This paper presents a novel approach for QRS complex detection and extraction of electrocardiogram signals for different types of arrhythmias. Firstly, the ECG signal is filtered by a band pass filter, and then it is differentiated. After that, the Hilbert transform and the adaptive threshold technique are applied for QRS detection. Finally, the Principal Component Analysis is implemented to extract features from the ECG signal. Nineteen different records from the MIT-BIH arrhythmia database have been used to test the proposed method. A 96.28% of sensitivity and a 99.71% of positive predictivity are reported in this testing for QRS complexity detection, being a positive result in comparison with recent researches. All Rights Reserved

The powder α-Eu 2 (MoO 4 ) 3 sample was prepared by the solid-state reaction method. The phase purity of the final powder product was verified by X-ray diffraction analysis. The constituent element core levels and valence band are measured by X-ray photoelectron spectroscopy as a function of Ar + ion (2.5 keV, 7−8 μA/cm 2 ) bombardment time. The formation of Mo 5+ and Mo 4+ states at high bombardment times was detected. The Eu−O and Mo−O bonding was considered in comparison with other Eu 3+ -and Mo 6+ -containing oxides using binding energy difference parameters. The transparency range obtained for the pure α-Eu 2 (MoO 4 ) 3 tablet is λ = 0.41−0.97 μm, as estimated at the transmission level of 5%. The short-wavelength cut edge in α-Eu 2 (MoO 4 ) 3 is governed by the direct allowed optical transitions within the band gap of E g = 3.74 eV (300 K). The band structure of α-Eu 2 (MoO 4 ) 3 was calculated by ab initio methods and strongly different results were obtained for the spin up/down configurations. The Eu-4f states are located around 2.2 eV and −4.0 eV for spin up (↑) and the structures situated at around 6.5 and 5.5 eV for spin down (↓) configuration. The calculated spin magnetic moments are in excellent relation to the Slater-Pauling rule and within the Eu sphere the magnetic moment of 4f electrons is ∼5.99 μB.

As the starting point for a comprehensive theoretical investigation of the linear and nonlinear optical susceptibilities, we have used our experimental crystallographic data for Ag0.5Pb1.75GeS3Se (Ag2Pb7Ge4S12Se4) reported. The experimental crystallographic positions were optimized by minimizing the forces acting on each atom to get meaningful theoretical predictions of the optical properties. The linear optical susceptibilities are calculated. We find that the optical band gap shows very good agreement with our measured gap. The second-order nonlinear optical (NLO) susceptibilities dispersion namely the optical second harmonic generation (SHG) is calculated and compared with our experimental measurements. The microscopic first order hyperpolarizability, β123, vector component along the principal dipole moment directions for the χ((2))(123)(ω) component was obtained theoretically and compared with our measured values at different temperatures. The dependence of the two-photon absorption (TPA) for the pump-probing at SHG of the microsecond CO2 laser was measured. In addition we explored the linear electro-optical effect in these crystals. This effect is described by the third rank polar tensors similarly to the SHG. However, for the Pockels effect besides the electronic contribution, the phonon subsystem also begins to play a principal role. As a consequence we study the dispersion of the linear electro-optical effects in the mentioned crystals.

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During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target. A real-time image-guided technique can be used to monitor such lung tumor motion for accurate dose delivery, but the system latency up to several hundred milliseconds for repositioning the radiation beam also affects the accuracy. In order to compensate the latency, neural network prediction technique with real-time retraining can be used. We have investigated real-time prediction of 3D time series of lung tumor motion on a classical linear model, perceptron model, and on a class of higher-order neural network model that has more attractive attributes regarding its optimization convergence and computational efficiency. The implemented static feed-forward neural architectures are compared when using gradient descent adaptation and primarily the Levenberg-Marquardt batch algorithm as the ones of the most common and most comprehensible learning algorithms. The proposed technique resulted in fast real-time retraining, so the total computational time on a PC platform was equal to or even less than the real treatment time. For one-second prediction horizon, the proposed techniques achieved accuracy less than one millimeter of 3D mean absolute error in one hundred seconds of total treatment time.