Since diffractive deep neural network (D2NN) provides a full optical solution to implement deep neural networks (DNNs), it offers ultrafast operation speed and virtually unlimited bandwidth, yielding an alternative-yet-competitive approach for computer-based neural networks. A D2NN is composed of several 3D-printed phase masks as hidden layers and a number of optical detectors at the output. To enable automatic and efficient design of D2NNs, we propose an iterative optimization method to determine the optimal design parameters of D2NNs. During each iteration step, we first optimize the physical parameters for masks (e.g., thicknesses) while fixing the detector parameters (e.g., locations). Next, we exhaustively search the detector parameters with fixed masks. These two steps are repeated until convergence is reached. Our numerical experiments demonstrate that the proposed optimization algorithm can produce a high-performance D2NN achieving 97% accuracy for recognizing handwritten digits.

Chronic diseases are gradually becoming the principal factors of harm to people's health. Fortunately, the development of e-health provides a novel thought for chronic disease prevention and treatment. This paper focuses on the research of cardiovascular disease (CVDs) prevention and early warning techniques using e-health and data mining. In this paper, we will use weighted associative classification algorithm to model the data in healthcare database to determine the level of cardiovascular risk. Besides, on the basis of data mining and knowledge discovery, intelligent warning mechanisms are proposed to provide different services to patients with different levels of risk. The experimental results show that the used classification algorithm is a more effective mining algorithm in the field of healthcare with higher accuracy and better comprehension. Our study is of definite significance to help control risk level of CVDs patients.

This paper presents a healthcare monitoring architecture coupled with a wireless sensor network and wearable sensor systems which monitor chronic patients in nursing house or the elderly in their home. With this architecture, we investigate how sensor nodes are deployed in the three-dimension (3D) monitoring region to achieve uniform distribution, which directly determines the Quality of Service (QoS). Based on the existing two-dimension (2D) coverage-enhancing algorithms for wireless sensor networks, a 3D sensing model and a coverage optimization algorithm are proposed in this paper. Firstly, an intelligent optimization algorithm is utilized to adjust the position of sensor nodes. Then, we pick out the redundant nodes with the set coverage algorithm, and move them into the uncovered area to increase the coverage ratio. The simulation results show that the coverage ratio increased by the coverage optimization algorithm compared with the other coverage algorithms.