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.