College of Engineering, Pune

About: College of Engineering, Pune is a based out in . It is known for research contribution in the topics: Computer science & Sliding mode control. The organization has 4264 authors who have published 3492 publications receiving 19371 citations. The organization is also known as: COEP.

An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis.

Abstract: Hand Radiography (RA) is one of the prime tests for checking the progress of rheumatoid joint inflammation in human bone joints. Recognizing the specific phase of RA is a difficult assignment, as human abilities regularly curb the techniques for it. Convolutional neural network (CNN) is the center for hand recognition for recognizing complex examples. The human cerebrum capacities work in a high-level way, so CNN has been planned depending on organic neural-related organizations in humans for imitating its unpredictable capacities. This article accordingly presents the convolutional neural network (CNN) which has the ability to naturally gain proficiency with the qualities and anticipate the class of hand radiographs from an expansive informational collection. The reproduction of the CNN halfway layers, which depict the elements of the organization, is likewise appeared. For arrangement of the model, a dataset of 290 radiography images is utilized. The result indicates that hand X-rays are rated with an accuracy of 94.46% by the proposed methodology. Our experiments show that the network sensitivity is observed to be 0.95 and the specificity is observed to be 0.82.

Embedded Implementation of Deep Learning-based Linear Model Predictive Control

Abstract: Model predictive control (MPC) has emerged as an excellent control strategy owing to its ability to include constraints in the control optimization and robustness to linear as well as highly non-linear systems There are many challenges in real-time implementation of MPC on embedded devices, including computational complexity, numerical instability, and memory constraints Advances in machine learning-based approaches have widened the scope to replace the traditional and intractable optimization algorithms with advanced algorithms In this paper, a novel deep learning-based model predictive control (DNN-MPC) is presented The proposed MPC uses recurrent neural network (RNN) to accurately predict the future output states based on the previous training data Using deep neural networks for the real-time embedded implementation of MPC, on-line optimization is completely eliminated leaving only the evaluation of some linear equations Closed-loop performance evaluation of the DNN-MPC is verified through hardware-in-loop (HIL) co-simulation on ARM microcontroller and a 4x speed-up in computational time for a single iteration is achieved over the conventional MPC Detailed analysis of DNNMPC complexity (speed and memory requirement) is presented and compared with traditional MPC Results show that the proposed DNN-MPC performs faster and with less memory footprints while retaining the controller performance

Characterization of Throughput in Wireless Sensor Network for MAC and Routing Protocol

Abstract: Wireless sensor network is set of sensor nodes which are accountable for transmission of data i.e. it send and receive data packets from one node to another node in network surround. Sensor nodes have various functionalities thus they requires more resources for improve performance in network. Among the different parameters in WSN throughput is vital parameter. Because when the nodes close to sink then it suffers heavy traffic load i.e. initially throughput increases when load increases at base station but at certain point it get decreases because of congestion or delay. Thus throughput value has to get optimized for better performance of WSN.

Sliding mode controller with state observer for TITO systems with time delay

Abstract: Many multi-variable systems are modeled as two-input-two-output (TITO) systems with time delay. Such systems are difficult to control due to interaction among the variables and time delay. In this paper, a decentralized sliding mode controller (SMC) is designed for TITO systems with time delay. To reduce the effect of interaction, TITO system is decoupled using ideal decoupler. A delay ahead predictor is used to make the system model delay free. To improve the accuracy of delay ahead prediction, a corrector with observer is designed. Independent SMCs are designed for each decoupled subsystem and the control signal of SMC is applied to TITO system through the ideal decoupler. The stability condition for the proposed controller is derived using direct Lyapunov stability analysis. A simulation example and real time experimentation is included to validate performance of proposed SMC.