Electrocardiogram Baseline Wander Suppression Based on the Combination of Morphological and Wavelet Transformation Based Filtering.

One of the major noise components in electrocardiogram (ECG) is the baseline wander (BW). Effective methods for suppressing BW include the wavelet-based (WT) and the mathematical morphological filtering-based (MMF) algorithms. However, the T waveform distortions introduced by the WT and the rectangular/trapezoidal distortions introduced by MMF degrade the quality of the output signal. Hence, in this study, we introduce a method by combining the MMF and WT to overcome the shortcomings of both existing methods. To demonstrate the effectiveness of the proposed method, artificial ECG signals containing a clinical BW are used for numerical simulation, and we also create a realistic model of baseline wander to compare the proposed method with other state-of-the-art methods commonly used in the literature. The results show that the BW suppression effect of the proposed method is better than that of the others. Also, the new method is capable of preserving the outline of the BW and avoiding waveform distortions caused by the morphology filter, thereby obtaining an enhanced quality of ECG.

Beamforming for hyperthermia treatment by training a weighted network of an ultrasonic array.

We propose a beamforming algorithm based on waveform diversity for hyperthermia treatment of breast cancer using an ultrasonic array. The introduced array has a structure with a network connecting the feeding nodes and the array elements, and the objective of the algorithm is to train the weight matrix of the network to minimize the difference between the generated beam pattern and the ideal one. The training procedure of the algorithm, which is inspired by the idea of machine learning, comprises three parts: forward calculation, comparison, and backward calculation. The forward calculation maps the weight matrix to the beam pattern, and in the comparison step, the generated beam pattern is modified based on the error, and finally, the backward calculation maps the modified beam pattern to a refined weight matrix which performs better than the original one. An optimal weight matrix is obtained by iterative training. The effectiveness of the algorithm is demonstrated by using numerical simulations.

Bio-inspired artificial functional photocatalyst: biomimetic enzyme-like TiO2/reduced graphene oxide nanocomposite with excellent molecular recognition ability.

An enzyme-like TiO(2)/reduced graphene oxide (enzyme-TiO(2)/rGO) nanocomposite with molecular recognition ability was fabricated by biomimicking the geometrical and chemical complementation of the enzyme and substrate. The anatase TiO(2) nanocrystals were densely dispersed on rGO nanosheets with close interfacial contacts. With geometrical and chemical matching of target molecules and memorized cavities, the adsorption capacity of enzyme-TiO(2)/rGO nanocomposites for 4-nitrophenol (4.71 mg g(-1)) is about six times that of control TiO(2)/rGO without the enzyme-like feature (0.79 mg g(-1)), and the enzyme-TiO(2)/rGO shows a relative selectivity coefficient of 7.24. Moreover, enzyme-TiO(2)/rGO exhibits molecular recognitive photocatalytic degradation for a particular contaminant. The results demonstrate that enzyme-substrate recognition provides a convenient and powerful basis on which to biomimic and construct efficient photocatalysts with high selectivity.