A novel adaptive classification scheme for digital modulations in satellite communication

To make the modulation classification system more suitable for signals in a wide range of signal to noise ratios （SNRs）, a novel adaptive modulation classification scheme is presented in this paper. Differ-ent from traditional schemes, the proposed scheme employs a new SNR estimation algorithm for small samples before modulation classification, which makes the modulation classifier work adaptively according to estimated SNRs. Furthermore, it uses three efficient features and support vector machines （SVM） in modulation classification. Computer simulation shows that the scheme can adaptively classify ten digital modulation types （i.e. 2ASK, 4ASK, 2FSK, 4FSK, 2PSK, 4PSK, 16QAM, TFM, π/4QPSK and OQPSK） at SNRS ranging from 0dB to 25dB and success rates are over 95% when SNR is not lower than 3dB. Accuracy, efficiency and simplicity of the proposed scheme are obviously improved, which make it more adaptive to engineering applications.

Real-Time Brain-Computer Interface System Based on Motor Imagery

Abstract-A brain-computer interface （BCI） real- time system based on motor imagery translates the user＇s motor intention into a real-time control signal for peripheral equipments. A key problem to be solved for practical applications is real-time data collection and processing. In this paper, a real-time BCI system is implemented on computer with electroencephalogram amplifier. In our implementation, the on-line voting method is adopted for feedback control strategy, and the voting results are used to control the cursor horizontal movement. Three subjects take part in the experiment. The results indicate that the best accuracy is 90%.

Classification of flaw by adaptive filtering deconvolution of ultrasonic backscattering echo

A convolution model of flaw scattering echoes and an adaptive filtering deconvolution method are presented. The effect of the method is analyzed by simulating a given system. By deconvolution, the influence of the transducer on echoes is reduced greatly and the flaw features stand out more clearly in the deconvolved echoes than in flaw echoes themselves. flaw echo signals of 18 flaw samples are processed by adaptive filtering deconvolution. As a result, flaws are classified successfully

Fast Adaptive Wavelet for Remote Sensing Image Compression

Remote sensing images are hard to achieve high compression ratio because of their rich texture. By analyzing the influence of wavelet properties on image compression, this paper proposes wavelet construction rules and builds a new biorthogonal wavelet construction model with parameters. The model parameters are optimized by using genetic algorithm and adopting energy compaction as the optimization object function. In addition, in order to resolve the computation complexity problem of online construction, according to the image classification rule proposed in this paper we construct wavelets for different classes of images and implement the fast adaptive wavelet selection algorithm （FAWS）. Experimental results show wavelet bases of FAWS gain better compression performance than Daubechies9/7.

A two-step robust adaptive filtering algorithm for GNSS kinematic precise point positioning

In kinematic navigation and positioning,abnormal observations and kinematic model disturbances are one of the key factors affecting the stability and reliability of positioning performance.Generally,robust adaptive filtering algorithm is used to reduce the influence of them on positioning results.However,it is difficult to accurately identify and separate the influence of abnormal observations and kinematic model disturbances on positioning results,especially in the application of kinematic Precise Point Positioning(PPP).This has always been a key factor limiting the performance of conventional robust adaptive filtering algorithms.To address this problem,this paper proposes a two-step robust adaptive filtering algorithm,which includes two filtering steps:without considering the kinematic model information,the first step of filtering only detects the abnormal observations.Based on the filtering results of the first step,the second step makes further detection on the kinematic model disturbances and conducts adaptive processing.Theoretical analysis and experiment results indicate that the two-step robust adaptive filtering algorithm can further enhance the robustness of the filtering against the influence of abnormal observations and kinematic model disturbances on the positioning results.Ultimately,improvement of the stability and reliability of kinematic PPP is significant.