基于时差法的复杂转子系统声发射源定位算法

在复杂转子系统的碰摩声发射源定位中,常规的广义互相关时延估计算法难以得到准确的时延值,进而无法进行准确定位。针对这一问题,引入基于平滑相干变换(Smoothed Coherence Transform, SCOT)的双加权二次互相关时延估计算法,计算声发射源信号的到达时间差;再利用Hilbere差值法对相关峰值进行锐化,减小在碰摩过程中噪声的干扰,以获得较为精确的时延估计结果;最后使用得到的时延值进行声发射源定位。实验结果表明,与常规的广义互相关算法相比,该算法对复杂转子系统碰摩声发射源定位具有较高的定位精度。     

Array geometry impact on music in the presence of spatially distributed sources

The MUltiple SIgnal Classification (MUSIC) estimator has been widely studied for a long time for its high resolution capability in the domain of the direction of arrival (DOA) estimation, with the sources assumed to be point. However, when the actual sources are spatially distributed with angular dispersion, the performance of the conventional MUSIC is degraded. In this paper, the impact of the array geometry on the DOA estimation of spatially distributed sources impinging on a sensor array is considered. Taking into account a coherently distributed source model, we establish closed-form expressions of the MUSIC-based DOA estimation error as a function of the positions of the array sensors in the presence of model errors due to the angular dispersion of the signal sources. The impact of the array geometry is studied and particular array designs are proposed to make DOA estimation more robust to source dispersion. The analytical results are validated by numerical simulations.¹     

A performance model of partial packet discard and early packet discard schemes in ATM switches

In this paper, we develop a concise performance model of partial packet discard (PPD) and early packet discard (EPD) schemes in ATM switches. We study the performance of PPD and EPD with heterogeneous traffic sources. The sources included Poisson, and ON–OFF with long-tailed sojourn time distribution, which is approximated by a hyperexponential distribution. The fairness of EPD is investigated. We automatically generate and numerically solve the underlying Markov chain using a high-level graphical paradigm known as the stochastic reward net. Our numerical results reveal that: (1) the benefit of PPD and EPD is not significant when the queuing system is underloaded with Poisson sources; (2) PPD and EPD can increase the goodput when the system is overloaded or loaded with ON–OFF sources; (3) in All Poisson case, PPD and EPD provide nearly fair service to the sources; (4) the ON–OFF source gets higher goodput than the Poisson source. Because the burstiness of a source will be alleviated by the statistical multiplexing of ATM switches, the ON–OFF source may be viewed as the source that just enters the network, and the Poisson source may be viewed as the source being far away from the network node we are considering and has been regulated and smoothed by the switches it traversed. Therefore, the queuing system with EPD algorithm gives a higher goodput to the sources near it. And the sources far away from the system will have lower goodput. One of our principal conclusions is that per-VC-based scheme is not needed at the core of large ATM networks because the traffic is less bursty at the core of the networks and PPD and EPD are fair under this kind of environment. A per-VC-based scheme may be used at the edge of the ATM cloud. Such a configuration can make the core of the ATM network work at high speed.     

GRAFT, a complete system for data fusion

Data fusion concerns the problem of merging information coming from independent sources. Also known as statistical matching, file grafting or microdata merging, it is a challenging problem for statisticians. The increasing growth of collected data makes combining different sources of information an attractive alternative to single source data. The interest in data fusion derives, in certain cases, from the impossibility of attaining specific information from one source of data and the reduction of the cost entailed by this operation and, in all cases, from taking greater advantage of the available collected information. The GRAFT system is presented. It is a multipurpose data fusion system based on the k-nearest neighbor (k-nn) hot deck imputation method. The system aim is to cope with many data fusion problems and domains. The k-nn is a very demanding algorithm. The solutions envisaged and their cost, which allow this methodology to be used in a wide range of real problems, are presented.     

Batch and Online Underdetermined Source Separation Using Laplacian Mixture Models

In this paper, we explore the problem of sound source separation and identification from a two-sensor instantaneous mixture. The estimation of the mixing and the sources is performed using Laplacian mixture models (LMM). The proposed algorithm fits the model using batch processing of the observed data and performs separation using either a hard or a soft decision scheme. An extension of the algorithm to online source separation, where the samples are arriving in a real-time fashion, is also presented. The online version demonstrates several promising source separation possibilities in the case of nonstationary mixing.     

Exact-differentiation-based leak detection and isolation in a plastic pipeline under temperature variations

This paper presents an algorithm to detect and locate a leak in a plastic pipeline which carries pressurized water, taking temperature variations into account. The algorithm is based on an appropriate modeling, as well as a Robust Exact Differentiation method for state variables and leak parameters estimation. The model includes temperature dependence in several parameters (friction factor and Equivalent Straight Length typically), and the exact differentiation method is based on so-called Higher-Order Sliding Modes. The algorithm only considers measurements coming from pressure head and flow rate sensors located at the pipeline ends, while temperature variations are monitored via a sensor in the upstream tank feeding the pipeline. Experimental results finally illustrate the performance of this algorithm, and more particularly in comparison with the case when temperature variations are not considered.     

基于决策树的羽流追踪机器人自主决策方法研究

针对无法获得可靠羽流流向信息不利于实现羽流追踪的问题，提出了一种基于决策树的羽流追踪移动机器人自主决策方法。该方法通过移动机器人两侧的浓度传感器采集到的浓度信息，利用追踪的行为规则建立决策树模型，获得行为决策信息，使机器人高效地追踪到羽流并精确地定位。由于浓度变化关系蕴含了羽流的流向及流速信息，从而取代了传统方法中流向及流速传感器。在扩散环境下，通过移动机器人羽流追踪实验，实现了良好的源定位效果。     

Binaural Localization Based on Weighted Wiener Gain Improved by Incremental Source Attenuation

This paper addresses the problem of direction-of-arrival (DOA) estimation both in azimuthal and elevation angle from binaural sound that is processed with a head-related transfer function (HRTF). Previously, we proposed a weighted Wiener gain (WWG) method for two-dimensional DOA estimation with two-directional microphones. However, for signals processed with HRTFs, peaks in the spatial spectra of WWG indicating true sources can mingle with spurious peaks. To resolve this situation, we propose to apply incremental source attenuation (ISA) in combination with WWG. In fact, ISA reduces spectral components originating from specified sound sources and thereby improves the localization accuracy of the next targeted source in the proposed incremental estimation procedure. We conduct computer simulations using directional microphones and four HRTF sets corresponding to four individuals. The proposed method is compared to two DOA estimation methods that are equivalent to two generalized cross-correlation functions and two high-resolution methods of multiple signal classification (MUSIC) and minimum variance method. For comparison purposes, we introduce binary coherence detection (BCD) to high-resolution methods for emphasizing valid spectral components for localization in multiple source conditions. Evaluation results demonstrate that, although MUSIC with BCD yield comparable performance to that of WWG in conditions where single speech source exists, WWG with ISA surpasses the other methods in conditions including two or three speech sources.     

Online Short-Term Multiple Sound Source Mapping for a Mobile Robot by Robust Motion Triangulation

The paper describes a two-dimensional (2-D) sound source mapping system for a mobile robot. The robot localizes the directions of sound sources while moving and estimates the positions of sound sources using triangulation from a short time period of directional localization results. Three key components are denoted. (i) Directional localization and separation method of different pressure sound sources by combining the Delay and Sum Beam Forming (DSBF) and the Frequency Band Selection (FBS) algorithms. (ii) The design of the microphone array by beam forming simulation to increase the resolution of the localization procedure and its robustness to ambient noise. (iii) Sound position estimation by using the RAndom SAmple Consensus (RANSAC) algorithm. Then we achieved 2-D multiple sound source mapping from time-limited data with high accuracy. Applying FBS as a binary filter after DSBF improves robustness for multiple sound source localization under robotic movement. In addition, a moving sound source separation method is shown by using segments of the DSBF enhanced signal derived from the localization process.     

A Bayesian method for multi-pollution source water quality model and seasonal water quality management in river segments

Excessive pollutant discharge from multi-pollution resources can lead to a rise in downriver contaminant concentration in river segments. A multi-pollution source water quality model (MPSWQM) was integrated with Bayesian statistics to develop a robust method for supporting load (I) reduction and effective water quality management in the Harbin City Reach of the Songhua River system in northeastern China. The monthly water quality data observed during the period 2005–2010 was analyzed and compared, using ammonia as the study variable. The decay rate (k) was considered a key factor in the MPSWQM, and the distribution curve of k was estimated for the whole year. The distribution curves indicated small differences between the marginal distribution of k of each period and that water quality management strategies can be designed seasonally. From the curves, decision makers could pick up key posterior values of k in each month to attain the water quality goal at any specified time. Such flexibility is an effective way to improve the robustness of water quality management. For understanding the potential collinearity of k and I, a sensitivity test of k for I_2i (loadings in segment 2 of the study river) was done under certain water quality goals. It indicated that the posterior distributions of I_2i show seasonal variation and are sensitive to the marginal posteriors of k. Thus, the seasonal posteriors of k were selected according to the marginal distributions and used to estimate I_2i in next water quality management. All kinds of pollutant sources, including polluted branches, point and non-point source, can be identified for multiple scenarios. The analysis enables decision makers to assess the influence of each loading and how best to manage water quality targets in each period. Decision makers can also visualize potential load reductions under different water quality goals. The results show that the proposed method is robust for management of multi-pollutant loadings under different water quality goals to help ensure that the water quality of river segments meets targeted goals.     

Simultaneous pose and reliability estimation using convolutional neural network and Rao–Blackwellized particle filter

In this study, we propose a novel localization approach that simultaneously estimates the reliability of estimation results. In the approach, a convolutional neural network (CNN) is used to make decision whether the localization process has failed or not. We train the CNN using a dataset that includes successful localization results and faults. However, the decision will contain some noise and many misdetection results may occur when the decision made by the CNN is used directly to detect faults. Therefore, we estimate both a robot's pose and reliability of the localization results based on the decision. To simultaneously estimate the robot's pose and reliability, we propose a new graphical model and implement a Rao–Blackwellized particle filter based on the model. We evaluated the proposed approach based on simulations and actual environments, which showed that the reliability estimated by the proposed approach can be used as an exact criterion for detecting localization faults. In addition, we show that the proposed approach can be applied in actual environments even when a dataset created from a simulation is used to train the CNN.     

Iterative constrained weighted least squares estimator for TDOA and FDOA positioning of multiple disjoint sources in the presence of sensor position and velocity uncertainties

Sensor position and velocity uncertainties are known to be able to degrade the source localization accuracy significantly. This paper focuses on the problem of locating multiple disjoint sources using time differences of arrival (TDOAs) and frequency differences of arrival (FDOAs) in the presence of sensor position and velocity errors. First, the explicit Cramér–Rao bound (CRB) expression for joint estimation of source and sensor positions and velocities is derived under the Gaussian noise assumption. Subsequently, we compare the localization accuracy when multiple-source positions and velocities are determined jointly and individually based on the obtained CRB results. The performance gain resulted from multiple-target cooperative positioning is also quantified using the orthogonal projection matrix. Next, the paper proposes a new estimator that formulates the localization problem as a quadratic programming with some indefinite quadratic equality constraints. Due to the non-convex nature of the optimization problem, an iterative constrained weighted least squares (ICWLS) method is developed based on matrix QR decomposition, which can be achieved through some simple and efficient numerical algorithms. The newly proposed iterative method uses a set of linear equality constraints instead of the quadratic constraints to produce a closed-form solution in each iteration. Theoretical analysis demonstrates that the proposed method, if converges, can provide the optimal solution of the formulated non-convex minimization problem. Moreover, its estimation mean-square-error (MSE) is able to reach the corresponding CRB under moderate noise level. Simulations are included to corroborate and support the theoretical development in this paper.     

Simultaneous direct position determination and pulse deinterleaving by a moving receiver

In this paper, a method for simultaneous deinterleaving and direct position determination (DPD) of multiple pulsed sources is presented, utilizing a pulse receiver on a moving platform. Traditional localization methods were based on the estimation of an intermediate parameter, and the intersection of these parameters results in source localization. DPD removes this step and determines the source's position by a single step processing. Previous works in DPD achieved suitable results in localization performance and multi-target resolution, but their problem structure is not applicable to the multiple pulsed sources, and a deinterleaving step is necessary. Moreover, traditional deinterleaving methods have limited performance. We present an approach, which localizes the sources and separates their pulses in a single-phase algorithm, based on a probability spectrum. Simulation results show that this combination of DPD and deinterleaving can enhance the performance of both phases.     

Auditory inspired methods for localization of multiple concurrent speakers

The use of microphone arrays offers enhancements of speech signals recorded in meeting rooms and office spaces. A common solution for speech enhancement in realistic environments with ambient noise and multi-path propagation is the application of so-called beamforming techniques. Such beamforming algorithms enhance signals at the desired angle using constructive interference while attenuating signals coming from other directions by destructive interference. However, these techniques require as a priori the time difference of arrival information of the source. Therefore, the source localization and tracking algorithms are an integral part of such a system. The conventional localization algorithms deteriorate in realistic scenarios with multiple concurrent speakers. In contrast to conventional methods, the techniques presented in this paper make use of pitch information of speech signals in addition to the location information. This “position–pitch”-based algorithm pre-processes the speech signals by a multiband gammatone filterbank that is inspired from the auditory model of the human inner ear. The role of this gammatone filterbank is analyzed and discussed in details. For a robust localization of multiple concurrent speakers, a frequency-selective criterion is explored that is based on a study of the human neural system's use of correlations between adjacent sub-band frequencies. This frequency-selective criterion leads to improved localization performance. To further improve localization accuracy, an algorithm based on grouping of spectro-temporal regions formed by pitch cues is presented. All proposed speaker localization algorithms are tested using a multichannel database where multiple concurrent speakers are active. The real-world recordings were made with a 24-channel uniform circular microphone array using loudspeakers and human speakers under various acoustic environments including moving concurrent speaker scenarios. The proposed techniques produced a localization performance that was significantly better than the state-of-the-art baseline in the scenarios tested.     

Self-organization of unattended wireless acoustic sensor networks for ground target tracking

A distributed, self-organization algorithm for ground target tracking using unattended acoustic sensor network is developed. Instead of using microphone arrays, each sensor node in the sensor network uses only a single microphone as its sensing device. This design can greatly reduce the size and cost of each sensor node and allow more flexible deployment of the sensor network. The self-organization algorithm presented in this paper can dynamically select proper sensor nodes to form the localization sensor groups that can work as a virtual microphone array to perform energy efficient target localization and tracking. To achieve this, we use a time-delay based bearing estimation plus triangulation for source localization in the sensor network. Major error sources of the localization method like time delay estimation, bearing calculation and triangulation are analyzed and sensor selection criteria are developed. Based on these criteria and neighborhood information of each sensor node, a distributed self-organization algorithm is developed. Simulation results show the effectiveness of the proposed algorithm.     

A unified neural-network-based speaker localization technique

Locating and tracking a speaker in real time using microphone arrays is important in many applications such as hands-free video conferencing, speech processing in large rooms, and acoustic echo cancellation. A speaker can be moving from the far field to the near field of the array, or vice versa. Many neural-network-based localization techniques exist, but they are applicable to either far-field or near-field sources, and are computationally intensive for real-time speaker localization applications because of the wide-band nature of the speech. We propose a unified neural-network-based source localization technique, which is simultaneously applicable to wide-band and narrow-band signal sources that are in the far field or near field of a microphone array. The technique exploits a multilayer perceptron feedforward neural network structure and forms the feature vectors by computing the normalized instantaneous cross-power spectrum samples between adjacent pairs of sensors. Simulation results indicate that our technique is able to locate a source with an absolute error of less than 3.5 degrees at a signal-to-noise ratio of 20 dB and a sampling rate of 8000 Hz at each sensor.     

Separation problem of industrial particles emissions using a stationary scattering model

This paper deals with some methods used to separate and to evaluate emission of dust particles coming from various industrial origins. Locations of all sources are supposed to be known by their 3D-Cartesian coordinates and steady-state dispersion is assumed to be reached. Our main contribution is to combine a particular stationary dispersion model with some efficient separation methods. The estimation of particles flow for the case of point sources over flat terrain is first addressed and then it is extended to line or area sources. Anyway, the approach is divided into two specific steps: the stationary model presentation and the separation techniques applied to the previous model. Finally, simulation results show the performances of the different methods in case of point. It turns out that small variations on the wind angle lead to large errors on flows estimate. Robust techniques with respect to model uncertainties appear to be of prime interest.     

Effective bandwidth estimation and testing for Markov sources

This work addresses the resource sharing problem in broadband communication networks that can guarantee some quality of service (QoS), and develops some results about data source and traffic modelling, especially in aspects of model testing and parameter estimation. The multiplexing of variable bit rate (VBR) sources poses a mathematical and statistical problem: the estimation of the resource requirements of a source or set of sources. The estimation method shall be simple enough to be practically implemented in the connection acceptance control (CAC) function.

In this paper, the VBR video sources are taken as a typical case of variable rate, with real-time constraints. This association of requirements makes the case especially interesting. A Markov model is assumed for the VBR sources. The validity of such models is under research; they seem to be appropriate at least in certain time scales. The model is tested against real video traces. In order to estimate the resource allocation or “channel occupation” of each source, the concept of equivalent bandwidth proposed by Kelly [Notes on effective bandwidth, in: F.P. Kelly, S. Zachary, I.B. Ziedins (Eds.), Stochastic Networks: Theory and Applications, Oxford University Press, Oxford, 1996, pp. 141] is used; it is based on a consistent mathematical theory, and has proven to be robust and useful for technical applications.

A calculation of the equivalent bandwidth of a Markov source, given its parameters, can be found in the literature [IEEE ACM Trans. Networking 1 (4) (1993) 424]. But in fact, one can only estimate model and parameters. In this work, an estimation of the equivalent bandwidth is given, which can be obtained from real data. The convergence and the consistency of the estimation are studied, and practical bounds are found. Illustrative calculations are performed from real video traces that were obtained using a software MPEG coder, developed by the authors. The mathematical and statistical results are valid for whatever phenomenon that can be modelled as a Markov process.     

Musical source separation using time-frequency source priors

This article deals with the source separation problem for stereo musical mixtures using prior information about the sources (instrument names and localization). After a brief review of existing methods, we design a family of probabilistic mixture generative models combining modified positive independent subspace analysis (ISA), localization models, and segmental models (SM). We express source separation as a Bayesian estimation problem and we propose efficient resolution algorithms. The resulting separation methods rely on a variable number of cues including harmonicity, spectral envelope, azimuth, note duration, and monophony. We compare these methods on two synthetic mixtures with long reverberation. We show that they outperform methods exploiting spatial diversity only and that they are robust against approximate localization of the sources.     

基于速率的IP接入网流量控制算法的研究

论文提出了一种基于速率的IP接入网流量控制算法的设计思路。其目的是通过源端数据发送率的调节,提高接入网中汇节点的吞吐率;降低数据包的平均时延。算法可分成两个部分,分别驻留于汇结点端和源端。汇结点端完成优化计算,为源端数据发送率的变化做决策。源端根据来自汇结点的反馈信号调节自身的发送速率。