Cramer-Rao lower bound on locations of sudden changes in a steplikesignal

Estimation of locations of sudden changes in a steplike signal has many signal processing applications; e.g., well-log signal segmentation, ionic-channel signal classification, edge detection, and segmentation of images. In this work, the Cramer-Rao lower bound (CRLB) on locations of steps in one-dimensional (1-D) steplike signals is calculated. The calculation is based on the use of a sigmoidal function to model a sudden-change (step) in the signal. The introduced model has an adjustable parameter that can be used to fit the CRLB calculation to a particular class of steplike signals     

Consistency of robust estimators in multi-structural visual data segmentation

A theoretical framework is presented to study the consistency of robust estimators used in vision problems involving extraction of fine details. A strong correlation between asymptotic performance of a robust estimator and the asymptotic bias of its scale estimate is mathematically demonstrated where the structures are assumed to be linear corrupted by Gaussian noise. A new measure for the inconsistency of scale estimators is defined and formulated by deriving the functional forms of four recent high-breakdown robust estimators. For each estimator, the inconsistency measures are numerically evaluated for a range of mutual distances between structures and inlier ratios, and the minimum mutual distance between the structures, for which each estimator returns a non-bridging fit, is calculated.     

Climate-related electricity demand-side management in oil-exporting countries—the case of the United Arab Emirates

The oil crisis of the 1970s has increased the concern about the continuity of oil imports flow to major oil-importing developed countries. Numerous policy measures including electricity demand-side management (DSM) programs have been adopted in such countries. These measures aim at reducing the growing need for electricity power that increases the dependency on imported foreign oil and damages the environment. On the other hand, the perception that energy can be obtained at very low cost in oil-rich countries led to less attention being paid to the potential of DSM policies in these countries. This paper discusses such potential using the case of the United Arab Emirates (UAE). Since air conditioning is a major source of electric energy consumption, the relationship between climate conditions and electric energy consumption is considered. An electricity demand model is constructed using time series techniques. The fitted model seems to represent these relationships rather well. Forecasts for electricity consumption using the estimated model indicate that a small reduction in cooling degrees requirement might induce a significant reduction in electric energy demand. Hence, a DSM program is proposed with policy actions to include, among others, measures to reduce cooling degrees requirement.     

High-breakdown-voltage AlInAs/GaInAs junction-modulated HEMT's(JHEMT's) with regrown ohmic contacts by MOCVD

A technology for increasing both the two-terminal gate-drain breakdown and subsequently the three-terminal-off-state breakdown of AlInAs/GaInAs high-electron-mobility transistors (HEMTs) to record values without substantial impact on other parameters is presented. The breakdown in these structures is dependent on the multiplication of electrons injected from the source (channel current) and the gate (gate leakage) into the channel. In addition, holes are generated by high fields at the drain and are injected back into the gate and source electrodes. These phenomena can be suppressed by increasing the gate barrier height and alleviating the fields at the drain. Both have been achieved by incorporating a p⁺-2DEG junction as the gate that modulates the 2DEG gas and by utilizing selective regrowth of the source and drain regions by MOCVD. The 1-μm-gate-length devices fabricated have two-terminal gate-drain and three-terminal-off-state breakdown voltages of 31 V and 28 V, respectively     

ECFGM: enriched control flow graph miner for unknown vicious infected code detection

Vicious codes, especially viruses, as a kind of impressive malware have caused many disasters and continue to exploit more vulnerabilities. These codes are injected inside benign programs in order to abuse their hosts and ease their propagation. The offsets of injected virus codes are unknown and their targets usually are latent until they are executed and activated, what in turn makes viruses very hard to detect. In this paper enriched control flow graph miner, ECFGM in short, is presented to detect infected files corrupted by unknown viruses. ECFGM uses enriched control flow graph model to represent the benign and vicious codes. This model has more information than traditional control flow graph (CFG) by utilizing statistical information of dependent assembly instructions and API calls. To the best of our knowledge, the presented approach in this paper, for the first time, can recognize the offset of infected code of unknown viruses in the victim files. The main contributions of this paper are two folds: first, the presented model is able to detect unknown vicious code using ECFG model with reasonable complexity and desirable accuracy. Second, our approach is resistant against metamorphic viruses which utilize dead code insertion, variable renaming and instruction reordering methods.     

A novel Jacob spectral method for multi-dimensional weakly singular nonlinear Volterra integral equations with nonsmooth solutions

The main purpose of this work is to develop a spectrally accurate collocation method for solving weakly singular integral equations of the second kind with nonsmooth solutions in high dimensions. The proposed spectral collocation method is based on a multivariate Jacobi approximation in the frequency space. The essential idea is to adopt a smoothing transformation for the spectral collocation method to circumvent the curse of singularity at the beginning of time. As such, the singularity of the numerical approximation can be tailored to that of the singular solutions. A rigorous convergence analysis is provided and confirmed by numerical tests with nonsmooth solutions in two dimensions. The results in this paper seem to be the first spectral approach with a theoretical justification for high-dimensional nonlinear weakly singular Volterra type equations with nonsmooth solutions.

     

An improved volleyball premier league algorithm based on sine cosine algorithm for global optimization problem

Volleyball premier league (VPL) simulating some phenomena of volleyball game has been presented recently. This powerful algorithm uses such racing and interplays between teams within a season. Furthermore, the algorithm imitates the coaching procedure within a game. Therefore, some volleyball metaphors, including substitution, coaching, and learning, are used to find a better solution prepared by the VPL algorithm. However, the learning phase has the largest effect on the performance of the VPL algorithm, in which this phase can lead to making the VPL stuck in optimal local solution. Therefore, this paper proposed a modified VPL using sine cosine algorithm (SCA). In which the SCA operators have been applied in the learning phase to obtain a more accurate solution. So, we have used SCA operators in VPL to grasp their advantages resulting in a more efficient approach for finding the optimal solution of the optimization problem and avoid the limitations of the traditional VPL algorithm. The propounded VPLSCA algorithm is tested on the 25 functions. The results captured by the VPLSCA have been compared with other metaheuristic algorithms such as cuckoo search, social-spider optimization algorithm, ant lion optimizer, grey wolf optimizer, salp swarm algorithm, whale optimization algorithm, moth flame optimization, artificial bee colony, SCA, and VPL. Furthermore, the three typical optimization problems in the field of designing engineering have been solved using the VPLSCA. According to the obtained results, the proposed algorithm shows very reasonable and promising results compared to others.

     

Fuzzy reliability analysis of nanocomposite ZnO beams using hybrid analytical-intelligent method

A hybrid analytical-intelligent approach is proposed for fuzzy reliability analysis of the composite beams reinforced by zinc oxide (ZnO) nanoparticle. The fuzzy reliability index corresponding to buckling failure mode of nanocomposite beam under thickness-direction external voltage is computed based on three-levels: (1) fuzzy analysis, (2) reliability analysis and (3) analytical buckling analysis. In fuzzy analysis level, an improved gravitational search algorithm has been applied to determine uncertainty interval for membership levels of reliability index. The adaptive formulation with a dynamical self-adjusting process is used for reliability analysis level based on conjugate first-order reliability method (FORM). The self-adjusting term in conjugate sensitivity vector is used to satisfy the sufficient descent condition for controlling instability of FORM formula while the proposed conjugate scalar factor is computed less than the original conjugate FORM, thus it may be provided with the efficient results for the convex problem. The new and previous sensitivity vectors obtained by conjugate and steepest descent vectors dynamically adjusted the proposed conjugate factor. In the buckling analysis level, an exponential theory in conjunction with the method of energy is utilized. Fuzzy random variables including applied voltage, the volume fraction of ZnO, thickness of beam, spring constant and shear constant of the foundation are considered in studied nanocomposite beam. Survey results indicated that the proposed method can provide stable and acceptable fuzzy membership functions for parametric study. Moreover, the ratio of length to thickness and spring constant of foundation are the more sensitive parameters which affect fuzzy reliability index significantly.