Chandigarh University

About: Chandigarh University is a education organization based out in Mohali, India. It is known for research contribution in the topics: Materials science & Computer science. The organization has 1358 authors who have published 2104 publications receiving 10050 citations.

Cloud Load Balancing Algorithms: A Comparative Assessment

Abstract: Nowadays, Cloud computing is developing quickly and customers are requesting more administrations and superior outcomes. In the cloud domain, load balancing has turned into an extremely intriguing and crucial research area. Numbers of algorithms were recommended to give proficient mechanism for distributing the cloud user’s requests for accessing pool cloud resources. Also load balancing in cloud should provide notable functional benefits to cloud users and at the same time should prove out to be eminent for cloud services providers. In this paper, the pre-existing load balancing techniques are explored. The paper intends to provide landscape for classification of distinct load balancing algorithms based upon the several parameters and also address performance assessment bound to various load balancing algorithms. The comparative assessment of various load balancing algorithms will helps in proposing a competent load balancing technique for intensify the performance of cloud data centers.

Message‐sensing classified transmission scheme based on mobile edge computing in the Internet of Vehicles

Abstract: With the rapid development of intelligent transportation, vehicle terminals generate a large number of data messages that need to be processed in real time, and the required computing and storage resources far exceed the load capacity of vehicle terminals. Mobile edge computing enables data resources to be processed near device terminals, and provides low‐latency and high‐reliability computing services to meet the power and service quality requirements of terminal devices. Therefore, in order to achieve better data resource management, this paper introduces mobile edge computing technology, and mainly researches secure message transmission optimization algorithms based on mobile edge computing. Firstly, we prioritize secure messages through the analytic hierarchy process. This can guarantee that the most urgent messages get the highest transmission level. Secondly, we establish an optimal task offloading model of delay and energy loss by assigning different weight factors to delay and energy loss. The Lagrangian relaxation method is used to transform the nonconvex problem into a convex problem. We use greedy algorithm to solve the main problem. Finally, the vehicle transmits secure messages through the topology of the local network within its defined communication range. Performance evaluation results show that the scheme not only reduces the redundant transmission of messages, but also improves the performance of end‐to‐end delay and message deliver success ratio of secure messages.

Condition Monitoring of Machinery: A Case Study

Abstract: Condition monitoring is a cutting-edge method in machine maintenance that analyzes the performance of machines on the basis of data gathered from sensors that have previously been placed on the machines themselves. The parameters of the machines are examined to see whether there is a significant change that indicates the presence of a defect in the manufacturing process. This constitutes a major portion of precognitive support. Plants and equipment have failed in the past, and this article seeks to determine why this has happened. Within a typical plant, monitoring frameworks and condition checking are coordinated in order to increase the plant and equipment observing frameworks. Observational apparatuses of the nature of the conditions used in the temperature, vibration, spillage, consumption, and splitting screens have been installed and are being used.

Image Forgery Detection using SIFT and PCA Classifiers for Panchromatic Images

Abstract: Objectives: The image forgery detection is the technique in which pixels are marked in the image, which are not similar to other pixels of the images. The Principal Component Analysis (PCA) is the classification of neural networks which will analyze each pixel of the image and classify pixels according to pixel type. Method: The PCA algorithm takes training and trained dataset as input and drive new values according to input image. In the paper improvement in the PCA algorithm with usage of Scale-Invariant-Feature-Transform algorithm (SIFT Algorithm), is proposed for image-forgery. The SIFT algorithm is the algorithm which analyze each pixel of the image and define type of pixels in the image. The output of the SIFT algorithm is given as input to PCA algorithm for data classification. The PCA algorithm will classify the data according to SIFT algorithm output. Findings: The results demonstrate that the proposed algorithm executes well in terms of “Peak Signal-to-Noise Ratio” (PSNR), “Mean-Square-Error” (MSE), fault detection rate and accuracy value.