Chaotic exploration and learning of locomotion behaviors

We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage.     

Time-of-flight sensor and color camera calibration for multi-view acquisition

This paper presents a multi-view acquisition system using multi-modal sensors, composed of time-of-flight (ToF) range sensors and color cameras. Our system captures the multiple pairs of color images and depth maps at multiple viewing directions. In order to ensure the acceptable accuracy of measurements, we compensate errors in sensor measurement and calibrate multi-modal devices. Upon manifold experiments and extensive analysis, we identify the major sources of systematic error in sensor measurement and construct an error model for compensation. As a result, we provide a practical solution for the real-time error compensation of depth measurement. Moreover, we implement the calibration scheme for multi-modal devices, unifying the spatial coordinate for multi-modal sensors. The main contribution of this work is to present the thorough analysis of systematic error in sensor measurement and therefore provide a reliable methodology for robust error compensation. The proposed system offers a real-time multi-modal sensor calibration method and thereby is applicable for the 3D reconstruction of dynamic scenes.     

Tracking servo method for holographic data storage using discrete pre-patterns

Page-oriented holographic data storage (HDS) is very sensitive to disturbances that affect the position of the recording medium. Accordingly, a more precise tracking servo is required for the recording process, and is also crucial for achieving high storage density. A compensation method is therefore essential for HDS recording. In this paper, we suggest some discrete pre-patterns for the tracking servo used in the recording process. This method is motivated by a tracking servo technique for a hard disk drive. Firstly, in designing the pattern shape, HDS characteristics are taken into account. Secondly, track error signals are analyzed. Thirdly, the discrete pre-pattern intervals are determined according to the track tolerance. Lastly, the feasibility of the new method is analyzed via simulations and experiments.     

Fractal analysis of pressure fluctuations in a three phase bubble column reactor operating at low pressure

Pressure fluctuations in a three phase bubble column reactor operating at relatively low pressure (92 KPa) have been analyzed by adopting the spectral and fractal analyses to get the engineering informations for the on-line control and fault diagnosis of the reactors. The mean value, standard deviation, skewness and kurtosis of the pressure fluctuations have been obtained. The local fractal dimension has been determined from the Pox diagram obtained by means of the rescaled range analysis of the pressure fluctuations based on the fractional Brownian motion. The local fractal dimension of pressure fluctuations has increased and thus the pressure fluctuation signals have become less persistent and irregular, with increases in the gas flow rate reaction temperature, particle size and solid content in the slurry phase. The local fractal dimension has been well correlated in terms of the operating variables.     

J resistance behavior in functionally graded materials using cohesive zone and modified boundary layer models

This paper describes elastic–plastic crack growth resistance simulation in a ceramic/metal functionally graded material (FGM) under mode I loading conditions using cohesive zone and modified boundary layer (MBL) models. For this purpose, we first explore the applicability of two existing, phenomenological cohesive zone models for FGMs. Based on these investigations, we propose a new cohesive zone model. Then, we perform crack growth simulations for TiB/Ti FGM SE(B) and SE(T) specimens using the three cohesive zone models mentioned above. The crack growth resistance of the FGM is characterized by the J-integral. These results show that the two existing cohesive zone models overestimate the actual J value, whereas the model proposed in the present study closely captures the actual fracture and crack growth behaviors of the FGM. Finally, the cohesive zone models are employed in conjunction with the MBL model. The two existing cohesive zone models fail to produce the desired K–T stress field for the MBL model. On the other hand, the proposed cohesive zone model yields the desired K–T stress field for the MBL model, and thus yields J _R curves that match the ones obtained from the SE(B) and SE(T) specimens. These results verify the application of the MBL model to simulate crack growth resistance in FGMs.     

Nanostructural and optical features of nc-Si:H films prepared by nickel-induced crystallization

A 30-nm-thick Ni layer was deposited on top of the nc-Si:H (hydrogenated nanocrystalline Si) films by rf-magnetron sputter, and then heat-treatments were carried out at temperatures of 350–500^∘C. Si nanocrystallites were formed in the Ni/nc-Si:H bilayer films during the post-deposition heat-treatments. The intensity of the photoluminescence spectra of the post-deposition heat-treated films gradually increased at wavelengths of ∼420 as well as ∼580 nm with raising the annealing temperature from 350 to 500^∘C. It is highly likely that the increase of the photoluminescence intensity is caused by the increase in the total volume of the nanocrystallites in the films. It was found that the nickel-induced crystallization processing enhanced the formation of Si cystallites with the size of ∼2 and ∼5 nm in the films.     

Recursive nonlinear observer design: beyond the uniform observability

We propose a recursive design scheme of a state observer for multiple-input-multiple-output, partly lower triangular nonlinear systems. The design begins from the subdynamics far from the output and propagates to the subdynamics close to the output, recalling the backstepping scheme for nonlinear control. The proposed class of systems is fairly general since it includes nonuniformly observable and/or detectable multioutput systems. Error convergence to zero is proved assuming boundedness of inputs a posteriori (i.e., after the design), which is preferable whereas most results in the literature assume the boundedness; a priori (i.e., before the design). A global observer is proposed with the global Lipschitz condition of the system, but without any restriction on the size of Lipschitz coefficient. The Lipschitz condition can be removed when a semiglobal observer is of interest.     

A flight control system for aerial robots: algorithms and experiments

This paper presents a hierarchical flight control system for unmanned aerial vehicles. The proposed system executes high-level mission objectives by progressively substantiating them into machine-level commands. The acquired information from various sensors is propagated back to the higher layers for reactive decision making. Each vehicle is connected via standardized wireless communication protocol for scalable multi-agent coordination. The proposed system has been successfully implemented on a number of small helicopters and validated in various applications. Results from waypoint navigation, a probabilistic pursuit-evasion game and vision-based target tracking demonstrate the potential of the proposed approach toward intelligent flying robots.     

WALRUS: a similarity retrieval algorithm for image databases

Approaches for content-based image querying typically extract a single signature from each image based on color, texture, or shape features. The images returned as the query result are then the ones whose signatures are closest to the signature of the query image. While efficient for simple images, such methods do not work well for complex scenes since they fail to retrieve images that match the query only partially, that is, only certain regions of the image match. This inefficiency leads to the discarding of images that may be semantically very similar to the query image since they may contain the same objects. The problem becomes even more apparent when we consider scaled or translated versions of the similar objects. We propose WALRUS (wavelet-based retrieval of user-specified scenes), a novel similarity retrieval algorithm that is robust to scaling and translation of objects within an image. WALRUS employs a novel similarity model in which each image is first decomposed into its regions and the similarity measure between a pair of images is then defined to be the fraction of the area of the two images covered by matching regions from the images. In order to extract regions for an image, WALRUS considers sliding windows of varying sizes and then clusters them based on the proximity of their signatures. An efficient dynamic programming algorithm is used to compute wavelet-based signatures for the sliding windows. Experimental results on real-life data sets corroborate the effectiveness of WALRUS'S similarity model.