Advances in Computer Engineering http://www.hindawi.com The latest articles from Hindawi Publishing Corporation © 2014 , Hindawi Publishing Corporation . All rights reserved. Application of Compressive Sampling in Computer Based Monitoring of Power Systems Wed, 26 Nov 2014 07:16:23 +0000 http://www.hindawi.com/journals/aceng/2014/524740/ Shannon’s Nyquist theorem has always dictated the conventional signal acquisition policies. Power system is not an exception to this. As per this theory, the sampling rate must be at least twice the maximum frequency present in the signal. Recently, compressive sampling (CS) theory has shown that the signals can be reconstructed from samples obtained at sub-Nyquist rate. Signal reconstruction in this theory is exact for “sparse signals” and is near exact for compressible signals provided certain conditions are satisfied. CS theory has already been applied in communication, medical imaging, MRI, radar imaging, remote sensing, computational biology, machine learning, geophysical data analysis, and so forth. CS is comparatively new in the area of computer based power system monitoring. In this paper, subareas of computer based power system monitoring where compressive sampling theory has been applied are reviewed. At first, an overview of CS is presented and then the relevant literature specific to power systems is discussed. Sarasij Das and Tarlochan Sidhu Copyright © 2014 Sarasij Das and Tarlochan Sidhu. All rights reserved. A Clustering Approach for the -Diversity Model in Privacy Preserving Data Mining Using Fractional Calculus-Bacterial Foraging Optimization Algorithm Tue, 16 Sep 2014 06:14:16 +0000 http://www.hindawi.com/journals/aceng/2014/396529/ In privacy preserving data mining, the -diversity and -anonymity models are the most widely used for preserving the sensitive private information of an individual. Out of these two, -diversity model gives better privacy and lesser information loss as compared to the -anonymity model. In addition, we observe that numerous clustering algorithms have been proposed in data mining, namely, -means, PSO, ACO, and BFO. Amongst them, the BFO algorithm is more stable and faster as compared to all others except -means. However, BFO algorithm suffers from poor convergence behavior as compared to other optimization algorithms. We also observed that the current literature lacks any approaches that apply BFO with -diversity model to realize privacy preservation in data mining. Motivated by this observation, we propose here an approach that uses fractional calculus (FC) in the chemotaxis step of the BFO algorithm. The FC is used to boost the computational performance of the algorithm. We also evaluate our proposed FC-BFO and BFO algorithms empirically, focusing on information loss and execution time as vital metrics. The experimental evaluation shows that our proposed FC-BFO algorithm derives an optimal cluster as compared to the original BFO algorithm and existing clustering algorithms. Pawan R. Bhaladhare and Devesh C. Jinwala Copyright © 2014 Pawan R. Bhaladhare and Devesh C. Jinwala. All rights reserved. A Water Flow-Like Algorithm for the Travelling Salesman Problem Thu, 07 Aug 2014 09:06:49 +0000 http://www.hindawi.com/journals/aceng/2014/436312/ The water flow-like algorithm (WFA) is a relatively new metaheuristic that performs well on the object grouping problem encountered in combinatorial optimization. This paper presents a WFA for solving the travelling salesman problem (TSP) as a graph-based problem. The performance of the WFA on the TSP is evaluated using 23 TSP benchmark datasets and by comparing it with previous algorithms. The experimental results show that the proposed WFA found better solutions in terms of the average solution and the percentage deviation of the average solution from the best-known solution. Ayman Srour, Zulaiha Ali Othman, and Abdul Razak Hamdan Copyright © 2014 Ayman Srour et al. All rights reserved. Real-Time Linux Analysis Using Low-Impact Tracer Thu, 05 Jun 2014 07:15:49 +0000 http://www.hindawi.com/journals/aceng/2014/173976/ Debugging real-time software presents an inherent challenge because of the nature of real-time itself. Traditional debuggers use breakpoints to stop the execution of a program and allow the inspection of its status. The interactive nature of a debugger is incompatible with the strict timing constraints of a real-time application. In order to observe the execution of a real-time application, it is therefore necessary to use a low-impact instrumentation solution. Tracing allows the collection of low-level events with minimal impact on the traced application. These low-level events can be difficult to use without appropriate tools. We propose an analysis framework to model real-time tasks from tracing data recovered using the LTTng tracer. We show that this information can be used to populate views and help developers discover interesting patterns and potential problems. François Rajotte and Michel R. Dagenais Copyright © 2014 François Rajotte and Michel R. Dagenais. All rights reserved.