In the past years, traditional pattern recognition methods have made great progress. However, these methods rely heavily on manual feature extraction, which may hinder the generalization model performance. With the increasing popularity and success of deep learning methods, using these techniques to recognize human actions in mobile and wearable computing scenarios has attracted widespread attention. In this paper, a deep neural network that combines convolutional layers with long short-term memory (LSTM) was proposed. This model could extract activity features automatically and classify them with a few model parameters. LSTM is a variant of the recurrent neural network (RNN), which is more suitable for processing temporal sequences. In the proposed architecture, the raw data collected by mobile sensors was fed into a two-layer LSTM followed by convolutional layers. In addition, a global average pooling layer (GAP) was applied to replace the fully connected layer after convolution for reducing model parameters. Moreover, a batch normalization layer (BN) was added after the GAP layer to speed up the convergence, and obvious results were achieved. The model performance was evaluated on three public datasets (UCI, WISDM, and OPPORTUNITY). Finally, the overall accuracy of the model in the UCI-HAR dataset is 95.78%, in the WISDM dataset is 95.85%, and in the OPPORTUNITY dataset is 92.63%. The results show that the proposed model has higher robustness and better activity detection capability than some of the reported results. It can not only adaptively extract activity features, but also has fewer parameters and higher accuracy. INDEX TERMS Human activity recognition, convolution, long short-term memory, mobile sensors.

Racquet sports can provide positive benefits for human healthcare. A reliable detection device that can effectively distinguish movement with similar sub-features is therefore needed. In this paper, a racquet sports recognition wristband system and a multilayer hybrid clustering model are proposed to achieve reliable activity recognition and perform number counting. Additionally, a Bluetooth mesh network enables communication between a phone and wristband, and sets-up the connection between multiple devices. This allows users to track their exercise through the phone and share information with other players and referees. Considering the complexity of the classification algorithm and the user-friendliness of the measurement system, the improved multi-layer hybrid clustering model applies three-level K-means clustering to optimize feature extraction and segmentation and then uses the density-based spatial clustering of applications with noise (DBSCAN) algorithm to determine the feature center of different movements. The model can identify unlabeled and noisy data without data calibration and is suitable for smartwatches to recognize multiple racquet sports. The proposed system shows better recognition results and is verified in practical experiments.

The traditional weighing and selling process of non-barcode items requires manual service, which not only consumes manpower and material resources but is also more prone to errors or omissions of data. This paper proposes an intelligent self-service vending system embedded with a single camera to detect multiple products in real-time performance without any labels, and the system realizes the integration of weighing, identification, and online settlement in the process of non-barcode items. The system includes a self-service vending device and a multi-device data management platform. The flexible configuration of the structure gives the system the possibility of identifying fruits from multiple angles. The height of the system can be adjusted to provide self-service for people of different heights; then, deep learning skill is applied implementing product detection, and real-time multi-object detection technology is utilized in the image-based checkout system. In addition, on the multi-device data management platform, the information docking between embedded devices, WeChat applets, Alipay, and the database platform can be implemented. We conducted experiments to verify the accuracy of the measurement. The experimental results demonstrate that the correlation coefficient R2 between the measured value of the weight and the actual value is 0.99, and the accuracy of non-barcode item prediction is 93.73%. In Yangpu District, Shanghai, a comprehensive application scenario experiment was also conducted, proving that our system can effectively deal with the challenges of various sales situations.

The optical features of mineral composition and texture in petrographic thin sections are an important basis for rock identification and rock evolution analysis. However, the efficiency and accuracy of human visual interpretation of petrographic thin section images have depended on the experience of experts for a long time. The application of image-based computer vision and deep-learning algorithms to the intelligent analysis of the optical properties of mineral composition and texture in petrographic thin section images (in plane polarizing light) has the potential to significantly improve the efficiency and accuracy of rock identification and classification. This study completed the transition from simple petrographic thin image classification to multitarget detection, to address more complex research tasks and more refined research scales that contain more abundant information, such as spatial, quantitative and category target information. Oolitic texture is an important paleoenvironmental indicator that widely exists in sedimentary records and is related to shallow water hydraulic conditions. We used transfer learning and image data augmentation in this paper to identify the oolitic texture of petrographic thin section images based on the faster region-based convolutional neural network (Faster RCNN) method. In this study, we evaluated the performance of Faster RCNN, a two-stage object detection algorithm, using VGG16 and ResNet50 as backbones for image feature extraction. Our findings indicate that ResNet50 outperformed VGG16 in this regard. Specifically, the Faster RCNN model with ResNet50 as the backbone achieved an average precision (AP) of 92.25% for the ooids test set, demonstrating the accuracy and reliability of this approach for detecting ooids. The experimental results also showed that the uneven distribution of training sample images and the complexity of images both significantly affect detection performance; however, the uneven distribution of training sample images has a greater impact. Our work is preliminary for intelligent recognition of multiple mineral texture targets in petrographic thin section images. We hope that it will inspire further research in this field.