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Measurement
Published in David M. Scott, Industrial Process Sensors, 2018
The sensor model S represented by equation 2.1 is a set of equations that describe the physical mechanism of the interaction between the physical observable and the process. It is possible to formulate a mathematical model for any process sensor; whether or not the model captures the physics of the measurement is another matter. For instance, a proximity sensor that is based on electrical capacitance may be sensitive to changes in humidity or barometric pressure due to its design. If the model used by the sensor fails to include the environmental effects on the capacitance, then it will fail to describe the output of the sensor under all operating conditions. The sensor will always generate a signal, but the signal could be wrong. If however the model adequately describes the sensor design, then the sensitivity to these extraneous effects can be determined or even corrected.
Soft Computing Approach to Safe Navigation of Autonomous Planetary Rovers
Published in Ali Zilouchian, Mo Jamshidi, Intelligent Control Systems Using Soft Computing Methodologies, 2001
Edward Tunstel, Homayoun Seraji, Ayanna Howard
In this section, fuzzy logic rules are presented which govern rover behavior based on the local information about en route obstacles, such as large rocks. In general, obstacles may belong to any variety of mobility and navigation hazards such as extreme slopes, sand/dust-covered pits, crevasses, cliffs and otherwise unstable terrain. Also included are so called negative obstacles such as ditches and craters, and their complements such as ridges and boulders. Rocks that are considered obstacles are those with sizes that exceed the obstacle climbing threshold for which the rover is designed. In the case of the Mars rover Sojourner, the threshold was 1.5 wheel diameters. Without loss of generality, we may refer to the general category of untraversable patches of terrain as navigation obstacles. This local obstacle information is acquired online and in real time by the proximity sensors mounted on the rover. For space robotics applications, different types of proximity sensors can be used, ranging from lowresolution infrared sensors to high-resolution and longer-range laser detectors [28]. A wider range of options is available for use in more general mobile robot applications [29]. The range of reliable operation of proximity sensors is typically 20 to 50cm, which is about an order of magnitude shorter than that of regional sensor coverage. Note, however, that precise measurement of the obstacle distance is not needed, because of the multivalued nature of the fuzzy sets used to describe it.
Alerting System for Gas Leakage in Pipelines
Published in K Hemachandran, Shubham Tayal, Preetha Mary George, Parveen Singla, Utku Kose, Bayesian Reasoning and Gaussian Processes for Machine Learning Applications, 2022
Nilesh Deotale, Pragya Chandra, Prathamesh Dherange, Pratiksha Repaswal, Saibaba V More
A proximity sensor is used to detect the presence of an object (commonly referred to as target). Proximity sensors are mainly used in smartphones, self-driving cars, industrial plants and terminals, anti-aircraft missiles, etc. The two most commonly used proximity sensors are the inductive proximity sensor and the capacitive proximity sensor. An inductive proximity sensor is used to detect metal targets, because it uses electromagnetic fields. The inductive characteristics of metal changes during contact with an electromagnetic field, and the object can be sensed at a larger or lesser distance based on how responsive the metal is (Figures 6.4 and 6.5).
Catalysing assistive solutions by deploying light-weight deep learning model on edge devices
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2023
Kanak Manjari, Madhushi Verma, Gaurav Singal, Vinay Chamola
Sensors are the instruments that are often used to detect and interact with electrical or optical signals. A sensor translates the physical parameter (e.g. temperature, blood pressure, humidity, velocity, etc.) into a signal that can be electrically measured. There are various types of sensors for different purposes such as temperature sensors, UV sensors, proximity sensors, and ultrasonic sensors. The temperature sensor gathers temperature information from a source and converts it into a form that other devices or individuals can understand. The UV sensor measures the strength or severity of the incident ultraviolet radiation. The proximity sensor detects the existence of artefacts that are almost positioned without a contact point. The ultrasonic sensor is an instrument that uses ultrasonic sound waves to measure the distance to an object. The RPi 3/4, NCS2 and NANO setup that was deployed on the cane (Manjari et al., 2019b,a) has been shown in Figure 3. The motive behind the development of this cane is to help the visually impaired in gaining information about their surroundings as there can be many objects in the nearby area that can be harmful to them. However, a mobile device has limited resources in terms of memory and power. We aim to use the edge devices available in the market and let the user know about their compatibility issues with various object detection models. The developer of the prototype should know the options of models that can be deployed on different edge devices along with the performance after deploying.
Foundations and affordances of workplace assistive technology: The case of mobile and enabling IT for workers with visual impairments
Published in Assistive Technology, 2020
Many smartphones are equipped with sensors designed to capture information regarding current location, the direction in which they are traveling, the rate at which they are traveling, their orientation and acceleration. Proximity sensors can determine how close they are to another object. Cameras and microphones allow them to gather additional information about the ambient environment. Using data from these sensors, smartphone apps can derive some degree of contextual awareness. Contextually aware computing is a common feature in the literature on ubiquitous computing (Capurso et al., 2018). Mark Weiser and his colleagues at the Xerox Palo Alto Research Center are generally credited with opening this stream of research. In their view, the most profound technologies are those that are sufficiently ubiquitous that they vanish into the background, “weaving themselves into the fabric of everyday life until they are indistinguishable from it” (Weiser, 1991).
Validity of proximity sensor-based wear-time detection using the ActiGraph GT9X
Published in Journal of Sports Sciences, 2018
Diego Arguello, Kristie Andersen, Alvin Morton, Patty S Freedson, Stephen S Intille, Dinesh John
The GT9X monitor (3.5 × 3.5 × 1 cm; 14 g) has a polycarbonate enclosure (thickness = 1.2 mm; dielectric constant = 2.9) and the capacitive proximity sensor (32 × 11.5 mm) is located inside and next to the backside of the device (opposite display screen). The proximity sensor is manufactured “in-house” and is a single electrode connected in a circuit with two port pins, which alternate between driving a 192 kHz signal and sensing a change in that signal. This alternating signal duty cycle charges the electrode and forms a 0.022μF capacitor with a sensitivity of 4.2 nF (personal communication with Doug Cross, Director of Engineering, ActiGraph). The charge in the capacitor varies with the type of material (e.g., air, human body) in close proximity or in contact with the sensor. Each time the GT9X is initialized, the GT9X is calibrated to establish a reference signal of this capacitor in free air. Through in-house experimentation, ActiGraph determined that a charge differential of 43 nF from the free air reference is indicative of skin-contact. I.e., a charging time differential of 52 microseconds. However, wear-time sensing using the GT9X is deliberately biased towards positive wear detection when motion is detected by the 8 g GT9X accelerometer. When this sensor detects an acceleration of at least 0.04 g lasting at least 0.125 s, the differential threshold of the proximity sensor that returns positive skin contact is halved. Reliance on motion was introduced in the firmware update 1.4.0. for the GT9X. The 6 monitors used in this study had firmware 1.5.0. or higher. The microcontroller in the GT9X measures differences in charging time at the end of a whole round minute, once every 60 seconds. Thus, the resolution of distinguishing between wear and non-wear using the proximity sensor-based method is 1-min. For optimal wear-time detection, ActiGraph recommends that the casing holding the GT9X be worn in contact with the skin.