Sensors And Transducers By D Patranabis Pdf 28
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In the literature, traditional methods to analyse and model output signal sensors have been introduced. For example, second-order models have been used in . In these models, various techniques can be used to conduct more accurate results in linearization, such as analogue, digital and computer look-up ROM tables [13,14,15,16,17]. Other work has used metal oxide semiconductor (MOS) technologies to solve the nonlinear response. The aforementioned traditional calibration methods is a time-consuming task because calibration is usually done by manual and repetitive identification . Moreover, the algorithm can be applied specifically for a particular sensor but is not appropriate to be used in a general measurement system.
Thus, as time elapses during manipulation in real time (RT), some pressure sensor parameters are changed due to hysteresis, variation in gain and lack of linearity [10,19]. This negatively affects the calibrated output data. Thereby, inaccurate pressure measurements appear. This implies that these sensors compensate to eliminate the systematic errors and to ease the calibration. Previous calibration algorithms for intelligent sensors have been implemented [11,20,21,22,23,24]. In detail, researchers have different options for algorithms, some of which are recursive algorithms [24,25] or artificial neural networks [10,26]. The former cannot be applied in a general way, one of which is the progressive polynomial algorithm . Neither its effectiveness, nor the number of readjustment points required to achieve a minimal error have yet been proven. Furthermore, the calibration cost is still high.
Generally, rehabilitation of the upper limbs is crucial for paralyzed patients; one hour of repetitive exercise every session is desired to recover the hand function, which is executed within three stages with 20 min for every stage. The target of this paper is to generate a secure grip for paralyzed patient by ensuring, and covering; the analysis of the pressure output signal within at least 20 min of dynamic manipulation as a first stage towards recovering hand functions. Thus, five FlexiForce sensors were used to conduct the experiment, which were later distributed to the five fingers of the wearable robotic hand glove. Subsequently, to evaluate how the sensor behaves over time, a dynamic calibration of 20 min was performed for each sensor with different maximum loading and holding times.
Moreover, as different sensors were utilized under the same conditions, different measurements of the relative output voltage were detected. For instance, the maximum output peak voltages for sensor 1 to sensor 5 during the whole period of calibration under the same applied pressure were 1.237357, 1.751546, 1.687251, 1.494452 and 1.883202 respectively. The changes in voltage output and the ultimate voltage of the sensor 5 were the highest due to material creep of piezoresistivity over time.
For the training algorithm, the generation of data sets is important. The data were collected from the pressure measurement system using five pressure sensors and the CT3 Texture Analyser machine as well as load cell force sensor that was used as a reference for the calibrated data. The training ANN was performed for each pressure sensor with more than 132,000 calibration data obtained during the 20 min of dynamic loading. The topology of the proposed LMBP-ANN model is demonstrated in Figure 5. For each sensor of experimental calibration, 132,243 dataset samples were obtained while applying dynamic pressure using CT3 Texture Analyser machine.
A.M.M.A. designed and performed the experiments. W.Z.W.H. contributed in robotic hand filed. C.W. and S.A.A. gave a share in signal processing and rehabilitation. K.H. and A.M.M.A. conceived implemented the algorithm. S.S. contributed materials and sensors. A.M.M.A. wrote the paper.
Sensors utilize a wide spectrum of transducer and signal transformation approaches with corresponding variations in technical complexity. These range from relatively simple temperature measurement based on a bimetallic thermocouple, to the detection of specific bacteria species using sophisticated optical systems. Within the healthcare, wellness, and environmental domains, there are a variety of sensing approaches, including microelectromechanical systems (MEMS), optical, mechanical, electrochemical, semiconductor, and biosensing. As outlined in Chapter 1, the proliferation of sensor-based applications is growing across a range of sensing targets such as air, water, bacteria, movement, and physiology. As with any form of technology, sensors have both strengths and weaknesses. Operational performance may be a function of the transduction method, the deployment environment, or the system components. In this chapter, we review the common sensing mechanisms that are used in the application domains of interest within the scope of this book, along with their respective strengths and weaknesses. Finally, we describe the process of selecting and specifying sensors for an application.
There are no uniform descriptions of sensors or the process of sensing. In many cases, the definitions available are driven by application perspectives. Taking a general perspective, a sensor can be defined as:
The words sensor and transducer are both commonly used in the context of measurement systems, and often in an interchangeable manner. Transducer is used more in the United States while sensor has greater popularity in Europe (Sutherland, 2004). The blurring of the lines between the exact meaning of sensors and transducers leads to a degree of confusion.
The National Research Council (NRC, 1995) found, however, that the scientific literature had not generally adopted the ANSI definition (AALIANCE, 2010). Instead, descriptions of transducers focusing on the process of converting a physical quality into a measurable output, electrical or optical, for example, have emerged. One such definition is:
However, it is difficult to find consensus on the distinction between sensors and transducers. This problem is exacerbated when the sensor becomes more sophisticated. For example, chemical sensors can be transducers that have been modified to become a sensor e.g. through the use of a sensitive coating covering the sample interface of the transducer. It is clear that strict definitions will always be contentious and driven in part by philosophical differences between engineers and scientists. These differences only hold academic interest when it comes to application development. So while there may be differences in the definitions of sensors and transducers, this has little impact on the ability to utilize sensors in applications. Within this book we use the simple and broad definition that a sensor measures something of interest using a variety of mechanisms, and a transducer converts the output of the sensing processing into a measurable signal. Sensor application developers simply focus on delivering a sensor system that can measure a quantity of interest with the required accuracy. A sensor system usually consists of sensors, measuring and processing circuits, and an output system (Wang, et al., 2011). The key hardware components of a sensor system are described in Chapter 3.
For any given quantity, there is usually more than one form of sensor that can be used to take a measurement. Each sensor type offers different levels of accuracy, sensitivity, specificity, or ability to operate in different environmental conditions. There are also cost considerations. More expensive sensors typically have more sophisticated features that generally offer better performance characteristics. Sensors can be used to measure quantities of interest in three ways:
Sample removal:This approach involves an invasive collection of a representative sample by a human or automated sampling system. Sample removal commonly occurs in healthcare and environmental applications, to monitor E. coli in water or glucose levels in blood, for example. Such samples may be analyzed using either sensors or laboratory-based analytical instrumentation.
With sensor-based approaches, small, hand-held, perhaps disposable sensors are commonly used, particularly where rapid measurements are required. The sensor is typically in close proximity to the sample collection site, as is the case with a blood glucose sensor. Such sensors are increasingly being integrated with computing capabilities to provide sophisticated features, such as data processing, presentation, storage, and remote connectivity.
Mechanical sensors are based on the principle of measuring changes in a device or material as the result of an input that causes the mechanical deformation of that device or material (Fink, 2012). Inputs, such as such motion, velocity, acceleration, and displacement that result in mechanical deformation that can be measured. When this input is converted directly into an electrical output, the sensor is described as being electromechanical. Other possible output signals include magnetic, optical, and thermal (Patranabis, 2004). 1e1e36bf2d