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With the enormous amount of data generated through the internet and sensors, Internet of Things, it becomes too overwhelming for humans to examine it all. One solution is to reduce the data to a set of statistics. The perspective in this paper is the opposite, namely that most of this data is just background noise, and the interesting parts are those that deviate from background noise, the parts that...
With the enormous amount of data generated through the internet and sensors, Internet of Things, it becomes too overwhelming for humans to examine it all. One solution is to reduce the data to a set of statistics. The perspective in this paper is the opposite, namely that most of this data is just background noise, and the interesting parts are those that deviate from background noise, the parts that...
With the exponential growth in information, “Big Data,” a key question is what to do with this information. A classic possibility is to characterize it through statistics. The perspective in this paper is the opposite, namely that most of the value in the information is in the parts that deviates from the average, that are unusual, atypical. Think of art: The valuable paintings or writings are those...
Atypicality is a new concept that uses a codelength-based deviation from the norm to find the interesting rare events. In a previous paper we have developed an information theoretic approach for discrete data. Then in the two other papers, we came up with an extension to the real-valued models for Gaussian and vector Gaussian cases. In the current paper we generalize our real-valued model to the class...
Atypical sequences are subsequences of long sequences that deviate from the ‘normal’ data. In previous papers we have developed an information-theoretic approach to such sequences for discrete and real-valued data. In the current paper we extend the principle of real-valued data that follows vector Gaussian models, which allows for finding relationship between data. We include a simple application...
Atypical sequences are subsequences of long sequences that deviates from the ‘normal’ data. In a previous paper we have developed an information theory approach to such sequences for discrete data. In the current paper we extend this principle to real-valued data, whereby it is possible to use signal processing tools to search for atypical data. The application of this principle is to extract a few...
One characteristic of the information age is the exponential growth of information, and the ready availability of this information through networks, including the internet — “Big Data.” The question is what to do with this enormous amount of information. One possibility is to characterize it through statistics — think averages. The perspective in this paper is the opposite, namely that most of the...
In this paper, we proposed a method based on time-frequency dependent features extracted from Intrinsic Mode Functions (IMFs) and physiological feature such as the number of premature beats (PBs) to predict the onset of Paroxysmal Atrial Fibrillation (PAF) by using electrocardiogram (ECG) signal. To extract IMFs, we used Empirical Mode Decomposition (EMD). In order to predict PAF, we used variance...
In this work, a novel local spline smoothing-based approach is proposed in order to despeckling medical ultrasound images. For better comparison, firstly nine conventional statistical spatial filters were applied on the ultrasound images and the results evaluated by quantitative indices of contrast to noise ratio (CNR) and speckle signal to noise ratio (SSNR). Secondly, one-dimensional cubic spline...
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