Optimizing source and receiver placement in multistatic sonar networks to monitor fixed targets

Multistatic sonar networks consisting of non-collocated sources and receivers are a promising development in sonar systems, but they present distinct mathematical challenges compared to the monostatic case in which each source is collocated with a receiver. This paper is the first to consider the optimal placement of both sources and receivers to monitor a given set of target locations. Prior publications have only considered optimal placement of one type of sensor, given a fixed placement of the other type. We first develop two integer linear programs capable of optimally placing both sources and receivers within a discrete set of locations. Although these models are capable of placing both sources and receivers to any degree of optimality desired by the user, their computation times may be unacceptably long for some applications. To address this issue, we then develop a two-step heuristic process, Adapt-LOC, that quickly selects positions for both sources and receivers, but with no guarantee of optimality. Based on this, we also create an iterative approach, Iter-LOC, which leads to a locally optimal placement of both sources and receivers, at the cost of larger computation times relative to Adapt-LOC. Finally, we perform computational experiments demonstrating that the newly developed algorithms constitute a powerful portfolio of tools, enabling the user to slect an appropriate level of solution quality, given the available time to perform computations. Our experiments include three real-world case studies.     

Training Data Selection and Optimal Sensor Placement for Deep-Learning-Based Sparse Inertial Sensor Human Posture Reconstruction

Although commercial motion-capture systems have been widely used in various applications, the complex setup limits their application scenarios for ordinary consumers. To overcome the drawbacks of wearability, human posture reconstruction based on a few wearable sensors have been actively studied in recent years. In this paper, we propose a deep-learning-based sparse inertial sensor human posture reconstruction method. This method uses bidirectional recurrent neural network (Bi-RNN) to build an a priori model from a large motion dataset to build human motion, thereby the low-dimensional motion measurements are mapped to whole-body posture. To improve the motion reconstruction performance for specific application scenarios, two fundamental problems in the model construction are investigated: training data selection and sparse sensor placement. The problem of deep-learning training data selection is to select independent and identically distributed (IID) data for a certain scenario from the accumulated imbalanced motion dataset with sufficient information. We formulate the data selection into an optimization problem to obtain continuous and IID data segments, which comply with a small reference dataset collected from the target scenario. A two-step heuristic algorithm is proposed to solve the data selection problem. On the other hand, the optimal sensor placement problem is studied to exploit most information from partial observation of human movement. A method for evaluating the motion information amount of any group of wearable inertial sensors based on mutual information is proposed, and a greedy searching method is adopted to obtain the approximate optimal sensor placement of a given sensor number, so that the maximum motion information and minimum redundancy is achieved. Finally, the human posture reconstruction performance is evaluated with different training data and sensor placement selection methods, and experimental results show that the proposed method takes advantages in both posture reconstruction accuracy and model training time. In the 6 sensors configuration, the posture reconstruction errors of our model for walking, running, and playing basketball are 7.25°, 8.84°, and 14.13°, respectively.     

Performance analysis of integer linear programming optical monitor placement for crosstalk attack detection in different network topologies

In this paper, an optimum optical power monitor placement is formulated in integer linear programming for crosstalk attack detection by exploiting monitoring information from test connections. The experimental results showed that equipping 6 out of 14, 8 out of 15 and 8 out of 16 nodes with optical power monitoring devices in NSF, MESH and GRID topologies are enough to detect the nodes that are traversed by the crosstalk attack in the networks.     

超大规模集成电路布局的优化模型与算法

布局确定集成电路单元在芯片中的具体位置,在单元互不重叠的基础上优化一些性能指标。该问题是NP困难的组合优化问题,是超大规模集成电路物理设计的核心问题之一,对集成电路的性能指标,如线网可布通性、时延、功耗、电路可靠性等有重大影响。在现代的集成电路设计中,布局问题通常包含数百万个集成电路单元,以及大小相异的异质性模块,和各种复杂的布局约束。目前的超大规模集成电路布局算法通常分解为总体布局、布局合法化和详细布局三个步骤。根据近年来集成电路布局算法的研究进展,综述并分析集成电路的总体布局、布局合法化和详细布局的相关优化模型和算法,并展望进一步的研究方向。     

A simulated annealing approach to the nesting problem in the textile manufacturing industry

The nesting problem in the textile industry is the problem of placing a set of irregularly shaped pieces (calledstencils) on a rectangularsurface, such that no stencils overlap and that thetrim loss produced when cutting out the stencils is minimized. Certain constraints may put restrictions on the positions and orientation of some stencils in the layout but, in general, the problem is unconstrained. In this paper, an algorithmic approach using simulated annealing is presented covering a wide variety of constraints which may occur in the industrial manufacturing process. The algorithm has high performance, is quite simple to use, is extensible with respect to the set of constraints to be met, and is easy to implement.The work of this author was supported in part by grant Le 491/3-1 from the German Research Association (DFG).     

BBL布局的V一H树

本文首先定义一种有序有色的出树,称为V-H树。用该树描述BBL布局中的slicing结群。然后提出两种产生V-H树的递归构造法:一种是利用整数划分和迭代的方法,另一种是先分类再根据若干定理递归。最后讨论几种特殊树和BBL布局的关系。     

Anti-Plane Shear Waves Scattering from a Partially Debonded Magneto-Electro-Elastic Circular Cylindrical Inhomogeneity

This article presents an analysis of the scattering of anti-plane shear waves from a single cylindrical inhomogeneity partially bonded to an unbounded magneto-electro-elastic matrix. The magneto-electric permeable boundary conditions are adopted. The crack opening displacement is represented by Chebyshev polynomials and a system of equations is derived and solved for the unknown coefficients. Some examples are calculated and the results are illustrated. The results show that the COD increases when the piezomagnetic coefficient of the inhomogeneity bonded to the piezoelectric matrix becomes larger, and that the COD decreases when the piezomagnetic coefficient of the matrix with the piezoelectric inhomogeneity increases.     

Optimal 3D Angle of Arrival Sensor Placement with Gaussian Priors

Sensor placement is an important factor that may significantly affect the localization performance of a sensor network. This paper investigates the sensor placement optimization problem in three-dimensional (3D) space for angle of arrival (AOA) target localization with Gaussian priors. We first show that under the A-optimality criterion, the optimization problem can be transferred to be a diagonalizing process on the AOA-based Fisher information matrix (FIM). Secondly, we prove that the FIM follows the invariance property of the 3D rotation, and the Gaussian covariance matrix of the FIM can be diagonalized via 3D rotation. Based on this finding, an optimal sensor placement method using 3D rotation was created for when prior information exists as to the target location. Finally, several simulations were carried out to demonstrate the effectiveness of the proposed method. Compared with the existing methods, the mean squared error (MSE) of the maximum a posteriori (MAP) estimation using the proposed method is lower by at least 25% when the number of sensors is between 3 and 6, while the estimation bias remains very close to zero (smaller than 0.15 m).     

Pole placement method of controlling chaos in DC--DC buck converters

Based on the mechanism for the generation of chaos in a buck converter, a pole placement method is proposed and applied to controlling the chaos in a circuit. The control circuit is designed and tested. Numerical calculation and circuit implementation demonstrate the validity of this chaos control method.     

快速泡泡布点方法

泡泡布点算法能够在复杂区域上不经过人工干预生成高质量的节点集,为提高其计算效率,本文对泡泡布点法做三方面修正:①让泡泡运动的粘性系数c随时间的推移逐渐增大以加速收敛,每轮模拟结束后需增删泡泡时再给c赋一个较小的值以确保泡泡分布的质量;②求解控制泡泡运动的常微分方程组时选用时间复杂度较小的数值算法.算例表明数值方法精度在一定程度上的降低对节点质量的影响不大,能有效的节约计算时间;③取消对泡泡交叠率的排序,通过设置阈值控制泡泡的增删.算例表明以上方案节约运行时间40%以上,所布置节点对应的Delaunay三角单元网格平均质量高于0.9.该快速算法高效且能够生成高质量的节点集.