Single channel signal component separation using Bayesian estimation

A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper. By using the basis function projection, the component separation becomes a problem of limited parameter estimation. Then, a Bayesian model for estimating parameters is set up. The reversible jump MCMC （Monte Carlo Markov Chain） algorithmis adopted to perform the Bayesian computation. The method can jointly estimate the parameters of each component and the component number. Simulation results demonstrate that the method has low SNR threshold and better performance.

Learning a discriminative high-fidelity dictionary for single channel source separation

Sparse-representation-based single-channel source separation,which aims to recover each source’s signal using its corresponding sub-dictionary,has attracted many scholars’attention.The basic premise of this model is that each sub-dictionary possesses discriminative information about its corresponding source,and this information can be used to recover almost every sample from that source.However,in a more general sense,the samples from a source are composed not only of discriminative information but also common information shared with other sources.This paper proposes learning a discriminative high-fidelity dictionary to improve the separation performance.The innovations are threefold.Firstly,an extra sub-dictionary was combined into a conventional union dictionary to ensure that the source-specific sub-dictionaries can capture only the purely discriminative information for their corresponding sources because the common information is collected in the additional sub-dictionary.Secondly,a task-driven learning algorithm is designed to optimize the new union dictionary and a set of weights that indicate how much of the common information should be allocated to each source.Thirdly,a source separation scheme based on the learned dictionary is presented.Experimental results on a human speech dataset yield evidence that our algorithm can achieve better separation performance than either state-of-the-art or traditional algorithms.

Single channel speech enhancement via time-frequency dictionary learning

A time-frequency dictionary learning approach is proposed to enhance speech con- taminated by additive nonstationary noise. In this framework, a time-frequency dictionary which is learned from noise data is incorporated into the convolutive nonnegative matrix fac- torization framework. The update rules for the time-varying gains and speech dictionary are derived by precomputing the noise dictionary. The magnitude spectra of speech are estimated using convolution operation between the learned speech dictionary and the time-varying gains. Finally, noise is removed via binary time-frequency masking. The experimental results indi- cate that the proposed scheme gives better enhancement results in terms of quality measures of speech. Moreover, the proposed algorithm outperforms the multiband spectra subtraction and the non-negative sparse coding based noise reduction algorithm in nonstationary noise conditions.

Stable error-free single channel 160-Gb/s OTDM 100-km transmission via high-precision dispersion management

A single channel with a 160-Gb/s optical time-division-multiplexing （OTDM） transmission over 100 km is fabricated. With the help of 500-GHz optical sampling oscilloscopes, the fiber length is adjusted to the order of 10 m, which corresponds to the accuracy of 0.4 ps for the dispersion compensation. The dispersion map is optimized for the 100-km transmission link. A completely error-free transmission with the power penalty of 3.6 dB is achieved for 2 h without using forward error correction.

Study on the Fine Configuration of Reservoir in River Facies Oilfield in Bohai Sea Area

The geological conditions of offshore shallow water delta oil reservoirs were complex, with limited well data and large well spacing. Taking A Oilfield in the Bohai Sea Area, China as an example, the target sand body was formed in a shallow water delta sedimentary environment, with well-developed underwater distributary channels and frequent branching and diversion. The reservoir was strong non-uniformity and uneven plane water cut pressure. To this end, based on the existing work of predecessors, combined with seismic, logging, and production dynamics data, and based on the genesis mechanism of shallow water delta reservoirs, the boundary of composite river channels was identified through seismic facies, and logging facies were used to subdivide them into single river levels within the composite river channels. Then, seismic waveform characteristics were applied to track and characterize the plane distribution of single river channels, guiding the efficient development of offshore shallow water delta oil fields and achieving increased storage and production in Bohai Oilfield, China.

Encryption and Decryption of Color Images through Random Disruption of Rows and Columns

In modern society,information is becoming increasingly interconnected through networks,and the rapid development of information technology has caused people to pay more attention to the encryption and the protection of information.Image encryption technology is a key technology for ensuring the security performance of images.We extracted single channel RGB component images from a color image using MATLAB programs,encrypted and decrypted the color images by randomly disrupting rows,columns and regions of the image.Combined with histograms and the visual judgments of encryption images,it is shown that the information of the original image cannot be obtained from the encryption image easily.The results show that the color-image encryptions with the algorithm we used have good effect and fast operation speed.Thus this algorithm has certain practical value.

EXPERIMENTAL STUDY OF THE NON-DARCY FLOW AND SOLUTE TRANSPORT IN A CHANNELED SINGLE FRACTURE

The characterization of fracture rocks is always a key issue in understanding the flow and solute transport in fractured media. This article studies the solute transport in a Channeled Single Fracture （CSF）, a single fracture with contact in certain areas. The flow in a CSF often has preferential pathways and the transport in a CSF often has Break Through Curves （BTCs） with long tails. The Surface Contact Ratio （SCR）, the ratio of the contact area to the total fracture area, is an important indicator for the fracture surface roughness. To study the flow and solute transport in a CSF, a controlled physical model is constructed and a series of flow and tracer test experiments are carried out. Under our experimental conditions, the flow in a CSF is found to follow the Forchheimer equation , where and are the hydraulic gradient and the average pore velocity, respectively and and are two parameters related to the viscous and inertial flow components, respectively. Furthermore, it is found that b decreases with the decrease of SCR. For the solute transport, it is found that the BTCs often deviate from the traditional Fickian behavior, by the early-arrival and the long tailing. More interestingly, the observed BTCs often have a double-peak or a multi-peak, that would be difficult to explain using the existing transport theory such as the Advection-Dispersion Equation （ADE）. In addition, the longitudinal dispersion coefficient is found to be scale-dependent in a CSF and the relationship is of exponential type.

Novel Scheme for Multiple Channel Pulse Generation from a Single Laser Diodefor 10 Gbit/s Based WDM Systems Using RZ Modulation Format

A new technique for the generation of multi-channel optical pulse from a single laser diode (LD) is presented in this paper. 35 channel pulse source with 6.5 GHz repetition rate per channel and 32.5 GHz channel spacing was generated from a subharmonically hybrid mode-locked two section monolithic laser with enhanced amplitude modulation. The obtained pulse source exhibits high extinction ratio (>10 dB) and low level of root mean square (RMS) phase noise (<0.11 rad) over all channels from 1556 nm to 1565...

Time Modulated Array Antennas: A Review

Time-modulated array(TMA)antennas,introduce the dimension of time into antenna design to control the radiation patterns and frequency spectral characteristics,thus improve the reconfigurability of array antennas and provide multiple functional-ities.They have great application potential in military and civilian fields,such as precision guidance and mobile communication,and are currently a hot spot of academic research.This article provides a review on the fundamentals and applications of TMAs.First,the basic theory and mathematical formulations of TMAs are introduced.Second,the most important applications of TMAs,namely time-modulated phased arrays(TMPA),are discussed from the perspectives of harmonic suppression and harmonic utiliza-tion,which are used for single-beam and multibeam radiation.Then,we survey the combination of TMA with various types of novel antenna arrays,such as single-channel digital beamforming(DBF)arrays,frequency diverse arrays(FDAs),and retrodirective arrays,to create new hardware implementation methods and enhance their performance.Next,recent advances in dedicated integrated chips for TMA,which have played a significant role in driving the progress of TMAs from academic research to practical applications,are presented.Finally,the challenges and prospects for TMAs are discussed,including new research directions and emerging applica-tion scenarios.

STUDY ON SST IN THE NORTHWESTERN PACIFIC OCEAN WITH SATELLITE OBSERVATIONS

On the basis of extracting the clear brightness temperature in GMS-4 infrared data,the tests have been carried out by using three methods for SST retrieval in the northwestern Pacific Ocean in August 1993.Considering error amplification is the inherent feature in the single channel physical method,two statistical methods are developed.One is a single channel method.Its precision is 1.186℃,which is superior to that of other similar methods.The other is a multi-channel method using both TOVS and GMS-4 data.Its precision is 1.061℃,which is matched with that of other current multi-channel statistical methods(0.5—1.36℃).The retrieval SST distributions obtained by the two methods are in good agreement with conventional observations.

A blind separation method of overlapped multi-components based on time varying AR model

A method utilizing single channel recordings to blindly separate the multicomponents overlapped in time and frequency domains is proposed in this paper. Based on the time varying AR model, the instantaneous frequency and amplitude of each signal component are estimated respectively, thus the signal component separation is achieved. By using prolate spheroidal sequence as basis functions to expand the time varying parameters of the AR model, the method turns the problem of linear time varying parameters estimation to a linear time invariant parameter estimation problem, then the parameters are estimated by a recursive algorithm. The computation of this method is simple, and no prior knowledge of the signals is needed. Simulation results demonstrate validity and excellent performance of this method.