在评估示波器时领会主要技术指标言外之意

在过去5年左右的时间中，工程师一直把重点更多地放在低压差分信令上，以明显提高系统性能。数据速率已经以几何级数提高，推动着设备之间的通信更广泛地采用复杂的串行协议，如PCI Express、Infiniband、XAUI等等。这些环境涵盖了各种数据速率和传输结构，但所有这些数据速率和传输结构都需要严格的设计和检验方法。

FREQUENCY ESTIMATION OF SINUSIODE FROM WIDEBAND USING SUB-NYQUIST SAMPLING

A novel frequency estimation algorithm for wideband signal with sub-Nyquist sampling is proposed in this paper. With the aid of information provided by the auxiliary delayed sampling channel and the aliased frequency estimation for wideband signal with sub-Nyquist sampling, the frequency aliasing due to sub-Nyquist sampling can be solved. This method can reduce the complexity of the overall hardware at the cost of an auxiliary sampling channel. Furthermore, in order to alleviate the computation burden for its practicability, a more simplified algorithm is put forward and its validity is proved by our numerical simulation results. The Cramer-Rao Lower Bound (CRLB) of the frequency estimation is also derived at the end of this paper.

Efficient Recovery of Structured Sparse Signals via Approximate Message Passing with Structured Spike and Slab Prior

Due to limited volume, weight and power consumption, micro-satellite has to reduce data transmission and storage capacity by image compression when performs earth observation missions. However, the quality of images may be unsatisfied. This paper considers the problem of recovering sparse signals by exploiting their unknown sparsity pattern. To model structured sparsity, the prior correlation of the support is encoded by imposing a transformed Gaussian process on the spike and slab probabilities. Then, an efficient approximate message-passing algorithm with structured spike and slab prior is derived for posterior inference, which, combined with a fast direct method, reduces the computational complexity significantly. Further, a unified scheme is developed to learn the hyperparameters using expectation maximization(EM) and Bethe free energy optimization. Simulation results on both synthetic and real data demonstrate the superiority of the proposed algorithm.

Comparative Analysis of EEG Signals Based on Complexity Measure

The aim of this study is to identify the functions and states of the brains according to the values of the complexity measure of the EEG signals. The EEG signals of 30 normal samples and 30 patient samples are collected. Based on the preprocessing for the raw data, a computational program for complexity measure is compiled and the complexity measures of all samples are calculated. The mean value and standard error of complexity measure of control group is as 0.33 and 0.10, and the normal group is as 0.53 and 0.08. When the confidence degree is 0.05, the confidence interval of the normal population mean of complexity measures for the control group is (0.2871,0.3652), and (0.4944,0.5552) for the normal group. The statistic results show that the normal samples and patient samples can be clearly distinguished by the value of measures. In clinical medicine, the results can be used to be a reference to evaluate the function or state, to diagnose disease, to monitor the rehabilitation progress of the brain.

Taming complexity in nonlinear dynamical systems by recycled signal

In this paper, the impacts of the recycled signal on the dynamic complexity have been studied theoretically and numerically xn a prototypical nonlinear dynamical system. The Melnikov theory is employed to determine the critical boundary, and the sta- tistical complexity measure （SCM） is defined and calculated to quantify the dynamic complexity. It has been found that one can switch the dynamics from the periodic motion to a chaotic one or suppress the chaotic behavior to a periodic one, merely via adjusting the time delay or the amplitude of the recycled signal, therefore, providing a candidate to tame the dynamic com- plexity in nonlinear dynamical systems.

Complexity and characteristic frequency studies in ECG signals of mice based on multiple scale factors

Existing methods of physiological signal analysis based on nonlinear dynamic theories only examine the complexity difference of the signals under a single sampling frequency.We developed a technique to measure the multifractal characteristic parameter intimately associated with physiological activities through a frequency scale factor.This parameter is highly sensitive to physiological and pathological status.Mice received various drugs to imitate different physiological and pathological conditions,and the distributions of mass exponent spectrum curvature with scale factors from the electrocardiogram (ECG) signals of healthy and drug injected mice were determined.Next,we determined the characteristic frequency scope in which the signal was of the highest complexity and most sensitive to impaired cardiac function,and examined the relationships between heart rate,heartbeat dynamic complexity,and sensitive frequency scope of the ECG signal.We found that all animals exhibited a scale factor range in which the absolute magnitudes of ECG mass exponent spectrum curvature achieve the maximum,and this range (or frequency scope) is not changed with calculated data points or maximal coarse-grained scale factor.Further,the heart rate of mice was not necessarily associated with the nonlinear complexity of cardiac dynamics,but closely related to the most sensitive ECG frequency scope determined by characterization of this complex dynamic features for certain heartbeat conditions.Finally,we found that the health status of the hearts of mice was directly related to the heartbeat dynamic complexity,both of which were positively correlated within the scale factor around the extremum region of the multifractal parameter.With increasing heart rate,the sensitive frequency scope increased to a relatively high location.In conclusion,these data provide important theoretical and practical data for the early diagnosis of cardiac disorders.