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The iterative closest point (ICP) algorithm is fast and accurate for rigid point set registration, but it works badly when there are many outliers and noises in the point sets. This paper instead proposes a novel method based on the ICP algorithm to deal with this problem. Firstly, correntropy is introduced into the rigid registration problem and then a new energy function based on maximum correntropy...
The traditional affine iterative closest point (ICP) algorithm is fast and accuracy for affine registration of point sets, but it performs worse when the point sets with large outliers. This paper introduces a novel algorithm based on correntropy for affine registration of point sets with outliers. First, a novel objective function is proposed by introducing the maximum correntropy criterion (MCC)...
A Gaussian mixture model (GMM), coupled with possibilistic clustering is used to build an adaptive system for analyzing streaming multi-dimensional activity feature vector with the goal of identifying signs of early diseases. The system is based on temporal analysis, including outlier detection, customization and adaption to new changes, together with the creation of new components for GMM in the...
The low-rank property of hyperspectral imagery is well exploited with low-rank decomposition methods recently. In our approach, a novel hyperspectral anomaly detector based on low-rank representation (LRR) and learned dictionary (LD) has been proposed. This method assumes that a two-dimensional matrix transformed from a three-dimensional hyperspectral imagery can be decomposed into two parts: a low...
We propose an effective subspace selection scheme as a post-processing step to improve results obtained by sparse subspace clustering (SSC). Our method starts by the computation of stable subspaces using a novel random sampling scheme. Thus constructed preliminary subspaces are used to identify the initially incorrectly clustered data points and then to reassign them to more suitable clusters based...
Robust optimization has gained increasing attention in the power system area due to its ability to model uncertainties using modest information while producing reliable solutions. However, a common concern with the robust approach is that it can be overly-conservative. To address this issue, we propose a data-driven method to construct uncertainty sets by using autoregressive integrated moving average...
We consider the problem of aggregating a large number of online ratings where there may be outliers, representing biased, missing or erroneous evaluations. The penalty-based method proposed comprises both outlier detection and reallocation of weights and we focus on models dependent on the relative order of inputs, i.e. based on OWA operators, however we also define the model for weighted means.
Hysteresis nonlinearity, which is an inherent characteristic of piezoelectric actuators (PEAs), decreases the tracking precision and may even result in limit cycle oscillations. A tracking control based on H∞ robust disturbance observer (DOB) is presented for PEAs in this paper. Hammerstein system is applied to modeling dynamic hysteresis nonlinearity of PEA. Then, a control scheme that combines H...
The scaled Complex Wishart distribution is a model that fits very well multilook full polarimetric Synthetic Aperture Radar (PolSAR) data from homogeneous regions. Its parameters, L and Σ, are intrinsicaly related to the physical process of image aquisition giving information about the number of looks (L) and brightness (|Σ|) of the image. Several techniques commonly used in image processing and understanding,...
The algorithms foundational to visualization are central to the production visualization tools running at computing centers around the world and consume tremendous amounts of finite, limited resources. We believe that understanding the performance characteristics of these algorithms is critical in being good stewards of computational centers' resources. In this paper, we report initial studies on...
This paper considers the problem of iterative learning control design for linear systems with data quantization, where the system matrices contain uncertain parameters. It is assumed that the control input update signals are quantized before they are transmitted to the iterative learning controller. A logarithmic quantizer is used to decode the signal with a number of quantization levels. Then, a...
Ultra-wideband (UWB) Synthetic Aperture Radars (SAR) operate over a large bandwidth ranging from under 100 MHz to over a few Ghz. They often share spectrum with other systems such as radio, TV and cellular networks. The mitigation of radio frequency interference(RFI) from these sources is an important problem for UWB SAR systems. Traditional RFI suppression techniques such as notch filtering introduce...
Positioning and orientation precision of a multirotor aerial robot can be increased by using additional control loops for each of the driving units. As a result, one can eliminate lack of balance between true thrust forces. A control performance comparison of two proposed thrust controllers, namely robust controller designed with coefficient diagram method (CDM) and proportional, integral and derivative...
This paper introduces a robust methodology for autotuning design parameters in the EPSAC (Extended Prediction Self-Adaptive Control) approach to MPC (Model based Predictive Control). The method requires from the user solely a well-chosen sampling period of the process and, in case of process with time delay, the amount of delayed samples. The main design parameter, the prediction horizon, is related...
In this paper, we propose a novel spatial variation modeling method based on robust dictionary learning for nanoscale integrated circuits. This method takes advantage of the historical data to efficiently improve the accuracy of wafer-level spatial variation modeling with extremely low measurement cost. Robust regression is adopted by our implementation to reduce the bias posed by outliers. An iterative...
In this paper, a new combination of features and normalization methods is investigated for robust biometric speaker identification. Mel Frequency Cepstral Coefficients (MFCC) are efficient for speaker identification in clean speech while Power Normalized Cepstral Coefficients (PNCC) features are robust for noisy environments. Therefore, combining both features together is better than taking each one...
In this paper, we propose a joint machine learning and human learning design approach to make the training data labeling task in linear regression problems more efficient and robust to noise, modeling mismatch, and human labeling errors. Considering a sequential active learning scheme which relies on human learning to enlarge training data set, we integrate it with sparse outlier detection algorithms...
The paper proposes rCV, a new randomised Cross Validation (CV) criterion specially designed for use with data acquired over non-uniformly scattered designs, like the linear transect surveys typical in environmental observation. The new criterion enables a robust parameterisation of interpolation algorithms, in a manner completely driven by the data and free of any modelling assumptions. The new CV...
This paper presents an Internal Model Based (IMC) tuned PI controller for real-time control of a coupled tank liquid level system. The IMC is a model based controller design technique. In present scenario the IMC Controller method is widely used in almost every process control industries as it allows good set point tracking along with sulky disturbance response especially for the process with a small...
The millimeter wave (mmWave) frequencies offer the potential for enormous capacity cellular systems. However, a key challenge in designing robust communication systems in these frequencies is channel intermittency: mmWave signals are extremely vulnerable to blocking and the channel can rapidly appear and disappear with small movement of obstacles and reflectors. This paper presents a novel statistical...
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