Special Issue "Intelligent Sensors"

Abstract: To achieve dynamic error compensation in CNC machine tools, a non-contactlaser probe capable of dimensional measurement of a workpiece while it is being machinedhas been developed and presented in this paper. The measurements are automatically fedback to the machine controller for intelligent error compensations. Based on a well resolvedlaser Doppler technique and real time data acquisition, the probe delivers a very promisingdimensional accuracy at few microns over a range of 100 mm. The developed opticalmeasuring apparatus employs a differential laser Doppler arrangement allowing acquisitionof information from the workpiece surface. In addition, the measurements are traceable tostandards of frequency allowing higher precision.

Abstract: With the ever increasing complex sensing and actuating tasks in manufacturingplants, intelligent sensors cluster in hybrid networks becomes a rapidly expanding area.They play a dominant role in many fields from macro and micro scale. Global object controland the ability to self organize into fault-tolerant and scalable systems are expected for highlevel applications. In this paper, new structural concepts of intelligent sensors and networkswith new intelligent agents are presented. Embedding new functionalities to dynamicallymanage cooperative agents for autonomous machines are interesting key enablingtechnologies most required in manufacturing for zero defects production.

Abstract: Load cells are transducers used to measure force or weight. Despite the fact thatthere is a wide variety of load cells, most of these transducers that are used in the weighingindustry are based on strain gauges. In this paper, an s-beam load cell based on strain gaugeswas suitably assembled to the mechanical structure of several seats of a bus underperformance tests and used to measure the resistance of their mechanical structure to tensionforces applied horizontally to the seats being tested. The load cell was buried in a broad-band noise background where the unwanted information and the relevant signal sometimesshare a very similar frequency spectrum and its performance was improved by using arecursive least-squares (RLS) lattice algorithm. The experimental results are satisfactoryand a significant improvement in the signal-to-noise ratio at the system output of 27 dB wasachieved, which is a good performance factor for judging the quality of the system.

Abstract: A rapid progress in intelligent sensing technology creates new interest in a development of analysis and design of non-conventional sampling schemes. The investigation of the event-based sampling according to the integral criterion is presented in this paper. The investigated sampling scheme is an extension of the pure linear send-on- delta/level-crossing algorithm utilized for reporting the state of objects monitored by intelligent sensors. The motivation of using the event-based integral sampling is outlined. The related works in adaptive sampling are summarized. The analytical closed-form formulas for the evaluation of the mean rate of event-based traffic, and the asymptotic integral sampling effectiveness, are derived. The simulation results verifying the analytical formulas are reported. The effectiveness of the integral sampling is compared with the related linear send-on-delta/level-crossing scheme. The calculation of the asymptotic effectiveness for common signals, which model the state evolution of dynamic systems in time, is exemplified.

Abstract: Sensor systems are not always equipped with the ability to track targets. Sudden maneuvers of a target can have a great impact on the sensor system, which will increase the miss rate and rate of false target detection. The use of the generic particle filter (PF) algorithm is well known for target tracking, but it can not overcome the degeneracy of particles and cumulation of estimation errors. In this paper, we propose an improved PF algorithm called PF-RBF. This algorithm uses the radial-basis function network (RBFN) in the sampling step for dynamically constructing the process model from observations and updating the value of each particle. With the RBFN sampling step, PF-RBF can give an accurate proposal distribution and maintain the convergence of a sensor system. Simulation results verify that PF-RBF performs better than the Unscented Kalman Filter (UKF), PF and Unscented Particle Filter (UPF) in both robustness and accuracy whether the observation model used for the sensor system is linear or nonlinear. Moreover, the intrinsic property of PF-RBF determines that, when the particle number exceeds a certain amount, the execution time of PF-RBF is less than UPF. This makes PF-RBF a better candidate for the sensor systems which need many particles for target tracking.

Abstract: In this paper, an approach is presented to detect faint signals with strong noises in sensors by stochastic resonance (SR). We adopt the power spectrum as the evaluation tool of SR, which can be obtained by the fast Fourier transform (FFT). Furthermore, we introduce the adaptive filtering scheme to realize signal processing automatically. The key of the scheme is how to adjust the barrier height to satisfy the optimal condition of SR in the presence of any input. For the given input signal, we present an operable procedure to execute the adjustment scheme. An example utilizing one audio sensor to detect the fault information from the power supply is given. Simulation results show that th

Abstract: This paper presents the construction and working principles of an intelligent fiber-optic intensity sensor used for examining the concentration of a mixture in conjunction with water. It can find applications e.g. in waste-water treatment plant for selection of a treatment process. The sensor head is the end of a large core polymer optical fiber, which constitutes one arm of an asymmetrical coupler. The head works on the reflection intensity basis. The reflected signal level depends on the Fresnel reflection from the air and from the mixture examined when the head is immersed in it. The sensor head is mounted on a lift. For detection purposes the signal can be measured on head submerging, submersion, emerging and emergence. Therefore, the measured signal depends on the surface tension, viscosity, turbidity and refraction coefficient of the solution. The signal coming from the head is processed electrically in an opto-electronic interface. Then it is fed to a neural network. The novelty of the proposed sensor lies in that it contains an asymmetrical coupler and a neural network that works in the generalization mode. The sensor resolution depends on the efficiency of the asymmetrical coupler, the precision of the opto-electronic signal conversion and the learning accuracy of the neural network. Therefore, the number and quality of the points used for the learning process is very important. By way of example, the paper describes a sensor intended for examining the concentration of liquid soap in water.

Abstract: The aim of this work is to develop a smart flexible sensor adapted to textile structures, able to measure their strain deformations. The sensors are “smart” because of their capacity to adapt to the specific mechanical properties of textile structures that are lightweight, highly flexible, stretchable, elastic, etc. Because of these properties, textile structures are continuously in movement and easily deformed, even under very low stresses. It is therefore important that the integration of a sensor does not modify their general behavior. The material used for the sensor is based on a thermoplastic elastomer (Evoprene)/carbon black nanoparticle composite, and presents general mechanical properties strongly compatible with the textile substrate. Two preparation techniques are investigated: the conventional melt-mixing process, and the solvent process which is found to be more adapted for this particular application. The preparation procedure is fully described, namely the optimization of the process in terms of filler concentration in which the percolation theory aspects have to be considered. The sensor is then integrated on a thin, lightweight Nylon fabric, and the electromechanical characterization is performed to demonstrate the adaptability and the correct functioning of the sensor as a strain gauge on the fabric. A normalized relative resistance is defined in order to characterize the electrical response of the sensor. Finally, the influence of environmental factors, such as temperature and atmospheric humidity, on the sensor performance is investigated. The results show that the sensor’s electrical resistance is particularly affected by humidity. This behavior is discussed in terms of the sensitivity of the carbon black filler particles to the presence of water.

Abstract: This paper is concerned with sensor data transmission strategy. The main focus of the paper is how to reduce the number of sensor data transmission while maintaining the dif- ference between the estimated sensor value and the real sensor value. The proposed method could be used in sensor networks and networked control systems, where number of transmis- sion should be minimal. A linear predictor is used to predict sensor values and sensor data are transmitted if the difference between the predicted sensor value and the real sensor value exceeds the specified limit. An analytic upper bound of the mean rate of messages is pro- vided. Through simulation, it is shown that the number of transmission could be significantly reduced compared with the periodic sampling and the conventional send-on-delta method.

Abstract: An intelligent textile is a textile structure that measures and reacts in front of external agents or stimulus with or without integrated electronic equipment. . The finality of the present textile is to take one more step towards intelligent textile, considering the integration of electronics and textile needs, to be industrially viable and to keep up the necessary competitiveness, raising the final price as little as possible. The finality of these experiments is to develop a textile that varies in conductivity and resistance in relation to the elongation of the textile, detecting changes caused by the alteration of a piece of clothing, from the pressure of a finger on the material, for example. One of the most important characteristics of textile is the capacity of reproducing measures, of varying the response in different tests. Two lines of research were opened: the study of the most adequate structure to achieve a response that can be reproduced and the study of the best way of taking measures without altering the behavior of the textile.

Abstract: How can the compound eye of insects capture the prey so accurately andquickly? This interesting issue is explored from the perspective of computer vision insteadof from the viewpoint of biology. The focus is on performance evaluation of noiseimmunity for motion recovery using the single-row superposition-type planar compound-like eye (SPCE). The SPCE owns a special symmetrical framework with tremendousamount of ommatidia inspired by compound eye of insects. The noise simulates possibleambiguity of image patterns caused by either environmental uncertainty or low resolutionof CCD devices. Results of extensive simulations indicate that this special visualconfiguration provides excellent motion estimation performance regardless of themagnitude of the noise. Even when the noise interference is serious, the SPCE is able todramatically reduce errors of motion recovery of the ego-translation without any type offilters. In other words, symmetrical, regular, and multiple vision sensing devices of thecompound-like eye have statistical averaging advantage to suppress possible noises. Thisdiscovery lays the basic foundation in terms of engineering approaches for the secret of thecompound eye of insects.

Abstract: The development of smart sensors involves the design of reconfigurable systemscapable of working with different input sensors. Reconfigurable systems ideally shouldspend the least possible amount of time in their calibration. An autocalibration algorithmfor intelligent sensors should be able to fix major problems such as offset, variation of gainand lack of linearity, as accurately as possible. This paper describes a new autocalibrationmethodology for nonlinear intelligent sensors based on artificial neural networks, ANN.The methodology involves analysis of several network topologies and training algorithms.The proposed method was compared against the piecewise and polynomial linearizationmethods. Method comparison was achieved using different number of calibration points,and several nonlinear levels of the input signal. This paper also shows that the proposedmethod turned out to have a better overall accuracy than the other two methods. Besides,experimentation results and analysis of the complete study, the paper describes theimplementation of the ANN in a microcontroller unit, MCU. In order to illustrate themethod capability to build autocalibration and reconfigurable systems, a temperaturemeasurement system was designed and tested. The proposed method is an improvement over the classic autocalibration methodologies, because it impacts on the design process of intelligent sensors, autocalibration methodologies and their associated factors, like time and cost.

Abstract: This paper is concerned with improving performance of a state estimationproblem over a network in which a send-on-delta (SOD) transmission method is used. TheSOD method requires that a sensor node transmit data to the estimator node only if itsmeasurement value changes more than a given specified δ value. This method has beenexplored and applied by researchers because of its efficiency in the network bandwidthimprovement. However, when this method is used, it is not ensured that the estimator nodereceives data from the sensor nodes regularly at every estimation period. Therefore, wepropose a method to reduce estimation error in case of no sensor data reception. When theestimator node does not receive data from the sensor node, the sensor value is known to bein a (−δ_i , δ_i ) interval from the last transmitted sensor value. This implicit information hasbeen used to improve estimation performance in previous studies. The main contribution ofthis paper is to propose an algorithm, where the sensor value interval is reduced to(−δ_i / 2, δ_i / 2) in certain situations. Thus, the proposed algorithm improves the overallestimation performance without any changes in the send-on-delta algorithms of the sensornodes. Through numerical simulations, we demonstrate the feasibility and the usefulness ofthe proposed method.

Abstract: A novel planar array sensor based on electrical conductivity measurements ispresented which may be applied to visualize surface fluid distributions. The sensor ismanufactured using printed-circuit board fabrication technology and comprises of 64 x 64interdigital sensing structures. An associated electronics measures the electricalconductivity of the fluid over each individual sensing structure in a multiplexed manner byapplying a bipolar excitation voltage and by measuring the electrical current flowing from adriver electrode to a sensing electrode. After interrogating all sensing structures, a two-dimensional image of the conductivity distribution over a surface is obtained which in turnrepresents fluid distributions over sensor’s surface. The employed electronics can acquire upto 2500 frames per second thus being able to monitor fast transient phenomena. The systemhas been evaluated regarding measurement accuracy and depth sensitivity. Furthermore, theapplication of the sensor in the investigation of two different flow applications is presented.

Abstract: Wireless control systems (WCSs) often have to operate in dynamic environmentswhere the network traffic load may vary unpredictably over time. The sampling in sensors isconventionally time triggered with fixed periods. In this context, only worse-than-possiblequality of control (QoC) can be achieved when the network is underloaded, whileoverloaded conditions may significantly degrade the QoC, even causing system instability.This is particularly true when the bandwidth of the wireless network is limited and sharedby multiple control loops. To address these problems, a flexible time-triggered samplingscheme is presented in this work. Smart sensors are used to facilitate dynamic adjustment ofsampling periods, which enhances the flexibility and resource efficiency of the system basedon time-triggered sampling. Feedback control technology is exploited for adapting samplingperiods in a periodic manner. The deadline miss ratio in each control loop is maintainedat/around a desired level, regardless of workload variations. Simulation results show that theproposed sampling scheme is able to deal with dynamic and unpredictable variations innetwork traffic load. Compared to conventional time-triggered sampling, it leads to muchbetter QoC in WCSs operating in dynamic environments.

Abstract: The position determination inside a building where no GPS signal is beingreceived can be ascertained using laser transmitters in industrial situations where there areno people or using triangulation of the signal strength, normally electro-magnetic signals,if the required accuracy is more than a metre. Our solution is aimed at situations wherepeople are present and where the required accuracy is less than 30 cm, such as in shoppingprecincts or supermarkets. To achieve this, a network of ultrasonic transmitters is fittedinto the ceiling which receives a synchronised time signal. Each transmitter has a uniqueidentifier code and emits its code with a delay with respect to the common time signalwhich is proportional to its code number with an ASK modulation over the ultrasonic bandcentred on 40 KHz. The receivers circulating beneath the transmitters receive the codes ofthose within their detection range, translate the time delays into distances and then obtaintheir position by triangulation since the receivers know the position of every transmitter.Since the receivers are not synchronised with the common time signal or the actual speedof the sound, whose value varies appreciably with temperature, relative humidity andatmospheric pressure, a consecutive approximation algorithm has been introduced. This isbased on the fact that the Z coordinator of the receiver is known and constant and thus it is possible, with only three different identifiers received, to deduce the phase of the common time signal and estimate the speed of the sound with a fourth identifier.

Abstract: By introducing a percentage of conductive material during the manufacture ofsewing thread, it is possible to obtain a fabric which is able to detect variations in pressurein certain areas. In previous experiments the existence of resistance variations has beendemonstrated, although some constrains of cause and effect were found in the fabric. Theresearch has been concentrated in obtaining a fabric that allows electronic detection of itsshape changes. Additionally, and because a causal behavior is needed, it is necessary thatthe fabric recovers its original shape when the external forces cease. The structure of thefabric varies with the type of deformation applied. Two kinds of deformation aredescribed: those caused by stretching and those caused by pressure. This last type ofdeformation gives different responses depending on the conductivity of the object used tocause the pressure. This effect is related to the type of thread used to manufacture thefabric. So, if the pressure is caused by a finger the response is different compared to theresponse caused by a conductive object. Another fact that has to be mentioned is that apressure in a specific point of the fabric can affect other detection points depending on theforce applied. This effect is related to the fabric structure. The goals of this article arevalidating the structure of the fabric used, as well as the study of the two types ofdeformation mentioned before.

Abstract: The paper addresses the problem of the mean access delay characteristics in termof the channel load for networked sensor/control systems in LonWorks/EIA-709 technology.The system modelling is focused on the Media Access Control protocol that provides theload prediction and determines the key network characteristics. The network model assumesthe consistency of load prediction between the nodes, and that the Transaction ControlSublayer does not introduce limitations on the data transmission. The latter means that thenumbers of concurrent outgoing transactions being in progress are unlimited. Furthermore, itis assumed that the destination addresses of transmitted messages are distributed rather thanconcentrated on particular nodes. The analytical approach based on Markov chains isapplied. The calculation of transition probabilities of the Markov chain is exemplified by theload scenario where all the transactions are acknowledged, unicast, and the optionalcollision detection is enabled. On the basis of the stochastic analysis, the probabilities of asuccessful transmission and collision, respectively, are computed. Furthermore, thenumerical results of the mean access delay are reported. The simulative validation ofanalytical results is provided.

Abstract: In this paper an input-output transfer function analysis based on the frequencyresponse of a photometer circuit based on operational amplifier (op amp) is carried out. Opamps are universally used in monitoring photodetectors and there are a variety of amplifierconnections for this purpose. However, the electronic circuits that are usually used to carryout the signal treatment in photometer circuits introduce some limitations in theperformance of the photometers that influence the selection of the op amps and otherelectronic devices. For example, the bandwidth, slew-rate, noise, input impedance and gain,among other characteristics of the op amp, are often the performance limiting factors ofphotometer circuits. For this reason, in this paper a comparative analysis between twophotodiode amplifier circuits is carried out. One circuit is based on a conventional currentto-voltage converter connection and the other circuit is based on a robust current-to-voltageconverter connection. The results are satisfactory and show that the photodiode amplifierperformance can be improved by using robust control techniques.

Abstract: In this paper, a novel identification method based on a machine vision system is proposed to recognize the score of dice. The system employs image processing techniques, and the modified unsupervised grey clustering algorithm (MUGCA) to estimate the location of each die and identify the spot number accurately and effectively. The proposed algorithms are substituted for manual recognition. From the experimental results, it is found that this system is excellent due to its good capabilities which include flexibility, high speed, and high accuracy.

Abstract: This paper describes artificial neural network (ANN) based prediction of theresponse of a fiber optic sensor using evanescent field absorption (EFA). The sensingprobe of the sensor is made up a bundle of five PCS fibers to maximize the interaction ofevanescent field with the absorbing medium. Different backpropagation algorithms areused to train the multilayer perceptron ANN. The Levenberg-Marquardt algorithm, aswell as the other algorithms used in this work successfully predicts the sensor responses.

Abstract: In this article, we report on the new design of a miniaturized strain microsensor. The proposed sensor utilizes the piezoresistive properties of doped single crystal silicon. Employing the Micro Electro Mechanical Systems (MEMS) technology, high sensor sensitivities and resolutions have been achieved. The current sensor design employs different levels of signal amplifications. These amplifications include geometric, material and electronic levels. The sensor and the electronic circuits can be integrated on a single chip, and packaged as a small functional unit. The sensor converts input strain to resistance change, which can be transformed to bridge imbalance voltage. An analog output that demonstrates high sensitivity (0.03mV/me), high absolute resolution (1μe) and low power consumption (100μA) with a maximum range of ±4000μe has been reported. These performance characteristics have been achieved with high signal stability over a wide temperature range (±50^oC), which introduces the proposed MEMS strain sensor as a strong candidate for wireless strain sensing applications under harsh environmental conditions. Moreover, this sensor has been designed, verified and can be easily modified to measure other values such as force, torque…etc. In this work, the sensor design is achieved using Finite Element Method (FEM) with the application of the piezoresistivity theory. This design process and the microfabrication process flow to prototype the design have been presented.

Abstract: In this paper, an integrated MEMS gyroscope array method composed of two levels of optimal filtering was designed to improve the accuracy of gyroscopes. In the firstlevel filtering, several identical gyroscopes were combined through Kalman filtering into a single effective device, whose performance could surpass that of any individual sensor. The key of the performance improving lies in the optimal estimation of the random noise sources such as rate random walk and angular random walk for compensating the measurement values. Especially, the cross correlation between the noises from different gyroscopes of the same type was used to establish the system noise covariance matrix and the measurement noise covariance matrix for Kalman filtering to improve the performance further. Secondly, an integrated Kalman filter with six states was designed to further improve the accuracy with the aid of external sensors such as magnetometers and accelerometers in attitude determination. Experiments showed that three gyroscopes with a bias drift of 35 degree per hour could be combined into a virtual gyroscope with a drift of 1.07 degree per hour through the first-level filter, and the bias drift was reduced to 0.53 degree per hour after the second-level filtering. It proved that the proposed integrated MEMS gyroscope array is capable of improving the accuracy of the MEMS gyroscopes, which provides the possibility of using these low cost MEMS sensors in high-accuracy application areas.

Abstract: The development of intelligent sensors involves the design of reconfigurable systems capable of working with different input sensors signals. Reconfigurable systems should expend the least possible amount of time readjusting. A self-adjustment algorithm for intelligent sensors should be able to fix major problems such as offset, variation of gain and lack of linearity with good accuracy. This paper shows the performance of a progressive polynomial algorithm utilizing different grades of relative nonlinearity of an output sensor signal. It also presents an improvement to this algorithm which obtains an optimal response with minimum nonlinearity error, based on the number and selection sequence of the readjust points. In order to verify the potential of this proposed criterion, a temperature measurement system was designed. The system is based on a thermistor which presents one of the worst nonlinearity behaviors. The application of the proposed improved method in this system showed that an adequate sequence of the adjustment points yields to the minimum nonlinearity error. In realistic applications, by knowing the grade of relative nonlinearity of a sensor, the number of readjustment points can be determined using the proposed method in order to obtain the desired nonlinearity error. This will impact on readjustment methodologies and their associated factors like time and cost.

Abstract: In this paper, a spatial information-theoretic model is proposed to locate sensors for detecting source-to-target patterns of special nuclear material (SNM) smuggling. In order to ship the nuclear materials from a source location with SNM production to a target city, the smugglers must employ global and domestic logistics systems. This paper focuses on locating a limited set of fixed and mobile radiation sensors in a transportation network, with the intent to maximize the expected information gain and minimize the estimation error for the subsequent nuclear material detection stage. A Kalman filtering-based framework is adapted to assist the decision-maker in quantifying the network-wide information gain and SNM flow estimation accuracy.