Search results for "Gram"
showing 10 items of 9069 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.
Unsupervised Eye Blink Artifact Identification in Electroencephalogram
2018
International audience; The most prominent type of artifact contaminating electroencephalogram (EEG) signals is the eye blink (EB) artifact. Hence, EB artifact detection is one of the most crucial pre-processing step in EEG signal processing before this artifact can be removed. In this work, an approach that identifies EB artifacts without human supervision and automated varying threshold setting is proposed and evaluated. The algorithm functions on the basis of correlation between two EEG electrodes, Fp1 and Fp2, followed by EB artifact threshold determination utilizing the amplitude displacement from the mean. The proposed approach is validated and evaluated in terms of accuracy and error…
Morphology-based measurement of activation time in human atrial fibrillation
2003
The measurement of the activation time is crucial to allow the correct automatic analysis and classification of intracardiac electrograms recorded in the human atria during atrial fibrillation (AF). This study proposes a method which accounts for the morphology of bipolar signals. After ventricular artifact removal and activation wave recognition, the fiducial point of the activation wave was set at its local barycentre (LB). The method was tested on a set of 30 AF bipolar recordings of increasing complexity class; its performance was compared with that of the traditional methods of maximum peak (MP) or maximum slope (MS) estimation, taking the manual measurements performed by an expert car…
Artificial intelligence techniques for cancer treatment planning
1988
An artificial intelligence system, NEWCHEM, for the development of new oncology therapies is described. This system takes into account the most recent advances in molecular and cellular biology and in cell-drug interaction, and aims to guide experimentation in the design of new optimal protocols. Further work is being carried out, aimed to embody in the system all the basic knowledge of biology, physiopathology and pharmacology, to reason qualitatively from first principles so as to be able to suggest cancer therapies.
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…
Comparison of approaches for generation of fully non-stationary artificial accelerograms
2019
The modelling of the seismic input is a critical aspect when non-linear time-history analyses (NLTHAs) are carried out. As a matter of fact, seismic response of structures is very sensitive to the input excitation time history. The present work aims to highlight the differences in the input modelling and the assessment of seismic response of three r.c. structures employing four generation methods of fully non-stationary artificial accelerogram sets at a given construction site. For each method, seven accelerograms are generated and employed to perform NLTHAs on three r.c. structures having irregular mass and stiffness distributions. The original contribution of the paper relies in the crite…
Highly Performant, Deep Neural Networks with sub-microsecond latency on FPGAs for Trigger Applications
2020
Artificial neural networks are becoming a standard tool for data analysis, but their potential remains yet to be widely used for hardware-level trigger applications. Nowadays, high-end FPGAs, often used in low-level hardware triggers, offer theoretically enough performance to include networks of considerable size. This makes it very promising and rewarding to optimize a neural network implementation for FPGAs in the trigger context. Here an optimized neural network implementation framework is presented, which typically reaches 90 to 100% computational efficiency, requires few extra FPGA resources for data flow and controlling, and allows latencies in the order of 10s to few 100s of nanoseco…
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).
Support Tool for the Combined Software/Hardware Design of On-Chip ELM Training for SLFF Neural Networks
2016
Typically, hardware implemented neural networks are trained before implementation. Extreme learning machine (ELM) is a noniterative training method for single-layer feed-forward (SLFF) neural networks well suited for hardware implementation. It provides fixed-time learning and simplifies retraining of a neural network once implemented, which is very important in applications demanding on-chip training. This study proposes the data flow of a software support tool in the design process of a hardware implementation of on-chip ELM learning for SLFF neural networks. The software tool allows the user to obtain the optimal definition of functional and hardware parameters for any application, and e…