Search results for "computer.software_genre"
showing 10 items of 3858 documents
Geostatistical computing of acoustic maps in the presence of barriers
2009
Acoustic maps are the main diagnostic tools used by authorities for addressing the growing problem of urban acoustic contamination. Geostatistics models phenomena with spatial variation, but restricted to homogeneous prediction regions. The presence of barriers such as buildings introduces discontinuities in prediction areas. In this paper we investigate how to incorporate information of a geographical nature into the process of geostatistical prediction. In addition, we study the use of a Cost-Based distance to quantify the correlation between locations.
Discriminating and simulating actions with the associative self-organising map
2015
We propose a system able to represent others’ actions as well as to internally simulate their likely continuation from a partial observation. The approach presented here is the first step towards a more ambitious goal of endowing an artificial agent with the ability to recognise and predict others’ intentions. Our approach is based on the associative self-organising map, a variant of the self-organising map capable of learning to associate its activity with different inputs over time, where inputs are processed observations of others’ actions. We have evaluated our system in two different experimental scenarios obtaining promising results: the system demonstrated an ability to learn discrim…
PQ Metrics Implementation on Low Cost Smart Metering Platforms. A Case Study Analysis
2018
The increasing interest on the quality of the energy supplied and generated in distribution networks is raising the need of distributed power quality (PQ) measurements. To allow such capabilities with cost effective metering solutions, this paper wants to investigate the feasibility of using smart meters, already installed for energy billing, also for power quality monitoring. To this aim, an electronic board (based on STCOMET metrology device), already used in many energy meters of different countries, has been selected as case study platform. The manufacturer firmware of this board was essentially aimed at implementing energy meter metrics and power line communications functionalities. In…
A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series
2021
Sleep scoring is a fundamental but time-consuming process in any sleep laboratory. Automatic sleep scoring is crucial and urgent to help address the increasing unmet need for sleep research. Therefore, this paper aims to develop an end-to-end deep learning architecture using raw polysomnographic recordings to automate sleep scoring. The proposed model adopts two-dimensional convolutional neural networks (2D-CNN) to automatically learn features from multi-modality signals, together with a "squeeze and excitation" block for recalibrating channel-wise feature responses. The learnt representations are finally fed to a softmax classifier to generate predictions for each sleep stage. The model pe…
A novel pilot study of automatic identification of EMF radiation effect on brain using computer vision and machine learning
2020
Abstract Electromagnetic field (EMF) radiations from mobile phones and cell tower affect brain of humans and other organisms in many ways. Exposure to EMF could lead to neurological changes causing morphological or chemical changes in the brain and other internal organs. Cellular level analysis to measure and identify the effect of mobile radiations is an expensive and long process as it requires preparing the cell suspension for the analysis. This paper presents a novel pilot study to identify changes in brain morphology under EMF exposure considering drosophila melanogaster as a specimen. The brain is automatically segmented, obtaining microscopic images from which discriminatory geometri…
Robust Automated Assessment of Human Blastocyst Quality using Deep Learning
2018
AbstractMorphology assessment has become the standard method for evaluation of embryo quality and selecting human blastocysts for transfer inin vitro fertilization(IVF). This process is highly subjective for some embryos and thus prone to human bias. As a result, morphological assessment results may vary extensively between embryologists and in some cases may fail to accurately predict embryo implantation and live birth potential. Here we postulated that an artificial intelligence (AI) approach trained on thousands of embryos can reliably predict embryo quality without human intervention.To test this hypothesis, we implemented an AI approach based on deep neural networks (DNNs). Our approac…
Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization
2019
AbstractVisual morphology assessment is routinely used for evaluating of embryo quality and selecting human blastocysts for transfer after in vitro fertilization (IVF). However, the assessment produces different results between embryologists and as a result, the success rate of IVF remains low. To overcome uncertainties in embryo quality, multiple embryos are often implanted resulting in undesired multiple pregnancies and complications. Unlike in other imaging fields, human embryology and IVF have not yet leveraged artificial intelligence (AI) for unbiased, automated embryo assessment. We postulated that an AI approach trained on thousands of embryos can reliably predict embryo quality with…
Towards Automatic Testing of Reference Point Based Interactive Methods
2016
In order to understand strengths and weaknesses of optimization algorithms, it is important to have access to different types of test problems, well defined performance indicators and analysis tools. Such tools are widely available for testing evolutionary multiobjective optimization algorithms. To our knowledge, there do not exist tools for analyzing the performance of interactive multiobjective optimization methods based on the reference point approach to communicating preference information. The main barrier to such tools is the involvement of human decision makers into interactive solution processes, which makes the performance of interactive methods dependent on the performance of huma…
Decision-cache based XACML authorisation and anonymisation for XML documents
2012
Author's version of an article in the journal: Computer Standards and Interfaces. Also available from the publisher at: http://dx.doi.org/10.1016/j.csi.2011.10.007 This paper describes a decision cache for the eXtensible Access Control Markup Language (XACML) that supports fine-grained authorisation and anonymisation of XML based messages and documents down to XML attribute and element level. The decision cache is implemented as an XACML obligation service, where a specification of the XML elements to be authorised and anonymised is sent to the Policy Enforcement Point (PEP) during initial authorisation. Further authorisation of individual XML elements according to the authorisation specifi…
Anomaly Detection and Classification of Household Electricity Data : A Time Window and Multilayer Hierarchical Network Approach
2022
With the increasing popularity of the smart grid, huge volumes of data are gathered from numerous sensors. How to classify, store, and analyze massive datasets to facilitate the development of the smart grid has recently attracted much attention. In particular, with the popularity of household smart meters and electricity monitoring sensors, a large amount of data can be obtained to analyze household electricity usage so as to better diagnose the leakage and theft behaviors, identify man-made tampering and data fraud, and detect powerline loss. In this paper, the time window method is first proposed to obtain the features and potential periodicity of household electricity data. Combining th…