Mepco Schlenk Engineering College

About: Mepco Schlenk Engineering College is a based out in . It is known for research contribution in the topics: Wavelet & Wavelet transform. The organization has 1307 authors who have published 1665 publications receiving 18690 citations.

Performance analysis of PV module connected in various configurations under uniform and non-uniform solar radiation conditions

Abstract: This paper focuses on investigating the I-V and P-V characteristics of a PV module connected in various configurations Here the parameters of the PV module have been obtained from the datasheet The performance analysis of PV module has been carried out under uniform and non-uniform conditions such as change in irradiation, change in temperature, accumulation of dust and change in wind forces using MATLAB-Simulink environment From the obtained results, it has been observed that for a given number of PV modules, the array configurations affects the maximum available output power where more local maxima are found under partially shaded conditions Moreover the comparative analysis of PV module has been performed under various configurations like series, parallel and series-parallel for the above disturbances condition From the above analysis it is evident that even under non-uniform conditions, the parallel arrangement of PV modules is more prominent were maximum output power is obtained and the results are reported

Iris recognition using combined statistical and co-occurrence multi-resolutional features

Abstract: Iris recognition is one of the most reliable personal identification methods. This paper presents a novel algorithm for iris recognition encompassing iris segmentation, fusion of statistical and co-occurrence features extracted from the curvelet and ridgelet transformed images. In this work, the pupil and iris boundaries are detected by using the equation of circle from three points on its circumference. Using Canny edge detection, the iris radius value is empirically chosen based on rigorous experimentation. Eyelash removal is done by using a horizontal 1-D rank filter. Iris normalization is done by mapping the detected iris region from the polar domain to the rectangular domain and the multi-resolution transforms such as curvelet and ridgelet transforms are applied for multi-resolutional feature extraction. The classification is done using Manhattan distance (Md) and multiclass classifier with logistic function and the two results are compared. Here, the benchmark database CASIA-IRIS-V3 (Interval) is used for identification and recognition. It is observed that the ridgelet transform increases the iris recognition rate.

Agent based bidirectional bidding mechanism for efficient scheduling of real time tasks in cloud computing

Abstract: The cloud computing has become an efficient paradigm to run real-time and complex applications to store and manage the voluminous amount of data. Scheduling plays a major role in managing the execution of real-time applications in cloud computing environment. Cloud provider provides services to run the tasks in cloud and maintain the quality of service for its customers. To ensure the cloud provider's QoS, we propose a novel agent-based scheduling mechanism to run the customers' tasks in cloud and provide dynamic resources. In agent-based scheduling, we introduce a bidirectional announcement based bidding mechanism to allocate tasks and resources dynamically. In addition, it consists of three phases, i.e., basic matching phase, forward announcement-bidding phase and backward announcement-bidding phase. However, the scalability is necessary when scheduling is performed in dynamically adding virtual machines which gives elasticity. We have designed calculation rules for both forward and backward announcement bidding mechanism which helps for selecting contractors. The experiments are evaluated for both random synthetic workload and Google cloud tracelogs. The experiment results show that our agent-based scheduling mechanism gives better performance compared to existing methods.

Fusion of spatial and dynamic CNN streams for action recognition

Abstract: Human action recognition is widely explored as it finds varied applications including visual navigation, surveillance, video indexing, biometrics, human–computer interaction, ambient assisted living, etc. This paper aims to design and analyze the performance of Spatial and Temporal CNN streams for action recognition from videos. An action video is fragmented into a predefined number of segments called snippets. For each segment, atomic poses portrayed by the individual is effectively captured by the representative frame and dynamics of the action is well described by the dynamic image. The representative frames and dynamic images are separately trained by Convolutional Neural Network for further analysis. The attained results on KTH, Weizmann UCF Sports and UCF101 datasets ascertain the efficiency of the proposed architecture for action recognition.