Computer simulation of tidal signal prediction in long range ocean acoustic travel-time measurements

When using long range sound travel-time measurements to monitor the ocean temperature changes, the tidal effects must be corrected from the data. On the basis of a linear model of the tidal signal, and using measurements taken within a specified period and at 4 hour intervals, a pseudorinverse method is used to predict the traveltime change due to the aggregate effect of barotropic tides along the sound path. The sampling period should be sufficiently long to give an acceptable prediction accuracy. In order to estimate all the major tidal constituents and separate closely spaced frequency components, a sampling period of 18 months is recommended. The linear model should include as many constituents as possible to minimize the predictioll error. This is feasible because in modeling the tide, the only parameter needed for each constituent is the frequency; and the freqencies of the astronomical components are known to a high precision, while the nonastronoIIilcal components are trivial in this application. Quantisation errors are reduced by means of multipath averaging.

PREDICTION OF OCEANIC DATA

The Kalman filter is used to predict the velocity of littoral current, the wave direction, the sea depth and the wave steepness. In this paper the Kazumasa model has been modified to deal with two cases: 1) For the positions a bit far from the shore, the interaction between the velocity of littoral current as well as the wave direction and the sea depth as well as the wave steepness must be considered. 2) For the positions very close to the shore, three new parameters describing the asymmetry wave are introduced to deal with wave breaking. The results from the modified model are compared with observed data, and the comparison indicates that the modified model is better and capable of giving more accurate results.

Discrete-time Markov-based dynamic control approach for compressed sampling

To solve the problem that the signal sparsity level is time-varying and not known as a priori in most cases,a signal sparsity level prediction and optimal sampling rate determination scheme is proposed.The discrete-time Markov chain is used to model the signal sparsity level and analyze the transition between different states.According to the current state,the signal sparsity level state in the next sampling period and its probability are predicted.Furthermore,based on the prediction results,a dynamic control approach is proposed to find out the optimal sampling rate with the aim of maximizing the expected reward which considers both the energy consumption and the recovery accuracy.The proposed approach can balance the tradeoff between the energy consumption and the recovery accuracy.Simulation results show that the proposed dynamic control approach can significantly improve the sampling performance compared with the existing approach.

ROLE OF EQUATORIAL PACIFIC OCEAN SUBSURFACE OCEANIC TEMPERATURE MODE IN ENSO CYCLE

Based on the simple ocean data assimilation(SODA) reanalysis dataset from the University of Maryland and the method of Empirical Orthogonal Functions(EOF),the characteristics of interannual and interdecadal variabilities of the equatorial Pacific subsurface oceanic temperature anomaly(SOTA) are captured.The first and second modes of the equatorial Pacific SOTA in the interannual and interdecadal variations are found respectively and the effect of the second mode on the ENSO cycle is discussed.Results show that the first mode of SOTA's interannual and interdecadal variabilities exhibit a dipole pattern,indicating that the warm and cold temperature anomalies appear simultaneously in the equatorial subsurface Pacific.The second mode shows coherent large-scale temperature anomalies in the equatorial subsurface Pacific,which is a dominant mode in the evolution of ENSO cycle.The temporal series of the second mode has a significant lead correlation with the Ni?o-3.4 index,which can make a precursory prediction signal for ENSO.The function of this prediction factor in SOTA is verified by composite and case analyses.

PASSIVE LOCATION AND TRACKING USING DOA AND TOA MEASUREMENTS OF SINGLE NONMANEUVERING OBSERVER

Passive location and tracking (PLAT) of a moving emitter can be implemented by multi-sited observers or by single maneuvering observer using DOA measurements only. In this article, the principle and method of passive location and tracking of a moving emitter by a single non-maneuvering observer using DOA and TOA measurements are presented and described. Computer simulation of PLAT of a moving emitter in two dimensional plane was implemented. It is shown that convergent and accurate tracking data can be obtained.

Distributed Filtering Algorithm Based on Tunable Weights Under Untrustworthy Dynamics

Aiming at effective fusion of a system state estimate of sensor network under attack in an untrustworthy environment, distributed filtering algorithm based on tunable weights is proposed. Considering node location and node influence over the network topology, a distributed filtering algorithm is developed to evaluate the certainty degree firstly. Using the weight reallocation approach, the weights of the attacked nodes are assigned to other intact nodes to update the certainty degree, and then the weight composed by the certainty degree is used to optimize the consensus protocol to update the node estimates. The proposed algorithm not only improves accuracy of the distributed filtering, but also enhances consistency of the node estimates. Simulation results demonstrate the effectiveness of the proposed algorithm. © 2014 Chinese Association of Automation.

Radio Disturbance Suppression for Ground Wave Radar

A new method of single sample polarization filtering is proposed. The algorithm is fast and suitable for the polarization processing of stationary or nonstationary polarized disturbed signals with one or more independent disturbances. A ground wave polarimetric radar with the ability of radio disturbance suppression is then introduced. Some numerical results demonstrate the effectiveness of single sample polarization filtering method for ground wave polarimetric radar.

Natural Gradient Approach to Multichannel Blind Deconvolution

In this paper we study the geometrical structures of FIR filters and their application to multichannel blind deconvolution. First we introduce a Lie group structure and a Riemannian structure on the manifolds of the FIR filters. Then we derive the natural gradients on the manifolds using the isometry of the Riemannian metric. Using the natural gradient, we present a novel learning algorithm for blind deconvolution based on the minimization of mutual information. Some properties of the learning algorithm, such as equivariance and stability are also studied. Finally, the simulations are given to illustrate the effectiveness and validity of the proposed algorithm.

Variation in the emission rate of sounds in a captive group of false killer whales Pseudorca crassidens during feedings: possible food anticipatory vocal activity?

This study examines whether a group of captive false killer whales(P seudorca crassidens) showed variations in the vocal rate around feeding times. The high level of motivation to express appetitive behaviors in captive animals may lead them to respond with changes of the behavioral activities during the time prior to food deliveries which are referred to as food anticipatory activity. False killer whales at Qingdao Polar Ocean World(Qingdao, China) showed signifi cant variations of the rates of both the total sounds and sound classes(whistles, clicks, and burst pulses) around feedings. Precisely, from the Transition interval that recorded the lowest vocalization rate(3.40 s/m/d), the whales increased their acoustic emissions upon trainers' arrival(13.08 s/m/d). The high rate was maintained or intensifi ed throughout the food delivery(25.12 s/m/d), and then reduced immediately after the animals were fed(9.91 s/m/d). These changes in the false killer whales sound production rates around feeding times supports the hypothesis of the presence of a food anticipatory vocal activity. Although sound rates may not give detailed information regarding referential aspects of the animal communication it might still shed light about the arousal levels of the individuals during different social or environmental conditions. Further experiments should be performed to assess if variations of the time of feeding routines may affect the vocal activity of cetaceans in captivity as well as their welfare.

Identifying the occurrence time of an impending mainshock:a very recent case

The procedure by means of which the occurrence time of an impending mainshock can be identified by analyzing in natural time the seismicity in the candidate area subsequent to the recording of a precursory seismic electric signals（SES） activity is reviewed. Here, we report the application of this procedure to an MW5.4 mainshock that occurred in Greece on 17 November 2014. This mainshock（which is pretty rare since it is the strongest in that area for more than half a century） was preceded by an SES activity recorded on 27 July 2014, and the results of the natural time analysis reveal that the system approached the critical point（mainshock occurrence） early in the morning on 15 November 2014.

Prediction of propeller non-cavitation noise by superimposing shifted sound signal from an isolated blade

The time-cost of the propeller non-cavitation noise prediction can be greatly re- duced by the isolated blade method, which is validated via hybrid URANS and acoustic analogy, followed by the acoustic characteristics of propeller in time domain are analyzed. Firstly, we predicted the sound of the E779A propeller operating in uniform inflow and found a typical periodic characteristic of the sound pressure distribution on propeller blade as well as the sound signal of the receiver, and the result by the superimposing shifted sound signal from an isolated blade （isolated blade method） agreed well with the result by the integration on total blades, which validated the credibility of the isolated blade method in uniform inflow. Finally, we pre- dicted the sound of a propeller running in the wake of submarine by the isolated blade method, and the result also agreed well with the result by the integration on total blades, which further indicated that the isolated blade method was also applicable for the non-cavitation noise prediction of the propeller running in non-uniform inflow. The noise prediction of the counter-rotating propeller, the pump-jet can also benefit from this method.

Acknowledgments

Epilepsy is the most common neurological disorder of the brain that affects people worldwide at any age from newborn to adult. It is characterized by recurrent seizures, which are brief episodes of signs or symptoms due to abnormal excessive or synchronous neuronal activity in the brain. The electroencephalogram, or EEG, is a physiological method to measure and record the electrical

Data-driven disturbance observer-based control: an active disturbance rejection approach

In this paper,a data-driven method for disturbance estimation and rejection is presented.The proposed approach is divided into two stages:an inner stabilization loop,to set the desired reference model,together with an outer loop for disturbance estimation and compensation.Inspired by the active disturbance rejection control framework,the exogenous and endogenous disturbances are lumped into a lotal disturbance signal.This signal is estimaed using an on-line algorithm based on a data-driven predictor scheme,whose parameters are chosen Io salisfy high robustnessperformance criteria.The above process is presented as a novel enhancement lo design a disturbance observer,w hich constitutes the main contribution of the paper.In addition,the control strategy is completely presented in discrete time,avoiding the use of discretization methods for its digital implementation.As a case study,the voltage control of a DC-DC synchronous buck converter aflected by disturbances in the input voltage and the load is considered.Finally,experimental results that validate the proposed stralegy and some comparisons with the classical disturbance observer-based control are presented.

Processing for laser radar range images

Imaging laser radar can give intensity and range images,which provide integrated 3-dimensional (3D) information about objects.However, dropouts and range anomalies exacerbate range images, which makes their background cluttered and target blurred.For background suppression,a new algorithm that combines intensity image and its mean is presented.By using this algorithm to process actual laser radar range images, most background noises are suppressed.According to range anomalies characteristics,multitemplate selection order mean filtering algorithm is presented and used for actual ladar range images where the distance between two targets is 77 m. This algorithm obtains the clear range image in which the interval of two objects is 75 m.The result shows that the processing algorithm is correct and effective.

Particle filter based visual tracking with multi-cue adaptive fusion

To improve the robustness of visual tracking in complex environments such as: cluttered backgrounds, partial occlusions, similar distraction and pose variations, a novel tracking method based on adaptive fusion and particle filter is proposed in this paper. In this method, the image color and shape cues are adaptively fused to represent the target observation; fuzzy logic is applied to dynamically adjust each cue weight according to its associated reliability in the past frame; particle filter is adopted to deal with non-linear and non-Gaussian problems in visual tracking. The method is demonstrated to be robust to illumination changes, pose variations, partial occlusions, cluttered backgrounds and camera motion for a test image sequence.

Robustness estimation of software-synchronized all-optical sampling for fiber communication systems

The robustness of the software-synchronized all-optical sampling for optical performance monitoring is estimated for 10-Gb/s fiber communication systems. It reveals that the software-synchronized algorithm is sensitive to the signal degradation caused by chromatic dispersion and nonlinearity in optical fibers. The influence of timing jitter and amplitude fluctuation of the sampling pulses is also investigated. It is found that stringent requirements are imposed on the quality of the sampling pulse and the tolerance of 1-dB Q penalty is measured. Considering the practically available optical sampling pulse sources, the results indicate that the amplitude fluctuation of the sampling pulses has the dominant impacts on the software-synchronized method.

All-optical NRZ-to-AMI conversion using linear filtering effect of silicon microring resonator

We experimentally demonstrate 10-Gb/s format conversion from non-return-to-zero （NRZ） to alternatemark-inversion （AMI） using the linear filtering effect of silicon microring resonator. Our discussion and analysis in simulation further show that a 10-Gb/s AMI signal with good quality can be obtained by a resonator with a notch depth larger than 25 dB when the 3-dB bandwidth is 0.4 nm.

Ultrahigh speed OOK-to-PSK conversion using linear filtering in silicon ring resonators

A scheme to achieve ultrahigh speed all-optical format conversion from on-off keying （OOK） to phase-shift keying （PSK） by using the linear filtering in the silicon ring resonators is proposed. It is shown that the OOK-to-PSK conversion can be achieved through a linear signal processing. Simulation results are provided for the 160-Gb/s non-return-to-zero （NRZ）-to-PSK and carrier-suppressed （CS） return-to-zero （RZ）-to-（CS）RZPSK conversions.

Morphological self-organizing feature map neural network with applications to automatic target recognition

The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

Track before detect for point targets with particle filter in infrared image sequences

The problem of detecting and tracking point targets in a sequence of infrared images with very low signalto-noise ratio (SNR) is investigated in this paper. A track before detect algorithm for infrared (IR) point target is developed based on particle filter. The particle filter is used to estimate the state of the target in track stage. The unnormalized weights of the output of the filter are used to approximately construct the likelihood ratio for hypothesis test in detection stage. Experiment results with the real image sequences that SNR is about 2.0 show that the proposed algorithm can successfully detect and track point target.