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.

Using optimal assembly relationships, companies can enhance product quality without significantly increasing production costs. However, predicting Assembly Geometric Errors presents a challenging real-world problem in the manufacturing domain. To address this challenge, this paper introduces a highly efficient Transformer-based neural network model known as Predicting Assembly Geometric Errors based on Transformer (PAGEformer). This model accurately captures long-range assembly relationships and predicts final assembly errors. The proposed model incorporates two unique features: firstly, an enhanced self-attention mechanism to more effectively handle long-range dependencies, and secondly, the generation of positional information regarding gaps and fillings to better capture assembly relationships. This paper collected actual assembly data for folding rudder blades for unmanned aerial vehicles and established a Mechanical Assembly Relationship Dataset (MARD) for a comparative study. To further illustrate PAGEformer performance, we conducted extensive testing on a large-scale dataset and performed ablation experiments. The experimental results demonstrated a 15.3% improvement in PAGEformer accuracy compared to ARIMA on the MARD. On the ETH, Weather, and ECL open datasets, PAGEformer accuracy increased by 15.17%, 17.17%, and 9.5%, respectively, compared to the mainstream neural network models.

Kinect is a motion sensing input device which is widely used in computer vision and other related fields. However, there are many inaccurate depth data in Kinect depth images even Kinect v2. In this paper, an algorithm is proposed to enhance Kinect v2 depth images. According to the principle of its depth measuring, the foreground and the background are considered separately. As to the background, the holes are filled according to the depth data in the neighborhood. And as to the foreground, a filling algorithm, based on the color image concerning about both space and color information, is proposed. An adaptive joint bilateral filtering method is used to reduce noise. Experimental results show that the processed depth images have clean background and clear edges. The results are better than ones of traditional Strategies. It can be applied in 3D reconstruction fields to pretreat depth image in real time and obtain accurate results.

Medical ultrasound imaging is one of the most commonly used imaging modalities for clinicians to make clinical diagnoses. Currently, medical ultrasound imaging equipment is transitioning towards portable ultrasound devices that can be more widely used. However, portable ultrasound devices suffer from the drawback of low imaging quality due to hardware limitations. Enhancing ultrasound images requires paired medical ultrasound image data with identical content but different styles for enhancement training, which is quite challenging. To address these issues, this paper proposes a DPM CycleGAN, a generative adversarial network that can convert low-quality ultrasound images into high-quality ones without the need for paired data. This network employs a maximum perception feature extraction module designed specifically for ultrasound images to extract global features. Subsequently, a diffusion feature enhancement module integrated with pseudo-labels is used to enhance these global features. The generator of DPM CycleGAN utilizes the enhanced global features to generate high-quality medical ultrasound images. Experimental results on the USenhance 2023 ultrasound dataset demonstrate that this method can effectively enhance low-quality ultrasound images of various organs, not only improving the resolution of ultrasound images but also enhancing the organ structure and texture information, and eliminating some noise in the ultrasound images. Compared with the latest ultrasound image enhancement methods, the model proposed in this paper performs optimally in all metrics, which fully demonstrates the promising potential of the proposed method in medical image enhancement and its valuable support in areas such as disease prediction and treatment.

The stark contrast in density between geological layers is a fundamental aspect in the examination of basic geological structures. The delineation between the crystalline basement and sedimentary layers, moreover, is pivotal in the pursuit of strategic energy resources, such as petroleum and natural gas. Traditional full space density inversion, however, is beleaguered by issues of stability and resolution, impeding the accurate characterization of the sharp density interface. To rectify these shortcomings, we introduce an innovative methodology for estimating 2-D depth-to-basement and overlying density distribution, employing a deep neural network with a leaky rectified linear unit as an activation function. Evaluation of the proposed method on simulated sedimentary basin models underscores its superior ability to discern complex geometries of basin boundaries and overlying density, despite the presence of various degrees of Gaussian noise. In practical application to the Poyang basin, the relief of the Cretaceous basement is proficiently recovered through vertical gravity field data, with validation provided by corresponding seismic sections and well-established stratigraphic markers.