Anticipating, measuring, and minimizing MEMS mirror scan error to improve laser scanning microscopy's speed and accuracy

Sparse Lissajous scanning reflectance confocal microscope with an adjustable field of view and fast iterative Fourier filtering reconstruction

Medical imaging devices are becoming increasingly compact, necessitating optimization research into different methods of actuation. Actuation influences important parameters of the imaging device such as size, weight, frame rate, field of view (FOV), and image reconstruction for imaging devices point scanning techniques. Current literature around piezoelectric fiber cantilever actuators focuses on device optimization with a fixed FOV but neglects adjustability. In this paper, we introduce an adjustable FOV piezoelectric fiber cantilever microscope and provide a characterization and optimization procedure. To overcome calibration challenges, we utilize a position sensitive detector (PSD) and address trade-offs between FOV and sparsity with a novel inpainting technique. Our work demonstrates the potential for scanner operation when sparsity and distortion dominate the FOV, extending the usable FOV for this form of actuation and others that currently only operate under ideal imaging conditions.

Modeling and optimization of galvanometric point-scanning temporal dynamics

Galvanometers are ubiquitous in point-scanning applications in optical imaging, display, ranging, manufacturing, and therapeutic technologies. However, galvanometer performance is constrained by finite response times related to mirror size and material properties. We present a model-driven approach for optimizing galvanometer response characteristics by tuning the parameters of the closed-loop galvanometer controller and demonstrate settling time reduction by over 50%. As an imaging proof-of-concept, we implement scan waveforms that take advantage of the optimized galvanometer frequency response to increase linear field-of-view, signal-to-noise ratio, contrast-to-noise ratio, and speed. The hardware methods presented may be directly implemented on galvanometer controllers without the need for specialized equipment and used in conjunction with customized scan waveforms to further optimize scanning performance.

Generalizing HMMs to Continuous Time for Fast Kinetics: Hidden Markov Jump Processes

The hidden Markov model (HMM) is a framework for time series analysis widely applied to single-molecule experiments. Although initially developed for applications outside the natural sciences, the HMM has traditionally been used to interpret signals generated by physical systems, such as single molecules, evolving in a discrete state space observed at discrete time levels dictated by the data acquisition rate. Within the HMM framework, transitions between states are modeled as occurring at the end of each data acquisition period and are described using transition probabilities. Yet, whereas measurements are often performed at discrete time levels in the natural sciences, physical systems evolve in continuous time according to transition rates. It then follows that the modeling assumptions underlying the HMM are justified if the transition rates of a physical process from state to state are small as compared to the data acquisition rate. In other words, HMMs apply to slow kinetics. The problem is, because the transition rates are unknown in principle, it is unclear, a priori, whether the HMM applies to a particular system. For this reason, we must generalize HMMs for physical systems, such as single molecules, because these switch between discrete states in "continuous time". We do so by exploiting recent mathematical tools developed in the context of inferring Markov jump processes and propose the hidden Markov jump process. We explicitly show in what limit the hidden Markov jump process reduces to the HMM. Resolving the discrete time discrepancy of the HMM has clear implications: we no longer need to assume that processes, such as molecular events, must occur on timescales slower than data acquisition and can learn transition rates even if these are on the same timescale or otherwise exceed data acquisition rates.

Application of Micro-Electro-Mechanical Systems (MEMS) as Sensors: A Review

This paper presents a review of the current applications of Micro-Electro-Mechanical Systems (MEMS) in the robotics and industrial applications. MEMS are widely used as actuators or sensors in numerous respects of our daily life as well as automation lines and industrial applications. Intersection of founding new polymers and composites such as silicon and micro manufacturing technologies performing micro-machining and micro-assembly brings about remarkable growth of application and efficacy of MEMS devices. MEMS indicated huge improvement in size reduction, higher reliability, multi-functionality, customized design, and power usage. Demonstration of various devices and technologies utilized in robotics and industrial applications are illustrated in this article along with the use and the role of silicon in the development of the sensors. Some future trends and its perspectives are also discussed.