Description:
Internet of Things (IoT) image sensors, social media, and smartphones generate huge volumes of digital images every day. Easy availability and usability of photo editing tools have made forgery attacks, primarily splicing and copy-move attacks, effortless, causing cybercrimes to be on the rise. While several models have been proposed in the literature for detecting these attacks, the robustness of those models has not been investigated when (i) a low number of tampered images are available for model building or (ii) images from IoT sensors are distorted due to image rotation or scaling caused by unwanted or unexpected changes in sensors' physical set-up. Moreover, further improvement in detection accuracy is needed for real-word security management systems. To address these limitations, in this paper, an innovative image forgery detection method has been proposed based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. First, images are divided into non-overlapping fixed size blocks and 2D block DCT is applied to capture changes due to image forgery. Then LBP is applied to the magnitude of the DCT array to enhance forgery artifacts. Finally, the mean value of a particular cell across all LBP blocks is computed, which yields a fixed number of features and presents a more computationally efficient method. Using Support Vector Machine (SVM), the proposed method has been extensively tested on four well known publicly available gray scale and color image forgery datasets, and additionally on an IoT based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training samples.
Description:
This research was funded by Research Priority Area (RPA) scholarship of Federation University Australia.
Description:
Internet of Things (IoT) image sensors, social media, and smartphones generate huge volumes of digital images every day. Easy availability and usability of photo editing tools have made forgery attacks, primarily splicing and copy-move attacks, effortless, causing cybercrimes to be on the rise. While several models have been proposed in the literature for detecting these attacks, the robustness of those models has not been investigated when (i) a low number of tampered images are available for model building or (ii) images from IoT sensors are distorted due to image rotation or scaling caused by unwanted or unexpected changes in sensors' physical set-up. Moreover, further improvement in detection accuracy is needed for real-word security management systems. To address these limitations, in this paper, an innovative image forgery detection method has been proposed based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. First, images are divided into non-overlapping fixed size blocks and 2D block DCT is applied to capture changes due to image forgery. Then LBP is applied to the magnitude of the DCT array to enhance forgery artifacts. Finally, the mean value of a particular cell across all LBP blocks is computed, which yields a fixed number of features and presents a more computationally efficient method. Using Support Vector Machine (SVM), the proposed method has been extensively tested on four well known publicly available gray scale and color image forgery datasets, and additionally on an IoT based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training samples.
Description:
This research was funded by Research Priority Area (RPA) scholarship of Federation University Australia.
Description:
This work has been supported by the National Key Research and Development Program of China (2017YFB0902705), State Key Laboratory of Electrical Insulation and Power Equipment (EIPE20210), the National Natural Science Foundation of China (51907148), the Youth Fund of State Key Laboratory of Electrical Insulation and Power Equipment (EIPE19308), the Foundation Project of State Grid in Shaanxi province section, China (SGSNKY00SPJS1900302). The authors thank the Natural Science Basic Research Plan in Shaanxi Province of China (2019JQ-070).
Description:
Algebraic-geometric (AG) codes over finite fields with respect to the Lee metric have been studied. A lower bound on the minimum Lee distance is derived, which is a Lee-metric version of the well-known Goppa bound on the minimum Hamming distance of AG codes. The bound generalises a lower bound on the minimum Lee distance of Lee-metric BCH and Reed-Solomon codes, which have been successfully used for protecting against bitshift and synchronisation errors in constrained channels and for error control in partial-response channels.
Description:
Let Fq[X1,...,Xm] denote the set of polynomials over Fq in m variables, and Fq[X1,...,Xm]≤u denote the subset that consists of the polynomials of total degree at most u. Let H(T) be a nontrivial polynomial in T with coefficients in Fq[X1,...,Xm]. A crucial step in interpolation-based list decoding of q-ary Reed-Muller (RM) codes is finding the roots of H(T) in Fq[X1,...,Xm]≤u. In this correspondence, we present an efficient root-finding algorithm, which finds all the roots of H(T) in Fq[X1,...,Xm]≤u. The algorithm can be used to speed up the list decoding of RM codes.
Description:
An L-2-boosting algorithm for estimation of a regression function from random design is presented, which consists of fitting repeatedly a function from a fixed nonlinear function space to the residuals of the data by least squares and by defining the estimate as a linear combination of the resulting least squares estimates. Splitting of the sample is used to decide after how many iterations of smoothing of the residuals the algorithm terminates. The rate of convergence of the algorithm is analyzed in case of an unbounded response variable. The method is used to fit a sum of maxima of minima of linear functions to a given data set, and is compared with other nonparametric regression estimates using simulated data.
Description:
An L-2-boosting algorithm for estimation of a regression function from random design is presented, which consists of fitting repeatedly a function from a fixed nonlinear function space to the residuals of the data by least squares and by defining the estimate as a linear combination of the resulting least squares estimates. Splitting of the sample is used to decide after how many iterations of smoothing of the residuals the algorithm terminates. The rate of convergence of the algorithm is analyzed in case of an unbounded response variable. The method is used to fit a sum of maxima of minima of linear functions to a given data set, and is compared with other nonparametric regression estimates using simulated data.
Description:
Vibration signals used for abnormality detection in machine health monitoring (MHM) suffer from significant variation in the patterns with fault severity. This variation results in overlap among the features extracted against different fault types and causes severe degradation in fault detection accuracy. This paper identifies a newfangled problem originated by severity variant features and mitigates this impact by using appropriate feature selection based on Fisher linear discriminant (FLD) and Bhattacharyya distance (BCD) to enhance fault classification accuracy. In order to validate the performance of the proposed scheme, training and testing data are obtained from different severity levels. To capture the non-stationary behavior of vibration signal, robust tools such as wavelet transform (WT) for time-frequency analysis is employed. Simulation studies show that the proposed scheme ensures good fault diagnostic accuracy even if training and testing data belong to different severity levels. [ABSTRACT FROM AUTHOR]
Description:
This paper presents a new algorithm for the control of a snake-like robot with passive joints and active wheels. Each segment has four autonomously driven and steered wheels. The algorithm approximates the ideal solution in which all wheels on a segment have the same centre of curvature with wheel speeds, providing cooperative redundancy. Each hitch point joining segments traverses the same path, which is determined by an operator, prescribing the path curvature and front hitch speed. The numerical algorithm developed in this paper is simulation tested against a previously derived analytical solution for a predetermined path. Further simulations are carried out to show the effects of changing curvature and front hitch speed on hitch path, wheel angles and wheel speeds for a one, two and three segment robot.
Description:
This paper presents a new algorithm for the control of a snake-like robot with passive joints and active wheels. Each segment has four autonomously driven and steered wheels. The algorithm approximates the ideal solution in which all wheels on a segment have the same centre of curvature with wheel speeds, providing cooperative redundancy. Each hitch point joining segments traverses the same path, which is determined by an operator, prescribing the path curvature and front hitch speed. The numerical algorithm developed in this paper is simulation tested against a previously derived analytical solution for a predetermined path. Further simulations are carried out to show the effects of changing curvature and front hitch speed on hitch path, wheel angles and wheel speeds for a one, two and three segment robot.
Description:
Condition based maintenance (CBM) in the process industry helps in determining the residual life of equipment, avoiding sudden breakdown and facilitating the maintenance staff to schedule repairs by optimizing demand–supply relationships. One of the prevalent issues in CBM is to predict the residual life of the equipment. This paper proposes a novel framework to predict the remnant life of the equipment, called residual life prediction, based on optimally parameterized wavelet transform and multi-step support vector regression (RWMS). In optimally parameterized wavelet transform, a generalized criterion is proposed to select the wavelet decomposition level which works for all the applications; decomposition nodes are selected by characterizing their dominancy level based upon relative fault signature–signal energy contents. The prediction model is based on multi-step support vector regression to determine the nonlinear crack propagation in the rotary machine according to Paris’s fatigue model. The results both for the simulated as well as the actual vibration datasets validate the enhanced performance of RWMS in comparison with the existing techniques to predict the residual life of the equipment.
Description:
Complex regulatory networks often have to be further expanded and improved with regard to the unknown effects of additional parameters and factors that can emit a disturbing influence on the key variables under consideration. The concept of target-environment (TE) networks provides a holistic framework for the analysis of such parameter-dependent multi-modal systems. In this study, we consider time-discrete TE regulatory systems with spline entries. We introduce a new regression model for these particular two-modal systems that allows us to determine the unknown system parameters by applying the multivariate adaptive regression spline (MARS) technique and the newly developed conic multivariate adaptive regression spline (CMARS) method. We obtain a relaxation by means of continuous optimization, especially, conic quadratic programming (CQP) that could be conducted by interior point methods. Finally, a numerical example demonstrates the efficiency of the spline-based approach.
Description:
We address for the first time the measurement of nonradiative decay rates in Cr-related centers in nanodiamonds. Compared to our previous quantum efficiency measurement of Cr centers created in bulk diamond, separate measurements of radiative and nonradiative decay rates in grown nanodiamonds prove more challenging due to size dependence effects. We demonstrate in this Letter that, using defocused dipole imaging and collection efficiency calculation via finite-difference time-domain (FDTD), a quantum efficiency up to 0.9 can be inferred to Cr-related centers showing a 2-level system photon statistics.
Description:
In predictive coding of integers real-valued residuals are mapped to integers before encoding, leaving room for improvement by reducing the loss due to rounding. In this paper, we propose a new prefix coding scheme where actual integer values, instead of the residuals, are encoded using cosets with real domain predictions as the side information. This novel coding scheme outperforms Golomb-based coding by reducing the rounding loss with similar computational and memory complexity.
Description:
Multiple-point defects and abraded surfaces in rotary machinery induce complex vibration signatures, and have a tendency to mislead defect diagnosis models. A challenging problem in machine defect diagnosis is to model and study defect signature dynamics in the case of multiple-point defects and surface abrasion. In this study, a multiple-point defect model (MPDM) that characterizes the dynamics of n-point bearing defects is proposed. MPDM is further extended to model degradation in a rotating machine as a special case of multiple-point defects. Analytical and experimental results for multiple-point defects and abrasions show that the location of the fundamental defect frequency shifts depending upon the relative location of the defects and width of the abrasive region. This variation in the defect frequency results in a degradation of the defect detection accuracy of the defect diagnostic model. Based on envelope detection analysis, a modification in existing defect diagnostic models is recommended to nullify the impact of multiple-point defects, and general abrasion in machine components.