2,134 research outputs found

    Computation in Complex Networks

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    Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin

    Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

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    Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects are considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at Elsevier Neural Network

    Data-driven Soft Sensors in the Process Industry

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    In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

    Detection and Prediction of Epileptic Seizures

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    Production of prebiotic oligosaccharides by novel enzymatic catalysis

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    Annales Mathematicae et Informaticae (44.)

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    Poster Session

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    Advancing Prediction of Headwater Flow Permanence and Stream Expansion and Contraction Using a Process-Based Hydrologic Model Developing ANN Model for Predicting Lake Michigan E.Coli Counts Examining Long Term Trends in Rainfall and Stream Flow at Upper Wabash River Basin Using Self Organizing Map Investigating Water and Sediment Transport Processes with High-Resolution Sensor Measurements and Hysteresis Analysis in the Cane Run Royal Spring Basin, Kentucky, USA Blue Water Farms: Edge-of-Field Water Quality Monitoring of Nutrient and Sediment Loss from No-Till Corn and Soybean Fields in the Lower Cumberland River Watershed Determination of Microcystin Cyanobacterial Toxins in Kentucky Lakes by Diffusive Gradients in Thin Films* Environmental Conditions on the Lower Ohio River with Comments on Phytoplankton Assemblages Restoring Kentucky Streams Containing the Threatened Arrow Darter Comparisons of Conductivity and Chloride Concentrations in the Upper Ohio River Valley During Summer and Winter Months Development and Optimization of Green Polymer and Solvent-Based Ultrafiltration Membranes for Water Treatment Applications Municipal Water Quality Concerns and Rebuilding Trust in a Rural Community Application of a Water Treatment Inspired Technique on a 3D Support for Air Filtration Comparison of Leaf Litter Bag and Environmental DNA in Detection of Salamanders in Maywoods Environmental and Educational Laboratory Reusable Polymeric Sorbents and their Applications in Water Remediation Investigating Plant-Soil Processes and Nitrate Seasonality Using High Resolution Sensors and Stable Isotope Measurements Development and Validation of qPCR Assays for use in eDNA Detection of Ambystoma Species Is Chloride Driving Specific Conductance in Streams in the Upper Ohio River Valley? The Use of Electrical Resistivity Tomography for Delineating Ridgetop Wetland Hydrogeology in the Daniel Boone National Forest in Eastern Kentucky
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