MIMO SAR OFDM chirp waveform design and GMTI with RPCA based method

Multiple-input multiple-output synthetic aperture radar (MIMO SAR) has drawn wide attention for its increased degrees of freedom (DOFs) compared to the traditional multi-channel SAR (MC-SAR) system. The MIMO SAR system, which makes each antenna illuminate one subswath, can widen the range swath without decreasing PRF. One of the foremost tasks involved in the MIMO SAR system is the waveform design at the transmitter. Moreover, at the receiver, how to accurately separate the auto-correlation signal from the cross-correlation interferences is the other important issue. In this paper, an orthogonal frequency division multiplexing (OFDM) chirp signal designing method is firstly proposed, which is based on low cross-correlation interferences and good peak-to-sidelobe ratio (PSLR) rules. Using the designed OFDM chirp signals, the focused signal of MIMO SAR system matched the robust principal component analysis (RPCA) basic model. The moving target (sparse signal), clutter (low-rank signal) and noise (noise signal) can be separated directly. Hence, RPCA based method can be employed for ground moving target indication (GMTI) with no need for extracting the matched signal or suppressing the unmatched interferences. Extensive simulations demonstrate the effectiveness of the waveform designing method and GMTI with RPCA based method for MIMO SAR system.     

掩埋小目标声探测技术研究

探测和识别沉底、掩埋水雷等小目标在军事上显得愈来愈迫切。在介绍掩埋小目标探测声纳现状的基础上,剖析了掩埋小目标声探测的技术难点,总结了小目标声探测技术的发展趋势,主要包括探测频率向低频发展,重点发展合成孔径探测技术,积极探索时反探测和MIMO探测方法,小目标识别技术向联合利用图像特征和散射特征的方向发展等,对沉底/掩埋小目标声探测技术研究及其声纳设计有借鉴指导意义。     

Mathematical modelling of abrasive waterjet footprints for arbitrarily moving jets: Part I—single straight paths

It is notoriously well-known that abrasive waterjet milling (AWJM) is difficult to perform controlled-depth owing to variable geometries of the footprints that depends not only on jet energy and the exposure time upon the workpiece, but also on the orientation of the jet relative to the target surface. An attempt is made to develop a model that can be generally applied to different machine system and to predict individual jet footprints that are one of the key steps for controlled-depth AWJM. To address this, the paper breaks new ground in geometrical modelling of AWJM with the benefit of having few variables for predicting the footprints obtained under the following conditions: (i) any time exposures (i.e. V_f, jet feed speeds); (ii) jet orientations (θ) relative to the target surface; (iii) arbitrarily moving straight jet-paths (β). These conditions reflect the real industrial conditions under which the process is run. The geometrical model results in a non-linear partial differential equation, a method to evaluate the material specific erosion rate from the characteristics of a shallow trench obtained experimentally using high jet feed speeds. Under these conditions, the governing equations can be linearised and solved analytically.The model validation for full profile of trenches generated at various tilt angles (θ=70-90°), jet feed rates (V_f=500-1000 mm/min) and jet path directions (β=0-270°) indicates that a high degree of accuracy (mean of the residuals R_M<3% and root-mean-square error of residuals R_RMS<6%) has been achieved. This innovative footprint modelling approach has the key advantage of being independent of the properties of the workpiece material and/or machine setup, since it calibrates the specific etching rate. By considering any orientation of the jet plume vector relative to the target surface, this approach becomes a powerful tool for the development of advanced jet path strategies to enable AWJM of complex geometries.     

移动对象无冗余周期模式发现算法

技术的发展进步和需求的多样性,产生了大量的数据,数据背后隐含模式的发现对问题的深入研究起到关键性的作用.为了挖掘移动对象的周期运动模式,提出了一种无冗余周期模式(Non-redundant period patterns,NRPP)发现算法.为解决噪声因素的影响,在现有方法基础上,引入相似性因子,删减冗余周期模式,使挖掘出的模式精简、准确.算法为不同模式设置不同阈值,不但解决了稀有项问题和组合爆炸问题,而且也使得挖掘效率更高效.采用公开移动对象数据实验,结果表明算法能高效挖掘出移动对象的周期模式.     

Segmentation of neuronal nuclei based on clump splitting and a two-step binarization of images

In this paper we present an algorithm to segment the nuclei of neuronal cells in confocal microscopy images, a key technical problem in many experimental studies in the field of neuroscience. We describe the whole procedure, from the original images to the segmented individual nuclei, paying particular attention to the binarization of the images, which is not straightforward due to the technical difficulties related to the visualization of nuclei as individual objects and incomplete and irregular staining. We have focused on the division of clusters of nuclei that appear frequently in these images. Thus we have developed a clump-splitting algorithm to separate touching or overlapping nuclei allowing us to accurate account for both the number and size of the nuclei. The results presented in the paper show that the proposed algorithm performs well on different sets of images from different layers of the cerebral cortex.     

Dimension reduction in principal component analysis for trees

The statistical analysis of tree structured data is a new topic in statistics with wide application areas. Some Principal Component Analysis (PCA) ideas have been previously developed for binary tree spaces. These ideas are extended to the more general space of rooted and ordered trees. Concepts such as tree-line and forward principal component tree-line are redefined for this more general space, and the optimal algorithm that finds them is generalized.An analog of the classical dimension reduction technique in PCA for tree spaces is developed. To do this, backward principal components, the components that carry the least amount of information on tree data set, are defined. An optimal algorithm to find them is presented. Furthermore, the relationship of these to the forward principal components is investigated, and a path-independence property between the forward and backward techniques is proven.These methods are applied to a brain artery data set of 98 subjects. Using these techniques, the effects of aging to the brain artery structure of males and females is investigated. A second data set of the organization structure of a large US company is also analyzed and the structural differences across different types of departments within the company are explored.     

Scalable visibility color map construction in spatial databases

Recent advances in 3D modeling provide us with real 3D datasets to answer queries, such as “What is the best position for a new billboard?” and “Which hotel room has the best view?” in the presence of obstacles. These applications require measuring and differentiating the visibility of an object (target) from different viewpoints in a dataspace, e.g., a billboard may be seen from many points but is readable only from a few points closer to it. In this paper, we formulate the above problem of quantifying the visibility of (from) a target object from (of) the surrounding area with a visibility color map (VCM). A VCM is essentially defined as a surface color map of the space, where each viewpoint of the space is assigned a color value that denotes the visibility measure of the target from that viewpoint. Measuring the visibility of a target even from a single viewpoint is an expensive operation, as we need to consider factors such as distance, angle, and obstacles between the viewpoint and the target. Hence, a straightforward approach to construct the VCM that requires visibility computation for every viewpoint of the surrounding space of the target is prohibitively expensive in terms of both I/Os and computation, especially for a real dataset comprising thousands of obstacles. We propose an efficient approach to compute the VCM based on a key property of the human vision that eliminates the necessity for computing the visibility for a large number of viewpoints of the space. To further reduce the computational overhead, we propose two approximations; namely, minimum bounding rectangle and tangential approaches with guaranteed error bounds. Our extensive experiments demonstrate the effectiveness and efficiency of our solutions to construct the VCM for real 2D and 3D datasets.     

Optimal conditions of isobutane dehydrogenation in radial flow moving bed hydrogen-permselective membrane reactors to enhance isobutene and hydrogen production

In the isobutane dehydrogenation process, coupling reaction and separation and optimization of the intensified process can improve the isobutane conversion and selectivity, reduce operational costs and lets to produce pure hydrogen. In this research, the radial flow moving bed reactors in the Olefex technology have been supported by Pd–Ag membrane plate to remove hydrogen from the reaction zone. The reactions occur in the tube side and the hydrogen is permeated from the reaction zone to the sweep gas stream. The proposed configuration has been modeled heterogeneously based on the mass and energy conservation laws considering reaction networks. To prove the accuracy of the considered model, the simulation results of the conventional process have been compared against available plant data. The Genetic algorithm as an effective method in the global optimization has been considered to optimize the operating condition of membrane reactors to enhance isobutene productivity. In this optimal configuration, the isobutene production has been enhanced about 3.7%.     

A Statistical Control Chart for Stationary Process Data

In the statistical process control environment, a primary method to deal with autocorrelated data is the use of a residual chart. Although this methodology has the advantage that it can be applied to any autocorrelated data, it needs some modeling effort in practice. In addition, the detection capability of the residual chart is not always great. This article proposes a statistical control chart for stationary process data. It is simple to implement, and no modeling effort is required. Comparisons are made among the proposed chart, the residual chart, and other charts. When the process autocorrelation is not very strong and the mean changes are not large, the new chart performs better than the residual chart and the other charts.     

Precedence constrained TSP arising in printed circuit board assembly

Component placement sequencing is a challenging problem that arises in automated assembly of printed circuit boards. While for some placement machines all placement sequences are acceptable, in other cases some sequences are not allowed because of the shape of the placement head. In such cases, while the head moves down to perform a placement, it might damage a previously placed component, and the problem of determining a minimum cost and at the same time acceptable sequence leads to a Precedence Constrained Travelling Salesman Problem formulation. In this study, a solution procedure to such a formulation is developed and its implementation in a real PCB assembly environment is discussed.