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Precise positioning in an indoor environment is a challenging task because it is difficult to receive a strong and reliable global positioning system (GPS) signal. For existing wireless indoor positioning methods, ultra-wideband (UWB) has become more popular because of its low energy consumption and high interference immunity. Nevertheless, factors such as indoor non-line-of-sight (NLOS) obstructions can still lead to large errors or fluctuations in the measurement data. In this paper, we propose a fusion method based on ultra-wideband (UWB), inertial measurement unit (IMU), and visual simultaneous localization and mapping (V-SLAM) to achieve high accuracy and robustness in tracking a mobile robot in a complex indoor environment. Specifically, we first focus on the identification and correction between line-of-sight (LOS) and non-line-of-sight (NLOS) UWB signals. The distance evaluated from UWB is first processed by an adaptive Kalman filter with IMU signals for pose estimation, where a new noise covariance matrix using the received signal strength indicator (RSSI) and estimation of precision (EOP) is proposed to reduce the effect due to NLOS. After that, the corrected UWB estimation is tightly integrated with IMU and visual SLAM through factor graph optimization (FGO) to further refine the pose estimation. The experimental results show that, compared with single or dual positioning systems, the proposed fusion method provides significant improvements in positioning accuracy in a complex indoor environment. [ABSTRACT FROM AUTHOR]

In this study, a system of automatic microparticle patterning that could enable the separation, trapping, and translation of single microbeads in liquid suspension using negative dielectrophoresis (DEP) tweezers was presented to form a single-bead pattern. A microchip with integrated electrodes was flipped and placed above the substrate through a micromanipulator. Microparticles laying on the substrate could be displaced to different positions relative to the electrodes on the microchip, and only the selected particles would be trapped by the electric fields generated from electrodes. Vision-based approaches were used to evaluate the necessary information, such as the gap distance and the positions of electrodes and microparticles in the image. A strategy for separating nearby particles was proposed to achieve single-bead patterning with high accuracy. A controller was used to guide the microparticles toward the position for trapping while avoiding flow disturbance. Different strategies were simulated to decrease the patterning time and find the minimum traveling distance and the best route of movement. The optimization problem is NP-hard. Hence, global optimization algorithms, such as genetic algorithm, particle swarm optimization, and ant colony optimization (ACO), were simulated, and the results were compared with those of the local optimization method. The comparison results showed that ACO obtained the best performance among the methods. The strategy for constructing high-quality microparticle patterns was also examined through experiments. Orange fluorescent polystyrene beads suspended in 6-aminohexanoic acid solution were considered and successfully patterned on a glass substrate by using the proposed system. Note to Practitioners—Micropatterning is an effective tool for pharmaceutical research and drug discovery. However, the reliability of results depends on the quality of patterns. Existing approaches, such as microfluidic devices, are limited to create a pattern from one chip for single use, and the entire process is sealed and isolated from the environment. In this study, a multielectrode microchip combined with a vision-based micromanipulator is introduced to create a novel noncontact approach for microparticle patterning, which offers high flexibility and guarantees the quality of the constructed patterns. The electrodes on the chip can be selectively energized to determine the shape of the final pattern. A real-time screening is performed so that the micromanipulator will only guide the particles in good condition for selection. An optimization algorithm is implemented to aid the particle selection with the electrodes, allowing high-quality microparticle patterns to be constructed in a short time for various applications. [ABSTRACT FROM AUTHOR]

Background: Cytoskeleton is a highly dynamic network that helps to maintain the rigidity of a cell, and the mechanical properties of a cell are closely related to many cellular functions. This paper presents a new method to probe and characterize cell mechanical properties through dielectrophoresis (DEP)-based cell stretching manipulation and actin cytoskeleton modeling.Methods: Leukemia NB4 cells were used as cell line, and changes in their biological properties were examined after chemotherapy treatment with doxorubicin (DOX). DEP-integrated microfluidic chip was utilized as a low-cost and efficient tool to study the deformability of cells. DEP forces used in cell stretching were first evaluated through computer simulation, and the results were compared with modeling equations and with the results of optical stretching (OT) experiments. Structural parameters were then extracted by fitting the experimental data into the actin cytoskeleton model, and the underlying mechanical properties of the cells were subsequently characterized.Results: The DEP forces generated under different voltage inputs were calculated and the results from different approaches demonstrate good approximations to the force estimation. Both DEP and OT stretching experiments confirmed that DOX-treated NB4 cells were stiffer than the untreated cells. The structural parameters extracted from the model and the confocal images indicated significant change in actin network after DOX treatment.Conclusion: The proposed DEP method combined with actin cytoskeleton modeling is a simple engineering tool to characterize the mechanical properties of cells. [ABSTRACT FROM AUTHOR]