一种可变形移动机器人协同构形变换方法

提出了一种可变形移动机器人AMOEBA-I的协同构形变换方法,建立了机器人系统的数学模型,对各个模块之间的协同变换及运动特性进行了分析.研究了机器人3个模块在协同变换过程中的电流变化情况,实现了3种特殊构形之间的变换.通过理论分析和实验比较了协同构形变换方法的特点,实验验证了在多种地面条件下机器人协同构形变换方法的有效性.

基于状态构形矢量和状态构形矩阵的可重构模块星球机器人构形水

可重构模块星球机器人系统由母体和多个子机器人模块组成,单个模块可以独立运动和操作,多个模块可以重构组合成不同构形,模块采用非对称式轮手一体机构,具有姿态方位性和运动方向性,重构目的是组成在某种环境下更好地完成有向性运动的构形。基于此,提出矢量构形概念,将运动趋势方向和方位性融合到构形拓扑结构中。在模块矢量分析模型基础上,提出并构建状态构形矢量(State configuration vector,SCV)和状态构形矩阵(State configuration matrix,SCM),对非对称式单模块和整体构形的状态信息进行描述,同时支持预定义的数学变换操作,可以表达并触发模块的基础动作、构形重构。提出离散模块组合重构成目标构形的优化分析算法,通过实例仿真计算获得优化分析的选择结果。

可重构星球探测机器人控制系统的设计与实现

介绍了一种新型可重构星球探测机器人系统.基于这种机器人功能和结构的分解特点,设计了模块化控制系统,使用CAN总线技术作为模块间主要通讯方式.提出了控制原理和集中式控制算法,有效地实现了一台子机器人在不同模式状态下自主运动和操作的控制,并通过原理样机实验验证了这套控制系统的可行性.

可重构模块化机器人模块及构形设计

对可重构模块化机器人模块的结构进行了研究,并归纳设计出7种功能模块,其中包括3种1自由度的关节模块,2种连杆模块和2种辅助模块·所有模块的功能都是独立的,并且每个模块的连接界面都设计成了圆筒形以便重组和提高其刚度·每一种模块都可设计成不同尺寸系列,这些不同类型和尺寸系列的模块便可构成一个模块库·作者对3个自由度串联机器人的构形进行了系统的研究,并应用制作的实验模块对研究结果的可行性进行了验证·

Constructing extensions of CP-maps via tensor dilations with rhe help of von Neumann modules

Gohm, Rolf; Skeide, M., (2005) 'Constructing extensions of CP-maps via tensor dilations with rhe help of von Neumann modules', Infinite Dimensional Analysis, Quantum Probability and Related Topics 8(2) pp.291-305 RAE2008

Papers presented at conference on the situation in China : under the auspices of the Committee of Reference and Counsel, representing the Foreign Missions Conference of North America : February 29, 1912, 156 Fifth Avenue, New York City.

http://www.archive.org/details/paperspresenteda00foreuoft

Catholic grievances in relation to the administration of Indian affairs : being a report presented to the Catholic Young Men's National Union, at its eighth annual convention, held in Boston, Massachusetts, May 10th and 11th, 1882

http://www.archive.org/details/catholicgrieva00mealrich

The Gift Presented as a Symbol

Abstract is not available.

Improved Tracking of Multiple Humans with Trajectory Prediction and Occlusion Modeling

A combined 2D, 3D approach is presented that allows for robust tracking of moving bodies in a given environment as observed via a single, uncalibrated video camera. Tracking is robust even in the presence of occlusions. Low-level features are often insufficient for detection, segmentation, and tracking of non-rigid moving objects. Therefore, an improved mechanism is proposed that combines low-level (image processing) and mid-level (recursive trajectory estimation) information obtained during the tracking process. The resulting system can segment and maintain the tracking of moving objects before, during, and after occlusion. At each frame, the system also extracts a stabilized coordinate frame of the moving objects. This stabilized frame is used to resize and resample the moving blob so that it can be used as input to motion recognition modules. The approach enables robust tracking without constraining the system to know the shape of the objects being tracked beforehand; although, some assumptions are made about the characteristics of the shape of the objects, and how they evolve with time. Experiments in tracking moving people are described.

Trajectory Guided Tracking and Recognition of Actions

A combined 2D, 3D approach is presented that allows for robust tracking of moving people and recognition of actions. It is assumed that the system observes multiple moving objects via a single, uncalibrated video camera. Low-level features are often insufficient for detection, segmentation, and tracking of non-rigid moving objects. Therefore, an improved mechanism is proposed that integrates low-level (image processing), mid-level (recursive 3D trajectory estimation), and high-level (action recognition) processes. A novel extended Kalman filter formulation is used in estimating the relative 3D motion trajectories up to a scale factor. The recursive estimation process provides a prediction and error measure that is exploited in higher-level stages of action recognition. Conversely, higher-level mechanisms provide feedback that allows the system to reliably segment and maintain the tracking of moving objects before, during, and after occlusion. The 3D trajectory, occlusion, and segmentation information are utilized in extracting stabilized views of the moving object that are then used as input to action recognition modules. Trajectory-guided recognition (TGR) is proposed as a new and efficient method for adaptive classification of action. The TGR approach is demonstrated using "motion history images" that are then recognized via a mixture-of-Gaussians classifier. The system was tested in recognizing various dynamic human outdoor activities: running, walking, roller blading, and cycling. Experiments with real and synthetic data sets are used to evaluate stability of the trajectory estimator with respect to noise.

ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network

This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is built up from a pair of Adaptive Resonance Theory modules (ARTa and ARTb) that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. During training trials, the ARTa module receives a stream {a^(p)} of input patterns, and ARTb receives a stream {b^(p)} of input patterns, where b^(p) is the correct prediction given a^(p). These ART modules are linked by an associative learning network and an internal controller that ensures autonomous system operation in real time. During test trials, the remaining patterns a^(p) are presented without b^(p), and their predictions at ARTb are compared with b^(p). Tested on a benchmark machine learning database in both on-line and off-line simulations, the ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half the input patterns in the database. It achieves these properties by using an internal controller that conjointly maximizes predictive generalization and minimizes predictive error by linking predictive success to category size on a trial-by-trial basis, using only local operations. This computation increases the vigilance parameter ρa of ARTa by the minimal amount needed to correct a predictive error at ARTb· Parameter ρa calibrates the minimum confidence that ARTa must have in a category, or hypothesis, activated by an input a^(p) in order for ARTa to accept that category, rather than search for a better one through an automatically controlled process of hypothesis testing. Parameter ρa is compared with the degree of match between a^(p) and the top-down learned expectation, or prototype, that is read-out subsequent to activation of an ARTa category. Search occurs if the degree of match is less than ρa. ARTMAP is hereby a type of self-organizing expert system that calibrates the selectivity of its hypotheses based upon predictive success. As a result, rare but important events can be quickly and sharply distinguished even if they are similar to frequent events with different consequences. Between input trials ρa relaxes to a baseline vigilance pa When ρa is large, the system runs in a conservative mode, wherein predictions are made only if the system is confident of the outcome. Very few false-alarm errors then occur at any stage of learning, yet the system reaches asymptote with no loss of speed. Because ARTMAP learning is self stabilizing, it can continue learning one or more databases, without degrading its corpus of memories, until its full memory capacity is utilized.

Working Memory Networks for Learning Temporal Order, with Application to 3-D Visual Object Recognition

Working memory neural networks are characterized which encode the invariant temporal order of sequential events. Inputs to the networks, called Sustained Temporal Order REcurrent (STORE) models, may be presented at widely differing speeds, durations, and interstimulus intervals. The STORE temporal order code is designed to enable all emergent groupings of sequential events to be stably learned and remembered in real time, even as new events perturb the system. Such a competence is needed in neural architectures which self-organize learned codes for variable-rate speech perception, sensory-motor planning, or 3-D visual object recognition. Using such a working memory, a self-organizing architecture for invariant 3-D visual object recognition is described. The new model is based on the model of Seibert and Waxman (1990a), which builds a 3-D representation of an object from a temporally ordered sequence of its 2-D aspect graphs. The new model, called an ARTSTORE model, consists of the following cascade of processing modules: Invariant Preprocessor --> ART 2 --> STORE Model --> ART 2 --> Outstar Network.

Novel smart modules for imaging, communications, and displays

This dissertation proposes and demonstrates novel smart modules to solve challenging problems in the areas of imaging, communications, and displays. The smartness of the modules is due to their ability to be able to adapt to changes in operating environment and application using programmable devices, specifically, electronically variable focus lenses (ECVFLs) and digital micromirror devices (DMD). The proposed modules include imagers for laser characterization and general purpose imaging which smartly adapt to changes in irradiance, optical wireless communication systems which can adapt to the number of users and to changes in link length, and a smart laser projection display that smartly adjust the pixel size to achieve a high resolution projected image at each screen distance. The first part of the dissertation starts with the proposal of using an ECVFL to create a novel multimode laser beam characterizer for coherent light. This laser beam characterizer uses the ECVFL and a DMD so that no mechanical motion of optical components along the optical axis is required. This reduces the mechanical motion overhead that traditional laser beam characterizers have, making this laser beam characterizer more accurate and reliable. The smart laser beam characterizer is able to account for irradiance fluctuations in the source. Using image processing, the important parameters that describe multimode laser beam propagation have been successfully extracted for a multi-mode laser test source. Specifically, the laser beam analysis parameters measured are the M2 parameter, w0 the minimum beam waist, and zR the Rayleigh range. Next a general purpose incoherent light imager that has a high dynamic range (>100 dB) and automatically adjusts for variations in irradiance in the scene is proposed. Then a data efficient image sensor is demonstrated. The idea of this smart image sensor is to reduce the bandwidth needed for transmitting data from the sensor by only sending the information which is required for the specific application while discarding the unnecessary data. In this case, the imager demonstrated sends only information regarding the boundaries of objects in the image so that after transmission to a remote image viewing location, these boundaries can be used to map out objects in the original image. The second part of the dissertation proposes and demonstrates smart optical communications systems using ECVFLs. This starts with the proposal and demonstration of a zero propagation loss optical wireless link using visible light with experiments covering a 1 to 4 m range. By adjusting the focal length of the ECVFLs for this directed line-of-sight link (LOS) the laser beam propagation parameters are adjusted such that the maximum amount of transmitted optical power is captured by the receiver for each link length. This power budget saving enables a longer achievable link range, a better SNR/BER, or higher power efficiency since more received power means the transmitted power can be reduced. Afterwards, a smart dual mode optical wireless link is proposed and demonstrated using a laser and LED coupled to the ECVFL to provide for the first time features of high bandwidths and wide beam coverage. This optical wireless link combines the capabilities of smart directed LOS link from the previous section with a diffuse optical wireless link, thus achieving high data rates and robustness to blocking. The proposed smart system can switch from LOS mode to Diffuse mode when blocking occurs or operate in both modes simultaneously to accommodate multiple users and operate a high speed link if one of the users requires extra bandwidth. The last part of this section presents the design of fibre optic and free-space optical switches which use ECVFLs to deflect the beams to achieve switching operation. These switching modules can be used in the proposed optical wireless indoor network. The final section of the thesis presents a novel smart laser scanning display. The ECVFL is used to create the smallest beam spot size possible for the system designed at the distance of the screen. The smart laser scanning display increases the spatial resoluti on of the display for any given distance. A basic smart display operation has been tested for red light and a 4X improvement in pixel resolution for the image has been demonstrated.

Display of cell surface sites for fibronectin assembly is modulated by cell adherence to (1)F3 and C-terminal modules of fibronectin.

BACKGROUND: Fibronectin-null cells assemble soluble fibronectin shortly after adherence to a substrate coated with intact fibronectin but not when adherent to the cell-binding domain of fibronectin (modules (7)F3-(10)F3). Interactions of adherent cells with regions of adsorbed fibronectin other than modules (7)F3-(10)F3, therefore, are required for early display of the cell surface sites that initiate and direct fibronectin assembly. METHODOLOGY/PRINCIPAL FINDINGS: To identify these regions, coatings of proteolytically derived or recombinant pieces of fibronectin containing modules in addition to (7)F3-(10)F3 were tested for effects on fibronectin assembly by adherent fibronectin-null fibroblasts. Pieces as large as one comprising modules (2)F3-(14)F3, which include the heparin-binding and cell adhesion domains, were not effective in supporting fibronectin assembly. Addition of module (1)F3 or the C-terminal modules to modules (2)F3-(14)F3 resulted in some activity, and addition of both (1)F3 and the C-terminal modules resulted in a construct, (1)F3-C, that best mimicked the activity of a coating of intact fibronectin. Constructs (1)F3-C V0, (1)F3-C V64, and (1)F3-C Delta(V(15)F3(10)F1) were all able to support fibronectin assembly, suggesting that (1)F3 through (11)F1 and/or (12)F1 were important for activity. Coatings in which the active parts of (1)F3-C were present in different proteins were much less active than intact (1)F3-C. CONCLUSIONS: These results suggest that (1)F3 acts together with C-terminal modules to induce display of fibronectin assembly sites on adherent cells.

The spacing effect depends on an encoding deficit, retrieval, and time in working memory: evidence from once-presented words.

The spacing effect in list learning occurs because identical massed items suffer encoding deficits and because spaced items benefit from retrieval and increased time in working memory. Requiring the retrieval of identical items produced a spacing effect for recall and recognition, both for intentional and incidental learning. Not requiring retrieval produced spacing only for intentional learning because intentional learning encourages retrieval. Once-presented words provided baselines for these effects. Next, massed and spaced word pairs were judged for matches on their first three letters, forcing retrieval. The words were not identical, so there was no encoding deficit. Retrieval could and did cause spacing only for the first word of each pair; time in working memory, only for the second.