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.

Mixed H∞ and passive consensus sampled-data control for nonlinear systems

Abstract: This paper studies the consensus problem of a second-order nonlinear multi-agent system with directed topologies. A distributed control protocol is proposed for each agent using the relative states among neighboring agents. A mixed H∞ and passivity-based control is maneuvered to deal the bounded disturbances enduring in the system. Based on the theory of the sampled-data control technique and Lyapunov stability theory, some novel conditions are given to realize the consensus of a class of second-order multi-agent nonlinear systems. A new set of delay dependent sufficient conditions is derived in terms of linear matrix inequalities, which guarantees that all agents asymptotically converge to the convex hull with the prescribed H∞ and passive performance. Finally, an example with simulation results is given to verify the theoretical results.

Modified Hardware Security Algorithms for Process Industries Using Internet of Things

Abstract: Embedded devices used in process industries are vulnerable to a variety of attacks due to its large number of deployments to reduce the measurement and process complexity. The security threats incr...

Classification of color objects like fruits using probability density function (PDF)

Abstract: Fruits like apples are valued based on their appearance (i.e. color, sizes, shapes, presence of surface defects) and hence classified into different grades. Grading process helps in achieving better standards and quality of fruits. Of the many available color models, HSI model provides a highly effective color evaluation particularly for analyzing biological products. Human assessment furnishes only qualitative data and such inspection is time consuming and cost-intensive. Machine vision systems with specialized image processing software provide a solution that may satisfy the demand. The analysis was carried out on images of 187 apple fruits, shows that classification done based on median of PDF. In order to avoid the mismatch in grading the same it has been classified further using Histogram Intersection, which determines the closeness between two images i.e. 1 if two images are similar and 0 if they are dissimilar.

Web Metrics Based on Page Features and Visitor's Web Behavior

Abstract: The objective of this paper is to propose new web metrics based on its usability and effectiveness for different web domain users of the page through the available utilities and its visual appearance. The earlier metrics concentrated on the number of visits, cost per visit and clicks to download ratio are not sufficient enough to fully assess the web quality and the impact of the page features on the number of visitors. To satisfy the small group of visitors of different business views and expectations, the web content and the provision for navigating within the site is playing a vital role The efficiency of a web page is viewed as the ratio of the relative cost of page features and network resources to the number of visits and the effectiveness is the ratio of the actual user expectation to the desired number of web users. The different classes of the web pages based on their domains are considered and new metrics are proposed. A visual simulator has been developed to determine the impact of the individual utility metrics on the total number of hit count values based on the survey results. The main focus in this work is to arrive at a matrix model for determining the numbers of visitors from various domains who expect to get the most recent and updated correct information content what they searching for.

RETRACTED ARTICLE: Optimal Hop Selection Based Novel Trust Based DDoS Attack Removal Framework for Reliable and Secured Transmission of Data in VANETs

Abstract: This paper proposes a new Trust based DDoS Attack Removal Framework (T-DARF) for effective trust management in VANETs. Based on a newly developed data centric validation unit and intrusion detection unit, all the misbehaving nodes are removed and allows the framework to prevent DDoS attacks in a distributed and collaborative manner. In other words, our proposed framework ensures a trusted, reliable communication between vehicles and delivering reduced network overhead by handling the DDoS attacks. T-DARF is built of three elements: (1) for the detection of dishonest nodes it includes a collaborative and distributed elements, (2) to filter the malicious data it includes a data-centric validation element, and (3) a delay checking element for the detection and prevention against DoS and DDoS attacks. Also, a new trust-based routing protocol is developed that uses the idea of companions (i.e. integrating the notions of link quality and trust value of neighbors) to select the trusted vehicles and to select the optimal path we imposed a hybrid Dragonfly based particle swarm optimization (DPSO) algorithm. The simulation results proved the efficiency of our proposed trust based framework on detecting dishonest nodes, the malicious data transmitted by the honest/dishonest nodes and also detecting the colluding attacks that destroys the network resources in a short period. Also, T-DARF in a worst case scenario can sustain its performance degrees and outperforms the effectiveness of existing schemes such as TRIP, AECFV and T-CLAIDS.