Orthogonal Time Sequency Multiplexing (OTSM) achieves transmission performance similar to Orthogonal Time Frequency Space (OTFS) modulation with lower complexity, providing a promising solution for future high-speed mobile communication systems that require low complexity transceivers. To address the issue of insufficient efficiency in existing time-domain based Gauss-Seidel (GS) iterative equalization, a secondary signal detection algorithm was proposed. First, Linear Minimum Mean Square Error (LMMSE) detection with low complexity was performed in the time domain, and then Successive Over Relaxation (SOR) iterative algorithm was used to further eliminate residual symbol interference. To further optimize convergence efficiency and detection performance, the SOR algorithm was linearly optimized to obtain an Improved SOR (ISOR) algorithm. The simulation experimental results show that compared with SOR algorithm, ISOR algorithm improves detection performance and accelerates convergence while increasing lower complexity. Compared with GS iterative algorithm, ISOR algorithm has a gain of 1.61 dB when using 16 QAM modulation with a bit error rate of 10 - 4 .
Focused on the issue that the category relationship between samples is not considered in the unsupervised Locally Invariant Robust Principal Component Analysis (LIRPCA) algorithm, a feature extraction model based on Neighbor Supervised LIRPCA (NSLIRPCA) was proposed. The category information between samples was considered by the proposed model, and a relationship matrix was constructed based on this information. The formulas of the model were solved and the convergences of the formulas were proved. At the same time, the proposed model was applied to various occlusion datasets. Experimental results show that compared with Principal Component Analysis (PCA), PCA based on L1-norm (PCA-L1), Non-negative Matrix Factorization (NMF), Locality Preserving Projection (LPP) and LIRPCA algorithms on ORL, Yale, COIL-Processed and PolyU datasets, the proposed model has the recognition rate improved by 8.80%, 7.76%, 20.37%, 4.72% and 4.61% at most respectively on the original image datasets, and the recognition rate improved by 30.79%, 30.73%, 36.02%, 19.65% and 17.31% at most respectively on the occluded image datasets. It can be seen that with the proposed model, the recognition performance of the algorithm is improved, and the complexity of the model is reduced, verifying that the model is obviously better than the comparison algorithms.
To effectively extract the temporal information between consecutive video frames, a prediction network IndRNN-VAE (Independently Recurrent Neural Network-Variational AutoEncoder) that fuses Independently Recurrent Neural Network (IndRNN) and Variational AutoEncoder (VAE) network was proposed. Firstly, the spatial information of video frames was extracted through VAE network, and the latent features of video frames were obtained by a linear transformation. Secondly, the latent features were used as the input of IndRNN to obtain the temporal information of the sequence of video frames. Finally, the obtained latent features and temporal information were fused through residual block and input to the decoding network to generate the prediction frame. By testing on UCSD Ped1, UCSD Ped2 and Avenue public datasets, experimental results show that compared with the existing anomaly detection methods, the method based on IndRNN-VAE has the performance significantly improved, and has the Area Under Curve (AUC) values reached 84.3%, 96.2%, and 86.6% respectively, the Equal Error Rate (EER) values reached 22.7%, 8.8%, and 19.0% respectively, the difference values in the mean anomaly scores reached 0.263, 0.497, and 0.293 respectively. Besides, the running speed of this method reaches 28 FPS (Frames Per Socond).
Concerning the shortcoming that the current feature-weighted Fuzzy Support Vector Machines (FSVM) only consider the influence of feature weights on the membership functions but ignore the application of feature weights to the kernel functions calculation during sample training, a new FSVM algorithm that considers the influence of feature weights on the membership function and the kernel function calculation simultaneously was proposed, namely Doubly Feature-Weighted FSVM (DFW-FSVM). Firstly, relative weight of each feature was calculated by using Information Gain (IG). Secondly, the weighted Euclidean distance between the sample and the class center was calculated in the original space based on the feature weights, and then the membership function was constructed by applying the weighted Euclidean distance; at the same time, the feature weights were applied to the calculation of the kernel function in the sample training process. Finally, DFW-FSVM algorithm was constructed according to the weighted membership functions and kernel functions. In this way, DFW-FSVM is able to avoid being dominated by trivial relevant or irrelevant features. The comparative experiments were carried out on eight UCI datasets, and the results show that compared with the best results of SVM, FSVM, Feature-Weighted SVM (FWSVM), Feature-Weighted FSVM (FWFSVM) and FSVM based on Centered Kernel Alignment (CKA-FSVM) , the accuracy and F1 value of the DFW-FSVM algorithm increase by 2.33 and 5.07 percentage points, respectively, indicating that the proposed DFW-FSVM has good classification performance.
Classifying similar, counterfeit and deteriorated slices in Chinese herbal slices plays a vital role in clinical application of Chinese medicine. Traditional manual identification methods are subjective and fallible. And the classification of traditional Chinese herbal slices based on computer vision is superior in speed and accuracy, which makes Chinese herbal slice screening intelligent. Firstly, general steps of Chinese medicine recognition algorithm based on computer vision were introduced, and technical development status of preprocessing, feature extraction and recognition model of Chinese medicine images were reviewed separately. Then, 12 classes of similar and easily confused Chinese herbal slices were selected as a case to study. By constructing a dataset with 9 156 pictures of Chinese herbal slices, the recognition performance differences of traditional recognition algorithms and various deep learning models were analyzed and compared. Finally, the difficulties and future development trends of computer vision in Chinese herbal slices were summarized and prospected.
During the operation of the Unmanned Surface Vehicles (USVs), the propellers are easily gotten entangled by waterweeds, which is a problem encountered by the whole industry. Concerning the global distribution, dispersivity, and complexity of the edge and texture of waterweeds in the water surface images, the U-Net was improved and used to classify all pixels in the image, in order to reduce the feature loss of the network, and enhance the extraction of both global and local features, thereby improving the overall segmentation performance. Firstly, the image data of waterweeds in multiple locations and multiple periods were collected, and a comprehensive dataset of waterweeds for semantic segmentation was built. Secondly, three scales of input images were introduced into the network to enable full extraction of the features via the network, and three loss functions for the upsampled images were introduced to balance the overall loss brought by the three different scales of input images. In addition, a hybrid attention module, including the dilated convolution branch and the channel attention enhancement branch, was proposed and introduced to the network. Finally, the proposed network was verified on the newly built waterweed dataset. Experimental results show that the accuracy, mean Intersection over Union (mIoU) and mean Pixel Accuracy (mPA) values of the proposed method can reach 96.8%, 91.22% and 95.29%, respectively, which are improved by 4.62 percentage points, 3.87 percentage points and 3.12 percentage points compared with those of U-Net (VGG16) segmentation method. The proposed method can be applied to unmanned surface vehicles for detection of waterweeds, and perform the corresponding path planning to realize waterweed avoidance.
To solve the problem of the regulation of badminton dynamic stable equilibrium, the particle influence coefficient method of feather piece was put forward. The method combined badminton quality models and quality feather piece, bending camber degree, angle of attack, and other related factors. The feather piece of particle influence coefficient was obtained by adjusting the height centroid which satisfied badminton dynamic stability requirements got by striking tilt minimum square. Compared with the traditional badminton dynamic stabilization which must depend on the experience accumulated for a long time, the badminton particle influence coefficient method of feather piece that was put forward by this paper formed a theoretical system. And it had less time consumption, high efficiency, etc. The numerical results show that the proposed method is correct and effective.
Most of the variants of Graph Cut algorithm do not impose any shape constraints on the segmentations, rendering it difficult to obtain semantic valid segmentation results. As for pedestrian segmentation, this difficulty leads to the non-human shape of the segmented object. An improved Graph Cut algorithm combining shape priors and discriminatively learned appearance model was proposed in this paper to segment pedestrians in static images. In this approach, a large number of real pedestrian silhouettes were used to encode the a'priori shape of pedestrians, and a hierarchical model of pedestrian template was built to reduce the matching time, which would hopefully bias the segmentation results to be humanlike. A discriminative appearance model of the pedestrian was also proposed in this paper to better distinguish persons from the background. The experimental results verify the improved performance of this approach.
Considering the complexity and inaccuracy of traditional theoretical modeling for rigid-flexible couple system, the frequency domain subspace method was used to identify the motor's model and piezoelectric ceramic piece's model in the experimental system. Due to the problem of chattering and long reaching time of traditional reaching law, a novel sliding mode control with power reaching law was proposed. Theoretical analysis shows that the reaching time can be shortened and the range of traditional power reaching law's parameter α can be expanded, which will not affect the chattering. Considering the effect of vibration characteristics of flexible beam on system performance, the method of sub-sliding surface was used to design the sliding mode controller. Lastly, experimental results show that the designed controller can track the angle of the center of the rigid body rapidly and suppress the vibration of the flexible beam quickly.