Search

The atomic structures and the electronic states of active and inactive dopant sites in semiconductor are elucidated successfully by using X-ray absorption near-edge structure (XANES), Auger electro...

This paper verifies the method of multi-sensor data fusion in the field of smart home. A multi-source sensor data fusion model based on fire detection is proposed, and the methods of fusion calculation and decision analysis are mainly studied. Nowadays, the traditional single sensor is replaced by a multi-source sensor especially in fire detection (Wang in Comp Intell Syst 1(1–4):1–22 [1]), and three main detector (CO, smoke, and temperature) are significantly changed when the fire occurs, and the data fusion algorithm is used to calculate the result, thereby improving the system accuracy and reducing the false alarm rate by using the self-learning and self-adaptive ability of BP neural network on the feature layer. Then, the result is transmitted into the decision-making layer, and the fuzzy logic inference algorithm is used to make the final decision and judgment. Simulation experiments show that the fusion system can meet the data collection, processing, fusion, and analysis of fire detection scenarios in smart home security, can effectively identify the accurate fire situation, and can respond quickly.

Cognitive radio is a promising technology. The spectrum scarcity problem can be overcome by enabling unlicensed users to access the licensed spectrum bands. In this paper, we focus on power efficiency maximization of cognitive system by jointly considering the parameters of spectrum sensing time and transmit power. In addition, we propose to investigate power efficiency of cognitive relay transmission scheme based on that of non-relay transmission. Finally, we provide the simulation results to show that the throughput and power efficiency of the proposed approach can be significantly improved compared to that of the non-relay transmission scheme in cognitive radio networks. Furthermore, numerical results demonstrate that there exist the optimal parameters to maximize power efficiency.

A relay selection algorithm is proposed to improve the system performance based on statistical channel state information(CSI) in amplify-and-forward(AF) cooperative networks. An exact and upper bound ergodic capacity formulation were derived for AF relay selection network. Theoretical analysis and simulation results validate the Tightness and feasibility of upper bound ergodic capacity.

Scarcity of the spectrum is becoming a major issue based on traditional frequency planning scheme, cognitive radio (CR) has been proposed for better spectrum utilization efficiency by intelligently sensing and opportunistic access. In this paper, a novel method is proposed to determine signal to noise ratio threshold (SNRT) based on transmission power and complexity of cognitive detector under lognormal shadowing conditions. Since there is tradeoff between detection probability and false alarm probability, we can maximize spectrum utilization efficiency with pre-determined detection performance,meanwhile SNRT is adjusted to void unacceptable interference to primary receiver (PR) and minimize complexity of cognitive detector according to transmission power. Numerical results will show that cognitive radio can adjust SNRT adaptively in order to guarantee the PR decoding signal from primary transmitter (PT) without unacceptable interference.