New receiver designs have been proposed to boost the heat transfer rate in the parabolic trough receiver (PTR) absorber tube, which can further improve the overall performances of the parabolic trough module. The objective of this study is to investigate the thermal performance and heat losses of the newly designed PTR wavy tube by means of a 1-D thermal model taking into account the change of the kinetic energy and pressure drop, and establishing a comparison with the conventional straight and smooth PTR absorber tube. According to the final results, the use of the S-curved absorber leads to higher thermal performance and simultaneously to lower heat losses. According to the thermal efficiency results, it is found an increase of 0.16% in the case of 450 K of the heat transfer fluid (HTF) inlet temperature and a straight length of 7.8 m of the receiver, while the heat losses decrease by a maximum of 7.72%.

Electrocardiogram (ECG) signal with artifact might lead to wrong analysis. In this paper, discrete cosine transform (DCT) denoising method for artifacts removal from ECG signals is proposed. Based on the energy properties of DCT transform is located in the low frequency coefficients and baseline wander (BW) noise is also a low frequency signal; therefore we deduce that the firsts coefficients of the DCT of the ECG signal is corresponds to the BW artifact in it. In our approach the BW is estimated from the low coefficients of the DCT transform, and the corrected ECG signal is obtained by its subtraction from noisy ECG signal in the time domain. The simulation results are done based on real ECG signals taken from MIT-BIH arrhythmia database whose corrupted with the BW noise from MIT-BIH noise stress test database. A comparison analysis of the median filtering (MF), discrete wavelet transform (DWT) decomposition and DCT transform for ECG signal denoising is done. The obtained results showed that our proposed method based on DCT transform gives better performances compared to MF and DWT methods in terms of Signal to Noise Ratio (SNR), and Pearson correlation parameter.