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It has been widely suggested that a key goal of visualization systems is to assist decision making, but is this true? We conduct a critical investigation on whether the activity of decision making is indeed central to the visualization domain. By approaching decision making as a user task, we explore the degree to which decision tasks are evident in visualization research and user studies. Our analysis suggests that decision tasks are not commonly found in current visualization task taxonomies and that the visualization field has yet to leverage guidance from decision theory domains on how to study such tasks. We further found that the majority of visualizations addressing decision making were not evaluated based on their ability to assist decision tasks. Finally, to help expand the impact of visual analytics in organizational as well as casual decision making activities, we initiate a research agenda on how decision making assistance could be elevated throughout visualization research. [ABSTRACT FROM AUTHOR]

Interaction is fundamental to data visualization, but what “interaction” means in the context of visualization is ambiguous and confusing. We argue that this confusion is due to a lack of consensual definition. To tackle this problem, we start by synthesizing an inclusive view of interaction in the visualization community – including insights from information visualization, visual analytics and scientific visualization, as well as the input of both senior and junior visualization researchers. Once this view takes shape, we look at how interaction is defined in the field of human-computer interaction (HCI). By extracting commonalities and differences between the views of interaction in visualization and in HCI, we synthesize a definition of interaction for visualization. Our definition is meant to be a thinking tool and inspire novel and bolder interaction design practices. We hope that by better understanding what interaction in visualization is and what it can be, we will enrich the quality of interaction in visualization systems and empower those who use them. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of synchronizing two sets of data where the size of the symmetric difference between the sets is small and, in addition, the elements in the symmetric difference are related through the Hamming distance metric. Upper and lower bounds are derived on the minimum amount of information exchange. Furthermore, explicit encoding and decoding algorithms are provided for many cases. [ABSTRACT FROM AUTHOR]

This paper presents a multidimensional computational method to predict the spatial variation data inside and across multiple dies of a wafer. This technique is based on tensor computation. A tensor is a high-dimensional generalization of a matrix or a vector. By exploiting the hidden low-rank property of a high-dimensional data array, the large amount of unknown variation testing data may be predicted from a few random measurement samples. The tensor rank, which decides the complexity of a tensor representation, is decided by an available variational Bayesian approach. Our approach is validated by a practical chip testing data set, and it can be easily generalized to characterize the process variations of multiple wafers. Our approach is more efficient than the previous virtual probe techniques in terms of memory and computational cost when handling high-dimensional chip testing data. [ABSTRACT FROM AUTHOR]

A pulsed electromagnetic inductive valve has been developed to achieve correct initial deposition of the propellant gas puff for the planar pulsed inductive plasma thruster. This paper aims to describe the method for modeling the valve. The most significant physical effects involved in the valve operation process are described by the electrical model, the magnetic model, the mechanical model, and the pneumatic model. These physical models are then linked together based on the couplings between them to form a complete multiphysics coupling model. In order to validate the proposed model, simulations and experiments are implemented for the prototype valve at different operating conditions. Response characteristics and throughput characteristics predicted by the model agree well with experimental data. The results demonstrate that the proposed model can be successfully applied to accurately capture the main performance of the valve. The principle of the analysis procedure is applicable to other electromagnetic valves of this type. [ABSTRACT FROM AUTHOR]

Scan test data compression is widely used in industry to reduce test data volume (TDV) and test application time (TAT). This paper shows how multiple scan chain expansion ratios can help to obtain high test data compression in system-on-chips. Scan chains are partitioned with a higher expansion ratio than normal in scan compression mode and then are gradually concatenated based on a cost function to detect any faults that could not be detected at the higher expansion ratios. It improves the overall test compression ratio since it potentially allows faults to be detected at the highest expansion ratio. This paper introduces a new cost function to choose scan chain concatenation candidates for concatenation for multiple expansion ratios. To avoid TDV and TAT increase by scan concatenation, the proposed method takes a logic structure and scan chain length into consideration. Experiment results show the proposed method reduces TAT and TDV by 53%–64% compared with a traditional scan compression method. [ABSTRACT FROM AUTHOR]

Light field (LF) acquisition faces the challenge of extremely bulky data. Available hardware solutions usually compromise the sensor resource between spatial and angular resolutions. In this paper, a compressed sensing framework is proposed for the sampling and reconstruction of a high-resolution LF based on a coded aperture camera. First, an LF dictionary based on perspective shifting is proposed for the sparse representation of the highly correlated LF. Then, two separate methods, i.e., subaperture scan and normalized fluctuation, are proposed to acquire/calculate the scene disparity, which will be used during the LF reconstruction with the proposed disparity-aware dictionary. At last, a hardware implementation of the proposed LF acquisition/reconstruction scheme is carried out. Both quantitative and qualitative evaluation show that the proposed methods produce the state-of-the-art performance in both reconstruction quality and computation efficiency. [ABSTRACT FROM PUBLISHER]

A call for papers for the special issue of the journal "IEEE Internet Computing" regarding the internet-scale data management for the January/February 2012 issue and the context-aware computing for the March/April 2012 issue is presented.

This paper studies the network utility maximization (NUM) problem in static-routing rechargeable sensor networks (RSNs) with the link and battery capacity constraints. The NUM problem is very challenging as these two constraints are typically coupling in RSNs, which cannot be directly tackled. Existing works either do not fully consider the two coupled constraints together, or heuristically remove the temporally coupled part, both of which are not practical, and will also degrade the network performance. In this paper, we attempt to jointly optimize the sampling rate and battery level by carefully tackling the spatiotemporally coupled link and battery capacity constraints. To this end, we first decouple the original problem equivalently into separable subproblems by means of dual decomposition. Then, we propose a distributed algorithm in the context of joint rate and battery control, called decouple spatiotemporally-coupled constraint (DSCC), which can converge to the globally optimal solution. Numerical results, based on the real solar data, demonstrate that the proposed algorithm always achieves higher network utility than existing approaches. In addition, the impact of link/battery capacity and initial battery level on the network utility is further investigated. [ABSTRACT FROM AUTHOR]

In Part 1 of this two-part paper, we bounded the centroid of a symmetric interval type-2 fuzzy set (T2 FS), and consequently its uncertainty, using geometric properties of its footprint of uncertainty (FOU). We then used these bounds to solve forward problems, i.e., to go from parametric interval T2 FS models to data. The main purpose of the present paper is to formulate and solve inverse problems, i.e., to go from uncertain data to parametric interval T2 FS models, which we call type-2 fuzzistics. Given interval data collected from people about a phrase, and the inherent uncertainties associated with that data, which can be described statistically using the first- and second-order statistics about the end-point data, we establish parametric FOUs such that their uncertainty bounds are directly connected to statistical uncertainty bounds. These results should find applicability in computing with words. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) systems are trained to solve complex problems and learn to perform specific tasks by using large volumes of data, such as prediction, classification, recognition, decision-making, etc. In the past three decades, AI research has focused mostly on the model-centric approach compared to the data-centric approach. In the model-centric approach, the focus is to improve the code or model architecture to enhance performance, whereas in data-centric AI, the focus is to improve the dataset to enhance performance. Data is food for AI. As a result, there has been a recent push in the AI community toward data-centric AI from model-centric AI. This paper provides a comprehensive and critical analysis of the current state of research in data-centric AI, presenting insights into the latest developments in this rapidly evolving field. By emphasizing the importance of data in AI, the paper identifies the key challenges and opportunities that must be addressed to improve the effectiveness of AI systems. Finally, this paper gives some recommendations for research opportunities in data-centric AI.

The capability to anticipate a contact with another device can contribute to improving the performance and user satisfaction of mobile social network applications and of any other relying on some form of data harvesting or hoarding. This paper presents a nine year data set of wireless access logs produced by more than 70,000 devices and 40,000 users. Research on the recurring contact patterns observed between groups of devices permitted to model the probabilities of occurrence of a contact at a predefined date between pairs of devices. As an example, the paper presents and evaluates an algorithm that provides daily contact predictions, based on the history of past pairwise contacts and its application on a reputation service. [ABSTRACT FROM AUTHOR]

In this paper, we study how to optimally manage the freshness of information updates sent from a source node to a destination via a channel. A proper metric for data freshness at the destination is the age-of-information, or simply age, which is defined as how old the freshest received update is, since the moment that this update was generated at the source node (e.g., a sensor). A reasonable update policy is the zero-wait policy, i.e., the source node submits a fresh update once the previous update is delivered, which achieves the maximum throughput and the minimum delay. Surprisingly, this zero-wait policy does not always minimize the age. This counter-intuitive phenomenon motivates us to study how to optimally control information updates to keep the data fresh and to understand when the zero-wait policy is optimal. We introduce a general age penalty function to characterize the level of dissatisfaction on data staleness and formulate the average age penalty minimization problem as a constrained semi-Markov decision problem with an uncountable state space. We develop efficient algorithms to find the optimal update policy among all causal policies and establish sufficient and necessary conditions for the optimality of the zero-wait policy. Our investigation shows that the zero-wait policy is far from the optimum if: 1) the age penalty function grows quickly with respect to the age; 2) the packet transmission times over the channel are positively correlated over time; or 3) the packet transmission times are highly random (e.g., following a heavy-tail distribution). [ABSTRACT FROM PUBLISHER]

Reversible data hiding (RDH) has unique advantage in copyright and integrity protection for multimedia contents. As a typical RDH scheme, histogram shifting technique (HS) has found wide applications due to its high quality of marked image. At present, most existing HS-based RDH schemes rely on single histogram generated from cover image to hide data. Since the single histogram-based approach (SH_RDH) commonly employs smooth regions in the cover image for data hiding, it might not well utilize the cover image and exploit the correlations among image contents of different texture characteristics. In this paper, a novel RDH general framework using multiple histograms modification (MH_RDH) is proposed, which involves two key issues as follows: 1) the construction of multiple histograms based on optimized multi-features and 2) the rate allocation among multiple histograms is formulated as the one of rate-distortion optimization and solved with evolutionary algorithms. The experimental results show that the proposed method could considerably increase the payload of current MH_RDH-based embedding (ranging from 0.2 to 0.7 bpp for most test images) and outperform the other state-of-the-art SH_RDH and MH_RDH schemes. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of thresholded monitoring in distributed data streams, that is, given multiple distributed data streams observed by multiple monitors during a certain period, finding the items whose global frequencies over all data streams exceeding a given threshold. We first derive a lower bound of communication overhead for any deterministic algorithm for this problem. Then, we propose two different schemas, i.e., Low-threshold Cascaded Cuckoo Filter (L-CCF) for low-threshold monitoring and High-threshold Cascaded Cuckoo Filter (H-CCF) for high-threshold monitoring. L-CCF and H-CCF can identify items whose frequencies are more than the given threshold while a desired false negative rate (FNR) is achieved and communication overhead is optimized. The key idea is to compress the communication overhead caused by transferring the ID and frequency information at the same time. First, to reduce the communication overhead of transferring IDs, we propose to encode the IDs into separate tiny parts and store these tiny parts in L-CCF or H-CCF. Second, to reduce the communication overhead of transferring frequencies, we adopt a carry-in counter technique in L-CCF and multiple sampling technique in H-CCF. We evaluated L-CCF and H-CCF on two real-world traces and compared their performance with two prior adapted algorithms. Our experimental results show that on average, L-CCF and H-CCF achieve FNRs with 55% and 65% better than that of comparison algorithms while FPRs is maintained at the level of 2%. [ABSTRACT FROM AUTHOR]

Incomplete information often occurs along with many database applications, e.g., in data integration, data cleaning, or data exchange. The idea of data imputation is often to fill the missing data with the values of its neighbors who share the same/similar information. Such neighbors could either be identified certainly by editing rules or extensively by similarity relationships. Owing to data sparsity, the number of neighbors identified by editing rules w.r.t. value equality is rather limited, especially in the presence of data values with variances. To enrich the imputation candidates, a natural idea is to extensively consider the neighbors with similarity relationship. However, the candidates suggested by these (heterogenous) similarity neighbors may conflict with each other. In this paper, we propose to utilize the similarity rules with tolerance to small variations (instead of the aforesaid editing rules with strict equality constraints) to rule out the invalid candidates provided by similarity neighbors. To enrich the data imputation, i.e., imputing the missing values more, we study the problem of maximizing the missing data imputation. Our major contributions include (1) the np-hardness analysis on solving as well as approximating the problem, (2) exact algorithms for tackling the problem, and (3) efficient approximation with performance guarantees. Experiments on real and synthetic data sets demonstrate the superiority of our proposal in filling accuracy. We also demonstrate that the record matching application is indeed improved, after applying the proposed imputation. [ABSTRACT FROM AUTHOR]

This paper presents an approach for detecting anomalous events in videos with crowds. The main goal is to recognize patterns that might lead to an anomalous event. An anomalous event might be characterized by the deviation from the normal or usual, but not necessarily in an undesirable manner, e.g., an anomalous event might just be different from normal but not a suspicious event from the surveillance point of view. One of the main challenges of detecting such events is the difficulty to create models due to their unpredictability and their dependency on the context of the scene. Based on these challenges, we present a model that uses general concepts, such as orientation, velocity, and entropy to capture anomalies. Using such a type of information, we can define models for different cases and environments. Assuming images captured from a single static camera, we propose a novel spatiotemporal feature descriptor, called histograms of optical flow orientation and magnitude and entropy, based on optical flow information. To determine the normality or abnormality of an event, the proposed model is composed of training and test steps. In the training, we learn the normal patterns. Then, during test, events are described and if they differ significantly from the normal patterns learned, they are considered as anomalous. The experimental results demonstrate that our model can handle different situations and is able to recognize anomalous events with success. We use the well-known UCSD and Subway data sets and introduce a new data set, namely, Badminton. [ABSTRACT FROM PUBLISHER]

High-cost specification tests of analog/RF devices have become a bottle-neck in reducing the overall cost of high-volume manufacturing (HVM) of systems on chip (SoCs) due to lengthy testing time and expensive test equipment. Especially for fabless semiconductor companies, test time reduction (TTR) is becoming an important priority for developing cost-effective design and manufacturing flow of SoCs. Toward this end, numerous approaches have been developed. In this paper, we point out an important practical issue in implementing feature-selection-based low-cost analog/RF test scheme: in HVM, similar specification tests are often bundled as test suites, which are tested as a whole function by automatic test equipment. Removing some tests in one test suite does not provide much saving on device testing time. We then propose a test-suite-based analog/RF TTR approach using canonical correlation, which aims at identifying correlations between two multivariate sets. We further enhance the incurred test escape by applying a data-driven defect-oriented approach. Experimental results in high-volume industrial data confirm the superiority of the proposed approach over existing methods in terms of both TTR and defective parts per million level. [ABSTRACT FROM PUBLISHER]

In sensor networks (SNs), how to allocate the resources so as to optimize data gathering and network utility is an important and challenging task. This paper studies the distributed optimization problem in SNs. A distributed hybrid-driven algorithm based on the coordinate descent method is presented for the optimization purpose. The proposed optimization algorithm differs from the existing ones since the hybrid driven scheme allows more choices of actuation time, resulting a tradeoff between communications and computation performance. Applying the proposed algorithm, each sensor node is driven in a hybrid event time manner, which removes the requirement of strict time synchronization. The convergence and optimality of the proposed algorithm are analyzed, and then verified by simulation examples. The developed results also show the tradeoff between communications and computation performance. [ABSTRACT FROM AUTHOR]

Locating high impedance fault (HIF) in a distribution network enhances the reliability of the system and improves the quality of the service for the customers. Due to low magnitudes of fault current and nonlinearity of fault impedance it is a very hard task and less considered by the distribution automation system. This paper introduces a novel method for estimating the location of high impedance arcing faults in radial distribution systems. The proposed method searches the feeders of the network at different locations, to find where the estimated parameters of the assumed HIF would result the most accurate estimation of the faulty phase voltage. The fault current and the arc parameters are estimated at each probable location employing an appropriate model to estimate the faulty phase voltage. As the proposed method uses the time domain analysis, it is applicable for nonhomogeneous distribution networks with unbalanced loading as well as symmetrical systems. The results of simulations of IEEE 34-node test feeder with various HIF parameters by EMTP-RV show that the proposed method is well capable of locating high impedance faults despite uncertainty in the load data. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) measurement and identification have great importance to condition monitoring of power transformers. In this paper, a new method for recognition of single and multi-source of PD based on extraction of high level image features has been introduced. A database, involving 365 samples of phase-resolved PD (PRPD) data, is developed by measurement carried out on transformer artificial defect models (having different sizes of defect) under a specific applied voltage, to be used for proposed algorithm validation. In the first step, each set of PRPD data is converted into grayscale images to represent different PD defects. Two “image feature extraction” methods, the Local Binary Pattern (LBP), and the Histogram of Oriented Gradient (HOG), are employed to extract features from the obtained gray scale images. Different variants of Support Vector Machine (SVM) are adjusted for optimal classification of PD sources in this process. Impact of the employed parameters in the image processing such as image resolution, random noise, and phase shift, on identification accuracy is investigated and addressed. It is shown that by using HOG-SVM method 99.3% accuracy can be achieved. This is hardly affected by various external factors. Two case studies have been conducted on multi-source PD for evaluating the performance of the proposed algorithm. A void defect is implemented into the transformer model and the resultant recorded signal is used for the study. The DBSCAN algorithm is used as the mean of PD source clustering and sub-PRPD pattern development. It is shown that HOG-SVM method has superior performance in identifying active sources, under sub-PRPD pattern application. [ABSTRACT FROM AUTHOR]

Weighted regression approach is one of the popular problems in robust regression analysis. Recently, robust fuzzy regression models have proven to be alternative approaches to fuzzy regression models attempting to identify, down-weight and/or ignore unusual points (outliers). This paper proposes a new robust fuzzy regression modeling technique known as weighted least squares (LS) fuzzy regression to construct a model for crisp input-fuzzy output data. We introduce a new weighted objective function to overcome the disadvantages of the ordinary LS approach in the presence of outliers. We derive and describe an iterative reweighted algorithm for minimization of the objective function. The algorithm is presented to approximate the weighted estimators of the fuzzy regression by solving the weighted optimization problem. The proposed algorithm decreases the affect of outliers on the model fit attempting to identify/down-weight them. To this end, experiments on datasets with different numbers of outliers are performed. The accuracy of our approach in a real setting is also tested on establishing a predictive model for evaluation of suspended load based on a real world dataset in hydrology engineering. The numerical results show that in the presence of unusual points the proposed weighted fit tracks the main body of the data considerably better than the ordinary LS fuzzy regression fit both in terms of the selected performance criteria and in terms of identifying and down weighting unusual data (outliers). The results of the numerical examples show that this approach has the capability to examine the behavior of value changes in the goodness-of-fit criteria of the fuzzy regression models when the downweighted observations are omitted. [ABSTRACT FROM AUTHOR]

Reactivity is widely used as the paramount means for defining nuclear reactor status, but there is no physical sensor available to measure this parameter directly. Similarly delayed neutron precursors’ concentrations, the source of the delayed neutrons which play important role in reactor control cannot be measured directly. Hence, these quantities must be calculated or estimated from observed neutron flux transients. Recently, Kalman filter (KF) and its variants have been used for the reactivity estimation. A key limitation of KF and all of its variants is that they cannot account for the constraints on state variables systematically, which may cause the filter’s instability. In this paper, a constrained estimator based on the recursive nonlinear dynamic data reconciliation technique is proposed for the estimation of reactivity and delayed neutron precursors’ concentrations in the nuclear reactor. The effectiveness of the proposed technique has been evaluated through the step test signal in the presence of measurement noise and experimental power variation data sets collected from one of the Indian reactors. [ABSTRACT FROM AUTHOR]

In the past decade, new communication schemes have been designed and retrofitted into substations by utilities to integrate data from relays and Intelligent Electronic Devices (IEDs). The idea of standardizing the language of communication between TEDs has evolved as the key for the advancement of connectivity and interoperability within a SA system. The most important out- come of this standardization process has been the IEC 61850 standard. IEC 61850 is an abstract application layer protocol aimed at providing interoperability between a variety of substation and feeder devices by describing how devices are to communicate in a substation as well as the related system requirements. In this paper, the authors describe the standard's application-view model and present the use of OO methodology and techniques for the modelling and implementation of the Data and Dataset application-view object models of the standard. The target audience for this paper includes anyone interested in getting an overview of the IEC 61850 international standard, and understanding how the object-oriented models proposed in the standard standardize the data found in IEDs and the communication of this date between substation devices. [ABSTRACT FROM AUTHOR]

Few existing visualization systems can handle large data sets with hundreds of dimensions, since high-dimensional data sets cause clutter on the display and large response time in interactive exploration. In this paper, we present a significantly improved multidimensional visualization approach named Value and Relation (VaR) display that allows users to effectively and efficiently explore large data sets with several hundred dimensions. In the VaR display, data values and dimension relationships are explicitly visualized in the same display by using dimension glyphs to explicitly represent values in dimensions and glyph layout to explicitly convey dimension relationships. In particular, pixel-oriented techniques and density-based scatterplots are used to create dimension glyphs to convey values. Multidimensional scaling, Jigsaw map hierarchy visualization techniques, and an animation metaphor named Rainfall are used to convey relationships among dimensions. A rich set of interaction tools has been provided to allow users to interactively detect patterns of interest in the VaR display. A prototype of the VaR display has been fully implemented. The case studies presented in this paper show how the prototype supports interactive exploration of data sets of several hundred dimensions. A user study evaluating the prototype is also reported in this paper. [ABSTRACT FROM AUTHOR]

Modern information processing relies on the axiom that high-dimensional data lie near low-dimensional geometric structures. This paper revisits the problem of data-driven learning of these geometric structures and puts forth two new nonlinear geometric models for data describing “related” objects/phenomena. The first one of these models straddles the two extremes of the subspace model and the union-of-subspaces model, and is termed the metric-constrained union-of-subspaces (MC-UoS) model. The second one of these models—suited for data drawn from a mixture of nonlinear manifolds—generalizes the kernel subspace model, and is termed the metric-constrained kernel union-of-subspaces (MC-KUoS) model. The main contributions of this paper in this regard include the following. First, it motivates and formalizes the problems of MC-UoS and MC-KUoS learning. Second, it presents algorithms that efficiently learn an MC-UoS or an MC-KUoS underlying data of interest. Third, it extends these algorithms to the case when parts of the data are missing. Last, but not least, it reports the outcomes of a series of numerical experiments involving both synthetic and real data that demonstrate the superiority of the proposed geometric models and learning algorithms over existing approaches in the literature. These experiments also help clarify the connections between this work and the literature on (subspace and kernel k-means) clustering. [ABSTRACT FROM AUTHOR]

This paper compares the three levels of data fusion with the goal of determining the optimal level of data fusion for multi-sensor human activity data. Using the data processing pipeline, gyroscope and accelerometer data was fused at the sensor-level, feature-level and decision-level. For each level of data fusion four different techniques were used with varying levels of success. This analysis was performed on four human activity publicly-available datasets along with four well-known machine learning classifiers to validate the results. The decision-level fusion (Acc = 0.7443±0.0850) outperformed the other two levels of fusion in regards to accuracy, sensor level (Acc = 0.5934 ± 0.1110) and feature level (Acc = 0.6742 ± 0.0053), but, the processing time and computational power required for training and classification were far greater than practical for a HAR system. However, Kalman filter appear to be the more efficient method, since it exhibited both good accuracy (Acc = 0.7536 ± 0.1566) and short processing time (time = 61.71ms ± 63.85); properties that play a large role in real-time applications using wearable devices. The results of this study also serve as baseline information in the HAR literature to compare future methods of data fusion. [ABSTRACT FROM AUTHOR]

In this paper, we present a method of ship size extraction for Sentinel-1 synthetic aperture radar (SAR) images, which is composed of the image processing stage and the regression stage. In order to achieve extraction with high accuracy, considering the data characteristics of Sentinel-1 images, we propose to use the dual-polarization fusion and the nonlinear regression with the gradient boosting. The experiments and analyses on a relatively large data set show that: 1) compared with the existing and related studies, the proposed method achieves an improved performance. The extraction errors are pushed under one pixel, and they are 4.66% (8.80 m) and 7.01% (2.17 m) for length and width, respectively; 2) the dual-polarization information fusion does improve the size extraction accuracy; and 3) the nonlinear regression does exploit the relationship between the influential factors and the size parameters and provide a better performance than the linear regression. The experimental results verify that the proposed design is suitable for ship size extraction in Sentinel-1 SAR images. [ABSTRACT FROM AUTHOR]

The aim of this paper was to test the capabilities of the Sentinel-1 radar data in downscaling Soil Moisture Active Passive (SMAP) radiometer data for high-resolution soil moisture estimation. Three different active–passive downscaling algorithms, including the brightness temperature-based downscaling algorithm (BTBDA), the soil moisture-based downscaling algorithm (SMBDA), and a change detection method (CDM), were analyzed using pairs of Sentinel-1 active and SMAP passive observations collected over a semiarid landscape in southeastern Australia from May 2015 to May 2016. While these algorithms have been tested previously, this is the first study to evaluate the three algorithms using real Sentinel-1 radar and SMAP radiometer data. The SMAP passive observations were disaggregated to 9-, 3-, and 1-km scales and then compared with ground soil moisture measurements. The results suggest that the root-mean-square error (RMSE) in downscaled soil moisture at 9-km resolution was 0.057, 0.056, and 0.067 cm3/cm3 for the BTBDA, SMBDA, and CDM, respectively. The accuracy of downscaling methods was generally decreased when applied at the finer spatial resolution. The SMBDA had overall better performance in terms of correctly detecting the soil moisture pattern and relatively lower RMSE values, and is, therefore, recommended for the combined Sentinel-1 radar and SMAP radiometer setup for soil moisture monitoring. The influence of incidence angle normalization of Sentinel-1 SAR data on downscaled soil moisture was also investigated and found to be minimal. [ABSTRACT FROM AUTHOR]

Flood inundation probability is critical for situation awareness, flood mitigation, emergency response, and postevent damage assessment. Current flood inundation mapping approaches can be categorized into real-time (RT) and near-RT (NRT) processes based on the timing of data acquisition. However, the intrinsic limitations of each category largely hamper their applications for flood mapping. Taking the 2015 South Carolina flood in downtown Columbia as a case study, this paper proposes a flood inundation reconstruction model by enhancing the NRT normalized difference water index (NDWI) derived from remote sensing imagery with the RT data including stream gauge readings and social media (tweets). Splitting into three modules: water height module, global enhancement module, and local enhancement module, the proposed model first incorporates the gauge readings and the NDWI image to reconstruct a macroscale flood probability layer, which is then locally enhanced using the verified flood-related tweets. The final output of the model matches well with the U.S. Geological Survey inundation map and its surveyed high-water marks. Results suggest that by enhancing NRT imagery with RT data sources, the proposed flood inundation probability reconstruction model renders a more robust, spatially enhanced flood probability index for emergency responders to quickly identify areas in need of urgent attention. [ABSTRACT FROM AUTHOR]

Dimensionality reduction is an essential task in hyperspectral image processing. How to preserve the original intrinsic structure information and enhance the discriminant ability is still a challenge in this area. Recently, with the advantage of preserving global intrinsic structure information, low-rank representation has been applied to dimensionality reduction and achieved promising performance. By exploiting the submanifold information of the original data set, multimanifold learning is effective in enhancing the discriminant ability of the processed data set. In addition, due to the ability of preserving the spatial neighborhood structure information, the tensor analysis has become a popular technique for hyperspectral image processing. Motivated by the above-mentioned analysis, a novel tensor-based low-rank graph with multimanifold regularization (T-LGMR) for dimensionality reduction of hyperspectral images is proposed in this paper. In the T-LGMR, a low-rank constraint is employed to preserve the global data structure while multimanifold information is utilized to enhance the discriminant ability, and tensor representation is used to preserve the spatial neighborhood information. Finally, dimensionality reduction is achieved in the graph embedding framework. Experimental results on three real hyperspectral data sets demonstrate the superiority of the proposed method over several state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Ocean color data are critical for the monitoring and understanding of biological and ecological processes and phenomena, and the data are also important sources of input data for physical and biogeochemical ocean models. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership has continued to provide global ocean color data since its launch in October 2011. However, there are always many missing pixels in the original VIIRS-measured ocean color images due to clouds and various other reasons. The data interpolating empirical orthogonal functions (DINEOF) is a method to reconstruct (gap filling) missing data in geophysical data sets based on the empirical orthogonal function. In this paper, the DINEOF is applied to VIIRS-derived global Level-3 binned ocean color data of 9-km spatial resolution, and the DINEOF-reconstructed ocean color data are used to fill the gaps of missing data. In particular, daily, eight-day, and monthly VIIRS global Level-3 binned ocean color data, including chlorophyll-a concentration, diffuse attenuation coefficient at 490 nm [ $K_{d}(490)$ ], and normalized water-leaving radiance spectra [ $nL_{w}(\lambda)$ ] at the five VIIRS visible bands, are tested and evaluated. To validate and evaluate the gap-filled data, a set of original valid nonmissing pixels in the VIIRS images are selected randomly and treated as missing pixels in the DINEOF process, so that the reconstructed pixels can be compared with the original data. Results show that the DINEOF method can successfully reconstruct and gap-fill meso-scale and large-scale spatial ocean features in the global VIIRS Level-3 images, as well as capture the temporal variations of these features. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method based on deformable dictionary learning for detecting the regions of change between multitemporal image pairs. We build on our previous work, which constructed a pair of dictionaries. The main shortcoming of this method was its dependence on a large amount of training data. In practice, there is often a shortage of ground-truthed training images, which limits the expression capability of the resulting dictionaries. This paper overcomes this challenge by incorporating the concept of deformation, wherein each atom of a dictionary is no longer a simple image patch, but instead is a flexible image deformation function. This enables the creation of more expressive dictionaries, capable of generalizing to a far greater variety of image patterns, while using a far smaller amount of ground-truthed images for supervised dictionary training. Deformation similarity is employed for patch matching to find the best set of atoms in the difference image (DI) dictionary for reconstructing image patches for a new input DI. Each such atom can be deformed to achieve a better match, thus extending generality while reducing the number of atoms needed in the dictionary. Multiple deformed atoms are weighted and combined to best reconstruct the input DI patch. Then, the same set of deformations and weights is projected to the corresponding atoms in the CD dictionary to obtain the output change-detection map. Experiments in six realistic synthetic aperture radar data sets demonstrate the robustness and efficiency of the proposed method in comparison with five other state-of-the-art methods from the literature. [ABSTRACT FROM AUTHOR]

In this paper, a dual-frequency and dual-baseline (DFDB) processing framework for the extraction of high-precision terrain information from airborne interferometric synthetic aperture radar (SAR) data is presented. Specifically, we propose the use of two single-pass data sets acquired simultaneously in two different frequency bands and two large-baseline repeat-pass data sets also acquired simultaneously in two frequency bands. The configuration profits from the stability of the single-pass derived elevation maps in relation to spatially correlated artifacts as well as from the increased sensitivity associated with large-baseline acquisitions. Moreover, the dual-frequency nature of the data set enables the tackling of the phase unwrapping issue, promoting the retrieval of unambiguous measurements. Several algorithms for the interferometric processing of the DFDB airborne data set are proposed, including the outline of multichannel phase calibration and unwrapping error correction strategies and approaches to remove spatially correlated artifacts and extract the common underlying topography. Elevation models generated from a DFDB data set acquired with the airborne F-SAR sensor over tidal flats in northern Germany are presented, and comparisons with an airborne laser scanner reference show errors with a standard deviation of around 14 cm and a mean absolute deviation of less than 10 cm. [ABSTRACT FROM AUTHOR]

Recent advances in computer vision and pattern recognition have demonstrated the superiority of deep neural networks using spatial feature representation, such as convolutional neural networks (CNNs), for image classification. However, any classifier, regardless of its model structure (deep or shallow), involves prediction uncertainty when classifying spatially and spectrally complicated very fine spatial resolution (VFSR) imagery. We propose here to characterize the uncertainty distribution of CNN classification and integrate it into a regional decision fusion to increase classification accuracy. Specifically, a variable precision rough set (VPRS) model is proposed to quantify the uncertainty within CNN classifications of VFSR imagery and partition this uncertainty into positive regions (correct classifications) and nonpositive regions (uncertain or incorrect classifications). Those “more correct” areas were trusted by the CNN, whereas the uncertain areas were rectified by a multilayer perceptron (MLP)-based Markov random field (MLP-MRF) classifier to provide crisp and accurate boundary delineation. The proposed MRF-CNN fusion decision strategy exploited the complementary characteristics of the two classifiers based on VPRS uncertainty description and classification integration. The effectiveness of the MRF-CNN method was tested in both urban and rural areas of southern England as well as semantic labeling data sets. The MRF-CNN consistently outperformed the benchmark MLP, support vector machine, MLP-MRF, CNN, and the baseline methods. This paper provides a regional decision fusion framework within which to gain the advantages of model-based CNN, while overcoming the problem of losing effective resolution and uncertain prediction at object boundaries, which is especially pertinent for complex VFSR image classification. [ABSTRACT FROM AUTHOR]

Because of the internal malfunction of satellite sensors and poor atmospheric conditions such as thick cloud, the acquired remote sensing data often suffer from missing information, i.e., the data usability is greatly reduced. In this paper, a novel method of missing information reconstruction in remote sensing images is proposed. The unified spatial–temporal–spectral framework based on a deep convolutional neural network (CNN) employs a unified deep CNN combined with spatial–temporal–spectral supplementary information. In addition, to address the fact that most methods can only deal with a single missing information reconstruction task, the proposed approach can solve three typical missing information reconstruction tasks: 1) dead lines in Aqua Moderate Resolution Imaging Spectroradiometer band 6; 2) the Landsat Enhanced Thematic Mapper Plus scan line corrector-off problem; and 3) thick cloud removal. It should be noted that the proposed model can use multisource data (spatial, spectral, and temporal) as the input of the unified framework. The results of both simulated and real-data experiments demonstrate that the proposed model exhibits high effectiveness in the three missing information reconstruction tasks listed above. [ABSTRACT FROM AUTHOR]

Most graphics hardware features memory to store textures and vertex data for rendering. However, because of the irreversible trend of increasing complexity of scenes, rendering a scene can easily reach the limit of memory resources. Thus, vertex data are preferably compressed, with a requirement that they can be decompressed during rendering. In this paper, we present a novel method to exploit existing hardware texture compression circuits to facilitate the decompression of vertex data in graphics processing unit (GPUs). This built-in hardware allows real-time, random-order decoding of data. However, vertex data must be packed into textures, and careless packing arrangements can easily disrupt data coherence. Hence, we propose an optimization approach for the best vertex data permutation that minimizes compression error. All of these result in fast and high-quality vertex data decompression for real-time rendering. To further improve the visual quality, we introduce vertex clustering to reduce the dynamic range of data during quantization. Our experiments demonstrate the effectiveness of our method for various vertex data of 3D models during rendering with the advantages of a minimized memory footprint and high frame rate. [ABSTRACT FROM PUBLISHER]

We present a method for outdoor markerless motion capture with sparse handheld video cameras. In the simplest setting, it only involves two mobile phone cameras following the character. This setup can maximize the flexibilities of data capture and broaden the applications of motion capture. To solve the character pose under such challenge settings, we exploit the generative motion capture methods and propose a novel model-view consistency that considers both foreground and background in the tracking stage. The background is modeled as a deformable 2D grid, which allows us to compute the background-view consistency for sparse moving cameras. The 3D character pose is tracked with a global-local optimization through minimizing our consistency cost. A novel $L_1$ motion regularizer is also proposed in the optimization to constrain the solution pose space. The whole process of the proposed method is simple as frame by frame video segmentation is not required. Our method outperforms several alternative methods on various examples demonstrated in the paper. [ABSTRACT FROM PUBLISHER]

An efficient 2-D joint full-waveform inversion method for electromagnetic and seismic data in a layered medium background is developed. The joint inversion method based on the integral equation (IE) method is first proposed in this paper. In forward computation, the IE method is employed, which usually has smaller discretized computation domain and less cumulative error compared with the finite-difference method. In addition, fast Fourier transform is used to accelerate the convolution between Green's functions and induced sources due to the shift invariance property of the layered Green's functions in the horizontal direction. In the inversion model, the cross-gradient function is incorporated into the cost function of the separate inversion to enforce the structure similarity between electric conductivity and seismic-wave velocity. We use the improved variational Born iteration method and two different iteration strategies to minimize the cost function and reconstruct the contrasts. Several typical models in geophysical applications are used to validate our joint inversion method, and the numerical simulation results show that joint inversion can improve the inversion results when compared with those from the separate inversion. [ABSTRACT FROM AUTHOR]

We have introduced a new decomposition method for seismic data, termed complex variational mode decomposition (VMD), and we have also designed a new filtering technique for random noise attenuation in seismic data by applying the VMD on constant-frequency slices in the frequency-offset (f-x) domain. The motivation behind this paper is to overcome the potential low performance of empirical mode decomposition (EMD) for energy preservation of the steeply dipping events when used for noise attenuation, and low resolution when used for signal decomposition. The VMD is proposed to decompose a signal into an ensemble of band-limited modes. For seismic data consisting of linear events, the constant-frequency slices of its f-x spectrum are exactly band-limited. The noise attenuation algorithm is summarized as follows. First, the Fourier transform is applied on the time axis of the 2-D seismic data. Next, the VMD is applied on each frequency slice of the f-x spectrum and the decomposed modes are combined to obtain the filtered frequency slice. Finally, an inverse Fourier transform is applied on the frequency axis of the f-x spectrum to obtain the denoised result. The resulting VMD-based noise attenuation method is equivalent to applying a Wiener filter on each decomposed mode, which is achieved during the decomposition progress. We also applied 2-D VMD on 3-D seismic data for denoising. Numerical results show that the proposed VMD-based method achieves a higher denoising quality than both the f-x deconvolution method and the EMD-based denoising method, especially for preserving the steep slopes. [ABSTRACT FROM AUTHOR]

Field Programmable Gate Array (FPGA) boast abundant resources with which to realise high-performance accelerators for computationally demanding operations. Highly efficient accelerators may be automatically derived from Signal Flow Graph (SFG) models by using architectural synthesis techniques, but in practical design scenarios, these currently operate under two important limitations - they cannot efficiently harness the programmable datapath components which make up an increasing proportion of the computational capacity of modern FPGA and they are unable to automatically derive accelerators to meet a prescribed throughput or latency requirement. This paper addresses these limitations. SFG synthesis is enabled which derives software-programmable multicore single-instruction, multiple-data (SIMD) accelerators which, via combined offline characterisation of multicore performance and compile-time program analysis, meet prescribed throughput requirements. The effectiveness of these techniques is demonstrated on tree-search and linear algebraic accelerators for 802.11n WiFi transceivers, an application for which satisfying real-time performance requirements has, to this point, proven challenging for even manually-derived architectures. [ABSTRACT FROM PUBLISHER]

Apache Kafka has become a popular solution for managing networked data streaming in a variety of applications, from industrial to general purpose. This paper systematically surveys the research literature in this field by carefully classifying it into key macro areas, namely algorithms, networks, data, cyber-physical systems, and security. Through this meticulous classification, the paper aims to identify and analyze the optimization aspects relevant to each area, drawing upon practical applications as the basis for analysis. In this respect, the paper synthesizes and consolidates existing knowledge, saving researchers valuable time and effort in searching for relevant information across multiple sources. The tangible benefits of this survey paper include providing a consolidated knowledge base about research-intensive Apache Kafka topics, highlighting practical insights and novel approaches, pointing up cross-domain applications, identifying related research challenges, and serving as a trusted reference for the Apache Kafka community.

Maximizing data usage and minimizing privacy risk are two conflicting goals. Organizations always apply a set of transformations on their data before releasing it. While determining the best set of transformations has been the focus of extensive work in the database community, most of this work suffered from one or both of the following major problems: scalability and privacy guarantee. Differential Privacy provides a theoretical formulation for privacy that ensures that the system essentially behaves the same way regardless of whether any individual is included in the database. In this paper, we address both scalability and privacy risk of data anonymization. We propose a scalable algorithm that meets differential privacy when applying a specific random sampling. The contribution of the paper is two-fold: 1) we propose a personalized anonymization technique based on an aggregate formulation and prove that it can be implemented in polynomial time; and 2) we show that combining the proposed aggregate formulation with specific sampling gives an anonymization algorithm that satisfies differential privacy. Our results rely heavily on exploring the supermodularity properties of the risk function, which allow us to employ techniques from convex optimization. Through experimental studies we compare our proposed algorithm with other anonymization schemes in terms of both time and privacy risk. [ABSTRACT FROM PUBLISHER]

The future wireless network, such as the Centralized Radio Access Network (C-RAN), will need to deliver data rate about 100–1000 times the current fourth-generation (4G) technology. For the C-RAN-based network architecture, there is a pressing need for tremendous enhancement of the effective data rate of the common public radio interface (CPRI). Compression of CPRI data is one of the potential enhancements. In this paper, we introduce a vector quantization based compression algorithm for CPRI links, utilizing the Lloyd algorithm. Methods to vectorize the I/Q samples and enhanced initialization of the Lloyd algorithm for codebook training are investigated for improved performance. Multistage vector quantization and unequally protected multigroup quantization are considered to reduce codebook search complexity and codebook size. Simulation results show that our solution can achieve compression of four times for uplink and 4.5 times for downlink, within $2\%$ error vector magnitude (EVM) distortion. Remarkably, vector quantization codebook proves to be quite robust against data modulation mismatch, fading, signal-to-noise ratio (SNR), and Doppler spread. [ABSTRACT FROM AUTHOR]

To cope with increasing vehicular traffic and extensive application demands on vehicular cellular networks, opportunistic vehicular networks are used to disseminate mobile data by high-capacity device-to-device communication contacts when vehicles come into the communication range of each other, which offloads significant traffic from the cellular network. The current opportunistic vehicular data transmission models often do not take into account the fact that the contact duration is usually very short, due to the high mobility of vehicles, which will limit the amount of data that can be transmitted during one opportunistic contact. In this paper, we consider a mobile data offloading system that integrates cellular network and vehicular opportunistic communications. Based on this proposed system, we establish a mathematical framework to study the problem of contact-aware optimal resource allocation for mobile data offloading by the explicit consideration of the contact duration. Based on theoretical analysis of this problem, we derive an optimal scheme for mobile data offloading to optimally allocate the network resources in terms of opportunistic contacts and offloading helpers’ storage. By carrying out extensive simulation using two realistic urban vehicular traces, we demonstrate the effectiveness of our contact duration aware optimal offloading scheme, in comparison with a wide range of existing schemes. [ABSTRACT FROM AUTHOR]

This paper investigates a practical energy minimization problem for multiuser relay-assisted downlink cellular networks. The system adopts a hybrid-automatic-repeat-request of type I (HARQ-I) protocol, and each user has an average delay constraint to be satisfied under a total power constraint for the system. The contribution of this paper lies in several folds. 1) By introducing new constraints and new variables in the original problem, the solution is analytically obtained by the dual method. 2) An algorithm that jointly allocates for every user the optimal downlink power at base station and relay station (RS) and selects the RS (if cooperation is decided) is proposed. 3) Another binary integer programming algorithm that selects the optimal modulation and coding scheme (MCS) and optimizes the number of time-frequency resource blocks (RBs) between users is proposed. In order to corroborate our findings, simulation results in terms of energy-delay tradeoff, average starvation user rates, and selected MCS statistics are presented. Our analyses show that relay-assisted strategy improves up to 30% the energy consumption, compared to a nonrelay-assisted one. [ABSTRACT FROM AUTHOR]