Distributed Adaptive Networks: A Graphical Evolutionary Game-Theoretic View

Distributed adaptive filtering has been considered as an effective approach for data processing and estimation over distributed networks. Most existing distributed adaptive filtering algorithms focus on designing different information diffusion rules, regardless of the nature evolutionary characteristic of a distributed network. In this paper, we study the adaptive network from the game theoretic perspective and formulate the distributed adaptive filtering problem as a graphical evolutionary game. With the proposed formulation, the nodes in the network are regarded as players and the local combiner of estimation information from different neighbors is regarded as different strategies selection. We show that this graphical evolutionary game framework is very general and can unify the existing adaptive network algorithms. Based on this framework, as examples, we further propose two error-aware adaptive filtering algorithms. Moreover, we use graphical evolutionary game theory to analyze the information diffusion process over the adaptive networks and evolutionarily stable strategy of the system. Finally, simulation results are shown to verify the effectiveness of our analysis and proposed methods.Comment: Accepted by IEEE Transactions on Signal Processin

A Multiple-Expert Binarization Framework for Multispectral Images

In this work, a multiple-expert binarization framework for multispectral images is proposed. The framework is based on a constrained subspace selection limited to the spectral bands combined with state-of-the-art gray-level binarization methods. The framework uses a binarization wrapper to enhance the performance of the gray-level binarization. Nonlinear preprocessing of the individual spectral bands is used to enhance the textual information. An evolutionary optimizer is considered to obtain the optimal and some suboptimal 3-band subspaces from which an ensemble of experts is then formed. The framework is applied to a ground truth multispectral dataset with promising results. In addition, a generalization to the cross-validation approach is developed that not only evaluates generalizability of the framework, it also provides a practical instance of the selected experts that could be then applied to unseen inputs despite the small size of the given ground truth dataset.Comment: 12 pages, 8 figures, 6 tables. Presented at ICDAR'1

Deep imaging survey of young, nearby austral stars: VLT/NACO near-infrared Lyot-coronographic observations

Context. High contrast and high angular resolution imaging is the optimal search technique for substellar companions to nearby stars at physical separations larger than typically 10 AU. Two distinct populations of substellar companions, brown dwarfs and planets, can be probed and characterized. As a result, fossile traces of processes of formation and evolution can be revealed by physical and orbital properties, both for individual systems and as an ensemble. Aims. Since November 2002, we have conducted a large, deep imaging, survey of young, nearby associations of the southern hemisphere. Our goal is detection and characterization of substellar companions with projected separations in the range 10–500 AU. We have observed a sample of 88 stars, primarily G to M dwarfs, younger than 100 Myr, and within 100 pc of Earth. Methods. The VLT/NACO adaptive optics instrument of the ESO Paranal Observatory was used to explore the faint circumstellar environment between typically 0.1 and 10". Diffraction-limited observations in H and K_s-band combined with Lyot-coronagraphy enabled us to reach primary star-companion brightness ratios as small as 10^(-6). The existence of planetary mass companions could therefore be probed. We used a standardized observing sequence to precisely measure the position and flux of all detected sources relative to their visual primary star. Repeated observations at several epochs enabled us to discriminate comoving companions from background objects. Results. We report the discovery of 17 new close (0.1–5.0") multiple systems. HIP 108195 AB and C (F1 III-M6), HIP 84642 AB (a~14 AU, K0-M5) and TWA22 AB (a~1.8 AU; M6-M6) are confirmed comoving systems. TWA22 AB is likely to be a rare astrometric calibrator that can be used to test evolutionary model predictions. Among our complete sample, a total of 65 targets were observed with deep coronagraphic imaging. About 240 faint companion candidates were detected around 36 stars. Follow-up observations with VLT or HST for 83% of these stars enabled us to identify a large fraction of background contaminants. Our latest results that pertain to the substellar companions to GSC 08047-00232, AB Pic and 2M1207 (confirmed during this survey and published earlier), are reviewed. Finally, a statistical analysis of our complete set of coronagraphic detection limits enables us to place constraints on the physical and orbital properties of giant planets between typically 20 and 150 AU

A Robust Approach to Optimal Matched Filter Design in Ultrasonic Non-Destructive Evaluation (NDE)

The matched filter was demonstrated to be a powerful yet efficient technique to enhance defect detection and imaging in ultrasonic non-destructive evaluation (NDE) of coarse grain materials, provided that the filter was properly designed and optimized. In the literature, in order to accurately approximate the defect echoes, the design utilized the real excitation signals, which made it time consuming and less straightforward to implement in practice. In this paper, we present a more robust and flexible approach to optimal matched filter design using the simulated excitation signals, and the control parameters are chosen and optimized based on the real scenario of array transducer, transmitter-receiver system response, and the test sample, as a result, the filter response is optimized and depends on the material characteristics. Experiments on industrial samples are conducted and the results confirm the great benefits of the method

Automatic Document Image Binarization using Bayesian Optimization

Document image binarization is often a challenging task due to various forms of degradation. Although there exist several binarization techniques in literature, the binarized image is typically sensitive to control parameter settings of the employed technique. This paper presents an automatic document image binarization algorithm to segment the text from heavily degraded document images. The proposed technique uses a two band-pass filtering approach for background noise removal, and Bayesian optimization for automatic hyperparameter selection for optimal results. The effectiveness of the proposed binarization technique is empirically demonstrated on the Document Image Binarization Competition (DIBCO) and the Handwritten Document Image Binarization Competition (H-DIBCO) datasets

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available