Among the existing spectrum sensing algorithms, energy detection can be implemented easily, but its detection performance depends on noise power. Spectrum sensing algorithms based on random matrix theory can skillfully avoid the influence of noise uncertainty on detection performance, but most of them make use of approximate distribution of the largest eigenvalue. The accuracy of threshold expression derived from it needs to be further improved. Aiming to above problems, by using the latest research results about random matrix theory, a spectrum sensing algorithm based on distribution of the least eigenvalue of sample covariance matrix of received signals was proposed. Cumulative distribution function of the least eigenvalue is not based on asymptotical assumptions, which is more suitable for realistic communication scenarios. The threshold expression derived from it was a function of false alarm probability, whose effectiveness and superiority were analyzed and verified with few samples. Simulations complied with single variable principle were conducted under the situation of few samples, few collaborative users, low signal to noise ratio and low false alarm probability, in comparison with classic maximum-minimum eigenvalue algorithm. Detection probability of the proposed algorithm was increased by 0.2 or so. The results show that the proposed algorithm can significantly improve the detection performance of system.

In this paper, the limited feedback techniques based on the codebook for Multiple-Input Multiple-Output (MIMO) system in the Long Term Evolution-Advanced (LTE-Advanced) applications were investigated. A new limited feedback technique based on multi-level linear prediction was proposed. This method utilized the time correlation of the channel to predict the value of the channel based on multi-level linear prediction. By minimizing the mean square error to design a quantization code book in the basis of the prediction error, code number in the code book was used for system feedback. The simulation results show that multi-level linear prediction can effectively reduce the system prediction error, equivalently, reducing feedback overhead of the system and the maximum reduction rate of 15% to the amount of system feedback that can be reached.

Adaptive Epidemic routing (Ad-EPI) algorithm based on control mechanism was proposed to overcome the performance defects of traditional Epidemic algorithm. The overall balance of the peak transmission control, bandwidth resource consumption, cache utilization and delay were achieved by using controlled flooding mechanism, and information copy control mechanism, introduction of information on survival time (lifetime of information) and adaptive control strategy under the condition to ensure that there is a high arrival rate. The Ad-EPI algorithm was used in VC++ 6.0 programming and simulation and compared with the classic Epidemic algorithm on the VanetMobiSim simulation platform. The simulation results confirm that the Ad-EPI algorithm not only pays a smaller delay cost than classic Epidemic algorithm but also obtains a return of bandwidth usage decreasing by 27.62%, peak reducing by 15.19% on average, cache utilization increasing by 92.14% and so on. The Ad-EPI algorithm has achieved performance improvements in the three above mentioned areas, and it has engineering significance and application value.

Multiple Sclerosis (MS) is a chronic disease that affects the central nervous system and MS lesions are visible in conventional Magnetic Resonance Imaging (cMRI). A new method for the automatic segmentation of MS White Matter Lesions (WML) on cMRI was presented, which enabled the efficient processing of images. Firstly the Kernel Fuzzy C-Means (KFCM) clustering was applied to the preprocessed T1-weight (T1-w) image for extracting the white matter image. Then region growing algorithm was applied to the white matter image to make a binary mask. This binary mask was then superimposed on the corresponding T2-weight (T2-w) image to yield a masked image only containing white matter, lesions and background. The KFCM was reapplied to the masked image to obtain WML. The testing results show that the proposed method is able to segment WML on simulated images of low noise quickly and effectively. The average Dice similarity coefficient of segmentation result is above 80%.