Coastal surveillance video helps officials to obtain on-site visual information on maritime traffic situations, which benefits building up the maritime transportation detection infrastructure. The previous ship detection methods focused on detecting distant small ships in maritime videos, with less attention paid to the task of ship detection from coastal surveillance video. To address this challenge, a novel framework is proposed to detect ships from coastal maritime images in three typical traffic situations in three consecutive steps. First the Canny detector is introduced to determine the potential ship edges in each maritime frame. Then, the self-adaptive Gaussian descriptor is employed to accurately rule out noisy edges. Finally, the morphology operator is developed to link the detected separated edges to connected ship contours. The model's performance is tested under three typical maritime traffic situations. The experimental results show that the proposed ship detector achieved satisfactory performance (in terms of precision, accuracy and time cost) compared with other state-of-the-art algorithms. The findings of the study offer the potential of providing real-time visual traffic information to maritime regulators, which is crucial for the development of intelligent maritime transportation.

Most previous research has handled the task of ship type recognition by exploring hand-craft ship features, which may fail to distinguish ships with similar visual appearances. This situation motivates us to propose a novel deep learning based ship type recognition framework which we have named coarse-to-fine cascaded convolution neural network (CFCCNN). First, the proposed CFCCNN framework formats the input training ship images and data, and provides trainable input data for the hidden layers of the CFCCNN. Second, the coarse and fine steps are run in a nesting manner to explore discriminative features for different ship types. More specifically, the coarse step is trained in a similar manner to the traditional convolution neural network, while the fine step introduces regularisation mechanisms to extract more intrinsic ship features, and fine tunes parameter settings to obtain better recognition performance. Finally, we evaluate the performance of the CFCCNN model for recognising the most common types of merchant ship (oil tanker, container, LNG tanker, chemical carrier, general cargo, bulk carrier, etc.). The experimental results show that the proposed framework obtains better recognition performance than the conventional methods of ship type recognition.

Conventional visual ship tracking methods employ single and shallow features for the ship tracking task, which may fail when a ship presents a different appearance and shape in maritime surveillance videos. To overcome this difficulty, we propose to employ a multi-view learning algorithm to extract a highly coupled and robust ship descriptor from multiple distinct ship feature sets. First, we explore multiple distinct ship feature sets consisting of a Laplacian-of-Gaussian (LoG) descriptor, a Local Binary Patterns (LBP) descriptor, a Gabor filter, a Histogram of Oriented Gradients (HOG) descriptor and a Canny descriptor, which present geometry structure, texture and contour information, and more. Then, we propose a framework for integrating a multi-view learning algorithm and a sparse representation method to track ships efficiently and effectively. Finally, our framework is evaluated in four typical maritime surveillance scenarios. The experimental results show that the proposed framework outperforms the conventional and typical ship tracking methods.

In this study, thermodynamic properties of BC2N under extreme conditions have been reported by using first-principle calculations and quasi-harmonic Debye model. Isochoric heat capacity (Cv) of BC2N at normal temperature and pressure is 23.15 kJ mol−1 K−1 and it increases with the temperature and decreases with the pressure. In the low temperature region, pressure has no obvious influence on phonons and thus the decrease of Cv is very slow. In the medium temperature region, the decrease of Cv becomes steep. The reason is that high pressure plays an important role in controlling the vibration of atoms. In the high temperature region, the decrease of Cv becomes slow. Debye temperature (θ) decreases with the temperature. However, the tendency is not obvious in the low temperature region but very clear in high temperature. Moreover, θ increases with pressure and the amplitude is larger in higher temperature. Because of the four covalent bonds with different strength and distribution asymmetric thermal expansion along different axes occurs. The value of thermal expansion coefficient along c axis is more than that of along a and b axes.

An all-electron scalar relativistic (AER) calculation on AunPtCO (n = 1–12) clusters had been performed using density functional theory (DFT) with the generalized gradient approximation (GGA) at Perdew-Wang 91 (PW91) exchange-correlation functional level. The CO molecule prefers to be adsorbed on Pt in AunPt (n = 1–12) clusters. The introduction of impurity Pt strengthens the adsorption toward CO molecule and then promotes the reactivity enhancement of CO molecule. The CO molecule is also more favorable to be adsorbed by the even-numbered AunPt clusters with closed-shell electronic structure.

We apply infrared spectroscopic ellipsometry (IRSE) in combination with near-infrared to vacuum-ultraviolet ellipsometry to study the concentration and mobility of holes in a set of Mg-doped In-polar InN samples of different Mg-concentrations. P-type behavior is found in the IRSE spectra for Mg-concentrations between 1x1018 cm-3 and 3x1019 cm-3. The free-charge carrier parameters are determined using a parameterized model that accounts for phonon-plasmon coupling. From the NIR-VUV data information about layer thicknesses, surface roughness, and structural InN layer properties are extracted and related to the IRSE results.

Epitaxy of InN films with N-polarity and In-polarity was investigated by RF-MBE with several in-situ monitoring/controlling systems. It was found that the epitaxy temperature for N-polarity growth could be as high as 600 °C and this was about 100 deg higher than that for In-polarity case. This temperature difference in two polarities drastically affected not only the growth behaviors but also the properties of InN epilayers, i.e. N-polarity growth was preferable in both view-points. The step-flow-like surface morphology was achieved for the InN films grown with N-polarity at 580 °C. The FWHMs of X-ray rocking curves for InN (002) and (102) of 5–8 μm-thick InN films grown in N-polarity were about 200–350 and 650–950 arcsec, respectively. The highest Hall mobility was above 2000 cm2/V·s with a background carrier concentration of 1–2×1018 cm−3 at room temperature. For both polarity films, N-rich condition was necessary for the stable InN growth to obtain 5–8 μm-thick InN films.