Data Projection Method Research Articles

Real-time notch filtering based on the modified subspace-based high-resolution frequency estimator

This paper presents the real-time notch filtering based on a modified subspace-based high-resolution frequency estimator. The modified subspace-based frequency estimator is proposed based on the propagator using total least squares (TLS) to enhance the robustness to noise, which can be extended to the recursive implementation using data projection method (DPM). In addition, a subspace-based amplitude estimation method is proposed based on the available frequency estimates. For notch filtering, a second-order infinite impulse response (IIR) notch filter is directly designed in the discrete-time domain with given notch frequency and notch bandwidth. The performance of the proposed method is validated through numerical simulations and a real-world example.

Detection of deltamethrin remains in mint with an electronic device coupled to chemometric methods

This article describes the possibility of an electronic device coupled with chemometric methods to detect and discriminate between mint treated with an insecticide containing deltamethrin and the untreated mint. A multisensor system is designed and realized mainly by a commercial metal oxide (MOS) gas sensors array, a data acquisition board, and a personal computer coupled with chemometric methods to achieve the objective. In each experiment, data were collected for 510 s using the multi-sensor system. Then, the principal component analysis (PCA) statistical data projection method and the support vector machine (SVM) machine learning method were exploited to prove the ability of our laboratory prototype to differentiate untreated mint from deltamethrin mint treated. The data projection with principal component analysis algorithm indicates that this method can classify the data with 98% of the variance by the first three main components (PC1, PC2, and PC3) with remarkable separation between mint groups while that the machine support vector (SVM) method was able to distinguish samples with a success rate of 95%. As such, this work offers the ability to identify the mint treated from untreated one using a simple, fast, and inexpensive multi-sensor system.

The confounding effect of multi-type human papillomavirus infections on type-specific natural history parameter identification

The natural history of human papillomavirus (HPV) from infection to cervical cancer differs between HPV types. Accordingly, type-specific natural history parameters are crucial for the mathematical models used to optimize the nearly life-long series of disease prevention measures. These parameters are estimated from genotyped data from trials and population level screening programs, typically one type at a time, which requires projecting the multiple-type data to the single type. To analyze impacts of such projection methods on the estimates, we compared estimating one type at a time using different projection methods with estimating all types together. We simulated genotyped data with chosen parameter values for two HPV types and analyzed the identifiability of the chosen values using the different estimation methods. We found the success of estimating one type at a time to be excessively sensitive to the data projection method, with potential to falsely identify the parameters at wrong values. Estimating all types together identified the parameters well. Our results were consistent both when trial and population level data were used. In conclusion, the potential confounding by multi-type infections has to be considered when choosing an estimation method for type-specific natural history parameters.

Multivariate variable selection by means of null-beamforming

This article aims to use beamforming, a covariate-assisted data projection method to solve the problem of variable selection for multivariate random-effects regression models. The new approach attempts to explore the covariance structure in the data with a small number of random-effects covariates. The basic premise behind the proposal is to scan through a covariate space with a series of forward filters named null-beamformers; each is tailored to a particular covariate in the space and resistant to interference effects originating from other covariates. Applying the proposed method to simulated and real multivariate regression data, we show that it can substantially outperform the existing methods of multivariate variable selection in terms of sensitivity and specificity. A theory on selection consistency is established under certain regularity conditions.

Efficient Channel Estimation in Massive MIMO Partially Centralized Cloud-Radio Access Network Systems

This article investigates channel estimation problem in massive MIMO partially centralized cloud-RAN (MPC-RAN). The channel estimation was realized through compressed data method to minimize the huge pilot overhead, then combined with parallel Givens data projection method (PGDPM) to form a semi-blind estimator. Comparison and analysis of improved minimum mean square error (MMSE), fast data projection method (FDPM), compressed data, and PGDPM techniques was evaluated for achievable normalized mean square error (NMSE) in MPC-RAN. The PGDPM-based estimator had the lowest normalized mean square error. The FDPM and PGDPM based methods are comparable in performance with PGDPM based estimator having a slight edge over FDPM-based estimator. This vindicates PGDPM-based estimator as a method to be utilized in channel estimation since it compresses the massive MIMO channel information, hence mitigating the fronthaul finite capacity problem, and at the same time, it is geared towards efficient parallelization for optimal BBU resource utilization.

Actuator fault tolerant control of disturbed switched hybrid systems

The present work treats the problem of actuator fault tolerant control (FTC) for disturbed switched hybrid system. By extending the data-projection method (EDPM), the fault signal is estimated fast and properly. Then, using the unknown input observers, the unavailable state is estimated. Based on the estimated state and fault estimation information the fault tolerant control is performed to compensate the fault and to guarantee the stability of the system. In fact, the proposed FTC is designed by combined an additive control computed from the EDPM to the stabilising control. The optimal gain matrices of the stabilising control are computed using the linear matrix inequalities (LMI) technique in order to ensure the stability of the system and to correct the effect of the fault correctly. The effectiveness of the proposed method was shown by a simulation example.

Robust Transceiver Design Based on Interference Alignment for Multi-User Multi-Cell MIMO Networks With Channel Uncertainty

In this paper, we first exploit the inter-user interference and inter-cell interference as useful references to develop a robust transceiver design based on interference alignment for a downlink multi-user multi-cell multiple-input–multiple-output interference network under channel estimation error. At transmitters, we propose a two-tier transmit beamforming strategy, and we first achieve the inner beamforming direction and allocated power by minimizing the interference leakage as well as maximizing the system energy efficiency, respectively. Then, for the outer beamformer design, we develop an efficient conjugate gradient Grassmann manifold subspace tracking algorithm to minimize the distances between the subspace spanned by interference and the interference subspace in the time-varying channel. At receivers, we propose a practical interference alignment based on the fast and robust Fast Data Projection method subspace tracking algorithm to achieve the receive beamformer under channel uncertainty. Numerical results show that our proposed robust transceiver design achieves better performance compared with some existing methods in terms of the sum rate and the energy efficiency.

Switching time estimation and active mode recognition using a data projection method

Abstract This paper proposes a data projection method (DPM) to detect a mode switching and recognize the current mode in a switching system. The main feature of this method is that the precise knowledge of the system model, i.e., the parameter values, is not needed. One direct application of this technique is fault detection and identification (FDI) when a fault produces a change in the system dynamics. Mode detection and recognition correspond to fault detection and identification, and switching time estimation to fault occurrence time estimation. The general principle of the DPM is to generate mode indicators, namely, residuals, using matrix projection techniques, where matrices are composed of input and output measured data. The DPM is presented in detail, and properties of switching detectability (fault detectability) and discernability between modes (fault identifiability) are characterized and discussed. The great advantage of this method, compared with other techniques in the literature, is that it does not need the model parameter values and thus can be applied to systems of the same type without identifying their parameters. This is particularly interesting in the design of generic embedded fault diagnosis algorithms.

A technique for classification and decomposition of muscle signal for control of myoelectric prostheses based on wavelet statistical classifier

Since surface electromyography is an electrical activity of superficial muscles and is an essential tool to investigate assessments protocols to be required for prosthetic design, so here, the wavelet transforms based interpretation of Surface Electromyogram signal for classifications of upper arm operations were investigated. The study presented methods of processing and analyzing Surface Electromyogram signal for upper arm motions for extracting accurate patterns of the signal. From these recorded signals, amplitude estimated features were extracted and explored significantly. Then a comparative study to evaluate the wavelet denoising for optimal motor unit action potential detection through the decomposition based on the different wavelet functions of Daubechies, Coiflet and Symmlets families were investigated and tabulated. Thereafter linear discriminating analysis pattern classifier approach was employed to analyze classification performance for different upper arm movements. Results inferred that Daubechies wavelet families were more suitable for the analysis of surface electromyogram signals of different upper arm motions and a classification accuracy of 85.0% was achieved. Finally data projection method of analysis of variance technique was implemented for the effectiveness of recorded surface electromyogram signals for class separability of upper arm motions.

Blind interference alignment tracking for two‐way interference channel

In this study, the authors propose a novel blind adaptive minor subspace tracking interference alignment (BAMST-IA) scheme for slowly fading two-way interference channel. In this scheme, a new frame structure is designed to exploit the dependency of the channels between consecutive fading blocks. Then, the IA problem is reformulated as a minor subspace tracking problem, and the low computational complexity fast data projection method algorithm is integrated with the BAMST-IA scheme to update the IA solutions. Moreover, the update uses only observed data without any channel state information, thus requires lower signalling overhead. Simulation results are presented to show that the proposed BAMST-IA scheme is fit for slowly fading channels.

Interference alignment for MIMO cognitive networks: a complex FDPM-based subspace tracking approach

Interference alignment (IA) for multiple-input multiple-output (MIMO) cognitive networks is considered by modelling the unlicensed secondary transmitter-receiver pairs which coexist with the licensed multi-antenna primary user as a K-user MIMO interference channel. Starting from investigating the constraint conditions of IA scheme in MIMO cognitive networks, a practical IA algorithm is developed by using a subspace tracking approach based on the complex fast data projection method (FDPM). In the proposed algorithm, first, each secondary transmitter aligns its transmitted signal into the null space of the channel matrix from itself to the primary user without causing any interference to the primary user. Then secondary transmitters and receivers, requiring no channel knowledge of secondary networks, alternately design the precoding and postprocessing matrices through a training period which exploits the complex FDPM-based minor subspace tracking, thus eliminating interference among secondary users. Moreover, the case where secondary transmitters have partial knowledge of channels from themselves to the primary user is also discussed. Simulation results reveal that the proposed algorithm can achieve a high sum rate performance while requiring low computational complexity.

Soft-input iterative channel estimation for bit-interleaved turbo-coded MIMO-OFDM systems

Bit-interleaved coded modulation (BICM) is a robust multiplexing technique for achieving multiplexing gain in multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems. However, in order to benefit maximally from the various advantages offered by BICM-based MIMO-OFDM systems, availability of accurate MIMO channel state information (CSI) at the receiver end of the system is essential. Without accurate MIMO CSI, accurate MIMO demapping and coherent detection and decoding of the transmitted message symbols at the system’s receiver would be impossible. In such cases, the multiplexing gain offered by the BICM technique, as well as the higher data rate made possible by the MIMO-OFDM system, is not benefitted from in full. In this paper, we propose a soft input based decision-directed channel estimation scheme for the provision of MIMO CSI for coherent detection of signals in MIMO-OFDM systems. The proposed channel estimator works in iterative mode with a MIMO demapper and a turbo decoder, and is based on the fast data projection method (FDPM) and the variable step size normalised least mean square (VSSNLMS) algorithm. Simulation results of the proposed estimator based on the FDPM and VSSNLMS algorithms indicate better performance in comparison with the same estimator employing minimum mean square error criteria and deflated projection approximation subspace tracking algorithms for both slow- and fast-fading channel scenarios. The proposed estimator would be suitable for use at the receiver end of MIMO-OFDM wireless communication systems operating in either slow- or fast-fading channels.

Local and global principal component analysis for process monitoring

In this paper, a novel data projection method, local and global principal component analysis (LGPCA) is proposed for process monitoring. LGPCA is a linear dimensionality reduction technique through preserving both of local and global information in the observation data. Beside preservation of the global variance information of Euclidean space that principal component analysis (PCA) does, LGPCA is characterized by capturing a good linear embedding that preserves local structure to find meaningful low-dimensional information hidden in the high-dimensional process data. LGPCA-based T2 (D) and squared prediction error (Q) statistic control charts are developed for on-line process monitoring. The validity and effectiveness of LGPCA-based monitoring method are illustrated through simulation processes and Tennessee Eastman process (TEP). The experimental results demonstrate that the proposed method effectively captures meaningful information hidden in the observations and shows superior process monitoring performance compared to those regular monitoring methods.

A Novel Orthonormalization Matrix Based Fast and Stable DPM Algorithm for Principal and Minor Subspace Tracking

We note that the well-known Fast Rayleigh's quotient-based Adaptive Noise Subspace (FRANS), FRANS with Householder transformation (HFRANS), and fast data projection method (FDPM) algorithms all inherit from the data projection method (DPM) algorithm, but with different orthonormalization matrices. Starting from the DPM, we analyze the orthonormalization matrices of all these algorithms and develop a novel orthonormalization matrix for our algorithm. Based on this novel orthonormalization matrix, a fast and stable implementation of the DPM algorithm which has the merits of both the FRANS and FDPM approaches is investigated for principal and minor subspace tracking. The proposed algorithm can switch between the principal and minor subspace tracking with a simple sign change of its step size parameter. Moreover, it reaches the 3 <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">np</i> lower bound of the dominant complexity and guarantees the orthonormality of the tracked subspace. The numerical stability of our algorithm is established theoretically and tested numerically. The strengths and weaknesses of the proposed algorithm to some existing subspace tracking algorithms are demonstrated using a de facto benchmark example. Simulation results are presented to demonstrate the effectiveness of the tracking algorithm advocated.

Subspace Tracking-Based Decision Directed CIR Estimator and Adaptive CIR Prediction

One of the essential prerequisites for coherent detection in Orthogonal Frequency Division Multiplexing (OFDM)-based communication systems is the availability of an accurate estimate of the channel state information (CSI). In the absence of symbol errors, the decision directed channel estimation (DDCE) scheme has been proved to provide better and more accurate CSI than the channel estimation scheme based purely on pilot symbols. In this paper a subspace algorithm, the fast data projection method (FDPM), is employed for estimation of the channel impulse response (CIR) of the OFDM channel in a decision directed mode. A variable step size normalized least mean square (VSSNLMS)-based predictor is derived for the implementation of the CIR prediction stage of the estimation scheme. The whole channel estimation scheme based on the two algorithms is simulated in the context of fractionally spaced (FS)-CIR based channel model, also known as non-sample spaced CIR model. The performance of the FDPM-based decision directed channel estimator is compared through computer simulation with another subspace algorithm, deflated projection approximation subspace tracking (PASTd)-based channel estimator, while employing normalized least mean square (NLMS) and the proposed VSSNLMS predictors, respectively. It was discovered from the simulation results that the proposed scheme performed better than the PASTd-based channel estimator. The channel estimation scheme employing VSSNLMS-predictor also shows enhanced performance in comparison with the scheme using NLMS-based predictor.

Suppression of local haze variations in MERIS images over turbid coastal waters for retrieval of suspended sediment concentration

Atmospheric correction over turbid waters can be problematic if atmospheric haze is spatially variable. In this case the retrieval of water quality is hampered by the fact that haze variations could be partly mistaken for variations in suspended sediment concentration (SSC). In this study we propose the suppression of local haze variations while leaving sediment variations intact. This is accomplished by a multispectral data projection (MDP) method based on a linear spectral mixing model, and applied prior to the actual standard atmospheric correction. In this linear model, the hazesediment spectral mixing was simulated by a coupled water-atmosphere radiative transfer (RT) model. As a result, local haze variations were largely suppressed and transformed into an approximately homogenous atmosphere over the MERIS top-of-atmosphere (TOA) radiance scene. The suppression of local haze variations increases the number of satellite images that are still suitable for standard atmospheric correction processing and subsequent water quality analysis.

Efficient Recursive Principal Component Analysis Algorithms for Process Monitoring

Principal component analysis (PCA) has been successfully applied in large scale process monitoring. However, classical PCA has some drawbacks: one of these aspects is the inability to deal with parameter-varying processes, where it interprets the natural changes in the process as faults, resulting in numerous false alarms. These false alarms threaten the credibility of the monitoring system. Therefore, recursive PCA (RPCA) algorithms are recommended. The most important challenge faced by these algorithms is the high computation costs, due to repeated eigenvalue decomposition (EVD) or singular value decomposition (SVD). Motivated by this issue, we present two RPCA algorithms that will greatly reduce the computation cost. The first algorithm is based on first-order perturbation analysis (FOP), which is a rank-one update of the eigenvalues and their corresponding eigenvectors of a sample covariance matrix. The second one is based on the data projection method (DPM), which is a simple and reliable approach for adaptive subspace tracking. The effectiveness of the presented RPCA algorithms is evaluated with an application of monitoring a nonisothermal continuous stirred tank reactor (CSTR) system. The results show the efficiency of these approaches compared to the classical PCA.

Fast and Stable Subspace Tracking

We consider the problem of adaptive subspace tracking, when the rank of the subspace we seek to estimate is assumed to be known. Starting from the data projection method (DPM), which constitutes a simple and reliable means for adaptively estimating and tracking subspaces, we develop a fast and numerically robust implementation of DPM, which comes at a considerably lower computational cost. Most existing schemes track subspaces corresponding either to the largest or to the smallest singular values, while our DPM version can switch from one subspace type to the other with a simple change of sign of its single parameter. The proposed algorithm provides orthonormal vector estimates of the subspace basis that are numerically stable since they do not accumulate roundoff errors. In fact, our scheme constitutes the first numerically stable, low complexity, algorithm for tracking subspaces corresponding to the smallest singular values. Regarding convergence towards orthonormality our scheme exhibits the fastest speed among all other subspace tracking algorithms of similar complexity.

Linear data projection using a feedforward neural network

Linear data projection methods provide useful tools in exploratory data analysis and pattern recognition. The most widely used methods in this respect are principal components analysis (PCA) and linear discriminant analysis (LDA). These methods are able to condense multidimensional information in two (or three) dimensions, allowing us to analyze the underlying structure of the data by visual inspection. This paper describes an alternative linear data projection method using a feedforward neural network. The method uses category information to obtain a projection maximally separating the predefined classes of data patterns (supervised data projection). The projection is constructed by training a feedforward network consisting of two subnetworks. The first subnetwork receives the training input and performs the actual projection, while the second one performs the desired classification using the output of the first subnetwork. After training the projection, subnetwork provides the 2D (or 3D) projection map, where as the complete network behaves as an ordinary classification network. As an example, the method is applied to a data set consisting of 13 dioxin concentrations for 145 samples of cow's milk, originating from the vicinity of five different sources of dioxin contamination. 2D projection maps were constructed using PCA, LDA and the proposed neural network using different classification subnets and starting values of the network weights. The neural network approach produced a number of significant different mappings, most of them showing an improved class separation as compared to PCA and LDA.