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(Zr47Cu44Al9)100−xSix (x = 0, 0.5, 1.5 and 2.5 at%) thin-film metallic glasses (TFMGs) were deposited at glass substrate by pulsed laser deposition. The influence of Si content and substrate temperature on the microstructure, optical and electrical properties was investigated. After adding Si in the Zr47Cu44Al9 system, the transmittance of the films enhances obviously, especially in the near-infrared region. The resistivity increases and becomes more sensitive to the temperature due to Si addition. With increasing substrate temperatures from room temperature to 430 °C, (Zr47Cu44Al9)98.5Si1.5 films display fully amorphous structure. Appropriate substrate temperature drastically decreases the resistivity of films without degrading the transmittance. (Zr47Cu44Al9)98.5Si1.5 TFMG deposited at 330 °C exhibits the visible transmittance of 49.38%, the infrared transmittance of 80.27% and the resistivity of 7.62 mΩ cm, which are useful properties for their potential applications in infrared technology with the high requirement of radiation resistance and corrosion resistance.

For better analyzing and adopting superpixel methods in image segmentation, an improved Simple Linear Iterative Clustering (SLIC) algorithm is put forward, which sustains that homogeneous regions show high consistence during clustering. Firstly, a primary iteration of SLIC approach is employed to generate uniform superpixels. Then an update strategy is introduced to determine whether to recluster the candidate region. In homogeneous regions, a cluster center often shows relatively small deviation of last position in spatial-color feature space when local iteration occurs, which can be regarded as redundant computation and be reduced. Experimental results show that the strategy brings a remarkable increased efficiency for traditional SLIC without significant affection on segmentation results.

Superpixel segmentation usually over-segments an image into fragments to extract regional features, thus linking up advanced computer vision tasks. In this work, a novel coarse-to-fine gradient ascent framework is proposed for superpixel-based color image adaptive segmentation. In the first stage, a speeded-up Simple Linear Iterative Clustering (sSLIC) method is adopted to generate uniform superpixels efficiently, which assumes that homogeneous regions preserve high consistence during clustering, consequently, much redundant computation for updating can be avoided. Then a simple criterion is introduced to evaluate the uniformity in each superpixel region, once a superpixel region is under-segmented, an adaptive marker-controlled watershed algorithm processes a finer subdivision. Experimental results show that the framework achieves better performance on detail-rich regions than previous superpixel approaches with satisfactory efficiency.

Multi-walled carbon nanotubes (MWCNTs) with strong visible fluorescence were synthesized via oxidation and amination. A large number of oxygen-containing surface functional groups contributing to fluorescence emission have been found, and the novel structure of carbon nanoclusters with the diameter of 20–30 nm are formed at the tips of MWCNTs. The fluorescent MWCNTs exhibit distinct excitation-wavelength dependence and pH response. Furthermore, the temperature sensitivity of fluorescence has been described carefully. The fluorescence intensity first increases and then decreases upon increasing temperature, and the maximum emission is achieved when the functionalized MWCNTs are dried at 150 °C.

Frequency-tuned (FT) saliency detector has been widely used in various image processing and computer vision applications. However, removing high frequency noise and textures by a Difference of Gaussians (DoG) filter is still time-consuming, which hinders real-time execution of FT. In this paper, an improved algorithm is proposed to tackle the obstacles to performance improvement. It exploits color quantization within natural images and substitute Gaussian filtering process. Experimental results demonstrate that the proposed method runs up to about two times as fast as FT while resulting in almost the same saliency map.

Aluminum-doped ZnO (AZO) thin films with thin film metallic glass of Zr50Cu50 as buffer are prepared on glass substrates by the pulsed laser deposition. The influence of buffer thickness and substrate temperature on structural, optical, and electrical properties of AZO thin film are investigated. Increasing the thickness of buffer layer and substrate temperature can both promote the transformation of AZO from amorphous to crystalline structure, while they show (100) and (002) unique preferential orientations, respectively. After inserting Zr50Cu50 layer between the glass substrate and AZO film, the sheet resistance and visible transmittance decrease, but the infrared transmittance increases. With substrate temperature increasing from 25 °C to 520 °C, the sheet resistance of AZO(100 nm)/Zr50Cu50(4 nm) film first increases and then decreases, and the infrared transmittance is improved. The AZO(100 nm)/Zr50Cu50(4 nm) film deposited at a substrate temperature of 360 °C exhibits a low sheet resistance of 26.7 Ω/□, high transmittance of 82.1% in the visible light region, 81.6% in near-infrared region, and low surface roughness of 0.85 nm, which are useful properties for their potential applications in tandem solar cell and infrared technology.

Promoting the development and utilisation of renewable energy is the current trend of energy policy in various regions. First, we divide the world into seven regions based on the Engineering News-Record (ENR) regional classification—Asia-Pacific, Middle East, Canada, the United States, Latin America, Europe and Africa—and analyse the status of renewable energy from political, technical, economic and social perspectives. Second, an integration forecasting model is proposed, which includes differential autoregressive integrated moving average model (ARIMA), neural network model (NNM), support vector machine model (SVM), and predicts the development prospects of renewable resources by one-way regression in various regions. Finally, corresponding countermeasures are proposed for these seven regions.

In recent years, significant advances have been gained in visual detection, and an abundance of outstanding models have been proposed. However, state-of-the-art object detection networks have some inefficiencies in detecting small targets. They commonly fail to run on portable devices or embedded systems due to their high complexity. In this workpaper, a real-time object detection model, termed as Tiny Fast You Only Look Once (TF-YOLO), is developed to implement in an embedded system. Firstly, the k-means++ algorithm is applied to cluster the dataset, which contributes to more excellent priori boxes of the targets. Secondly, inspired by the multi-scale prediction idea in the Feature Pyramid Networks (FPN) algorithm, the framework in YOLOv3 is effectively improved and optimized, by three scales to detect the earlier extracted features. In this way, the modified network is sensitive for small targets. Experimental results demonstrate that the proposed TF-YOLO method is a smaller, faster and more efficient network model increasing the performance of end-to-end training and real-time object detection for a variety of devices.