KCG College of Technology

About: KCG College of Technology is a based out in . It is known for research contribution in the topics: Computer science & The Internet. The organization has 427 authors who have published 381 publications receiving 2193 citations.

New paradigms for innovation in teaching and learning process

Abstract: Innovation is the key to success in every sphere of Life. Changes are happening in everyday life and one has to adapt to new situations and face challenges. Technical Education is no exception to this phenomenon. There is need to change curriculum and traditional teaching methods. This paper focuses on ‘Change Management’ and Re-engineering of Educational processes. IT has enormous potential and we can improve learning processes dramatically with IT as change enabler and innovation happens as a side product. Some of the paradigms discussed are Business Process Re-Engineering, Kaizen, and Six Sigma, e-learning etc. In spite of its complexity and long time taken for its implementation, reengineering the educational processes is the only solution to bring forth drastic changes and improvement in current Educational systems. Some of the problems in implementing BPR are resistance to change and lack of management support etc, resulting in failures. These problems can be circumvented by addressing critical success factors of Re-engineering. E-learning can be used successfully by harnessing the innovations happening in Internet and World Wide Web. In recent times emphasis is shifting from Instruction paradigm to learning paradigm and a learner centric approach will promote active learning. A case study was presented which introduces a new paradigm for students, an E-learning system which offers opportunities for alternative form of learning using powerful internet web site content creation, maintenance and web analytic tools.

Fault Tolerant and Energy Efficient Signal Processing on FPGA Using Evolutionary Techniques

Abstract: In this paper, an energy efficient approach using field-programmable gate array (FPGA) partial dynamic reconfiguration (PDR) is presented to realize autonomous fault recovery in mission-critical (space/defence) signal processing applications at runtime. A genetic algorithm (GA) based on adaptive search space pruning is implemented, for reducing repair time thus increasing availability. The proposed method utilizes dynamic fitness function evaluation, which reduces the test patterns for fitness evaluation. Hence, the scalability issue and large recovery time associated with refurbishment of larger circuits is addressed and improved. Experiments with case study circuits, prove successful repair in minimum number of generations, when compared to conventional GA. In addition, an autonomous self-healing system for FPGA based signal processing system is proposed using the presented pruning based GA for intrinsic evolution with the goal of reduced power consumption and faster recovery time.

Uncovering hidden community structures in evolving networks based on neighborhood similarity

Abstract: Detection of densely interconnected nodes also called modules or communities in static or dynamic networks has become a key approach to comprehend the topology, functions and organizations of the networks. Over the years, numerous methods have been proposed to detect the accurate community structure in the networks. State-of-the-art approaches only focus on finding non-overlapping and overlapping communities in a network. However, many networks are known to possess a hidden or embedded structure, where communities are recursively grouped into a hierarchical structure. Here, we reinvent such sub-communities within a community, which can be redefined based on nodes similarity. We term those derived communities as hidden or hierarchical communities. In this work, we present a method called Hidden Community based on Neighborhood Similarity Computation (HCNC) to uncover undetected groups of communities that embedded within a community. HCNC can detect hidden communities irrespective of density variation within the community. We define a new similarity measure based on the degree of a node and it’s adjacent nodes degree. We evaluate the efficiency of HCNC by comparing it with several well-known community detectors through various real-world and synthetic networks. Results show that HCNC has better performance in comparison to the candidate community detectors concerning various statistical measures. The most intriguing findings of HCNC is that it became the first research work to report the presence of hidden communities in Les Miserables, Karate and Polbooks networks.

Energy monitoring and management using internet of things

Abstract: Energy management is becoming a trending topic in the 21st century. It includes planning and operation of energy production and energy consumption units. One of initial steps for an effective energy cost control program is the assessment of energy consumption and pattern of energy usage. This is the place where Energy Monitoring comes into picture. It is a technique based on the standard management axiom stating that "You cannot manage what you cannot measure". It serves as an effective guide for energy management. Unlike energy meters which are installed at locations which cannot be accessed easily, this monitoring system can be installed within the house i.e. nearby the switchboard or distribution box. By this way the people can get an idea of how much energy they are consuming in real time, inside their house itself. By uploading the data into the cloud, detailed consumption of energy will be available for every month, every day and every hour. This serves as a tool for Energy management. Apart from this, the loads in the houses can also be controlled remotely via computers/smart phones in case of overloads or when a person is not available at home.