Finding Academic Experts on a MultiSensor Approach using Shannon's Entropy

Expert finding is an information retrieval task concerned with the search for the most knowledgeable people, in some topic, with basis on documents describing peoples activities. The task involves taking a user query as input and returning a list of people sorted by their level of expertise regarding the user query. This paper introduces a novel approach for combining multiple estimators of expertise based on a multisensor data fusion framework together with the Dempster-Shafer theory of evidence and Shannon's entropy. More specifically, we defined three sensors which detect heterogeneous information derived from the textual contents, from the graph structure of the citation patterns for the community of experts, and from profile information about the academic experts. Given the evidences collected, each sensor may define different candidates as experts and consequently do not agree in a final ranking decision. To deal with these conflicts, we applied the Dempster-Shafer theory of evidence combined with Shannon's Entropy formula to fuse this information and come up with a more accurate and reliable final ranking list. Experiments made over two datasets of academic publications from the Computer Science domain attest for the adequacy of the proposed approach over the traditional state of the art approaches. We also made experiments against representative supervised state of the art algorithms. Results revealed that the proposed method achieved a similar performance when compared to these supervised techniques, confirming the capabilities of the proposed framework

Vertical wind profile characterization and identification of patterns based on a shape clustering algorithm

Wind power plants are becoming a generally accepted resource in the generation mix of many utilities. At the same time, the size and the power rating of individual wind turbines have increased considerably. Under these circumstances, the sector is increasingly demanding an accurate characterization of vertical wind speed profiles to estimate properly the incoming wind speed at the rotor swept area and, consequently, assess the potential for a wind power plant site. The present paper describes a shape-based clustering characterization and visualization of real vertical wind speed data. The proposed solution allows us to identify the most likely vertical wind speed patterns for a specific location based on real wind speed measurements. Moreover, this clustering approach also provides characterization and classification of such vertical wind profiles. This solution is highly suitable for a large amount of data collected by remote sensing equipment, where wind speed values at different heights within the rotor swept area are available for subsequent analysis. The methodology is based on z-normalization, shape-based distance metric solution and the Ward-hierarchical clustering method. Real vertical wind speed profile data corresponding to a Spanish wind power plant and collected by using a commercialWindcube equipment during several months are used to assess the proposed characterization and clustering process, involving more than 100000 wind speed data values. All analyses have been implemented using open-source R-software. From the results, at least four different vertical wind speed patterns are identified to characterize properly over 90% of the collected wind speed data along the day. Therefore, alternative analytical function criteria should be subsequently proposed for vertical wind speed characterization purposes.The authors are grateful for the financial support from the Spanish Ministry of the Economy and Competitiveness and the European Union —ENE2016-78214-C2-2-R—and the Spanish Education, Culture and Sport Ministry —FPU16/042

Application of data fusion techniques and technologies for wearable health monitoring

Technological advances in sensors and communications have enabled discrete integration into everyday objects, both in the home and about the person. Information gathered by monitoring physiological, behavioural, and social aspects of our lives, can be used to achieve a positive impact on quality of life, health, and well-being. Wearable sensors are at the cusp of becoming truly pervasive, and could be woven into the clothes and accessories that we wear such that they become ubiquitous and transparent. To interpret the complex multidimensional information provided by these sensors, data fusion techniques are employed to provide a meaningful representation of the sensor outputs. This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications. The application of these techniques are then described in the context of healthcare including activity and ambulatory monitoring, gait analysis, fall detection, and biometric monitoring. A snap-shot of current commercially available sensors is also provided, focusing on their sensing capability, and a commentary on the gaps that need to be bridged to bring research to market

Multisensor acoustic tracking of fish and seabird behavior around tidal turbine structures in Scotland

Despite rapid development of marine renewable energy, relatively little is known of the immediate and future impacts on the surrounding ecosystems. Quantifying the behavior and distribution of animals around marine renewable energy devices is crucial for understanding, predicting, and potentially mitigating any threats posed by these installations. The Flow and Benthic Ecology 4D (FLOWBEC) autonomous seabed platform integrated an Imagenex multibeam echosounder and a Simrad EK60 multi-frequency echosounder to monitor marine life in a 120◦ sector over ranges up to 50 m, seven to eight times per second. Established target detection algorithms fail within MRE sites, due to high levels of backscatter generated by the turbulent physical dynamics, limiting and biasing analysis to only periods of low current speed. This study presents novel algorithms to extract diving seabirds, fish, and fish schools from the intense backscatter caused by turbulent dynamics in flows of 4ms−1. Filtering, detection, and tracking using a modified nearest neighbor algorithm provide robust tracking of animal behavior using the multibeam echosounder. Independent multifrequency target detection is demonstrated using the EK60 with optimally calculated thresholds, scale-sensitive filters, morphological exclusion, and frequency-response characteristics. This provides sensitive and reliable detection throughout the entire water column and at all flow speeds. Dive profiles, depth preferences, predator–prey interactions, and fish schooling behavior can be analyzed, in conjunction with the hydrodynamic impacts of marine renewable energy devices. Coregistration of targets between the acoustic instruments increases the information available, providing quantitative measures including frequency response from the EK60, and target morphology and behavioral interactions from the multibeam echosounder. The analyses draw on deployments at a tidal energy site in Scotland to compare the presence and absence of renewable energy structures across a range of physical and trophic levels over complete spring-neap tidal cycles. These results can be used to inform how animals forage in these sites and whether individuals face collision risks. This quantitative information can de-risk the licensing process and, with a greater mechanistic understanding at demonstration scales, its predictive power could reduce the monitoring required at future arrays

Development of Optimized Phenomic Predictors for Efficient Plant Breeding Decisions Using Phenomic-Assisted Selection in Soybean

The rate of advancement made in phenomic-assisted breeding methodologies has lagged those of genomic-assisted techniques, which is now a critical component of mainstream cultivar development pipelines. However, advancements made in phenotyping technologies have empowered plant scientists with affordable high-dimensional datasets to optimize the operational efficiencies of breeding programs. Phenomic and seed yield data was collected across six environments for a panel of 292 soybean accessions with varying genetic improvements. Random forest, a machine learning (ML) algorithm, was used to map complex relationships between phenomic traits and seed yield and prediction performance assessed using two cross-validation (CV) scenarios consistent with breeding challenges. To develop a prescriptive sensor package for future high-throughput phenotyping deployment to meet breeding objectives, feature importance in tandem with a genetic algorithm (GA) technique allowed selection of a subset of phenotypic traits, specifically optimal wavebands. The results illuminated the capability of fusing ML and optimization techniques to identify a suite of in-season phenomic traits that will allow breeding programs to decrease the dependence on resource-intensive end-season phenotyping (e.g., seed yield harvest). While we illustrate with soybean, this study establishes a template for deploying multitrait phenomic prediction that is easily amendable to any crop species and any breeding objective

Cognitively-Engineered Multisensor Data Fusion Systems for Military Applications

The fusion of imagery from multiple sensors is a field of research that has been gaining prominence in the scientific community in recent years. The technical aspects of combining multisensory information have been and are currently being studied extensively. However, the cognitive aspects of multisensor data fusion have not received so much attention. Prior research in the field of cognitive engineering has shown that the cognitive aspects of any human-machine system should be taken into consideration in order to achieve systems that are both safe and useful. The goal of this research was to model how humans interpret multisensory data, and to evaluate the value of a cognitively-engineered multisensory data fusion system as an effective, time-saving means of presenting information in high- stress situations. Specifically, this research used principles from cognitive engineering to design, implement, and evaluate a multisensor data fusion system for pilots in high-stress situations. Two preliminary studies were performed, and concurrent protocol analysis was conducted to determine how humans interpret and mentally fuse information from multiple sensors in both low- and high-stress environments. This information was used to develop a model for human processing of information from multiple data sources. This model was then implemented in the development of algorithms for fusing imagery from several disparate sensors (visible and infrared). The model and the system as a whole were empirically evaluated in an experiment with fighter pilots in a simulated combat environment. The results show that the model is an accurate depiction of how humans interpret information from multiple disparate sensors, and that the algorithms show promise for assisting fighter pilots in quicker and more accurate target identification

A Bayesian fusion model for space-time reconstruction of finely resolved velocities in turbulent flows from low resolution measurements

The study of turbulent flows calls for measurements with high resolution both in space and in time. We propose a new approach to reconstruct High-Temporal-High-Spatial resolution velocity fields by combining two sources of information that are well-resolved either in space or in time, the Low-Temporal-High-Spatial (LTHS) and the High-Temporal-Low-Spatial (HTLS) resolution measurements. In the framework of co-conception between sensing and data post-processing, this work extensively investigates a Bayesian reconstruction approach using a simulated database. A Bayesian fusion model is developed to solve the inverse problem of data reconstruction. The model uses a Maximum A Posteriori estimate, which yields the most probable field knowing the measurements. The DNS of a wall-bounded turbulent flow at moderate Reynolds number is used to validate and assess the performances of the present approach. Low resolution measurements are subsampled in time and space from the fully resolved data. Reconstructed velocities are compared to the reference DNS to estimate the reconstruction errors. The model is compared to other conventional methods such as Linear Stochastic Estimation and cubic spline interpolation. Results show the superior accuracy of the proposed method in all configurations. Further investigations of model performances on various range of scales demonstrate its robustness. Numerical experiments also permit to estimate the expected maximum information level corresponding to limitations of experimental instruments.Comment: 15 pages, 6 figure

Advancing the Monitoring Capabilities of Mountain Snowpack Fluctuations at Various Spatial and Temporal Scales

Snow is a critical water resource for the western US and many regions across the globe. However, our ability to accurately monitor changes in snow mass from satellite remote sensing, specifically its water equivalent, remains a challenge in mountain regions. No single sensor currently has the ability to directly measure snow water equivalent (SWE) from space at a spatial scale suitable for water supply forecasting in mountain environments. This knowledge gap calls for the innovative use of remote sensing techniques, computational tools, and data science methods to advance our ability to estimate mountain snowpacks across a range of spatial and temporal scales. The goal of this dissertation is to advance our capabilities for understanding snowpack across watershed-relevant spatial and temporal scales. Two research approaches were used to accomplish this goal: quantifying the physiographic controls and sensitivities of hydrologically important snow metrics and progressing our ability to use L-band interferometric synthetic aperture radar (InSAR) to measure SWE changes. First, we quantify the physiographic controls and various snowpack metrics in the Sierra Nevada using a novel gridded SWE reanalysis dataset. Such work demonstrates the complexity of snowpack processes and the need for fine-resolution snowpack information. Next, using L-band Interferometric Synthetic Aperture Radar (InSAR) from the NASA SnowEx campaign, both snow ablation and accumulation are estimated in the Jemez Mountains, NM. The radar-derived retrievals are evaluated utilizing a combination of optical snow-cover data, snow pits, meteorological station data, in situ snow depth sensors, and ground-penetrating radar (GPR). Lastly, we compare multisensor optical-radar approaches for SWE retrievals and find that moderate-resolution legacy satellite products provide sufficient results. The results of this work show that L-band InSAR is a suitable technique for global SWE monitoring when used synergistically with optical SCA data and snowpack modeling. While two distinctive methods are present in this research, they both work towards advancing our ability to understand the dynamics of mountain snowpack