Explore the vast range of titles available.

In this paper, a compressed adaptive image-sensing method based on an overcomplete ridgelet dictionary is proposed. Some low-complexity operations are designed to distinguish between smooth blocks and texture blocks in the compressed domain, and adaptive sampling is performed by assigning different sampling rates to different types of blocks. The efficient, sparse representation of images is achieved by using an overcomplete ridgelet dictionary; at the same time, a reasonable dictionary-partitioning method is designed, which effectively reduces the number of candidate dictionary atoms and greatly improves the speed of classification. Unlike existing methods, the proposed method does not rely on the original signal, and computation is simple, making it particularly suitable for scenarios where a device’s computing power is limited. At the same time, the proposed method can accurately identify smooth image blocks and more reasonably allocate sampling rates to obtain a reconstructed image with better quality. The experimental results show that our method’s image reconstruction quality is superior to that of existing ARCS methods and still maintains low computational complexity.

To solve the problems of poor estimation of full-body shape and inaccurate capture results in human motion capture in mobile sensor networks, a method of capturing image features of human full-body motion posture in mobile sensor networks is studied. The method uses Markov random fields to cooperate with sensors to extract human full-body motion foreground images and combines guided filtering to enhance the extraction effect of foreground images. Based on the foreground images, a human tree-structured model is established to simulate the actions of human movements. The extracted foreground images are used as input to the convolutional neural network to extract edge features and spatio-temporal features of human motion posture. After fusion, a human motion posture feature matrix is constructed. Based on the least squares method, a strong regression mapping model is constructed. According to the structure of the human tree model, multi-dimensional iterative mapping is performed from top to bottom between the human motion posture feature matrix and the human tree model. The joint positions corresponding to the human motion posture feature matrix in the human tree model are calculated, and the two-dimensional position information of all joint points of the moving human body is obtained. The capture of human full-body motion posture in mobile networks is completed. Experimental data show that the method has clear foreground image extraction, can effectively obtain human motion features, and has accurate capture results of human full-body motion posture.

In the modern era, the wireless sensor networks play a vital role in scientific and industrial applications. The enduring energy is a crucial metric parameter to be considered in the process of calculating the life span of the wireless sensor networks. Because each sensor node having non-renewable battery supply. In the first generation of wireless sensor applications, data has been transferred from each sensor node to the sink. Further evolution suggests a cluster head approach for transferring data from sensor field to sink. Different strategies of cluster head selection have provided better energy utilization. In the context of effective energy utilization, this work proposes an algorithmic technique called Phase Adjournment Data Capture Technique for a mobile object. In this technique, data will be captured by the mobile object based on its present traveling layer count. When a mobile object goes beyond the receiving limit, it will initiate the sleep mode flag to indicate the master node to stop transferring the data. This technique extends the improvement in energy consumption and also provides effective control of data flooding. It extends and eradicates the need for an erudite antenna essential for setting the direction of the data.

There is a growing interest in 3D content following the recent developments in 3D movies, 3D TVs and 3D smartphones. However, 3D content creation is still dominated by professionals, due to the high cost of 3D motion capture instruments. The availability of a low-cost motion capture system will promote 3D content generation by general users and accelerate the growth of the 3D market. In this paper, we describe the design and implementation of a real-time motion capture system based on a portable low-cost wireless camera sensor network. The proposed system performs motion capture based on the data-driven 3D human pose reconstruction method to reduce the computation time and to improve the 3D reconstruction accuracy. The system can reconstruct accurate 3D full-body poses at 16 frames per second using only eight markers on the subject’s body. The performance of the motion capture system is evaluated extensively in experiments.

Wireless rechargeable sensor nodes are usually equipped with capacitors which collect the the energy from RF signals and support the functioning of sensing, computation, and communication components. However, the highly limited capacitor size as well as the intrinsic energy cost during the node activation imposes critical design challenge for effective energy management. It is also desirable to realize effective coordination among nodes without too much intercommunication due to the highly limited energy capacity. In this paper, we consider the problem of how to schedule the activation of wireless rechargeable sensor nodes in order to maximize the event capture rate for random event process with the consideration of both highly limited capacitor size and activation energy cost. For the events following Poisson distribution, we theoretically prove that one optimal scheduling scheme for single node case is to activate the node only when the capacitor is fully charged and sleep only when the remaining energy is unable to support one activation. Then for the more general multi-node case, we propose a coordinated periodic scheduling scheme which reduces the activation overlap among different nodes without requiring the intercommunication. Extensive simulations are conducted to verify the effectiveness of our proposed methods.