Monitoring and Recognition of Debris Flow Infrasonic Signals

Low frequency infrasonic waves are emitted during the formation and movement of debris flows, which are detectable in a radius of several kilometers, thereby to serve as the precondition for their remote monitoring.However, false message often arises from the simple mechanics of alarms under the ambient noise interference.To improve the accuracy of infrasound monitoring for early-warning against debris flows, it is necessary to analyze the monitor information to identify in them the infrasonic signals characteristic of debris flows.Therefore, a large amount of debris flow infrasound and ambient noises have been collected from different sources for analysis to sum up their frequency spectra, sound pressures, waveforms, time duration and other correlated characteristics so as to specify the key characteristic parameters for different sound sources in completing the development of the recognition system of debris flow infrasonic signals for identifying their possible existence in the monitor signals.The recognition performance of the system has been verified by simulating tests and long-term in-situ monitoring of debris flows in Jiangjia Gully,Dongchuan, China to be of high accuracy and applicability.The recognition system can provide the local government and residents with accurate precautionary information about debris flows in preparation for disaster mitigation and minimizing the loss of life and property.

Simulation of the Physical Performance of Optical Packet Switching Nodes

A graphical and visual simulation system for the study of optical packet switching (OPS) nodes is accomplished. With the simulation system, the effect on physical performance-bit error rate (BER) due to a variety of factors such as the crosstalk parameters of OPS nodes, number of cascaded OPS nodes, length of optical output buffer, traffic load and fluctuation of amplitude of optical signals are evaluated. Reliability of the simulation system is proved by the analytical results obtained in all the above cases.

Application of empirical mode decomposition in early diagnosis of magnetic memory signal

In order to eliminate noise interference of metal magnetic memory signal in early diagnosis of stress concentration zones and metal defects, the empirical mode decomposition method combined with the magnetic field gradient characteristic was proposed. A compressive force periodically acting upon a casing pipe led to appreciable deformation, and magnetic signals were measured by a magnetic indicator TSC-1M-4. The raw magnetic memory signal was first decomposed into different intrinsic mode functions and a residue, and the magnetic field gradient distribution of the subsequent reconstructed signal was obtained. The experimental results show that the gradient around 350 mm represents the maximum value ignoring the marginal effect, and there is a good correlation between the real maximum field gradient and the stress concentration zone. The wavelet transform associated with envelop analysis also exhibits this gradient characteristic, indicating that the proposed method is effective for early identifying critical zones.

Heterogeneous performance analysis of the new model of CFAR detectors for partially-correlated χ~2-targets

To mitigate the deleterious effects of clutter and jammer, modern radars have adopted adaptive processing techniques such as constant false alarm rate(CFAR) detectors which are widely used to prevent clutter and noise interference from saturating the radar’s display and preventing targets from being obscured.This paper concerns with the detection analysis of the novel version of CFAR schemes(cell-averaging generalized trimmed-mean,CATM) in the presence of additional outlying targets other than the target under research. The spurious targets as well as the tested one are assumed to be fluctuating in accordance with the χ~2-model with two-degrees of freedom. In this situation, the processor performance is enclosed by the swerling models(SWI and SWII). Between these bounds, there is an important class of target fluctuation which is known as moderately fluctuating targets. The detection of this class has many practical applications. Structure of the CATM detector is described briefly. Detection performances for optimal, CAM, CA, trimmed-mean(TM) and ordered-statistic(OS) CFAR strategies have been analyzed and compared for desired probability of false alarm and determined size of the reference window. False alarm rate performance of these processors has been evaluated for different strengths of interfering signal and the effect of correlation among the target returns on the detection and false alarm performances has also been studied. Our numerical results show that, with a proper choice of trimming parameters,the novel model CAM presents an ideal detection performance outweighing that of the Neyman-Pearson detector on condition that the tested target obeys the SWII model in its fluctuation. Although the new models CAS and CAM can be treated as special cases of the CATM algorithm, their multi-target performance is modest even it has an enhancement relative to that of the classical CAcheme. Additionally, they fail to maintain the false alarm rate constant when the operating environment is of type target multiplicity. Moreover, the non-coherent integration of M pulses ameliorates the processor performance either it operates in homogeneous or multi-target environment.

ANNOYANCE AND SPEECH INTERFERENCE OF IMPULSE AND TRAFFIC NOISE

By using the modernly developed techniques of possibility and set-valued statistics, this paper determined the thresholds of various categories of noise annoyance and defined the dose-response relation between the levels of noise and their annoyance. Three factors controlled in the experiment were types of noise: impulse and traffic; level of noise: 5Leq from 45 - 85 dB (A); and performance task: with and without speech recognition. Noise interference with speech recognition was measured with the SDT method. Our ex-perimental results showed that Leq of impulse noise should be higher than that of traffic noise to get equal annoyance; the speech recognition task might lighten the experience of annoyance; the higher the level of noise, the more its interference with the speech recognition, that is. the less the value of d '.

Multi-Attribute Preferences Mining Method for Group Users with the Process of Noise Reduction

Traditional researches on user preferences mining mainly explore the user’s overall preferences on the project,but ignore that the fundamental motivation of user preferences comes from their attitudes on some attributes of the project.In addition,traditional researches seldom consider the typical preferences combination of group users,which may have influence on the personalized service for group users.To solve this problem,a method with noise reduction for group user preferences mining is proposed,which focuses on mining the multi-attribute preference tendency of group users.Firstly,both the availability of data and the noise interference on preferences mining are considered in the algorithm design.In the process of generating group user preferences,a new path is used to generate preference keywords so as to reduce the noise interference.Secondly,the Gibbs sampling algorithm is used to estimate the parameters of the model.Finally,using the user comment data of several online shopping websites as experimental objects,the method is used to mine the multi-attribute preferences of different groups.The proposed method is compared with other methods from three aspects of predictive ability,preference mining ability and preference topic similarity.Experimental results show that the method is significantly better thap other existing methods.

High-impedance Fault Section Location for Distribution Networks Based on t-distributed Stochastic Neighbor Embedding and Variable Mode Decomposition

When high-impedance faults(HIFs)occur in resonant grounded distribution networks,the current that flows is extremely weak,and the noise interference caused by the distribution network operation and the sampling error of the measurement devices further masks the fault characteristics.Consequently,locating a fault section with high sensitivity is difficult.Unlike existing technologies,this study presents a novel fault feature identification framework that addresses this issue.The framework includes three key steps:(1)utilizing the variable mode decomposition(VMD)method to denoise the fault transient zero-sequence current(TZSC);(2)employing a manifold learning algorithm based on t-distributed stochastic neighbor embedding(t-SNE)to further reduce the redundant information of the TZSC after denoising and to visualize fault information in high-dimensional 2D space;and(3)classifying the signal of each measurement point based on the fuzzy clustering method and combining the network topology structure to determine the fault section location.Numerical simulations and field testing confirm that the proposed method accurately detects the fault location,even under the influence of strong noise interference.

Study on fractal features of modulation signals

Based on fractal theory, the note presents a novel method of modulation signals classification that adopts box dimension and information dimension extracted from received signals as features of classification. These features contain the characteristics of magnitude, frequency and phase of signals, and collect discriminatory information among various modulation modes. They are effective features in classification sense, and are insensitive to noises interfering. The theoretical analysis also proves the above conclusion. The classifier design is very simple based on such features. The simulation results show that the performances of signal classification are superior.

Modified fast Fourier transform in FBMC for satellite communications

In this paper,a Doppler scaling fast Fourier transform（Doppler-FFT）algorithm for filter bank multi-carrier（FBMC）is proposed,which can efficiently eliminate the impact of the Doppler scaling in satellite communications.By introducing a Doppler scaling factor into the butterfly structure of the fast Fourier transform（FFT）algorithm,the proposed algorithm eliminates the differences between the Doppler shifts of the received subcarriers,and maintains the same order of computational complexity compared to that of the traditional FFT.In the process of using the new method,the Doppler scaling should be estimated by calculating the orbital data in advance.Thus,the inter-symbol interference（ISI）and the inter-carrier interference（ICI）can be completely eliminated,and the signal to interference and noise ratio（SINR）will not be affected.Simulation results also show that the proposed algorithm can achieve a 0.4 d B performance gain compared to the frequency domain equalization（FDE）algorithm in satellite communications.

Distributed power allocation with a novel signaling in dense OFDMA small cell networks

A distributed power allocation scheme was presented to maximize the system capacity in dense small cell networks. A new signaling called inter-cell-signal to interference plus noise ratio （ISINR） as well as its modification was defined to show the algebraic properties of the system capacity. With the help of ISINR, we have an easy way to identify the local monotonicity of the system capacity. Then on each subchannel in iteration, we divide the small cell evolved node B＇s （SeNBs） into different subsets. For the first subset, the sum rate is convex with respect to the power domain and the power optimally was allocated. On the other hand, for the second subset, the sum rate is monotone decreasing and the SeNBs would abandon the subchannel in this iteration. The two strategies are applied iteratively to improve the system capacity. Simulations show that the proposed scheme can achieve much larger system capacity than the conventional ones. The scheme can achieve a promising tradeoffbetween performance and signaling overhead.