scalability will be tested for deployment in smart city environments.

The prototype:

Figure 1. Image of the prototype

Figure 2. Working prototype

Figure 3. Block Diagram of the prototype

Prompt and timely disease detection forms an essential part of any treatment, Dr Pradyut Kumar Sanki, Dr Swagata Samanta, and research scholar Ms Pushpavathi Kothapalli from the Department of Electronics and Communication Engineering have worked towards a timely and accurate disease detection when it comes to kidney disease diagnosis through medical images. Their innovative research titled, “A System and a Method for Automated Segmentation of Kidney Abnormalities in Medical Images” has been published in the patent journal with Application No. 202441074765 and has significant potential for clinical adoption, improving patient care in kidney disease detection and treatment.

Abstract:

This research work aimed to develop an effective method for segmenting kidney diseases, including kidney stones, cysts, and tumours. The method achieved high accuracy in segmenting kidney diseases, with a good mean precision, and recall. The study employed techniques to efficiently select the most relevant features for kidney disease segmentation, identifying key features related to imaging and patient health. The method outperformed other approaches in terms of accuracy, precision, and recall. The study utilized a comprehensive dataset of kidney disease patients to train and test the segmentation method effectively. The results suggest that this method has the potential to be widely adopted in clinical settings, contributing to more accurate and efficient diagnostic tools for kidney disease segmentation and improving patient care in an effective manner.

Practical Implementation:

The practical implementation of the research involves deploying a system for real-time segmentation of kidney diseases, including kidney stones, cysts, and tumours. The method achieved high accuracy in segmenting kidney diseases using deep learning techniques. The model can quickly identify and delineate diseased areas within the kidney. The study employed techniques to select the most relevant features for kidney disease segmentation, focusing on key imaging and health-related characteristics. The method outperformed other approaches in terms of accuracy, precision, and recall. The study utilized a comprehensive dataset of kidney disease patients to train and test the segmentation method. The results suggest that the method has the potential to be widely adopted in clinical settings, contributing to more accurate and efficient diagnostic tools for kidney disease segmentation and improving patient care.

Future Research Plans:

The future plans for the work on chronic kidney disease (CKD) detection and segmentation involve several key areas:

Expanding Disease Coverage: Future research could involve adapting and expanding the segmentation model to detect and segment other kidney-related abnormalities and diseases, such as renal infections or congenital disorders, thereby increasing the versatility and applicability of the tool.

Improving Model Accuracy and Robustness: To further improve accuracy, additional deep learning techniques, such as ensemble learning or advanced attention mechanisms, could be explored. Testing on larger and more diverse datasets could help make the model more robust and generalizable across various patient demographics and imaging devices.

Integration with Multi-modal Data: Incorporating other data types, such as blood test results, genetic markers, or electronic health records, could be an exciting avenue to explore. This would create a multi-modal approach, combining imaging data with clinical information, potentially improving diagnostic accuracy and providing more comprehensive insights into kidney health.

Real-world Clinical Trials: Conducting clinical trials in real-world settings to validate the effectiveness of the segmentation tool with healthcare professionals. Gathering feedback from these trials would provide valuable insights into user experience and model performance, facilitating further refinement.

Developing a User-friendly Interface: Future work could involve creating an easy-to-use interface that seamlessly integrates with hospital systems. This interface would allow healthcare providers to interact with the segmentation results, adjust parameters, and view comprehensive diagnostic reports.

Exploring Semi-supervised and Unsupervised Learning Approaches: To reduce the reliance on labeled data, which can be time-consuming to obtain, exploring semi-supervised or unsupervised learning techniques could be beneficial. These approaches might help in training the model on large datasets without extensive labeling, thereby improving scalability.

Longitudinal Studies for Prognostic Analysis: Research could also focus on tracking patients over time to understand how kidney disease progresses and how segmentation results correlate with long-term health outcomes. This could help in creating predictive models for disease prognosis.

Prof. Rupesh Kumar from the Department of Electronics and Communication Engineering and his research cohort, Mr Venkata Naga Sai Kiran Damarla, Mr Prasannakumar Seelam, Ms V L S Tanmay P, and Mr Venkata Ramana Murthy Pondala (BTech ECE students), has published a patent titled “An Integrated Iot System for Sensing, Monitoring, and Geo-Information Storage of Sensor Data” with Application No: 202441065318 for their breakthrough research on developing an intelligent IoT system that can detect environmental changes.

Abstract

This research presents an integrated IoT system designed to monitor environmental conditions and store the corresponding data along with geo-information. The system autonomously collects data from its surroundings, processes it, and stores the information for future retrieval. The system is designed for use in remote monitoring applications where consistent tracking of environmental variables and location data is essential.

Explanation of the Research in Layperson’s Terms

The project aims to create a smart system that can sense changes in its environment—like temperature or humidity—and record those changes alongside its current location and time stamp. The collected data is stored so that it can be retrieved later for analysis, making the system useful in situations like monitoring climate conditions in farming or tracking environmental changes in various locations.

Practical Implementation/Social Implications of Research

The practical implementation of this system spans various fields such as agriculture, environmental management, and logistics. For example, in agriculture, the system can help farmers track environmental changes, allowing them to optimize crop management. Similarly, it can monitor environmental conditions in remote areas, ensuring timely interventions when necessary. The system could also aid in asset tracking, providing both environmental data and the location of critical resources. Its broader social impact lies in its ability to automate data collection, improve accuracy, and reduce human intervention in critical monitoring tasks.

Collaboration

This project was completed with the support and guidance of the Wireless Sensing & Imaging (WSI) Lab. The research team worked closely within the lab to develop the system, utilising the lab’s resources and expertise to achieve the research goals. There were no external collaborations involved in this project.

Prof. Rupesh and his team plan to explore radar design and focus on working with radar technology in their future research. By developing and integrating radar systems, the team aims to enhance IoT applications with advanced sensing capabilities, such as distance measurement, object detection, and environmental monitoring. Their goal is to contribute to more accurate and reliable solutions through the implementation of IoT Integrated Radar technology.

In today’s fast-paced technological world, ensuring the quality and reliability of electronic devices is essential. Associate Professor Dr Ramesh Vaddi and his research Scholar Mr Vinod Kumar Ancha from the Department of Electrical and Electronics Engineering introduce an innovative system for real-time detection and classification of defects in PCBAs, leveraging advanced machine learning techniques. Their research titled, “System And Method For Real-Time Detection And Classification of Defects in Assembled Printed Circuit Boards (PCBA)” was published in the Patent Journal with Application number 202441045761.

Abstract:

This study presents a new system for real-time detection and classification of defects in Assembled Printed Circuit Boards (PCBAs), which are critical in electronic products and systems. It employs an efficient model with pretrained weights to detect defects for enhanced quality control. The model is initially trained and fine-tuned on a computer, 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 PCBA image featuring clearly labeled 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 or flaws in Assembled Printed Circuit Boards (PCBAs). Which are the “backbone” of most electronic devices, like computers and phones. This system uses a powerful computer model to “look” at these boards and quickly identify any defects, like missing holes, mouse byte, open circuit, short circuit, spur and spurious copper in real-time. The research starts by training this model using a deep learning object detection model on a regular computer, teaching it to recognize what a normal PCBA looks like and what various defects might look like. Once it’s ready, we transfer the model to a small, efficient computer edge board, which does all the processing. A camera is used to capture images of the PCBAs, and the system analyzes these images to find respective defects. The results are displayed on a screen, where it clearly marks where the defects are and what kind of defects they are. Overall, this system helps companies detect defects in their electronics manufacturing process quickly and accurately, which can save time, reduce waste, and improve the quality of their products.

Practical Implementation:

The practical implementation of our research involves deploying a system for real-time detection and classification of defects in Assembled Printed Circuit Boards (PCBAs) a crucial component in nearly all electronic devices. By using advanced Deep learning techniques, our system can quickly identify manufacturing defects, allowing electronics manufacturers to detect the defect early in the production process. This can lead to significant improvements in quality control, reduced waste, and lower production costs. By improving quality control in electronics manufacturing, the system can help reduce electronic waste, which is a significant environmental concern. Early detection of defects also reduces the chances of faulty electronic products reaching consumers, thereby improving safety and reducing the need for product recalls. Additionally, the efficiency and accuracy of our system could lead to more reliable electronics, contributing to greater consumer trust in electronic products. This, in turn, could encourage companies to invest in higher-quality manufacturing processes, ultimately leading to a more sustainable and responsible electronics industry.

Collaborations:

To develop our system, we first trained a computer model to recognize defects in Assembled Printed Circuit Boards (PCBAs). This training process involved feeding the model a large dataset of PCBA images, some with defects and some without. By analyzing these examples, the model learned to identify common defects, like Missing hole, mouse byte, open circuit, short circuit, Spur and Spurious copper. Once the model was trained, we implemented it in a real-time setting. This meant integrating it with equipment that could inspect PCBAs as they were being produced. The system used a camera to capture images of each PCBA and then applied the trained model to analyze those images, checking for any defects. With the model 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 helped improve the quality of the final product and reduced the chances of defective electronics reaching consumers. It also sped up the quality control process and reduced waste, making the entire manufacturing process more efficient.

Future Research Plans:

Our future research plans focus on enhancing and expanding our system for defect detection in Assembled Printed Circuit Boards (PCBAs):

Model Optimization: We aim to further refine our machine learning model to improve accuracy and speed. This includes experimenting with different architectures and training techniques to boost performance.

Expanded Defect Library: We plan to gather a more extensive dataset of PCBA defects, allowing our model to identify a wider range of issues. This will make the system more versatile and capable of handling various manufacturing environments.

Real-World Testing: We intend to test our system in a broader range of manufacturing settings to ensure its robustness and adaptability. This will help us understand how it performs in diverse real-world scenarios and how we can fine-tune it for optimal results.

Integration with Manufacturing Systems: Our goal is to integrate our system with other manufacturing processes and technologies. This will allow for seamless communication between defect detection and other quality control systems, enhancing the overall manufacturing workflow.

Automation and Robotics: We’re interested in exploring the use of automation and robotics to streamline the defect detection process. This could lead to a more automated manufacturing line, reducing human intervention and potential errors.

Collaboration and Partnerships: We plan to collaborate with more industry partners and academic institutions to accelerate our research and development. These partnerships will provide valuable insights and resources for advancing our system.