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.

Supercapacitor based energy recovery system for an elevator

Abstract: As cities grow vertically, the energy requirement for elevators tend to increase substantially. Due to their unique drive characteristics in the four quadrants, the elevator motor generates energy as well as consumes it. At present, most of the generated energy is dissipated as heat in resistors. Energy recovery using supercapacitors could be a solution to this wastage and can make the system more energy efficient. This study tries to address this issue by using a supercapacitor bank to store the energy produced and use it for the next motoring cycle of the elevator. The proposed system also incorporates a ride through facility during mains failure.

Feature level fusion of face and fingerprint modalities using Gabor filter bank

Abstract: This paper proposes a biometric authentication system based on feature level fusion of face and fingerprint modalities. The proposed method utilizes Gabor filter bank with two scales and eight orientations, to extract directional features from source data. Usage of a small set of Gabor filters typically reduces the system processing time. To introduce a good discriminating ability and to avoid curse of dimensionality in feature set, we have used Principal Component Analysis (PCA) +Linear Discriminant Analysis (LDA) framework. The framework enables us to use only 39 features as input to classifier stage of the system. Distance classifiers are employed to authenticate a person based on the distance between input image and stored database templates. Experimental results showcase the advantages of feature level fusion over a uni-modal framework. The system achieves recognition accuracy up to 99.25%. The experiments have been carried out on ORL face database and FVC2002 fingerprint database.

A survey on innovative approach for improvement in efficiency of caching technique for big data application

Abstract: Big Data is playing important role in scientific, industrial and academic areas. Information is being generated everyday by millions of computing machines and collected for future use. The big data is useful for business need, scientific research, future predictions for community welfare, lifestyle enhancements etc. The data collected at Google is around 50TB, Twitter 20TB everyday which is huge in volume, velocity and variety. There is need to resolve big data issues through high computing machines and large scale nodes with the help of distributed processing technologies and software tools like MapReduce from Google, Hadoop of Apache foundation and its eco system. These technologies and tools needs to be modified so as to make usefulness of intermediate data, improvement in efficiency of output, minimize overhead on processors, efficient storage technologies and improve security.

Ontology Based Multi-Agent Intrusion Detection System for Web Service Attacks Using Self Learning

Abstract: Web Services (WS) have become a significant part of the Internet. They employ many features, each of them having specific drawbacks and security threats that are being exploited currently. According to current market researches majority of cyber attacks/exploits are done on these vulnerabilities in WS. Some are direct head on attacks while others are highly coordinated ones. To detect these attacks so that their further attempts can be prevented, highly intelligent Intrusion Detection Systems (IDS) are required. This can be done by having vast databases with high update frequencies or by employing a self learning ontology. Since, rules cannot be added to the database every minute and hence the ontology is preferred since attacks are of varying nature and new forms of attacks arise every day. For coordinated attacks, a single, stand alone IDS’s becomes obsolete here. Hence the use of Distributed Intrusion Detection Systems (DIDS) along with firewalls is essential. The communication between these IDS’s can be done using agents or any set standard of communication between these IDS’s. On recognition of an attack on a single member or number of members of the DIDS System rules are added to the ontology knowledge base and learning occurs. This is the basic idea of an ontology based DIDS. The objective is to detect multiple kinds of attacks with good efficiency in least possible time practically.