A minimum time seek controller for a disk drive

A time optimal controller design is proposed for the purpose of steering the read/write head of a magnetic disk drive during a seek manuever. The design includes the development of a switch-on-time (SOT) open loop slew control using a third order model of the voice coil motor. The SOT algorithm employs a look up table of switch times versus angle. A perturbation correction stage is included to tailor the switch times to adjust for variations in the operating characteristic of the disk drive (due, for example to heat buildup, wear, etc.). The design also includes a terminal stage servo control that employs an optimal feedback/feedforward controller which is used to provide precise positioning at the end of the seek maneuver     

具有最陡主瓣的最小方差时频分析

针对非平稳时间序列,基于最小方差谱估计提出自适应加窗的时频分析方法,所叠加的时间窗能够自适应地调整尺寸,使得所估计的谱具有最陡的瞬时主瓣以适应时频分析,从而获得满意的非平稳信号的时频分布。仿真结果以及与参考文献的方法比较表明,该方法能够提供更好的频率分辨率,时频分布性能更好。     

Broadband minimum variance beamforming for ultrasound imaging

A minimum variance (MV) approach for nearfield beamforming of broadband data is proposed. The approach is implemented in the frequency domain, and it provides a set of adapted, complex apodization weights for each frequency subband. The performance of the proposed MV beamformer is tested on simulated data obtained using Field II. The method is validated using synthetic aperture data and data obtained from a plane wave emission. Data for 13 point targets and a circular cyst with a radius of 5 mm are simulated. The performance of the MV beamformer is compared with delay-and-sum (DS) using boxcar weights and Hanning weights and is quantified by the full width at half maximum (FWHM) and the peak-side-lobe level (PSL). Single emission {DS boxcar, DS Hanning, MV} provide a PSL of {-16, -36, -49} dB and a FWHM of {0.79, 1.33, 0.08} mm. Using all 128 emissions, {DS boxcar, DS Hanning, MV} provides a PSL of {-32, -49, -65} dB, and a FWHM of {0.63, 0.97, 0.08} mm. The contrast of the beamformed single emission responses of the circular cyst was calculated as {-18, -37, -40} dB. The simulations have shown that the frequency subband MV beamformer provides a significant increase in lateral resolution compared with DS, even when using considerably fewer emissions. An increase in resolution is seen when using only one single emission. Furthermore, the effect of steering vector errors is investigated. The steering vector errors are investigated by applying an error of the sound speed estimate to the ultrasound data. As the error increases, it is seen that the MV beamformer is not as robust compared with the DS beamformer with boxcar and Hanning weights. Nevertheless, it is noted that the DS does not outperform the MV beamformer. For errors of 2% and 4% of the correct value, the FWHM are {0.81, 1.25, 0.34} mm and {0.89, 1.44, 0.46} mm, respectively.     

A new linearly constrained minimum variance beamformer for reconstructing EEG sparse sources

Brain source imaging based on EEG aims to reconstruct the neural activities producing the scalp potentials. This includes solving the forward and inverse problems. The aim of the inverse problem is to estimate the activity of the brain sources based on the measured data and leadfield matrix computed in the forward step. Spatial filtering, also known as beamforming, is an inverse method that reconstructs the time course of the source at a particular location by weighting and linearly combining the sensor data. In this paper, we considered a temporal assumption related to the time course of the source, namely sparsity, in the Linearly Constrained Minimum Variance (LCMV) beamformer. This assumption sounds reasonable since not all brain sources are active all the time such as epileptic spikes and also some experimental protocols such as electrical stimulations of a peripheral nerve can be sparse in time. Developing the sparse beamformer is done by incorporating L1-norm regularization of the beamformer output in the relevant cost function while obtaining the filter weights. We called this new beamformer SParse LCMV (SP-LCMV). We compared the performance of the SP-LCMV with that of LCMV for both superficial and deep sources with different amplitudes using synthetic EEG signals. Also, we compared them in localization and reconstruction of sources underlying electric median nerve stimulation. Results show that the proposed sparse beamformer can enhance reconstruction of sparse sources especially in the case of sources with high amplitude spikes.     

Eigenspace-based minimum variance beamforming applied to medical ultrasound imaging

Recently, adaptive beamforming methods have been successfully applied to medical ultrasound imaging, resulting in significant improvement in image quality compared with non-adaptive delay-and-sum (DAS) beamformers. Most of the adaptive beamformers presented in the ultrasound imaging literature are based on the minimum variance (MV) beamformer which can significantly improve the imaging resolution, although their success in enhancing the contrast has not yet been satisfactory. It is desirable for the beamformer to improve the resolution and contrast at the same time. To this end, in this paper, we have applied the eigenspace-based MV (EIBMV) beamformer to medical ultrasound imaging and have shown a simultaneous improvement in imaging resolution and contrast. EIBMV beamformer utilizes the eigenstructure of the covariance matrix to enhance the performance of the MV beamformer. The weight vector of the EIBMV is found by projecting the MV weight vector onto a vector subspace constructed from the eigenstructure of the covariance matrix. Using EIBMV weights instead of the MV ones leads to reduced sidelobes and improved contrast, without compromising the high resolution of the MV beamformer. In addition, the proposed EIBMV beamformer presents a satisfactory robustness against data misalignment resulting from steering vector errors, outperforming the regularized MV beamformer.     

CW脉冲回波信号的最小方差无畸变响应自适应检测

建立了适用于解析时间序列信号检测的最小方差无畸变响应(Minimum Variance Distortionless Response,MVDR)自适应处理模型(Time Series MVDR,TSMVDR),并针对CW声纳脉冲回波信号,通过构造解析信号和分段近似处理给出了相应算法(Algorithms of TSMVDR on CW pulse signal,AWMVDR),并进行了数值仿真研究。研究结果表明:对于CW脉冲信号:(1)在较高采样率的条件下,AWMVDR可以实现CW脉冲信号稳定的自适应检测;(2)AWMVDR较常规线性相关检测具有更高的脉冲参数估计精度和时间分辨力;(3)AWMVDR的实现需要付出较高采样率和更大计算量的代价。     

基于最小方差的声线跟踪自适应分层算法

针对高精度水声定位系统中定位精度与计算量相互矛盾的问题,提出了基于最小方差的声线跟踪自适应分层算法。该算法根据声速在有限范围内规律变化的特点,对声速剖面全局搜索,通过各段声速散点到其拟合直线的总方差表示拟切分段与原始声速剖面的差别,利用最大差别找出最佳切分点,保留原始声速剖面特征,实现对声速剖面的简化。实验结果表明,该算法在保证定位精度的同时能够提高计算效率,具有良好的工程应用价值。     

Repetitive controller design for minimum track misregistration in hard disk drives

Repetitive control is a widely used technique for the compensation of repeatable error in systems that contain rotating mechanisms or repeat a trajectory. Generally, it includes delay chains and a low-pass filter in the positive feedback loop, which generate a periodic signal. The controller has typically been implemented in a plug-in fashion and designed heuristically with the simplest form of the filter. However, this design approach is somewhat ambiguous in the selection of controller parameters because of its influence over nonharmonic frequencies. Also, it leaves the possibility for further improvement. This paper presents an improved design method for the repetitive controller that provides minimum track misregistration (TMR) in a hard disk drive (HDD). For TMR prediction, the method identifies disturbances acting on an HDD and estimates servo performance, using the identification result. We have confirmed the identification and estimation procedure through experiments. In our method, first the basic tracking controller is designed and later the repetitive controller is designed in conjunction with a Q filter. A cost function based on Parseval's theorem, reflecting the servo performance as TMR, is defined. Then the servo performance is estimated from the identified disturbance, and the plant and designed controller's frequency response are modified as necessary by changing the parameters of the controller, whose optimization is carried out with a commercial nonlinear optimization tool. The design strategy facilitates the controller design by providing an accurate estimation for the attainable servo performance and design criteria under the optimization framework.     

Iterative bit and power allocation for multi-cell orthogonal frequency division multiplexing with minimum variance distortionless response beamforming

This paper develops bit and power allocation schemes with beamforming for multi-cell orthogonal frequency division multiplexing (OFDM) systems on uplink. The model of the multi-cell channel with frequency reuse is considered. The transmit signal from each mobile causes interference to the received signals of other base stations. The schemes aim to minimise the total mobile transmit power while satisfying the required data rate and the bit error rate (BER) of each mobile. The proposed schemes offer better performance than that of the fixed bit allocation method. The proposed distributed allocation scheme reduces computational complexity compared to the proposed centralised multi-user greedy method with insignificant performance degradation. The simulation results are presented to demonstrate the efficacy of the proposed schemes.     

Contrast enhancement and robustness improvement of adaptive ultrasound imaging using forward-backward minimum variance beamforming

In adaptive ultrasound imaging, accurate estimation of the array covariance matrix is of great importance, and biases the performance of the adaptive beamformer. The more accurately the covariance matrix can be estimated, the better the resolution and contrast can be achieved in the ultrasound image. To this end, in this paper, we have used the forward-backward spatial averaging for array covariance matrix estimation, which is then employed in minimum variance (MV) weights calculation. The performance of the proposed forward-backward MV (FBMV) beamformer is tested on simulated data obtained using Field II. Data for two closely located point targets surrounded by speckle pattern are simulated showing the higher amplitude resolution of the FBMV beamformer in comparison to the forward-only (F-only) MV beamformers, without the need for diagonal loading. A circular cyst with a diameter of 6 mm and a phantom containing wire targets and two cysts with different diameters of 8 mm and 6 mm are also simulated. The simulations show that the FBMV beamformer, in contrast to the F-only MV, could estimate the background speckle statistics without the need for temporal smoothing, resulting in higher contrast for the FBMV-resulted image in comparison to the MV images. In addition, the effect of steering vector errors is investigated by applying an error of the sound speed estimate to the ultrasound data. The simulations show that the proposed FBMV beamformer presents a satisfactory robustness against data misalignment resulted from steering vector errors, outperforming the regularized F-only MV beamformer. These improvements are achieved without compromising the good resolution of the MV beamformer and resulted from more accurate estimation of the covariance matrix and consequently, the more accurate setting of the MV weights.     

Maximum likelihood estimates of mean and variance of occupation radiation doses subjected to minimum detection levels

Data collection and its analysis in the field of nuclear safety is an important task in the sense that it powers the improvement of safety as well as reliability of the plant. Thus, occupational exposure data analysis is presented to measure the safety or reliability of radiation protection of a given facility. It also is required as a basic input in making decisions on radiation protection regulations and recommendations. A common practice in radiation protection is to record a zero for observation below minimum detection limit (MDL) doses, which leads to an underestimation of true doses and overestimation of the dose-response relationship. Exposure data (both external and internal) are collected by monitoring each individual and this kind of monitoring generally is graded as low-level monitoring. So, in such low-level monitoring, the occurrence of exposure below MDL invites statistical complications for estimating mean and variance because the data are generally censored, i.e observations below MDL are marked. In Type I censoring, the point of censoring (e.g. the detection limit) is 'fixed' a priori for all observations and the number of the censored observations varies. In Type II censoring, the number of censored observations is fixed a priori, and the point of censoring vary. The methodology generally followed in estimating mean and variance with these censored data was the replacement of missing dose by half the MDL. In this paper, authors have used the maximum likelihood estimation (MLE) approach for the estimation of mean and standard deviation. A computer code BDLCENSOR has been developed in which all these MLE-based advanced algorithms are implemented. In addition to the MLE-based method, an expectation maximisation algorithm has also been implemented. The code is written using Visual BASIC 6.0. The paper describes the details of the algorithms adopted for handling such censored data to estimate bias free mean and standard deviation.     

A minimum violations ranking method

We present a rating method that, given information on the pairwise comparisons of n items, minimizes the number of inconsistencies in the ranking of those items. Our Minimum Violations Ranking (MVR) Method uses a binary linear integer program (BILP) to do this. We prove conditions when the relaxed LP will give an optimal solution to the original BILP. In addition, the LP solution gives information about ties and sensitivities in the ranking. Lastly, our MVR method makes use of bounding and constraint relaxation techniques to produce a fast algorithm for the linear ordering problem, solving an instance with about one thousand items in less than 10 minutes.     

Analysis of the improvement in sky coverage for multiconjugate adaptive optics systems obtained using minimum variance split tomography

The scientific utility of laser-guide-star-based multiconjugate adaptive optics systems depends upon high sky coverage. Previously we reported a high-fidelity sky coverage analysis of an ad hoc split tomography control algorithm and a postprocessing simulation technique. In this paper, we present the performance of a newer minimum variance split tomography algorithm, and we show that it brings a median improvement at zenith of 21 nm rms optical path difference error over the ad hoc split tomography control algorithm for our system, the Narrow Field Infrared Adaptive Optics System for the Thirty Meter Telescope. In order to make the comparison, we also validated our previously developed sky coverage postprocessing software using an integrated simulation of both high- (laser guide star) and low-order (natural guide star) loops. A new term in the noise model is also identified that improves the performance of both algorithms by more properly regularizing the reconstructor.     

Estimating Construction Material Indices with ARIMA and Optimized NARNETs

Construction Industry operates relying on various key economic indicators. One of these indicators is material prices. On the other hand, cost is a key concern in all operations of the construction industry. In the uncertain conditions, reliable cost forecasts become an important source of information. Material cost is one of the key components of the overall cost of construction. In addition, cost overrun is a common problem in the construction industry, where nine out of ten construction projects face cost overrun. In order to carry out a successful cost management strategy and prevent cost overruns, it is very important to find reliable methods for the estimation of construction material prices. Material prices have a time dependent nature. In order to increase the foreseeability of the costs of construction materials, this study focuses on estimation of construction material indices through time series analysis. Two different types of analysis are implemented for estimation of the future values of construction material indices. The first method implemented was Autoregressive Integrated Moving Average (ARIMA), which is known to be successful in estimation of time series having a linear nature. The second method implemented was Non-Linear Autoregressive Neural Network (NARNET) which is known to be successful in modeling and estimating of series with non-linear components. The results have shown that depending on the nature of the series, both these methods can successfully and accurately estimate the future values of the indices. In addition, we found out that Optimal NARNET architectures which provide better accuracy in estimation of the series can be identified/discovered as result of grid search on NARNET hyperparameters.     

Enhanced generally weighted moving average variance charts for monitoring process variance with individual observations

The generally weighted moving average variance (GWMAV) chart is effective in detecting increases in process variance when only individual observations are available. Recently, the combination of exponentially weighted moving average and cumulative sum (CUSUM) charts for the effective detection of small process shifts has emerged. Inspired by the features, we propose the mixed GWMAV-CUSUM chart and its reverse order CUSUM-GWMAV to enhance the detection ability of the GWMAV chart and compare with the existing counterparts. Numerical simulation revealed that the mixed GWMAV-CUSUM and mixed CUSUM-GWMAV charts are sensitive to small upward shifts in the process variance and efficient structures compared with their prototypes and their separate charts, that is, GWMAV and CUSUM charts. An industrial dataset was used to illustrate the application of the proposed mixed charts.     

A weighted three-point-based methodology for variance estimation

It is widely accepted that variations in manufacturing processes are inevitable and should be taken into account during analysis and design processes. However, estimating uncertainty propagation in an end-product caused by these variations is a very challenging task, especially when a computationally expensive effort is already needed in deterministic models, such as simulations of sheet metal forming. The focus of this article is on the variance estimation of a system response using sensitivity-based methods. A weighted three-point-based strategy for efficiently and effectively estimating the variance of a system response is proposed. Three first-order derivatives of each variable are used to describe the non-linear behaviour and estimate the variance of a system. A methodology for determining the optimal locations and weights of the three points along each axis is proposed and illustrated for the cases where each variable follows either a normal distribution or a uniform distribution. An extension of the weighted three-point-based strategy is introduced to take into account the interaction between parameters. In addition, an extension is given for mean estimation of the system response without requiring more data. The considerable improvement in accuracy compared with the traditional first-order approximation is demonstrated in a number of test problems. The proposed method requires significantly less computational effort than the Monte Carlo method.     

CSTC 12: A CAMAC streamer tubes controller with on-line preprocessing

A new programmable microprocessor based controller for streamer tubes is presented. The identification or the rejection of undesirable events both on-line and off-line is implemented.