Accurate and time efficient estimation of the probability of error in bursty channels

A method and a technique for the probability of error estimation in digital channels with memory are developed and demonstrated. The expressions for the mean and variance of a random variable, representing a block of bits transmission in a bursty channel (channel with memory), are derived. The influence of the memory is expressed by a parameter called the memory factor. It is shown that the traditional Monte Carlo method can be applied for the probability of error estimation. In order to control the accuracy and increase the time efficiency of estimation this method is modified and a new method, called the modified Monte Carlo method, is proposed. Based on this modified method a technique of estimation with controlled accuracy is developed and demonstrated using data obtained by simulation. According to this technique the sample size is adjusted in the course of estimating procedure to give an accurate estimate of the probability of error for a minimum required time of estimation. Copyright © 2003 John Wiley & Sons, Ltd.     

调频遥测信号载波频率的精确估计

传统调频遥测信号载波频率估计算法对输入信号降采样后直接进行快速傅里叶变换，实现方法虽然简单，但测量精度较差，无法适应高动态、低信噪比等复杂场景。为此，提出了一种调频遥测信号载波频率的精确估计算法。两并联补偿支路先分别采用正、负调频频率对输入信号进行频率预先补偿，低通滤波后完成降采样处理，削弱调频频率的频谱影响；频率搜索状态对采样数据进行载波多普勒变化率的频率补偿，经过快速傅里叶变换、非相干积分和频谱重心搜索完成频率解算，提高载波频率的检测性能。试验与分析表明，所提算法在高动态、低信噪比等复杂场景下可显著提高调频遥测信号载波频率的估测性能。     

Accurate dense optical flow estimation using adaptive structure tensors and a parametric model

An accurate optical flow estimation algorithm is proposed in this paper. By combining the three-dimensional (3D) structure tensor with a parametric flow model, the optical flow estimation problem is converted to a generalized eigenvalue problem. The optical flow can be accurately estimated from the generalized eigenvectors. The confidence measure derived from the generalized eigenvalues is used to adaptively adjust the coherent motion region to further improve the accuracy. Experiments using both synthetic sequences with ground truth and real sequences illustrate our method. Comparisons with classical and recently published methods are also given to demonstrate the accuracy of our algorithm.     

基于Proteus的LPC2124精确计数系统的设计与仿真

本文设计了精确计数系统的硬件和软件结构,硬件结构采用LPC2124微处理器通过I/O口的精确控制实现计数,软件系统的设计主要是对LPC2124微处理器相关寄存器的设置。并对精确计数系统在Proteus中进行了仿真,仿真结果表明系统达到了设计的目标要求,表明Proteus在LPC2124微处理器的设计与仿真中有着广阔的应用前景。     

A computationally efficient method of timing and phase estimation in TDMA systems using a preamble sequence

Optimum and near optimum methods of burst parameter estimation based on the discrete Fourier transform are presented. The methods are applicable to single sample per symbol demodulators when the burst preamble is chosen to be a discrete frequency component at one quarter of the symbol rate. The proposed methods do not require the squaring of the signal, and hence avoid the loss associated with squaring the noise components. The optimum method is shown to reach the Cramer-Rao bound for symbol timing and carrier phase estimation for the chosen preamble, whereas the more computationally efficient method shows a loss of 1.0dB and yields information regarding the timing offset only. The performances of the two methods in the presence of carrier frequency offset and additive white Gaussian noise are analysed, and implementations are given together with a comparison of the respective computational complexities.     

使用PRONY方法的OFDM频偏估计

对OFDM频偏的盲估计算法进行了研究,采用PRONY方法,利用对OFDM码元的过取样来构成估计所需的空间。着重对多径环境下的性能进行了仿真,证明该方法在多普勒频移下有较好的性能,并进一步分析了估计性能与过采样倍数的关系。     

Cramer‐Rao lower bound and parameter estimation for multibeam satellite links

Data throughput and availability of multibeam satellite links are limited by interference problems, in particular when the design is determined by an aggressive reuse of frequency bands. Usually, this is mitigated by appropriately selected techniques like precoding or multi‐user detection. Before such methods are applicable, however, the most important transmission parameters have to be recovered successfully even under very challenging conditions caused by (full) frequency reuse. The detailed analysis of the log‐likelihood function characterizing the multibeam scenario shows that a beamwise decoupling of the estimation task is possible, if the following three conditions are met: (i) alignment of all frequency offsets; (ii) symbol synchronicity; (iii) orthogonal synchronization sequences. Based on these assumptions, it turns out that the modified Cramer‐Rao lower bound (MCRLB) for carrier frequency and phase, symbol timing and signal amplitude is the same, no matter whether we are dealing with a single or a multiple beam situation. Furthermore, because a maximum likelihood framework for parameter estimation is not available in closed form, we introduce a sub‐optimal concept of low‐complex algorithms. Finally, it is verified by simulation results that the jitter variance of each recovery scheme is close to the related MCRLB, when the decoupling conditions are satisfied. Copyright © 2016 John Wiley & Sons, Ltd.     

组合幂调频信号的时频移估计

为了准确估计接收信号的时频移参数和降低估计复杂度,设计了一种组合幂调频(CPFM)信号,该信号由具有时间间隔的正负幂调频(PFM)信号构成,并提出了基于CPFM信号的时频移估计算法.在算法中首先将CPFM信号降阶,然后独立地估计时延和频移,即采用三次相位函数(CPF)估计时延,通过正负PFM相位相消后再估计频移.相比于分数阶傅里叶变换(FRFT)时频移估计算法,所提算法避免了时延估计对频移估计的影响,而且仅需两次一维搜索,降低了计算量.仿真结果表明,该算法能准确地估计CPFM信号的时延和频移,并且频移估计均方误差(MSE)接近克拉美劳下界(CRLB).     

Accurate direction‐of‐arrival estimation of multiple sources using a genetic approach

In this paper, we present an accurate direction‐of‐arrival (DOA) estimation method, which is based on the maximum likelihood (ML) principle and implemented using a modified and refined genetic algorithm (GA). With the newly introduced features—intelligent initialization and the emperor‐selective (EMS) mating scheme, carefully selected crossover and mutation operators and fine‐tuned parameters such as the population size, the probability of crossover and mutation etc., the GA‐ML estimator achieves fast global convergence. A GA operator and parameter standard is suggested for this application, which is independent of the source and array configurations except the number of sources. Simulation results demonstrate that in general scenarios, the proposed estimator is the most efficient in computation and its statistical performance is the best among all popular ML‐based DOA estimation methods. Copyright © 2004 John Wiley & Sons, Ltd.     

Parameters estimation and detection of MIMO-LFM signals using MWHT

This article proposed an improved Wigner–Hough Transform (WHT) for multicarrier LFM signals of MIMO radars (MIMO-LFM). First, the signal model of the intercepted MIMO-LFM signals and the localisation of conventional WHT for this signal model are analysed. Therefore, we present the new WHT with multiple matching components, which is called as multicomponent WHT (MWHT). Then the detection and parameters estimation performance of MWHT are deduced, and analytical results indicate that MWHT is superior to conventional WHT for MIMO-LFM. In order to reduce the computation cost, a coarse estimation method is introduced. Finally, the numerical simulations demonstrate the validity of MWHT, as well as analytical results.     

An investigation of time delay estimation in room acoustic environment using magnitude ratio

This paper investigates the relation between the nonstationary sound source and the frequency domain magnitude ratio of two microphones based on short-term frequency analysis. The fluctuation level of nonstationary sound sources is modeled by the exponent of polynomials from the concept of moving pole model. According to this model, the sufficient condition for utilizing the fluctuation level and magnitude ratio to estimate the time delay between two microphones is suggested. Simulation results are presented to show the performance of the suggested method.     

Toward a deep CNN and RS-GOA framework for vehicle detection,traffic flow estimation,and optimal path selection from surveillance videos

Several autonomous traffic monitoring systems have been created as a result of the growing number of vehicles in urban areas. Traffic surveillance systems that use roadside cameras, in particular, are becoming widely used for traffic management. For an efficient traffic control and vehicle navigation system, accurate traffic flow information must be obtained based on the vehicles detected in surveillance videos. However, vehicles of various scales are difficult to spot in traffic surveillance videos due to the presence of barricades, other vehicles, and the impact of poor lighting. Also, adverse weather conditions like snow, fog, and heavy rain diminish the visual quality of the surveillance footage. This paper proposes multi-scale dense nested deep CNN (MSDN-DCNN) and regional search grasshopper optimization algorithm (RS-GOA) framework to accurately detect the vehicles, estimate the traffic flow, and find the optimal path with less travel time. First, the surveillance videos are pre-processed, which includes frame conversion, redundancy removal, and image enhancement. The pre-processed frames are given as input to the MSDN-DCNN for multi-scale vehicle detection. The detected results are used for vehicle counting and estimating the traffic flow. Finally, the optimal path is chosen based on the traffic flow information by using the RS-GOA algorithm. The performance of the proposed method is compared with the existing vehicle detection and path selection techniques. The results illustrate that the proposed Deep CNN-RS-GOA framework has improved performance with high detection accuracy (91.03%), high speed (53.9 fps), less running time (1,000 ms), less travel time, and faster convergence.     

Uncertainty Quantification for parameter estimation of an industrial electric motor using hierarchical Bayesian inversion

In this work, we employ hierarchical Bayesian inference to estimate aleatory parameter uncertainty of a “black-box” simulation model of an industrial electric motor using noisy measurements obtained from a real test bench. Standard sampling-based approaches, like Metropolis–Hastings (MH), require a huge amount of expensive simulation model evaluations in order to compute the likelihood. This is prohibitive from a computational point of view. Instead, Hamiltonian Monte Carlo (HMC) can be used to reduce the number of samples significantly. One key ingredient in HMC, however, is the availability of a function evaluating the gradient of the simulation model with respect to its parameters. In our problem setting, the simulation model is a “black-box” model — a situation which is common in many industrial engineering problems. There is no function available for evaluating the gradients, which therefore must be either approximated numerically or evaluated from a suitable analytical approximation of the simulation model using a surrogate. In this work, we introduce a new approach to enable HMC for complex black box simulations based on B-splines surrogates trained on spatially adaptive sparse grids. The B-spline surrogates are able to accurately represent the simulation model and in addition its gradients. We show how they can be used within HMC in order to infer the probability distribution of all five electric motor parameters accurately and efficiently, and we demonstrate its superiority in convergence compared to MH.     

Aircraft motion parameter estimation via multipath time-delay using a single ground-based passive acoustic sensor

The time-frequency analysis of the signal acquired by a single ground-based microphone shows a two-dimensional interference pattern in the time-frequency plane,which is caused by the time delay of the received signal emitted from a low flying aircraft via the direct path and the ground-re-flected path. A model is developed for estimating the motion parameters of an aircraft flying along a straight line at a constant height and with a constant speed. Monte Carlo simulation results and ex-perimental results are presented to validate the model,and an error analysis of the model is presented to verify the effectiveness of the estimation scheme advocated.     

Fall detection using body geometry and human pose estimation in video sequences

According to the World Health Organization, falling is a significant health problem that causes thousands of deaths every year. Fall detection and fall prediction tasks enable accurate medical assistance to vulnerable populations whenever required, allowing local authorities to predict daily health care resources and to reduce fall damages accordingly. We present in this paper, a fall detection approach that explores human body geometry available at different frames of the video sequence. Especially, pose estimation, the angle and the distance between the vector formed by the head-centroid of the identified facial image and the center hip of the body, and the vector aligned with the horizontal axis of the center hip, are employed to construct new distinctive image features. A two-class Support Vector Machine (SVM) classifier and a Temporal Convolution Network (TCN) are trained on the newly constructed feature images. At the same time, a Long-Short-Term Memory (LSTM) network is trained on the calculated angle and distance sequences to classify fall and non-fall activities. We perform experiments on the Le2i FD dataset and the UR FD dataset, where we also propose a cross-dataset evaluation. The results demonstrate the effectiveness and efficiency of the developed approach.     

A RD-ESPRIT algorithm for coherent DOA estimation in monostatic MIMO radar using a single pulse

This paper discusses the problem of coherent direction of arrival (DOA) estimation in a monostatic multi-input multi-output (MIMO) radar using a single pulse, and proposes a reduced dimension (RD)-estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. We reconstruct the received data and then utilise it to construct a set of Toeplitz matrices. After that, we use RD-ESPRIT to obtain the DOAs of the sources. The proposed algorithm is effective for coherent angle estimation based on a single pulse, and it has much better angle estimation performance than the forward backward spatial smoothing (FBSS)-ESPRIT algorithm and the ESPRIT-like of Li, as well as very close angle estimation performance to the ESPRIT-like of Han. For complexity comparison, our algorithm has very close complexity to the FBSS-ESPRIT algorithm, and lower complexity than the ESPRIT-like of Han and the ESPRIT-like of Li. Simulation results present the effectiveness and improvement of our approach.     

Order and pole locator estimation in fractional order systems using bode diagram

This paper deals with estimation of fractional order and pole locator in fractional order systems. The estimation is based on Bode diagram of the system that is obtained using input and output measurements. Here the magnitude diagram is approximated with number of straight lines depending on the level of complexity and in consequence a very good estimation of fractional order and acceptable approximations of pole locators are determined. Relying on the proposed method, complexity of fractional order system identification which is mostly due to the estimation of fractional order is substantially resolved. Some example simulation results are provided to explain the work and show its effectiveness.     

Accurate and Simple Time Synchronization and Frequency Offset Correction in OFDM System

1　Introduction　OFDMisaneffectivetransmissionschemetocopewithmultipathfading[1] .Byinsertingaguardintervalbetweensymbolsblockscalledcyclicprefix ,theInterSymbolInterference (ISI)canbemitigated .OFDMwasadoptedasthemodulationschemeforaDigitalAu dioBroadcasting (DAB)system[2 ] andisalsoproposedastheterrestrialHDTVtransportinEurope[3] .　SymboltimingoffsetsinOFDMreceiversnotonlyin troduceISIbutalsocauselinearphaserotationsofthesignalconstellationofthereceiverFastFourierTrans form (F…     

Combining Capon and APES for estimation of spectral lines

We propose combining the Capon and the APES spectral estimators for estimation of both the amplitude and the frequency of spectral lines. The so-obtained estimator does not suffer from Capon's biased amplitude estimates nor from APES' biased frequency estimates or resolution problem. Furthermore, the combined estimator is computationally simpler than APES and has about the same complexity as Capon. Numerical simulations are presented illustrating the increased performance.This work was supported in part by the Swedish Foundation for Strategic Research.     

Fast and efficient motion estimation using diamond zonal-based algorithms

Motion estimation has always been an important part of video encoding systems because it can reduce temporal redundancy effectively and thus has significant impact on the bit rate and the output visual quality of the encoded sequence. Unfortunately, when using the brute-force full search algorithm, motion estimation consumes a very large portion of the encoding time. Previously, several algorithms have been proposed which try to reduce complexity, usually, with a significant loss in visual quality. Based on the diamond zonal search framework we introduced recently, we propose in this paper a novel algorithm called advanced diamond zonal search (ADZS), which was submitted to and well received by the Moving Pictures Experts Group (MPEG) standard committee for possible inclusion as an encoder optimization tool. ADZS was criticized in MPEG for using fixed thresholds, which may not be suitable for all video sequences. To address this issue, we further propose a threshold-adaptive version called threshold-adaptive advanced diamond zonal search (TAADZS). Simulation results verify the superior performance of ADZS and TAADZS over other fast algorithms and the robustness of TAADZS over ADZS.This project has been supported by the Research Grants Council of the Hong Kong Special Administrative Region, China (project no. HKUST6057/99E) and the Hong Kong Telecom Institute of Information Technology. Parts of this paper have been previously published in conference proceedings.