The present paper studies break down concept in specially N job 3 machine flow shop scheduling in which processing times are associated with weight of the job. The objective of the study is to get optimal or near optimal sequence of jobs in order to minimize the total weighted mean production flow time of the jobs. The algorithm is made clear by numerical example.

we consider the problem of decomposing a video sequence into a superposition of (a given number of) moving layers. For this problem, we propose an energy minimization approach based on the coding cost. Our contributions affect both the model (what is minimized) and the algorithmic side (how it is minimized). The novelty of the coding-cost model is the inclusion of a refined model of the image formation process, known as super resolution. This accounts for camera blur and area averaging arising in a physically plausible image formation process. It allows us to extract sharp high-resolution layers from the video sequence. The algorithmic framework is based on an alternating minimization scheme and includes the following innovations. 1) A video labeling, we optimize the layer domains. This allows regularizing the shapes of the layers and a very elegant handling of occlusions. 2) We present an efficient parallel algorithm for extracting super-resolved layers based on TV filtering.

A mutual exclusion is a program object that prevents simultaneous access to a shared resource. This concept is used in concurrent programming with a critical section, a piece of code in which processes or threads access a shared resource. A number of mutual exclusion algorithms are available in the literature, with different performance metrics and with different techniques. The Selection for a “good” mutual exclusion algorithm is a key point. These mutual exclusion algorithms can be broadly classified into token and permission based algorithm. This paper surveys the algorithms which have been reported in the literature for Mutual exclusion in distributed systems and their comparison

In mobile cloud computing, a fundamental application is to outsource the mobile data to external cloud servers for scalable data storage. The outsourced data, however, need to be encrypted due to the privacy and confidentiality concerns of their owner. This results in the distinguished difficulties on the accurate search over the encrypted mobile cloud data. To tackle this issue, in this paper, the searchable encryption for multi-keyword ranked search over the storage data was developed. Specifically, by considering the large number of outsourced documents (data) in the cloud, the relevance score and k-nearest neighbor techniques to develop an efficient multi-keyword search scheme was utilized which can return the ranked search results based on the accuracy. Within this framework, an efficient index to further improve the search efficiency, and adopt the blind storage system to conceal access pattern of the search user was leveraged. Security analysis demonstrates that our scheme can achieve confidentiality of documents and index, trapdoor privacy, trapdoor unlink ability, and concealing access pattern of the search user. Finally, using extensive simulations, proposal can achieve much improved efficiency in terms of search functionality and search time compared with the existing proposals.