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Practical Aspects of Localization Microscopy
Published in Guy Cox, Fundamentals of Fluorescence Imaging, 2019
Mark B. Cannell, Christian Soeller, David Baddeley
In order to generate a quality, high-precision, super-resolution image,alarge number of molecules must be independently localized. This requires the collection of a large number of image frames (typically on the order of 20–50,000). If collecting data at a typical rate of 20 camera frames per second (FPS), this corresponds to 15–40 minutes of continuous imaging. Obviously, any instrumental drift over this time period will result in degradation in the accuracy of the resulting image data. As conventional microscope hardware can experience a drift of several microns over this time period, this is a critical issue for localization microscopy. There are two principal approaches to minimizing or correcting for drift: (i) adapting the microscope hardware to minimize drift, and (ii) measuring and correcting the drift after data acquisition. Most localization microscopes combine aspects of both approaches. Postprocessing-based drift correction can either use fiduciary markers such as gold beads embedded in the sample to report drift (e.g. [6]), or attempt to infer drift from the structure being imaged itself [19]. Focus drift is especially problematic and difficult to correct in postprocessing, so some form of active focus control is often employed during imaging [20, 21].
Assessment of MEMS-based sensors for inclination measurements
Published in Nigel Powers, Dan M. Frangopol, Riadh Al-Mahaidi, Colin Caprani, Maintenance, Safety, Risk, Management and Life-Cycle Performance of Bridges, 2018
Literature related to MEMS sensors is mainly focused on the technological development and not on the metrological performance, which is critical for application of these novel sensor types for monitoring of structures. Especially, the effect of temperature changes needs to be known, to allow for an appropriate compensation. The long-term stability of the sensors is impaired by sensor drift. In this regard, drift is the occurrence of slowly increasing changes of the measured quantity, e.g. acceleration or inclination, due to aging of the sensor leading to selfinduced losses in accuracy.
Gas Detection Technologies
Published in James McNay, A Guide to Fire and Gas Detection Design in Hazardous Industries, 2023
Well-documented limitations associated with the technology include the poisoning of the catalyst and blocking of the sintered disks (both of which can lead to unrevealed failures). Where the device is poisoned, it is credible that no alarm will be generated. The devices also generally cannot be used in an inert atmosphere. Sensors can also ‘drift’ and require regular calibration. Exposure to high concentrations of gas can also damage the sensor and impair future performance. Gas-rich environments may also saturate the device, meaning that an alarm is not generated.
IoT-based patient stretcher movement simulation in smart hospital using type-2 fuzzy sets systems
Published in Production Planning & Control, 2023
C. B. Sivaparthipan, M. Anand, Nidhi Agarwal, Mallika Dhingra, Laxmi Raja, Akila Victor, S. A. Amala Nirmal Doss
Fuzzification helps in the movements in the privacy of the Universal Host Controller (UHC). A fuzzification unit transforms the precise input into fuzzy input and enables the use of various fuzzification techniques. The translation of precise input into fuzzy input results in the formation of a knowledge base a collection of rule bases and database systems. The fuzzy inference system recognises the scope of the activity for the flexion of the upper limb. This is measured by the process of fuzzy logic and the features that affect the sensor signal to make the dynamic and posture motions. Sensor signals for delivering dynamic motions can be affected by features such as noise, drift, hysteresis, and nonlinearity. Noise is characterised by random fluctuations in the signal, while drift is caused by slow changes in the signal over time. Hysteresis refers to the dependence of the output on previous input history, while nonlinearity refers to deviations from a linear relationship between input and output. To prevent these affecting features, measures such as filtering techniques, calibration, and linearisation techniques can be used. Other preventive measures such as appropriate sensor selection, proper installation and handling, and regular maintenance and inspection can also be taken to ensure optimal sensor performance. The motion that makes the movement measured in the final interface act as the management of the wireless sensor node. In this paper, these sensors identify the stretcher’s location and the patient’s activity; thus, the fuzzification process is needed (Thyla et al. 2021).
Sliding mode observer-based fault detection for helicopter system
Published in Journal of Control and Decision, 2022
M. Raghappriya, S. Kanthalakshmi
The most common faults impacting the helicopter are sensor, actuator and component faults. Precision position and attitude data from the sensors are essential for helicopter control and stabilisation. This paper focuses on sensor drift, which is caused by temperature fluctuations or changes in sensor calibration. Drift is represented as a vector that has an impact on the system's measurements. As a result, when there is a sensor error, the system output changes as where n denotes the number of sensors. Here is a column vector. In a helicopter, a failure of an actuator can be dangerous since it can cause the helicopter to lose control and crash. The sort of actuator fault discussed in this work is the loss of actuator control efficacy. A factor reflects the loss of control efficacy of actuators, where j denotes the number of actuators. As a result, the actuator fault is modelled as The efficacy of loss of control factor ranges from 0 to 1. A value of 0 indicates that there is no actuator defect, whereas a value of 1 indicates that the actuator has completely failed. The actuator faults considered are a constant factor that is time invariant. Component faults are essentially characterised as changes in the system's physical properties, which are considered as changes in the system state. This is denoted as .
Characteristic analysis and motion control of a novel ball double-screw hydraulic robot joint
Published in Engineering Applications of Computational Fluid Mechanics, 2022
Jie Shao, Yongming Bian, Meng Yang, Guangjun Liu
By cyclic iterative calculation of Equations (25)–(27), the output value and deviation at the time of the moving window can be obtained. The corrected compensation value is as follows: where denotes the compensation value of the previous correction and denotes the correction coefficient. The system drift can be compensated adaptively through continuous correction. This method can improve the control accuracy.