Dynamic Strain Measured by Mach-Zehnder Interferometric Optical Fiber Sensors

Optical fibers possess many advantages such as small size, light weight and immunity to electro-magnetic interference that meet the sensing requirements to a large extent. In this investigation, a Mach-Zehnder interferometric optical fiber sensor is used to measure the dynamic strain of a vibrating cantilever beam. A 3 × 3 coupler is employed to demodulate the phase shift of the Mach-Zehnder interferometer. The dynamic strain of a cantilever beam subjected to base excitation is determined by the optical fiber sensor. The experimental results are validated with the strain gauge

A Developmental Learning Approach of Mobile Manipulator via Playing

Inspired by infant development theories, a robotic developmental model combined with game elements is proposed in this paper. This model does not require the definition of specific developmental goals for the robot, but the developmental goals are implied in the goals of a series of game tasks. The games are characterized into a sequence of game modes based on the complexity of the game tasks from simple to complex, and the task complexity is determined by the applications of developmental constraints. Given a current mode, the robot switches to play in a more complicated game mode when it cannot find any new salient stimuli in the current mode. By doing so, the robot gradually achieves it developmental goals by playing different modes of games. In the experiment, the game was instantiated into a mobile robot with the playing task of picking up toys, and the game is designed with a simple game mode and a complex game mode. A developmental algorithm, “Lift-Constraint, Act and Saturate,” is employed to drive the mobile robot move from the simple mode to the complex one. The experimental results show that the mobile manipulator is able to successfully learn the mobile grasping ability after playing simple and complex games, which is promising in developing robotic abilities to solve complex tasks using games

Local Activities of the Membranes Associated with Glycosaminoglycan-Chitosan Complexes in Bone Cells

Chitosan is a cationic polysaccharide derived from the partial deacetylation of chitin. Hyaluronic acid (HA), chondroitin sulfate (CS) and heparin (HP) are anionic glycosaminoglycans (GCGs) which can regulate osteogenic activity. In this study, chitosan membranes were prepared by glutaraldehyde crosslinking reaction and then complexed with three different types of GCGs. 7F2 osteoblasts-like cells and macrophages Raw264.7 were used as models to study the influence of chitosan membranes on osteometabolism. Although chitosan membranes are highly hydrophilic, the membranes associated with GCG-chitosan complexes showed about 60-70% cell attachment. Furthermore, the membranes associated with HP-chitosan complexes could increase ALP activity in comparison with chitosan films only. Three types of the membranes associated with GCG-chitosan complexes could significantly inhibit LPS induced-nitric oxide expression. In addition, chitosan membranes associated with HP and HA can down-regulate tartrate-resistant acid phosphatase (TRAP) activity but not CS-chitosan complexes. Based on these results, we conclude that chitosan membranes associated with HP can increase ALP activity in osteoblasts and chitosan membranes associated with HP and HA reduce TRAP activity in osteoclasts

Img2Logo:Generating Golden Ratio Logos from Images

Logos are one of the most important graphic design forms that use an abstracted shape to clearly represent the spirit of a community. Among various styles of abstraction, a particular golden-ratio design is frequently employed by designers to create a concise and regular logo. In this context, designers utilize a set of circular arcs with golden ratios (i.e., all arcs are taken from circles whose radii form a geometric series based on the golden ratio) as the design elements to manually approximate a target shape. This error-prone process requires a large amount of time and effort, posing a significant challenge for design space exploration. In this work, we present a novel computational framework that can automatically generate golden ratio logo abstractions from an input image. Our framework is based on a set of carefully identified design principles and a constrained optimization formulation respecting these principles. We also propose a progressive approach that can efficiently solve the optimization problem, resulting in a sequence of abstractions that approximate the input at decreasing levels of detail. We evaluate our work by testing on images with different formats including real photos, clip arts, and line drawings. We also extensively validate the key components and compare our results with manual results by designers to demonstrate the effectiveness of our framework. Moreover, our framework can largely benefit design space exploration via easy specification of design parameters such as abstraction levels, golden circle sizes, etc

Use of Automatic Chinese Character Decomposition and Human Gestures for Chinese Calligraphy Robots

Conventional Chinese calligraphy robots often suffer from the limited sizes of predefined font databases, which prevent the robots from writing new characters. This paper presents a robotic handwriting system to address such limitations, which extracts Chinese characters from textbooks and uses a robot’s manipulator to write the characters in a different style. The key technologies of the proposed approach include the following: (1) automatically decomposing Chinese characters into strokes using Harris corner detection technology and (2) matching the decomposed strokes to robotic writing trajectories learned from human gestures. Briefly, the system first decomposes a given Chinese character into a set of strokes and obtains the stroke trajectory writing ability by following the gestures performed by a human demonstrator. Then, it applies a stroke classification method that recognizes the decomposed strokes as robotic writing trajectories. Finally, the robot arm is driven to follow the trajectories and thus write the Chinese character. Seven common Chinese characters have been used in an experiment for system validation and evaluation. The experimental results demonstrate the power of the proposed system, given that the robot successfully wrote all the testing characters in the given Chinese calligraphic style

GANCCRobot:Generative Adversarial Nets based Chinese Calligraphy Robot

Robotic calligraphy, as a typical application of robot movement planning, is of great significance for the inheritance and education of calligraphy culture. The existing implementations of such robots often suffer from its limited ability for font generation and evaluation, leading to poor writing style diversity and writing quality. This paper proposes a calligraphic robotic framework based on the generative adversarial nets (GAN) to address such limitation. The robot implemented using such framework is able to learn to write fundamental Chinese character strokes with rich diversities and good quality that is close to the human level, without the requirement of specifically designed evaluation functions thanks to the employment of the revised GAN. In particular, the type information of the stroke is introduced as condition information, and the latent codes are applied to maximize the style quality of the generated strokes. Experimental results demonstrate that the proposed model enables a calligraphic robot to successfully write fundamental Chinese strokes based on a given type and style, with overall good quality. Although the proposed model was evaluated in this report using calligraphy writing, the underpinning research is readily applicable to many other applications, such as robotic graffiti and character style conversion

Decoder Choice Network for Metalearning

Metalearning has been widely applied for implementing few-shot learning and fast model adaptation. Particularly, existing metalearning methods have been exploited to learn the control mechanism for gradient descent processes, in an effort to facilitate gradient-based learning in gaining high speed and generalization ability. This article presents a novel method that controls the gradient descent process of the model parameters in a neural network, by limiting the model parameters within a low-dimensional latent space. The main challenge for implementing this idea is that a decoder with many parameters may be required. To tackle this problem, the article provides an alternative design of the decoder with a structure that shares certain weights, thereby reducing the number of required parameters. In addition, this work combines ensemble learning with the proposed approach to improve the overall learning performance. Systematic experimental studies demonstrate that the proposed approach offers results superior to the state of the art in performing the Omniglot classification and miniImageNet classification tasks