Predicting future behavior of chaotic time series system is a challenging area in the literature of nonlinear systems. The prediction's accuracy of chaotic time series is extremely dependent on the model and the learning algorithm. On the other hand the cyclic solar activity as one of the natural chaotic systems has significant effects on earth, climate, satellites and space missions. Several m...

Quantitative phase-contrast microscopy (QPCM) provides an effective approach for label-free detection of transparent samples. In this study, we propose a condenser-free quantitative (CF-QPCM), in which several light-emitting diodes (LEDs) distributed on ring are used direct ultra-oblique illumination. Such design greatly simplifies the system’s structure and releases space installing phase maps...

An approach to prognosis of qualitative behavior of an unknown dynamical system (DS) from weakly nonstationary chaotic time series (TS) containing significant measurement noise is proposed. The approach is based on construction of a global time-dependent parametrized model of discrete evolution operator (EO) capable of reproducing nonstationary dynamics of a reconstructed DS. A universal model ...

The use of light diffraction for the microbiological diagnosis of bacterial colonies was a significant breakthrough with widespread implications for the food industry and clinical practice. We previously confirmed that optical sensors for bacterial colony light diffraction can be used for bacterial identification. This paper is focused on the novel perspectives of this method based on digital i...

In this paper, a local predictor approach based on proven powerful regression algorithm which is support vector regression (SVR) combined with space reconstruction of time series is introduced. In addition, real value genetic algorithm (GA) has been utilized in the proposed method for optimization of the parameters of the SVR. In the proposed approach, the embedding dimension and the time delay...

To solve chaotic time series prediction problem, a novel Prediction approach for chaotic time series based on Immune Optimization Theory (PIOT) is proposed. In PIOT, the concepts and formal definitions of antigen, antibody and affinity being used for time series prediction are given, and the mathematical models of immune optimization operators being used for establishing time series prediction ...

Introduction In MRI with continuously moving table (CMT) breathing motion is an important issue. Recently motion compensation techniques for CMT-MRI during free breathing have been introduced [1, 2]. They apply parallel imaging to reconstruct multiple snapshots of the breathing motion from a fully sampled k-space and combine these snapshots subsequently. In this study the compressed sensing (CS...

Introduction Asymmetry between odd and even echoes generates Nyquist ghost artifact in a single-shot EPI , and further enhances multiple ghost artifacts in a segmented EPI. These ghost artifacts not only severely degrade EPI image quality, but may lead to incorrect ADC values in EPI based diffusion measurements (1). Methods currently available for odd-even-echo asymmetry compensation include on...

Magnetic field inhomogeneity causes artifacts in MRI. For example, in single-shot echo-planar imaging (EPI), they often appear as severe geometric distortions along the phase-encoding direction. Sensitivity encoding (SENSE) is useful in reducing the distortion in EPI since it only acquires partial k-space data using multiple receiver channels. In SENSE, a reference scan usually needs to be perf...

PURPOSE Utilization of respiratory correlated four-dimensional cone-beam computed tomography (4DCBCT) has enabled verification of internal target motion and volume immediately prior to treatment. However, with current standard CBCT scan, 4DCBCT poses challenge for reconstruction due to the fact that multiple phase binning leads to insufficient number of projection data to reconstruct and thus c...