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With highly correlated input signal, the kernel least-mean-square algorithm(KLMS) always possess a low convergence rate. To overcome this problem the input signal should be decorrelated before adaptive filtering. A decorrelated kernel least-mean-square algorithm(DKLMS) is proposed, which is the combination of KLMS and decorrelation. Using the characteristics of Gaussian kernel, the correlation coefficient...
In response to the demand on data-analytic tools that monitor time-varying connectivity patterns within brain networks, the present paper extends the framework of [Slavakis et al., SSP'16] to include kernel-based partial correlations as a tool for clustering dynamically evolving connectivity states of networks. Such an extension becomes feasible due to the argument which runs beneath also this work:...
Correlation filter based tracking method has been widely used for its high efficiency and robustness. However, reducing model drifting while achieving both high robustness and fast scale estimation is still an open problem. In this paper, we represent the target in kernel feature space and train a classifier on a scale pyramid to achieve adaptive scale estimation. We then integrate three complementary...
PM2.5 concentration can have significant impacts on solar irradiation and thus on photovoltaic (PV) power output. This paper presents a method to model impacts of PM2.5 concentration on PV power. A non-parametric kernel density estimation is used to fit the probability distribution of PM2.5 concentration. An incremental relation between the increase of PM2.5 concentration and the decrease of solar...
Location-aware mobile sensors have fundamentally changed the ways radiation levels are detected and tracked. These changes have raised many exciting research questions related to nuclear forensics. This paper investigates the relationship between a mobile sensors' velocity and radiation counts. The study compares the correlations of the sensors' velocity and measurment radiation counts with a source...
In a number of applications, positron emission tomography (PET) requires two or more scans to observe and even quantify changes in function (e.g. tissue metabolism, or receptor binding potentials). Conventionally the raw datasets are reconstructed into images independently, allowing no sharing of information. Kernelised EM (KEM) is a recently proposed PET reconstruction method that utilises one or...
This paper is about possibility of using the support vector regression method for telecommunication networks monitoring. The paper defines a role of regression analysis in the formation of the correlations between telecommunication networks quality parameters. An example of using the regression analysis by support vector machine with scikit-learn implementation is given.
Because the contrast of the image for guiding the high-speed infrared air-to-air missile is low, its signal to noise ratio is poor and the target and its background gray-scale coupling is strong, the paper analyzes the reasons why the threshold value segmentation method and the fuzzy C-means clustering method have the over-segmentation and under-segmentation in segmenting the above type of image....
In this contribution, classification of two main neuromuscular diseases namely Myopathy and Neuropathy and Healthy signals is performed using cross-correlation based feature extraction technique. For this purpose, cross-correlation of Healthy, Myopathy and Neuropathy disease EMG signal is done with a reference Healthy signal. Selective features like Hjorth, Adaptive Autoregressive and statistical...
Object tracking is an important task within the field of computer vision. Object tracking methodology is divided into three categories: point-, kernel-; and silhouette-based tracking. Recently, a correlation-based kernel tracking has been used in object tracking with high accuracy and performance. However, the correlation-based approach heavily relies on luminance information, thus it has many problems...
Research on iris recognition have observed that iris texture has inherent radial correlation. However, currently, there lacks a deeper insight into iris textural correlation. Few research focus on a quantitative and comprehensive analysis on this correlation. In this paper, we perform a quantitative analysis on iris textural correlation. We employ steering kernels to model the textural correlation...
In this paper, the multidimensional output Gaussian process (GP) is applied to model urban environmental data collected by sensor networks. Measurements from sensors at different locations are correlated. Moreover, we observe that the pollution level in urban area is highly coupled with human activities and shows periodic patterns accordingly. Based on these observations, we discuss the design of...
In this paper, we focused on the problem of automatic modulation classification of digital signals. Several useful characteristic parameters which can be used for modulation analysis are extracted from spectral correlation, for different types of modulated signals have different power spectral density functions. A density estimation approach based on Support Vector Machine (SVM) is developed. Also,...
In recent years, correlation filter based trackers outperform better than other trackers. Nevertheless, they only employ one feature and a single kernel, so they are usually not robust in complex scenes. In this paper, we derive a multi-feature and multi-kernel correlation filter based tracker which fully takes advantage of the invariance-discriminative power spectrums of various features and kernels...
A personal or enterprise collection of a large set of face images may contain many types of tags used for querying the collection. Often the tags have many irrelevant content that may not reflect the image content in terms of the facial characteristics. In this paper, we propose a data curation method to filter out the irrelevant face images using a face recognition based subgraph identification....
Robust scale and rotation estimation is an important and challenging problem in visual object tracking. There have been proposed many sophisticated trackers to track the location of a target accurately, but most of them do not take much attention to the scale and rotation estimation. Inspired by the success of the correlation filters in visual tracking, we proposed a novel scale-and-rotation correlation...
The recent decade has witnessed remarkable developments of SIFT-based approaches for image retrieval. However, such approaches are inherently insufficient in handling the semantic gap and large viewpoint changes, leading to inferior performance. To address these limitations, this paper extends SIFT-based match kernels by integrating the match functions for SIFT and CNN features. Specifically, a thresholded...
In this paper we study the problem of estimating the unknown delay(s) in a system where we receive a linear combination of several delayed copies of a known transmitted waveform. This problem arises in many applications such as timing-based localization or wireless synchronization. Since accurate delay estimation requires wideband signals, traditional systems need high-speed AD converters which poses...
The metric in the reproducing kernel Hilbert space (RKHS) is known to be given by the Gram matrix (which is also called the kernel matrix). It has been reported that the metric leads to a decorrelation of the kernelized input vector because its autocorrelation matrix can be approximated by the (down scaled) squared Gram matrix subject to some condition. In this paper, we derive a better metric (a...
Sleep spindles (SSs) are characteristic electroencephalographic (EEG) waveforms of sleep stages N2 and N3. One of the main problems associated with SS detection is the high number of false positives. In this paper we propose a new periodogram based on correntropy to detect SSs and enhance their characterization. Correntropy is a generalized correlation, under the information theoretic learning framework...
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