An Information Fusion-Based Multiobjective Security System With a Multiple-Input/ Single-Output Sensor

In the framework of sensor fusion, multiple sensors corresponding to the number of physical variables that must be measured are used. In this paper, we propose a novel sensing approach that simultaneously deals with heterogeneous physical variables with a sensor. It is fundamentally different from sensor fusion. The proposed approach takes into consideration the fact that any sensor that detects a certain physical variable is influenced to a degree by other physical variables, which are designated as noise. The objective in conventional sensor design has been the minimization of noise. In contrast, the proposed approach takes advantage of sensors that are easily influenced by many physical variables and makes full use of the multisensing characteristics of these sensors. The system designed using this concept has advantages in terms of cost performance and system simplification compared to existing approaches. This concept can be realized by developing a novel multiple-input/single-output sensor that can detect various variables, including pressure, acceleration, temperature and incandescent light emission, by a single device. We apply the sensor to monitor the symptoms of fire, earthquakes, and break-ins for the purpose of home security. The proposed security system is realized through statistical signal processing and machine learning techniques     

Vehicle Localization Using Sensors Data Fusion Via Integration of Covariance Intersection and Interval Analysis

Achieving an innovative integrated sensor fusion architecture with a robust vehicle navigation and localization using an extended Kalman filter, interval analysis and covariance intersection that can overcome the uncertainty in the system model and sensor noise statistics. There are various approaches to the problem, but here the focus is on an approach which can guaranteed performance of sensor-based navigation. The guaranteed performance is quantified by explicit bounds of position estimate of a ground vehicle. Ground vehicles generally carry dead reckoning sensors such as wheel encoders and inertial sensors, to measure acceleration and angle rate, while obstacle detection and mapmaking is done with time-of-flight ultrasonic sensors. Most of these sensors give overlapping or complementary information and sometimes are redundant as well, which offers scope for exploiting data fusion. The purpose here is to achieve data fusion for ground vehicles with low-cost sensors by forming an intelligent sensor system. This is accomplished by combining the sensors' measurements and processing these measurements with data fusion algorithms. The algorithms are complementary in the sense that they compensate for each other's limitations, so that the resulting performance of the sensor system is better than its individual components.     

A Full Fingerprint Verification System for a Single-Line Sweep Sensor

This paper presents a full fingerprint verification system. It is composed of a tactile fingerprint sensor, integrated read out and conversion circuits, and dedicated recognition algorithms. The sensor is a single-line sweep mode sensor, e.g., it is made of a single line of sensing elements, thus covering the minimum surface of silicon. Compared with cm² sized touch sensors, it offers a large cost reduction and possibility of easy integration into portable devices. The use of a single line to measure a fingerprint requires the user to sweep its finger along the sensor. This sensing scheme produces fingerprint images with several distortions that needs further image processing to allow efficient fingerprint recognition. This is why we developed and present here specific algorithms to take care of the sensor's specifications. This paper will present measurement results, as well as a performance evaluation of the entire verification system.     

网格式柔性应变传感器的制备及应用

鉴于柔性应变传感器在人体运动监测、健康监测等领域的广泛应用,设计出兼具高灵敏度和大应变范围的柔性应变传感器具有重要的意义。本文基于Ecoflex-石墨烯复合材料,通过模板法制备了四边形和六边形网格式柔性应变传感器。通过对比两种不同网格结构传感器的应变范围与拉伸断裂极限,发现六边形网格柔性应变传感器的综合性能更优异,并在80%应变条件下进行拉伸/释放疲劳寿命检测,此传感器表现出良好的可靠性,同时该传感器在手肘关节运动和人体不同呼吸状况监测方面表现良好。将六边形网格柔性应变传感器组合构建多通道检测系统,实现了多种手势识别,这在人工智能和运动识别领域具有广阔的市场应用前景。     

Flying triangulation--an optical 3D sensor for the motion-robust acquisition of complex objects

Three-dimensional (3D) shape acquisition is difficult if an all-around measurement of an object is desired or if a relative motion between object and sensor is unavoidable. An optical sensor principle is presented-we call it "flying triangulation"-that enables a motion-robust acquisition of 3D surface topography. It combines a simple handheld sensor with sophisticated registration algorithms. An easy acquisition of complex objects is possible-just by freely hand-guiding the sensor around the object. Real-time feedback of the sequential measurement results enables a comfortable handling for the user. No tracking is necessary. In contrast to most other eligible sensors, the presented sensor generates 3D data from each single camera image.     

A Bio-Inspired Pattern Recognition System for Tin-Oxide Gas Sensor Applications

In this paper, a bio-inspired pattern recognition system for tin-oxide gas sensor applications is proposed. To mimic the biological olfactory system, temperature modulation is first used to virtually increase the number of sensors by periodically sampling the sensors' response at different temperatures. A convex microhotplate is used in order to improve the thermal properties of the structure enabling efficient temperature modulation process to be carried out. Temperature modulation is shown to increase the number of effective sensors from 16 physically available sensors (integrated on a single chip) to 12 000 virtual sensors (VSs). This enables the emulation of a very large number of sensors typically found in biological systems. The response of each sensor is seen as a fingerprint map, which is further processed using various image processing techniques. Self organized maps (SOMs) algorithm is used to create a 2D map for each gas and to combine the huge number of VSs in order to reduce the dimensionality. Image moments are used as a feature enabling to characterize the spatial distribution within the image lattice and to retrieve the brighter regions in the SOM' nodes exhibiting high activity to the input gas. Experiments on real sensors data show improved performance (96%) as compared with standard gas discrimination algorithms.      

High-resolution angular and displacement sensing based on the excitation of surface plasma waves

The possibility of building angular and displacement sensors based on the phenomenon of attenuated total reflection (ATR) is explored both numerically and experimentally. ATR occurs when a surface wave is excited by an incoming TM electromagnetic wave through a resonant phase-matching process, as in the Kretschmann coupling scheme. The reflected intensity strongly depends on the angle of incidence of the beam. We first show some computations of the sensitivity and the linearity of an ATR-based sensor, then proceed to the experiment, illustrating how an angular resolution of the order of 0.1 arc sec can be obtained with moderate effort. Finally we show how the sensor, combined with a simple optical arrangement, can be used to detect and measure nanometric displacements, as those provided by piezoelectric actuators.     

Mechanocombinatorially Screening Sensitivity of Stretchable Strain Sensors

Stretchable strain sensors have aroused great interest for their application in human activity recognition, health monitoring, and soft robotics. For various scenarios involving the application of different strain ranges, specific sensitivities need to be developed, due to a trade‐off between sensor sensitivity and stretchability. Traditional stretchable strain sensors are developed based on conductive sensing materials and still lack the function of customizable sensitivity. A novel strategy of mechanocombinatorics is proposed to screen the sensor sensitivity based on mechanically heterogeneous substrates. Strain redistribution over substrates is optimized by mechanics and structure parameters, which gives rise to customizable sensitivity. As a proof of concept, a local illumination method is used to fabricate heterogeneous substrates with customizable mechanics and structure parameters. A library of mechanocombinatorial strain sensors is created for extracting the specific sensitivity. Thus, not only is an effective strategy for screening of sensor sensitivity demonstrated, but a contribution to the mechanocombinatorial strategy for personalized stretchable electronics is also made.     

Model-based sea mine classification with synthetic aperture sonar

The quality and effectiveness of sensor information provided by mine-hunting autonomous underwater vehicles (AUVs) equipped with high-resolution sonars has improved drastically in recent years. In parallel, data rates have significantly increased resulting in information overload. Automatic target recognition (ATR) is regarded as a solution for this problem. This study describes a specific ATR technique based on model matching for application to high-resolution data. A sonar model for generation of high-resolution synthetic aperture sonar (SAS) images is described and applied both as database generator and classification. The performance of the model matching, which is attained by correlation and stochastically, is evaluated using a large data set covering the variety expected in mine-hunting operations. The model-based features generated in this way are able to reach an acceptable classification performance. The article is concluded with one real data example, which is easily classified when training with the simulated database. Further work is next aimed to confirm performance on real data.     

Optimum sensors for color constancy in scenes illuminated by daylight

The apparent color of an object within a scene depends on the spectrum of the light illuminating the object. However, recording an object's color independent of the illuminant spectrum is important in many machine vision applications. In this paper the performance of a blackbody-model-based color constancy algorithm that requires four sensors with different spectral responses is investigated under daylight illumination. In this investigation sensor noise was modeled as gaussian noise, and the responses were quantized using different numbers of bits. A projection-based algorithm whose output is invariant to illuminant is investigated to improve the results that are obtained. The performance of both of these algorithms is then improved by optimizing the spectral sensitivities of the four sensors using freely available CIE standard daylight spectra and a set of lightness-normalized Munsell reflectance data. With the optimized sensors the performance of both algorithms is shown to be comparable to the human visual system. However, results obtained with measured daylight spectra show that the standard daylights may not be sufficiently representative of measured daylight for optimization with the standard daylight to lead to a reliable set of optimum sensor characteristics.     

A novel thermal sensor concept for flow direction and flow velocity

This paper presents a unified theory for different measurement concepts of a thermal flow sensor. Based on this theory, a new flow sensor concept is derived. The concept allows measuring both direction and velocity of a fluid flow with a heater and an array of temperature sensors. This paper first analyzes the two-dimensional (2-D) forced convection problem with a laminar flow. The two operation modes of a constant heating power and of a constant heater temperature are considered in the analytical model. A novel estimation algorithm was derived for the flow direction. Different methods for velocity measurement were presented: the hot-wire method, the calorimetric method, and the novel average-temperature method. The only geometric parameter of the sensor, the dimensionless position of the sensor array, is optimized based on the analytical results. Furthermore, the paper presents the experimental results of the sensor prototype. In order to verify the analytical model, an array of temperature sensors was used for recording the 2-D temperature profile around the heater. Temperature values are transferred to a computer by a multiplexer. A program running on a personal computer extracts the actual flow velocity and flow direction from the measured temperature data. This paper discusses different evaluation algorithms, which can be used for this sensor. A simple Gaussian estimator was derived for the direction measurement. This estimator provides the same accuracy as the analytical estimator. Velocity results of both the calorimetric concept and the novel average-temperature concept are also presented.     

Improved Method for Object Recognition in Complex Scenes by Fusioning 3-D Information and RFID Technology

This work analyzes a new method for object recognition in complex scenes combining vision-based techniques applied to the 3-D data obtained using range sensors and object identification coming from radio frequency tags (radio frequency identification (RFID) technology). Three-dimensional vision-based algorithms for object recognition have many restrictions in practical applications, i.e., uncertainty, incapability for real-time tasks, etc., but they work well for pose determination once the object is recognized. On the other hand, RFID technology allows us to detect the presence of specific objects in a scene, but it cannot provide their localization, at least not with the accuracy required in applications such as ours. In this paper, we present a new and powerful recognition method obtained by fusing both techniques. The phases of the method are described, and abundant experimentation results are included. An in-depth performance analysis has been carried out to demonstrate the recognition improvements achieved by the algorithm when RFID assistance is considered. It helps to confirm the robustness of this fusion approach and prove its effectiveness. A final discussion is included, concerning what should be the most adequate size of the object database for optimal algorithm exploitation.     

Model Discrimination for Nonlinear Regression Models

Wireless sensor networks (WSNs) are becoming important tools in various tasks, including monitoring and tracking of spatially occurring phenomena. These networks offer the capability of densely covering a large area, but at the same time are constrained by the limiting sensing, processing and power capabilities of their sensors. To complete the task at hand, the information collected by the sensor nodes needs to be appropriately fused. In this article we study the problems of estimating the location of a target and estimating its signal intensity. The proposed algorithms are based on the local vote decision fusion (LVDF) mechanism, where sensors first correct their original decisions using decisions of neighboring sensors. These corrected decisions are more accurate and robust and improve detection; however, they are correlated, which makes maximum likelihood estimation intractable. We adopt a pseudolikelihood formulation and examine several variants of localization and signal estimation algorithms based on original and corrected decisions using direct optimization methods, as well as an EM approach. Uncertainty assessments about the parameters of interest are provided using a parametric bootstrap technique. An extensive simulation study of the developed algorithms, along with several benchmarks, establishes the overall superior performance of the LVDF-based algorithms, especially in low signal-to-noise ratio environments. Extensions to tracking moving targets and localizing multiple targets also are considered.     

Local,hierarchic, and iterative reconstructors for adaptive optics

Adaptive optics systems for future large optical telescopes may require thousands of sensors and actuators. Optimal reconstruction of phase errors using relative measurements requires feedback from every sensor to each actuator, resulting in computational scaling for n actuators of n2. The optimum local reconstructor is investigated, wherein each actuator command depends only on sensor information in a neighboring region. The resulting performance degradation "global" modes is quantified analytically, and two approaches are considered for recovering global performance. Combining local and global estimators in a two-layer hierarchic architecture yields computations scaling with n(4/3); extending this approach to multiple layers yields linear scaling. An alternative approach that maintains a local structure is to allow actuator commands to depend on both local sensors and prior local estimates. This iterative approach is equivalent to a temporal low-pass filter on global information and gives a scaling of n(3/2). The algorithms are simulated by using data from the Palomar Observatory adaptive optics system. The analysis is general enough to also be applicable to active optics or other systems with many sensors and actuators.     

A ray-tracing approach for simulating recognition abilities of active infrared sensor arrays

The construction and setup of sensors or sensor arrays determines their maximum resolution and recognition abilities. Therefore, the analysis of certain setups is an important and mandatory task during the design process of a new sensor system. This paper deals with the simulation and evaluation of the recognition abilities of active infrared sensors for autonomous systems. Additionally, the simulation method as well as the results provide useful information for other applications, where infrared sensors are used. The simulation method is based on a Monte Carlo algorithm, which uses ray tracing to calculate the impulse response of the optical channel consisting of the sending and receiving components and the environment. In order to allow a fast simulation of several configurations, an efficient and flexible computation is realized. This means that all rays contribute maximally to the final result, and different sensor characteristics can easily be calculated. Extensive experiments are carried out, and the results show different evaluation options.     

Diagnosis based on reliability analysis using monitors and sensors

We develop a process for using monitors or sensors to optimize diagnostic decision trees (DDTs) generated for large systems. We present algorithms for optimizing the diagnosis process, which combines evidence data captured from monitors or sensors into the diagnostic tree generation process to produce DDTs. Since evidence data can be extracted from monitors and sensors, we developed a method for sensor modeling. Our method allows modeling monitors or sensors as an abstract layer on top of a systems fault tree model. This method of modeling allows the designer to graphically link monitors or sensors to the components that they monitor, without impacting the reliability analysis. We use a real system from the industry to demonstrate the practicality and effectiveness of our algorithms and methods.     

Recovering fluorescent spectra with an RGB digital camera and color filters using different matrix factorizations

The aim of a multispectral system is to recover a spectral function at each image pixel, but when a scene is digitally imaged under a light of unknown spectral power distribution (SPD), the image pixels give incomplete information about the spectral reflectances of objects in the scene. We have analyzed how accurately the spectra of artificial fluorescent light sources can be recovered with a digital CCD camera. The red-green-blue (RGB) sensor outputs are modified by the use of successive cutoff color filters. Four algorithms for simplifying the spectra datasets are used: nonnegative matrix factorization (NMF), independent component analysis (ICA), a direct pseudoinverse method, and principal component analysis (PCA). The algorithms are tested using both simulated data and data from a real RGB digital camera. The methods are compared in terms of the minimum rank of factorization and the number of sensors required to derive acceptable spectral and colorimetric SPD estimations; the PCA results are also given for the sake of comparison. The results show that all the algorithms surpass the PCA when a reduced number of sensors is used. The experimental results suggest a significant loss of quality when more than one color filter is used, which agrees with the previous results for reflectances. Nevertheless, an RGB digital camera with or without a prefilter is found to provide good spectral and colorimetric recovery of indoor fluorescent lighting and can be used for color correction without the need of a telespectroradiometer.     

Analysis and application of multiframe superresolution processing for conventional imaging systems and lenslet arrays

Low-cost compact sensors for ultrasmall systems are a pressing need in many new applications. One potential solution is a shallow aspect ratio system using a lenslet array to form multiple undersampled subimages of a scene on a single focal plane array, where processing techniques then produce an upsampled restored image. We have investigated the optimization and theoretical limits of the performance of such arrays. We have built a hardware simulator and developed algorithms to process imagery similar to that of a full lenslet imaging sensor, which allowed us to quickly test optical components, algorithms, and complete system designs for future lenslet imaging systems.     

Triple-sensor multiplexed frequency-modulated continuous-wave interferometric fiber-optic displacement sensor

A triple-sensor multiplexed fiber-optic displacement sensor, which can measure the displacements of three different objects or the three-dimensional displacement of a single object, is introduced. The sensor is based on the principles of optical frequency-modulated continuous-wave interference and frequency-division multiplexing. The beat signals from the individual sensors are assigned in the frequency domain and separated with different electrical bandpass filters. The displacements of objects can be determined simultaneously by detecting the phase shifts of the corresponding signals. The cross talk between the individual sensors is evaluated, and an accuracy of 0.08 microm in a dynamic range of 1000 microm is achieved.     

Characterization of Integrated Tin Oxide Gas Sensors With Metal Additives and Ion Implantations

Post-treatment of the sensing film in tin oxide gas sensor arrays is widely used to improve the selectivity in gas recognition applications. This letter describes the characterization study of an integrated tin oxide gas sensor array chip in which the sensing films are modified using metal additives and ion implantations. Measurement results reveal that metal additives present a higher impact on the sensor sensitivity compared with ion implantations. The latter has no significant effect on the sensing properties. The drift is increased for the sensors with only ion implantation compared with the ones with metal additives. An array combining both post-treatment techniques is expected to improve the overall recognition performance.