A Detection Algorithm to Anomaly Network Traffic Based on Spectral Kurtosis Analysis

2013 ◽  
Vol 765-767 ◽  
pp. 1461-1464 ◽  
Author(s):  
Ding De Jiang ◽  
Cheng Yao ◽  
Wei Han Zhang ◽  
Zheng Zheng Xu
Keyword(s):  
Network Traffic ◽  
Detection Algorithm ◽  
Time Frequency ◽  
Spectral Kurtosis ◽  
Analysis Theory ◽  
Time Signals ◽  
Local Properties ◽  
Simulation Results ◽  
Detailed Nature ◽  
New Time

This paper presents a detection algorithm for anomaly network traffic, which is based on spectral kurtosis analysis. Firstly, we turn network traffic into time-frequency signals at different scales. These time-frequency signals hold the more detailed nature corresponding to different scales. Secondly, the time-frequency signals at different scales are transformed into a series of new time signals by time-frequency analysis theory. These new time signals hold obvious narrowband nature and embody the local properties of network traffic. Thirdly, we calculate the spectral kurtosis values of the new time signals and then perform the feature extractions. As a result, the abnormal network traffic can be correctly identified. Simulation results show that our algorithm is feasible and promising.

Frequency hopping signal detection based on wavelet decomposition and Hilbert–Huang transform

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740078 ◽  
Author(s):  
Yang Zheng ◽  
Xihao Chen ◽  
Rui Zhu
Keyword(s):  
Signal Detection ◽  
Frequency Analysis ◽  
Wavelet Decomposition ◽  
Frequency Hopping ◽  
Detection Algorithm ◽  
Frequency Resolution ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Low Probability ◽  
Simulation Results

Frequency hopping (FH) signal is widely adopted by military communications as a kind of low probability interception signal. Therefore, it is very important to research the FH signal detection algorithm. The existing detection algorithm of FH signals based on the time-frequency analysis cannot satisfy the time and frequency resolution requirement at the same time due to the influence of window function. In order to solve this problem, an algorithm based on wavelet decomposition and Hilbert–Huang transform (HHT) was proposed. The proposed algorithm removes the noise of the received signals by wavelet decomposition and detects the FH signals by Hilbert–Huang transform. Simulation results show the proposed algorithm takes into account both the time resolution and the frequency resolution. Correspondingly, the accuracy of FH signals detection can be improved.

scholarly journals Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3524
Author(s):  
Rongru Wan ◽  
Yanqi Huang ◽  
Xiaomei Wu
Keyword(s):  
Ventricular Fibrillation ◽  
Cross Validation ◽  
Rhythmic Activity ◽  
Detection Algorithm ◽  
Motion Artifacts ◽  
Cardiac Monitoring ◽  
Mechanical Activity ◽  
Time Frequency ◽  
S Transform ◽  
Fatal Arrhythmia

Ventricular fibrillation (VF) is a type of fatal arrhythmia that can cause sudden death within minutes. The study of a VF detection algorithm has important clinical significance. This study aimed to develop an algorithm for the automatic detection of VF based on the acquisition of cardiac mechanical activity-related signals, namely ballistocardiography (BCG), by non-contact sensors. BCG signals, including VF, sinus rhythm, and motion artifacts, were collected through electric defibrillation experiments in pigs. Through autocorrelation and S transform, the time-frequency graph with obvious information of cardiac rhythmic activity was obtained, and a feature set of 13 elements was constructed for each 7 s segment after statistical analysis and hierarchical clustering. Then, the random forest classifier was used to classify VF and non-VF, and two paradigms of intra-patient and inter-patient were used to evaluate the performance. The results showed that the sensitivity and specificity were 0.965 and 0.958 under 10-fold cross-validation, and they were 0.947 and 0.946 under leave-one-subject-out cross-validation. In conclusion, the proposed algorithm combining feature extraction and machine learning can effectively detect VF in BCG, laying a foundation for the development of long-term self-cardiac monitoring at home and a VF real-time detection and alarm system.

scholarly journals New Time-Frequency Transient Features for Nonintrusive Load Monitoring

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1437
Author(s):  
Mahfoud Drouaz ◽  
Bruno Colicchio ◽  
Ali Moukadem ◽  
Alain Dieterlen ◽  
Djafar Ould-Abdeslam
Keyword(s):  
Transient Signal ◽  
Learning Tools ◽  
Time Frequency ◽  
Energy Measure ◽  
Load Monitoring ◽  
Feature Based ◽  
Stockwell Transform ◽  
Processing Techniques ◽  
Equipment Laboratory ◽  
New Time

A crucial step in nonintrusive load monitoring (NILM) is feature extraction, which consists of signal processing techniques to extract features from voltage and current signals. This paper presents a new time-frequency feature based on Stockwell transform. The extracted features aim to describe the shape of the current transient signal by applying an energy measure on the fundamental and the harmonic frequency voices. In order to validate the proposed methodology, classical machine learning tools are applied (k-NN and decision tree classifiers) on two existing datasets (Controlled On/Off Loads Library (COOLL) and Home Equipment Laboratory Dataset (HELD1)). The classification rates achieved are clearly higher than that for other related studies in the literature, with 99.52% and 96.92% classification rates for the COOLL and HELD1 datasets, respectively.

scholarly journals TMaR: a two-stage MapReduce scheduler for heterogeneous environments

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Neda Maleki ◽  
Hamid Reza Faragardi ◽  
Amir Masoud Rahmani ◽  
Mauro Conti ◽  
Jay Lofstead
Keyword(s):  
Task Scheduling ◽  
Network Traffic ◽  
Input Data ◽  
Power Reduction ◽  
Heterogeneous Environments ◽  
Two Stage ◽  
Finish Time ◽  
Remote Storage ◽  
Simulation Results

Abstract In the context of MapReduce task scheduling, many algorithms mainly focus on the scheduling of Reduce tasks with the assumption that scheduling of Map tasks is already done. However, in the cloud deployments of MapReduce, the input data is located on remote storage which indicates the importance of the scheduling of Map tasks as well. In this paper, we propose a two-stage Map and Reduce task scheduler for heterogeneous environments, called TMaR. TMaR schedules Map and Reduce tasks on the servers that minimize the task finish time in each stage, respectively. We employ a dynamic partition binder for Reduce tasks in the Reduce stage to lighten the shuffling traffic. Indeed, TMaR minimizes the makespan of a batch of tasks in heterogeneous environments while considering the network traffic. The simulation results demonstrate that TMaR outperforms Hadoop-stock and Hadoop-A in terms of makespan and network traffic and achieves by an average of 29%, 36%, and 14% performance using Wordcount, Sort, and Grep benchmarks. Besides, the power reduction of TMaR is up to 12%.

Detection of EEG-Based Eye-Blinks Using A Thresholding Algorithm

2021 ◽  
Vol 6 (4) ◽  
pp. 6-12
Author(s):  
Dang-Khoa Tran ◽  
Thanh-Hai Nguyen ◽  
Thanh-Nghia Nguyen
Keyword(s):  
Detection Algorithm ◽  
Time Frequency ◽  
Eye Blink ◽  
Eye Blinks ◽  
User Training ◽  
Electrical Potentials ◽  
Ocular Artifact ◽  
Blink Detection ◽  
Thresholding Algorithm ◽  
Frequency Properties

In the electroencephalography (EEG) study, eye blinks are a commonly known type of ocular artifact that appears most frequently in any EEG measurement. The artifact can be seen as spiking electrical potentials in which their time-frequency properties are varied across individuals. Their presence can negatively impact various medical or scientific research or be helpful when applying to brain-computer interface applications. Hence, detecting eye-blink signals is beneficial for determining the correlation between the human brain and eye movement in this paper. The paper presents a simple, fast, and automated eye-blink detection algorithm that did not require user training before algorithm execution. EEG signals were smoothed and filtered before eye-blink detection. We conducted experiments with ten volunteers and collected three different eye-blink datasets over three trials using Emotiv EPOC+ headset. The proposed method performed consistently and successfully detected spiking activities of eye blinks with a mean accuracy of over 96%.

A New Echo Cancellation Algorithm without Double-Talk Detection Based on Correlation Function

2011 ◽  
Vol 225-226 ◽  
pp. 996-999
Author(s):  
Li Jun Sun ◽  
Shou Yong Zhang ◽  
Wei Sheng Wang ◽  
Xiao Ning Zhang
Keyword(s):  
Computer Simulation ◽  
Correlation Function ◽  
Adaptive Filter ◽  
Detection Algorithm ◽  
Echo Cancellation ◽  
Acoustic Echo Cancellation ◽  
Error Signal ◽  
Echo Canceller ◽  
Filter Performance ◽  
Simulation Results

In an adaptive echo canceller, the detection algorithm able to distinguish echo path change (EPC) from double-talk (DT) is vital to ensure that adaptive filter tap coefficients are updated in case of EPC and frozen during the DT period. The paper presents a new echo cancel algorithm, which can protect the adaptive filter performance during double-talk in acoustic echo cancellation of teleconference without setting a detector. A judgment value can be directly used in the iteration formula to control the iteration speed of the filter, which composed of the correlation of the far-end signal and near-end received signal, the pre-correlation of the error signal. The computer simulation results verify that the mentioned algorithm has the good double talk protection performance, and it is very useful and efficient in distinguishing EPC from DT but with less computational complexity contrast to the congener algorithm.

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