An Electroactive and Transparent Haptic Interface Utilizing Soft Elastomer Actuators with Silver Nanowire Electrodes

An electroactive and transparent haptic interface having a rectangular void pattern creates tunable surface textures by controlling the wavelength and amplitude of independent void‐lines. To make an active tactile surface, the transparent haptic interface employs a silver nanowire (AgNW) electrode to be compliant with the deformed elastomer surface. Here, the dielectric elastomer is newly blended with polydimethylsiloxane and Ecoflex prepolymer to simultaneously control the mechanical stiffness and transparency. The relative resistance of the AgNW electrode on a single void line is nearly unchanged under bending test, confirming the high stretchability and conductivity of the nanowire‐networked electrode. The optical transparencies are 92–85%, depending on the ratio of the Ecoflex solution. Transparency values decreas by 7 and 16% after coating with AgNWs at densities of 30 and 140 mg m^?2, respectively. Using EP31, the void line is deformed by 90 µm under a field intensity of 13.0 V µm^?1. The haptic surface is successfully controlled by applying voltage, which produces four different surface textures, from relatively smooth to rough feeling, depending on the distance between deformed void lines. This haptic interface can be applied to diverse display systems as an external add‐on screen and will help to realize programmable surface textures in the future.     

SSPQL: Stochastic shortest path-based Q-learning

Reinforcement learning (RL) has been widely used as a mechanism for autonomous robots to learn state-action pairs by interacting with their environment. However, most RL methods usually suffer from slow convergence when deriving an optimum policy in practical applications. To solve this problem, a stochastic shortest path-based Q-learning (SSPQL) is proposed, combining a stochastic shortest path-finding method with Q-learning, a well-known model-free RL method. The rationale is, if a robot has an internal state-transition model which is incrementally learnt, then the robot can infer the local optimum policy by using a stochastic shortest path-finding method. By increasing state-action pair values comprising of these local optimum policies, a robot can then reach a goal quickly and as a result, this process can enhance convergence speed. To demonstrate the validity of this proposed learning approach, several experimental results are presented in this paper.     

A Data‐driven Segmentation for the Shoulder Complex

The human shoulder complex is perhaps the most complicated joint in the human body being comprised of a set of three bones, muscles, tendons, and ligaments. Despite this anatomical complexity, computer graphics models for motion capture most often represent this joint as a simple ball and socket. In this paper, we present a method to determine a shoulder skeletal model that, when combined with standard skinning algorithms, generates a more visually pleasing animation that is a closer approximation to the actual skin deformations of the human body. We use a data‐driven approach and collect ground truth skin deformation data with an optical motion capture system with a large number of markers (200 markers on the shoulder complex alone). We cluster these markers during movement sequences and discover that adding one extra joint around the shoulder improves the resulting animation qualitatively and quantitatively yielding a marker set of approximately 70 markers for the complete skeleton. We demonstrate the effectiveness of our skeletal model by comparing it with ground truth data as well as with recorded video. We show its practicality by integrating it with the conventional rendering/animation pipeline.     

Interactive visualization of hyperspectral images of historical documents

This paper presents an interactive visualization tool to study and analyze hyperspectral images (HSI) of historical documents. This work is part of a collaborative effort with the Nationaal Archief of the Netherlands (NAN) and Art Innovation, a manufacturer of hyperspectral imaging hardware designed for old and fragile documents. The NAN is actively capturing HSI of historical documents for use in a variety of tasks related to the analysis and management of archival collections, from ink and paper analysis to monitoring the effects of environmental aging. To assist their work, we have developed a comprehensive visualization tool that offers an assortment of visualization and analysis methods, including interactive spectral selection, spectral similarity analysis, time-varying data analysis and visualization, and selective spectral band fusion. This paper describes our visualization software and how it is used to facilitate the tasks needed by our collaborators. Evaluation feedback from our collaborators on how this tool benefits their work is included.     

Multiscale Bayesian texture segmentation using neural networks and Markov random fields

This paper presents a wavelet-based texture segmentation method using multilayer perceptron (MLP) networks and Markov random fields (MRF) in a multi-scale Bayesian framework. Inputs and outputs of MLP networks are constructed to estimate a posterior probability. The multi-scale features produced by multi-level wavelet decompositions of textured images are classified at each scale by maximum a posterior (MAP) classification and the posterior probabilities from MLP networks. An MRF model is used in order to model the prior distribution of each texture class, and a factor, which fuses the classification information through scales and acts as a guide for the labeling decision, is incorporated into the MAP classification of each scale. By fusing the multi-scale MAP classifications sequentially from coarse to fine scales, our proposed method gets the final and improved segmentation result at the finest scale. In this fusion process, the MRF model serves as the smoothness constraint and the Gibbs sampler acts as the MAP classifier. Our texture segmentation method was applied to segmentation of gray-level textured images. The proposed segmentation method shows better performance than texture segmentation using the hidden Markov trees (HMT) model and the HMTseg algorithm, which is a multi-scale Bayesian image segmentation algorithm.     

Design of micromirror actuator by ionic polymer metal composites

Microactuators for micromirror system have found many applications in various areas including projection displays, optical switches, RF switches and so on. In this paper we demonstrated micromirror actuator using ionic polymer metal composites (IPMC) that is a suitable candidate, since it has many attractive qualities such as durability, aquatic, miniature and light-weighted. Specially, IPMC has extraordinary advantages which are simple bending motion for low driving voltage (1–2 V), low power consumption, and simple structure. The IPMC actuator is made of Nafion NE-1110 (Dupont Co, Ltd., 260 µm thick) layer and electrode (platinum) layers and driven by 1–4 V. The displacement measured vertically is 0.25 mm and tilting angle is 11.3°. The angular motion, which is more than 10°, is a good advantage in the field of display module. This paper shows that the IPMC actuator has enough possibility for other applications.     

Decentralised sampled-data control for large-scale systems with nonlinear interconnections

This paper presents a decentralised sampled-data control technique for a class of large-scale systems, which are considered to consist of linear subsystems and nonlinear interconnections. The decentralised sampled-data controller design problem is established using a closed-loop subsystem. Based on the controller design problem, the stability condition is derived for a closed-loop large-scale system, and the maximum interconnection bound is guaranteed to satisfy the stability condition. Also, its sufficient condition is formulated in terms of linear matrix inequalities. Finally, the effectiveness of the proposed technique is verified by using an example of the multi-machine power system.     

Fuzzy filter for nonlinear sampled-data systems: Intelligent digital redesign approach

This paper presents a fuzzy filter design method for nonlinear sampled-data systems using an intelligent digital redesign (IDR) technique. Based on a Takagi–Sugeno (T–S) fuzzy model, discretized closed-loop systems with pre-designed analog fuzzy and digital fuzzy filters are presented. An IDR problem is given to guarantee both state-matching condition and asymptotic stability. Sufficient conditions for solving the IDR problem are proposed and are derived in terms of linear matrix inequalities (LMIs). Finally, a simulation example is given to show the effectiveness of the proposed method.