Controllability of Flow-Conservation Transportation Networks with Fractional-Order Dynamics

In this paper, we adapt the fractional derivative approach to formulate the flow-conservation transportation networks, which consider the propagation dynamics and the users’ behaviors in terms of route choices. We then investigate the controllability of the fractional-order transportation networks by employing the Popov-Belevitch-Hautus rank condition and the QR decomposition algorithm. Furthermore, we provide the exact solutions for the full controllability pricing controller location problem, which includes where to locate the controllers and how many controllers are required at the location positions. Finally, we illustrate two numerical examples to validate the theoretical analysis.

A Training Set Subsampling Strategy for the Reduced Basis Method

We present a subsampling strategy for the offline stage of the Reduced Basis Method. The approach is aimed at bringing down the considerable offline costs associated with using a finely-sampled training set. The proposed algorithm exploits the potential of the pivoted QR decomposition and the discrete empirical interpolation method to identify important parameter samples. It consists of two stages. In the first stage, we construct a low-fidelity approximation to the solution manifold over a fine training set. Then, for the available low-fidelity snapshots of the output variable, we apply the pivoted QR decomposition or the discrete empirical interpolation method to identify a set of sparse sampling locations in the parameter domain. These points reveal the structure of the parametric dependence of the output variable. The second stage proceeds with a subsampled training set containing a by far smaller number of parameters than the initial training set. Different subsampling strategies inspired from recent variants of the empirical interpolation method are also considered. Tests on benchmark examples justify the new approach and show its potential to substantially speed up the offline stage of the Reduced Basis Method, while generating reliable reduced-order models.

A New Technique for ARMA-System Identification Based on QR-Decomposition of Third Order Cumulants Matrix

In this paper a new technique to estimate the coefficients of a general Autoregressive Moving Average (ARMA) (p, q) model is proposed. The ARMA system is excited by an un-observable independently identically distributed (i.i.d) non-Gaussian process. The proposed ARMA coefficients estimation method uses the QR-Decomposition (QRD) of a special matrix built with entries of third order cumulants (TOC) of the available output data only. The observed output may be corrupted with additive colored or white Gaussian noise of unknown power spectral density. The proposed technique was compared with several good methods such as the residual time series (RTS) and the Q-slice algorithm (QSA) methods. Simulations for several examples were tested. The results for these examples confirm the good performance of the proposed technique with respect to existing well-known methods.

High Payload Qr-Based Data Hiding Using Secured Compressed Watermark in Polar Domain

Audio Watermarking is a method to insert a copyright marker on audio. This method inserts a watermark in the information form and in a way that does not damage the audio. This technique is one of the ways to solve the problem of copyright infringement. The embedded watermark has to meet the condition of not damaging the audio and must have robustness, imperceptibility, and good capacity. The data hiding technique use the combined method of Lifting Wavelet Transform (LWT), Fast Fourier Transform (FFT), QR Decomposition and Reconstruction, and Cartesian-Polar Transformation (CPT) based on Quantization Index Modulation (QIM) with the secured and compressed watermark using Compressive Sampling (CS) technique. The proposed scheme is blind Audio Watermarking as it no needs for original audio in the detection process. The combination of methods overcomes multiple attacks with guaranteed quality watermarking and high capacity. Compared to the existing technique, the data hiding technique can withstand LPF attacks, Resampling, Linear speed change (LSC), and MP3 compression. This proposed technique is also secured due to the coded watermark by a particular random key using CS. Combining CS and Audio Watermarking techniques can perform well in capacity, imperceptibility, security, and attack resistance.