nils-herrmann/clean_scirepeval huggingface dataset:
Geo-enabled synchrophasor data exchange framework based on Sensor Web

Information exchange is difficult and often time consuming in power system because of incompatibility in hardware and software being used in power industry. National Institute of Standards and Technology (NIST) has prescribed several guidelines to ensure interoperability in future smart grid applications. In this paper, we will propose a geo-enabled framework based on Sensor Web to help synchrophasor data exchange without altering existing data acquisition technology being used in industry. It encapsulates existing incompatibility of power system by providing users a web-enabled querying mechanism for synchrophasor data in standards as prescribed by NIST. Data can be queried based on temporal, spatial and comparative parameters giving more control to user to be concise on requirement of data depending upon application. We have utilized open source technologies such as OpenPDC, Sensor Observation Service (SOS) to maximize interoperability.
Investigation of evolutionary feature subset selection in multi-temporal datasets for harmful algal bloom detection

In the present study we investigate the evolutionary feature subset selection using wrapper based genetic algorithms on Multi-temporal datasets. Feature subset selection helps in reducing the original feature dimension and also yields high performance. The evolutionary strategy attains a global optimum by reducing the computations iteratively and by traversing intelligently in the entire feature space. This method gave a very high performance improvement up to 0.97 kappa accuracy with a best reduced feature dimension for harmful algal bloom detection.
Nonparametric detection of cloudy pixels in AVHRR NDVI data

Two nonparametric detectors, based on the Sign Test and the Wilcoxon Signed Rank Test, were applied to 10-day composites of Normalized Difference Vegetation Index (NDVI) data to detect cloud contaminated pixels. The performances of these detectors were found to be no better than a detector based on randomly chosen thresholds. The detector thresholds need to account for the climatologically induced biases in the data for each region and time period every year.
Improved methods for investigating boron related defects in 6H-SiC using deep level transient spectroscopy

A new covariance technique is introduced for parameter estimation of DLTS capacitance transients using Singular Value Decomposition and Prony's method (SVD-Prony). The SVD-Prony method is shown to be effective at Signal-to-Noise Ratio's (SNR's) above 6 dB. The algorithm is shown to accurately estimate the time constants associated with non single-exponential capacitance transients. This paper describes the software written to implement the algorithm using a multi-platform MATLAB Graphic User Interface (GUI). The software also has a wavelet preprocessing tool for SNR's less than 6 dB.
Quality analysis of pansharpened images

In general, image fusion or sharpening is of great importance to various remote sensing and mapping applications, where the spatial information from a "high-resolution" image is merged with the radiometric information from a "low-resolution" image. Several pansharpening algorithms have been developed to improve the spatial resolution of the multispectral image while simultaneously retaining its spectral information. However, a quantitative measure performance of these algorithms has not been studied in details. This paper presents a quality analysis of pansharpening methods and assesses their performance in terms of quality metrics
The cumulant based MUSIC and its use in frequency estimation

A technique for estimating the frequency components of a signal with additive colored noise is presented. This technique is a cumulant-based MUSIC-type method that effectively operates based on good estimates of the cumulants and the separation of signal and noise subspaces of the fourth-order cumulant matrix. The cumulant-based MUSIC offers the same performance as the noiseless case and provides infinite resolution. Results for simulated colored noisy data with decreasing signal-to-noise ratio values are presented, and a comparison with the correlation-based MUSIC method is made to demonstrate the validity of proposed technique. Due to its insensitivity to Gaussian and non-Gaussian processes, the cumulant-based MUSIC provides better performance and higher frequency resolution than the correlation-based MUSIC.<<ETX>>
Coalescing ICA and Wavelets Coefficients for Image Information Mining in Earth Observation Data Archives

Reflectance pattern and spatial pattern characterize the geospatial data. Current semantic-enabled framework retrieval system extract primitive features based on color, texture (Spatial Gray Level Dependency - SGLD matrices), and shape from the segmented homogenous region. This system can use only three bands (true color or false color) at a time to capture color information as it converts RGB space into HSV space. Thus it fails to capture the complete reflectance pattern, an important characteristic of geospatial data. This paper describes a new method to perform image segmentation using the features obtained by coalescing of Independent Component Analysis and Wavelet transform, which are later on used for the region-based retrieval in the earth observation data archives. Experimental results show effectiveness of the proposed method for image information mining in Earth observation data archives.
Particle swarm optimization-based band selection for hyperspectral target detection

This paper proposes particle swarm optimization (PSO)-based band selection approach for target detection from hyperspectral imagery. Specifically, typical target detectors such as constrained energy minimization (CEM) and adaptive coherence estimator (ACE) are studied. Due to the lack of training samples in the detection problem, it is more difficult than classification-purposed band selection. Several objective functions are proposed for target detection during PSO searching. In our experiments, we used the PSO with certain criteria to find the best solution for band selection, and show that it can outperform other searching method such as sequential forward search (SFS) in terms of target detection performance.
Underground cable fault detection and identification via fourier analysis

In this paper, the analysis of underground power cables is performed using Fourier analysis with the objective of detecting fault and average life of the cable. Three types of cables are used in this experiment; a normal cable, a shorted cable, and a cable with holes. The impedance in each case is computed and Fourier transformation is applied so that the resulting impedance magnitude and impedance phase can be examined in the frequency domain. Various windowing techniques are applied to the experimental data to eliminate any interference. Fourier analysis is then applied to the impedance data calculated from both sending end voltage and differential voltage. This analysis reveals differences in the frequency response of the three different types of a cable and eventually can be used as a measure for fault detection. Preliminary results reveal the differences in the frequency response. Accordingly, Fourier type methods can be effectively used as low cost and viable solutions to identify and detect faults in underground cables.
On using adaptive filtering to extract the absorption cross section of SO/sub 2/ in "DALAS" systems

In this paper, an adaptive filter routine based on adaptive line enhancement is used to effectively extract the absorption coefficient of SO/sub 2/ in a differential absorption laser spectroscopy system. The ALE technique requires no a priori knowledge of the signal nor of the noise. Results for raw laser data obtained from this system is presented to illustrate the applicability of the technique.<<ETX>>
Optimized Spectral Transformation for Detection and Classification of Buried Radioactive Materials

We investigate the detection and classification of buried radioactive materials of interest using data collected by a sodium iodide (NaI) detector with a short sensor dwell time (i.e., less than or equal to 1 s). The objective of detection is to detect a target from background and nontarget materials, while the objective of classification is to classify targets buried at different depths. Binned energy windows can reduce data dimensionality, help alleviate the negative impact from background, and suppress trivial spectral variations. However, the performance is sensitive to bin partition parameters including the number of bins and their bin widths. We have developed a particle swarm optimization (PSO)-based automatic system to determine these parameters. We also propose to apply a multiobjective PSO to optimize both the detection and classification accuracy simultaneously. The experimental results show that the PSO-based algorithm can outperform the Powell's direction set optimization method. The multiobjective PSO can achieve the balance between the two objectives, and it may provide even better individual accuracy than a single-objective PSO.
Infrared satellite precipitation estimate using waveletbased cloud classification and radar calibration

We have developed a methodology to enhance an infrared-based high resolution rainfall retrieval algorithm by intelligently calibrating the rainfall estimates using space-based observations. Our approach involves the following four steps: 1) segmentation of infrared cloud images into patches; 2) feature extraction using a wavelet-based method; 3) clustering and classification of cloud patches; and 4) dynamic application of brightness temperature (Tb) and rain rate relationships, derived using satellite observations. The results show that using wavelet features along with other features increase the performance of rainfall estimate in terms of quantitative rain/no rain area estimates. In addition, using lightning data as a feature improves the estimates as well.
Standards-Based Middleware and Tools for Coastal Sensor Web Applications

Coastal buoys and stations provide frequent, high quality marine observations for oceanographic study, weather service, atmospheric, and public safety. There is a great need for sharing the generated data sets to enable timely decision-making across various agencies/institutions. However, this requires tremendous efforts and coordination among different sensor network agencies to come to a shared understanding for disseminating the information in a uniform and standardized way. To address these needs and achieve interoperability, syntactic standardization provides to a certain extent the data description models. In addition, semantic enrichment of the information sources is also required to understand the context of the data. In this paper we adopt standardized data models based on the Open Geospatial Consoritum (OGC) sensor web enablement framework for coastal sensor networks, which facilitates improved information retrieval on a variety of spatio-temporal scales. The semantic enrichment is achieved in terms of conceptualization of selected sensor types and other terminology involved in the coastal domain in the form of Ontologies. A coastal semantic mapping (COSEM-Map) tool developed as a part of this work facilitates the harmonization of different representations. This ontological model is amenable to querying using SPARQL, which is a standardized RDF query language. Further, sensor web services discovery aspects have been addressed by augmenting the Universal Description, discovery and Integration (UDDI) registry with Web Ontology Language for Services (OWL-S) and using a semantic matching algorithm for services discovery. Both web-based and mobile-enabled clients were developed to process syntactic and semantic queries.
Hyperspectral soil texture classification

A soil texture classification system is developed and exploited in the hyperspectral domain. The hyperspectral signatures of three different pure soil textures, i.e., sand, silt and clay, combined with a linear mixture model, are used to generate signals representing different types of soil textures. Feature extraction via the discrete wavelet transform and linear discriminant analysis for feature vector reduction and optimization are used. Different types of classifiers, which include the nearest mean and maximum likelihood, are incorporated to test the system's applicability. Classification accuracy is evaluated using a leave-one-out method. Experimental results are presented and possible future works are discussed.
On-line adaptive assessment of the synchronizing and damping torque coefficients using Kalman filtering

This paper presents an on-line optimal approach for dynamic stability assessment of a single-machine infinite-bus system. The approach is based on estimating the synchronizing and damping torque coefficients of the synchronous machine using a two-state linear Kalman filter. The coefficients are estimated from the time responses of the changes in the rotor angle, rotor speed, and electromagnetic torque. These coefficients can be used as indices to provide insight into the relative stability of the synchronous machine. The two indices can also predict the possible unstable conditions and hence help the operator to take the correct control action beforehand. The performance of the Kalman filter is compared with the least-square error technique. The effect of system noise on the performance of the Kalman filter is also investigated. Compared with the least square technique, the paper shows that the Kalman filter offers several advantages. This includes significant reduction in computing time and storage needed for the estimation of the synchronizing and damping torque coefficients and its robustness in dealing with noisy measurements. This approach results in a remarkable reduction in the computational complexity associated with this problem and hence allows for on-line implementation needed for continuous monitoring of the dynamic stability indices.
Soil texture classification using wavelet transform and maximum likelihood approach

In this paper, a wavelet-based soil texture classification system is proposed for identifying soil with different textures. The wavelet transform is used for feature extraction. The wavelet is a systematic and powerful tool for signal and image analysis due to its multiresolution characteristic. The maximum likelihood (ML) classifier is designed using a set of training samples. The ML parameter estimation method has been shown to give out optimal results. During the process of training and classification, the Fisher's Linear Discrimination Analysis (FLDA) is incorporated for feature vector dimension reduction and optimization. Three different soil texture images, i.e., sand, silt, and clay are used for training and classification. Experimental results and discussion are presented.
Feature Identification via a Combined ICA–Wavelet Method for Image Information Mining

Image transformation is required for color-texture image segmentation. Various techniques are available for the transformation along the spatial and spectral axes. For instance, the HSV-wavelet technique is shown to be very effective for image information mining in remote-sensing applications. However, the HSV transformation approach uses only three spectral bands at a time. In this letter, a new feature set, obtained by combining independent component analysis and wavelet transformation for image information mining in geospatial data, is presented. Experimental results show the effectiveness of the presented method for image information mining in Earth observation data archives.
Pan-sharpening via the contourlet transform

The wavelet transform has been a popular choice for the spatial transformation in the pan-sharpening process. However, the wavelet transform do not represent the directional information efficiently. On the other hand, the contourlet transform, which also has a property of multiresolution decomposition similar to the wavelet, is known to provide efficient directional information and is also useful in capturing intrinsic geometrical structures of the objects. This property of contourlet transformation is very useful for images that contain geometric features. Principal component analysis (PCA) is generally used for the spectral transformation. In this paper, an alternative algorithm based on the merger of PCA-contourlet transform for pan-sharpening is presented. The efficiency of this method is tested by performing pan-sharpening of the high resolution (IKONOS and Quickbird) and the medium resolution (LandSat7 ETM+) datasets. The resulting pan-sharpened images are evaluated in terms of known global validation indexes. These indexes reveal that this method provides better fusion results than the PCA-wavelet approach.
Evaluation of JP3D for lossy and lossless compression of hyperspectral imagery

The performance of the recent JPEG2000 Part 10 standard, known as JP3D, is evaluated for the lossy and lossless compression of hyperspectral imagery. Experimental results using a Karhunen-Loève transform (KLT) for spectral decorrelation and a 2D wavelet transform for spatial decorrelation compare the performance of JP3D against 2D JPEG2000 as specified by Part 2 of the standard. JP3D is used with both the 2D arithmetic-coding contexts as specified in the JP3D standard as well as non-standard experimental 3D contexts. Results reveal that, while for lossless coding, JP3D very slightly surpasses the performance of JPEG2000 Part 2, for lossy coding, JP3D fails to match the rate-distortion performance of the 2D Part-2 coder.
Contourlet Spectral Histogram for Texture Classification

Texture classification is a very important image analysis application. To successfully distinguish among texture categories, common features that can effectively characterize texture images are in need. The contourlet transform is a recently proposed two-dimensional technique for image analysis. It has been proved very efficient for representing images with fine geometrical structures, of which texture images are typical examples. In this paper, contourlet based feature extraction for texture classification has been investigated. A new feature design based on the contourlet spectral histogram has been successfully developed. With this feature design, satisfactory classification accuracy has been achieved for some typical sets of Brodatz textures. It has also been demonstrated that the design outperformed several other comparable schemes
Contourlet Image Modeling with Contextual Hidden Markov Models

The contourlet transform is a recently developed two-dimensional transform technique. It is reported to be more effective than wavelets in representing smooth curvature details typical of natural images. To fully exploit the potential of contourlets in image processing and analysis applications, appropriate models are needed to describe statistical characteristics of images in the contourlet domain. In this paper, statistical contourlet image modeling techniques have been investigated. A contextual hidden Markov model, which was successfully applied to wavelet image denoising, has been adapted into the contourlet domain. The resulting contourlet contextual HMM has been tested in a denoising application with promising results, which verified its effectiveness in characterizing contourlet images
Natural frequencies extraction of a radar target from a measured response using the Prony method

It is shown that the Prony method can be used to accurately extract the natural frequencies of a radar target from a measured response. This can be accomplished by filtering the response via a serial correlation filter. This filter requires no a priori knowledge of the signal and estimates the noise level. Results are presented for both simulated data with additive white Gaussian noise and measured data with unknown noise characteristics in order to demonstrate the application and accuracy of this technique.<<ETX>>
Estimation of the Number of Decomposition Levels for a Wavelet-Based Multiresolution Multisensor Image Fusion

The wavelet-based scheme for the fusion of multispectral (MS) and panchromatic (PAN) imagery has become quite popular due to its ability to preserve the spectral fidelity of the MS imagery while improving its spatial quality. This is important if the resultant imagery is used for automatic classification. Wavelet-based fusion results depend on the number of decomposition levels applied in the wavelet transform. Too few decomposition levels result in poor spatial quality fused images. On the other hand, too many levels reduce the spectral similarity between the original MS and the pan-sharpened images. If the shift-invariant wavelet transform is applied, each excessive decomposition level results in a large computational penalty. Thus, the choice of the number of decomposition levels is significant. In this paper, PAN and MS image pairs with different resolution ratios were fused using the shift-invariant wavelet transform, and the optimal decomposition levels were determined for each resolution ratio. In general, it can be said that the fusion of images with larger resolution ratios requires a higher number of decomposition levels. This paper provides the practitioner an understanding of the tradeoffs associated with the computational demand and the spatial and spectral quality of the wavelet-based fusion algorithm as a function of the number of decomposition levels
A Proposal for the Standardization of Image Information Mining Systems via OGC Web Services Framework

In this paper we present details of a proposal to develop standards for the processes involved in image information mining (IIM) and also to initiate a discussion within the IIM community to evolve and develop specifications. The proposal is based on the current Services Oriented Architectures (SOA's) which provides loosely coupled services that enable cross domain information integration and querying. Web services decouple objects that are platform specific and facilitate interactions among platform independent objects, which are able to access data from anywhere on the Web. They rely on loose, rather than tight coupling among the web components which enables a flexible and dynamic interchange in open, distributed web environments. The Open Geospatial Consortium (OGC) provides a platform for government organizations, academia, and industry to come to a consensus and standardization of geospatial technologies. This paper proposes that image information mining systems need standardization in terms of OGC specifications and in describing the IIM framework in an OGC perspective. This would facilitate interoperability with several existing OGC web services and foster the clear separation of the business logic layer and presentation layer.
Innovative data mining techniques in support of GEOSS: A workshop's findings

It is envisaged that the Global Earth Observation System of Systems (GEOSS) will provide data streams to decision-support tools for a wide variety of users and applications. As with the Internet, GEOSS will be a global and flexible network of content providers facilitating decision makers' access to an extraordinary range of information products at their desk. However, the challenge for the global users community is to have access to fast and reliable tools to process the data streams into actionable information. On August 31 — September 2, 2009 a workshop was held in Sinaia, Romania to discuss the current state-of-the-art in data mining tools and technologies for Earth Observation (EO) data by a group of international experts. The intent of the workshop was to lead to an understanding of and an objective assessment of the world's practice in extracting information from Earth Observation data and in developing a strategic plan for advancing the tools, as well as important open issues. This paper summarizes some of the major findings and recommendations resulting from this workshop.