In-vacuum active colour sensor and wireless communication across a vacuum-air interface.

In situ sensing with wireless digital-data transfer is a potential processing scheme that works very closely to the location of an event monitored by a sensor and converts the sensor's raw output into digitized and informative small-volume bits, as suggested by recent proposals for edge computing and the Internet of Things (IoT). Colour perception may be a target of in situ sensor data acquisition; however, in contrast to from other sensing devices, colour sensors that detect visible light signals are usually located away from light-emitting sources, collecting light transmitting through the space and attenuating it in some manner. For example, in a vacuum chamber whose gas pressure is much less than the ambient atmosphere in which the sensors usually work, there are many veiled light sources, such as discharge plasma, for various industrial purposes including nanoscale manufacturing. In this study, we designed an in-vacuum colour sensor that can work with analogue-to-digital conversion and transfer data by wireless communication; this sensor is active in a low-pressure plasma chamber, detecting light signals and transferring them to a personal computer located outside the vacuum chamber. In addition to detecting lights with controlled spectra from outside successfully, we achieved complete operation of our in-vacuum active sensor for plasma emissions generated at 100 Pa. Comparing the signals with data from simultaneous monitoring by a monochromator, we established that the recorded signals arose from the plasma, confirming successful direct detection of low-pressure plasma emissions without any filtering effects between the sensor and the target object.

Classifying Dysphagic Swallowing Sounds with Support Vector Machines.

Swallowing sounds from cervical auscultation include information related to the swallowing function. Several studies have been conducted on the screening tests of dysphagia. The literature shows a significant difference between the characteristics of swallowing sounds obtained from different subjects (e.g., healthy and dysphagic subjects; young and old adults). These studies demonstrate the usefulness of swallowing sounds during dysphagic screening. However, the degree of classification for dysphagia based on swallowing sounds has not been thoroughly studied. In this study, we investigate the use of machine learning for classifying swallowing sounds into various types, such as normal swallowing or mild, moderate, and severe dysphagia. In particular, swallowing sounds were recorded from patients with dysphagia. Support vector machines (SVMs) were trained using some features extracted from the obtained swallowing sounds. Moreover, the accuracy of the classification of swallowing sounds using the trained SVMs was evaluated via cross-validation techniques. In the two-class scenario, wherein the swallowing sounds were divided into two categories (viz. normal and dysphagic subjects), the maximum F-measure was 78.9%. In the four-class scenario, where the swallowing sounds were divided into four categories (viz. normal subject, and mild, moderate, and severe dysphagic subjects), the F-measure values for the classes were 65.6%, 53.1%, 51.1%, and 37.1%, respectively.