The societal benefits of understanding climate change through identification of global carbon dioxide sources and sinks led to the desired NASA's active sensing of carbon dioxide emissions over nights, days, and seasons (ASCENDS) space-based missions of global carbon dioxide measurements. For more than 15 years, NASA Langley Research Center (LaRC) have developed several carbon dioxide active remote sensors using the differential absorption lidar (DIAL) technique operating at the two-micron wavelength. Currently, an airborne two-micron triple-pulse integrated path differential absorption (IPDA) lidar is under development. This IPDA lidar measures carbon dioxide as well as water vapor, the dominant interfering molecule on carbon dioxide remote sensing. Advancement of this triple-pulse IPDA lidar development is presented.
In electrical distribution networks many automation applications, such as voltage and power control, require to access the status of the system. Such information can be obtained by aggregating the heterogeneous measurements available in the grid and then applying state estimation algorithms. Different factors may have an impact on the estimation errors when the grid status evolves dynamically. The scope of this paper is to point out the different contributions affecting the global uncertainty of state estimation results when fast dynamics are present in the grid, for example due to fast variations of renewable energy sources generation or customers power consumption. The so-called "4 quadrants' method" is proposed to quantify and decompose such uncertainty components. The results of the 4 quadrants method may be useful in the process of design of the monitoring infrastructure.
In pulsed eddy-current testing (PECT), material thickness and defect depth are indicated by features such as time to peak and peak value. Before testing, the relationship between thickness/depth and features must be established in advance. This requires multiple reference samples with known thickness/depth. On the other hand, the parameters of each material, such as electrical conductivity and magnetic permeability should be known in advance when calculating by computer. It is time consuming and expensive. In this paper, a new processing scheme is proposed to separate the geometric and conductivity parameters. The thickness/depth of the specimen is quantified into a series of formal unit thickness. There is an excitation frequency which corresponds to every unit thickness level. Using the rich frequency components of the pulsed eddy current response signal, they can be used to scan the thickness of the specimen step by step. When the scanned thickness is close to the thickness, the current frequency is very different with the reference frequency. So this frequency is used to calculate the thickness. In this method, the initial values of the thickness and frequency are detected first to calculate the thickness. It is help to separate the geometric and conductivity parameters. Thus this scheme only requires one reference sample and one known thickness/depth specimen for each material, which significantly reduces the cost of testing.
Subcutaneous veins localization is basic and important step for any intravenous medication administration. Due to different physiological characteristics, mainly darker skin tone, scars or dehydrated condition of patients, medical staff face difficulty in veins localization. Through near infrared imaging technology the veins can be visualized due to high contrast between veins and skin tissue in this modality. Information on the depth of veins is equally important for proper catheterization or venipuncture procedures. Patients have different veins depth due to the different amount of fat present in the subcutaneous layer. The depth of veins from the skin surface cannot be estimated by simple imaging technique. In this paper a mathematical model to estimate the depth of veins based on measured diffused reflectance is presented. A layered model of Monte Carlo simulations for light transport in turbid medium was used to validate the results.
This paper presents a new matrix algebraic approach to the direct solution of inverse boundary value problems (IBVP). The synthesis of admissible functions and differentiating matrices is given particular attention. All the necessary mathematical elements are derived from basic principles. The method yields a linear operator for the solution of IBVPs. A single matrix multiplication is required at run-time to determine the solution. The number of FLOPS required is constant and known a-priory making the solution suitable for use in embedded real-time systems. The concept of discrete basis function design is introduced for the first time. The method enables the design of special discrete basis functions which yield optimal noise performance and numerical efficiency for specific tasks. All the methods are also verified in a laboratory test system and compared with results from an optical reference measurement.
The properties of the image acquired from a general camera contain a large field of view (FOV) and low resolution. In contrast, the microscopy usually offers higher magnification image a smaller FOV but high resolution. This study attempts to develop a modified super resolution (MSR) method to obtain both of high resolution and large FOV properties for criminal identified microscopic images. The MSR method can analysis the image's pixels either from the intensity of single point (ISP) or the intensity of neighbor point (INP), when a microscopic image (150X) captured under white light, and three macro images (60X) captured under three different wavelength lights (red-620nm, green-520nm and blue-460nm), and then can reconstruct a new criminal identified image with high resolution and large FOV. Finally the simulated experiments in an ant model show that, the ISP analysis offers a reconstructed image with higher contrast and identification degree, synchronously the INP analysis affords an image has smoother edge.
Sequential Monte Carlo (SMC) or particle filtering (PF) has demonstrated effectiveness in non-linear and non-Gaussian system estimation, due to its unique approach for posterior probability density function estimation. However, classical PF techniques suffer from particle degeneracy and sample impoverishment. This paper proposes a new scheme for joint state and parameter estimation, based on the sequential importance resampling (SIR) particle filter. First, a local search strategy is proposed as a resampling strategy to overcome particle impoverishment. Second, a switching multiple modes filter is adopted to handle sudden changes of parameters in the state evolution model due to faults, which cannot be processed by conventional PF that assumes gradual parameter variations over a long period. The proposed estimation method is applied to degradation and remaining useful life (RUL) prediction of dynamic systems, such as heat exchanger in the heating, ventilation and air conditioning (HVAC) systems. Both natural and transient degradations are evaluated, while parameters dominating the degradation models are assumed to change before and after transient decay. The developed method is evaluated using simulation data, and results demonstrate the effectiveness of proposed method in state estimation and degradation prediction in heat exchanger.
The paper describes algorithms of the nonparametric power estimation by summation of the products of the amplitude discrete Fourier transform (DFT) coefficients when simultaneously of sampling is assumed. As with energy-based approach for the amplitude square estimation of the one-channel signal, the apparent power in the two-channel case can be estimated with the multiplied amplitude DFT coefficients around the component peak and a suitable interpolation algorithm. The use of the Rife-Vincent windows class I, which are designed for maximization of the window spectrum side-lobes fall-off, performs minimal leakage influences in estimations with acceptable noise contributions. The main criterion, which order of window and how many points of interpolation should be used, is the systematic error behavior in dependence of the number of cycles in the measurement interval.
Electro-optical detection systems should be calibrated prior to use, a commonly used method with an autocollimator and a high precision turntable may introduce large error in the calibration process. This paper analyzed the calibration process, and showed that the nature of the problem was attributed to the plane rotation around a non-orthogonal axis. The mirror's non-orthogonal rotation was divided into position rotation and mirror spinning, and the effects on autocollimator readout were obtained. Simulation results demonstrated that the coupling effects on elevation and azimuth become serious as the inclined angle and rotated angle increase. It is therefore necessary to separate the coupling error in the calibration process, which would provide more accurate data for further compensation.
Minimizing the crest factor of a multitone waveform helps to increase the signal to noise ratio in the measurement and decrease the nonlinear distortions. This paper uses the ac Josephson voltage standard to synthesize multitone waveforms and the L∞ algorithm to optimize the harmonic phases and thus to minimize the crest factor. In the optimization, both random and Schroeder harmonic phases are adopted as initial phases for the algorithm. With the crest factor minimized, the operating margin is increased significantly and the maximum output tone amplitude is doubled from 400 μVrms to 800 μVrms for the multitone waveform composed of 200 continuous harmonics.
High quality face image acquisition from huge video data obtained in visual sensor network is of great significance in applications related to face processing, such as face recognition and reconstruction. This paper proposes an optimal face image acquisition method in visual sensor network, which is based on collaborative face frames acquisition and heterogeneous feature fusion-based face quality assessment. Gaussian-probability-distribution-based multi-view data fusion and kalman filter are used for collaborative target localization and tracking. To achieve primary screening of face frames, a lightweight face frames quality evaluation method is presented. Importantly, new face quality assessment criterion calculation methods are proposed to make fine screening of face images more applicable in visual sensor network. The new face quality assessment criterion calculation methods are based on heterogeneous feature fusion of pedestrian tracking and static face image features analysis. Fuzzy inference engine is used to combine these criteria to generate a face quality assessment score. Experimental results show that the proposed method can acquire optimal face images accurately and robustly.
A novel acoustic emission location approach was presented for large anisotropic composite plates with unknown lay-up and wave velocity. The proposed algorithm was based on the differences of acoustic emission signals measured by four piezoelectric sensors surface-bonded in a small area. Wavelet scale spectrum combined with cubic splines interpolation was employed to obtain the time of arrivals (TOA) of different acoustic emission signals. Then, the source location would be identified by solving a set of non-linear equations. The performance of the proposed approach was validated through pencil-lead breaks performed on a large anisotropic composite plate. The results conclusively demonstrated that the proposed method can obtain a reasonable accuracy (maximum error was approximately 3%) when the distance between acoustic source and the origin of coordinates was more than 120 mm.
A novel method of small target detection in infrared image via sparse representation is proposed in this paper. At the beginning, plenty small target image patches are generated based on mathematical model to construct an overcomplete dictionary which can be used to approximate a test image patch. However, all image patches distribute only in a small part of the whole space because their entries are non-negative which denote image intensity. To make them easier to discriminate, each image patch is changed into a vector by stacking its columns and zero-mean-unit-norm normalization is applied. Then patch at each pixel position of test image is approximated linearly with fewest dictionary atoms and positive correlation index(PCI) is calculated based on the coefficient to evaluate the similarity between a test image patch and small target dictionary. Finally, a two-dimensional map will be produced based on PCI after lexicographically scanning and target can be located with a suitable threshold. Experimental results of infrared images with a wide variety of background demonstrate the effectiveness and robustness of the proposed method.
Emotion recognition is a process in which emotions are identified and recognized according to emotion-related bio signals. Wearable biosensor network expands the application of emotion recognition by measuring different emotion-related bio signals with wearable and portable hardware structures to meet specific needs in complicated measuring environment. This paper develops a multimodal emotion recognition method in wearable biosensor network. Reputation-driven Support Vector Machine (RSVM) classification algorithm is proposed to reduce the classification error caused by wearable sensor nodes. Reputations are figured out by similarity evaluation based on correlation calculation and are used for training sample selection and fuzzy membership degree determination. The experiment results indicate that this framework realizes reliable emotion recognition in wearable web-enable sensing environment and provides a solution to primary recognition and monitoring of emotional states and spiritual health.
A wind farm often consists of a number of wind turbines (WTs) operating simultaneously, which not only challenges the operation of the wind farm but makes the condition monitoring (CM) more costly than ever before if each turbine is equipped with a standard commercially available WT CM system (e.g. SKF's WindCon system). With the continual increase of wind farm size, the operator is under increasing pressure to lower the CM cost so that the energy cost can be accordingly reduced in the end. The research presented in this paper is for meeting such a requirement by developing a new cost-effective CM technique based on detecting the transient period of a single WT generator stator current signal. Instead of collecting multiple numbers of CM signals from WT key components, the new technique relies on the use of only a single electric current signal collected from WT generator stator. Moreover, the new technique directly deals with the time waveform of the signal without using any artificial transforms (e.g. Fourier transform). Therefore, it is potentially able to provide more reliable CM result. Experiments have shown that the proposed technique is valid for detecting both the electrical and mechanical faults emulated on a specially designed WT drive train test rig.
Within the trend of smart grid, distributed and intelligent metering has gained its popularity among large scale residential power network. Supervising and assessing power quality in time is the method to guarantee secure and trustworthy energy for the demand side, and harmonics measurement and analysis are of necessity. Harmonics metering under the large-scale distributed metering architecture faces the out-of-sequence measurement (OOSM) problem which brings latency in communication and data fusion. This paper depicts a distributed measuring schedule for large scale harmonic analysis, in which compressive sensing (CS) is utilized to decrease the harmonic sampling length and transform packets, and Nonlinear Autoregressive model with Exogenous inputs (NARX) model is exploited to reorder the out-of-sequence measuring data at the meter data center. Experiments on a practical residential power utilization environment are implemented and harmonic identification accuracy is adopted to evaluate the measuring mechanism under distributed metering network. Results demonstrate a improvement of the harmonics analysis precision and validate the asynchronous measuring method in large scale residential power network.
This paper deals with the characterization of magnetic iron oxide nanoparticles. Pure magnetite (Fe3O4) and noble metal doped magnetite were synthesized following a new sustainable microwave-assisted method. Furthermore, an experimental analysis was carried out to investigate the chemical and magnetic properties of these new nanopowders. Morphological and chemical characteristics of the pure magnetite and magnetite modified by noble metals such as palladium, platinum and ruthenium are provided together with the relevant B-H magnetic curve. Among the adopted measurement techniques, the purity of magnetite phase was analyzed via Fourier Transform Infrared Spectroscopy (FT-IR) and X-Ray Diffraction (XRD), while a Vibrating Sample Magnetometer (VSM) characterized the magnetic behavior of the nanopowder samples.
The keystone of many applications associated to the vision of the Internet of Things is a distributed measurement and data acquisition system. Its design represents a major challenge, because it must enable concurrent measurement of different signals, with heterogeneous sensors and communication protocols. It must also be secure and scalable (in the sense of low marginal cost of adding new features and sensors). We propose the architecture of such a system and demonstrate two different use cases: a distributed system for electric power metering, and a wearable ECG monitoring system for multiple patients. We show that our proposed solution is flexible in terms of measured quantities, and can easily adapt to different data rates. In addition, it allows us to reach record performance in terms of energy consumption per effective number of quantization levels, as we demonstrate in the case of ECG sensors.
