UPPER EXTREMITY KINEMATICS OF AN ELITE SNOOKER PLAYER ON STOP SHOTS

Purpose: The purpose of this study was to identify kinematic characteristics of stroking upper extremity during final stroke when an elite snooker player using stop shots and compared them with the description of technical books. Methods: The participant was Stephen Maguire, an elite player and the data was recorded by Qualisys Analysis System. The sucessful stop shots was performed 3 times totally. Results: The hitting moment occurs near the minimum value of the forearm angle, as well as the elbow-shoulder-wrist projecting angle remained stable and was less than -4°. There was little displacement of the shoulder joint and the elbow joint. The Coefficient of Multiple Correlation (CMC) of all three joint movements in an upper limb was greater than 0.95. Conclusion: The elite snooker player might have a stable movement pattern in final stroke and the stop shot is not a pendulum movement theoretically. Elbow joint is in the inner side of shoulder and wrist joint and hitting moment always occurs when the elbow joint is at lowest position

Graphene-based Yagi-Uda antenna with reconfigurable radiation patterns

This paper presents a radiation pattern reconfigurable Yagi-Uda antenna based on graphene operating at terahertz frequencies. The antenna can be reconfigured to change the main beam pattern into two or four different radiation directions. The proposed antenna consists of a driven dipole radiation conductor, parasitic strips and embedded graphene. The hybrid graphene-metal implementation enables the antenna to have dynamic surface conductivity, which can be tuned by changing the chemical potentials. Therefore, the main beam direction, the resonance frequency, and the front-to-back ratio of the proposed antenna can be controlled by tuning the chemical potentials of the graphene embedded in different positions. The proposed two-beam reconfigurable Yagi-Uda antenna can achieve excellent unidirectional symmetrical radiation pattern with the front-to-back ratio of 11.9 dB and the10-dB impedance bandwidth of 15%. The different radiation directivity of the two-beam reconfigurable antenna can be achieved by controlling the chemical potentials of the graphene embedded in the parasitic stubs. The achievable peak gain of the proposed two-beam reconfigurable antenna is about 7.8 dB. Furthermore, we propose a four-beam reconfigurable Yagi-Uda antenna, which has stable reflection-coefficient performance although four main beams in reconfigurable cases point to four totally different directions. The corresponding peak gain, front-to-back ratio, and 10-dB impedance bandwidth of the four-beam reconfigurable antenna are about 6.4 dB, 12 dB, and 10%, respectively. Therefore, this novel design method of reconfigurable antennas is extremely promising for beam-scanning in terahertz and mid-infrared plasmonic devices and systems

Multi-view Self-supervised Disentanglement for General Image Denoising

With its significant performance improvements, the deep learning paradigm has become a standard tool for modern image denoisers. While promising performance has been shown on seen noise distributions, existing approaches often suffer from generalisation to unseen noise types or general and real noise. It is understandable as the model is designed to learn paired mapping (e.g. from a noisy image to its clean version). In this paper, we instead propose to learn to disentangle the noisy image, under the intuitive assumption that different corrupted versions of the same clean image share a common latent space. A self-supervised learning framework is proposed to achieve the goal, without looking at the latent clean image. By taking two different corrupted versions of the same image as input, the proposed Multi-view Self-supervised Disentanglement (MeD) approach learns to disentangle the latent clean features from the corruptions and recover the clean image consequently. Extensive experimental analysis on both synthetic and real noise shows the superiority of the proposed method over prior self-supervised approaches, especially on unseen novel noise types. On real noise, the proposed method even outperforms its supervised counterparts by over 3 dB.Comment: International Conference on Computer Vision 2023 (ICCV 2023

Change detection in SAR images based on the salient map guidance and an accelerated genetic algorithm

This paper proposes a change detection algorithm in synthetic aperture radar (SAR) images based on the salient image guidance and an accelerated genetic algorithm (S-aGA). The difference image is first generated by logarithm ratio operator based on the bi-temporal SAR images acquired in the same region. Then a saliency detection model is applied in the difference image to extract the salient regions containing the changed class pixels. The salient regions are further divided by fuzzy c-means (FCM) clustering algorithm into three categories: changed class (set of pixels with high gray values), unchanged class (set of pixels with low gray values) and undetermined class (set of pixels with middle gray value, which are difficult to classify). Finally, the proposed accelerated GA is applied to explore the reduced search space formed by the undetermined-class pixels according to an objective function considering neighborhood information. In S-aGA, an efficient mutation operator is designed by using the neighborhood information of undetermined-class pixels as the heuristic information to determine the mutation probability of each undetermined-class pixel adaptively, which accelerates the convergence of the GA significantly. The experimental results on two data sets demonstrate the efficiency of the proposed S-aGA. On the whole, S-aGA outperforms five other existing methods including the simple GA in terms of detection accuracy. In addition, S-aGA could obtain satisfying solution within limited generations, converging much faster than the simple GA

Fuzzy Superpixels based Semi-supervised Similarity-constrained CNN for PolSAR Image Classification

Recently, deep learning has been highly successful in image classification. Labeling the PolSAR data, however, is time-consuming and laborious and in response semi-supervised deep learning has been increasingly investigated in PolSAR image classification. Semi-supervised deep learning methods for PolSAR image classification can be broadly divided into two categories, namely pixels-based methods and superpixels-based methods. Pixels-based semi-supervised methods are liable to be affected by speckle noises and have a relatively high computational complexity. Superpixels-based methods focus on the superpixels and ignore tiny detail-preserving represented by pixels. In this paper, a Fuzzy superpixels based Semi-supervised Similarity-constrained CNN (FS-SCNN) is proposed. To reduce the effect of speckle noises and preserve the details, FS-SCNN uses a fuzzy superpixels algorithm to segment an image into two parts, superpixels and undetermined pixels. Moreover, the fuzzy superpixels algorithm can also reduce the number of mixed superpixels and improve classification performance. To exploit unlabeled data effectively, we also propose a Similarity-constrained Convolutional Neural Network (SCNN) model to assign pseudo labels to unlabeled data. The final training set consists of the initial labeled data and these pseudo labeled data. Three PolSAR images are used to demonstrate the excellent classification performance of the FS-SCNN method with data of limited labels

Nanosheet-Assembled ZnO Microflower Photocatalysts

Large scale ZnO microflowers assembled by numerous nanosheets are synthesized through a facile and effective hydrothermal route. The structure and morphology of the resultant products are characterized by X-ray diffraction (XRD) and scanning electron microscope (SEM). Photocatalytic properties of the as-synthesized products are also investigated. The results demonstrate that eosin red aqueous solution can be degraded over 97% after 110 min under UV light irradiation. In addition, methyl orange (MO) and Congo red (CR) aqueous solution degradation experiments also are conducted in the same condition, respectively. It showed that nanosheet-assembled ZnO microflowers represent high photocatalytic activities with a degradation efficiency of 91% for CR with 90 min of irradiation and 90% for MO with 60 min of irradiation. The reported ZnO products may be promising candidates as the photocatalysts in waste water treatment

Hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methods

Focussing on the problem that redundant nodes in the kernel incremental extreme learning machine (KI-ELM) which leads to ineffective iteration increase and reduce the learning efficiency, a novel improved hybrid intelligent deep kernel incremental extreme learning machine (HI-DKIELM) based on a hybrid intelligent algorithms and kernel incremental extreme learning machine is proposed. At first, hybrid intelligent algorithms are proposed based on differential evolution (DE) and multiple population grey wolf optimization (MPGWO) methods which used to optimize the hidden layer neuron parameters and then to determine the effective hidden layer neurons number. The learning efficiency of the algorithm is improved by reducing the network complexity. Then, we bring in the deep network structure to the kernel incremental extreme learning machine to extract the original input data layer by layer gradually. The experiment results show that the HI-DKIELM methods proposed in this paper with more compact network structure have higher prediction accuracy and better ability of generation compared with other ELM methods

Accelerated genetic algorithm based on search-space decomposition for change detection in remote sensing images

Detecting change areas among two or more remote sensing images is a key technique in remote sensing. It usually consists of generating and analyzing a difference image thus to produce a change map. Analyzing the difference image to obtain the change map is essentially a binary classification problem, and can be solved by optimization algorithms. This paper proposes an accelerated genetic algorithm based on search-space decomposition (SD-aGA) for change detection in remote sensing images. Firstly, the BM3D algorithm is used to preprocess the remote sensing image to enhance useful information and suppress noises. The difference image is then obtained using the logarithmic ratio method. Secondly, after saliency detection, fuzzy c-means algorithm is conducted on the salient region detected in the difference image to identify the changed, unchanged and undetermined pixels. Only those undetermined pixels are considered by the optimization algorithm, which reduces the search space significantly. Inspired by the idea of the divide-and-conquer strategy, the difference image is decomposed into sub-blocks with a method similar to down-sampling, where only those undetermined pixels are analyzed and optimized by SD-aGA in parallel. The category labels of the undetermined pixels in each sub-block are optimized according to an improved objective function with neighborhood information. Finally the decision results of the category labels of all the pixels in the sub-blocks are remapped to their original positions in the difference image and then merged globally. Decision fusion is conducted on each pixel based on the decision results in the local neighborhood to produce the final change map. The proposed method is tested on six diverse remote sensing image benchmark datasets and compared against six state-of-the-art methods. Segmentations on the synthetic image and natural image corrupted by different noise are also carried out for comparison. Results demonstrate the excellent performance of the proposed SD-aGA on handling noises and detecting the changed areas accurately. In particular, compared with the traditional genetic algorithm, SD-aGA can obtain a much higher degree of detection accuracy with much less computational time

A Survey of Deep Learning-Based Object Detection

Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning networks for detection tasks, the performance of object detectors has been greatly improved. In order to understand the main development status of object detection pipeline, thoroughly and deeply, in this survey, we first analyze the methods of existing typical detection models and describe the benchmark datasets. Afterwards and primarily, we provide a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors. Moreover, we list the traditional and new applications. Some representative branches of object detection are analyzed as well. Finally, we discuss the architecture of exploiting these object detection methods to build an effective and efficient system and point out a set of development trends to better follow the state-of-the-art algorithms and further research.Comment: 30 pages,12 figure