In this paper, we propose a new visual tracking method in light of salience information and deep learning. Salience detection is used to exploit features with salient information of the image. Complicated representations of image features can be gained by the function of every layer in convolution neural network (CNN). The characteristic of biology vision in attention-based salience is similar to the neuroscience features of convolution neural network. This motivates us to improve the representation ability of CNN with functions of salience detection. We adopt the fully-convolution networks (FCNs) to perform salience detection. We take parts of the network structure to perform salience extraction, which promotes the classification ability of the model. The network we propose shows great performance in tracking with the salient information. Compared with other excellent algorithms, our algorithm can track the target better in the open tracking datasets. We realize the 0.5592 accuracy on visual object tracking 2015 (VOT15) dataset. For unmanned aerial vehicle 123 (UAV123) dataset, the precision and success rate of our tracker is 0.710 and 0.429.

Quantum dots (QDs) are promising candidates for the next-generation optical and electronic devices due to the outstanding photoluminance efficiency, tunable bandgap and facile solution synthesis. Nevertheless, the limited optoelectronic performance and poor lifetime of QDs devices hinder their further applications. As a gas-phase surface treatment method, atomic layer deposition (ALD) has shown the potential in QDs surface modification and device construction owing to the atomic-level control and excellent uniformity/conformality. In this perspective, the attempts to utilize ALD techniques in QDs modification to improve the photoluminance efficiency, stability, carrier mobility, as well as interfacial carrier utilization are introduced. ALD proves to be successful in the photoluminance quantum yield (PLQY) enhancement due to the elimination of QDs surface dangling bonds and defects. The QDs stability and devices lifetime are improved greatly through the introduction of ALD barrier layers. Furthermore, the carrier transport is ameliorated efficiently by infilling interstitial spaces during ALD process. Attributed to the ultra-thin and dense coating on the interface, the improvement on optoelectronic performance is achieved. Finally, the challenges of ALD applications in QDs at present and several prospects including ALD process optimization, in-situ characterization and computational simulations are proposed.

This paper is devoted to reviewing the results achieved so far in the application of the single-pixel imaging technique to terahertz (THz) systems. The use of THz radiation for imaging purposes has been largely explored in the last twenty years, due to the unique capabilities of this kind of radiation in interrogating material properties. However, THz imaging systems are still limited by the long acquisition time required to reconstruct the object image and significant efforts have been recently directed to overcome this drawback. One of the most promising approaches in this sense is the so-called "single-pixel" imaging, which in general enables image reconstruction by patterning the beam probing the object and measuring the total transmission (or reflection) with a single-pixel detector (i.e., with no spatial resolution). The main advantages of such technique are that i) no bulky moving parts are required to raster-scan the object and ii) compressed sensing (CS) algorithms, which allow an appropriate reconstruction of the image with an incomplete set of measurements, can be successfully implemented. Overall, this can result in a reduction of the acquisition time. In this review, we cover the experimental solutions proposed to implement such imaging technique at THz frequencies, as well as some practical uses for typical THz applications.