Global detection of DDoS attack based on time and frequency analysis

Third order time and frequency analysis has exhibited great potential for correlation analysis of multi-sensor datasets, but is usually presented as separate time domain and frequency domain approaches. A combined framework of both frequency domain and time domain has rarely been used. This paper proposes a non-parametric third order time and frequency domain framework which used two dimensional Fourier transforms to bridge the gap between time domain and frequency domain. A unified framework offers flexibility and efficiency to apply to data. In this paper we study neural spike train data treated as stochastic point processes. In time domain direct analysis, third order cumulant densities of spike trains are applied, which need all first-, secondand third order product densities to be calculated before constructing the third order cumulant density, which brings additional challenges. The novelty in this study is that a new framework is proposed which can offer an alternative approach without calculating lower order quantities and can reveal nonlinear relationship between neural recordings. The results show that the present framework provides a novel non-parametric method to estimate both time and frequency domain measurements which is applicable to neural spike trains.

In this paper, we propose a novel method for the detection of road accidents by analyzing audio streams for road surveillance application. In the last decade, due to the increase in a number of people and transportation vehicles, traffic accidents have become one of a major public issue worldwide. The vast number of injuries and death due to road traffic accident reveals the story of a global crisis of road safety. A total of 52,160 road traffic accidents (RTA), 1130 injuries and 85 fatalities were registered during the year 2000, in the state of Qatar. An increase in the number of transportation vehicles around cities has risen the need for more security and safety in public environments. The most obvious reason for a person's death during accidents is the absence or prolong response time of the first aid facility, which is due to the delay in the information of the accident being reached to the police, hospital or ambulance team. In the last couple of years, several surveillance systems have been proposed based on image and video processing for automatically detecting road accidents and car crashes to ensure a quick response by emergency teams. However, in some situations such as adverse weather conditions or cluttered environments, the visual information is not sufficient enough, whereas analyzing the audio tracks can significantly improve the overall reliability of surveillance systems. In this paper we propose a novel method that automatically identifies hazardous situations such as tire skidding and car crashes in presence of background noises, by analyzing the audio streams. Previous studies show that methods for the detection, estimation, and classification of nonstationary signals can be enhanced by utilizing the time-frequency (TF) characteristics of such signals. The TF-based techniques have been proved to outperform classical techniques based on either time or frequency domains, in analyzing real-life nonstationary signals. Time-frequency distributions (TFDs) give additional information about signals that cannot be extracted from either the time domain or frequency domain representations, i.e. the instantaneous frequency of the components of a signal. In order to utilize this extra information provided by TF domain representation, the proposed approach extracts TF image features from quadratic time-frequency distributions (QTFDs), for the detection of audio anomalies. The extended modified-B distribution (EMBD) is utilized to transform a 1-dimensional audio signal into 2-dimensional TF representation which is interpreted as an image. The image descriptors based features are then extracted from the TF representation to classify visually the audio signals into background or abnormal activity patterns in the TF domain. The proposed features are based on Haralick's texture features extracted from the TF representation of audio signals considered and processed as a textured image. These features are used to characterize and hence classify audio signals into M classes. This research study demonstrates that a TF image pattern recognition approach offers significant advantages over standard signal classification methods that utilize either t-domain only or f-domain only features. The proposed method has been experimentally validated on a large open source database of sounds, including several kinds of background noise. The events to be recognized are superimposed on different background sounds of roads and traffic jam. The obtained results are compared with a recent study, utilizing the same large and complex data set of audio signals, and the same experimental setup. The overall classification results confirm the superior performance of the proposed approach with accuracy improvement up to 6%.

Conventional time-frequency Transformers primarily focus on the global features of signals in the time-frequency domain while neglecting the local features in both the time domain and frequency domain. This limitation hinders the ability of the model to effectively capture the shared features among time, frequency, and time-frequency representations. To address this issue, a Multi-Branch Joint Time-Frequency Transformer (MBJTF-Transformer) is proposed for domain generalization (DG) fault diagnosis of rotating machinery. Specifically, a time-branch Transformer is designed to extract temporal features, while a frequency-branch Transformer captures frequency-domain information. In addition, a time-frequency Transformer is employed to learn the shared representations across time, frequency, and time-frequency domains. Finally, a multi-decision fusion strategy of MBJTF-Transformer is adopted to enhance the generalization capability of the model. Experimental results on both the SCARA (Selective Compliance Assembly Robot Arm, SCARA) dataset and the PU (Paderborn University) bearing dataset demonstrate that the proposed MBJTF-Transformer achieves superior DG performance compared to multiple state-of-the-art sequential models.

PurposePenetration state is one of the most important factors for judging the quality of a gas tungsten arc welding (GTAW) joint. The purpose of this paper is to identify and classify the penetration state and welding quality through the features of arc sound signal during robotic GTAW process.Design/methodology/approachThis paper tried to make a foundation work to achieve on‐line monitoring of penetration state to weld pool through arc sound signal. The statistic features of arc sound under different penetration states like partial penetration, full penetration and excessive penetration were extracted and analysed, and wavelet packet analysis was used to extract frequency energy at different frequency bands. The prediction models were established by artificial neural networks based on different features combination.FindingsThe experiment results demonstrated that each feature in time and frequency domain could react the penetration behaviour, arc sound in different frequency band had different performance at different penetration states and the prediction model established by 23 features in time domain and frequency domain got the best prediction effect to recognize different penetration states and welding quality through arc sound signal.Originality/valueThis paper tried to make a foundation work to achieve identifying penetration state and welding quality through the features of arc sound signal during robotic GTAW process. A total of 23 features in time domain and frequency domain were extracted at different penetration states. And energy at different frequency bands was proved to be an effective factor for identifying different penetration states. Finally, a prediction model built by 23 features was proved to have the best prediction effect of welding quality.

In this article, theoretical modeling and simulations are introduced to explore the contact failure characteristics for connectors both in time and frequency domains. The equivalent models are developed to describe the impedance performances of un‐degraded and degraded connectors with the time domain and frequency domain, respectively. The experiments using a Network Analyzer for time and frequency domains analyses are conducted to verify the proposed models. The measured results agree well with the simulation results in the time and frequency domains.

During the long-term operation of wind turbines, due to environmental factors and equipment aging, the health and reliability of each component will gradually decline, leading to failure. To assess the health status of wind turbines, timely grasp the subsequent changes and development trends, it is necessary to extract degradation characteristics, including time domain, frequency domain, and time-frequency domain characteristics. These degradation characteristics can reflect the operating status of the equipment, help build health indicator curves, and evaluate the health status of high-speed shaft bearings of wind turbines. Selecting reasonable degradation characteristics is an important prerequisite for constructing a health index curve, and using evaluation indicators to construct a comprehensive evaluation function to screen degradation characteristics. The feature fusion method based on a self-organizing feature mapping network is used to fuse multiple selected degradation features and fuse the selected multiple degradation features into a curve that can reflect the bearing degradation process. Finally, a quantitative analysis is performed on the health index curve to scientifically assess the health status of bearings. Bearings are one of the key components of wind turbines. Based on the health index curve constructed in this article, an appropriate prediction model is selected to predict the health index trend of bearings. A timely and effective grasp of the health trends of wind turbine bearings is of great practical significance for formulating scientific and reasonable maintenance measures for wind farms. The work of this article will be divided into the following four parts: (1) Extracting the time domain, frequency domain, and time-frequency domain degradation characteristics of high-speed shaft bearing vibration signals of wind turbines; (2) Comprehensive evaluation using monotonicity, correlation, and robustness constructs function to screen degenerate features; (3) Use self-organizing feature mapping network. The network integrates the selected degradation features and constructs a health index curve; (4) Based on the constructed health index curve, optimize the BiLSTM network hyperparameters through Bayesian and establish a BO-BiLSTM network model to achieve a more accurate and scientific prediction of the health index trend.

Corona-generated audible noise is induced by the collisions between space charges and air molecules. It has been proven that there is a close correlation between audible noise and corona current from DC corona discharge. Analysis on the correlation between audible noise and corona current can promote the cognition of the generation mechanism of corona discharge. In this paper, time-domain waveforms of AC corona-generated audible noise and corona current are measured simultaneously. The one-to-one relationship between sound pressure pulses and corona current pulses can be found and is used to remove the interferences from background noise. After the interferences are removed, the linear correlated relationships between sound pressure pulse amplitude and corona current pulse amplitude are obtained through statistical analysis. Besides, frequency components at the harmonics of power frequency (50 Hz) can be found both in the frequency spectrums of audible noise and corona current through frequency analysis. Furthermore, the self-correlation relationships between harmonic components below 400 Hz with the 50 Hz component are analyzed for audible noise and corona current and corresponding empirical formulas are proposed to calculate the harmonic components based on the 50 Hz component. Finally, based on the AC corona discharge process and generation mechanism of audible noise and corona current, the correlation between audible noise and corona current in time domain and frequency domain are interpreted qualitatively. Besides, with the aid of analytical expressions of periodic square waves, sound pressure pulses, and corona current pulses, the modulation effects from the AC voltage on the pulse trains are used to interpret the generation of the harmonic components of audible noise and corona current.

Objectives:Pre-diabetes represents the initial stage of type 2 diabetic disease. This study aimed to highlight the importance of the 6-month integrated approach of yoga therapy (IAYT) on the time domain and frequency domain of heart rate variability (HRV) in pre-diabetic subjects.Method:The study was conducted on 250 pre-diabetic adults aged 30–50 years attending a tertiary care hospital. The patients were divided into two groups: The study group (n = 125) was subjected to the IAYT and the control group (n = 125) was not engaged in yoga therapy. Biochemical parameters such as blood glucose, glycated hemoglobin, and the time domain and frequency domain of HRV were recorded at baseline and after 6 months of yoga intervention.Results:Participants had a mean age of 45.4 ± 6.4 years, the post-yoga intervention resulted in a significant decline in blood glucose, glycated hemoglobin, and significantly decreased frequency domain parameters low frequency (LF), and LF/high-frequency ratio (LF/HF ratio), and significantly increased in high frequency (HF) and in time-domain parameter standard deviation of normal-normal (NN) interval, mean percentage of differences higher than 50 ms in RR intervals (pRR50), and root mean square of successive differences between normal heartbeat (RMSSD). The number of pairs of successive NN (R-R) intervals that differ by more than 50 ms (NN50) significantly increased.Conclusion:The study clearly indicates that after 6 months of yoga intervention, autonomic nervous system shifted toward parasympathetic dominance, which was assessed by time domain and frequency domain parameters of HRV.

BackgroundReverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs) and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality.ResultsHere we focused on Granger causality both in the time and frequency domain and in local and global networks, and applied our approach to experimental data (genes and proteins). For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered.ConclusionsThe results on the proteomic data and gene data confirm that Granger causality is a simple and accurate approach to recover the network structure. Our approach is general and can be easily applied to other types of temporal data.

We describe the main properties of the EQUATOR airborne geophysical system and how the system can be employed to solve some engineering and environmental problems. To date, the EQUATOR system has been used for groundwater search in 2014 and 2015 in Siberia. The name “EQUATOR” originates from the intention to EQUATE, or to balance time and frequency domain approaches in airborne electromagnetics, which is why the system was originally designed. To the best of our knowledge, EQUATOR is the only system to collect electromagnetic data both in time and in frequency domains. We further explain the difference between the two methods on some numerical and field examples. Also we describe some advantages of their coupling. In particular, by virtue of our approach, a numerically more robust solution of the one-dimensional electromagnetic inverse problem may be achieved. The recorded full-time measurements are further converted to in-phase, quadrature and off-time responses, as opposed to standard frequency or time domain solutions, which have been widely adapted within the industry. When inverting the data, our main focus is within the top 100 m of the Earth, which are the most relevant for engineering and environmental studies. We emphasize that a combined use of frequency and time domain data gives more detailed information about geological structures in the near-surface. As an example, we present some case studies obtained over a platform (Siberia) and a crystalline shield (Western Africa) setting. We further suggest using these results in such near-surface studies as sediment thickness estimation, soil state analysis, groundwater search, and geological structural analysis.

Abstract: The principal objectives of this study were to examine in-cylinder combustion pressure oscillation characteristics of soybean biodiesel in time domain and time-frequency domain, and their influences on the control and operational parameters, such as injection timing, exhaust gas recirculation (EGR) ratio, engine load and engine speed. In this study, the combustion pressure oscillation characteristics of biodiesel engine for various injection timing, EGR ratio and engine speed were investigated. The corresponding relation of pressure characteristics in the time domain and frequency domain were obtained. The results showed that the pressure oscillation and peak pressure rise acceleration occurred mainly in the diffusion combustion, and the peak pressure rise rate located in the premixed combustion. The in-cylinder pressure level curve can be divided into three stages. The pressure levels of stage 1, stage 2 and stage 3 represent the peak in-cylinder pressure, the maximum amplitude of pressure rise rate and pressure rise acceleration, respectively. As the injection timing retards, the pressure levels of stage 1 and stage 3 decrease gradually. The pressure level curve of stage 3 with 25o before top dead center (BTDC) is the highest and the oscillation is the most significant. It is worth noting that the location of each stage with various operate conditions is not fixed. At 0.41 MPa indicated mean effective pressure (IMEP), with the increase of EGR rate, the pressure levels of stage 1 and stage 2 decrease gradually. The pressure level curve of stage 3 and the maximum amplitude of pressure rise acceleration with 0% EGR rate are the highest. The oscillation with 0% EGR rate is the most significant at 0.41 MPa IMEP. Compared to 0.41 MPa IMEP, the frequency bands of stage 1 and stage 2 at 1.1 MPa IMEP are relatively low due to the soft combustion in the cylinder. As EGR rate increases, the pressure level of stage 1 decreases, and those of stage 2 and stage 3 increase gradually. The oscillation with 30% EGR rate is the most significant. With the increase of engine speed, the pressure levels of stage 1 and stage 2 decrease, and move to the low frequency. The pressure level in the high frequency domain at 1600 r/min is less than that at 1100 r/min, and the combustion process is smooth. Keywords: combustion pressure oscillation, soybean biodiesel, injection timing, exhaust gas recirculation, engine speed DOI: 10.3965/j.ijabe.20160906.1680 Citation: Ma P, Liu H L, Liu S Y. Experimental study on combustion pressure oscillation of soybean bio diesel oil. Int J Agric & Biol Eng, 2016; 9(6): 156－166.

Evaluation of heart rate variability (HRV) detects the early subclinical alterations of the autonomic nervous system. Thus, impaired HRV is the earliest subclinical marker of cardiac autonomic neuropathy (CAN) in type 1 diabetes mellitus (T1DM). We aimed to explore the HRV parameters in asymptomatic T1DM patients and compare them with the results obtained in healthy subjects. Potential associations between HRV parameters and the established risk factors for CAN and cardiovascular diseases were also investigated. Seventy T1DM patients (38 ± 12 years, 46 females) and 30 healthy subjects were enrolled into the study. For HRV analysis, beat-to-beat heart rate was recorded for 30min. The less noisy 5-min segment of the recording was analyzed by Bittium Cardiac Navigator HRV analysis software. Time domain, frequency domain, and nonlinear indices were calculated. Regarding ratio of low to high frequency component (LF/HF), no differences were found between the two populations (p = 0.227). All the further, time domain, frequency domain, and nonlinear HRV indices were significantly lower in T1DM patients (each p < 0.001). In multiple linear models, disease duration remained the only independent predictor of LF/HF ratio (p = 0.019). HbA1c was found to be significant independent predictor of all further time domain (SDNN, p < 0.001; rMSSD, p < 0.001), frequency domain (VLF, p < 0.001; LF, p = 0.002; HF, p = 0.006; Total Power, p = 0.002), and nonlinear indices (SD1, p = 0.006; SD2, p = 0.007), alone, or in combination with other factors, such as age or body mass index. Asymptomatic T1DM patients have significantly reduced overall HRV as compared with healthy subjects, indicating subclinical CAN. Quality of the glycemic control is important determinant of HRV among T1DM patients. This relationship is independent of other risk factors for CAN or cardiovascular diseases.

This paper presents a three-dimensional model of a helical two-stage gear system. Excitation is induced by periodic variation of the mesh stiffness. This case describes the real working of the gearing. The mesh phasing variation is related to the number of teeth and also to the location of the gears. First, dynamic response is calculated using Newmark's step-by-step time integration method. The numerical results are presented in both the frequency and time domains. The dynamic behaviour of a defect-free gear system and a defective one are compared in both the time domain and frequency domain. Two types of defect are considered: a cracked tooth and misalignment. The cracked tooth produces an amplitude modulation with the mesh frequency and its first harmonics on the dynamic response. An amplitude modulation also occurs when a misalignment is introduced. The Wigner–Ville distribution method is used to show the dynamic behaviour in the joint time–frequency domain and to localise the cracked teeth.

Low-rate Denial of service (LDoS) attacks has become one of the biggest threats to the Internet, cloud computing platforms, and big data centers. As an evolutionary species of DDoS attack, LDoS attack is essentially different from the DDoS attack. DDoS attacks are the behavior of malicious blocking legitimate network traffic by destroying the targets and the infrastructure around it with huge network traffic. While, LDoS attacks are the behavior of intentional degrading the quality of TCP links by throttling TCP flows to a small fraction of its ideal rate with periodic small pulse sequence. Hence, LDoS attack has a very small flow (around 10&#x0025;&#x2013;20&#x0025; of the background traffic), it is easy to eluding the detection of routers and counter-DoS mechanisms. We try to reveal the mechanism of the LDoS attack and attempt to figure out the generation principle of LDoS attack in this paper. We classify the LDoS attacks and existing defense methods according to time domain and frequency domain in which detection and defense are performed. Furthermore, we highlight the filter approach to defense against LDoS attack. The initial purpose of our work is to encourage researchers to study effective ways to detect and defend against LDoS attacks with innovation and aggressiveness.

We investigate the statistical similarity of partially polarized, partially coherent electromagnetic fields in time and frequency domains, and the relationship between statistical similarity and complete coherence. We find that, both in time domain and frequency domain, the complete coherence of two fields is equivalent to the fields being both fully polarized and statistically similar. Unlike in scalar coherence theory, statistical similarity alone is found not to constitute a sufficient condition for complete coherence. We derive the conditions under which spectrally completely coherent fields are also temporally fully coherent, and we point out that temporally completely coherent fields are necessarily fully spectrally coherent at all frequencies. Complete temporal and spectral coherence of electromagnetic fields are found to be related to the recently introduced concept of strict cross-spectral purity, but in contrast to the scalar case, strict cross-spectral purity is not a necessary condition for complete temporal coherence if the fields have different spectral polarization states.