An effective algorithm for global abnormal detection from surveillance video is proposed in this paper. The algorithm is based on sparse representation. To deal with the illumination change in video scenes, specific f...
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ISBN:
(纸本)9781510830981
An effective algorithm for global abnormal detection from surveillance video is proposed in this paper. The algorithm is based on sparse representation. To deal with the illumination change in video scenes, specific feature extract methods are designed for corresponding illumination conditions. In the case of non-uniform illumination, features are extracted directly on the original image;in the case of uniform illumination, features are extracted on the binary image obtained by threshold segmentation on the difference image, where the thresholds are computed by the Otsu's method. The features extracted on normal video are used to learn an over-complete dictionary. Then, the sparse reconstruction cost over the dictionary is used to detect abnormal events. Experiments on the open global abnormal dataset and the comparison to the state-of-the-art methods validate effectiveness and quickness of our algorithm.
To distinguish different level of occupational stress of students and nurses, this paper applied inner composition alignment (IOTA) algorithm to analyse differences between the students' Electroencephalogram (EEG)...
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ISBN:
(纸本)9781509037117
To distinguish different level of occupational stress of students and nurses, this paper applied inner composition alignment (IOTA) algorithm to analyse differences between the students' Electroencephalogram (EEG) and the nurses' EEG and to explore the influence of occupational stress on EEG. First, we selected nine cases of students EEG data and the same quantity of nurses EEG data from General Hospital of Nanjing Military Region, then, constructed two different brain networks by utilizing IOTA method and analyzed the networks characteristics which include IOTA coefficient, Clustering coefficient and Average degree. In order to verify the accuracy of the results, we used SPSS to analyse the significant difference hypothesis testing. The IOTA coefficients are inconsistent between students' EEG and nurses' EEG. Compared with the students, nurses' brain network is more complex. The level of occupational stress of nurses is higher than students. The IOTA algorithm can significantly distinguish occupational stress between students and nurses, and all results demonstrated the validity of the algorithm.
Purpose. Since fractal image coding is time-consuming and is prone to causing "blocking artifact", the article aims to combine fractal image coding, wavelet transform and compressed sensing to put forward a ...
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The physiological analysis of electroencephalogram (EEG) signals is of great significance in assessing the activity of the brain function and the physiological state. EEG is a means of clinical examination of brain di...
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ISBN:
(纸本)9781509037117
The physiological analysis of electroencephalogram (EEG) signals is of great significance in assessing the activity of the brain function and the physiological state. EEG is a means of clinical examination of brain diseases. Age is one of the important factors that affect the results of the EEG. EEG signal analysis is mainly to analyze the time series of the signal, multiscale entropy (MSE) analysis [1-3] is the method that used to analyze the finite length of the time series. Multiscale sign series entropy (MSSE) method is proposed for the analysis of EEG signals in the young and middle-aged. We use the proposed method to analyze the signals from several aspects of data length, word length, noise, multi scale etc. By analyzing the influence of these factors, we can still distinguish the EEG signals of different ages. Multiscale sign series entropy (MSSE) analysis algorithm can effectively separate the brain electrical signals from the young and middle aged, which is expected to have a certain reference value for the traditional pathological analysis of the EEG signals.
In this paper,we applied wavelet permutation entropy to analyze the Ventricular Fibrillation(VF) signals and Sudden Cardiac Death(SCD) signals for making an effective distinction from normal sinus rhythm(NSR) ***,thre...
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In this paper,we applied wavelet permutation entropy to analyze the Ventricular Fibrillation(VF) signals and Sudden Cardiac Death(SCD) signals for making an effective distinction from normal sinus rhythm(NSR) ***,three different ECG signals are decomposed by wavelet and reconstructed in each single *** highly discriminated frequency band will be chosen as our target ***,under the circumstances of different series length,embedding dimension and delay time,the main work is to distinguish the three ECG signals in different frequency bands based on the permutation entropy(PE).The results show that permutation entropy method can make a distinction between normal and abnormal ECG signals which aren't decomposed,but the effect of decomposing with wavelets is better *** the highest discriminated frequency band is from 15.625 Hz to 31.25 *** the point of different data length,embedding dimension and delay time,it was found that permutation entropy method have different effects and the findings may assist cardiac clinical diagnosis.
The difference of protein sequence or protein structure can be used for the construction of molecular evolutionary tree or phytogenetic tree with certain hierarchy and topology. The divergent points in the tree sugges...
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Sleep Electroencephalogram(Sleep EEG) detection and treatment can provide the basis for clinical diagnosis and treatment. According to the non-stationary random character of EEG itself, the paper proposed multiscale s...
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ISBN:
(纸本)9781510806450
Sleep Electroencephalogram(Sleep EEG) detection and treatment can provide the basis for clinical diagnosis and treatment. According to the non-stationary random character of EEG itself, the paper proposed multiscale sign series entropy(MSSE) method and applied it to the state of sleep EEG analysis. Numerical results showed that, MSSE method can effectively differentiate awake period β wave and sleep stage β wave even if under the influence of the noise. The results show that the algorithm can aid in clinical diagnosis of sleep EEG.
Target extraction is a key technology for image measurement of moving particles distributed in fluidic system. In this paper, we propose a novel moving particle extraction method based on multimodal characteristic of ...
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Epilepsy is a common neurological diseases caused by abnormal discharge of neurons in the brain. the attack is sudden and repeated characteristics. Therefore, in order to advance seizure prediction has important meani...
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ISBN:
(纸本)9781510806450
Epilepsy is a common neurological diseases caused by abnormal discharge of neurons in the brain. the attack is sudden and repeated characteristics. Therefore, in order to advance seizure prediction has important meaning for patients to take timely measures in this paper, the seizures in patients with EEG by using the method of symbolic transfer entropy are research and analysis, Through the EEG signal of epilepsy patients during attack and normal human alpha wave is extracted, By using the method of symbolic transfer entropy for analysis and research, Prior to the transfer characteristics which have been analyzed under entropy alpha wave component, this paper starts from beta wave components. then make a study by using the method of symbolic transfer entropy, the study found that using this method can differentiate the normal EEG and EEG in patients with epilepsy, Also found that the existence of nonlinear large amount of time series of EEG. This method is also proved symbolic transfer entropy based algorithm can be used to analyze the EEG signals fully, reveals the difference between epileptic EEG and normal EEG, clinical contribution made certain detection and prediction of epilepsy.
Detection of groups of interacting people is a difficult task, especially in an unconstrained and crowded environment. In this paper, as a main contribution, we present a novel and efficient framework for social group...
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