A Micromachined Permalloy Magnetic Actuator Array for Micro Robotics Assembly Systems

Arrays of permalloy magnetic actuators have been studied for the use as precision micro robotics assembly systems. The actuator arrays have been tested for lifting and moving silicon and glass chips. The actuator unit consists of a permalloy plate 1 mm x 1 mm X 5µm in size together with polysilicon bending supports. Experimentally, it can lift a 87 µN (or 8.88 mg) force under a magnetic field of approximately 2 x 10^4 A/m. A proposed synchronous driving mode has been observed, and both translation and rotation of a silicon chip has been demonstrated

Debris Flow Risk Assessment and Land-Use Planning – A Case Study of Jhonglun Hot Spring Area

The Jhonglun Scenic Area in Chiayi County, is famous for its hot spring, the region was hit by debris flow with tremendous losses and resulted with dramatic change of the landscape during Typhoon Morakot in 2009. The most effective strategy for reducing natural hazard risks is through land-use planning. Following the concept of Risk=Hazard*Exposure*Vulnerability, this study conducted risk identification through the collection of landslide inventory and history debris flow hazard mapping of Chiayi DF051 potential debris flow torrent. Together with elements at risk information from field investigations, the risk analysis was conducted with several return periods debris flow simulation to recognize the possible economic losses and fatalities by debris flow. The identified high risk areas in Jhonglun Scenic Area were compared to the current special district planning to understand the spatial distribution of high risk areas. The result shows that some of the designated zones were among the areas with high debris flow risks, which further indicates that land-use planning should consider the consequences of natural hazards. The result of this study provides one of the first steps for land use planning restrictions within the potential debris flow region

A wafer-scale MEMS and analog VLSI system for active drag reduction

We describe an analog CMOS VLSI system that can process real-time signals from integrated shear stress sensors to detect regions of high shear stress along a surface in an airflow. The outputs of the CMOS circuit control the actuation of integrated micromachined flaps with the goal of reducing this high shear stress on the surface and thereby lowering the total drag. We have designed, fabricated, and tested components of this system in a wind tunnel in both laminar and turbulent flow regimes with the goal of building a wafer-scale system

Assessing the Decision-Making Process in Human-Robot Collaboration Using a Lego-like EEG Headset

Human-robot collaboration (HRC) has become an emerging field, where the use of a robotic agent has been shifted from a supportive machine to a decision-making collaborator. A variety of factors can influence the effectiveness of decision-making processes during HRC, including the system-related (e.g., robot capability) and human-related (e.g., individual knowledgeability) factors. As a variety of contextual factors can significantly impact the human-robot decision-making process in collaborative contexts, the present study adopts a Lego-like EEG headset to collect and examine human brain activities and utilizes multiple questionnaires to evaluate participants’ cognitive perceptions toward the robot. A user study was conducted where two levels of robot capabilities (high vs. low) were manipulated to provide system recommendations. The participants were also identified into two groups based on their computational thinking (CT) ability. The EEG results revealed that different levels of CT abilities trigger different brainwaves, and the participants’ trust calibration of the robot also varies the resultant brain activities

Surface micromachined magnetic actuators

A surface micromachined micromagnetic actuator (also called a magnetic flap) as an integrated part of an active micro electromechanical fluid control system is described. The flaps are fabricated by surface micromachining technology and are capable of achieving large deflections (above 100 /spl mu/m) with magnetic forces in stationary air. Special microfabrication issues involved in fabricating magnetic flaps with large areas are discussed. Mechanical characterizations of the flaps, including the intrinsic stresses of thin film materials and frequency response, are presented. Thermal actuation is capable of producing large flap displacements and has been theoretically and experimentally studied

An integrated MEMS system for turbulent boundary layer control

The goal of this project is a first attempt to achieve active drag reduction using a large-scale integrated MEMS system. Previously, we have reported the successful development of a shear-stress imager which allows us to "see" surface vortices (1996). Here we present the promising results of the interaction between micro flap actuators and vortices. It is found that microactuators can actually reduce drag to values even lower than the drag associated with pure laminar flow, and that the microactuators can reduce shear stress values in turbulent flow as well. Based on these results, we have attempted the first totally integrated system that consists of 18 shear stress sensors, 3 magnetic flap-type actuators and control electronics for use in turbulent boundary layer control studies

ECG Signal Super-resolution by Considering Reconstruction and Cardiac Arrhythmias Classification Loss

With recent advances in deep learning algorithms, computer-assisted healthcare services have rapidly grown, especially for those that combine with mobile devices. Such a combination enables wearable and portable services for continuous measurements and facilitates real-time disease alarm based on physiological signals, e.g., cardiac arrhythmias (CAs) from electrocardiography (ECG). However, long-term and continuous monitoring confronts challenges arising from limitations of batteries, and the transmission bandwidth of devices. Therefore, identifying an effective way to improve ECG data transmission and storage efficiency has become an emerging topic. In this study, we proposed a deep-learning-based ECG signal super-resolution framework (termed ESRNet) to recover compressed ECG signals by considering the joint effect of signal reconstruction and CA classification accuracies. In our experiments, we downsampled the ECG signals from the CPSC 2018 dataset and subsequently evaluated the super-resolution performance by both reconstruction errors and classification accuracies. Experimental results showed that the proposed ESRNet framework can well reconstruct ECG signals from the 10-times compressed ones. Moreover, approximately half of the CA recognition accuracies were maintained within the ECG signals recovered by the ESRNet. The promising results confirm that the proposed ESRNet framework can be suitably used as a front-end process to reconstruct compressed ECG signals in real-world CA recognition scenarios