A facile one-pot aqueous solution method has been developed for the fast and straightforward synthesis of SnTe nanoparticles in more than ten gram quantities per batch. The synthesis involves boiling an alkaline Na₂SnO₂ solution and a NaHTe solution for short time scales, in which the NaOH concentration and reaction duration play vital roles in controlling the phase purity and particle size, respectively. Spark plasma sintering of the SnTe nanoparticles produces nanostructured compacts that have a comparable thermoelectric performance to bulk counterparts synthesised by more time- and energy-intensive methods. This approach, combining an energy-efficient, surfactant-free solution synthesis with spark plasma sintering, provides a simple, rapid, and inexpensive route to p-type SnTe nanostructured materials. Full article

Background subtraction without a separate training phase has become a critical task, because a sufficiently long and clean training sequence is usually unavailable, and people generally thirst for immediate detection results from the first frame of a video. Without a training phase, we propose a background subtraction method based on three-dimensional (3D) discrete wavelet transform (DWT). Static backgrounds with few variations along the time axis are characterized by intensity temporal consistency in the 3D space-time domain and, hence, correspond to low-frequency components in the 3D frequency domain. Enlightened by this, we eliminate low-frequency components that correspond to static backgrounds using the 3D DWT in order to extract moving objects. Owing to the multiscale analysis property of the 3D DWT, the elimination of low-frequency components in sub-bands of the 3D DWT is equivalent to performing a pyramidal 3D filter. This 3D filter brings advantages to our method in reserving the inner parts of detected objects and reducing the ringing around object boundaries. Moreover, we make use of wavelet shrinkage to remove disturbance of intensity temporal consistency and introduce an adaptive threshold based on the entropy of the histogram to obtain optimal detection results. Experimental results show that our method works effectively in situations lacking training opportunities and outperforms several popular techniques. Full article