Dynamic Global-Principal Component Analysis Sparse Representation for Distributed Compressive Video Sampling

Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dynamic global-Principal Component Analysis (PCA) sparse representation algorithm for video based on the sparse-land model and nonlocal similarity. First, grouping by matching is realized at the decoder from key frames that are previously recovered. Second, we apply PCA to each group (sub-dataset) to compute the principle components from which the sub-dictionary is constructed. Finally, the non-key frames are reconstructed from random measurement data using a Compressed Sensing (CS) reconstruction algorithm with sparse regularization. Experimental results show that our algorithm has a better performance compared with the DCT and K-SVD dictionaries.

Key parameter optimization and analysis of stochastic seismic inversion

Stochastic seismic inversion is the combination of geostatistics and seismic inversion technology which integrates information from seismic records, well logs, and geostatistics into a posterior probability density function （PDF） of subsurface models. The Markov chain Monte Carlo （MCMC） method is used to sample the posterior PDF and the subsurface model characteristics can be inferred by analyzing a set of the posterior PDF samples. In this paper, we first introduce the stochastic seismic inversion theory, discuss and analyze the four key parameters： seismic data signal-to-noise ratio （S/N）, variogram, the posterior PDF sample number, and well density, and propose the optimum selection of these parameters. The analysis results show that seismic data S/N adjusts the compromise between the influence of the seismic data and geostatistics on the inversion results, the variogram controls the smoothness of the inversion results, the posterior PDF sample number determines the reliability of the statistical characteristics derived from the samples, and well density influences the inversion uncertainty. Finally, the comparison between the stochastic seismic inversion and the deterministic model based seismic inversion indicates that the stochastic seismic inversion can provide more reliable information of the subsurface character.

Power distribution analysis for multiple modulation formats in an all-optical sampling wavelength division multiplexing system

An optimal power distribution analysis for an all-optical sampling orthagonal frequency division multiplexing(OFDM) scheme with multiple modulation formats including diferential phase shift keyed(DPSK), diferential quadrature phase shift keyed(DQPSK), and non-return-to-zero(NRZ) is proposed. The noise tolerances of different modulation formats are analyzed, and the optimal input power ratio between phase and intensity modulation formats for the best overall receiving performance is investigated under unchanged total input power. Moreover, this scheme can seamlessly coexist with the traditional WDM channel.

Application of Transfer Learningin Mechanical Equipment Intelligent Diagnosis:Literature Review

Accelerating the process of intelligent manufacturing and the demand for new industrial productivity,the operating conditions of machinery and equipment have become ever more severe.As an important link to ensure the stable operation of the production process,the condition monitoring and fault diagnosis of equipment have become equally important.The fault diagnosis of equipment in actual production is often challenged by variable working conditions,large differences in data distribution,and lack of labeled samples,etc.Traditional fault diagnosis methods are often difficult to achieve ideal results in these complex environments.Transfer learning(TL)as an emerging technology can effectively utilize existing knowledge and data to improve the diagnostic performance.Firstly,this paper analyzes the trend of mechanical equipment fault diagnosis and explains the basic concept of TL.Then TL based on parameters,TL based on features,TL based on instances and domain adaptive(DA)methods are summarized and analyzed in terms of existing TL methods.Finally,the problems faced in the current TL research are summarized and the future development trend is pointed out.This review aims to help researchers in related fields understand the latest progress of TL and promote the application and development of TL in mechanical equipment diagnosis.

Small-time sampling behavior of a Fleming-Viot process

The Fleming-Viot process with parent-independent mutation process is one particular neutral pop- ulation genetic model. As time goes by, some initial species are replaced by mutated ones gradually. Once the population mutation rate is high, mutated species will elbow out all the initial species very quickly. Small-time behavior in thls case seems to be the key to understand this fast transition. The small-time asymptotic results related to time scale t and a（O）t, where lim0→∞θ θa（θ） = O, are obtained by Dawson and Shni （1998, 2001）, Shui and Xiong （2002）, and Xiang and Zhang （2005）, respectively. Only the behavior under the scale t（θ）, where limθ→∞θt（θ） = 0 and limθ→∞（O） =∞ was left untouched. In this paper, the weak limits under various small-time scales are obtained. Of particular interest is the large deviations for the small-time transient sam- pling distributions, which reveal interesting phase transition. Interestingly, such a phase transition is uniquely determined by some species diversity indices.

Unseen head pose prediction using dense multivariate label distribution

Accurate head poses are useful for many face-related tasks such as face recognition, gaze estimation,and emotion analysis. Most existing methods estimate head poses that are included in the training data(i.e.,previously seen head poses). To predict head poses that are not seen in the training data, some regression-based methods have been proposed. However, they focus on estimating continuous head pose angles, and thus do not systematically evaluate the performance on predicting unseen head poses. In this paper, we use a dense multivariate label distribution(MLD) to represent the pose angle of a face image. By incorporating both seen and unseen pose angles into MLD, the head pose predictor can estimate unseen head poses with an accuracy comparable to that of estimating seen head poses. On the Pointing'04 database, the mean absolute errors of results for yaw and pitch are 4.01?and 2.13?, respectively. In addition, experiments on the CAS-PEAL and CMU Multi-PIE databases show that the proposed dense MLD-based head pose estimation method can obtain the state-of-the-art performance when compared to some existing methods.

Distribution of persistent organochlorine pesticides in samples from the Canadian Arctic

SOME soil, moss, kelp and reindeer liver were collected at 5 sites in a wide range of the Canadi an Arctic from 58°N to 81°31′N by the First Scientific Expedition of China to Canadian Arctic during April through May, 1995. The concentrations of persistent organochlorine HCH and DDT of these samples were determined. After sampling, the samples were extracted with appropriate solvents, analyzed by packed column gas chromatography with electron capture

Cuckoo search with varied scaling factor

Cuckoo search （CS）, inspired by the obligate brood parasitic behavior of some cuckoo species, iteratively uses L6vy flights random walk （LFRW） and biased/selective random walk （BSRW） to search for new solutions. In this study, we seek a simple strategy to set the scaling factor in LFRW, which can vary the scaling factor to achieve better performance. However, choosing the best scaling factor for each problem is intractable. Thus, we propose a varied scal- ing factor （VSF） strategy that samples a value from the range [0,1] uniformly at random for each iteration. In addition, we integrate the VSF strategy into several advanced CS vari- ants. Extensive experiments are conducted on three groups of benchmark functions including 18 common test functions, 25 functions proposed in CEC 2005, and 28 functions intro- duced in CEC 2013. Experimental results demonstrate the ef- fectiveness of the VSF strategy.

Multiwavelength generation using an add-drop microring resonator integrated with an InGaAsP/InP sampled grating distributed feedback

A system of an add-drop microring resonator integrated with a sampled grating distributed feedback （SG-DFB） is investigated via modeling and simulation with the time-domain traveling wave （TDTW） method. The proposed microring resonator comprises a SiO2 waveguide integrated with an InGaAsP/InP SG-DFB, and the SiO2 waveguide consists of a silicon core having a refractive index of 3.48 and Kerr co- efficient of 4.5 × 10^-18 m2/W. The SG-DFB consists of a series of grating bursts that are constructed using a periodic apodization function with a burst spacing in the grating of 45 μm, a burst length of 5 μm, and I0 bursts across the total length of the SG-DBR. Transmission results of the through and drop port of the microring resonator show the significant capacity enhancement of the generated center wavelengths. The Q-factor of the microring resonator system, defined as the center wavelength （λ0） divided by 3 dB FWHM, without and with integration with the SG-DFB is calculated as 1.93 × 10^5 and 2.87 × 10^5, respectively. Analysis of the dispersion of the system reveals that increasing the wavelength results in a decrease of the dispersion. The higher capacity and efficiency are the advantages of integrating the microring resonator and the InGaAsP/InP SG-DFB.

Influence of facet reflection of SOA on SOA-integrated SGDBR laser

A combined model of the transmission-line laser model （TLLM） and the digital filter approach is developed to simulate the shuttering characteristic of a semiconductor optical amplifier （SOA）, which is inte- grated with a sampled grating distributed Bragg reflector （SGDBR） laser, to create a so called SOA-SGDBR laser. The SOA section acts as a shutter to blank the laser output during wavelength switching events. Simulated results show that the turn-on edge of the SOA blanking process will oscillate when the facet reflection of SOA is relatively high. This phenomenon is also observed by experiments.

Novel parametric optimum processing method for airborne radar

In radar target detection, an optimum processor needs to automatically adapt its weights to the environment change. Conventionally, the optimum weights are obtained by substantial independently and identically distributed (i.i.d.) interference samplings, which is not always realistic in an inhomogeneous clutter background of airborne radar. The lack of i.i.d. samplings will inevitably lead to performance deterioration for optimum processing. In this paper, a novel parametric adaptive processing method is proposed for airborne radar target detection based on the modified Doppler distributed clutter (DDC) model with contribution of clutter's internal motion. It is different from the conventional methods in that the adaptive weights are determined by two parameters of DDC model, i.e., angular center and spread. A low-complexity nonlinear operators approach is also proposed to estimate these parameters. Simulation and performance analysis are also provided to show that the proposed method can remarkably reduce the dependence of i.i.d. samplings and it is computationally efficient for practical use.