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The aim of this letter is the construction of a new model order reduction algorithm generalized to the multi-input/multi-output systems model order reduction. It is essentially based on the complete order model dominant modes retention. This involves the conversion of the overall significant information contained in the original complete order system into the reduced order approximant, permitting...
Electrocardiogram (ECG) signal plays an important role in the primary diagnosis, prognosis and survival analysis of heart diseases. Several noise types are sources of ECG signal corruption such as electrode movement, strong electromagnetic effect and muscle noise. The principle of techniques based wavelet depends on shrinking the wavelet coefficients in the wavelet domain. These techniques prove their...
The propose of this study is the calculation of optimal approximants for complete order digital filters, by the use of three different schemes based on special projections of frequency Weighted model order reduction (MOR) algorithms, based on namely, the Gradient Flow without Frequency Weighted (GF), the Frequency Weighted Balanced Truncation (FWBT), and the Gradient Flow with Frequency Weighted (FWGF)...
Using a new model order reduction based on frequency selection and optimal genetic algorithm, a very low order speech model is presented. In our approach, the modeling process starts with a full-order classical all poles model obtained by some known methods. The original model is then reduced using a new proposed approach based on the genetic algorithms and the full order speech production system...
Image denoising is the process to remove the noise from the image naturally corrupted by the noise. The wavelet method is one among various methods for recovering infinite dimensional objects like curves, densities, images, … etc. The wavelet techniques are very effective to remove the noise because of their ability to capture the energy of a signal in few energy transform values. The wavelet methods...
The work presented in this paper concerns with analysis and synthesis of the two-dimensional Infinite Impulse Response (IIR) filters based on model order reduction. The synthesis is performed with the method of Semi-Definite Programming (SDP), in the frequency domain. After synthesis, we make an order reduction of the filter model by the quasi-Gramians method. From Several results and their interpretations,...
Three recent methods for model reduction of linear discrete systems are presented. They are based on the impulse response gramian which contains information on the input-output behavior of the system. The corresponding low order approximants retain the first r Markov parameters and the first r×r elements of the impulse response gramian of the original system. To see the efficiency of each algorithm,...
In this paper, we use a reduced order model based on a model frequency representation, factor division and an error criterion. We show from the input-output behavior (time and frequency analysis), the interesting properties of this approximate, mainly once we comparing it to several recent model reduction techniques. A criterion is used to well appreciate the distance judge how good is the proposed...
In this paper, an efficient approach is proposed for the synthesis of an economical digital 2D-IIR filter with high information efficiency, by means of model reduction. As a result a linear phase IIR filter whose frequency response is very close to that of the initial filter and is very suitable for directional filtering and image processing applications.
We use the two-dimensional windowing method to design a digital 2D-FIR filter with linear phase, circularly symmetric with respect to the origin of the frequency plane. To get an economical filter with high information efficiency, an interesting way is applying the balanced realization method to this full-order filter. As a result, a linear phase IIR filter is obtained whose frequency response is...
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