一种新型本征倏逝波型光纤传感器的研究

研究开发了一种新型本征型光纤倏逝波传感器.采用有机玻璃板和大半圆弧高抛光PVC管构成光纤支架,将60/125 μm阶跃多模光纤隔一定距离剥除等长度涂覆层后缠绕于光纤支架上,其无涂覆层部分位于上下两PVC管之间,经化学腐蚀后获得裸芯总长度大于0.5m的新型高灵敏本征型光纤倏逝波传感器.实验结果表明:该传感器对亚甲基蓝的探...     

Indoor robot gardening: design and implementation

This paper describes the architecture and implementation of a distributed autonomous gardening system with applications in urban/indoor precision agriculture. The garden is a mesh network of robots and plants. The gardening robots are mobile manipulators with an eye-in-hand camera. They are capable of locating plants in the garden, watering them, and locating and grasping fruit. The plants are potted cherry tomatoes enhanced with sensors and computation to monitor their well-being (e.g. soil humidity, state of fruits) and with networking to communicate servicing requests to the robots. By embedding sensing, computation, and communication into the pots, task allocation in the system is de-centrally coordinated, which makes the system scalable and robust against the failure of a centralized agent. We describe the architecture of this system and present experimental results for navigation, object recognition, and manipulation as well as challenges that lie ahead toward autonomous precision agriculture with multi-robot teams.     

Peer-exchange schemes to handle mismatch in peer-to-peer systems

A self-organizing peer-to-peer system is built upon an application level overlay, whose topology is independent of an underlying physical network. A well-routed message path in such systems may result in a long delay and excessive traffic due to the mismatch between logical and physical networks. In order to solve this problem, we present a family of Peer-exchange Routing Optimization Protocols (PROP) to reconstruct the overlay. It includes two policies: PROP-G for generic condition and PROP-O for optimized one. Both theoretical analysis and simulation experiments show that these two protocols greatly reduce the average latency of the overlay and achieve a better logical topology with low overhead. Their overall performance can be further improved if combined with other recent approaches. Specifically, PROP-G can be easily applied to both structured and unstructured systems without the loss of their primary characteristics, such as efficient routing and anonymity. PROP-O, on the other hand, is more efficient, especially in a heterogenous environment where nodes have different processing capabilities.

Contact type dependency of texture classification in a whiskered mobile robot

Actuated artificial whiskers modeled on rat macrovibrissae can provide effective tactile sensor systems for autonomous robots. This article focuses on texture classification using artificial whiskers and addresses a limitation of previous studies, namely, their use of whisker deflection signals obtained under relatively constrained experimental conditions. Here we consider the classification of signals obtained from a whiskered robot required to explore different surface textures from a range of orientations and distances. This procedure resulted in a variety of deflection signals for any given texture. Using a standard Gaussian classifier we show, using both hand-picked features and ones derived from studies of rat vibrissal processing, that a robust rough-smooth discrimination is achievable without any knowledge of how the whisker interacts with the investigated object. On the other hand, finer discriminations appear to require knowledge of the target’s relative position and/or of the manner in which the whisker contact its surface. Electronic Supplementary Material  The online version of this article () contains supplementary material, which is available to authorized users.

On partial-information optimal singular control problem for mean-field stochastic differential equations driven by Teugels martingales measures

This paper is concerned with partial-information mixed optimal stochastic continuous–singular control problem for mean-field stochastic differential equation driven by Teugels martingales and an independent Brownian motion, where the Teugels martingales are a family of pairwise strongly orthonormal martingales associated with Lévy processes. The control variable has two components; the first being absolutely continuous, and the second singular. Partial-information necessary and sufficient conditions of optimal continuous–singular control for these mean-field models are investigated. As an illustration, this paper studies a partial-information linear quadratic control problem of mean-field type involving continuous–singular control.     

Simultaneous state estimation and parameter identification in linear fractional order systems using coloured measurement noise

In the present paper, the identification and estimation problem of a single-input–single-output (SISO) fractional order state-space system will be addressed. A SISO state-space model is considered in which parameters and also state variables should be estimated. The canonical fractional order state-space system will be transformed into a regression equation by using a linear transformation and a shift operator that are appropriate for identification. The identification method provided in this paper is based on a recursive identification algorithm that has the capability of identifying the parameters of fractional order state-space system recursively. Another subject that will be addressed in this paper is a novel fractional order Kalman filter suitable for the systems with coloured measurement noise. The promising performance of the proposed methods is verified using two stable fractional order systems.     

Iterative scheme for MS image pansharpening based on the combination of multi-resolution decompositions

Image pansharpening in the remote-sensing domain may be defined as the technique of extracting high-resolution details from the panchromatic (PAN) image and injecting them into the multispectral (MS) one in a way to preserve the spectral signature and improve the spatial resolution. In this article, the authors propose an image fusion framework that tries to derive sharpened MS image such that: (i) when decimated taking into account the imagery system Modulation Transfer Function (MTF), it equals the original MS image; (ii) when decomposed using discrete wavelet transform (DWT), its geometrical details are those of the PAN image weighted by the compatibility PAN/MS. Indeed, MS sharpening is carried out in two steps. First, pre-pansharpened MS image is obtained using inverse DWT taking as approximations those of the upsampled original MS image and as details those of PAN (to reduce spectral distortion, PAN detail injection is performed proportionally to the similarity PAN/MS). Second, to satisfy (i) and to remove the PAN-MS disagreement, an iteration algorithm (alternatively corrects approximations and details) has been proposed. The proposed approach is designed in two versions inspired by the Generalized Laplacian Pyramid (GLP) and the Gram–Schmidt (GS) transformation, respectively.

To validate our approach, Pléiades-1A, Geoeye-1, and Landsat Enhanced Thematic Mapper Plus (ETM+) images are tested. The results of qualitative and quantitative scores are presented and discussed. Compared to well-known techniques, our approach shows generally better results, particularly the one based on GLP formalism.     

Advection‐Based Function Matching on Surfaces

A tangent vector field on a surface is the generator of a smooth family of maps from the surface to itself, known as the flow. Given a scalar function on the surface, it can be transported, or advected, by composing it with a vector field's flow. Such transport is exhibited by many physical phenomena, e.g., in fluid dynamics. In this paper, we are interested in the inverse problem: given source and target functions, compute a vector field whose flow advects the source to the target. We propose a method for addressing this problem, by minimizing an energy given by the advection constraint together with a regularizing term for the vector field. Our approach is inspired by a similar method in computational anatomy, known as LDDMM, yet leverages the recent framework of functional vector fields for discretizing the advection and the flow as operators on scalar functions. The latter allows us to efficiently generalize LDDMM to curved surfaces, without explicitly computing the flow lines of the vector field we are optimizing for. We show two approaches for the solution: using linear advection with multiple vector fields, and using non‐linear advection with a single vector field. We additionally derive an approximated gradient of the corresponding energy, which is based on a novel vector field transport operator. Finally, we demonstrate applications of our machinery to intrinsic symmetry analysis, function interpolation and map improvement.     

Pool Evolution: A Parallel Pattern for Evolutionary and Symbolic Computing

We introduce a new parallel pattern derived from a specific application domain and show how it turns out to have application beyond its domain of origin. The pool evolution pattern models the parallel evolution of a population subject to mutations and evolving in such a way that a given fitness function is optimized. The pattern has been demonstrated to be suitable for capturing and modeling the parallel patterns underpinning various evolutionary algorithms, as well as other parallel patterns typical of symbolic computation. In this paper we introduce the pattern, we discuss its implementation on modern multi/many core architectures and finally present experimental results obtained with FastFlow and Erlang implementations to assess its feasibility and scalability.     

Applying Dynamic Training‐Subset Selection Methods Using Genetic Programming for Forecasting Implied Volatility

Volatility is a key variable in option pricing, trading, and hedging strategies. The purpose of this article is to improve the accuracy of forecasting implied volatility using an extension of genetic programming (GP) by means of dynamic training‐subset selection methods. These methods manipulate the training data in order to improve the out‐of‐sample patterns fitting. When applied with the static subset selection method using a single training data sample, GP could generate forecasting models, which are not adapted to some out‐of‐sample fitness cases. In order to improve the predictive accuracy of generated GP patterns, dynamic subset selection methods are introduced to the GP algorithm allowing a regular change of the training sample during evolution. Four dynamic training‐subset selection methods are proposed based on random, sequential, or adaptive subset selection. The latest approach uses an adaptive subset weight measuring the sample difficulty according to the fitness cases' errors. Using real data from S&P500 index options, these techniques are compared with the static subset selection method. Based on mean squared error total and percentage of non‐fitted observations, results show that the dynamic approach improves the forecasting performance of the generated GP models, especially those obtained from the adaptive‐random training‐subset selection method applied to the whole set of training samples.