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• The proposed method improves SR and measurement accuracy simultaneously. • SVM model is adopted to capture the main peak of power cumulative average data. • Noise is linearly fitted and removed from the main peak data to improve accuracy. • The Brillouin frequency shift of Double-peak Spectrum is precisely determined. • The precision of edge detection in the transition section is improved to 0.1-m. The Brillouin scattering signal is weak and the signal-to-noise ratio (SNR) is low in Brillouin optical time domain reflectometer (BOTDR), these result in a difficulty to improve the spatial resolution (SR) and measurement accuracy at the same time. In this paper, the denoising principle of power cumulative average (PCA) is analyzed theoretically, then an improved Lorentzian-curve fitting (ILCF) algorithm based on PCA data is proposed, in which the support vector machine (SVM) model is adopted to capture the main peak, and the Brillouin frequency shift (BFS) of double-peak spectrum is determined precisely. Furtherly, by adjusting the step of the sliding window, the precision of edge detection in the transition section is improved to 0.1-m. For the fiber under test (FUT) with a length of 12-km, 500 groups of time-domain data are processed under the probe pulse with 50 ns width and 1 GHz sampling rate. The average error of temperature accesses to 0.8 ° C and the SR achieves 6.4-m simultaneously. The algorithm in this paper can reduce BFS standard deviation from 28.21 MHz to 1.27 MHz, improve the SR to the maximum limitation by the probe pulse, determine the BFS of double-peak spectrum and reduce the measurement time. Therefore, the proposed method can improve SR and measurement accuracy at the same time. [ABSTRACT FROM AUTHOR]

• A new compounding imaging algorithm integrating amplitude and phase information of signal based on Lamb wave phased array is proposed. • Sigmoid function with variable gradient is used for establishing the nonlinear mapping between imaging index and phase difference. • The proposed imaging algorithm is validated on plates of different materials and dimensions with multiple types of defects. • The proposed imaging algorithm can effectively improve the signal-to-noise ratio of the images and suppress artifacts, implementing imaging without threshold. Aimed at improving the imaging performance of Lamb wave phased array based on Delay-and-Sum (DAS) of signal amplitude, a compounding imaging algorithm integrating amplitude and phase information of signal is proposed in this paper. By applying back propagation dispersion compensation, the pulse echoes caused by defects will be in-phase with the excitation signal, implementing signal focusing. In the proposed imaging algorithm, the amplitude imaging index is calculated by classical total focusing method (TFM) and the phase imaging index is calculated by phase coherence factor (PCF), which establishes the nonlinear mapping between imaging index and phase difference by sigmoid function with variable gradient. The final imaging results are the compound of two imaging indexes. The experimental validation is carried out on plates of different materials and dimensions with multiple types of defects based on uniform linear array. Compared with the imaging results of conventional TFM, the proposed imaging algorithm can effectively improve the signal-to-noise ratio (SNR) of the images and suppress artifacts, implementing threshold-free imaging. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we propose a new method for utilising phase information by complementing it with traditional magnitude-only spectral subtraction speech enhancement through complex spectrum subtraction (CSS). The proposed approach has the following advantages over traditional magnitude-only spectral subtraction: (a) it introduces complementary information to the enhancement algorithm; (b) it reduces the total number of algorithmic parameters; and (c) is designed for improving clean speech magnitude spectra and is therefore suitable for both automatic speech recognition (ASR) and speech perception applications. Oracle-based ASR experiments verify this approach, showing an average of 20% relative word accuracy improvements when accurate estimates of the phase spectrum are available. Based on sinusoidal analysis and assuming stationarity between observations (which is shown to be better approximated as the frame rate is increased), this paper also proposes a novel method for acquiring the phase information called Phase Estimation via Delay Projection (PEDEP). Further oracle ASR experiments validate the potential for the proposed PEDEP technique in ideal conditions. Realistic implementation of CSS with PEDEP shows performance comparable to state of the art spectral subtraction techniques in a range of 15–20dB signal-to-noise ratio environments. These results clearly demonstrate the potential for using phase spectra in spectral subtractive enhancement applications, and at the same time highlight the need for deriving more accurate phase estimates in a wider range of noise conditions. [Copyright &y& Elsevier]

Abstract: This paper works on joint approximate diagonalization of simplified fourth order cumulant matrices for very fast and large scale blind separation of instantaneous mixing model sources. The JADE algorithm is widely accepted but only limited to small scale separation tasks. The SHIBBS algorithm calculates a fraction of the fourth order cumulant set and avoids eigenmatrix decomposition to reduce calculation cost. However, it was seen to be slower than JADE at the time of its first publication and is hence less known. On the other hand, the SJAD algorithm using the same approach is shown to be very fast. This paper studies the iteration convergence criterion and proposes to use a signal to noise ratio based iteration stopping threshold approach. The improved SHIBBS/SJAD algorithm is very fast, and capable of large scale separation. Experimental separation comparisons between the SHIBBS/SJAD and FastICA are presented. [Copyright &y& Elsevier]

Motivated by the observation that most methods for accelerating the generalized Lloyd algorithm (GLA) normally lack the capability to improve the quality of its end result and that most methods for improving the quality of the end result of GLA usually lack the capability to speed it up, an efficient and effective method is presented in this paper to enhance the performance of GLA and its variants, in terms of both the computation time and the quality of the end result, by leveraging the strengths of several reduction methods and the multiple stage mechanism. Simulation results show that the proposed method can efficiently and effectively reduce the computation time of GLA by up to about 93% while at the same time improving its quality by up to about 1 dB in terms of the peak-signal-to-noise-ratio in most cases. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to find algorithms accompanied by explicit error bounds for phase retrieval from noisy magnitudes of frame coefficients when the underlying frame has a low redundancy. We achieve these goals with frames consisting of N = 6 d − 3 vectors spanning a d -dimensional complex Hilbert space. The two algorithms we use, phase propagation or the kernel method, are polynomial time in the dimension d . To ensure a successful approximate recovery, we assume that the noise is sufficiently small compared to the squared norm of the vector to be recovered. In this regime, we derive an explicit error bound that is inverse proportional to the signal-to-noise ratio, with a constant of proportionality that depends only on the dimension d . Properties of the reproducing kernel space of complex polynomials and of trigonometric polynomials are central in our error estimates. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for speckle noise removal. We propose a nonlocal low-rank regularization (NLR) approach toward exploiting structured sparsity and explore its application into speckle noise removal. A nonconvex surrogate functions for the rank instead of the convex nuclear norm is proposed. To further improve the computational efficiency of the proposed algorithm, we have developed a fast implementation using augmented Lagrange multiplier (ALM) method. We experimentally demonstrate the excellent performance of the technique, in terms of both Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM). [ABSTRACT FROM AUTHOR]

Reversible data hiding (RDH) algorithms allow data protection and exact recovery of the original cover image upon data extraction. Most of RDH algorithms attempt to embed data while maintaining high peak signal-to-noise ratio. However, it has been deemed recently that some applications may demand improving the image contrast while embedding data. Additionally, stego images with better contrast could be less attractive to attacker, given the fact they have no idea about the original cover image. In this paper, we propose a sharpening-aware RDH (SARDH) algorithm that is capable of embedding significant amount of data in addition to sharpening the image. Experimental results proved the ability of SARDH algorithm in embedding large payloads and providing sharper stego images. [ABSTRACT FROM AUTHOR]

The total variation (TV) model has been considered to be one of the most successful and representative model for compressive sensing (CS) sparse image reconstruction due to its advantage of preserving image edges. However, TV regularized term often favors piecewise constant solution and therefore it fails to preserve the details and textures. To overcome this defect and reconstruct the fine details, this paper proposes a two-dimensional CS sparse image reconstruction model by introducing the fractional-order TV (FrTV) regularization constraint into CS optimization problem. Furthermore, in order to achieve sparser representation flexibly, a combination of discrete wavelet transform and curvelet transform ℓ 1 -norm regularization is also incorporated into the cost function and a method for estimating the regularization parameter that trades off the two terms in the cost function is proposed. By using a smooth approximation of the ℓ 1 -norm, a gradient projection algorithm is derived to solve the combined FrTV and sparsifying transforms constrained minimization problem effectively. Compared with several state-of-the-art reconstruction algorithms, the proposed algorithm is more efficient and robust, not only yielding higher peak-signal-to-noise ratio, but also reconstructing the fine details and textures more efficiently. [ABSTRACT FROM AUTHOR]

The amino acid sequence of a protein determines its chemical properties, chain conformation and biological functions. Protein sequence comparison is of great importance to identify similarities of protein structures and infer their functions. Many properties of a protein correspond to the low-frequency signals within the sequence. Low frequency modes in protein sequences are linked to the secondary structures, membrane protein types, and sub-cellular localizations of the proteins. In this paper, we present Ramanujan Fourier transform (RFT) with a fast algorithm to analyze the low-frequency signals of protein sequences. The RFT method is applied to similarity analysis of protein sequences with the Resonant Recognition Model (RRM). The results show that the proposed fast RFT method on protein comparison is more efficient than commonly used discrete Fourier transform (DFT). RFT can detect common frequencies as significant feature for specific protein families, and the RFT spectrum heat-map of protein sequences demonstrates the information conservation in the sequence comparison. The proposed method offers a new tool for pattern recognition, feature extraction and structural analysis on protein sequences. [ABSTRACT FROM AUTHOR]

• BFS extraction technology based on K Nearest Neighbor Algorithm provides a new research idea for BOTDA to extract BFS in low SNRs. • BFS extraction accuracy decreases with the decrease of SNR and the increase of frequency step. • The uncertainty of BFS extraction by KNN can be 0.44 with frequency scanning step of 1MHz and SNR of 11dB. The extraction of Brillouin frequency shifts from a Brillouin gain spectrum is a common problem in the engineering applications of Brillouin distributed fiber sensors. In this paper, extraction of Brillouin frequency shifts from Brillouin gain spectrum in Brillouin distributed fiber sensors using K nearest neighbor algorithm is proposed and experimentally demonstrated. The Brillouin frequency shift extraction is treated as a model-free supervised classification problem. The influence of Signal-to-noise ratio and frequency scanning step is studied. The performance of Brillouin frequency shift extraction degrades with the increase of frequency scanning step, and with the decrease of signal-to-noise ratio. The result shows that the algorithm of K nearest neighbor algorithm is a better way to extract the Brillouin frequency shift from Brillouin gain spectrum, comparing with curve-fitting methods, the uncertainty can be 0.44 with frequency scanning step of 1 MHz and SNR of 11 dB. [ABSTRACT FROM AUTHOR]

• A novel two-stage sparse algorithm is proposed to localize and characterize the local nonlinearities in structures. • The Duhamel's integral is adopted to avoid performing the second-order derivative in the identification process. • A nonparametric de-noising method is developed to reduce the effect of noise. • The proposed algorithm for identification of local nonlinearities can be used to localize and characterize local damages in structures. This paper considers the localization and characterization of nonlinear components in local nonlinear structures with noisy measurements, and a novel two-stage sparse identification method is proposed. In this algorithm, one set of candidate nonlinear basis functions are leveraged to characterize system nonlinearities. To effectively localize and characterize nonlinear components, the algorithm introduces two sparse stages. At the first sparse stage, the adaptive group Lasso is adopted to localize nonlinear components in structures. At the second sparse stage, the adaptive Lasso is used to further characterize the contributing nonlinear basis functions in the identified nonlinear positions. Since the first sparse stage can greatly decrease the number of candidate nonlinear basis functions, the contributing functions can be effectively selected and characterized at the second sparse stage. Compared with the one-stage sparse algorithms, the proposed algorithm is more robust to noise, i.e., the new method can correctly identify the local nonlinear structures with a lower Signal to Noise Ratio (SNR). In addition, many existed nonlinear identification methods, simultaneously require the synchronous data of vibration displacements, velocities and accelerations. If only vibration displacements are measured, the first and second order derivatives of displacement are conducted to estimate velocities and accelerations, respectively. Nevertheless, the noise mixed into the displacement measurement will be severely amplified in the process of derivative calculation, which will make the estimation of velocities and accelerations deviating from the actual value and reduce the identification accuracy. To avoid performing the second order derivative, the Duhamel's integral is adopted to rewrite the dynamic relationship between the system input and output. In addition, a non-parametric de-noising method based on reproducing kernel Hilbert space (RKHS) is further developed to reduce the effect of noise in vibration displacements and obtain vibration velocities, which can effectively reduce the effect of noise and improve the identification accuracy. The numerical and experimental studies verify the potential and effectiveness of the proposed algorithm in localizing and characterizing nonlinear components in local nonlinear structures. [ABSTRACT FROM AUTHOR]

The deployment of systems experiencing high-rate dynamic events, such as hypersonic vehicles, advanced weaponry, and active blast mitigation systems, require high-rate structural health monitoring (HRSHM) capabilities in the sub-millisecond realm to ensure continuous operations and safety. However, the development of high-rate feedback systems is a complex task because these dynamic systems are uniquely characterized by (1) large uncertainties in their external loads, (2) high levels of non-stationarity and heavy disturbance, and (3) unmodeled dynamics from changes in system configuration. In this paper, we present a deep learning algorithm specifically engineered for HRSHM applications. It consists of an ensemble of recurrent neural networks (RNNs) constructed with long short-term memory (LSTM) cells with transfer learning capabilities to cope with the highly limited availability of training data as it is typical for high-rate systems. The use of an ensemble of RNNs empowers multi-rate sampling capability to capture multi-temporal features of the time series, thus enabling modeling of non-stationarities. Also, because the RNNs use short-sequence LSTMs and are arranged in parallel, the computation time is substantially reduced to the sub-millisecond range. The performance of the algorithm is investigated on experimental high-rate dynamic data produced from accelerated drop tower tests and benchmarked against results from a prior investigation using a purely on-the-edge algorithm termed variable input space observer (VIO) and its hybrid architecture (hybrid VIO) that incorporated physical knowledge and is considered as an upper bound on the algorithm performance. Numerical results show that the ensemble of RNNs, composed of five RNNs, significantly outperformed the VIO, with performance close to that of the hybrid VIO when it comes to the estimation error metrics, with an average computation time of 25 μ s per prediction step. Yet, the ensemble of RNNs exhibits chattering for low-amplitude excitations, likely attributable to the behavior of RNNs sampling at small time delays. An examination of the RNN weights and hidden states confirms that the algorithm can capture multi-temporal features, and an investigation with respect to noise in the training dataset showed that the algorithm is robust for signal-to-noise ratios up to 20 dB. • Machine learning-based architecture for non-stationary time-series analysis. • Transfer learning for accelerated training with limited data. • Ensemble-based system estimation using parallel recurrent neural networks integrated with attention mechanism is proposed. • Sib-millisecond estimation of a high-rate system is achieved though the proposed architecture. [ABSTRACT FROM AUTHOR]

• This manuscript applies the WNNM algorithm to the ROTDR system for the first time. • Compared with the WD algorithm, the WNNM algorithm can reduce the data fluctuation after denoising. • Compared with the NLM algorithm, WNNM can reduce the impact of data similarity on the denoising results. • In addition, the WNNM algorithm does not increase the hardware cost of the system. Raman Optical Time Domain Reflectometer (ROTDR) is widely used in industrial fields because of its distributed temperature measurement characteristics. Temperature accuracy is an important parameter of the ROTDR system, and there are some ways including wavelet denoising and nonlocal mean to improve the temperature accuracy of the system. However, these methods either increase the cost of the system or suffer from the limitations of the method itself. In this paper, we propose and experimentally demonstrate a weighted nuclear norm minimization (WNNM) algorithm for temperature accuracy enhancement. WNNM combines singular value decomposition and adaptive threshold to improve the signal-to-noise ratio (SNR) while avoiding data loss, better preserving the information from the original data, and being less sensitive to data similarity. In addition, the WNNM algorithm does not increase the hardware cost of the system. The experimental results show that the WNNM algorithm can achieve better temperature accuracy in the case of low sampling rates and high SNR. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this paper is to propose a vibration-based automated framework dealing with local faults occurring on bearings in the transmission of a helicopter. The knowledge of the shaft speed and kinematic computation provide theoretical frequencies that reveal deteriorations on the inner and outer races, on the rolling elements or on the cage. In practice, the theoretical frequencies of bearing faults may be shifted. They may also be masked by parasitical frequencies because the numerous noisy vibrations and the complexity of the transmission mechanics make the signal spectrum very profuse. Consequently, detection methods based on the monitoring of the theoretical frequencies may lead to wrong decisions. In order to deal with this drawback, we propose to readjust the fault frequencies from the theoretical frequencies using the redundancy introduced by the harmonics. The proposed method provides the confidence index of the readjusted frequency. Minor variations in shaft speed may induce random jitters. The change of the contact surface or of the transmission path brings also a random component in amplitude and phase. These random components in the signal destroy spectral localization of frequencies and thus hide the fault occurrence in the spectrum. Under the hypothesis that these random signals can be modeled as cyclostationary signals, the envelope spectrum can reveal that hidden patterns. In order to provide an indicator estimating fault severity, statistics are proposed. They make the hypothesis that the harmonics at the readjusted frequency are corrupted with an additive normally distributed noise. In this case, the statistics computed from the spectra are chi-square distributed and a signal-to-noise indicator is proposed. The algorithms are then tested with data from two test benches and from flight conditions. The bearing type and the radial load are the main differences between the experiences on the benches. The fault is mainly visible in the spectrum for the radially constrained bearing and only visible in the envelope spectrum for the “load-free” bearing. Concerning results in flight conditions, frequency readjustment demonstrates good performances when applied on the spectrum, showing that a fully automated bearing decision procedure is applicable for operational helicopter monitoring. [Copyright &y& Elsevier]

Abstract: In this paper, consistent multiple-input frequency-domain estimators will be presented based on a right matrix-fraction description of the frequency response function. This matrix-fraction description leads to a fast algorithm in the same way as the least-squares complex frequency-domain estimator (LSCF). The use of multiple inputs simultaneously in the estimation (so-called polyreference estimation) has the advantage that it allows to separate closely spaced modes. The main drawback of the LSCF estimator is that it is theoretically inconsistent, i.e.the estimates do not converge to the true values when the number of measurements increases to infinity. However, the noise information available from most measurements can be used to construct a maximum likelihood-like weighting for the LSCF estimators, giving consistent estimates. The results are fast, polyreference, and consistent weighted generalised total-least-squares (WGTLS) estimators. The iterative quadratic maximum likelihood (IQML) estimator is practically consistent for high signal-to-noise ratios, with the additional advantage that it yields clear stabilisation charts. The performance of the presented WGTLS and IQML estimators is evaluated by means of ground vibration test data and demonstrated on flight flutter test data. [Copyright &y& Elsevier]

Abstract: Traditional single particle reconstruction methods use either the Fourier or the delta function basis to represent the particle density map. This paper proposes a more flexible algorithm that adaptively chooses the basis based on the data. Because the basis adapts to the data, the reconstruction resolution and signal-to-noise ratio (SNR) is improved compared to a reconstruction with a fixed basis. Moreover, the algorithm automatically masks the particle, thereby separating it from the background. This eliminates the need for ad hoc filtering or masking in the refinement loop. The algorithm is formulated in a Bayesian maximum-a-posteriori framework and uses an efficient optimization algorithm for the maximization. Evaluations using simulated and actual cryogenic electron microscopy data show resolution and SNR improvements as well as the effective masking of particle from background. [Copyright &y& Elsevier]

Abstract: Modal strain energy has been widely utilized as a damage-sensitive feature to detect structural damage. Based on the closed-form of element modal strain energy sensitivity, a statistic structural damage detection algorithm is proposed in this paper. The method is oriented to the ambient vibration measurements where only the operational mode shapes are available. The established element damage equations are incorporated with the modal scaling factors so that the damage and modal scaling factor can be solved simultaneously. The statistic analysis is implemented to take into account the uncertainties existing in the structural model and measured structural modal parameters. The sensitivity formulas of system matrix and residual vector with respect to noise are derived. The proposed statistics based element damage equations have a compact algebraic form, which does not require a large computation time and a high storage capacity. The method is verified by a numerical simply supported beam with different assumed damage. The effects of noise and damage levels on damage detection are investigated. It is demonstrated that the proposed method can identify most likely damaged elements. [Copyright &y& Elsevier]

Abstract: Previous research based on theoretical simulations has shown the potential of the wavelet transform to detect damage in a beam by analysing the time-deflection response due to a constant moving load. However, its application to identify damage from the response of a bridge to a vehicle raises a number of questions. Firstly, it may be difficult to record the difference in the deflection signal between a healthy and a slightly damaged structure to the required level of accuracy and high scanning frequencies in the field. Secondly, the bridge is going to have a road profile and it will be loaded by a sprung vehicle and time-varying forces rather than a constant load. Therefore, an algorithm based on a plot of wavelet coefficients versus time to detect damage (a singularity in the plot) appears to be very sensitive to noise. This paper addresses these questions by: (a) using the acceleration signal, instead of the deflection signal, (b) employing a vehicle–bridge finite element interaction model, and (c) developing a novel wavelet-based approach using wavelet energy content at each bridge section, which proves to be more sensitive to damage than a wavelet coefficient line plot at a given scale as employed by others. [Copyright &y& Elsevier]

Abstract: In a transparent wavelength routed optical network, physical impairments degrade optical signal to noise ratio (OSNR) of a transmitted signal. Therefore, the quality of a lightpath must be checked during connection setup procedure. Since a lightpath setup may inject crosstalk on all previously established lightpaths with which it shares links or nodes, OSNR for those lightpaths should be checked as well as the OSNR of the tentative lightpath. On the other hand, by increasing the number of hops for a lightpath, the amount of OSNR degradation increases due to crosstalk. Therefore, long paths are subject to high quality of transmission (QoT) degradation due to crosstalk. In this paper, we enhance OSNR degradation for different lightpaths with different hop numbers in such a way that OSNR degradation for long-hop lightpaths becomes closer to short-hop lightpaths, i.e., a fair OSNR degradation. We provide a mechanism called Crosstalk Risk Estimation (CRE) by which we can find the nodes with positive crosstalk risk (i.e., the node in which generated crosstalk noise violates its crosstalk tolerance bound). Then, after establishing a lightpath, a rerouting mechanism is employed to reduce crosstalk risks of these nodes by migrating some of the lightpaths that pass through them. Actually, each node that is positive crosstalk risk decides to reroute some lightpaths traversing from itself. This algorithm tries to minimize the impact of establishing a new lightpath on the already established lightpaths. Our performance evaluations show that the CRE algorithm can provide better QoT and network access fairness because it can reduce the crosstalk risk and successfully mitigate crosstalk effects. [Copyright &y& Elsevier]

Abstract: Two-dimensional analysis of projections of single-particles acquired by an electron microscope is a useful tool to help identifying the different kinds of projections present in a dataset and their different projection directions. Such analysis is also useful to distinguish between different kinds of particles or different particle conformations. In this paper we introduce a new algorithm for performing two-dimensional multireference alignment and classification that is based on a Hierarchical clustering approach using correntropy (instead of the more traditional correlation) and a modified criterion for the definition of the clusters specially suited for cases in which the Signal-to-Noise Ratio of the differences between classes is low. We show that our algorithm offers an improved sensitivity over current methods in use for distinguishing between different projection orientations and different particle conformations. This algorithm is publicly available through the software package Xmipp. [Copyright &y& Elsevier]

Abstract: Automatic alignment (registration) of 3D images of adult fruit fly brains is often influenced by the significant displacement of the relative locations of the two optic lobes (OLs) and the center brain (CB). In one of our ongoing efforts to produce a better image alignment pipeline of adult fruit fly brains, we consider separating CB and OLs and align them independently. This paper reports our automatic method to segregate CB and OLs, in particular under conditions where the signal to noise ratio (SNR) is low, the variation of the image intensity is big, and the relative displacement of OLs and CB is substantial. We design an algorithm to find a minimum-cost 3D surface in a 3D image stack to best separate an OL (of one side, either left or right) from CB. This surface is defined as an aggregation of the respective minimum-cost curves detected in each individual 2D image slice. Each curve is defined by a list of control points that best segregate OL and CB. To obtain the locations of these control points, we derive an energy function that includes an image energy term defined by local pixel intensities and two internal energy terms that constrain the curve’s smoothness and length. Gradient descent method is used to optimize this energy function. To improve both the speed and robustness of the method, for each stack, the locations of optimized control points in a slice are taken as the initialization prior for the next slice. We have tested this approach on simulated and real 3D fly brain image stacks and demonstrated that this method can reasonably segregate OLs from CBs despite the aforementioned difficulties. [Copyright &y& Elsevier]

Abstract: A wireless ad hoc network is often composed of a set of n wireless devices distributed in a two-dimensional domain. For each wireless device (also called node) , there is a transmission region within which signal-to-noise-ratio (SNR) is at least a threshold so that the signal transmitted by u can be correctly received by other nodes with high probability. The transmission region is often modeled as a disk centered at the node u. In addition, for each node u, there is an interference region within which the transmission from u makes the signal-to-interference-and-noise-ratio (SINR) of the legitimate receiver smaller than the threshold so that the legitimate receiver cannot correctly receive the message from the legitimate transmitter. In this paper, we first present new graph models to model the communication graphs and the interference graphs defined by wireless ad hoc networks with attention to interference-free channel assignment or scheduling. Then we propose some simple approximation algorithms and/or PTASs (polynomial time approximation scheme) to approximate several classical graph problems such as maximum independent set, minimum vertex cover and minimum vertex coloring in these graph models. In addition, we also discuss various possible applications for these simple approximation algorithms and/or PTASs in wireless ad hoc networks. [Copyright &y& Elsevier]

• Theory and derivation for ID uncertainty of close modes under asymptotic conditions. • Applicable for long data, high S/N ratio, small damping and wide usable band. • Derivation is tedious but results reveal remarkable mathematical beauty. This work takes up the challenge of deriving the 'uncertainty law' for close modes, i.e., closed form analytical expressions for the remaining uncertainty of modal parameters identified using (output-only) ambient vibration data. In principle the uncertainty law can be obtained from the inverse of the Fisher information matrix of modal parameters. The key mathematical challenges stem from analytical treatment of entangled stochastic dynamics with a large number of modal parameters of different nature and the quest for closed form expressions for identification uncertainty, whose possibility is questionable. Fortunately the problem still admits insightful closed form solution under long data, high signal-to-noise ratio and wide resonance band for identification. Up to modelling assumptions and the use of probability, the uncertainty law dictates the achievable precision of modal properties regardless of the identification algorithm used. A companion paper discusses the insights, verification, scientific implications and recommendation for ambient test planning. [ABSTRACT FROM AUTHOR]

• The PC-SCMA technology can enhance the performance of communication system. • JIDD scheme can make full use of the intermediate messages to improve performance. • S-JIDD can effectively reduce complexity and achieve ideal error performance. • Adding CRC early stopping criterion can further reduce the BER of the system. Polar coding and sparse code multiple access (SCMA) technique are excellent candidates for 5G, and have a promising prospect in optical communication, the joint design of polar coded sparse code multiple access (PC-SCMA) system is important to enhance the overall system performance. In this paper, a serial joint iterative detection and decoding scheme is proposed by passing message between serial max-log message passing algorithm and soft cancellation (SCAN) algorithm, the strategy can accelerate the algorithm convergence speed by reducing the number of required iterations, decrease the computational complexity and achieve ideal bit error rate (BER) performance. In order to further improve the BER performance, a cyclic redundancy check (CRC) is added in the joint iterative process as an early stopping criterion to avoid decoding divergence caused by a low signal to noise ratio (SNR). Simulation results demonstrate that the addition of a termination mechanism can obtain an excellent performance gain compares with the conventional JIDD algorithm. [ABSTRACT FROM AUTHOR]