Lane keeping systems for a keeping a vehicle in the desired lane is key to advanced driving assistance system in autonomous vehicles. This paper presents a cost-effective image sensor with efficient processing algorithm for lane detection and lane control applications to autonomous delivery systems. The algorithm includes (1) lane detection by inverse perspective mapping and random sample consensus parabola fitting and (2) lane control by pure pursuit steering controller and classical proportional integral speed controller based on a nonholonomic kinematic model. The image sensor experiments conducted on a 1/10 scale model car maneuvering in a straight⁻curve⁻straight lane validate the better processing performance before, during, and after the turning section over previous work. The image sensor with the processing algorithm achieves the average lane detection error within 5% and maximum cross-track error within 9% in real-time. The development shall pave the way to cost-effective autonomous delivery systems.
This work presents a real-time bed-exit alarm system using ultrasonic sensors, infrared (IR) sensors, and an accelerometer. The ultrasonic sensors detect the elderly in bed; the IR sensors detect the motion of the elderly across the gap at the end of the bed, and the accelerometer detects the bed rail being lowered. To achieve early notification, one-against-one support vector machine classifiers are implemented to recognize the early stages of bed-exit events with a sensitivity of 97.7% and a precision of 93.8%. The proposed system is verified with ten elderly subjects and provides an average lead time of 11.4 s for real bed-exit events, indicating that the proposed system has the ability to issue early warnings to caregivers for immediate assistance.
Objective: Blood circulation is an important indicator of wound healing. In this study, a tissue oxygen saturation detecting (TOSD) system that is based on multispectral imaging (MSI) is proposed to quantify the degree of tissue oxygen saturation (StO 2 ) in cutaneous tissues. Methods: A wound segmentation algorithm is used to segment automatically wound and skin areas, eliminating the need for manual labeling and applying adaptive tissue optics. Animal experiments were conducted on six mice in which they were observed seven times, once every two days. The TOSD system illuminated cutaneous tissues with two wavelengths of light - red (λ = 660 nm) and near-infrared (λ = 880 nm), and StO 2 levels were calculated using images that were captured using a monochrome camera. The wound segmentation algorithm using ResNet34-based U-Net was integrated with computer vision techniques to improve its performance. Results: Animal experiments revealed that the wound segmentation algorithm achieved a Dice score of 93.49%. The StO 2 levels that were determined using the TOSD system varied significantly among the phases of wound healing. Changes in StO 2 levels were detected before laser speckle contrast imaging (LSCI) detected changes in blood flux. Moreover, statistical features that were extracted from the TOSD system and LSCI were utilized in principal component analysis (PCA) to visualize different wound healing phases. The average silhouette coefficients of the TOSD system with segmentation (ResNet34-based U-Net) and LSCI were 0.2890 and 0.0194, respectively. Conclusion: By detecting the StO 2 levels of cutaneous tissues using the TOSD system with segmentation, the phases of wound healing were accurately distinguished. This method can support medical personnel in conducting precise wound assessments.
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