Functional source separation to finger tappingrelated magnetoencephalography signal

It has been verified that the ICA can isolate sources from multi channel magnetoencephalography (MEG) signals. Based on the route of constrained ICA (cICA), this paper achieves a new solution of MEG inverse problem called functional source separation (FSS) by adding a functional constraint to the cost function of a basic ICA model. Source activity is obtained by applying this method to one MEG signal dataset under a self paced finger tapping task. The result is proved effective by calculating correlation coefficients between the weight vectors of function source separation method and the spatial filter coefficients of SAM method. It is found that finger tapping related functional source was localized in motor cortex of precentral gyrus. At the same time, the temporal and frequency information provided by FSS method could be a basis of exploring cortical control timing mechanisms associated with finger movements and extracting time frequency characteristics of the functional source.     

A floating threshold quantization algorithm to suppressmixing interferences in partial discharge

Due to the obvious difference of energy distribution frequencies from partial discharge (PD) signal and its mixing interferences (white noise and narrow brand), we uses the characteristic that node decomposition coefficients of wavelet packet transform can effectively show the energy change of signals to build up a floating threshold quantization algorithm (FTQA) varying with the noise energy of PD decomposition coefficients. It makes the node thresholds under the optimal base various with the noise strength in decomposition coefficients to self adaptively reality the choice of optimal threshold to finely partition PD decomposition coefficients. For simulated and real PD signals with mixing interferences, the conditional global threshold quantization algorithm (GTQA) and the proposed floating threshold quantization algorithm are employed to suppress the mixing interferences in PD signals and compared, and the results show that the proposed algorithm has the stronger suppression ability to mixing interference on PD signal and keeps perfect PD waveform via suppression.     

Principle and Algorithm lmplement of Ehanced Full-rate Speech Coding

Speech coders have assumed considerable importance in radio communication systems as their performance,to a large extent,determines the quality of the recovered speech and the capacity of the system.In order to enhance the quality of speech,GSM puts forward the EFR speech coding algorithm.It bases on the LPC coder,the encoding information includes not only speech feature parameters,but also some wave information,due to the application of A_B_S and VQ. It can afford TD_SCDMA a feasible encodec algorithm,for its high quality and the 12.2kbps speech rate. This article starting with the principal concepts of speech coding,introduces the pringciple of speech coding and ACELP algebraic code book search algorithm in detail.     

Application of Power Spectral Density Analysis in STM

The tunneling-current signals which acquired by STM,include both the sample's molecular structure and the dynamic characteristics of STM system.The Short Time Fourier Transform(STFT) is utilized to estimate the power spectral density(PSD)of the tunneling-current signals and the data's feature vector was extracted in the frequency space.The results obtained by PSD estimation reveal that spectral-space-based feature vector can distinguish the interference from power line(50 Hz),and it can explain and analyze the dynamic characteristics of STM efficiently by using a damped harmonic oscillator model.Meanwhile,the recognition is helpful in the control of the practice scanning condition of STM and the 3-D recreation for the images of STM.     

对马铃薯类受体激酶CRK基因家族的鉴定及响应病原真菌信号的表达分析

富含半胱氨酸的类受体激酶(cysteine-rich receptor-like kinase,CRK)在植物生长发育和环境适应过程中发挥重要的作用。本研究鉴定了马铃薯CRK(StCRK)家族成员,并对其理化性状、进化特征、亚细胞定位、染色体位置和表达模式进行分析。鉴定获得8个StCRKs,其氨基酸序列大小为459~686 aa,分子量介于50.75~77.50 kD,等电点介于5.84~8.75,主要位于质膜。进化分析将来自马铃薯、拟南芥、香蕉、苹果、水稻、番茄和棉花的CRKs分为9个亚组,2号、3号和5号染色体上的StCRKs分布于亚组I(6个成员)和VI(2个成员);存在2个串联重复基因簇,包含4个成员。StCRKs启动子区域存在多种顺式调控元件,主要响应激素、低温、防卫和逆境等信号。接种晚疫病菌(Phytophthora infestans,Pi)和干腐病菌(Fusarium sulphureum,Fs)后,分别发现8个和6个StCRKs为差异表达。其中,StCRK4和StCRK8响应Pi和Fs信号,在接种以上2种病原菌后,表达量上调8倍以上,推测其响应多个真菌信号,可能在马铃薯对真菌病害的广谱抗性中起重要作用,可作为进一步抗病研究和功能分析的候选基因。     

甘薯近缘野生种Ipomoea trifida(4x) GISH分析

以甘薯近缘野生种I. trifida (2x)为探针, 与I. trifida (4x) 2个株系“695104”和“697288”的体细胞染色体进行基因组荧光原位杂交, 结果显示, 2株系都与I. trifida (2x)有很近的亲缘关系, 但2株系的信号存在差异。“695104”几乎所有染色体整条都有均匀明亮的信号, 应为I. trifida (2x)基因组直接加倍而来;而 “697288”与“695104”不同, 虽然各条染色体也均有杂交信号, 但信号的区域与亮度有差异, 较为复杂, 可分为三种情况。第1种是整条染色体有均匀明亮的信号, 亮度与分布区域同“695104” , 有41条;第2种是几乎整条染色体有信号, 但亮度较第一种暗, 有14条;第3种为染色体部分区域有信号, 亮度较前二者更暗, 有5条。推测 “697288”是在加倍同时或之后又发生了基因组重组与部分变异。     

A blind adaptive reduced-rank receiver for ultra-wideband communication systems

A low-complexity blind adaptive receiver for ultra-wideband (UWB) systems in the presence of both multiple access interference (MAI) and inter symbol interference (ISI) is proposed, which is composed of two stages. In the first stage, a reduced-rank algorithm based on the multi-stage Wiener filter (MSWF) is considered to suppress the MAI and extract multi-path components. Channel estimation using the projection approximation subspace tracking with deflation (PASTd) algorithm and multi-path combining are then performed to further enhance the signal to interference plus noise ratio (SINR) of the desired user in the second stage. The simulation results show that the proposed receiver exhibits reasonably good bit error ratio (BER) performance compared with those of matched filter, conventional Rake receiver, decorrelating Rake receiver and adaptive receiver based on constant modulus algorithm (CMA). Moreover, it has faster convergence speed and less complexity.     

Signal Processing of Electroencephalogram and It''s Application

Electroencephalogram (EEG) signals are the electrical activities in the cortex or on the surface of scalp caused by the physiological activities of the brain which play a key role in the diagnosis of brain and the functional determination of brain. The authors discuss some methods for processing EEG signals from the view of the frequency domain and time domain, especially introduce some time-frequency analysis methods such as Wigner distribution, wavelet transform and matching pursuit etc. , ANN and non-linear analysis for EEG signals processing.     

利用原位杂交及CAPS标记分析芸薹属A、B和C基因组间的关系

以来源于Brassica rapa基因组(AA)的重复序列(151 bp)为探针, 分别同二倍体白菜型油菜(AA, 2n=20)、甘蓝(CC, 2n=18)和异源四倍体芥菜型油菜(AABB, 2n=36)的中期染色体杂交, 白菜型油菜和甘蓝的所有染色体上都有杂交信号, 芥菜型油菜的染色体上显示出20个明显的信号, 其余染色体上信号很弱或无, 可以区分出A和B基因组。对来源于油菜3个基本种与3个复合种FAE1基因进行CAPS分析表明, 3个基本种表现出不同的酶切式样, 用Mbo I和Msp I酶切表现出多态性, 基因组A和C非常相似, 而基因组B与A、C关系较远, 同时3个复合种也并不是2个基本种的简单相加, 表明异源四倍体在长期进化过程中可能发生了重排和重组。     

Fault diagnosis of rotating machinery based on SVM 2PTMC

A method is proposed to determine the priority class of binary tree SVM. Firstly, the vibration signals of rotating machinery are colleted through a rotating machinery fault experimental platform and data acquisition system. The signals are from 5 different conditions, i.e. rotor normal, rotor unbalance, rotor misalignment, rotor bearing inner ring cracks and rotor bearing outer ring cracks. Then the signals are disposed by zero-mean and the main frequency band of the vibration signals are reconstructed to, extract the dimensionless time domain as characteristic value. Finally, the priority class of SVM 2PTMC can be determined by the correct inspection rate of parallel SVM. Training samples can be completely divided in experiments, which verifies the effectiveness of this method.     

The method for removing the spurious modes of eigensystem realization algorithm

Due to the influence of spurious modes on the eigensystem realization algorithm results,singular value decomposition(SVD) and model energy level are introduced to remove the spurious modes of eigensystem realization algorithm,reduce part of the noise modes and improve the accuracy by reducing measurement noise by SVD. The energy matrix of each mode can be calculated by the selection matrices,the eigenvalues and eigenvectors of the state matrix and the input distribution matrix. The largest singular value of the energy matrix obtained by SVD is a measure for the energy contribution of each mode,which is named mode energy level. Spurious modes resulting from noise or model redundancy are indicated according their mode energy level. A numerical example and an experimental example are presented to demonstrate the efficacy of the method.     

Analysis About the Coupling Circuit of the Communication With Low-voltage Power Line

In the interest of realizing carrier communication using LVPL, it is sticking point to analyse and design the coupling circuit of the communication. Higher efficiency of loading carrier signals is necessary. On the other hand, disturbs from LVPL should be overcome. Penman commendably resolved the difficult problem with the "complex-coupling-technology" of combining " electromagnetism - coupling" to "RC-coupling". Based on the " complex - coupling-technology" , "the interface circuit of the communication is analyzed and designed with LVPL. Simulation results and experimentation results show : the coupling circuit not only has higher efficiency of loading carrier signals but also can completely close off 50 Hz signal from LVPL. Therefore, the coupling circuit can apply to communication systems on LVPL.     

Study on separation algorithm of convolutive mixing model and separation of mixing signals of multiple partial discharges

To identify individual partial discharges(PD)signals produced by multiple insulation defects in gas insolated switchgear (GIS),this paper analyses the mechanism of propagation and mixing of multiple PD electromagnetic wave signals in GIS cylinder and proposes the convolutive mixing model to describe it for a separation algorithm to acquire individual PD signals. With the non-stationary property,mixing PD signals are changed at time domain into a set consisting of short-time stationary PD signals and then the Molgedey-Schuster decorrelation approach is employed to separate these stationary PD signals at frequency domain. The correlation of the envelope of separated PD signals in this set is used to reconstruct PD signals to realize the separation of non-stationary UHF PD mixtures. The effective separation of actual UHF PD mixing signals validates the assumption of convolutive mixing process in GIS and also offers a new approach to the identification of mixing PD signals by external ultra-high frequency detection scheme from multiple insulation defects in GIS.     

QRS complexes detection using quadratic b-spline wavelet transform algorithm

Aiming at the low accuracy and low adaptability of wave detection, a QRS complexes detection algorithm is proposed based on quadratic b-spline wavelet, while combined with binary search algorithm and arc approximating curve algorithm. The signal is decomposed with quadratic b-spline wavelet through Mallat algorithm and the R wave is detected by adjusting the threshold with binary search and modulus maximumizing. The T wave and P wave are detected by using arc approximating curve algorithm based on the least square. This algorithm is certified with the ECG signals from MIT-BIH database and is demonstrated that the algorithm enhanced the adaptability of R wave detection and improved the accuracy of T wave and P wave detection. The simulation experiment shows that the improved algorithm can effectively improve the automatic detection capabilities of ECG signals.     

Multi-target tracking algorithm based on adaptive sampling interval in wireless sensor networks

Multi-target tracking is a hot topic of current research on wireless sensor networks (WSN). Based on adaptive sampling interval, we propose a multi-target tracking algorithm in order to save energy consumption and prevent tracking lost for WSN. We contrast the targets moving model by using the position metadata, and predicte the targets moving status based on extended Kalman filter (EKF).we adopt the probability density function (PDF) of the estimated targets to establish the tracking cluster. By defining the tracking center, we use Markov distance to quantify the election process of the main node (MN). We comput targets impact strength through the targets importance and the distance to MN node, and then use it to build tracking algorithm. We do the simulation experiment based on MATLAB, and the experiment results show that the proposed algorithm can accurate predict the trajectory of the targets, and adjust the sampling interval while the targets were moving. By analyzing the experiments data, we know that the proposed algorithm can improve the tracking precision and save the energy consumption of WSN obviously.     

Wavelet Edge Detection Method Based-on Multi-scale Fuzzy Logic

A novel edge detection algorithm is proposed by implementing fuzzy image enhancement at different scales.The implement of this algorithm can reduce the negative influence bring from the non-maximum suppression algorithm which select only one threshold.The success of the approach depends on the selection of a general fuzzy operator(GFO) for the enhancement of contrast of region after smoothing of image at different scales.Consequently,it is more obvious that coarse intensity changes are obtained at a large scale whereas the fine details of intensity changes are obtained at a small scale.A pretty performance is obtained by used this work.     

A Data Detector Based on Channel Equalizer Designed for the Downlink of CDMA Systems

In code-division multiple-access (CDMA) systems,sub-optimum multi-user or joint detection algorithms have been studied recently, which are mostly designed for the uplink situation. The algorithms are too complex to implement for the downlink situation. The conventional receiver consisting of matched filters is often inefficient in eliminating MAI. The authors propose a data detection algorithm designed for downlink of CDMA systems which introduce channel equalization to eliminate MAI. It consists of a channel equalizer and code matched filter. The equalization algorithm is based on zero forcing (ZF) criterion.The performance of the algorithm is better than metched filter's and its complexity is cower than tunt of sub-optimum multi-user detection.     

The Extended Kalman Filter Data Fusion Algorithm Based on Discrete Stationary Wavelet Transformation

It is difficult to establish precise mathematical model in extended Kalman filter data fusion algorithm for GPS/ DR integrated vehicle navigation system, an improved extended Kalman filter data fusion algorithm is put forward based on Discrete stationary wavelet transformation. The simulation results show that the algorithm is better than the extended Kalman filter data fusion algorithm, and is available for GPS/DR integrated vehicle navigation system.     

OBS网络的拥塞控制汇聚算法分析

传统OBS网络汇聚算法没有综合考虑边缘节点汇聚算法和核心节点的数据调度算法二者之间的相互联系，其通信性能受限。在分析OBS网络边缘节点汇聚算法对核心节点数据调度的影响后，提出了一种新的边缘节点汇聚算法——基于OBS网络的拥塞控制汇聚算法（CCAA）。该算法通过分析边缘节点汇聚参数对数据调度的影响，调整汇聚算法实现对核心节点调度成功率的影响，最终实现了提高核心节点数据调度的成功概率。     

Partial discharge pattern recognition based on S transform and two-directional 2DPCA

A new feature extraction method is proposed to recognize different types of partial discharge (PD) signals. Firstly,four typical categories of PD artificial defect models are made and S transform (ST) is employed to obtain a time-frequency representation of the recorded UHF signals. Then,two-directional two-dimensional principal component analysis ((2D) 2PCA) is applied to compress the ST amplitude (STA) matrix to extract features. Finally,support vector machine (SVM) combined with particle swarm optimization (PSO) algorithm is introduced to accomplish the recognition of experimental samples. Classification results demonstrate that the average recognition rate of (10,5) combination is the highest while the one of (5,5) combination is the lowest among four kinds of feature dimension combinations. Moreover,PSO can obviously improve the classification performance of SVM. Specifically,all the average recognition rates of PSO-SVM are higher than 94.43%and the maximum value comes to 97.67%. Therefore,the feature sets extracted by ST and (2D) 2PCA can not only achieve dramatic dimension reduction,but also retain the major information of original data. It is proved that the proposed algorithm can obtain ideal results in PD pattern recognition.