Advances in Computer Engineering The latest articles from Hindawi Publishing Corporation © 2016 , Hindawi Publishing Corporation . All rights reserved. Problem Detection in Real-Time Systems by Trace Analysis Wed, 06 Jan 2016 12:38:54 +0000 This paper focuses on the analysis of execution traces for real-time systems. Kernel tracing can provide useful information, without having to instrument the applications studied. However, the generated traces are often very large. The challenge is to retrieve only relevant data in order to find quickly complex or erratic real-time problems. We propose a new approach to help finding those problems. First, we provide a way to define the execution model of real-time tasks with the optional suggestions of a pattern discovery algorithm. Then, we show the resulting real-time jobs in a Comparison View, to highlight those that are problematic. Once some jobs that present irregularities are selected, different analyses are executed on the corresponding trace segments instead of the whole trace. This allows saving huge amount of time and execute more complex analyses. Our main contribution is to combine the critical path analysis with the scheduling information to detect scheduling problems. The efficiency of the proposed method is demonstrated with two test cases, where problems that were difficult to identify were found in a few minutes. Mathieu Côté and Michel R. Dagenais Copyright © 2016 Mathieu Côté and Michel R. Dagenais. All rights reserved. Linux Low-Latency Tracing for Multicore Hard Real-Time Systems Mon, 17 Aug 2015 09:21:48 +0000 Real-time systems have always been difficult to monitor and debug because of the timing constraints which rule out any tool significantly impacting the system latency and performance. Tracing is often the most reliable tool available for studying real-time systems. The real-time behavior of Linux systems has improved recently and it is possible to have latencies in the low microsecond range. Therefore, tracers must ensure that their overhead is within that range and predictable and scales well to multiple cores. The LTTng 2.0 tools have been optimized for multicore performance, scalability, and flexibility. We used and extended the real-time verification tool rteval to study the impact of LTTng on the maximum latency on hard real-time applications. We introduced a new real-time analysis tool to establish the baseline of real-time system performance and then to measure the impact added by tracing the kernel and userspace (UST) with LTTng. We then identified latency problems and accordingly modified LTTng-UST and the procedure to isolate the shielded real-time cores from the RCU interprocess synchronization routines. This work resulted in extended tools to measure the real-time properties of multicore Linux systems, a characterization of the impact of LTTng kernel and UST tracing tools, and improvements to LTTng. Raphaël Beamonte and Michel R. Dagenais Copyright © 2015 Raphaël Beamonte and Michel R. Dagenais. All rights reserved. High Performance Discrete Cosine Transform Operator Using Multimedia Oriented Subword Parallelism Mon, 02 Mar 2015 07:06:17 +0000 In this paper an efficient two-dimensional discrete cosine transform (DCT) operator is proposed for multimedia applications. It is based on the DCT operator proposed in Kovac and Ranganathan, 1995. Speed-up is obtained by using multimedia oriented subword parallelism (SWP). Rather than operating on a single pixel, the SWP-based DCT operator performs parallel computations on multiple pixels packed in word size input registers so that the performance of the operator is increased. Special emphasis is made to increase the coordination between pixel sizes and subword sizes to maximize resource utilization rate. Rather than using classical subword sizes (8, 16, and 32 bits), multimedia oriented subword sizes (8, 10, 12, and 16 bits) are used in the proposed DCT operator. The proposed SWP DCT operator unit can be used as a coprocessor for multimedia applications. Shafqat Khan, Emmanuel Casseau, and Daniel Menard Copyright © 2015 Shafqat Khan et al. All rights reserved. An Improvement Technique Based on Structural Similarity Thresholding for Digital Watermarking Thu, 18 Dec 2014 00:10:02 +0000 Digital watermarking is extensively used in ownership authentication and copyright protection. In this paper, we propose an efficient thresholding scheme to improve the watermark embedding procedure in an image. For the proposed algorithm, watermark casting is performed separately in each block of an image, and embedding in each block continues until a certain structural similarity threshold is reached. Numerical evaluations demonstrate that our scheme improves the imperceptibility of the watermark when the capacity remains fixed, and at the same time, robustness against attacks is assured. The proposed method is applicable to most image watermarking algorithms. We verify this issue on watermarking schemes in discrete cosine transform (DCT), wavelet, and spatial domain. Amin Banitalebi-Dehkordi, Mehdi Banitalebi-Dehkordi, Jamshid Abouei, and Said Nader-Esfahani Copyright © 2014 Amin Banitalebi-Dehkordi et al. All rights reserved. Feature Extraction with Ordered Mean Values for Content Based Image Classification Wed, 17 Dec 2014 13:39:45 +0000 Categorization of images into meaningful classes by efficient extraction of feature vectors from image datasets has been dependent on feature selection techniques. Traditionally, feature vector extraction has been carried out using different methods of image binarization done with selection of global, local, or mean threshold. This paper has proposed a novel technique for feature extraction based on ordered mean values. The proposed technique was combined with feature extraction using discrete sine transform (DST) for better classification results using multitechnique fusion. The novel methodology was compared to the traditional techniques used for feature extraction for content based image classification. Three benchmark datasets, namely, Wang dataset, Oliva and Torralba (OT-Scene) dataset, and Caltech dataset, were used for evaluation purpose. Performance measure after evaluation has evidently revealed the superiority of the proposed fusion technique with ordered mean values and discrete sine transform over the popular approaches of single view feature extraction methodologies for classification. Sudeep Thepade, Rik Das, and Saurav Ghosh Copyright © 2014 Sudeep Thepade et al. All rights reserved. Application of Compressive Sampling in Computer Based Monitoring of Power Systems Wed, 26 Nov 2014 07:16:23 +0000 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 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 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 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.