Ashok Krishnamurthy

Anupam Gupta, Ashok Krishnamurthy, Lisa Singh

This paper introduces a novel segmentation framework that integrates a classifier network with a reverse HRNet architecture for efficient image segmentation. Our approach utilizes a ResNet-50 backbone, pretrained in a semi-supervised manner, to generate feature maps at various scales. These maps are then processed by a reverse HRNet, which is adapted to handle varying channel dimensions through 1x1 convolutions, to produce the final segmentation output. We strategically avoid fine-tuning the backbone network to minimize memory consumption during training. Our methodology is rigorously tested across several benchmark datasets including Mapillary Vistas, Cityscapes, CamVid, COCO, and PASCAL-VOC2012, employing metrics such as pixel accuracy and mean Intersection over Union (mIoU) to evaluate segmentation performance. The results demonstrate the effectiveness of our proposed model in achieving high segmentation accuracy, indicating its potential for various applications in image analysis. By leveraging the strengths of both the ResNet-50 and reverse HRNet within a unified framework, we present a robust solution to the challenges of image segmentation.

Vijay Gadepally, Ashok Krishnamurthy, Umit Ozguner

Cyber Physical Systems (CPS) are the conjoining of an entities' physical and computational elements. The development of a typical CPS system follows a sequence from conceptual modeling, testing in simulated (virtual) worlds, testing in controlled (possibly laboratory) environments and finally deployment. Throughout each (repeatable) stage, the behavior of the physical entities, the sensing and situation assessment, and the computation and control options have to be understood and carefully represented through abstraction. The CPS Group at the Ohio State University, as part of an NSF funded CPS project on "Autonomous Driving in Mixed Environments", has been developing CPS related educational activities at the K-12, undergraduate and graduate levels. The aim of these educational activities is to train students in the principles and design issues in CPS and to broaden the participation in science and engineering. The project team has a strong commitment to impact STEM education across the entire K-20 community. In this paper, we focus on the K-12 community and present a two-week Summer Program for high school juniors and seniors that introduces them to the principles of CPS design and walks them through several of the design steps. We also provide an online repository that aids CPS researchers in providing a similar educational experience.