This paper proposed the H ∞ state feedback and H ∞ output feedback design methods for unstable plants, which improved the original H ∞ state feedback and H ∞ output feedback. For the H ∞ state feedback design of unstable plants, it presents the complete robustness constraint which is based on solving Riccati equation and Bode integral. For the H ∞ output feedback design of unstable plants, the medium-frequency band should be considered in particular. Besides, this paper presents the method to select weight function or coefficients in the H ∞ design, which employs Bode integral to optimize the H ∞ design. It takes a magnetic levitation system as an example. The simulation results demonstrate that the optimal performance of perturbation suppression is obtained with the design of robustness constraint. The presented method is of benefit to the general H ∞ design.
Cell microinjection is a technique of precise delivery of substances into cells and is widely used for studying cell transfection, signaling pathways, and organelle functions. Microinjection of the embryos of zebrafish, the third most important animal model, has become a very useful technique in bioscience. However, factors such as the small cell size, high cell deformation tendency, and transparent zebrafish embryo membrane make the microinjection process difficult. Furthermore, this process has strict, specific requirements, such as chorion softening, avoiding contacting the first polar body, and high-precision detection. Therefore, highly accurate control and detection platforms are critical for achieving the automated microinjection of zebrafish embryos. This article reviews the latest technologies and methods used in the automated microinjection of zebrafish embryos and provides a detailed description of the current developments and applications of robotic microinjection systems. The review covers key areas related to automated embryo injection, including cell searching and location, cell position and posture adjustment, microscopic visual servoing control, sensors, actuators, puncturing mechanisms, and microinjection.
The traditional basketball training is unable to be quantified and shared with others for it heavily relies on the coach's experience. In this paper, we develop an inertial measurement unit (IMU) to collect movement data of the basketball player and identify his postures, which helps improve the coach's guidance and the athletes' skills in a quality manner. Additionally, the IMU sensor is designed to recognize nine kinds of basic basketball movements, such as stand, walk, run, jump, in-situ dribble, dribble while walking, dribble while running, set shot, and jump shot. Experimentally, the IMU sensor is worn on the player's right wrist. Meanwhile, the player's movements are captured by the sports camera (GoPro Hero 6) for reference. Further, five features are extracted from y-axis acceleration (YAA) data and z-axis angular velocity (ZAAV) data for the analysis of basketball movements. Finally, a 98.9% accuracy rate of recognizing each basic basketball movement of one player is achieved by using a neural network algorithm.
Nanocalorimeters, or microfabricated calorimeters, provide a promising way to characterize the thermal process of biological processes, such as biomolecule interactions and cellular metabolic activities. They enabled miniaturized heat measurement onto a chip device with potential benefits including low sample consumption, low cost, portability, and high throughput. Over the past few decades, researchers have tried to improve nanocalorimeters' performance, in terms of sensitivity, accuracy, and detection resolution, by exploring different sensing methods, thermal insulation techniques, and liquid handling methods. The enhanced devices resulted in new applications in recent years, and here we have summarized the performance parameters and applications based on categories. Finally, we have listed the current technical difficulties in nanocalorimeter research and hope for future solutions to overcome them.
Small defects on the rails develop fast under the continuous load of passing trains, and this may lead to train derailment and other disasters. In recent years, many types of wireless sensor systems have been developed for rail defect detection. However, there has been a lack of comprehensive reviews on the working principles, functions, and trade-offs of these wireless sensor systems. Therefore, we provide in this paper a systematic review of recent studies on wireless sensor-based rail defect detection systems from three different perspectives: sensing principles, wireless networks, and power supply. We analyzed and compared six sensing methods to discuss their detection accuracy, detectable types of defects, and their detection efficiency. For wireless networks, we analyzed and compared their application scenarios, the advantages and disadvantages of different network topologies, and the capabilities of different transmission media. From the perspective of power supply, we analyzed and compared different power supply modules in terms of installation and energy harvesting methods, and the amount of energy they can supply. Finally, we offered three suggestions that may inspire the future development of wireless sensor-based rail defect detection systems.
In this paper, we present a new scheme for implementing virtual keyboards, which uses only two to four motion-recognition rings per hand and a two-dimensional keyboard template (e.g., an A4 size paper with printed key positions). It has the benefit of portability, customizability, and low-cost when compared with existing approaches. Essentially, we have shown that wearing two wireless IoT rings on the middle phalanges of two fingers of each hand, users can input the alphabetic characters into a computing device by typing on a flat paper on a desk, and potentially in mid-air. We have demonstrated that two rings are sufficient in capturing the gestures and motions of all fingers in a typing hand for keystrokes recognition. A single wireless IoT ring, which weighs 7.8 grams, consists of a Bluetooth low energy (BLE) unit, a micro inertial measurement unit (mIMU), and a cell battery. The 3-axes attitude angles and the Z-axis acceleration of each ring are adopted as the features for keystroke recognition. The overall keystroke recognition accuracy rate can reach as high as 94.8% when two IoT rings are worn by a user on each hand; this accuracy rate increases to 98.6%, when four rings are worn on each typing hand. INDEX TERMS Wearable sensors, wireless IoT ring, keystroke recognition, virtual keyboard, micro IMU.
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