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Canine parvovirus type 2 (CPV-2) is among the most important and highly contagious pathogens that cause enteric or systemic infections in domestic and nondomestic carnivores. However, the spillover of CPV-2 to noncarnivores is rarely mentioned. Taiwanese pangolins (

This paper addresses precision tracking control of dual-stage actuators for machining of noncircular engine piston profiles. To achieve desired tracking performance, the capacity limits, dynamic characteristics, external disturbances, and model uncertainties of the dual-stage actuators pose significant challenges in control design and real-time implementation. The master–slave linear control configuration previously applied to similar applications is inadequate when nonlinear dynamics are triggered by constraint violations. Optimal predictive control, which minimizes the tracking error subject to physical constraints, is desirable, but the substantial computation forbids real-time implementation at the sampling rate required by the application. A hybrid approach is proposed where an offline constrained optimal feedforward tracking utilizing the model-predictive control framework (FFMPC) is added to a two-parameter robust repetitive control (TPRRC) to achieve high-precision tracking performance, which would otherwise be unattainable solely by each individual control action. Multirate digital control implementation on a dual-stage actuator system, which is comprised of a long-stroke low-bandwidth linear motor and a short-stroke high-bandwidth piezoelectric actuator for tracking noncircular engine piston profiles is presented. The experimental results demonstrate the effectiveness of the approach, where the tracking error by TPRRC is reduced by 84.8 % when FFMPC is added.

Precision motion control is often challenged by asynchronous sensor update and quantization noise, causing abrupt and untimely state variation to limit the machine precision. To provide smooth and accurate sensor interpretation, this study developed a novel event-triggered sub-count estimation method that addresses continuity on system states. By enforcing such state continuity on important properties such as position and velocity, the proposed method relaxes the requirement for accurate model as in the exiting model-based methods. Also, because it does not rely on long horizon of past measurements as the nonmodel-based methods do, it is able to adapt to abrupt reference changes much more quickly. When applying the proposed sub-count estimation method, a precision motion system can utilize the sensor reading more effectively and improve the tracking performance beyond the sensor resolution given available only quantized and asynchronous measurements.

An online fast path following control algorithm subject to contouring error tolerance and other prototypical constraints, analogous to a racing car within track boundaries, is presented. A receding horizon quadratic programming (QP) for real-time implementation on electromechanical systems is proposed. A key feature of the algorithm is that the challenging constrained minimal-time optimization is approximated by minimizing the distance between an unattainable target and actual location when moving along the contour, mimicking pursuing rabbit lures in greyhound racing. Modeling errors and other uncertainties in implementation are compensated for by observer state feedback, which provides real-time updates of initial states for every receding horizon optimization. Applying the proposed online method, the requirement of an accurate model from conventional offline trajectory planning methods is relaxed. The proposed method is demonstrated by experimental results from a 1 kHz sampling rate implementation on a multi-axis nanolithographic position system.

Maskless nanolithography is an agile and cost effective approach if their throughputs can be scaled for mass production purposes. Using plasmonic nanolithography (PNL) approach, direct pattern writing was successfully demonstrated with around 20 nm half-pitch at high speed. Here, we report our recent efforts of implementing a high-throughput PNL prototype system with unique metrology and control features, which are designed to use an array of plasmonic lenses to pattern sub-100 nm features on a rotating substrate. Taking the advantage of air bearing surface techniques, the system can expose the wafer pixel by pixel with a speed of ∼10 m/s, much faster than any conventional scanning based lithography system. It is a low-cost, high-throughput maskless approach for the next generation lithography and also for the emerging nanotechnology applications, such as nanoscale metrology and imaging.

This paper develops an automated vascular access system (A-VAS) with novel vision-based vein and needle detection methods and real-time pressure feedback for murine drug delivery. Mouse tail vein injection is a routine but critical step for preclinical imaging applications. Due to the small vein diameter and external disturbances such as tail hair, pigmentation, and scales, identifying vein location is difficult and manual injections usually result in poor repeatability. To improve the injection accuracy, consistency, safety, and processing time, A-VAS was developed to overcome difficulties in vein detection noise rejection, robustness in needle tracking, and visual servoing integration with the mechatronics system.

This paper develops an efficient vision-based real-time vein detection algorithm for preclinical vascular insertions. Mouse tail vein injections perform a routine but critical step in most preclinical applications. Compensating for poor manual injection stability and high skill requirements, Vascular Access System (VAS) has been developed so a trained technician can manually command the system to perform needle insertions and monitor the operation through a near-infrared camera. However, VAS’ vein detection algorithm requires much computation and is, therefore, difficult to reflect the real-time tail movement during an insertion. Furthermore, the detection performance is often disturbed by tail hair and skin pigmentation. In this work, an effective noise filtering algorithm is proposed based on convex optimization. Effectively eliminating false-positive detections and preserving cross-sectional continuity, this algorithm provides vein detection results approximately every 200 ms at the presence of tail hair and skin pigmentation. This developed real-time tail vein detection method is able to capture the tail movement during insertion, therefore allow for the development of an automated Vascular Access System (A-VAS) for preclinical injections.Copyright © 2013 by ASME

Murine models are used extensively in biological and translational research. For many of these studies it is necessary to access the vasculature for the injection of biologically active agents. Among the possible methods for accessing the mouse vasculature, tail vein injections are a routine but critical step for many experimental protocols. To perform successful tail vein injections, a high skill set and experience is required, leaving most scientists ill-suited to perform this task. This can lead to a high variability between injections, which can impact experimental results. To allow more scientists to perform their own tail vein injections and to decrease the variability between injections a vascular access system (VAS) that semi-automatically inserts a needle into the tail vein of a mouse was developed. The VAS uses near infrared (NIR) light, image processing techniques, computer controlled motors, and a pressure feedback system to insert the needle and to validate its proper placement within the vein. The VAS was tested by injecting a commonly used radiolabeled probe (FDG) into the tail veins of five mice. These mice were then imaged using micro-positron emission tomography (PET) to measure the percentage of the injected probe remaining in the tail. These studies showed that, on average, the VAS leaves 3.4% of the injected probe in the tail. With these preliminary results, the VAS system demonstrates the potential for improving the accuracy of tail vein injections in mice.

This paper presents a model predictive control based optimal feedforward tracking integrated with a robust repetitive feedback control aimed for generating complex engine piston profiles in non-circular engine piston turning using dual-stage long stroke linear motor/high bandwidth piezoelectric actuators. By formulating and solving receding horizon optimization, which explicitly takes into account practical constraints and specific dynamic capabilities of the dual-stage actuators integrated with repetitive feedback control, the methodology is demonstrated in simulation to precisely track fast oscillating but non-periodic reference signals derived from an industrial engine piston profile. In comparison, linear repetitive feedback control alone would violate dual-stage actuator constraints and as a result generate tracking errors 50 times larger than the proposed approach.Copyright © 2012 by ASME