Threshold Extraction Framework for Software Metrics

Software metrics are used to measure different attributes of software. To practically measure software attributes using these metrics, metric thresholds are needed. Many researchers attempted to identify these thresholds based on personal experiences. However, the resulted experience-based thresholds cannot be generalized due to the variability in personal experiences and the subjectivity of opinions. The goal of this paper is to propose an automated clustering framework based on the expectation maximization (EM) algorithm where clusters are generated using a simplified 3-metric set (LOC, LCOM, and CBO). Given these clusters, different threshold levels for software metrics are systematically determined such that each threshold reflects a specific level of software quality. The proposed framework comprises two major steps:the clustering step where the software quality historical dataset is decomposed into a fixed set of clusters using the EM algorithm, and the threshold extraction step where thresholds, specific to each software metric in the resulting clusters, are estimated using statistical measures such as the mean (μ) and the standard deviation (σ) of each software metric in each cluster. The paper's findings highlight the capability of EM-based clustering, using a minimum metric set, to group software quality datasets according to different quality levels.     

A new perceptual video fingerprinting system

Search, retrieval and storage of video content over the Internet and online repositories can be efficiently improved using compact summarizations of this content. Robust and perceptual fingerprinting codes, extracted from local video features, are astutely used for identification and authentication purposes. Unlike existing fingerprinting schemes, this paper proposes a robust and perceptual fingerprinting solution that serves both video content identification and authentication. While content identification is served by the robustness of the proposed fingerprinting codes to content alterations and geometric attacks, their sensitivity to malicious attacks makes them fit for forgery detection and authentication. This dual usage is facilitated by a new concept of sequence normalization based on the circular shift properties of the discrete cosine and sine transforms (DCT and DST). Sequences of local features are normalized by estimating the circular shift required to align each of these sequences to a reference sequence. The fingerprinting codes, consisting of normalizing shifts, are properly modeled using information-theoretic concepts. Security, robustness and sensitivity analysis of the proposed scheme is provided in terms of the security of the secret keys used during the proposed normalization stage. The computational efficiency of the proposed scheme makes it appropriate for large scale and online deployment. Finally, the robustness (identification-based) and sensitivity (authentication-based) of the proposed fingerprinting codes to content alterations and geometric attacks is evaluated over a large set of video sequences where they outperform existing DCT-based codes in terms of robustness, discriminability and sensitivity to moderate and large size intentional alterations.     

Theory for fast and cost-effective frequency response estimation of systems

Frequency response, and hence transfer function, estimation of systems is of vital importance in several engineering fields such as signal processing, communications, control, system modelling and identification. Any digital frequency response scheme with attractive features such as low cost, high speed, good accuracy and excellent noise and harmonic rejection capabilities will therefore be of great practical benefit to practitioners in the above-mentioned fields. The author reports on a new frequency response estimation theory, based on the concept of non-subtractively dithered quantisation, which endows the classical quadrature correlation-based frequency response estimation scheme with all the above-mentioned attractive features. A theoretical analysis of the estimation (bias and variance) accuracy of this technique is given. The theory is tested on a number of filters and under some stringent conditions characterised by the coarsest possible (1-bit) quantisation scheme and very noisy (low SNR) environments. Simulation results substantiate the proposed theory quite well, thus making the VLSI implementation of the proposed 1-bit estimation scheme on a chip an attractive proposition.     

A Multiscale Approach to Leak Detection and Localization in Water Pipeline Network

Leak detection and localization in water pipeline networks is of paramount importance to industry, especially in regions where water is scarce. In this paper, we present a novel multi-modal and multi-scale approach for leak detection and localization in water pipeline networks, in which pressure measurements at various points on the network are used to localize the pipe segment in which the leak is occurring, and then the vibration sensors are used to localize the leak within this segment. In some situations where the complete pipeline model is not available, pressure data alone may not be effective in localizing the leak. However, in such a situation, by supplementing pressure data with vibration data, the leak can be localized, as these additional data are easier to acquire at arbitrary points, since vibration sensors are non-invasive. In order to validate the effectiveness of the approach that needs both pressure and vibration data, we simulate the pipeline model using EPANET that includes models for flow and pressure at various points on the pipeline, then integrate the vibration model with it in MATLAB, since EPNAET does not include models for vibration measurements. A case study of a pipeline network is considered, and the proposed scheme is used to detect and localize the leak. Extensive simulation results show the effectiveness of the proposed scheme in providing accurate leak detection and localization.     

Robust perceptual color image hashing using randomized hypercomplex matrix factorizations

Multimedia Tools and Applications - Compact representations of color image and video content allow efficient search, retrieval and storage of this content over the Internet and online repositories....     

Comparisons between unsteady sediment-transport modeling

The comparative study between unsteady flow models in alluvial streams shows a chaotic residue as for the choices of a forecasting model. The difficulty resides in the choice of the expressions of friction resistance and sediment transport. Three types of mathematical models were selected. Models of type one and two are fairly general, but require a considerable number of boundary conditions, which related to each size range of sediments. It can be a handicap during rivers studies which are not very well followed in terms of experimental measurements. Also, the use of complex models is not always founded. But then, the model of type three requires a limited number of boundary conditions and solves only a system of three equations at each time step. It allows a considerable saving in calculating times.     

Invariance property of Gaussian signals: A new interpretation, extension and applications

This paper addresses the invariance property of Gaussian signals, originally derived by Bussgang, which characterizes the input/output moment relation of a hybrid nonlinear moment (HNM) estimator based on a zero-memory nonlinearity (ZMN) g(y). Some re-derivations of this property are reviewed, and an original, direct, and simple proof is presented (Appendix 1). The paper then derives a new interpretation of this property (Theorem 1) that shows a moment-sense equivalence between g(y) and a linear mappingh ₁(y) whose coefficients a₀ and a₁ are completely characterized in terms ofg(y) and are shown to be optimal in a mean square error (MSE) sense. A direct and very interesting byproduct of this interpretation is a simple linear relationship between the input and output of the HNM estimator involved. The property is then generalized (Theorem 2) to signals other than Gaussian, resulting in an infinite cumulant series expansion of the HNM estimator output, whose coefficients are all characterized in terms ofg(y). Applications of Theorem 1 to some ZMNs commonly used in signal processing and control theory are presented that clearly illustrate the power and elegance of the invariance property. Finally, some conclusions are given.Author acknowledges financial support by KFUPM.