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.

GUI Based Pick and Place Robotic Arm for Multipurpose Industrial Applications

Abstract: As the technology is growing day by day the world is adapting robotic for their comfort in daily life. So we have developed a GUI based, trainable robotic arm which is being automated for multipurpose industrial applications. The special feature which we are highlighting in our work is that, the arm is easily manipulated and has all in one solution for the certain range of pick and place application. The basic aim is to achieve the ability of commanding and controlling the arm through MATLAB Graphical User Interface (GUI) and to make the system more efficient. The designed system has been divided into 2 parts: (1) The AVR microcontroller has been programmed for the central controlling station which has GUI access and the arm control, and (2) to program the GUI for making the robot trainable and user-friendly. As it is a time-saving controlling method, we can use it for picking and placing the material over the conveyor assembly.

A survey on data routing and aggregation techniques for Wireless Sensor Networks

Abstract: Sensor nodes are operating at low voltage and working on battery supply. The main challenge while designing the Wireless Sensor Network is to reduce energy utilization and construct effective route strategy to increase lifetime and relia- bility of the network. For various applications, Wireless Sensor Networks (WSNs) are getting deployed frequently, continuously and they are increasing day by day. Because of high density of WSN nodes there is high probability that redundant data will be sensed by surrounding nodes. Energy conservation is most affecting factor in building a network. It is not suitable to send data individually or separately by each node. Thus the data aggregation is desired in which certain node collects the data and then finally forwards towards destination node. Using this aggregation technique we can save cost and energy consumption in networking. Data routing in network aggregation is also called as DRINA. It provides a better solution like reduced number of message in routing tree, secure data aggregation, high aggregation rate, reliable, and transmission in wireless sensor network. In this survey, we highlights various approaches and algorithms for data routing and aggregation.

NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution.

Abstract: Negation is an important characteristic of language, and a major component of information extraction from text. This subtask is of considerable importance to the biomedical domain. Over the years, multiple approaches have been explored to address this problem: Rule-based systems, Machine Learning classifiers, Conditional Random Field models, CNNs and more recently BiLSTMs. In this paper, we look at applying Transfer Learning to this problem. First, we extensively review previous literature addressing Negation Detection and Scope Resolution across the 3 datasets that have gained popularity over the years: the BioScope Corpus, the Sherlock dataset, and the SFU Review Corpus. We then explore the decision choices involved with using BERT, a popular transfer learning model, for this task, and report state-of-the-art results for scope resolution across all 3 datasets. Our model, referred to as NegBERT, achieves a token level F1 score on scope resolution of 92.36 on the Sherlock dataset, 95.68 on the BioScope Abstracts subcorpus, 91.24 on the BioScope Full Papers subcorpus, 90.95 on the SFU Review Corpus, outperforming the previous state-of-the-art systems by a significant margin. We also analyze the model’s generalizability to datasets on which it is not trained.

IoT based data processing for automated industrial meter reader using Raspberry Pi

Abstract: With the increasing importance of information processing in industries arises the need for efficient data logging systems that are compatible with existing measuring devices such as LCD and LED meters. This paper presents the framework for an Internet of Things (IoT) device as an automated industrial meter reader that uploads the collected numeral data to a cloud storage for centralized data processing. The implementation of the device is done using Raspberry Pi as the platform. The device follows a four-step process-Image Acquisition using Raspberry Pi camera module, Optical Character Recognition using feature extraction technique, Internet Upload Mechanism using Google Forms and Online Data Processing using Google Spreadsheet. The performance of the device and techniques for debugging are also discussed.