Modeling and numerical simulation of crystal growth of Si film and heat transport in 3D structure were made for optimization of physical and geometrical parameters used during laser recrystallization. Based on simulations a new concept called micro-absorber was introduced for obtaining defect-free Si films.

Super resolution (SR) image reconstruction has attracted increasing attention these years and many SR image reconstruction algorithms have been proposed for restoring a high-resolution image from one or multiple low-resolution images. However, how to objectively evaluate the quality of SR reconstructed images remains an open problem. Although a great number of image quality metrics have been proposed, they are quite limited to evaluate the quality of SR reconstructed images. Inspired by this, this paper presents a blind quality index for SR reconstructed images using first- and second-order structural degradation. First, the SR reconstructed image is decomposed into multi-order derivative magnitude maps, which are effective for first- and second-order structural representation. Then, log-energy based features are extracted on these multi-order derivative magnitude maps in the frequency domain. Finally, support vector regression is used to learn the quality model for SR reconstructed images. The results of extensive experiments that were conducted on one public database demonstrate the superior performance of the proposed method over the existing quality metrics. Moreover, the proposed method is less dependent on the number of training images and has low computational cost.

This letter presents an objective quality index for benchmarking image inpainting algorithms. Under the guidance of the masks of damaged areas, the boundary region and the inpainting region are first located. Then, the statistical features are extracted from the boundary and inpainting regions respectively. For the boundary region, we utilize Weibull distribution to fit the gradient magnitude histograms of the exterior and interior regions around the boundary, and the Kullback-Leibler Divergence (KLD) is calculated to measure the boundary distortions caused by imperfect inpainting. Meanwhile, the quality of the inpainting region is measured by comparing the naturalness factors between the inpainted image and the reference image. Experimental results demonstrate that the proposed metric outperforms the relevant state-of-the-art quality metrics.

This paper proposes a technique for efficient standing multipath mitigation of geostationary earth orbit (GEO) satellites. The performance of traditional filtering methods for GEO multipath errors can be effectively improved by using carrier frequency variation approach. Conventional multipath mitigation methods do not pay much attention to the GEO satellite multipath effects, and they are less effective due to the non-zero average characteristics of GEO multipath errors for short epochs. By varying carrier frequency, the multipath error average becomes approaching zero in short epochs due to the faster multipath carrier phase variation. Therefore, it could enhance the traditional filtering method performance on the multipath signals. By varying the carrier frequency or the carrier phase offset, the average multipath error will approach zero as a result of the frequent multipath carrier phase variations. This method aims to explore the potential for signal pattern design while improving the performance of current satellite navigation systems. The results show that the root mean square error (RMSE) for pseudo-range multipath errors of the proposed approach improves about 0.3m with a frequency variation range of 15MHz.

Application-Specific Network-on-Chips (ASNoCs) have been proposed as a more promising solution than regular NoCs to the global communication challenges for particular applications in nanoscale System-on-Chip (SoC) designs. In ASNoC Design, one of the key challenges is to generate the most suitable and power efficient NoC topology under the constraints of the application specification. In this work, we present a two-step floorplanning (TSF) algorithm, integrating topology synthesis into floorplanning phase, to automate the synthesis of such ASNoC topologies. At the first-step floorplanning, during the simulated annealing, we explore the optimal positions and clustering of cores and implement an incremental path allocation algorithm to predictively evaluate the power consumption of the generated NoC topology. At the second-step floorplanning, we explore the optimal positions of switches and network interfaces on the floorplan. A power and timing aware path allocation algorithm is also integrated into this step to determine the connectivity across different switches. Experimental results on a variety of benchmarks show that our algorithm can produce greatly improved solutions over the latest works.

The rapid development of a global navigation satellite system (GNSS) has raised the demand for spacecraft navigation, particularly for lunar spacecraft (LS). First, instead of the traditional approach of combining the united X-band system (UXB) with very-long-baseline interferometry (VLBI) by a terrestrial-based observing station in Chinese deep-space exploration, the spacecraft navigation based on inter-satellite link (ISL) is proposed because the spatial coverage of the GNSS downstream signals is too narrow to be used for LS navigation. Second, the feasibility of LS navigation by using ISL wide beam signals has been analyzed with the following receiving parameters: the geometrical dilution of precision (GDOP) and the carrier-to-noise ratio (C/N0) for satellites autonomously navigation of ISL and LS respectively; the weighting distance root-mean-square (wdrms) for the combination of both navigation modes. Third, to be different from all existing spacecraft ISL and GNSS navigation methods, an ISL annular beam transmitting antenna has been simulated to minimize the wdrms (1.138m) to obtain the optimal beam coverage: 16°-47° on elevation angle. Theoretical calculations and simulations have demonstrated that both ISL autonomous navigation and LS navigation can be satisfied at the same time. Furthermore, an onboard annular wide beam ISL antenna with optimized parameters has been designed to provide a larger channel capacity with a simpler structure than that of the existing GPS ISL spot beam antenna, a better anti-jamming performance than that of the former GPS ISL UHF-band wide band antenna, and a wider effectively operating area than the traditional terrestrial-based measurement. Lastly, the possibility and availability of applying an ISL receiver with an annular wide beam antenna on the Chinese Chang'E-5T (CE-5T) reentry experiment for autonomous navigation are analyzed and verified by simulating and comparing the ISL receiver with the practiced GNSS receiver.

Global navigation satellite system (GNSS) receivers equipped with the frequency domain interference suppression (FDIS) filter can operate in environments with harsh interference. The FDIS will not cause tracking error bias for an ideal analog receiver channel as its magnitude response and phase response are constant. However, the analog receiver channel distortion is induced by RF cables, amplifiers, and mixers. The distortion of the channel caused asymmetry correlation function. The correlation function is further deformed by the FDIS filter. More seriously, since the FDIS filter is adaptive, the bias will vary with the jamming pattern, especially when the frequency of interference is varying. For precision navigation applications, this bias must be mitigated. Fortunately, to prevent power loss, the analog receiver channel filter is a real function or the imaginary part is negligible. Therefore, the magnitude response and the phase response are even functions. Based on these channel features, a new FDIS filter based on mirror frequency amplitude compensation (MFAC) method is proposed in this paper. The amplitude of the symmetry position of the notch frequency is doubled in the MFAC method in order to mitigate the tracking bias. Simulation results show that the MFAC-based FDIS method is capable of reducing the bias error to less than 0.1ns, which is significant smaller than that achieved by the traditional FDIS method.

The properties of the surface-conduction electron-emitter display (SED) are mainly decided by the surface-conduction electron emitters (SCE), which are normally made from the expensive metal Pd. In this study, we propose to use metal Zn instead of Pd as the emitter material. Both the device electrode and ZnO thin film are deposited by a sputter, and the electron emitters (SCE) are formed by the electro-forming process. The electron emission characteristic is obtained and the luminescence is observed.

A novel multipath mitigation algorithm for binary offset carrier (BOC) signals in the global navigation satellite system (GNSS) is presented. Based on the W2 code correlation reference waveform (CCRW) structure, a series of bipolar reference waveforms (BRWs) is introduced to shape the unambiguous s-curve. The resulted s-curve has a single stable zero-crossing point such that the tracking unambiguity in BOC (1,1) can be solved. At the same time, multipath mitigation capability is improved as well. As verified by simulations, the proposed method matches the multipath mitigation performance of W2 CCRW, and is superior to conventional receiver correlation techniques. This method can be applied in GPS L1 and Galileo E1.

Recently, deep reinforcement learning (DRL) methods have significantly improved the performance of target-driven indoor navigation tasks. However, the rich semantic information of environments is still not fully exploited in previous approaches. In addition, existing methods usually tend to overfit on training scenes or objects in target-driven navigation tasks, making it hard to generalize to unseen environments. Human beings can easily adapt to new scenes as they can recognize the objects they see and reason the possible locations of target objects using their experience. Inspired by this, we propose a DRL-based target-driven navigation model, termed MVC-PK, using Multi-View Context information and Prior semantic Knowledge. It relies only on the semantic label of target objects and allows the robot to find the target without using any geometry map. To perceive the semantic contextual information in the environment, object detectors are leveraged to detect the objects present in the multi-view observations. To enable the semantic reasoning ability of indoor mobile robots, a Graph Convolutional Network is also employed to incorporate prior knowledge. The proposed MVC-PK model is evaluated in the AI2-THOR simulation environment. The results show that MVC-PK (1) significantly improves the cross-scene and cross-target generalization ability, and (2) achieves state-of-the-art performance with 15.2% and 11.0% increase in Success Rate (SR) and Success weighted by Path Length (SPL), respectively.

Robot motion planning is an important part of the unmanned supermarket. The challenges of motion planning in supermarkets lie in the diversity of the supermarket environment, the complexity of obstacle movement, the vastness of the search space. This paper proposes an adaptive Search and Path planning method based on the Semantic information and Deep reinforcement learning (SPSD), which effectively improves the autonomous decision-making ability of supermarket robots. Firstly, based on the backbone of deep reinforcement learning (DRL), supermarket robots process real-time information from multi-modality sensors to realize high-speed and collision-free motion planning. Meanwhile, in order to solve the problem caused by the uncertainty of the reward in the deep reinforcement learning, common spatial semantic relationships between landmarks and target objects are exploited to define reward function. Finally, dynamics randomization is introduced to improve the generalization performance of the algorithm in the training. The experimental results show that the SPSD algorithm is excellent in the three indicators of generalization performance, training time and path planning length. Compared with other methods, the training time of SPSD is reduced by 27.42% at most, the path planning length is reduced by 21.08% at most, and the trained network of SPSD can be applied to unfamiliar scenes safely and efficiently. The results are motivating enough to consider the application of the proposed method in practical scenes. We have uploaded the video of the results of the experiment to https://www.youtube.com/watch?v=h1wLpm42NZk.

With the widespread use of indoor positioning technology, the need for high-precision positioning services is rising; nevertheless, there are several challenges, such as the difficulty of simulating the distribution of interior location data and the enormous inaccuracy of probability computation. As a result, this paper proposes three different neural network model comparisons for indoor location based on WiFi fingerprint - indoor location algorithm based on improved back propagation neural network model, RSSI indoor location algorithm based on neural network angle change, and RSSI indoor location algorithm based on depth neural network angle change - to raise accurately predict indoor location coordinates. Changing the action range of the activation function in the standard back-propagation neural network model achieves the goal of accurately predicting location coordinates. The revised back-propagation neural network model has strong stability and enhances indoor positioning accuracy based on experimental comparisons of loss rate (loss), accuracy rate (acc), and cumulative distribution function (CDF).