This work introduces a new methodology for the early detection of epileptic seizure based on the WiSARD weightless neural network model and a new approach in terms of preprocessing the electroencephalogram (EEG) data....
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This work introduces a new methodology for the early detection of epileptic seizure based on the WiSARD weightless neural network model and a new approach in terms of preprocessing the electroencephalogram (EEG) data. WiSARD has, among other advantages, the capacity of perform the training phase in a very fast way. This speed in training is due to the fact that WiSARD's neurons work like Random Access Memories (RAM) addressed by input patterns. Promising results were obtained in the anticipation of seizure onsets in four representative patients from the European Database on Epilepsy (EPILEPSIAE). The proposed seizure early detection WNN architecture was explored by varying the detection anticipation (δ) in the 2 to 30 seconds interval, and by adopting 2 and 3 seconds as the width of the Sliding Observation Window (SOW) input. While in the most challenging patient (A) one obtained accuracies from 99.57% (δ=2s; SOW=3s) to 72.56% (δ=30s; SOW=2s), patient D seizures could be detected in the 99.77% (δ=2s; SOW=2s) to 99.93% (δ=30s; SOW=3s) accuracy interval.
This paper describes the design, control and implementation of a sensorized robotic platform for versatile rehabilitation of stroke patients living with lower extremity neuromuscular deficit. The proposed device is co...
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ISBN:
(纸本)9781479918096
This paper describes the design, control and implementation of a sensorized robotic platform for versatile rehabilitation of stroke patients living with lower extremity neuromuscular deficit. The proposed device is composed of a six-degree-of-freedom actuation mechanism with a large workspace for lower extremity rehabilitation regimens. With a small footprint, lightweight, and low-cost design and a wireless interface this device is portable and well-suited for at-home and in-clinic use. A custom six-degree-of-freedom force/torque sensor was developed to measure real-time patient forces, and an admittance controller was implemented to provide assistive motion therapy. The results obtained show the suitability of this device for human-robot interaction for the implementation of lower extremity rehabilitation therapy.
In the last decades, studies on swarm robotics have grown significantly, with different new aspects becoming of interest in research. Although the primary interest in swarm robotics should be the application, which is...
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Flux Balance Analysis (FBA) is a widely used approach for studying biochemical networks, and in particular the genome-scale metabolic network reconstructions. It formulates the problem of predicting a cell's chemi...
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ISBN:
(纸本)9781479978878
Flux Balance Analysis (FBA) is a widely used approach for studying biochemical networks, and in particular the genome-scale metabolic network reconstructions. It formulates the problem of predicting a cell's chemical reaction fluxes as the linear optimization problem of maximizing a cellular objective (e.g., growth) subject to constraints capturing stoichiometry mass balances of the metabolic network and bounds that reflect the composition of the growth medium. In practice, however, reaction fluxes of the cells under specific growth conditions are available to be measured, but the primal FBA objective function is not necessarily known. Understanding its structure can elucidate the cellular metabolic control mechanisms and infer important information regarding an organism's evolution. To that end, we have developed an Inverse Flux Balance Analysis (InvFBA) method which is a novel inverse optimization-based framework for inferring metabolic objective functions. Within this framework, we present three different forms of objective functions: linear, quadratic, and non-parametric. We show that in all cases, the inverse problem is tractable and can be solved efficiently. We provide several numerical examples to show that the inference of the objective function is consistent with simulated flux data and actual measurements.
Motivation Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expan...
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Motivation Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables Included The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format csv and. SQL.
In this paper, a new kind of swarm-based metaheuristic search method, called Elephant Herding Optimization (EHO), is proposed for solving optimization tasks. The EHO method is inspired by the herding behavior of eleph...
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In this paper, a new kind of swarm-based metaheuristic search method, called Elephant Herding Optimization (EHO), is proposed for solving optimization tasks. The EHO method is inspired by the herding behavior of elephant group. In nature, the elephants belonging to different clans live together under the leadership of a matriarch, and the male elephants will leave their family group when they grow up. These two behaviors can be modelled into two following operators: clan updating operator and separating operator. In EHO, the elephants in each clan are updated by its current position and matriarch through clan updating operator. It is followed by the implementation of the separating operator which can enhance the population diversity at the later search phase. To demonstrate its effectiveness, EHO is benchmarked by fifteen test cases comparing with BBO, DE and GA. The results show that EHO can find the better values on most benchmark problems than those three metaheuristic algorithms.
Technical debt is a metaphor that describes the effect of immature artefacts in software development. One of its types is documentation debt, which can be identified by locating missing, inadequate or incomplete artef...
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Technical debt is a metaphor that describes the effect of immature artefacts in software development. One of its types is documentation debt, which can be identified by locating missing, inadequate or incomplete artefacts in software projects. Nowadays, we can observe more organizations using agile methods to support their activities. In particular, the use of user stories reduces the focus on requirement specification tasks and, as a consequence, creates difficulties that need to be overcame by the development team. In order to investigate these difficulties and assess whether they create a favourable scenario for incurring documentation debt, this paper presents the results of a literature review and an exploratory study. The results from both studies allowed us to identify a list of causes that can lead the development team to incur documentation debt when working with agile requirements. This is an important step in order to manage the technical debt from a preventive perspective.
In this paper we describe an approach to resolve strategic games in which players can assume different types along the game. Our goal is to infer which type the opponent is adopting at each moment so that we can incre...
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The radar ambiguity function, described as an autocorrelation of shifts in time and frequency, is useful for determining a waveform's accuracy at detecting targets in certain range-Doppler combinations. An algorit...
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The radar ambiguity function, described as an autocorrelation of shifts in time and frequency, is useful for determining a waveform's accuracy at detecting targets in certain range-Doppler combinations. An algorithm is proposed which uses a generalized method of alternating projections to synthesize waveforms with desired ambiguity function properties. In practice, it is often desirable to minimize the magnitude of the ambiguity function at range-Doppler combinations where targets other than the detection are likely to cause interference. The algorithm alternates between projections in the time, frequency, and range-Doppler domains until an optimal solution which fits desired ambiguity function properties is found. This work provides a computationally intelligent methodology to dynamically optimize detection in radar applications and a foundation for future work in joint circuit optimization for spectral compliance.
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