Eigenspace Transformation Research Articles

Adaptive control design for active Pogo suppression based on vibration-acceleration feedback

Adaptive control design for active Pogo suppression based on vibration-acceleration feedback

Partial source separation from unknown correlation mixture for eliminating unknown periodic disturbances from random measured signals

Separating and eliminating periodic disturbances from measured signals are a key problem to obtain original responses used for further system identification and evaluation. Actual periodic disturbances are partial unknown sources in measured signals and have certain correlation with random noise sources in time domain. In this paper, a separation problem on partial unknown sources such as periodic sources correlated with random noises is introduced. A partial unknown source separation technique is proposed by combining signal eigenspace transformation, covariance joint diagonalization and decorrelation of correlation sources. The partial source separation procedure has two main stages: obtain uncorrelated sources by eigenspace transformation and joint diagonalization; and obtain partial periodic sources correlated with random noises from the uncorrelated sources by decorrelation. The proposed partial source separation technique is supported by several theorems. Under given assumptions, the separation technique will result in accurate partial sources. The separation technique has main features such as partial unknown sources separated from measured signals, separated periodic sources correlated with random noise sources, and being suitable for dominant random noises and non-dominant periodic disturbance sources in measured signals. Numerical results are presented to illustrate the effectiveness of the separation technique.

Tensor projection mechanism and algorithm implementation

This paper analyses the mechanism of tensor projection transformation in depth and introduces a high-efficiency original algorithm developed in a quantum computing language for forward and backward projection between multidimensional tensors and one-dimensional vectors. Additionally, the author compares this algorithm with similar methods from both the Python scientific computing package and other relative development kits in method calls and source code to demonstrate the innovation of the tensor projection algorithm. On this basis, the classical convolution operation program commonly used in machine learning has been parallelized and improved, the analysis algorithm of the Beidou communication satellite view area has been parallelized and improved, and the actual operating efficiency has been greatly improved. After verification, the tensor projection transformation successfully solves the problem of location index mapping of the entity units among different dimensions, can provide a means for optimizing the traditional model of traversal algorithm, and can have significant reference value in eigenspace transformation against a tensor field.

Robust and Lightweight Ensemble Extreme Learning Machine Engine Based on Eigenspace Domain for Compressed Learning

Compressive sensing (CS) is applied to electrocardiography (ECG) telemonitoring system to address the energy constraint of signal acquisition in sensors. In addition, on-sensor-analysis transmitting only abnormal data further reduces the energy consumption. To combine both advantages, “On-CS-sensor-analysis” can be achieved by compressed learning (CL), which analyzes signals directly in compressed domain. Extreme learning machine (ELM) provides an effective solution to achieve the goal of low-complexity CL. However, single ELM model has limited accuracy and is sensitive to the quality of data. Furthermore, hardware non-idealities in CS sensors result in learning performance degradation. In this work, we propose the ensemble of sub-eigenspace-ELM (SE-ELM), including two novel approaches: 1) We develop the eigenspace transformation for compressed noisy data, and further utilize a subspace-based dictionary to remove the interferences, and 2) Hardware-friendly design for ensemble of ELM provides high accuracy while maintaining low complexity. The simulation results on ECG-based atrial fibrillation show the SE-ELM can achieve the highest accuracy with 61.9% savings of the required multiplications compared with conventional methods. Finally, we implement this engine in TSMC 90 nm technology. The postlayout results show the proposed CL engine can provide competitive area- and energy-efficiency compared to existing machine learning engines.

Code-Switching Event Detection by Using a Latent Language Space Model and the Delta-Bayesian Information Criterion

This paper proposes a new paradigm for code-switching event detection based on latent language space models (LLSMs) and the delta-Bayesian information criterion ( $\Delta {\hbox {BIC}}$ ). A phone-based Mandarin–English speech recognizer was first employed for obtaining the senone sequence of a speech utterance. For each senone, acoustic features and the posterior probability of the articulatory features (AFs) were extracted and applied to an eigenspace transformation, based on principal component analysis (PCA). Latent semantic analysis (LSA) was then adopted for constructing a matrix to model the importance of each principal component in the eigenspace for the senones and AFs in each language. The spatial relationships among the senones (or AFs) represented by the PCA-transformed eigenvalues in the LSA-based matrix were employed to construct an LLSM for characterizing a language. In code-switching event detection, the language likelihood between the input speech LLSM and each of the language-dependent LLSMs was estimated. The Euclidian-distance-based similarities and cosine-angle-distance-based similarities were adopted for estimating the language likelihood for senones and AFs. The $\Delta {\hbox {BIC}}$ was then used for estimating the language transition score for each hypothesized code-switching event. Finally, the dynamic programming algorithm was employed for obtaining the most likely code-switching language sequence. The proposed approach was evaluated using a Mandarin–English code-switching speech database and outperformed other conventional methods. A duration accuracy of 72.45% can be obtained from the proposed system with optimized parameters.

A 30 meter land cover mapping of China with an efficient clustering algorithm CBEST

Remote sensing based land cover mapping at large scale is time consuming when using either supervised or unsupervised classification approaches. This article used a fast clustering method—Clustering by Eigen Space Transformation (CBEST) to produce a land cover map for China. Firstly, 508 Landsat TM scenes were collected and processed. Then, TM images were clustered by combining CBEST and K-means in each pre-defined ecological zone (50 in total for China). Finally, the obtained clusters were visually interpreted as land cover types to complete a land cover map. Accuracy evaluation using 2159 test samples indicates an overall accuracy of 71.7% and a Kappa coefficient of 0.64. Comparisons with two global land cover products (i.e., Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) and GlobCover 2009) also indicate that our land cover result using CBEST is superior in both land cover area estimation and visual effect for different land cover types.

Clustering based on eigenspace transformation – CBEST for efficient classification

Large remote sensing datasets, that either cover large areas or have high spatial resolution, are often a burden of information mining for scientific studies. Here, we present an approach that conducts clustering after gray-level vector reduction. In this manner, the speed of clustering can be considerably improved. The approach features applying eigenspace transformation to the dataset followed by compressing the data in the eigenspace and storing them in coded matrices and vectors. The clustering process takes the advantage of the reduced size of the compressed data and thus reduces computational complexity. We name this approach Clustering Based on Eigen-space Transformation (CBEST). In our experiment with a subscene of Landsat Thematic Mapper (TM) imagery, CBEST was found to be able to improve speed considerably over conventional K-means as the volume of data to be clustered increases. We assessed information loss and several other factors. In addition, we evaluated the effectiveness of CBEST in mapping land cover/use with the same image that was acquired over Guangzhou City, South China and an AVIRIS hyperspectral image over Cappocanoe County, Indiana. Using reference data we assessed the accuracies for both CBEST and conventional K-means and we found that the CBEST was not negatively affected by information loss during compression in practice. We discussed potential applications of the fast clustering algorithm in dealing with large datasets in remote sensing studies.

Improved gait recognition by multiple-projections normalization

Recognizing people by gait promises to be useful for identifying individuals from a distance; in this regard, improved techniques are under development. In this paper, an improved method for gait recognition is proposed. Binarized silhouette of a motion object is first represented by four 1-D signals that are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Fourier Transform is employed as a preprocessing step to achieve translation invariant for the gait patterns accumulated from silhouette sequences that are extracted from the subjects’ walk in different speed and/or different time. Then, eigenspace transformation is applied to reduce the dimensionality of the input feature space. Support vector machine (SVM)-based pattern classification technique is then performed in the lower-dimensional eigenspace for recognition. The input feature space is alternatively constructed by using two different approaches. The four projections (1-D signals) are independently classified in the first approach. A fusion task is then applied to produce the final decision. In the second approach, the four projections are concatenated to have one vector and then pattern classification with one vector is performed in the lower-dimensional eigenspace for recognition. The experiments are carried out on the most well-known public gait databases: the CMU, the USF, SOTON, and NLPR human gait databases. To effectively understand the performance of the algorithm, the experiments are executed and presented as increasing amounts of the gait cycles of each person available during the training procedure. Finally, the performance of the proposed algorithm is comparatively illustrated to take into consideration the published gait recognition approaches.

Silhouette analysis-based gait recognition for human identification

Human identification at a distance has recently gained growing interest from computer vision researchers. Gait recognition aims essentially to address this problem by identifying people based on the way they walk. In this paper, a simple but efficient gait recognition algorithm using spatial-temporal silhouette analysis is proposed. For each image sequence, a background subtraction algorithm and a simple correspondence procedure are first used to segment and track the moving silhouettes of a walking figure. Then, eigenspace transformation based on principal component analysis (PCA) is applied to time-varying distance signals derived from a sequence of silhouette images to reduce the dimensionality of the input feature space. Supervised pattern classification techniques are finally performed in the lower-dimensional eigenspace for recognition. This method implicitly captures the structural and transitional characteristics of gait. Extensive experimental results on outdoor image sequences demonstrate that the proposed algorithm has an encouraging recognition performance with relatively low computational cost.

Dualband FLIR fusion for automatic target recognition

We investigate the potential benefits of fusing two bands of forward-looking infrared (FLIR) data for target detection and clutter rejection. We propose a similar set of neural-based clutter rejecters and target detectors, each of which consists of an eigenspace transformation and a simple multilayer perceptron. The same architecture is used to operate on either single band or dualband FLIR input images, so that the net effects of dualband fusion can be demonstrated. When the dualband inputs are used, the component bands are combined at either pixel or feature level, thus providing insight into methods of performing data fusion in this particular application. A large set of real FLIR images is used in two series of experiments, one for clutter rejection tasks and the other for target detection tasks. In both series, the results indicate that the dualband input images do improve the performance of the clutter rejecters and target detectors over their single band counterparts. On the other hand, results of the pixel and feature level fusions are quite similar, suggesting that dimensionality reduction by the eigenspace transformation can be performed independently on the two bands.

Eigenspace transformation for automatic clutter rejection

The goal of our research is to develop an effective and efficient clutter rejector with the use of an eigenspace transformation and a multilayer perceptron (MLP) that can be incorporated into an automatic target recognition system. An eigenspace transformation is used for feature extraction and dimensionality reduction. The transformations considered in this research are principal-component analysis (PCA) and the eigenspace separation transformation (EST). We fed the result of the eigenspace transformation to an MLP that predicts the identity of the input, which is either a target or clutter. Our proposed clutter rejector was tested on two huge and realistic datasets of second-generation forwardlooking infrared imagery for the Comanche helicopter. In general, both the PCA and EST methods performed satisfactorily with minor differences. The EST method performed slightly better when a smaller amount of transformed data was fed to the MLP, or when the positive and negative EST eigentargets were used together.

Recognising humans by gait via parametric canonical space

Based on principal component analysis (PCA), eigenspace transformation (EST) was demonstrated to be a potent metric in automatic face recognition and gait analysis by template matching, but without using data analysis to increase classification capability. Gait is a new biometric aimed to recognise subjects by the way they walk. In this article, we propose a new approach which combines canonical space transformation (CST) based on Canonical Analysis (CA), with EST for feature extraction. This method can be used to reduce data dimensionality and to optimise the class separability of different gait classes simultaneously. Each image template is projected from the high-dimensional image space to a low-dimensional canonical space. Using template matching, recognition of human gait becomes much more accurate and robust in this new space. Experimental results on a small database show how subjects can be recognised with 100% accuracy by their gait, using this method.

Human gait recognition in canonical space using temporal templates

A system for automatic gait recognition without segmentation of particular body parts is described. Eigenspace transformation (EST) has already proved useful for several tasks including face recognition, gait analysis, etc; it is optimal in dimensionality reduction by maximising the total scatter of all classes but is not optimal for class separability. A statistical approach that combines EST with canonical space transformation (CST) is proposed for gait recognition using temporal templates from a gait sequence as features. This method can be used to reduce data dimensionality and to optimise the class separability of different gait sequences simultaneously. Incorporating temporal information from optical-flow changes between two consecutive spatial templates, each temporal template extracted from computation of optical flow is projected from a high-dimensional image space to a single point in a low-dimensional canonical space. Using template matching, recognition of human gait becomes much faster and simpler in this new space. As such, the combination of EST and CST is shown to be of considerable potential in an emerging new biometric.

Visual Surveillance and Tracking of Humans by Face and Gait Recognition

Increased emphasis on automated real time intelligent surveillance system has led to the need to identify and track people in complex environments. Independent features such as face, gait provide valuable clues as to identity, which coupled with data fusion and tracking algorithms offer a potential solution to this problem. In this paper we address the first problem of recognizing humans in real time, data fusion and tracking will be performed by neurofuzzy state estimators. A new approach which combines eigenspace transformation with canonical space transformation is proposed here. This method can be used to reduce data dimensionality and to optimize the class separability of different classes simultaneously.

AN EIGEN-FORCE METHOD FOR FINITE ELEMENT ANALYSIS AND REANALYSIS

This paper presents a force-based finite element method that involves eigen-space transformation of element stiffness matrices in the first analysis. In each subsequent analysis (‘reanalysis’) associated with structural variations, the solution obtained previously is modified making use of intrinsic properties of eigen solutions and avoiding the time-consuming task of solving a large system of equations. The structural variations may involve changes in material properties, birth or death of elements, or change in boundary conditions. Numerical examples are presented to compare the accuracy and computational efficiency of the proposed method with the displacement-based finite element method. © 1997 by John Wiley & Sons, Ltd.