Data signal processing via manchester coding-decoding method using chaotic signals generated by PANDA ring resonator

We investigate the nonlinear behaviors of light recognized as chaos during the propagation of Gaussian laser beam inside a nonlinear polarization maintaining and absorption reducing （PANDA） ring resonator system. It aims to generate the nonlinear behavior of light to obtain data in binary logic codes for transmission in fiber optics communication. Effective parameters, such as refractive indices of a silicon waveguide, coupling coefficients （~）, and ring radius ring （R）, can be properly selected to operate the nonlinear behavior. Therefore, the binary coded data generated by the PANDA ring resonator system can be decoded and converted to Manchester codes, where the decoding process of the transmitted codes occurs at the end of the transmission link. The simulation results show that the original codes can be recovered with a high security of signal transmission using the Manchester method.

Gradient method for blind chaotic signal separation based on proliferation exponent

A new method to perform blind separation of chaotic signals is articulated in this paper, which takes advantage of the underlying features in the phase space for identifying various chaotic sources. Without incorporating any prior information about the source equations, the proposed algorithm can not only separate the mixed signals in just a few iterations, but also outperforms the fast independent component analysis （FastlCA） method when noise contamination is considerable.

Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating

In this paper, a new method to reduce noises within chaotic signals based on ICA （independent component analysis） and EMD （empirical mode decomposition） is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the indepen- dent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions （IMFs） of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals. Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor.

HARMONIC COMPONENT EXTRACTION FROM A CHAOTIC SIGNAL BASED ON EMPIRICAL MODE DECOMPOSITION METHOD

A novel approach of signal extraction of a harmonic component fRom a chaotic signal generated by a Duffing oscillator was proposed. Based on empirical mode decomposition （EMD） and concept that any signal is composed of a series of the simple intrinsic modes, the harmonic components were extracted f^om the chaotic signals. Simulation results show the approach is satisfactory.

Health Monitoring of Long-Haul Fiber Communication System Using Chaotic OTDR

A novel chaotic optical time-domain reflectometry(OTDR)-based approach was proposed for monitoring long-haul fiber communication systems with multiple fiber segments. The self-phase modulation and group velocity dispersion effects of the optical cable was considered in demonstrating the proof-of-concept experiment and simulation. In experiments, the correlation peaks are clearly obtained from the correlation trace between the reference and reflected(or scattered) light signals propagating in three optical-fiber segments. The technique affords a high spatial resolution of 2 m, and further long-haul fiber simulations indicate that the sensing distance can be more than 3300 km. Thus, the new proposed technique can be effectively applied for health monitoring of long-haul fiber communication systems.

Filtering noisy chaotic signal via sparse representation based on random frame dictionary

The denoising problem of impure chaotic signals is addressed in this paper. A method based on sparse representation is proposed, in which the random frame dictionary is generated by a chaotic random search algorithm. The numerical simulation shows the proposed algorithm outperforms those recently reported alternative denoising methods.

Chaotic signal denoising algorithm based on sparse decomposition

Denoising of chaotic signal is a challenge work due to its wide-band and noise-like characteristics.The algorithm should make the denoised signal have a high signal to noise ratio and retain the chaotic characteristics.We propose a denoising method of chaotic signals based on sparse decomposition and K-singular value decomposition(K-SVD)optimization.The observed signal is divided into segments and decomposed sparsely.The over-complete atomic library is constructed according to the differential equation of chaotic signals.The orthogonal matching pursuit algorithm is used to search the optimal matching atom.The atoms and coefficients are further processed to obtain the globally optimal atoms and coefficients by K-SVD.The simulation results show that the denoised signals have a higher signal to noise ratio and better preserve the chaotic characteristics.

Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter

For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks （WSNs）, a new signal reconstruction algorithm based on a cubature Kalman particle filter （CKPF） is proposed in this paper. We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter （CKF） to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter （UKPF） and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF.

Denoising via truncated sparse decomposition

This paper proposes a denoising algorithm called truncated sparse decomposition （TSD） algorithm, which combines the advantage of the sparse decomposition with that of the minimum energy model truncation operation. Experimental results on two real chaotic signals show that the TSD algorithm outperforms the recently reported denoising algorithmsnon-negative sparse coding and singular value decomposition based method.

Impact of polarization mode dispersion and nonlinearities on 2-channel DWDM chaotic communication systems

This paper has designed 2-channel dense wavelength division multiplexing （DWDM） chaotic sys- tem at the frequencies of 193.1 and 193,2THz, respec- tively. The optical chaotic signals were produced by using the semiconductor laser that is numerically modeled by employing laser rate equations. These two channels were multiplexed and then propagated through single mode optical fiber （SMF） of 80kin length with dispersion compensating fiber of 16 km length. Erbium doped fiber amplifier （EDFA） was used to compensate the power losses in the SMF. In lhis paper, we investigated the effects of polarization mode dispersion （PMD） and nonlinearities especially stimulated Raman scattering （SRS） on 2 channel DWDM chaotic communication system by varying the length of the SMF and value of differential group delay （DGD）.

An approach for parameter estimation of combined CPPM and LFM radar signal

In this paper, the problem of parameter estimation of the combined radar signal adopting chaotic pulse position modulation （CPPM） and linear frequency modulation （LFM）, which can be widely used in electronic countermeasures, is addressed. An approach is proposed to estimate the initial frequency and chirp rate of the combined signal by exploiting the second-order cyclostationarity of the intra-pulse signal. In addition, under the condition of the equal pulse width, the pulse repetition interval （PRI） of the combined signal is predicted using the low-order Volterra adaptive filter. Simulations demonstrate that the proposed cyclic autocorrelation Hough transform （CHT） algorithm is theoretically tolerant to additive white Gaussian noise. When the value of signal noise to ratio （SNR） is less than 4 dB, it can still estimate the intra-pulse parameters well. When SNR = 3 dB, a good prediction of the PRI sequence can be achieved by the Volterra adaptive filter algorithm, even only 100 training samples.