Reconstructive Oscillator Based Sinusoidal Signal Generator for ADC BIST

A sigma-delta (/spl Sigma//spl Delta/) modulation based sinusoidal signal generator is exploited which can produce high frequency analog and digital signals concurrently. Except for a 1-bit DAC and band-pass filter, the circuits are entirely digital circuits. Using digital circuits, it is easier to integrate the function in silicon and verify itself. Also, signal amplitude and oscillation frequency can be precisely controlled compared with pure analog signal generator. The area of the proposed high frequency sinusoidal signal generator is low and thus it is suitable to be a built-in signal generator for mixed-signal ICs testing.

Wavelet transform can decompose the input signal into high and low frequency signal. The low frequency signal includes main performances of the signal. However, the high frequency signal contains many noises. Wavelet de-noising can be realized by reconstructing the rest signal after the high frequency signal is flatted. According to the effect of noise on hydrological time series, this paper studies hydrological time series including measured noise with wavelet de-noising method. Firstly, wavelet de-noising is done with MATLAB, and then trend component, periodic component and random component are extracted from the hydrological time series respectively. At last, the time series model based on wavelet de-noising is established by superposition to simulate and forecast the groundwater dynamic variation of Chahayang irrigation district. The forecasting precision of this model is good. So it is reasonable to research the groundwater dynamic variation of Chahayang irrigation district through the model. At the same time, this paper can provide scientific basis for sustainable utilization of groundwater resources, making reasonable irrigation system and the development of paddy field in Chahayang irrigation district.

In the attack of the encryption algorithm ARCFOUR(RC4), new method of machine learning based on Wavelet Transform(WT) and Principal Component Analysis(PCA)is proposed. WT is used to reconstruct the new signals to extract information from the original signals effectively. Components in low and high frequency and reconstructed signals are made by WT. In order to analyze the impact of the signals on the success rate of prediction, four kinds of signals are adapted respectively for dimensionality reduction, which contains initial signals, reconstructed signals, and the signals in low and high frequency. By the classification of Support Vector Machine (SVM), the results show that the effect of the reconstructed signals is the best one. The reconstructed signals reduce the noise influence. In the range of 500 dimensions, the classification effect of the reconstructed signals is obviously better than others. As the dimension increases, the effect becomes small. The effect of signals in low frequency is more effective than that of the original in most of dimensions. The classification success rate is still high with fewer dimensions. In the four kinds of signals, the effect of the signals in high frequency is the worst. The results show that WT combined with PCA is a good method to handle with classification.

To improve the accuracy of forecasting stock prices, a new stock index prediction approach is proposed, which based on wavelet analysis combines the autoregressive integrated moving average (ARIMA) and artificial neural network. The non-stationary share price index series are decomposed and reconstructed into one low frequency signal and several high frequency signals by wavelet; the approximate stationary low frequency signal is predicted using ARIMA forecasting model, and the high frequency signals are forecasted using Elman neural network models; the prediction result of each layer are mixed by the radial basis function(RBF)neural network and the result is the final prediction. Examples show that the prediction of the combined forecasting model is precise.

A FPGA-based two-channel low frequency signal generator and analysis system has been designed to analyze the occurrence and analysis of low frequency signal. It uses FPGA for low frequency signal producing control chip module and analysis module. Control signal is sent to the FPGA by keys, FPGA generates frequency range step adjustable low frequency signal. Through the adder, fast Fourier transform is used to extract frequency domain signal by analysis module FPGA, the oscilloscope measures frequency amplitude and other information, to achieve low frequency signal frequency domain analysis capabilities. The system whose encapsulation is compact has friendly interface and it is very easy to operate and demonstrate.

A new wavelet domain algorithm for speech enhancement is presented. The main idea of the proposed algorithm can be described as follows: noisy speech is first split into low frequency band signal and high frequency band signal via one scale of DWT. Then, three scales of DWT and three scales of WPD are applied to the low frequency signal and the high frequency signal respectively. The denoised speech is reconstructed finally. To prevent the degradation of unvoiced sounds during noise reduction, this algorithm divides noisy speech samples into unvoiced segments and voiced segments and then applies multi-threshold to them. Compared with DWT and WPD, the new algorithm can preserve more unvoiced sounds and provide better performance as shown by computer simulation results

A neuron carries out its functions in networks receiving contacts from roughly 104 presynaptic neurons. Such a dense connectivity profile for a single neuron may give rise to enormous complex neuronal topologies, which might be very difficult to understand the underlying mechanisms of neurological functions and diseases. Extensive experimental data from neuroanatomical studies have uncovered that neural networks include some recurring topologies of microcircuits, known as network motifs which serve as characteristic building blocks of complex networks [1,2]. Therefore, it is widely assumed that a clear explanation on dynamical and functional features of these network motifs can be considered as the first step to understand large networks. Following this motivation, in this study, we investigate the Vibrational Resonance (VR) phenomenon in a triple-neuron feed-forward-loop (FFL) which is one of the most significant brain network motifs, shown in Figure ​Figure1.1. VR is a physical phenomenon found in nonlinear systems where a weak signal can be detected and processed by the system with the assistance of another high frequency signal. It is very similar to the well-known stochastic resonance phenomenon, where the role of noise is replaced in VR by a high frequency signal. In recent years, there exists a growing interest in applications of VR to neuroscience because two frequency signals are pervasive in neural systems, i.e. bursting neurons exhibit two widely different time scales, simultaneous arrival of vocal signals having distinct frequencies to auditory neurons. Due to the significance of its potential in neural signal processing, the VR phenomenon has already been studied with computational models of neurons and their networks [3,4]. However, a population of excitatory and inhibitory neurons has not been considered, yet. Here, we study the VR dynamics depending on whether the neurons in the FFL motifs are excitatory or inhibitory considering eight possible structural configurations of the considered microcircuit (Table ​(Table1).1). Such an approach provides us to test the functional influences of structural configuration on VR dynamics in neural populations. Figure 1 Schematic illustration of the considered feed-forward-loop motif. LF and HF refers to low and high frequency signals, respectively. Neuron 1 and 3 is considered as input and output neurons, respectively. Table 1 Eight possible FFL types. We found that the weak signal transmission in the network via the VR mechanism is possible depending on both the coupling strength between neurons and the network structure. For instance, when a weak coupling strength is present between connected neurons, weak signal injected to the input neuron can be transmitted to the output with only T8-FFL motif. In this case, signal transmission is impossible for other types of network motifs. For intermediate coupling strengths, signal transmission performance is high for all motifs and motif type does not change very much VR dynamics. Finally, for strong coupling strengths, the best VR performance is obtained with T1-FFL motif where all the neurons in the network are excitatory. We also clarify the mechanisms that underlies the differences in performance of network motifs.

The present work deals with the mechanical characterization of a woven jute fabrics reinforced polyester resin composite. Two stacking sequences were considered: [0]8 and [+45/−45]2S. In-situ acoustic emission technique, X-ray computed tomography (X-ray CT) and post-mortem microscopic observations were used to characterise the damage mechanisms and to follow their evolutions during uniaxial tension. The microstructural analysis and X-ray CT revealed the following damage modes for the two lay-ups: fibre-matrix debondings which constitute the dominant mechanism, matrix crackings and fibre breakages and pull-outs. The acoustic emission data were processed using an unsupervised pattern recognition technique which combines principal components analysis and k-means optimized by a genetic algorithm. Both temporal and frequential features of acoustic emission signals were considered. The Laplacian score and dendrogram were used to determine the relevant and uncorrelated descriptors for clustering. Three clusters of events were obtained and the waveforms of each one were examined. Furthermore, the frequency contents of signals of each cluster were accurately investigated using power density spectrum and smoothed pseudo Wigner-Ville time-frequency distribution and the discrepancies between clusters are highlighted. Cluster 1 (Cl 1) characterized by signals of low frequency and intermediate amplitude, cluster 2 (Cl 2) characterized by signals of higher frequency and an amplitude similar to that of Cl 1 and cluster 3 (Cl 3) characterized by signals of higher amplitude. A correlation between the clusters and the damage mechanisms was established by means of interrupted tensile tests: Cl 1 is assigned to the matrix cracking, Cl 2 to the fibre-matrix debonding and Cl 3 to the fibre breakage and pull-out. The kinetic of evolution of each damage mode was monitored for the two stacking sequences.

Non-unit transient based boundary protection for UHV transmission lines

Background/Aims: Overactive bladder is a frequently encountered urological disease that has debilitating effects on quality of life. Some hypotheses have been brought forward in order to explain the main pathophysiology underlying this clinical phenomenon, however the exact mechanism remains unclear and the relevant reports are limited in number, confined to female patients and have conflicting results. Therefore, we aimed to examine one of these hypotheses -autonomic nervous system dysfunction- in male patients with overactive bladder. Methods: We enrolled 41 male patients with overactive bladder and 43 healthy males in this study. Their electrocardiographic signals are recorded in the prevoiding and postvoiding periods and then converted to heart rate variability parameters which may reveal autonomic nervous system dysfunction. Results: Standard deviation of normal-to-normal interval, square root of the mean squared differences of successive normal-to-normal intervals, low frequency signal and low frequency/high frequency ratio were found to be significantly lower while high frequency signal was significantly higher in the patient group compared to healthy controls (p<0.05). Conclusions: Heart rate variability was attenuated in male patients with overactive bladder and parasympathetic predominance was observed during both prevoiding and postvoiding periods, contrary to some of the previous reports revealing sympathetic predominance in female patients.

1. The upstroke of the action potential was used as a signal of variable frequency in order to measure the apparent membrane capacity of cardiac Purkyne fibres.2. The upstroke in Na-Tyrode can be regarded as a high frequency signal. Action potentials in Na-free, Sr-Tyrode are characterized by a very small rate of depolarization (5 V/sec) and conduction velocity (15 cm/sec) and were used as a low frequency signal.3. On changing from Na- to Sr-Tyrode the apparent membrane capacity increased by a factor of 3.77 and approached the value estimated by the square-current technique.4. Cable properties (space and time constants, internal core and membrane resistance) were found to be constant when changing from Na- to Sr-Tyrode.5. The significance of the frequency dependent character of the membrane capacity is discussed in relation to propagation and form of the action potential.

Finite element method (FEM) based simulation has been carried out, and an analytic model of microcantilevers using piezoelectric excitations is proposed. The model is based on the type of the selected material and geometry of the structure. The investigations are carried out with rectangular microcantilevers using silicon as the substrate. The high frequency analytic signals are applied to the input piezoelectric electrodes, and the resultant signal generated at the output piezoelectric electrode is recorded and analyzed. The analysis of the results showed that the proposed system is capable of generating a low frequency signal. Two microcantilevers with different dimensional aspects are used, and the results verified the application of microcantilever array as a low frequency signal generator.

The optimum vibrational resonance in a time-delay bistable system driven by bihiarmonic signals is discussed in this paper. The theoretically expression for the response amplitude gain of low frequency signal in the time-delay bistable system is deduced, and the effects of time delay parameter on the optimum vibrational resonance peak and the required amplitude of high frequency signal are investigated. It is shown that the optimum vibrational resonance can be achieved by adjusting the high frequency signal amplitude and time delay parameter jointly. Meanwhile, the optimum vibrational resonance appeared periodically with time delay parameter and the period is equal to the period of low-frequency signal. The amplitude of high-frequency signal required for the optimum vibrational resonance can be fixed or varied with different time delay parameter depending on the ratio of the frequencies between biharmonic signals.

Cosine lobes for interactive direct lighting in dynamic scenes

Considering the nonlinear bistable vibrational resonance(VR) system, the mean signal-noise-ratio gain(MSNRI) is taken as performance index and VR phenomenon subjected to the action of high and low frequency signals is studied under α stable noise. Then the action laws between the amplitude B and frequency Ω of a high frequency drive signal on the resonant output effect is explored. Finally, the multi-high frequency weak signals detection(w i >> 1Hz) with a stable noise can be realized combing with the method of twice sampling. Results show that in bistable VR system, when a (or s) is given different values, the curve of MSNRI-B has multiple peaks with B increasing for a determined Ω, and the intervals of B which can induce VRs are multiple. Similarly, the same conclusion can be obtained from the curve of MSNRI-Ω for a determined B. Besides, the resonance intervals of B and Ω don't change with a or s. The above conclusions lay a foundation for realizing multi-frequency weak signals detection of the industrial sites using bistable VR theory, which have potential applications in signal processing.