Search results for "computing"
showing 10 items of 25279 documents
What Factors Facilitate Good Learning Experiences in Clinical Studies in Nursing: Bachelor Students’ Perceptions
2013
Published version of an article from the journal:ISRN Nursing. Also available from the publisher: http://dx.doi.org/10.1155/2013/628679 Clinical studies constitute 50% of the bachelor program in nursing education in Norway, and the quality of these studies may be decisive for the students’ opportunities to learn and develop their professional competences. The aim of this study was to explore what bachelor students’ in nursing perceived to be important for having good learning experiences in clinical studies. Data was collected in a focus group interview with eight nursing students who were in the last year of the educational program. The interview was transcribed verbatim, and qualitative c…
Fuzzy C-Means Segmentation on Brain MR Slices Corrupted by RF-Inhomogeneity
2007
Brain MR Images corrupted by RF-Inhomogeneity exhibit brightness variations in such a way that a standard Fuzzy C-Means (fcm) segmentation algorithm fails. As a consequence, modified versions of the algorithm can be found in literature, which take into account the artifact. In this work we show that the application of a suitable pre-processing algorithm, already presented by the authors, followed by a standard fcm segmentation achieves good results also. The experimental results ones are compared with those obtained using SPM5, which can be considered the state of the art algorithm oriented to brain segmentation and bias removal.
Illumination Correction on MR Images
2006
Objective. An important artifact corrupting Magnetic Resonance Images is the rf inhomogeneity, also called bias artifact. This anomaly produces an abnormal illumination fluctuation on the image, due to variations of the device magnetic field. This artifact is particularly strong on images acquired with a device specialized on upper and lower limbs due to their coil configuration. A method based on homomorphic filtering aimed to suppress this artifact was proposed by Guillemaud. This filter has two faults: it doesnt provide an indication about the cutoff frequency (cf) and introduces another illumination artifact on the edges of the foreground. This work is an improvement to this method because i…
Automated and Online Eye Blink Artifact Removal from Electroencephalogram
2019
Eyeblink artifacts often contaminates electroencephalogram (EEG) signals, which could potentially confound EEG's interpretation. A lot offline methods are available to remove this artifact, but an online solution is required to remove eyeblink artifacts in near real time for EEG signal to be beneficial in applications such as brain computer interface, (BCI). In this work, approaches that combines unsupervised eyeblink artifact detection with Empirical Mode Decomposition (EMD) and Canonical Correlation Analysis (CCA) are proposed to automatically identify eyeblink artifacts and remove them in an online setting. The proposed approaches are analysed and evaluated in terms of artifact removal a…
Effect of the high-level trigger for detecting long-lived particles at LHCb.
2022
Long-lived particles (LLPs) show up in many extensions of the Standard Model, but they are challenging to search for with current detectors, due to their very displaced vertices. This study evaluated the ability of the trigger algorithms used in the Large Hadron Collider beauty (LHCb) experiment to detect long-lived particles and attempted to adapt them to enhance the sensitivity of this experiment to undiscovered long-lived particles. A model with a Higgs portal to a dark sector is tested, and the sensitivity reach is discussed. In the LHCb tracking system, the farthest tracking station from the collision point is the scintillating fiber tracker, the SciFi detector. One of the challenges i…
Error-Based Interference Detection in WiFi Networks
2017
In this paper we show that inter-technology interference can be recognized by commodity WiFi devices by monitoring the statistics of receiver errors. Indeed, while for WiFi standard frames the error probability varies during the frame reception in different frame fields (PHY, MAC headers, payloads) protected with heterogeneous coding, errors may appear randomly at any point during the time the demodulator is trying to receive an exogenous interfering signal. We thus detect and identify cross-technology interference on off-the-shelf WiFi cards by monitoring the sequence of receiver errors (bad PLCP, bad PCS, invalid headers, etc.) and develop an Artificial Neural Network (ANN) to recognize t…
Exploring the use of multi-gene genetic programming in regional models for the simulation of monthly river runoff series
2023
The use of new data-driven approaches based on the so-called expert systems to simulate runoff generation processes is a promising frontier that may allow for overcoming some modeling difficulties related to more complex traditional approaches. The present study highlights the potential of expert systems in creating regional hydrological models, for which they can benefit from the availability of large database. Different soft computing models for the reconstruction of the monthly natural runoff in river basins are explored, focusing on a new class of heuristic models, which is the Multi-Gene Genetic Programming (MGGP). The region under study is Sicily (Italy), where a regression based rain…
A very brief history of soft computing: Fuzzy Sets, artificial Neural Networks and Evolutionary Computation
2013
This paper gives a brief presentation of history of Soft Computing considered as a mix of three scientific disciplines that arose in the mid of the 20th century: Fuzzy Sets and Systems, Neural Networks, and Evolutionary Computation. The paper shows the genesis and the historical development of the three disciplines and also their meeting in a coalition in the 1990s.
Prediction of surface treatment effects on the tribological performance of tool steels using artificial neural networks
2019
The present paper discussed the development of a reliable and robust artificial neural network (ANN) capable of predicting the tribological performance of three highly alloyed tool steel grades. Experimental results were obtained by performing plane-contact sliding tests under non-lubrication conditions on a pin-on-disk tribometer. The specimens were tested both in untreated state with different hardening levels, and after surface treatment of nitrocarburizing. We concluded that wear maps via ANN modeling were a user-friendly approach for the presentation of wear-related information, since they easily permitted the determination of areas under steady-state wear that were appropriate for use…
A Segmentation System for Soccer Robot Based on Neural Networks
2000
An innovative technique for segmentation of color images is proposed. The technique implements an approach based on thresholding of the hue histogram and a feed-forward neural network that learns to recognize the hue ranges of meaningful objects. A new function for detecting valleys of the histogram has been devised and tested. A novel blurring algorithm for noise reduction that works effectively when used over hue image has been employed. The reported experimental results show that the technique is reliable and robust even in presence of changing environmental conditions. Extended experimentation has been carried on the framework of the Robot Soccer World Cup Initiative (RoboCup).