Packet Features Research Articles

Scattering of a Dirac electron on a mass barrier

The interaction of a wave packet (and in particular the wave front) with a mass barrier is investigated in one dimension. We discuss the main features of the wave packet that are inherent to two-dimensional wave packets, such as compression during reflection, penetration in the case when the energy is lower than the height of the barrier, waving tails, precursors, and the retardation of the reflected and penetrated wave packets. These features depend on the wave-packet envelope function which we demonstrate by considering the case of a rectangular wave packet with sharp front and trailing edges and a smooth Gaussian wave packet. The method of Fourier integral for obtaining the nonstationary solutions is used.

How to block Tor’s hidden bridges: detecting methods and countermeasures

Tor network has been widely used for protecting the privacy of users while accessing various online services. Since Tor can be easily blocked by blacklisting the publicly published Tor relays, the hidden bridges-based blocking-resistance mechanism is designed and implemented in the current Tor network. Any user can subscribe a tuple of three bridges via email, https, twitter etc. However, we have found that there exist high correlations among those published tuples, which can be exploited to effectively detect hidden bridges by monitoring the outbound traffic from a controlled network. When Tor clients try to connect chosen hidden bridges, multiple SYN packets with consecutive source ports will be sent almost simultaneously, destining for different hosts. If any destination IP contained among such packets belongs to a known bridge, all others can then be inferred to be of bridges too. By recording and analyzing a series of traffic segments satisfying the above packet features, the hidden bridges used in a controlled network can be detected and further blocked. According to different available computing and storage resources, we proposed both online and offline detecting methods. Both analytical and simulation results verify the high correlation among published bridge tuples, validating the feasibility of our methods. By configuring optimized detecting parameters in the real-world experiments, we can achieve a detection rate of 86.7 % with a 0.85 % false-positive rate for online detection, and a 98.4 % detection rate with a 0.62 % false-positive rate for offline detection. To make up the flaws in Tor's current blocking-resistance mechanism, we also provide some countermeasures from the perspective of Tor network and users, respectively.

Characteristic imaging features of body packers: a pictorial essay

The drug-trafficking business has risen tremendously because of the current increased demand for illegal narcotics. The smugglers conceal the drugs in their bodies (body packers) in order to bypass the tight security at international borders. A suspected body packer will normally be sent to the hospital for imaging investigations to confirm the presence of drugs in the body. Radiologists, therefore, need to be familiar with and able to identify drug packets within the human body because they shoulder the legal responsibilities. This pictorial essay describes the characteristic imaging features of drug packets within the gastrointestinal tract.

Securing Data using Pre-filtering and Traceback Method

In this paper, we propose RegEx-Filter(pre-filtering approach) and IP traceback method to trace an unauthorized access incidents in the Internet, The current control technologies cannot stop specific way of access. The basic idea is to generate the RegEx print from RegEx set and use it in prefiltering most unmatched items and trace out the unauthorized user by means of traceback method. There are two key challenges in RegEx: the generation of RegEx print and the matching process of RegEx print. The generation of RegEx is tricky as it needs to tradeoff between two conflicting goals: filtering effectiveness, which means to filter out as many unmatched items as possible, and matching speed, which means that we want RegEx print to be as high as possible. Here we describe the development and the evaluation of our prototype system. The main features of our proposed method are the RegEx-Filter which filters the unmatched items. Packet feature, which is composed of specific packet information contained in a packet for identification of an unauthorized packet. And the algorithm using datalink identifier to identify a routing of a packet. We show the development of the prototype system, RegEx-Filter equipped with tracing functions on routers and its processing result.

A SVM Based Automated Detection of Uterine Fibroids Using Gabor and Wavelet Features

This paper suggests an automated detection procedure for identification of ultrasonic imaging based uterine fibroids using Gabor filters and wavelet features with support vector machines as classifier. A classification accuracy of 100% is achieved for 86 test images using wavelet packet features, which indicates the potential suitability of the proposed scheme for clinical diagnosis.

Classification of sleep apnea types using wavelet packet analysis of short-term ECG signals

Obstructive sleep apnea (OSA) causes a pause in airflow with reduced breathing effort. In contrast, central sleep apnea (CSA) event is not accompanied with breathing effort. The aim of this study is to differentiate CSA and OSA events using wavelet packet analysis and support vector machines of ECG signals over 5s period. Eight level wavelet packet analysis was performed on each 5s clip using Daubechies (DB3) mother wavelet and for comparison discrete wavelet analysis was performed using Symlet (SYM3) wavelets. The choice of wavelet basis function was based on a grid search using Daubechies, Symlet and biorthogonal wavelets with decomposition levels varying between 2 and 5. Support vector machine is used for two-class classification. Out of 29 overnight polysomnographic studies, 23 of them were used in the training phase and 6 patients were used for independent testing. The proposed algorithm is shown to perform better in classifying CSA and OSA with wavelet packet features (accuracy-91%, sensitivity-88.14% and specificity-91.11%) than with the traditional wavelet decomposition based features (accuracy-83.79%, sensitivity-89.18% and specificity-83.59%). The independent test resulted in overall classification accuracy, sensitivity and specificity of 91.08, 91.02 and 91.09% respectively using wavelet packet analysis. The classification result indicates the possibility of non-invasively classifying CSA and OSA events based on shorter segments of ECG signals.

Research on Acoustic Emission Test Method for Metal Fatigue Fracture Based on Wavelet Packets Feature Extraction

The appearance and growth of the microcracks in the structure is an important factor that influences the structure safety and its service life. Thus it is very important to detect the crack and monitor its growth at the beginning of the crack. Aiming at the main style of failures in metal structure - fatigue fracture, this paper research acoustic emission waveforms analysis that base on wavelet packets feature extraction, through processing acoustic emission signal to test metal fatigue fracture. First, this paper analyses the reason of metal fatigue fracture and introduces the theory of acoustic emission. Based on that, we establish the time domain module of acoustic emission signal and extract the feature of acoustic emission signal using wavelet packets. According to the experimental results bending specimen, acoustic emission techniques monitoring fatigue crack propagation is certificated not only to resemble variable rule of fatigue crack propagation but also to catch generation of fatigue crack in real time. Compared with the method of parameter extraction, this method can not only realize real-time and dynamic monitoring, but also get the result that is similar with fatigue crack expanding rate curve.

Gear fault detection based on adaptive wavelet packet feature extraction and relevance vector machine

In this article, a novel fault detection method based on adaptive wavelet packet feature extraction and relevance vector machine (RVM) is proposed for incipient fault detection of gear. First, ten statistical characteristics in time domain and all node energies of full wavelet packet tree are extracted as candidate features. Then, Fisher criterion is applied to evaluate the discrimination power of each feature. Finally, two optimal features from time domain and wavelet domain, respectively, are selected to be used as inputs to the RVM. Furthermore, moving average is applied to each feature to improve accuracy for online continuous fault detection. By combining wavelet packet transform with Fisher criterion, it is able to adaptively find the optimal decomposition level and select the global optimal features. The RVM, a Bayesian learning framework of statistical pattern recognition, is adopted to train the fault detection model. The RVM was compared with the popular support vector machine (SVM) with the increase of training samples. Experimental results validate the effectiveness of the proposed method, and indicate that RVM is more suitable than SVM for online fault detection.

Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection

In this study, a hierarchical electroencephalogram (EEG) classification system for epileptic seizure detection is proposed. The system includes the following three stages: (i) original EEG signals representation by wavelet packet coefficients and feature extraction using the best basis-based wavelet packet entropy method, (ii) cross-validation (CV) method together with k-Nearest Neighbor ( k-NN) classifier used in the training stage to hierarchical knowledge base (HKB) construction, and (iii) in the testing stage, computing classification accuracy and rejection rate using the top-ranked discriminative rules from the HKB. The data set is taken from a publicly available EEG database which aims to differentiate healthy subjects and subjects suffering from epilepsy diseases. Experimental results show the efficiency of our proposed system. The best classification accuracy is about 100% via 2-, 5-, and 10-fold cross-validation, which indicates the proposed method has potential in designing a new intelligent EEG-based assistance diagnosis system for early detection of the electroencephalographic changes.

Component-level fault diagnostics of a bevel gear using a wavelet packet transform

This article presents a novel diagnostic algorithm based on wavelet packet transform. Although existing fault diagnostic techniques may detect the occurrence of failure or excessive malfunction in a system, they rarely reveal such detailed information as fault position, not to mention their reduced effectiveness in a system whose fault feature is not so pronounced. The proposed algorithm takes advantage of time—frequency localized features of wavelet packets in order to classify the system condition and pinpoint the fault position. Fisher's linear discriminant is used to classify system conditions, all the wavelet packets are exhaustively searched in order to unravel the optimal base (or packet) that discriminates different system conditions based on the Fisher value. It is the time—frequency localized feature of the wavelet packet that further makes it possible to detect fault position and even faulty component in the bevel gear. The proposed algorithm is validated using a machine fault simulator that consists of a motor, a bevel gear, and a bearing, where the condition of the bevel gear may be varied to simulate normal, cracked, and broken conditions using vibration signal. It turns out that the proposed algorithm precisely locates fault position as well as identifies system conditions.

Adapted filter banks for feature extraction in transcranial magnetic stimulation evoked responses

A novel adaptive and approximate shift-invariant wavelet packet feature extraction scheme for event-related potentials (ERPs) in the electroencephalogram (EEG) is introduced in this paper. In this algorithm, the shift-invariant wavelet packed decomposition is done by integrating a cost function for decimation decision in each sub-band expansion. Additionally, a shape adaptation of the wavelet is implemented to find the best adapted wavelet shape for a given class of ERPs. This scheme is used to analyze the time course of the impact of single-pulse transcranial magnetic stimulation (TMS) to the auditory ERPs. We show that the proposed scheme is able to extract even slightest impacts of TMS, making it a promising tool for the extraction of weak ERPs components, particularly in hybrid TMS-EEG/ERP setups.

Dynamics of electron wave packet in a disordered chain with delayed nonlinear response

Abstract We investigate the dynamics of one electron wave packet in a linear chain with random on-site energies and a nonadiabatic electron–phonon interaction which is described by a delayed cubic nonlinear term in the time-dependent Schrodinger equation. We show that in the regime where the wave packet is delocalized in the case with only the delayed nonlinearity, the wave packet becomes localized when the disorder is added and the localization is enhanced by increasing the disorder. In the regime where the self-trapping phenomenon occurs in the case with only the delayed nonlinearity, by adding the disorder the general dynamical features of the wave packet do not change if the nonlinearity parameter is small, but the dynamics shows the subdiffusive behavior if the nonlinearity parameter is large. The numerical results demonstrate complicated wave packet dynamics of systems with both the disorder and nonlinearity.

Quantitative Analysis of Variant Selection and Orientation Relationships during the γ-to-α Phase Transformation in Hot-Rolled TRIP Steels

The orientation relationships that apply to the fcc (γ) – bcc (α) phase transformation in high-performance hot-rolled TRIP-aided steels were characterised by EBSD techniques. A statistical treatment of the experimental data allows the mean orientation relationship to be determined. This mean orientation relationship was compared to the models commonly proposed in the literature and confronted qualitatively to the predictions of the phenomenological theory of martensite crystallography (PTMC). The variant selection phenomenon was also characterized quantitatively at the level of individual austenite grains. The reconstruction of the EBSD maps evidences that bainite grows by packets in which the bainite laths share a common {111}γ plane in the austenite. This growth mechanism is not influenced by the prior hot deformation of the austenite. The hot deformation has a critical influence on the number of packets that forms. The analysis of the crystallographic features of the bainite packets reveals that all possible variants are formed in a packet, though in different proportions.

Modulational instability of ion-acoustic wave envelopes in magnetized quantum electron-positron-ion plasmas

The amplitude modulation of quantum ion-acoustic waves (QIAWs) along an external magnetic field is studied in a quantum electron-positron-ion (e-p-i) magnetoplasma. Reductive perturbation technique is used to derive the three-dimensional nonlinear Schrödinger equation which governs the slow modulation of QIAW packets. Accounting for the effects of the electron to ion number density ratio (μ), the normalized ion-cyclotron frequency (ωc) as well as the ratio (H) of the “plasmonic energy density” to the Fermi energy, new regimes for the modulational instability of QIAWs are obtained and analyzed. In contrast to one-dimensional unmagnetized e-p-i plasmas, the instability growth rate is shown to suppress with increasing μ or decreasing the values of H. The predicted results could be important for understanding the salient features of modulated QIAW packets in dense astrophysical plasmas as well as to the next generation intense laser solid density plasma experiments.

Coherent structures in turbulent boundary layers with adverse pressure gradients

The coherent structures in turbulent boundary layers (TBLs) subjected to adverse pressure gradients (APGs) were investigated by analyzing a database of direct numerical simulations. The equilibrium adverse pressure gradient flows were established by using a power law free-stream distribution. The population trends of the spanwise vortices show that the outer regions of the APG TBLs are densely populated with hairpin-like vortices. These vortical structures induce low-momentum regions in the middle of the boundary layers, which result in an outer peak in the Reynolds shear stress. The 3-D features of the hairpin packets were deduced from their spatial characteristics in the spanwise-wall-normal plane. The conditionally averaged velocity fields show that there are counter-rotating v−w swirling motions that represent cross-sectional evidences of the packets. Moreover, two-point correlations and linear stochastic estimations were used to provide statistical information about the hairpin packet motions in the ...

Estimation of the knee joint angle from surface electromyographic signals for active control of leg prostheses

The surface electromyographic (SEMG) signal is very convenient for prosthesis control because it is non-invasively acquired and intrinsically related to the user's intention. This work presents a feature extraction and pattern classification algorithm for estimation of the intended knee joint angle from SEMG signals acquired using two sets of electrodes placed on the upper leg. The proposed algorithm uses a combination of time-domain and frequency-domain approaches for feature extraction (signal amplitude histogram and auto-regressive coefficients, respectively), a self-organizing map for feature projection and a Levenberg–Marquardt multi-layer perceptron neural network for pattern classification. The new algorithm was quantitatively compared with the method proposed by Wang et al (2006 Med. Biol. Eng. Comput. 44 865–72), which uses wavelet packet feature extraction, principal component analysis and a multi-layer perceptron neural classifier. The proposed method provided lower error-to-signal percentage and peak error amplitudes, higher correlation and fewer error events. The algorithm presented in this work may be useful as part of a myoelectric controller for active leg prostheses designed for transfemoral amputees.

Neurowavelet packet analysis based on current signature for weld joint strength prediction in pulsed metal inert gas welding process

The monitoring of welding process is crucial for the development of a real time quality control system for the pulsed metal inert gas welding (PMIGW) process. This work introduces an intelligent system for weld joint strength prediction in a PMIGW process based on the analysis of acquired current signal by wavelet packet transform. A thirteen-dimensional array of process features, i.e. six process parameters and seven wavelet packet features, are used to describe various welding conditions. These process features obtained from a set of experiments are employed as input vectors of an artificial neural network model to predict the corresponding weld joint strengths. The results, i.e. the prediction errors, show that the use of wavelet packet features gives much accurate prediction as compared to the use of the purely time domain features.

ESTIMATION OF MENTAL FATIGUE BASED ON WAVELET PACKET PARAMETERS AND KERNEL LEARNING ALGORITHMS

A new method by combining wavelet packet transform with kernel learning algorithms is proposed to estimate the mental fatigue state in this paper. The first step of this method is to investigate the impact of long term mental arithmetic task on psychology and physiology of subjects by subjective self-reporting measures, action performance test, power spectral indices of HRV and wavelet packet parameters of EEG. The second step is to calculate the wavelet packet features of all EEG data segments, including relative wavelet packet energy parameters in four frequency bands, wavelet packet entropy and three ratio indices. Finally, kernel principal component analysis (KPCA) and support vector machine (SVM) are jointly applied to differentiate two mental fatigue states. The investigation suggests that the joint KPCA-SVM method is able to effectively reduce the dimensionality of the feature vectors, speed up the convergence in the training of SVM and achieve higher classification accuracy (88%) of the mental fatigue state. Hence KPCA-SVM could be a promising model for the estimation of mental fatigue.

Features of wave packets in the plane Poiseuille-Couette flow

Within the framework of the triple-deck theory, it is shown that the growth of the relative wall velocity in the Poiseuille-Couette flow leads to perturbations splitting into two wave packets, of which one grows faster and propagates at a higher velocity.

Refracting characteristics of backward propagating Lamb modes

Lamb waves are acoustic modes propagating along thin plates or shells and result from the coupling of longitudinal waves and shear vertical waves (polarized along the thickness direction), taking place at the surface boundaries for satisfying the boundary conditions of the waveguide. Their nature being a composition of two differently polarized components, it permits that, for some combination of frequency and wavenumber, a Lamb mode is propagating which has its group velocity contradirected with respect to the phase velocity. These are the so-called “backward” Lamb modes, and one of them takes place at the starting region (low wave number) of the S1 mode dispersion curve in aluminum. Peculiar features of a wave packet of a symmetric Lamb mode S1, backward propagating in an aluminum waveguide, are observed and anomalous behavior in refraction, diffraction and interference of this peculiar mode is experimentally put in evidence. Particularly, contradirected refraction is put in evidence, as it takes place at the boundary where a forward propagating mode is coupled to a backward propagating one, and change from forward to backward mode is obtained in case of a wedge shaped laminar plate, which goes through a zero point of the group velocity.