Gesture recognition is an important human- computer interaction interface. This paper introduces a novel hand gesture recognition system based on Leap Motion gen. 2. In this system, a spatial fuzzy matching (SFM) algorithm is firstly presented by matching and fusing spatial information to construct a fused gesture dataset. For dynamic hand recognition, an initial frame correction strategy based on SFM is proposed to fast initialize the trajectory of test gesture with respect to the gesture dataset. A notable feature of this system is that it can run on ordinary laptops due to the small size of the fused dataset, which accelerates the calculation of recognition rate. Experiment results show that the system recognizes static hand gestures at recognition rates of 94%-100% and over 90% of dynamic gestures using our collected dataset. This can greatly enhance the usability of Leap Motion.

When it comes to the studies concerning human-computer interaction, human posture recognition which was established on the basis of Kinect is widely acknowledged as a vital studying field. However, there exist some drawbacks in respect of the studying methods nowadays, for instance, limitations, insignificantly postures which can be recognized as well as the recognition rate which is relatively low. This study proposed a brand new approach which is hybrid in order to recognize human postures. The approach synthetically used depth data, skeleton data, knowledge of anthropometry, and backpropagation neural network (BPNN). First, the ratio of the height of the human head and that of body posture is ought to be evaluated. The distinguished four types of postures according to the ratio were standing, sitting or kneeling, sitting cross-legged, and other postures. Second, sitting and kneeling were judged according to the 3D spatial relation of the special points. Finally, feature vectors were extracted, transformed, and input to the BPNN according to the characteristics of the other postures, and bending and lying were recognized. Experiments proved the timeliness and robustness of the hybrid approach. The recognition accuracy was high, in which the average value was 99.09%.

Due to the significant bandwidth and memory requirements for transmitting and storing large-scale point clouds, considerable progress has been made in recent years in the field of large-scale point cloud geometry compression. However, challenges remain, including suboptimal compression performance and complex encoding–decoding processes. To address these issues, we propose an efficient large-scale scene point cloud geometry compression algorithm. By analyzing the sparsity of large-scale point clouds and the impact of scale on feature extraction, we design a cross-attention module in the encoder to enhance the extracted features by incorporating positional information. During decoding, we introduce an efficient generation module that improves decoding quality without increasing decoding time. Experiments on three public datasets demonstrate that, compared to the state-of-the-art G-PCC v23, our method achieves an average bitrate reduction of −46.64%, the fastest decoding time, and a minimal network model size of 2.8 M.

In this study, we demonstrate the significant development potential of diffusion U-Net extraction features transferred to the frequency domain, opening up a new perspective for diffusion models and generating a new optimization idea for diffusion model-related research. The generating quality of Text-to-Image (T2I) or Text-to-Video (T2V) can be significantly enhanced by modifying the key indicators of U-Net frequency domain features. We first investigated the two types of modules in the sampling process, CrossAttnUpBlock and UpBlock, on U-Net, and then examined the effect of fine-tuning modules on U-Net feature extraction from backbone and lateral skip connections. Finally, it is decided to modify the feature extraction procedure of the CrossAttnUpBlock as the entrance to increase the overall diffusion generation quality. At the same time, employing the traditional Fourier transform method in image processing, the influence of frequency domain elements such as frequency, amplitude, and phase on image generation is investigated in the upsampling CrossAttnUpBlock of diffusion models, resulting in the method DMFFT, which can improve T2I or T2V quality without training or fine-tuning. During the experiments, the scaling factors for high frequency, low frequency, amplitude, and phase are adapted, generating a variety of characteristic performance results. A large number of experiments show that the proposed method, DMFFT, can significantly improve the semantic alignment, structural layout, color texture, and temporal consistency of images or videos generated by baseline models, as well as improving the artistry and diversity of the generation effect.

Diagnosability is an important property in the field of fault diagnosis. In this paper, a novel approach based on logical formula is proposed to verify diagnosability of Discrete event systems(DESs). CNFFSM is defined to represent a new model for DES. Each transition in DES can be described as a clause. According to CNF-FSM, we construct a CNF-diagnoser. Based on the resolution principle and CNF-diagnoser, an algorithm is presented to test whether the failure events can be detected or not in a finite number of observable events. Our algorithm can be applied in both off-line diagnosis and on-line diagnosis. Experimental results show that our algorithm can solve the diagnosability problem efficiently.