To improve energy-efficiency and robustness of CR(Cognitive Radio), we investigate the robust energy-efficiency maximization algorithm in an OFDM(Orthogonal Frequency Division Multiplexing)-based CR networks with multiuser scenario in a given bounded uncertainty region. We use convex optimization method to obtain quasi-concave mathematic property and solve this problem via fractional programming technique. Simulation results show that this algorithm can strictly meet QoS(Quality of Service) requirement of primary users and promote energy-efficient performance of secondary users under noise plus interference uncertainty compared with non-robust scheme.

Aiming at the issues that the conventional TFPF(Time-Frequency Peak Filtering) doesn't consider the correlation between seismic channels and the radial-trace TFPF doesn't consider the real direction of the reflected event and the reflected event is locally linear, we propose self-adaptive radial-trace Time-frequency peak filtering. The new method finds the direction of the reflected event in the local area by utilizing the self-adaptive function and maps the event to radial-trace area along the direction by RTT(Radial-Trace Transform), and can improve the linearity of the signal. Consequently, it is more close to the unbiased TFPF and can reduce the error caused by the low linearity. Experiment results indicate that this method can provide better performance in both
random-noise attenuation and reflected signal preservation. When selecting same window length, the method can increase the SNR(Signal-to Noise Ratio) by 5 dB more than traditional TFPF and 3 dB more than radial-trace TFPF.

In order to balance energy consumption of WSN (Wireless Sensor Network), a multi-hop unequal clustering routing protocol, ECBUC (Energy Consumption Balanced Unequal Clustering), was put forward. By adopting an energy factor in the primary stage of cluster-head selection, the possibility of high-energy node being elected increased. Using competition algorithm in clustering stage, the network area was divided into unequal area and the “hot spots冶problem of wireless sensor network could be improved. When building a routing between clusters, ECBUC considered the relative position and residual energy of the intermediate nodes. It could coordinate the communication cost between clusters and the energy distribution of the network. Experimental results show that compared with EEUC (Energy Efficient Uneven Clustering) the ECBUC protocol greatly prolongs the lifetime of network by an average of 22. 29%, and balances the network energy consumption.

In order to solve the problem that the mobile botnet attacks against mobile intelligent terminal, which leads to a large number of personal privacy data disclosure, the author designs and implements a mobile botnet oriented Android platform based on Internet botnet technology. Based on micro-blogging control, the mobile botnet can complete information stealing, information destroying, SMS spam, etc. On this foundation, the author makes an in-depth analysis of the characteristics of the mobile botnet, finds the attack vulnerabilities and proposes targeted security defense strategy. The results of this study are significant to the research of mobile internet security technology and improve the security of mobile intelligent terminal. It is helpful for further research on the communication, command control mechanism and control protocol of mobile botnet, which reduce the loss caused by the mobile botnet to individual users.

In order to lighten the labor intensity of nurses, and reduce the incidence of medical accidents, an intelligent monitoring system is designed to ensure the safety of patients. This intelligent system uses CC2530 microcontroller as the master chip, infrared equipment as the detection method, ZigBee as transmission equipment, upper computer and mobile applications, etc. The system also makes a reasonable improvement to the hardware circuit, which can minimize the detection board, and make full use of the ZigBee network to make it easy to use. Experimental results show that the system is simple, highly sensitive and stable, can meet the needs of the actual monitoring of intravenous infusion.

In order to evaluate the comfort level of stereoscopic video, to provide some reference research material for the experts to study. Refer the parallax as the main influencing factors when human are viewing stereoscopic video and make the contrast as compensation for evaluating stereoscopic video comfort. Firstly,graphic segmentation method is used to separate foreground and background, scenes related significant regions extraction method is used to extract the salient region, the salient region parallax and the contrast from the foreground and background. and combining with depth perception and least square fitting method to establish evaluation model. Finally, the subjective evaluation methods are used to evaluate the model, hereby a method for three-dimensional video comfort evaluation is got. Evaluation shows that this method is basically the same as stereoscopic vision of the human eye, providing comfort considerations for further study on the human eye viewing stereoscopic.

In order to accurately extract the logistics process, the picture information, we put forward the logistics information collection system based on Android platform. Using the built-in CMOS(Complementary Metal Oxide Semiconductor) camera, image acquisition of information by loading third party map software and GPS(Global Positioning System) positioning module, information collection, trigger mode by unique way, remain the unique, the reliability of the data. Through the wireless network, broadcasting the data package to the remote server, realize the data storage. In the Android 4. 2 embedded hardware development platform,information collection andinformation transmission function interface design. Test results show that, the system can effectively extract the position information and pictures, can quickly and accurately to upload the data,through the application of Android system, the system can be widely applied.

In order to realize real-time scheduling and automatical lease of the city public bicycle, and improve efficiency in the use of vehicles, an intelligent lease system is designed based on the framework of client and server, which combined with radio frequency identification, general packet radio service and embedded technology. The client uses S3C2440 processor, and transplanted linux operating system. The client is in charge of the lease of bicycles automatically and transmits user information, distributed sites geography information and terminal state to server in TCP(Transmission Control Protocol); at the same time the server application is developed in windows system based on TCP(Transmission Control Protocol), for the purpose of data receiving
and distribution statistic of the bicycles and making scheduling strategy if necessary. After programming and debugging, the test results show that the system is stable and the client's data can be transmitted to server safely and reliably via the GPRS(General Packet Radio Service) network in real time and achieve the requirement of real-time transmission of the data.

Radar performance is greatly impacted by atmospheric environment, the propagation characteristics,such as the fading due to atmosphere, frog, rain, snow, and sand are simulated based on 79 GHz frequency band. Radar performance in different weather conditions are also simulated and analyzed. Results show that 79 GHz frequency band radar has a certain attenuation under different weather conditions, and the rainfall is the main influence. This research provides a theoretical basis for further development and utilization of 79 GHz frequency band used in the intelligent transportation system.

For SI(Spark Ignition) engine AFR(Air-Fuel Ratio) control system, the NMPC(Nonlinear Model Predict Control) method based on the NARX (Nonlinear Auto Regressive model with eXogenous inputs)model is developed. First the algorithm of recursive least squares is used to identify the NARX model. And then use the method of non-linear model to predict control in SI engine. This method has high prediction accuracy, and the optimal control sequence. Thus it improves the SI engine AFR control performance. Finally, the control system is evaluated using a widely used SI engine benchmark model which is Mean engine model by using Matlab, and compared with the PI(Proportional Integral)controller algorithm which current widespread use of car and Volterra model. Simulation results demonstrate the effectiveness of the NARX model based nonlinear model predict control.

Due to the phenomenon of engine misfire in single cylinder and dual cylinders often occured in vehicles, PNN(Probabilistic Neural Network) was used to detect the engine misfire by analyzing the engine rotary velocity and crankshaft angular displacement. Under the circumstance of AMEsim software, we construct a kind of four cylinders in line engine and use the injecting approach to simulate engine misfire. Then, extract the engine velocity and crankshaft angular displacement, put these data in the Matlab software to process and classify them. A PNN for training and testing is established. The experiment results indicate that the engine rotary velocity and crankshaft displacement reflect the real operation condition of the engine, the tested PNN can diagnose and probe single cylinder or double cylinders misfire in the engine. This technique has series of advantages of simplicity, economy, efficiency and high accuracy.

Eight degrees of freedom vehicle model contains many control variables, it is difficult to directly analyze and control. Based on the eight degrees of freedom vehicle model, a model decomposition method is proposed, which decomposes the original model into three parts. Every part includes less control variables. It is easy to design a controller for each part. Considering the roll angle and the roll angular speed of the decomposed model, the linear quadratic optimal control method is introduced to design the optimal state controller, which can achieve vehicle's stability control. It is proved that the designed controller shows more excellent control effect.

Objective body temperature and breathing out gas has a great influence on the temperature and humidity in enclosed spaces. We use the temperature and humidity sensors to collect temperature and humidity information in enclosed spaces. Fusion process the collected information, calibration of enclosed space crowd density level, the actual monitoring data to compare with the calibration data. And through curve fitting, build number and linear relationship of temperature and humidity vector norm. Estimate the crowd density in the current space effectively, revealing monitoring data on the labview regulatory interface, set the crowd density level threshold, flash and sound alarm function. After test, the system design is reasonable and practical, and has a good stability, and suitable for confined space the crowd density monitoring.

In order to solve the problem of the error system in the following control of autonomous vehicle,the following controller is designed based on Leader-follower strategies. The kinematic modeling is done.Leader-follower model is used to describe the following structure and L-method is used to build error system. The speed controller for the follower is designed to achieve the objectives. Finally, the simulation is done by Matlab, the results show that the controller can make the error converge to zero so that it can achieve its goal.

In order to solve the problem of eye tracking, we put forward a fast real-time algorithm of eye tracking based on video image. Firstly,convert RGB color space to YCbCr space, and locate human faces by skin color model. After clipping, use Sobel operator edge detection algorithm in convolution processing, and then find the approximate location of eyes through horizontal gray-level projection to determine rough location of the eyes. Divide this area into left and right eye areas with the help of gray-scale projection,then respectively locate the left and right eye, finally get the precise localization of human eyes. Based on the algorithm, we got the results based on video sequence.

To improve the practicality and convenience of intelligent electric rice cooker, the usability evaluation technology is used to study the display control interface of intelligent electric cooker. According to the characteristics of Chinese consumers and intelligent electric cookers, by means of studying the product interface usability theory. On the index system, the G1 method is applied to define weighting coefficient of all evaluation index. The establishment of the evaluation index system provides scientific basis for the evaluation of intelligent electric cooker display and control interface, and guides the design and redesign of the intelligent electric cookers display control interface in the future.

Aiming at improving the accuracy and directionality of the information acquisition to meet the requirements of the big data processing, we design and implement a personalized information customization system based on Hadoop cloud platform under the background of colleges and universities. It uses Mahout to train the Bayes classifier to distribute data mining algorithm, then the analysis results are stored in the Hbase database. We also develop the Android-based client, through which the mobile terminal connects to the distributed information processing platform. Test results shows that the system could provide the personalized service for users. Customized information could be accessed by users at any time conveniently, which improves the pertinence and interaction of the information service, meeting the demand of the growing network access.

To solve the problem of weak robustness in image segmentation of general active contour, an active contour model combined region-based model with gradient-based model is proposed using the entropy energy and gradient sum. Firstly, active contour model is constructed via fitting the gradient information and region information by use of entropy energy and gradient of image. Secondly, the level set is embed in the mentioned model to follow the curve change of topological structure. Finally, the narrow band method is used to increase the speed of evolution. The experiment results show that the proposed model has a better robustness with faster evolution speed.The study of the model is able to provide a new research way to develop the theory of image segmentation.

With people爷s daily life becoming more diversified, it makes ordinary video monitoring technology no longer needed. With the evolution of mobile communication technology, the popularity of video compression technology advances, smart phones and other mobile devices, video surveillance systems to simple words, network. According to the special needs of network monitoring system aspects, the development of monitoring systems and technology currently available in the background for a simple introduction, combined with the analysis and playback of the video data, a designs of remote video monitoring system based on Android platform is presented. This system provides the function of video surveillance, and it can make reasonable early warning if there is an exception.

In order to solve the issue that recognizing object from different angles and the mismatching of the contour center of the object in left and right images, this work combines the SURF ( Speeded Up Robust Features) algorithm and GrabCut algorithm. Firstly, we capture object images from different angles to establish object template image database in offline mode and use SURF algorithm for object recognition. Secondly,automatically initialize the GrabCut algorithm using SURF algorithm and realize the object contour extraction.Finally, we realize the matching of the contour center of the object based on gray relevant regional matching algorithm and get the localization of the object according to 3d reconstruction principle. Experiments show that the proposed method can recognize object successfully and locate the object accurately.

In order to solve the issues of traditional PC and other electronic equipment which monitoring the temperature and humidity is not easy and the accuracy is not precise enough. Combined with the latest Bluetooth 4. 0 technology and currently widely used Android mobile-terminal a design is presented for Temperature and Humidity monitor, data-transmission and data-display. The core of the System is the module of the Bluetooth low energy NRF51822. Use SHT20 Sensor collects the end data. Use the Android mobile terminal to read and display the datas. Compared to traditional PC-test, android has the advantages of convenient and fast.