Robotics and IoT: Interdisciplinary Applied Research in the RIoT Zone

Short Abstract: Robotics and the Internet of Things are intrinsically multi-disciplinary subjects that investigate the interaction between the physical and the cyber worlds and how they impact society. As a result, they not only demand careful consideration of digital and analog technologies, but also the human element. The “RIoT Zone” brings together disparate people and ideas to address intuitive autonomy. Full Abstract: Robotics and the Internet of Things are intrinsically multi-disciplinary subjects that investigate the interaction between the physical and the cyber worlds and how they impact society. As a result, they not only demand careful consideration of digital and analog technologies, but also the human element. The “RIoT Zone” brings together disparate people and ideas to address a human-centric form of intelligence we call “intuitive autonomy”. This talk will describe human/robot interaction and the programming of robots by human demonstration from the perspectives of Engineering Technology, Computer Information Technology, Industrial Engineering and Psychology

Modeling of the Electrical Characteristics of an Organic Field Effect Transistor in Presence of the Bending Effects

An analytical model incorporating the density of trap states for a bendable organic field effect transistor (OFET) is presented in this paper. The aim of this work is to propose a novel modeling framework to quantitatively characterize the bending effects on the electrical properties of an OFET in the linear and saturation regimes. In this model, the exponentially distributed shallow trap states are introduced into the Poisson equation to describe the carrier transports in the channel. The carrier mobility takes into account the low field mobility enhancement under gradual channel approximation and high field degradation. As a result, the generalized current-voltage transistor equations are derived for the first time to reflect the transconductance relationships of the OFET with trap states. In addition, an electro-mechanical coupling relationship is established per the metaphorical analogy between inorganic and organic semiconductor energy band models to quantify the stress-induced variations of the carrier mobility, and the threshold voltage. It is revealed that the before- and after-bending transconductances, predicted from the derived analytical model, are in good agreement with the experimental data measured from DNTT-based OFET bending tests

Expert-Agnostic Ultrasound Image Quality Assessment using Deep Variational Clustering

Ultrasound imaging is a commonly used modality for several diagnostic and therapeutic procedures. However, the diagnosis by ultrasound relies heavily on the quality of images assessed manually by sonographers, which diminishes the objectivity of the diagnosis and makes it operator-dependent. The supervised learning-based methods for automated quality assessment require manually annotated datasets, which are highly labour-intensive to acquire. These ultrasound images are low in quality and suffer from noisy annotations caused by inter-observer perceptual variations, which hampers learning efficiency. We propose an UnSupervised UltraSound image Quality assessment Network, US2QNet, that eliminates the burden and uncertainty of manual annotations. US2QNet uses the variational autoencoder embedded with the three modules, pre-processing, clustering and post-processing, to jointly enhance, extract, cluster and visualize the quality feature representation of ultrasound images. The pre-processing module uses filtering of images to point the network's attention towards salient quality features, rather than getting distracted by noise. Post-processing is proposed for visualizing the clusters of feature representations in 2D space. We validated the proposed framework for quality assessment of the urinary bladder ultrasound images. The proposed framework achieved 78% accuracy and superior performance to state-of-the-art clustering methods.Comment: Accepted in IEEE International Conference on Robotics and Automation (ICRA) 202