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This paper is concerned with determining the number of correlated signals between two data sets using canonical correlation analysis (CCA) when a principal component analysis (PCA) preprocessing step is performed for initial rank reduction. In signal processing applications, it is commonplace in scenarios with large dimensions, insufficient samples, or knowledge of low-rank underlying signals to extract...
A desired speech signal in hands-free communication systems is often degraded by background noise and interferers. Data-dependent spatial filters for desired speech extraction depend on the power spectral density (PSD) matrices of the desired and the undesired signals, which are commonly estimated recursively using a signal model-based speech presence probability (SPP). The SPP and the PSD matrix...
Non-negative matrix factorization (NMF) has become a popular tool for exploratory analysis due to its part based easy interpretable representation. Sparseness is commonly invoked in NMF (SNMF) by regularizing by the l1 - norm both to alleviate the non-uniqueness of the NMF representation as well as promote sparse (i.e. part based) representations. While sparseness can prune excess components thereby...
The Radon transform is a powerful method that has been used to filter coherent noise from seismic records and to reconstruct seismic data. In addition, it has a long history in image processing as a tool for feature extraction. An important shortcoming in exploration seismology, however, is the requirement of simple integration paths that often do not match well enough the spatio-temporal structure...
An extension of Independent Component Analysis (ICA) to the situation when the mixture of signals is contaminated by multiplicative noise is proposed in this paper. The ICA methods search for the most independent output after a linear transformation of the data vector. If the ICA model is followed by these data, the result of this search is the inverse of the unknown mixture. On the other hand, if...
A point pattern matching algorithm based on Mahalanobis distance is proposed, which effect is analyzed and confirmed by experiments. Secondly, the Graph Transformation Matching algorithm and Weighted Graph Transformation Matching algorithm are studied deeply. To overcome the limitation of Mahalanobis distance and WGTM, a novel and robust point pattern matching algorithm based on Weighted Graph Transformation...
SEIR (Susceptible-Exposed-Infected-Recovered) is a general and widely-used diffusion model that can model the diffusion in different contexts such as idea spreading and disease propagation. Here, we tackle the problem of inferring graph edges if we can only observe a SEIR diffusion process spreading over the nodes of a graph. This problem is of importance in the common case where node states can be...
Optimal experimental design is used to either reduce the number of experiments to be performed or to get as much information from the available data and data retrieved from a pre-determined set of experiments. An optimal experimental design aims at minimizing the cost of experimentation and still getting useful data to determine the process properties. This data in turn can be used to construct a...
Single-tone X-parameters-based models are time-invariant non-linear mappings, where the output is a multi-harmonic vector mapping of the incident and scattered waves under periodic steady-state conditions. An envelope simulation combines aspects of both the frequency and time domains, where the input waveforms may be represented as discrete carriers in the frequency domain and the modulation envelopes...
Volatility of the stock price is the key to the pricing problem of stock related derivatives in finance. Volatility appears in the diffusion term of the usual modeling of stock prices. One popular approach is to take volatility to be stochastic, and assumes that it satisfies a stochastic differential equation. Taking the stock price to be the observation, we may then pose the filtering problem of...
This paper is concerned with easy-to-use identification method for various types of continuous-time systems. First, it is shown how to identify continuous-time systems by using the Particle Swarm Optimization, which attracts a lot of attention recently in the evolutionary computation area due to its empirical evidence of its superiority in solving various non-convex problems. Second, its effectiveness...
In this contribution we discuss some variance properties of a two-step ARX estimation scheme. An expression for the co-variance of the final low order model is calculated and it is discussed how one should minimize this covariance. The implication of the results is that identification of the dynamics of a system could very easily be performed with standard linear least squares (two times), even if...
We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of the approach in a number of industrial applications. Bayesian approach provides a principled way to handle the problem of overfitting, by averaging over all model complexities weighted by their posterior probability given the data sample. The approach also facilitates estimation of the confidence intervals...
A method is presented that provides the exact equivalent representation of the periodogram by means of an MA(w-1) model. This method will be compared to inferring an MA(q) model directly from the data by estimating the parameters and selecting the optimal model order. Representing the periodogram by means of an MA(n-1) model, enables the use of The Prediction Error to make an exact quantitative comparison...
Noise pollution is a significant problem in cities due to its various effects on health, but the modeling of noise data and the generation of accurate noise pollution maps suffer from the high cost and restricted scale of sensing performed using static municipal sensors. In this paper, we present our approach for augmenting municipally sensed data using participatory sensing-based information collected...
This paper shows by simulation and by a relative total variance performance measures that in control of lag dominated first order time delayed plants appropriately filtered PID control may significantly increase the loop performance with respect to filtered PI control. By analyzing the impact of higher order low pass filters designed by a modified multiple real dominant pole approach it shows that...
When faced with the struggle to extract insights from complex and noisy data, often the end user may assume that there exist no significant relation between the features and target in the dataset and is forced to either quit the study or resort to alternate means. Artificial Neural Networks (ANNs) might be of help to predict some of the most complex data used in the industry. But it is neither easy...
In this paper we present a stochastic model for multi-area wind production that is used for planning reserves in transmission-constrained systems with large amounts of integrated renewable power supply. The stochastic model accounts for the inter-temporal and spatial dependencies of multi-area wind power production. Results are presented for two case studies of the California and the German power...
Purpose: Patient-specific respiratory mechanics can be used to guide mechanical ventilation therapy. However, even in controlled ventilation modes, underlying respiratory mechanics can be masked by spontaneous breathing efforts. The aim of this study is to accurately assess respiratory mechanics for breathing cycles affected by these spontaneous breathing efforts. Methods: A pressure reconstruction...
The RANdom SAmple Consensus (RANSAC) algorithm, as a robust parameter estimator, has been widely used to remove gross errors. However, there is less work on analyzing the uncertainty produced by the RANSAC. This paper fills this gap by presenting an uncertainty estimation algorithm for the RANSAC. Based on a thorough analysis on the uncertainty of the model parameters generated during the random hypothesis...
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