Animating 3D characters using motion capture data requires basic expertise and manual labor. To support the creativity of animation design and make it easier for common users, we present a sketch-based interface DualMotion, with rough sketches as input for designing daily-life animations of characters, such as walking and jumping. Our approach enables to combine global motions of lower limbs and the local motion of the upper limbs in a database by utilizing a two-stage design strategy. Users are allowed to design a motion by starting with drawing a rough trajectory of a body/lower limb movement in the global design stage. The upper limb motions are then designed by drawing several more relative motion trajectories in the local design stage. We conduct a user study and verify the effectiveness and convenience of the proposed system in creative activities.

The objective of this paper is to provide an effective approach to infrared spectrum recognition. Traditionally, recognizing infrared spectra is a quantitative analysis problem. However, only using quantitative analysis has met two difficulties in practice: (1) quantitative analysis generally very complex, and in some cases it may even become intractable; and (2) when spectral data are inaccurate, it is hard to give concrete solutions. Our approach performs qualitative reasoning before complex quantitative analysis starts so that the above difficulties can be efficiently overcome. We present a novel model for qualitatively decomposing and analyzing infrared spectra. A list of candidates can be obtained based on the solutions of the model, then quantitative analysis will only be applied to the limited candidates. We also present a novel model for handling inaccuracy of spectral data. The model can capture qualitative features of infrared spectra, and can consider qualitative correlations among spectral data as evidence when spectral data are inaccurate. We have tested the approach against about 300 real infrared spectra. This paper also introduces the implementation of the approach.

The haze situation will seriously affect the quality of license plate recognition and reduce the performance of the visual processing algorithm. In order to improve the quality of haze pictures, a license plate recognition algorithm based on haze weather is proposed in this paper. The algorithm in this paper mainly consists of two parts: The first part is MPGAN image dehazing, which uses a generative adversarial network to dehaze the image, and combines multi-scale convolution and perceptual loss. Multi-scale convolution is conducive to better feature extraction. The perceptual loss makes up for the shortcoming that the mean square error (MSE) is greatly affected by outliers; the second part is to recognize the license plate, first we use YOLOv3 to locate the license plate, the STN network corrects the license plate, and finally enters the improved LPRNet network to get license plate information. Experimental results show that the dehazing model proposed in this paper achieves good results, and the evaluation indicators PSNR and SSIM are better than other representative algorithms. After comparing the license plate recognition algorithm with the LPRNet algorithm, the average accuracy rate can reach 93.9%.

The Earth's ionosphere can hinder radio propagation with two serious problems: group delay and phase advance. Ionospheric irregularities are significantly troublesome since they make the amplitude and phase of the radio signals fluctuate rapidly, which is known as ionospheric scintillation. Severe ionospheric scintillation could cause loss of phase lock, which would degrade the positioning accuracy and affect the performance of navigation systems. Based on the phase screen model, this paper presents a novel power spectrum model of phase scintillation and a model of amplitude scintillation. Preliminary results show that, when scintillation intensity increases, the random phase and amplitude fluctuations become stronger, coinciding with the observations. Simulations of the scintillation effects on the acquisition of Beidou signals predict acquisition probability. In addition, acquisition probabilities of GPS and Beidou signals under different scintillation intensities are presented. And by the same SNR the acquisition probability decreases when the scintillation intensity increases. The simulation result shows that scintillation could cause the loss of the acquisition performance of Beidou navigation system. According to the comparison of Beidou and GPS simulations, the code length and code rate of satellite signals have an effect on the acquisition performance of navigation system.

Gait recognition distinguishes one individual from others according to the natural patterns of human gaits. Gait recognition is a challenging signal processing technology for biometric identification due to the ambiguity of contours and the complex feature extraction procedure. In this work, we proposed a new model - the convolutional neural network (CNN) joint attention mechanism (CJAM) - to classify the gait sequences and conduct person identification using the CASIA-A and CASIA-B gait datasets. The CNN model has the ability to extract gait features, and the attention mechanism continuously focuses on the most discriminative area to achieve person identification. We present a comprehensive transformation from gait image preprocessing to final identification. The results from 12 experiments show that the new attention model leads to a lower error rate than others. The CJAM model improved the 3D-CNN, CNN-LSTM (long short-term memory), and the simple CNN by 8.44%, 2.94% and 1.45%, respectively.

The large Doppler offset that exists in high dynamic environments poses a serious impediment to the acquisition of direct sequence spread spectrum (DSSS) signals. To ensure acceptable detection probabilities, the frequency space has to be finely divided, which leads to complicated acquisition structures and excessively long acquisition time at low SNR. A local frequency folding (LFF) method designed for combined application with established techniques dedicated to PN-code synchronization is proposed in this paper. Through modulating local PN-code block with a fixed waveform obtained by folding all frequency cells together, we eliminate the need for frequency search and ease the workload of acquisition. We also analyze the performance of LFF and find that the detection performance degradation from folding can be compensated by FFT-based coherent integration. The study is complemented with numerical simulations showing that the proposed method has advantages over unfolding methods with respect to detection probability and mean acquisition time, and the advantage becomes obvious but limited if the folded number gets larger.

Based on a proposed frame structure with an unequal sensing slot duration for each channel, and two sensing scenarios (with or without cooperation), a joint channel and sensing time assignment is suggested to maximize the uplink throughput of the centralized multi-band cognitive radio network with the consideration of the mutual interference among the secondary users (SUs). Firstly, the channel assignment is performed by using the proposed Delta Non-square Hungarian (DNH), which is a modified iterative Hungarian algorithm distinguished by throughput increment maximization and non-square weight matrix. Simulation results illustrate that DNH has significant advantages in enhancing the throughput and reducing the computational complexity. Moreover, a hybrid channel assignment, also performed by DNH, is improved based on the two sensing scenarios to maximize the throughput while efficiently limiting the interference power to primary users. Secondly, the convexity of the throughput functions within the range of sensing time is proved under the proposed frame structure, and then the maximum throughput is achieved through the steepest descent method-based sensing time assignment. Both of these results are corroborated by simulations.