Falls are one of the main causes of elderly injuries. If the faller can be found in time, further injury can be effectively avoided. In order to protect personal privacy and improve the accuracy of fall detection, this paper proposes a fall detection algorithm using the CNN-Casual LSTM network based on three-axis acceleration and three-axis rotation angular velocity sensors. The neural network in this system includes an encoding layer, a decoding layer, and a ResNet18 classifier. Furthermore, the encoding layer includes three layers of CNN and three layers of Casual LSTM. The decoding layer includes three layers of deconvolution and three layers of Casual LSTM. The decoding layer maps spatio-temporal information to a hidden variable output that is more conducive relative to the work of the classification network, which is classified by ResNet18. Moreover, we used the public data set SisFall to evaluate the performance of the algorithm. The results of the experiments show that the algorithm has high accuracy up to 99.79%.

Many techniques have been developed for wireless signal recognition in many fifth generation (5G) enabled derivatives. Many harsh constraints, such as the large amount of model parameters and complex signal characteristics, drives intelligent recognition method in real-world settings. In this paper, we propose a generalizable, practical method for raw IQ signal recognition. Specifically, deep complex-valued convolutional neural network models, including a Complex-valued Visual Geometry Group (VGG) (CvVGG) model and a Complex-valued ResNet (CvRN) model, are proposed for handling raw signal IQ data. We examine the merit of complex-valued neural networks (CvNN) and validate their performance with experiments using two public datasets. With an SNR of 10dB, the proposed algorithm achieves a recognition accuracy of 96% on the RadioML2018.10a dataset. When performing drone recognition, CvNN can achieve a recognition accuracy of 99%. Our experimental results verify that deep complex-valued neural network models can achieve considerably improved accuracy with lower computation complexity and fewer model parameters than their real-valued counterparts.

This letter proposes a novel saving-sensing-transmitting (SST) frame structure for energy harvesting cognitive radio networks. As in the conventional SST, the proposed SST takes the energy causality constraint (EC-constraint) into consideration, which is referred to as the normal mode in this letter. Besides, it includes the other two modes, sensing mode and sleeping mode, to make full use of the residual battery energy and ensure enough time for sensing and transmission. These two modes are activated based on the residual battery energy and a predefined save-ratio threshold. For the normal mode, we maximize the energy utilization efficiency of the secondary user by jointly optimizing the save-ratio and transmission power under both the EC-constraint and the minimum throughput constraint. This problem is solved by first determining the best save-ratio in closed-form and then optimizing the power allocation using Dinkelbach method. The sensing mode is optimized using a similar method. Finally, numerical results validate the efficiency of the proposed SST.

Human pose estimation is a complex detection task in which the network needs to capture the rich information contained in the images. In this paper, we propose MSTPose (Multi-Scale Transformer for human Pose estimation). Specifically, MSTPose leverages a high-resolution convolution neural network (CNN) to extract texture information from images. For the feature maps from three different scales produced by the backbone network, each branch performs the coordinate attention operations. The feature maps are then spatially and channel-wise flattened, combined with keypoint tokens generated through random initialization, and fed into a parallel Transformer structure to learn spatial dependencies between features. As the Transformer outputs one-dimensional sequential features, the mainstream two-dimensional heatmap method is abandoned in favor of one-dimensional coordinate vector regression. The experiments show that MSTPose outperforms other CNN-based pose estimation models and demonstrates clear advantages over CNN + Transformer networks of similar types.

The increase in fire accidents caused by indoor charging of electric bicycles has raised concerns among people. Monitoring EBs in elevators is challenging, and the current object detection method is a variant of YOLOv5, which faces problems with calculating the load and detection rate. To address this issue, this paper presents an improved lightweight method based on YOLOv5s to detect EBs in elevators. This method introduces the MobileNetV2 module to achieve the lightweight performance of the model. By introducing the CBAM attention mechanism and the Bidirectional Feature Pyramid Network (BiFPN) into the YOLOv5s neck network, the detection precision is improved. In order to better verify that the model can be deployed at the edge of an elevator, this article deploys it using the Raspberry Pi 4B embedded development board and connects it to a buzzer for application verification. The experimental results demonstrate that the model parameters of EBs are reduced by 58.4%, the computational complexity is reduced by 50.6%, the detection precision reaches 95.9%, and real-time detection of electric vehicles in elevators is achieved.