Sensors (Basel, Switzerland)
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Infectious diseases such as pneumonia take the lives of millions of children in low- and middle-income countries every year. Many of these deaths could be prevented with the availability of robust and low-cost diagnostic tools using integrated sensor technology. Pulse oximetry in particular, offers a unique non-invasive and specific test for an increase in the severity of many infectious diseases such as pneumonia. ⋯ Many lives could be saved if this technology was disseminated effectively in the affected regions of the world to rescue patients from the fatal consequences of these infectious diseases. We describe the implementation of such an oximeter that interfaces a conventional clinical oximeter finger sensor with a smartphone through the headset jack audio interface, and present a simulator-based systematic verification system to be used for automated validation of the sensor interface on different smartphones and media players. An excellent agreement was found between the simulator and the audio oximeter for both oxygen saturation and heart rate over a wide range of optical transmission levels on 4th and 5th generations of the iPod TouchTM and iPhoneTM devices.
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In this paper we propose a robust lane detection and tracking method by combining particle filters with the particle swarm optimization method. This method mainly uses the particle filters to detect and track the local optimum of the lane model in the input image and then seeks the global optimal solution of the lane model by a particle swarm optimization method. The particle filter can effectively complete lane detection and tracking in complicated or variable lane environments. ⋯ Since the particle swarm optimization method is a global optimization algorithm based on iterative computing, it can find the global optimal lane model by simulating the food finding way of fish school or insects under the mutual cooperation of all particles. In verification testing, the test environments included highways and ordinary roads as well as straight and curved lanes, uphill and downhill lanes, lane changes, etc. Our proposed method can complete the lane detection and tracking more accurately and effectively then existing options.
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Particle separation is of great interest in many biological and biomedical applications. Flow-based methods have been used to sort particles and cells. However, the main challenge with flow based particle separation systems is the need for a sheath flow for successful operation. ⋯ In this paper, we demonstrated two different particle size-resolution separations; (1) 3 μm and 10 μm and (2) 3 μm and 5 μm. Also, the effects of the input power, the flow rate, and particle concentration on the separation efficiency were investigated. These technologies have potential to impact broadly various areas including the essential microfluidic components for lab-on-a-chip system and integrated biological and biomedical applications.
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The photoplethysmographic waveform sits at the core of the most used, and arguably the most important, clinical monitor, the pulse oximeter. Interestingly, the pulse oximeter was discovered while examining an artifact during the development of a noninvasive cardiac output monitor. This article will explore the response of the pulse oximeter waveform to various modes of ventilation. ⋯ The effect of ventilation on the photoplethysmographic waveform has long been thought of as a source of artifact. The primary goal of this article is to improve the understanding of the underlying physiology responsible for the observed phenomena, thereby encouraging the utilization of this understanding to develop new methods of patient monitoring. The reader will be presented with a review of respiratory physiology followed by numerous examples of the impact of ventilation on the photoplethysmographic waveform.
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Studies on fault detection and diagnosis of planetary gearboxes are quite limited compared with those of fixed-axis gearboxes. Different from fixed-axis gearboxes, planetary gearboxes exhibit unique behaviors, which invalidate fault diagnosis methods that work well for fixed-axis gearboxes. It is a fact that for systems as complex as planetary gearboxes, multiple sensors mounted on different locations provide complementary information on the health condition of the systems. ⋯ In this method, two features developed for planetary gearboxes are used to characterize the gear health conditions, and an adaptive neuro-fuzzy inference system (ANFIS) is utilized to fuse all features from different sensors. In order to demonstrate the effectiveness of the proposed method, experiments are carried out on a planetary gearbox test rig, on which multiple accelerometers are mounted for data collection. The comparisons between the proposed method and the methods based on individual sensors show that the former achieves much higher accuracies in detecting planetary gearbox faults.