1,720,984 research outputs found
Pulse coupled neural networks for automatic oil spill detection from satellite SAR images
No full textIt is known that one of the most critical issues for the implementation of a fully automatic processing dedicated to the detection of oil spills from SAR imagery is the extraction of the oil spill candidate. In fact, the segmentation of the image is the first of three necessary steps, the other two being the characterization of the extracted black spot by using a set of features and the classification between oil spill and look-alike. In this paper we investigate an unsupervised neural network approach for automatically extracting oil spill candidates from ERS and ENVISAT SAR images. The technique is based on the use of Pulse-Coupled Neural Networks (PCNN) which is a relatively novel technique based on models of the visual cortex of small mammals. When applied to image processing, it yields a series of binary pulsed signals, each associated to one pixel or to a cluster. In literature, interesting results have been already reported by several authors in applications of this model to image segmentation, including, in few cases, the use of satellite data. The architecture of PCNN is rather simpler than most other neural network implementations. PCNN do not have multiple layers and receive input directly from the original image, forming a resulting “pulse” image. The network consists of multiple nodes coupled together with their neighbors within a definite distance, forming a grid (2D-vector). The PCNN neuron has two input compartments: linking and feeding. The feeding compartment receives both an external and a local stimulus, whereas the linking compartment only receives a local stimulus. When the internal activity becomes larger than an internal threshold, the neuron fires and the threshold sharply increases. Afterward, it begins to decay until once again the internal activity becomes larger. This process gives rise to the pulsing nature of PCNN, forming a wave signature which is invariant to rotation, scale, shift or skew of an object within the image. This study discusses the use of PCNN technique in a fully automatic chain for oil spill detection from SAR images. The objects segmented by the PCNN are successively processed by a more standard Multi-Layer Perceptron Neural Network, which provides the classification response between real oil spill and look-alike. The performance yielded by the PCNN-MLP chain is evaluated and critically discussed for a set of ERS-SAR and ENVISAT ASAR images. The application of the methodology to the very-high resolution SAR images taken by COSMO-Skymed and TerraSAR-X satellites will be also considered
Correspondence: Left ventricular pacing rate lower than expected during manual pacing threshold test in a biventricular defibrillator
No full textNo abstract availabl
Automatic object extraction from VHR satellite SAR images using pulse coupled neural networks
Full text linkFeasibility of the transseptal approach for fast and unstable left ventricular tachycardia mapping and ablation with a non-contact mapping system
No full textBackground Radiofrequency ablation of fast and unstable
left ventricular tachycardia (VT) usually requires noncontact
mapping. The procedure is usually performed by a
retrograde-transaortic route, requiring a double femoral
artery puncture, for the 9F multielectrode catheter and the
7F ablation catheter which are advanced through the aorta
and aortic valve into the left ventricle (LV). Reported limitations
of the procedure are due to the stiffness of the
balloon catheter, particularly in patients with tortuous peripheral
arteries, atherosclerotic aorta, or with aortic stenosis.
The aim of our study was to test the feasibility and
assess the safety of a transseptal approach for left VT noncontact
mapping and ablation.
Materials and methods Ten patients with multiple cardiac
defibrillator shocks because of fast and unstable VT were
selected for non-contact mapping and ablation. After a
double transseptal puncture the multielectrode catheter
(Ensite ArrayTM, St. Jude Medical) was advanced through
a standard 10F introducer to a stable position in the LVapex
over a 260 cm length 0.035 J-tip guidewire. The ablation
catheter (CelsiusTM Thermo-cool, Biosense Webster) was
then inserted through the second 8F introducer. Twenty-five
monomorphic sustained ventricular tachycardia were induced
and ablated at the level of the diastolic pathway or
exit point revealed by unipolar isopotential mapping. The
total procedural and fluoroscopy times were 209 ± 32 min
and 28.5±9.27 min, respectively, which were comparable
to those described with the traditional retrograde-transaortic
approach. No major complication related with the transseptal
approach were reported.
Conclusion A transseptal approach can be a feasible and effective
alternative approach for mapping and ablation of fast
and unstable left VT with a non-contact mapping system
Sedation with midazolam for electrical cardioversion
No full textBackground: Electrical cardioversion (ECV) usually requires the assistance of the anesthesiology team.
To avoid this dependence, previous studies have considered the use of sedation with benzodiazepines
administered by cardiologists. We describe our experience with intravenous Midazolam during cardioversion.
Methods:We performed 280 ECV in 202 patients sedated with intravenous Midazolam, without anesthesiology
supervision. In scheduled cardioversions, we tested two protocols of Midazolam administration:
a bolus of 3 mg, followed by 2 mg each minute until necessary, and a loading dose of 0.09–0.1 mg/kg.
In cardioversions performed during electrophysiology studies or defibrillator implant, Midazolam was
administered by small repeated doses during the entire procedure.
Results: Midazolam was effective to obtain adequate sedation in 99% of cases. All patients had amnesia
with regards of the cardioversion. A loading dose of Midazolam allowed a shortening of the procedural
time without serious adverse events. Intubation or the assistance of an Anesthetist was never necessary.
Conclusion: Sedation with Midazolam for ECV is effective and well tolerated, with some cautions discussed.
A loading dose of Midazolam is well tolerated and further reduces the procedural time
Atrial fibrillation and recurrent ventricular fibrillation during hypokalemia in Brugada syndrome
No full textAtrial Fibrillation and Recurrent Ventricular Fibrillation During Hypokalemia
in Brugada Syndrome. A 41-year-old man with Brugada syndrome (BS) and no previous
episodes of aborted sudden death or syncope referred to local emergency room for an episode of symptomatic
atrial fibrillation. Blood chemistry results showed hypokalemia (2.9 mEq/L). The other parameters
were within the normal range. After few minutes, an episode of ventricular fibrillation treated with biphasic
DC shock 150 J occurred. In successive 2 hours, the patient experienced recurrent episodes of ventricular
tachycardia and fibrillation. Each biphasic DC shock 150 J was effective to restore sinus rhythm. No further
episodes occurred after normalization of serum levels of potassium. Before discharge, an implantable
cardioverter defibrillator was inserted to prevent sudden cardiac death. Hypokalemia increases the risk
of arrhythmic events in BS. (PACE 2005; 28:1350–1353
Neural networks ensemble for automatic detection of changes from Cosmo-Skymed SAR images
No full textRemote observations in the optical part of the spectrum are generally used to monitor land cover and its changes. However, atmospheric conditions can seriously degrade the performance of optical sensors, which, furthermore, can only operate in daylight. As a consequence, to meet the requirements of promptness, timeliness and reliability, use of synthetic aperture radar (SAR) must be considered. A crucial step forward in Earth observation has been facilitated by the recent (2011) full availability of SARs on the COSMO-SkyMed (CSK) satellite constellation, operated by the Italian Space Agency (ASI). In fact, the four CSK X-band SAR sensors now in orbit are able to provide images not only at 1 m spatial resolution, but also with a very short revisit time, presently as short as 12 hours, irrespective of cloud cover and light conditions. To take advantage of the unique capabilities of the CSK observing system, adequate exploitation of the information contained in the meter-resolution multi-temporal SAR images is necessary. In particular, the large amount of data contained in each image calls for the development of suitable automatic techniques to manage in near-real time the information on land cover changes which are provided by the SAR observations. This paper presents and discusses a novel change detection method, based on the joint use of different neural networks architectures. It is well known that neural networks (NNs) can be very effective in classifying optical and SAR satellite images. Nevertheless, since the relative novelty of the VHR X-band CSK data, their understanding in this case is still under investigation, and only few studies dealing with the land cover characterization and change detection in CSK images have been carried out
Completely automatic classification of satellite multi-spectral imagery for the production of land cover maps
No full textThe increasing number of satellite missions providing more and more data for updating land cover and land use maps requires to upgrade the level of automatism for the processing of remotely sensed imagery. In this paper we try to pursue the ambitious goal of designing a completely automatic (no human interaction) supervised scheme for the classification, in terms of land cover, of a multi-spectral image. An expert system, using appropriate spectral and textural features, drives the selection of suitable training
pixels in the image. These are used for the learning of a neural network algorithm that successively performs the pixel-based land cover classification of the whole image.
The processing scheme has been tested on a set of Landsat images taken on different European urban areas
Toward fully automatic detection of changes in suburban areas from VHR SAR images by combining multiple neural network models
No full textRecent X-band SAR missions, such as COSMOSkyMed (CSK), which is able to provide very high spatial resolution
images of an area of interest with a short revisit time, are expected to be quite useful sources of information for monitoring the terrestrial environment and its changes. On the other hand, the huge amount of data involved, as well as the need to promptly act in case of emergency, requires the development of automatic change detection tools. This paper reports on a novel automatic change detection algorithm combining multilayer perceptron neural networks (NNs) and pulse coupled NNs, which has been implemented and tested on pairs of Stripmap and Spotlight CSK images acquired on the Tor Vergata University area in the southeast outskirts of Rome, Italy, where a significant and continuous urbanization process is occurring
- …
