nils-herrmann/clean_scirepeval huggingface dataset:
Accelerating Big Data processing chain in Image Information Mining using a hybrid HPC approach

The recent development in sensor technology shows the unprecedented growth of Remote Sensing (RS) data archives-Big Data. However, this growth in RS archives has resulted in many processing challenges. The three V's of big data- Volume, Velocity and Variety is highly relevant in situations such as flood, earthquake disaster, where real/near real time processing of data from different RS data sources is vital to deploy rescue operations. In this work, we have demonstrated a high-performance analytics approach- Message Passing Interface (MPI) along with the emerging Graphics Processing Units (GPUs) (i.e. hybrid MPI+GPU) technology to overcome the big data processing limitation. The different processing/analysis stages of our Spatial Image Information Mining (SIIM) system are parallelized using the above approach. The experimental results for parallel segmentation process show the applicability of MPI+GPU hybrid approach in disaster scenario.
Asymptotic analysis of the cumulant-based MUSIC method in the presence of sample cumulant errors

The paper presents an asymptotical analysis of the cumulant-based MUSIC for harmonic retrieval. Assuming that measurement errors are zero mean and uncorrelated, it is shown that the harmonic estimates obtained by the cumulant-based MUSIC are asymptotically unbiased and statistically efficient. Furthermore, the asymptotical distribution of the estimates is also investigated for non-Gaussian measurement errors.
Development of power system test bed for data mining of synchrophasors data, cyber-attack and relay testing in RTDS

This paper describes the development of a hardware-in-the-loop Real Time Digital Simulator (RTDS) enabled power system test bed used to support various types of research. Lack of access to actual utility synchrophasors data at the university level hinders studies on various subjects of interest including power system event detection, situational awareness, wide area monitoring and control, and cyber security. This paper explains a way to facilitate the researcher with reliable synchrophasor data and a power system test bed for different applications such as event detection, and cyber security resiliency studies. Various power system events and scenarios are modeled and simulation results are presented.
Tarp Filtering of Block-Transform Coefficients for Embedded Image Coding

Tarp filtering, an image coder with a simple implementation, is coupled with a block-based discrete cosine transform equipped with pre- and postfiltering. The prefilter reduces intra-block and inter-block correlation of the block-based coefficients, resulting in coefficients that are less correlated and thereby more suitable to tarp filtering. Experimental results show that the proposed coder achieves a significant improvement in rate-distortion performance as compared to the corresponding tarp coder in its original wavelet-based formulation for images with highly detailed content. A similar gain over JPEG2000 is seen for these same images, while, for images that are mostly smooth, the proposed coder performs comparably to JPEG2000
Hyperspectral pixel unmixing using singular value decomposition

A case study in pixel unmixing is performed using the singular value decomposition method. Using a linear mixing model, mixed pixels are created from a subset of the hyperspectral data from an analytical spectral devices handheld spectroradiometer. These pixels are unmixed using the other portion of the hyperspectral data. Simulation results are presented to illustrate the applicability of the presented technique.
Underground power cable fault detection using complex wavelet analysis

Fault detection in underground power cables can be accomplished by examining voltage and current signals using signal processing techniques. In this paper, we explore the feasibility of applying complex wavelet analysis to fault detection. We combine complex wavelets with continuous wavelet transform (CWT), and calculate the impedance from the voltage and current data in the wavelet domain. We then examine the magnitude and phase distributions of the impedance under various conditions. We test our analysis approach with measurement data from different types of cables. The results show that the complex wavelet analysis based approach is able to provide unique signatures for distinguishing between the cables, thus very promising for fault detection.
JPEG2000 coding strategies for hyperspectral data

In using JPEG2000 for the coding of multiple-component, or multiband, images such as hyperspectral imagery, one must consider spectral decorrelation and rate allocation between image components, issues that concern the design of the JPEG2000 encoder and are, consequently, outside the scope of the JPEG2000 standard. Spectral decorrelation via a wavelet transform, as well as three alternative strategies for extending to multiple components the optimal codeblock-bitstream-truncation process widely used for spatial rate allocation in JPEG2000 coding of single-component imagery, are considered. Results indicate that the strategy of simultaneously truncating all code-block bitstreams from all codeblocks from all image components coupled with wavelet-based spectral decorrelation significantly outperforms the other techniques considered in terms of not only rate-distortion performance but also accuracy of unsupervised classification.
Hyperspectral image cube compression combining JPEG-2000 and spectral decorrelation

In this paper, a combined JPEG-2000 and spectral correlation method for hyperspectral image compression is presented. This compression scheme shows promising results. Also, various spectral decorrelation techniques are compared. The decorrelation using Karhunen-Loeve transform performs the best in terms of PSNR gain. But, since it is computationally expensive, there is no much gain over discrete cosine transform.
Noise cancellation in sub-bands using the conjugate gradient method

An adaptive filter bank is considered for noise cancellation with the conjugate gradient (CG) adaptive algorithm being used as the optimization tool. It uses the exponentially windowed pass instantaneous gradients and it has convergence properties superior to the LMS and a lower computational complexity than the RLS. With the appropriately designed analysis and synthesis filter banks and the decimation factor such that aliasing can be ignored, the proposed technique provides satisfactory performance with faster convergence and high computational efficiency. Therefore, it is an attractive alternative for real time noise cancellation.<<ETX>>
Classification of levees using polarimetric Synthetic Aperture Radar (SAR) imagery

The recent catastrophe caused by hurricane Katrina emphasizes the importance of examination of levees to improve the condition of those that are prone to failure during floods. On-site inspection of levees is costly and time-consuming, so there is a need to develop efficient techniques based on remote sensing technologies to identify levees that are more vulnerable to failure under flood loading. This research uses NASA JPL's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) backscatter data for classification and analysis of earthen levees. The overall purpose of this research is to detect the problem areas along the levee such as through-seepage, sand boils and slough slides. This paper focuses on detection of slough slides. Since the UAVSAR is a quad-polarized L-band (λ = 25 cm) radar, the radar signals penetrate into the soil which aids in detecting soil property variations in the top layer. The research methodology comprises three steps: initially the SAR image is classified into three scattering components using the Freeman-Durden decomposition algorithm; then unsupervised classification is performed based on the polarimetric decomposition parameters: entropy (H) and alpha (α); and finally reclassified using the Wishart classifier. A 3×3 coherency matrix is calculated for each pixel of the radar's compressed Stokes matrix multi-look backscatter data and is used to retrieve these parameters. Different scattering mechanisms like surface scattering, dihedral scattering and volume scattering are observed to distinguish different targets along the levee. The experimental results show that the Wishart classifier can be used to detect slough slides on levees.
A Partially Supervised Approach for Detection and Classification of Buried Radioactive Metal Targets Using Electromagnetic Induction Data

The analysis of the data obtained from electromagnetic induction (EMI) sensors is one of the most viable tools for the detection of metallic objects buried under soil. The existing detection methods usually consist of sophisticated EM modeling of the source/target geometry to build suitable discriminators. The major technical challenge in this field is the reduction of false alarms with an increase of the detection probability. In this paper, we propose a partially supervised approach to detect buried radioactive targets, i.e., depleted uranium, without sophisticated EM modeling. Using the EMI data obtained by a GEM-3 sensor for a field survey, our proposed algorithm can successfully detect and discriminate the targets from nontarget metals, compared to other unsupervised and supervised approaches.
Noise-Adjusted Principal Component Analysis for Buried Radioactive Target Detection and Classification

We present a noise-adjusted principal component analysis (NAPCA)-based approach to the detection and classification of buried radioactive targets with short sensor dwell time. The data used in the experiments is the gamma spectroscopy collected by a Sodium Iodide (NAI) scintillation detector. Spectral transformation methods are first applied to the data, followed by NAPCA. Then k-nearest neighbor (kNN) clustering is applied to the NAPCA-transformed feature subspace to achieve detection or classification. This method is evaluated using a database of 240 spectral measurements consisting of background (construction sand), benign material measurements (uranium ore), and target measurements (depleted uranium) at various depths. Compared to other widely used algorithms for depleted uranium, the proposed technique can provide better performance.
Particle swarm optimization based spectral transformation for radioactive material detection and classification

We investigate buried depleted uranium detection and classification using data collected with short sensor dwell time (i.e., less than or equal to 1s). Under this circumstance, the gamma spectroscope collected by a NaI detector can be sparse and random, and may be severely affected by energy counts from the background. Several spectral transformations using binned energy windows can help alleviate the negative effect from background spectral noisy variation. The simplest way for such spectral partition is to use a fixed bin-width for uniform partition. In this paper, we propose a particle swarm optimization (PSO)-based optimization method to automatically determine the varied bin-width for each energy window. The experimental result shows that the spectral transformation methods using PSO-selected bins with variable widths can outperform those with a fixed bin-width.
Applying modular classifiers to mammographic mass classification

Classification is the last step in the computer aided diagnosis (CAD) system for determining whether a breast mass segmented from a digital mammogram is malignant or benign. Hence it is important to improve sensitivity at this stage. This work investigates the use of modular classifier (MoC) schemes, namely bagging and adaboost algorithms, for automated classification of mammographic masses. CAD systems containing a MoC are compared to CAD systems that contain traditional classifiers (TrC), for example single nearest mean or maximum likelihood classifiers. This study included 200 digitized mammograms, each manually segmented by a radiologist. In order to test the MoC and TrC approaches, conventional shape based features were extracted from the segmented masses. These features were then optimized using Fischer's linear discriminant analysis (LDA). When no LDA was utilized, it was observed that MoC schemes increased the sensitivity from 74% to 83% over the TrC approaches. After performing LDA, the sensitivity increased from 83% to 88% for TrC and MoC schemes, respectively.
On the Use of a Cluster Ensemble Cloud Classification Technique in Satellite Precipitation Estimation

In this paper, the link-based cluster ensemble (LCE) method is utilized to improve cloud classification and satellite precipitation estimation. High resolution Satellite Precipitation Estimation (SPE) is based on the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Network Cloud Classification (PERSIANN-CCS) algorithm. This modified SPE with the incorporation of LCE involves the following four steps: 1) segmentation of infrared cloud images into patches; 2) cloud patch feature extraction; 3) clustering cloud patches using LCE; and 4) dynamic application of brightness temperature (Tb) and rain-rate relationships, derived using satellite observations. In order to cluster the cloud patches, the LCE method combines multiple data partitions from different clustering methods. The results show that using the cluster ensemble increases the performance of rainfall estimates compared to the SPE algorithm using a Self Organizing Map (SOM) neural network. The false alarm ratio (FAR), probabilities of detection (POD), equitable threat score (ETS), and bias are used as quantitative measures to assess the performance of the algorithm. It is shown that both the ETS and bias provide improvement in the summer and winter seasons. Almost 5% ETS improvement is obtained at some threshold values for the winter season using the cluster ensemble.
An Information Semantics Approach for Knowledge Management and Interoperability for the Global Earth Observation System of Systems

The global earth observation system of systems (GEOSS) is built on current international cooperation efforts among existing distributed earth observing and processing systems. The goal is to formulate an end-to-end process that enables the collection and distribution of accurate, reliable earth observation (EO) data, information, products, and services to both suppliers and consumers worldwide. EOs are obtained from a multitude of sources and require tremendous efforts and coordination among different governments and user groups to come to a shared understanding on a set of concepts involved in a domain. Semantic metadata play a crucial role in resolving the differences in meaning, interpretation, and usage of the same or related data. Also, the knowledge about the geopolitical background of the originating datasets could be encoded in the metadata that would address the diversity on a global scale. In distributed environments like GEOSS, modularization is inevitable. In this paper, we describe the need for an information semantics-based approach for knowledge management and interoperability between heterogeneous GEOSS systems. Further, considering the magnitude of concepts involved in GEOSS, we explore the possibility of using modular ontologies for formulating smaller interconnected ontologies.
Evaluating transfer learning approaches for image information mining applications

The recent explosion of data from various Earth observation (EO) systems requires new ways to rapidly harness the information and synthesize it for decision making. Currently several image information mining (IIM) systems have some form of supervised statistical learning models that relate the image content to the various semantic classes. However, this kind of approach is constrained by the paucity of training information in several EO domains due to limited ground truth. Although, semi-supervised learning methods alleviate this problem to a certain extent by using unlabelled data from various spatial databases, however these methods require that the training data and future unseen data should conform to the same statistical distribution and feature space. To overcome this problem a more recent approach is focused on using small amounts of labeled information from closely related or similar learning task and somehow adapt that information in developing new semantic models. The above methodology called transfer learning can be applied in several processes of supervised and unsupervised learning. In this paper, we propose Transfer learning methods for IIM and discuss various techniques and their implications for content-based retrieval in the EO domain. Specifically, we explore traffer learning application in a rapid disaster response scenarios during coastal events. The adopted methodology for knowledge transfer is based on harnessing prior knowledge from similar concepts to learn new ones and uses a modified weighted least squares support vector machine (SVM).
Transmission line design for detonating exploding bridge wires

A common way to directly initiate explosives is the use of exploding bridge wire devices. A critical factor for detonating a bridge wire is the energy provided from the firing set. The amount of energy transmitted depends mainly on both the firing system and the shooting line parameters. A design procedure that optimizes the firing set parameters so that proper detonation occurs is developed. Transmission line theory is employed to develop a formulation to determine the bridge wire current and/or the burst current in terms of the firing set parameters. Measurements showed that a coaxial shooting line performs substantially better than a two-wire line. It is shown that the line length is an important factor in the occurrence of misfires.<<ETX>>
Application of the contourlet transform for image information mining in earth observation data archives

This paper presents an image segmentation method using contourlets for spatial transformation. Independent component analysis (ICA) is used to obtain features that are independent and uncorrelated. Feature reduction is also performed during the preprocessing stage of the ICA. A kernel- based approach for clustering the dataset will eliminate the need to calculate cross-correlation energies explicitly. Instead of performing clustering over the whole image, two stages of kernel- based clustering help in reducing the computation complexity of the segmentation method. The segmentation method is applied to LandSat 7 ETM+ imagery. Results show a promising use of the presented approach for image information mining within a semantic framework. These primitive features can subsequently used for object identification.
Interpolation of Missing Values in AMSR-E Soil Moisture Data Using Modified SSA

Soil moisture data available from the Advanced Microwave Scanning Radiometer-Earth Observation System (AMSR-E) onboard the National Aeronautic and Space Administration's Aqua satellite have many inherent gaps. For a region in the Southeast United States, data are collected for spring 2006. This data set has nearly 28% missing data due to radio interference and instrument errors, just to mention a few. To address this issue, an improved singular spectral analysis (SSA)-based interpolation scheme is presented, where a lag covariance matrix is computed for a smaller spatial-temporal subset, as opposed to using the entire spatial grid for the computation of the lag covariance structure. An AMSR-E soil moisture data set with a size of 28 × 22 × 90 is used, and the corresponding results are compared with the ones obtained from the original SSA gap-filling method to validate the applicability of this method. It is shown that our approach provides an improvement over the existing method by utilizing the local variations in the observations for estimating the missing values and thus significantly improving the computational efficiency of the algorithm. It is also found that a spatiotemporal block of 11 × 11 × 28 is optimal for interpolation, where the resulting optimal block information is used to fill the real gaps in the experimental data set.
Spectral analysis of timestamp data on unsynchronized computer systems

The MSU ERC UltraScope system consists of a distributed computing system, custom PCI cards, and GPS receivers. The UltraScope system allows precision timestamping of events in a distributed application on a system where the CPU and PCI clocks are synchronous. This paper investigates methods by which the UltraScope system can be expanded to asynchronous systems. The count offset and clock properties for an asynchronous system are computed. The spectrum of these characteristics are analyzed to try to improve the accuracy of the UltraScope system on this class of computing systems.
On the Performance Evaluation of Pan-Sharpening Techniques

The limitations of the currently existing pan-sharpening quality indices are analyzed: the absolute difference between pixel values, mean shifting, and dynamic range change is frequently used as spatial fidelity measurement, but they may not correlate well with the actual change of image content; and spectral angle is a widely used metric for spectral fidelity, but the spectral angle remains the same if two vectors are multiplied by two individual constants, which means the average spectral angle between two multispectal images is zero even if pixel vectors are multiplied by different constants. Therefore, it is important to evaluate the quality of a pan-sharpened image under a task of its practical use and to assess spectral fidelity in the context of an image. In this letter, three data analysis techniques in linear unmixing, detection, and classification are applied to evaluate spectral information within a spatial scene context. It is demonstrated that those old but simplest approaches, i.e., Brovey and multiplicative (or after straightforward adjustment) methods, can generally yield acceptable data analysis results. Thus, it is necessary to consider the tradeoff between computational complexity, actual improvement on application, and hardware implementation when developing a pan-sharpening method.
Sensor web and data mining approaches for Harmful algal bloom detection and monitoring in the Gulf of Mexico region

Harmful Algal Blooms (HABs) pose an enormous threat to the U.S. marine habitation and economy in the coastal waters. Federal and state coastal administrators have been working in devising a state-of-the-art monitoring and forecasting system for these HAB events. These modernized HAB systems provide useful and forewarning information to a varied user community. However, the lack of standardization in the data exchange mechanism with the current available systems causes an impediment to the wide area coastal observation and management. Hence, there is a need for the system to adapt the services oriented architecture and the OGC (Open Geospatial Consortium) sensor web enablement framework. We propose a HAB monitoring system by adopting the standardized OGC sensor web and using machine learning approaches for the detection of HAB events in the region of Gulf of Mexico. Various feature extraction techniques have been used in obtaining features of both HAB and Non-HAB data. Kernel based Support vector machines have been used as a classifier in the detection of HAB's. The performance of this approach is analyzed by accuracy measures like Kappa Coefficient, N-fold cross validation average and Confusion Matrix on a considerable test data.