Recovered Signals Research Articles

Joint Inverse Problems for Signal Reconstruction via Dictionary Splitting

Sparse signal recovery from limited and/or degraded samples is fundamental to many applications, such as medical imaging, remote sensing, astronomical and seismic imaging. Discrete wavelet transform (DWT) has been commonly used for sparse representation of signals; nevertheless, due to its shift-variant nature, pseudo-Gibbs artifacts are present in the recovered signals. Using the redundant shift-invariant wavelet transform (SWT) is the ideal solution to obtain shift invariance; however, high redundancy factor of SWT limits its application in practical settings. We propose a dictionary splitting approach for sparse recovery from incomplete data, which leverages the ideas of cycle spinning in combination with Bregman splitting. The proposed method significantly improves the conventional signal reconstruction with DWT, offers the advantages of SWT, and overcomes high redundancy factor of SWT. We solve parallel sparse recovery problems with orthogonal dictionaries (DWT and its permuted versions), while we impose consistency between the results by updating the recovered image at each iteration. Our experiments demonstrate that few shifts are sufficient to achieve reconstruction accuracy as high as recovery with SWT, and significantly reduces its computational cost and redundancy factor.

Impact of ADC parameters on linear optical sampling systems

Linear optical sampling (LOS), based on the coherent photodetection of an optical signal under test with a low repetition-rate signal originating from a pulsed local oscillator (LO), enables the characterization of the temporal electric field of optical sources. Thanks to this technique, low-speed photodetectors and analog-to-digital converters (ADCs) can be integrated in the LOS system providing a cost-effective tool for characterizing high-speed signals. However, the impact of photodetector and ADC parameters on such LOS systems has not been explored in detail so far. These parameters, including the integration time of the track-and-hold function, the effective number of bits (ENOB) of the ADC, as well as the combined limited bandwidth of the photodetector and ADC are experimentally and numerically investigated in a LOS system for the first time. More specifically, by reconstructing 10-Gbit/s non-return-to-zero on–off keying (NRZ-OOK) and 10-Gbaud NRZ-quadrature phase-shift-keying (QPSK) signals, it is shown that a short integration time provides a better recovered signal fidelity. Furthermore, an ENOB of 6 bits and an ADC bandwidth normalized to the sampling rate of 2.8 are found to be sufficient in order to reliably monitor the considered signals.

Strobe-frame sampling of subnanosecond radio pulses

Technology of high-speed digitization of subnanosecond radio pulses, which is constructed via counting frame sampling, is discussed. The basis of the circuit architecture of the frame sampler involving the high-speed logic built around hyperfine complementary metal‒oxide‒semiconductor structures, as well as the basis of the array of picosecond delay lines, is formulated. It is demonstrated that the strobe-frame-sampling technology excludes high-frequency clocking and, consequently, minimizes the circuit power consumption and ensures picosecond time resolution of a recovered signal, corresponding to a sampling rate of up to 100 G sample/s implemented in practice. The software model of the frame sampler making it possible to investigate and parametrically synthesize the circuit topology of high-speed digital devices is developed in the Simulink environment.

A Novel Blind Source Separation Algorithm using Bussgang Criterion and Natural Gradient

Objectives: In this paper, a novel algorithm based on Transform Domain Least Mean Square (TDLMS) is proposed for Blind Source Separation (BSS). The proposed algorithm is compared with several other BSS algorithms in detail and the results are discussed extensively. Methods/Statistical Analysis: To solve the problem of BSS employing the present approach, the ordinary gradient used in conventional LMS algorithm is replaced by natural gradient on the Stiefel manifold. The natural gradient is computed from a cost function based on Bussgang criterion. The proposed algorithm is compared with previously reported LMS type and Recursive Least Square (RLS) type algorithms for four different performance criteria – cross–talk error convergence, harmonic distortion in recovered signals, average deviation from orthogonality of demixing matrix and time complexity. Findings: Using simulations it is found that the proposed algorithm has best cross-talk error-convergence and least harmonic distortion as compared to other algorithms. However, the average deviation from orthogonality for demixing matrix and average simulation time for the proposed algorithm are comparable to LMS-type algorithms and estimates 35% as compared to RLS-type algorithms. Application/Improvements: The use of natural gradient and the pre-whitening process improves the performance of the algorithm. This algorithm is applied to separate signals from its mixture.Keywords: Bussgang Criterion, Bind Source Separation (BSS), Cost-Function, LMS type BSS, Natural Gradient, RLS type BSS

GVD-insensitive stable radio frequency phase dissemination for arbitrary-access loop link

We propose and experimentally demonstrate a stable radio frequency (RF) phase dissemination scheme for a long-haul optical fiber loop link based on frequency mixing. Using a single optical source in both directions of the loop link, additional timing jitter caused by group velocity dispersion (GVD) can be eliminated. Impressive scalability provided by the optical link ensures that arbitrary-access node can obtain an RF signal with a stabilized phase to meet the requirements of multiple users. In our experiment, a 2.4 GHz RF signal is distributed to arbitrary points along a 100 km fiber-optic loop link steadily. Stabilities of the recovered signals from two accessing nodes are recorded. The root-mean-square (RMS) phase jitter of the received signal at either accessing node is reduced from 1.87 rad to no more than 0.027 rad during 1800-second measuring time.

A BAYESIAN ESTIMATE OF THE CMB–LARGE-SCALE STRUCTURE CROSS-CORRELATION

ABSTRACT Evidences for late-time acceleration of the universe are provided by multiple probes, such as Type Ia supernovae, the cosmic microwave background (CMB), and large-scale structure (LSS). In this work, we focus on the integrated Sachs–Wolfe (ISW) effect, i.e., secondary CMB fluctuations generated by evolving gravitational potentials due to the transition between, e.g., the matter and dark energy (DE) dominated phases. Therefore, assuming a flat universe, DE properties can be inferred from ISW detections. We present a Bayesian approach to compute the CMB–LSS cross-correlation signal. The method is based on the estimate of the likelihood for measuring a combined set consisting of a CMB temperature and galaxy contrast maps, provided that we have some information on the statistical properties of the fluctuations affecting these maps. The likelihood is estimated by a sampling algorithm, therefore avoiding the computationally demanding techniques of direct evaluation in either pixel or harmonic space. As local tracers of the matter distribution at large scales, we used the Two Micron All Sky Survey galaxy catalog and, for the CMB temperature fluctuations, the ninth-year data release of the Wilkinson Microwave Anisotropy Probe (WMAP9). The results show a dominance of cosmic variance over the weak recovered signal, due mainly to the shallowness of the catalog used, with systematics associated with the sampling algorithm playing a secondary role as sources of uncertainty. When combined with other complementary probes, the method presented in this paper is expected to be a useful tool to late-time acceleration studies in cosmology.

Lossy Compression for Wireless Seismic Data Acquisition

In this paper, we rigorously compare compressive sampling (CS) to four state of the art, on-mote, lossy compression algorithms [ $K$ -run-length encoding (KRLE), lightweight temporal compression (LTC), wavelet quantization thresholding and run-length encoding (WQTR), and a low-pass filtered fast Fourier transform (FFT)]. Specifically, we first simulate lossy compression on two real-world seismic data sets, and we then evaluate algorithm performance using implementations on real hardware. In terms of compression ratios, recovered signal error, power consumption, on-mote execution runtime, and classification accuracy of a seismic event detection task (on decompressed signals), results show that CS performs comparable to (and in many cases better than) the other algorithms evaluated. A main benefit to users is that CS, a lightweight and nonadaptive compression technique, can guarantee a desired level of compression performance (and thus, radio usage and power consumption) without subjugating recovered signal quality. Our contribution is a novel and rigorous comparison of five state of the art, on-mote, lossy compression algorithms in simulation on real-world data sets and in implementations on hardware.

The Matrix Completion Method for Phase Retrieval from Fractional Fourier Transform Magnitudes

Inspired by the implementation of the fractional Fourier transform (FRFT) and its applications in optics, we address the problem of reconstructing a signal from its several FRFT magnitudes (or intensities). The matrix completion method is adopted here. Through numerical tests, the matrix completion method is proven effective in both noisy and noise-free situations. We also compare our method with the Gerchberg-Saxton (GS) algorithm based on FRFT. Numerical tests show that the matrix completion method gains a certain advantage in recovering uniqueness and convergence over the GS algorithm in the noise-free case. Furthermore, in terms of noisy signals, the matrix completion method performs robustly and adding more measurements can generally increase accuracy of recovered signals.

A Comparative Study of Physiological Monitoring with a Wearable Opto-Electronic Patch Sensor (OEPS) for Motion Reduction

This paper presents a comparative study in physiological monitoring between a wearable opto-electronic patch sensor (OEPS) comprising a three-axis Microelectromechanical systems (MEMs) accelerometer (3MA) and commercial devices. The study aims to effectively capture critical physiological parameters, for instance, oxygen saturation, heart rate, respiration rate and heart rate variability, as extracted from the pulsatile waveforms captured by OEPS against motion artefacts when using the commercial probe. The protocol involved 16 healthy subjects and was designed to test the features of OEPS, with emphasis on the effective reduction of motion artefacts through the utilization of a 3MA as a movement reference. The results show significant agreement between the heart rates from the reference measurements and the recovered signals. Significance of standard deviation and error of mean yield values of 2.27 and 0.65 beats per minute, respectively; and a high correlation (0.97) between the results of the commercial sensor and OEPS. T, Wilcoxon and Bland-Altman with 95% limit of agreement tests were also applied in the comparison of heart rates extracted from these sensors, yielding a mean difference (MD: 0.08). The outcome of the present work incites the prospects of OEPS on physiological monitoring during physical activities.

Market turning points forecasting using wavelet analysis

Based on the system adaptation framework we previously proposed, a frequency domain based model is developed in this paper to forecast the major turning points of stock markets. This system adaptation framework has its internal model and adaptive filter to capture the slow and fast dynamics of the market, respectively. The residue of the internal model is found to contain rich information about the market cycles. In order to extract and restore its informative frequency components, we use wavelet multi-resolution analysis with time-varying parameters to decompose this internal residue. An empirical index is then proposed based on the recovered signals to forecast the market turning points. This index is successfully applied to US, UK and China markets, where all major turning points are well forecasted.

Microwave Stepped Frequency Head Imaging Using Compressive Sensing With Limited Number of Frequency Steps

Compressive sensing (CS) can be used to recover sparse data (signal) from limited measurements by solving a constrained convex optimization problem. If this approach is applied on microwave stepped frequency imaging technique, the required number of frequency steps to get clear images can be significantly reduced resulting in simple systems with fast data acquisition and real time results. To that end, three different CS techniques are applied on head imaging systems aiming at the detection of brain injuries by utilizing the sparse characteristic of the correlated time domain scattered signals. The presented measured results using a head imaging system indicate that the time domain correlation signals are indeed sparse and thus can be recovered using a limited number of frequency steps. Those recovered signals are then used to successfully generate clear images that show brain injuries. A comparison between using the proposed and the traditional approaches using two quality metrics indicates superiority of the presented CS-based approach in not just the limited needed frequency steps, but also in the quality of the obtained images.

최소 Disturbance 기법을 적용한 향상된 CMA 적응 등화 알고리즘

This paper related with the ECMA (Enchanced CMA) algorithm performance which is possible to simultaneously compensation of the amplitude and phase by appling the minimum disturbance techniques in the CMA adatpve equalizer. The ECMA can improving the gradient noise amplification problem, stability and roburstness performance by the minimum disturbance technique that is the minimization of the equalizer tap weight variation in the point of squared euclidiean norm and the decision directed mode, and then the now cost function were proposed in order to simultaneouly compensation of amplitude and phase of the received signal with the minimum increment of computational operations. The performance of ECMA algorithm was compared to present MCMA by the computer simulation. For proving the performance, the recovered signal constellation that is the output of equalizer output signal and the residual isi and Maximum Distortion charateristic and MSE learning curve that are presents the convergence performance in the equalizer and the overall frequency transfer function of channel and equalizer were used. As a result of computer simulation, the ECMA has more better compensation capability of amplitude and phase in the recovered constellation, and the convergence time of adaptive equalization has improved compared to the MCMA.

A Bayesian approach to removal of incoherent scattering from neutron total-scattering data

A Bayesian statistics approach for subtraction of incoherent scattering from neutron total-scattering data has been developed and implemented in a public domain software package. In this approach, the estimated background signal associated with incoherent scattering maximizes the posterior probability, which combines the likelihood of this signal in reciprocal and real spaces with the prior that favors smooth lines. The probability distributions are constructed according to the principle of maximum entropy. The method enables robust subtraction of incoherent-scattering backgrounds while providing estimated uncertainties for recovered signals. The developed procedure was first tested using simulated data and then demonstrated using three representative experimental data sets, collected on bulk materials and nanoparticles, featuring distinct ratios of coherent to incoherent scattering.

Joint delay and direction of arrivals estimation in mobile communications

In this paper, we present a novel and precise way of estimating the direction and delay of arrivals in multipath environment for channel estimation purposes. Recently, super-resolution methods have been widely used for high-resolution direction of arrival (DOA) or time difference of arrival (TDOA) estimation. The proposed algorithm, called JDTDOA, is applicable to space–time channel estimation for space–time processing systems that employ hybrid DOA/TDOA technology. The estimator is based on conventional MUSIC algorithm to find the DOA and uses a standard correlator along with spline interpolation to find the TDOA of each arrival. In the interest of estimating the channel’s characteristics, each direction must be associated with its proper delay of arrival. To achieve this, we suggest a very simple and optimum beamforming by performing maximum variance distortionless response applied to each DOA found. The output at each DOA beamforming process gives the recovered signal from the relevant direction. A correlation is then made between each recovered signals which can be interpolated by cubic spline. The peak in correlation figure indicates the specific delay between the signal arrivals coming from the two considered direction.

Application of Synchronization Controlling for the Unified Chaos System in Secure Communication

The linear feedback synchronization of the unified chaos system was researched, which was applied in the secure communication based on the chaotic parameter modulation. Information signals were used to modulate some parameter of the unified chaotic system in the sending terminal, and in the receiving terminal the chaotic content was then obtained based on the chaotic synchronization signal, then the information signals recover effectively. The simulation results show that the synchronization time is very short and the recovered signals are close to the information signals and it can realize secret communication successfully, having strong security and practicability.

음영 지역을 위한 주파수 공간 블록 부호화 중계기 전송 시스템

Relay-assisted wireless communication systems have been studied widely to cope with shadow areas and extend the cell coverage. This paper proposes a space-frequency (SF) block coded single carrier-frequency division multiple access (SC-FDMA) transmission system in a relaying multi-path shadow area and present the performance comparison of SC-FDMA systems based on the signal-to-noise power ratio (SNR) between a relay and a destination station. The performance of relaying SC-FDMA systems can be improved by applying SF block code to the recovered signals of relays before re-transmitting them. The simulation result showed that the SNR performance of the proposed SF block coded relaying SC-FDMA system was approximately 5 dB better than the SNR performance of the single-path relaying SC-FDMA system at a symbol error rate (SER) of    .

음영 지역을 위한 다중 중계기 기반의 주파수 공간 블록 부호화 OFDMA 전송 시스템

음영 지역 해소 및 셀 커버리지 확대를 위하여 중계기를 활용하는 이동 통신 시스템이 널리 연구되고 있다. 본 논문에서는 중계기를 통해 다중 경로가 확보된 음영 지역에서 주파수 공간 블록 부호를 적용한 OFDMA(orthogonal frequency division multiple access) 전송 시스템을 제안한다. 다중 중계기에서 복원된 전송 신호에 대하여 주파수 공간 블록 부호를 적용하여 재전송함으로서 음영 지역 내 단말기의 수신 성능을 크게 향상시킬 수 있다. 실험을 통해, 제안된 주파수 공간 블록 부호화 OFDMA 전송 시스템의 성능이 기존의 단일 경로 OFDMA 전송 시스템의 성능보다 우수함을 확인한다. In order to cope with shadow areas and to extend cell coverage, relay-assisted wireless communication systems have been widely studied. In this paper, we propose a space-frequency(SF) block coded orthogonal frequency division multiple access(OFDMA) transmission system in a relaying multi-path shadow area. The receiving performance of mobile station in a shadow area can be improved by applying SF block code to the recovered signals of multiple relays before re-transmitting them. The simulation result shows that the proposed SF block coded OFDMA transmission system considerably outperforms the conventional single-path OFDMA transmission system.

Dreamlet-based interpolation using POCS method

Due to incomplete and non-uniform coverage of the acquisition system and dead traces, real seismic data always has some missing traces which affect the performance of a multi-channel algorithm, such as Surface-Related Multiple Elimination (SRME), imaging and inversion. Therefore, it is necessary to interpolate seismic data. Dreamlet transform has been successfully used in the modeling of seismic wave propagation and imaging, and this paper explains the application of dreamlet transform to seismic data interpolation. In order to avoid spatial aliasing in transform domain thus getting arbitrary under-sampling rate, improved Jittered under-sampling strategy is proposed to better control the dataset. With L0 constraint and Projection Onto Convex Sets (POCS) method, performances of dreamlet-based and curvelet-based interpolation are compared in terms of recovered signal to noise ratio (SNR) and convergence rate. Tests on synthetic and real cases demonstrate that dreamlet transform has superior performance to curvelet transform.

일반화된 공간천이변조 시스템에서 압축센싱기술을 이용한 수신신호 복호 알고리즘

In this paper, we propose a signal detection technique based on the parallel orthogonal matching pursuit (POMP) is proposed for generalized shift space keying (GSSK) systems, which is a modified version of the orthogonal matching pursuit (OMP) that is widely used as a greedy algorithm for sparse signal recovery. The signal recovery problem in the GSSK systems is similar to that in the compressed sensing (CS). In the proposed POMP technique, multiple indexes which have the maximum correlation between the received signal and the channel matrix are selected at the first iteration, while a single index is selected in the OMP algorithm. Finally, the index yielding the minimum residual between the received signal and the M recovered signals is selected as an estimate of the original transmitted signal. POMP with Quantization (POMP-Q) is also proposed, which combines the POMP technique with the signal quantization at each iteration. The proposed POMP technique induces the computational complexity M times, compared with the OMP, but the performance of the signal recovery significantly outperform the conventional OMP algorithm.

A Fast Reweighted Alternating Direction Method for Wideband Spectrum Sensing

In this paper, we propose a fast algorithm based on Reweighted Alternating Direction Method of Multiplier (R-ADMM) for cooperative compressive wideband spectrum sensing. The R-ADMM alternately updates the recovered signal matrix, the Lagrangian multiplier and the residue, and all update rules only involve matrix or vector multiplications and summations. To seek joint sparse solutions in a fully distributed scheme, multiple cognitive users collaborate during the sensing period by enforcing consensus among local spectral estimates. Meanwhile, adding weight in the target term and suppressing non-zero elements with large weights for getting the solution close to minimumnorm. The experimental results show that detection probability and detection speed of the algorithm has been improved under low SNR.