Foreground Detection Based on Nonlinear Independent Component Analysis

Motionless foreground objects are key targets in applications of home care monitoring and abandoned object detection, and pose a great challenge to foreground detection. Most algorithms incorporate the motionless foreground objects into their background models because they have to adapt to environmental changes. To overcome this challenge, a foreground detection method based on nonlinear independent component analysis （ICA） was proposed. Considering that each video frame was actually a nonlinear mixture of the background image and the foreground image, the nonlinear ICA was employed to accurately separate the independent components from each frame. Then, the entropy of grayscale image was calculated to classify which resulting independent component was the foreground image. The proposed nonlinear ICA model was trained offiine and this model was not updated online, so the method can cope with the motionless foreground objects. Experimental results demonstrate that, the method achieves remarkable results and outperforms several advanced methods in dealing with the motionless foreground objects.

Dimensionality Reduction with Input Training Neural Network and Its Application in Chemical Process Modelling

Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Mo-mentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propyl-ene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.

Multivariate Statistical Process Monitoring Using Robust Nonlinear Principal Component Analysis

The principal component analysis （PCA） algorithm is widely applied in a diverse range of fields for performance assessment, fault detection, and diagnosis. However, in the presence of noise and gross errors, the nonlinear PCA （NLPCA） using autoassociative bottle-neck neural networks is so sensitive that the obtained model differs significantly from the underlying system. In this paper, a robust version of NLPCA is introduced by replacing the generally used error criterion mean squared error with a mean log squared error. This is followed by a concise analysis of the corresponding training method. A novel multivariate statistical process monitoring （MSPM） scheme incorporating the proposed robust NLPCA technique is then investigated and its efficiency is assessed through application to an industrial fluidized catalytic cracking plant. The results demonstrate that, compared with NLPCA, the proposed approach can effectively reduce the number of false alarms and is, hence, expected to better monitor real-world processes.

Reconstruction based approach to sensor fault diagnosis using auto-associative neural networks

Fault diagnostics is an important research area including different techniques.Principal component analysis(PCA)is a linear technique which has been widely used.For nonlinear processes,however,the nonlinear principal component analysis(NLPCA)should be applied.In this work,NLPCA based on auto-associative neural network(AANN)was applied to model a chemical process using historical data.First,the residuals generated by the AANN were used for fault detection and then a reconstruction based approach called enhanced AANN(E-AANN)was presented to isolate and reconstruct the faulty sensor simultaneously.The proposed method was implemented on a continuous stirred tank heater(CSTH)and used to detect and isolate two types of faults(drift and offset)for a sensor.The results show that the proposed method can detect,isolate and reconstruct the occurred fault properly.

Taiwan’ Chi-Chi Earthquake Precursor Detection Using Nonlinear Principal Component Analysis to Multi-Channel Total Electron Content Records

This research uses eigenvalue characteristics of nonlinear principal component analysis （NLPCA） and principal component analysis （PCA） to investigate total electron content （TEC） anomalies associated with Taiwan＇s Chi-Chi earthquake of 21 September 1999 （LT） （M_w=7.6）. The transforms are used for ionospheric TEC from 01 August to 20 September 1999 （local time） using data from 13 GPS receivers. The data were collected at 22°N-26°N Lat. and 120°E-122°E Long.. Applying the NLPCA to the multi-channel total electron content records of GPS receivers, the earthquake-associated TEC anomalies were represented by large principal eigenvalues of NLPCA （〉0.5 in a normalized set） on 14 August and 17, 18, and 20 September, with allowance given for the Dst index, which was quiet for the study period. Comparisons were then made with other researchers who also found TEC anomalies on September 17, 18, and 19 associated with the Chi-Chi earthquake, which cannot be detected by PCA.Consideration is also given for reported ground level geomagnetic field activity that occurred between mid-August and late October, leading up to and including the Chi-Chi and Chia-Yi earthquakes, which are associated with the same series of faults. It is possible that Aug. 14 is representative of an earthquake-associated TEC anomaly. This is an interesting result given how much earlier than the earthquake it occurred.

Nonlinear Principal Component Analysis Using Strong Tracking Filter

The paper analyzes the problem of blind source separation （BSS） based on the nonlinear principal component analysis （NPCA） criterion. An adaptive strong tracking filter （STF） based algorithm was developed, which is immune to system model mismatches. Simulations demonstrate that the algorithm converges quickly and has satisfactory steady-state accuracy. The Kalman filtering algorithm and the recursive leastsquares type algorithm are shown to be special cases of the STF algorithm. Since the forgetting factor is adaptively updated by adjustment of the Kalman gain, the STF scheme provides more powerful tracking capability than the Kalman filtering algorithm and recursive least-squares algorithm.

Advances in adaptive nonlinear manifolds and dimensionality reduction

Recent decades have witnessed a much increased demand for advanced,effective and efficient methods and tools for analyzing,understanding and dealing with data of increasingly complex,high dimensionality and large volume.Whether it is in biology,neuroscience,modern medicine and social sciences or in engineering and computer vision,data are being sampled,collected and cumulated in an unprecedented speed.It is no longer a trivial task to analyze huge amounts of high dimensional data.A systematic,automated way of interpreting data and representing them has become a great challenge facing almost all fields and research in this emerging area has flourished.Several lines of research have embarked on this timely challenge and tremendous progresses and advances have been made recently.Traditional and linear methods are being extended or enhanced in order to meet the new challenges.This paper elaborates on these recent advances and discusses various state-of-the-art algorithms proposed from statistics,geometry and adaptive neural networks.The developments mainly follow three lines:multidimensional scaling,eigen-decomposition as well as principal manifolds.Neural approaches and adaptive or incremental methods are also reviewed.In the first line,traditional multidimensional scaling(MDS)has been extended not only to be more adaptive such as neural scale,curvilinear component analysis(CCA)and visualization induced self-organizing map(ViSOM)for online learning,but also to be more local scaling such as Isomap for enhanced flexibility for nonlinear data sets.The second line extends linear principal component analysis(PCA)and has attracted a huge amount of interest and enjoyed flourishing advances with methods like kernel PCA(KPCA),locally linear embedding(LLE)and Laplacian eigenmap.The advantage is obvious:a nonlinear problem is transformed into a linear one and a unique solution can then be sought.The third line starts with the nonlinear principal curve and surface and links up with adaptive neural network approaches such as self-organizing map(SOM)and ViSOM.Many of these frameworks have been further improved and enhanced for incremental learning and mapping function generalization.This paper discusses these recent advances and their connections.Their application issues and implementation matters will also be briefly enlightened and commented on.

An all-digital synthesizable baseband for a delay-based LINC transmitter with reconfigurable resolution

The linear amplification with nonlinear component transmitter is a promising solution to high efficiency and high linearity amplification for non-constant envelope signals. An all-digital synthesizable baseband for a delay-based LINC transmitter is implemented. This paper proposes a standard-cell based synthesizable methodology which can be applied in the ASIC process efficiently without performance degradation compared to the manual layout. A scheme to overcome the limited resolution of conventional phase detectors is proposed. It employs alter- native phase detector structures to provide reconfigurability for higher resolution after fabricating, resulting in an 11 ps resolution improvement. Due to the PVT variation, an adaptive calibration scheme focusing on the inherent imbalance between two delay lines is depicted, which reveals an effective EVM enhancement of 5.37 dB. This baseband chip is implemented in 0.13 μm CMOS technology, and the transmitter with the baseband has an EVM of-28.96 dB and an ACPR of-29.51 dB, meeting the design requirement.