Mixed spectral-structural classification of very high resolution images with summation kernels

In this paper, mixed spectral-structural kernel machines are proposed for the classification of very-high resolution images. The simultaneous use of multispectral and structural features (computed using morphological filters) allows a significant increase in classification accuracy of remote sensing images. Subsequently, weighted summation kernel support vector machines are proposed and applied in order to take into account the multiscale nature of the scene considered. Such classifiers use the Mercer property of kernel matrices to compute a new kernel matrix accounting simultaneously for two scale parameters. Tests on a Zurich QuickBird image show the relevance of the proposed method : using the mixed spectral-structural features, the classification accuracy increases of about 5%, achieving a Kappa index of 0.97. The multikernel approach proposed provide an overall accuracy of 98.90% with related Kappa index of 0.985.

Land use/cover classification in the Brazilian Amazon using satellite images

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation‑based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi‑resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Among the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, have the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical‑based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

Contribution of neural networks to Alzheimer disease's progression.

The neuropathology of Alzheimer disease is characterized by senile plaques, neurofibrillary tangles and cell death. These hallmarks develop according to the differential vulnerability of brain networks, senile plaques accumulating preferentially in the associative cortical areas and neurofibrillary tangles in the entorhinal cortex and the hippocampus. We suggest that the main aetiological hypotheses such as the beta-amyloid cascade hypothesis or its variant, the synaptic beta-amyloid hypothesis, will have to consider neural networks not just as targets of degenerative processes but also as contributors of the disease's progression and of its phenotype. Three domains of research are highlighted in this review. First, the cerebral reserve and the redundancy of the network's elements are related to brain vulnerability. Indeed, an enriched environment appears to increase the cerebral reserve as well as the threshold of disease's onset. Second, disease's progression and memory performance cannot be explained by synaptic or neuronal loss only, but also by the presence of compensatory mechanisms, such as synaptic scaling, at the microcircuit level. Third, some phenotypes of Alzheimer disease, such as hallucinations, appear to be related to progressive dysfunction of neural networks as a result, for instance, of a decreased signal to noise ratio, involving a diminished activity of the cholinergic system. Overall, converging results from studies of biological as well as artificial neural networks lead to the conclusion that changes in neural networks contribute strongly to Alzheimer disease's progression.

Image-Based Coral Reef Classification and Thematic Mapping

This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either k-nearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos

Sistema de presa de decisions distribuït

DDM is a framework that combines intelligent agents and artificial intelligence traditional algorithms such as classifiers. The central idea of this project is to create a multi-agent system that allows to compare different views into a single one.

Supervised localimage feature detection

This thesis is about detection of local image features. The research topic belongs to the wider area of object detection, which is a machine vision and pattern recognition problem where an object must be detected (located) in an image. State-of-the-art object detection methods often divide the problem into separate interest point detection and local image description steps, but in this thesis a different technique is used, leading to higher quality image features which enable more precise localization. Instead of using interest point detection the landmark positions are marked manually. Therefore, the quality of the image features is not limited by the interest point detection phase and the learning of image features is simplified. The approach combines both interest point detection and local description into one phase for detection. Computational efficiency of the descriptor is therefore important, leaving out many of the commonly used descriptors as unsuitably heavy. Multiresolution Gabor features has been the main descriptor in this thesis and improving their efficiency is a significant part. Actual image features are formed from descriptors by using a classifierwhich can then recognize similar looking patches in new images. The main classifier is based on Gaussian mixture models. Classifiers are used in one-class classifier configuration where there are only positive training samples without explicit background class. The local image feature detection method has been tested with two freely available face detection databases and a proprietary license plate database. The localization performance was very good in these experiments. Other applications applying the same under-lying techniques are also presented, including object categorization and fault detection.

Investigation of interaction between native and impurity defects in ZnSe

Zinc selenide is a prospective material for optoelectronics. The fabrication of ZnSe­based light-emitting diodes is hindered by complexity of p-type doping of the component materials. The interaction between native and impurity defects, the tendency of doping impurity to form associative centres with native defects and the tendency to self-compensation are the main factors impeding effective control of the value and type of conductivity. The thesis is devoted to the study of the processes of interaction between native and impurity defects in zinc selenide. It is established that the Au impurity has the most prominent amphoteric properties in ZnSe among Cu, Ag and Au impurities, as it forms a great number of both Au; donors and Auz„ acceptors. Electrical measurements show that Ag and Au ions introduced into vacant sites of the Zn sublattice form simple single-charged Agz„+ and Auzn+ states with d1° electron configuration, while Cu ions can form both single-charged Cuz„ (d1) and double-charged Cuzr`+ (d`o) centres. Amphoteric properties of Ag and Au transition metals stimulated by time are found for the first time from both electrical and luminescent measurements. A model that explains the changes in electrical and luminescent parameters by displacement of Ag ions into interstitial sites due to lattice deformation forces is proposed. Formation of an Ag;-donor impurity band in ZnSe samples doped with Ag and stored at room temperature is also studied. Thus, the properties of the doped samples are modified due to large lattice relaxation during aging. This fact should be taken into account in optoelectronic applications of doped ZnSe and related compounds.

Escuela, familia de origen inmigrante y participación

Las relaciones entre las familias y la escuela se inscriben en la articulación entre dos instituciones con asimetría de poder y en un contexto social y político que las sitúa en el debate entre intereses públicos y privados. Aunque deben considerarse espacios yuxtapuestos, a menudo lo que se percibe es la separación, la distancia, cuando no el conflicto, entre ambos. Y esto comporta que el territorio de la escuela y el de la familia se vigile, se controle, por la amenaza de invasión o intrusión. El artículo analiza la participación de los progenitores de origen inmigrante en la escuela en España.Realizando una breve referencia a la legislación, se centra en la situación organizativa confederal, federal y asociativa (utilizando como fuente de información datos propios obtenidos en cinco grupos de discusión organizados en los diferentes niveles organizativos) y, por último, se aproxima la realidad de las Asociaciones de Padres de Alumnos (a través de una encuesta a 594 presidentes de asociaciones).Además de constatar la baja participación general y, en particular, la de las familias de origen inmigrante (menor entre unos orígenes que entre otros) se evidencia la necesidad de trabajar para incorporarlos al movimiento de padres, hecho que se considera imprescindible para su desarrollo.

APOE status modulates the changes in network connectivity induced by brain stimulation in non-demented elders.

Behavioral consequences of a brain insult represent an interaction between the injury and the capacity of the rest of the brain to adapt to it. We provide experimental support for the notion that genetic factors play a critical role in such adaptation. We induced a controlled brain disruption using repetitive transcranial magnetic stimulation (rTMS) and show that APOE status determines its impact on distributed brain networks as assessed by functional MRI (fMRI).Twenty non-demented elders exhibiting mild memory dysfunction underwent two fMRI studies during face-name encoding tasks (before and after rTMS). Baseline task performance was associated with activation of a network of brain regions in prefrontal, parietal, medial temporal and visual associative areas. APOE ε4 bearers exhibited this pattern in two separate independent components, whereas ε4-non carriers presented a single partially overlapping network. Following rTMS all subjects showed slight ameliorations in memory performance, regardless of APOE status. However, after rTMS APOE ε4-carriers showed significant changes in brain network activation, expressing strikingly similar spatial configuration as the one observed in the non-carrier group prior to stimulation. Similarly, activity in areas of the default-mode network (DMN) was found in a single component among the ε4-non bearers, whereas among carriers it appeared disaggregated in three distinct spatiotemporal components that changed to an integrated single component after rTMS. Our findings demonstrate that genetic background play a fundamental role in the brain responses to focal insults, conditioning expression of distinct brain networks to sustain similar cognitive performance.

Keraamisten laattojen luokittelu väripiirteiden avulla

Vaikka keraamisten laattojen valmistusprosessi onkin täysin automatisoitu, viimeinen vaihe eli laaduntarkistus ja luokittelu tehdään yleensä ihmisvoimin. Automaattinen laaduntarkastus laattojen valmistuksessa voidaan perustella taloudellisuus- ja turvallisuusnäkökohtien avulla. Tämän työn tarkoituksena on kuvata tutkimusprojektia keraamisten laattojen luokittelusta erilaisten väripiirteiden avulla. Oleellisena osana tutkittiin RGB- ja spektrikuvien välistä eroa. Työn teoreettinen osuus käy läpi aiemmin aiheesta tehdyn tutkimuksen sekä antaa taustatietoa konenäöstä, hahmontunnistuksesta, luokittelijoista sekä väriteoriasta. Käytännön osan aineistona oli 25 keraamista laattaa, jotka olivat viidestä eri luokasta. Luokittelussa käytettiin apuna k:n lähimmän naapurin (k-NN) luokittelijaa sekä itseorganisoituvaa karttaa (SOM). Saatuja tuloksia verrattiin myös ihmisten tekemään luokitteluun. Neuraalilaskenta huomattiin tärkeäksi työkaluksi spektrianalyysissä. SOM:n ja spektraalisten piirteiden avulla saadut tulokset olivat lupaavia ja ainoastaan kromatisoidut RGB-piirteet olivat luokittelussa parempia kuin nämä.

Jauhatuspiirin ilmakierron optimointi

Luokittuminen erilaisine mekanismeineen aiheuttaa yleisesti ongelmia, kun on kysymyksessä kiintoaineen väliaikainenkin varastointi siilossa. Sitä voidaan vähentää kiintoaineiden, prosessin ja laitesuunnittelun muutoksilla. Tässä työssä tutkittiin mahdollisuuksia vähentää ilmeniitin luokittumista sen jauhatuspiirin ilmakiertoa optimoimalla. Suljetun kuivajauhatuspiirin keskeisimmäksi laitteeksi voitaisiin ajatella siinä oleva luokitin, joka voi olla esim. sykloni. Tässä piirissä tapahtuva kiintoaineen liikkuminen voidaan saada aikaiseksi esim. pneumaattisella kuljetuksella. Ilmeniitin jauhatus tapahtuu suljetussa kuivajauhatuspiirissä, jonka ajavana voimana on siinä oleva ilmakierto. Piirin oleellisia laitteita ovat kuulamylly, luokitin, erotussykloni ja pölykaappi sekä kiertoilma- ja poistoilmapuhaltimet. Ilmakierron optimointia varten suoritettiin kahden vastaavan jauhatuspiirin ainetasemääritykset. Lisäksi määritettiin yhden isomman piirin perustila. Jauhatuspiirien ainetasemäärityksissä määritettiin niiden massa- ja ilmavirrat sekä kiertokuorma ja luokittimen erotusterävyys, kuten myös ilmeniitin hiukkaskokojakaumat. Perustilamittauksissa määritettiin ainoastaan piirin ilmavirrat ja ilmeniitin hiukkaskokojakaumat. Optimointimittauksissa pienennettiin pikkumyllypiirin ilmamäärät vastaamaan kutakuinkin vastaavan toisen piirin määriä. Tällä yritettiin selvittää näiden toisiaan vastaavien piirien ilmamäärien ja varsinkin kiertokuormien eroavuutta. Tämä ilmamäärien pienentäminen ei tuottanut mainittavampaa muutosta piirin ainetaseisiin, joten voitaneen todeta, että piirin ilmamääriä pienentämällä saadaan aikaiseksi säästöjä, lähinnä kiertoilmapuhaltimen tehon alennuksen kautta.

Sex differences in brain plasticity: a new hypothesis for sex ratio bias in autism.

Several observations support the hypothesis that differences in synaptic and regional cerebral plasticity between the sexes account for the high ratio of males to females in autism. First, males are more susceptible than females to perturbations in genes involved in synaptic plasticity. Second, sex-related differences in non-autistic brain structure and function are observed in highly variable regions, namely, the heteromodal associative cortices, and overlap with structural particularities and enhanced activity of perceptual associative regions in autistic individuals. Finally, functional cortical reallocations following brain lesions in non-autistic adults (for example, traumatic brain injury, multiple sclerosis) are sex-dependent. Interactions between genetic sex and hormones may therefore result in higher synaptic and consecutively regional plasticity in perceptual brain areas in males than in females. The onset of autism may largely involve mutations altering synaptic plasticity that create a plastic reaction affecting the most variable and sexually dimorphic brain regions. The sex ratio bias in autism may arise because males have a lower threshold than females for the development of this plastic reaction following a genetic or environmental event.

Cooperative and Competitive Spreading Dynamics on the Human Connectome.

Increasingly detailed data on the network topology of neural circuits create a need for theoretical principles that explain how these networks shape neural communication. Here we use a model of cascade spreading to reveal architectural features of human brain networks that facilitate spreading. Using an anatomical brain network derived from high-resolution diffusion spectrum imaging (DSI), we investigate scenarios where perturbations initiated at seed nodes result in global cascades that interact either cooperatively or competitively. We find that hub regions and a backbone of pathways facilitate early spreading, while the shortest path structure of the connectome enables cooperative effects, accelerating the spread of cascades. Finally, competing cascades become integrated by converging on polysensory associative areas. These findings show that the organizational principles of brain networks shape global communication and facilitate integrative function.

Hiokerejektien käsittely MC-sakeudessa

Työn tavoitteena oli optimoida LWC-paperitehtaan kahden hiomolinjan rejektinkäsittelyt. Uusituilla rejektilinjoilla on käytössä keskisakeusrejektinjauhatus. Työn keskeinen osa oli teräkoeajot, teräsarjoja tutkittiin kuusi, kolme molemmilla linjoilla. Kahdessa ensimmäisessä teräkoeajossa oli molemmilla linjoilla samanlaiset jauhinterät. Teräkoeajojen tuloksista havaittiin yleisellä tasolla, että suurin osa mitatuista ominaisuuksista parani jauhatusastetta nostettaessa. Ainoastaan repäisylujuus heikkeni. Terävaihtoehdoista pystyttiin poimimaan molemmille linjoille sopiva terävaihtoehto. Rejektinlajittelun havaittiin parantavan edelleen massan laatuominaisuuksia, paitsi repäisylujuutta. Toisena osuutena vertailtiin keskisakeusjauhimen terävaihtoehtoa, jolla saavutettiin hyviä tuloksia, toisen paperitehtaan korkeassa sakeudessa jauhettuun rejektiin. Korkeasakeusjauhimen terää ei erityisesti valikoitu koeajoa varten. Tuloksista havaittiin, että keskisakeusjauhimella saadaan aikaan varsin hyvää LWC-paperiin käytettävää massaa. Keskisakeudessa jauhettu massa oli monilta ominaisuuksiltaan jopa parempaa kuin korkeasakeusjauhimen massa. Työn kolmannessa osuudessa ajettiin rejektilinjalla sakeuskoeajo. Sakeuskoeajosta havaittiin, että optiset ominaisuudet olivat parhaimmillaan jauhimen MC-sakeusalueen keskivaiheilla. Jauhatussakeuden noustessa kuidut jäivät jäykemmiksi ja karkeammiksi. Tikkupitoisuus oli sitä pienempi, mitä alhaisempaa jauhatussakeutta käytettiin. Sakeudella ei ollut selvää vaikutusta lujuusominaisuuksiin. Tulosten perusteella paras jauhatussakeus keskisakeusjauhimella oli sakeusalueen puoliväli. Työn viimeisenä osana selvitettiin miten kytkentämuutos, jossa rejektilinjan viimeisen lajittimen rejekti käännettiin palaamaan jälkilajittelun sijaan rejektilinjan kaariseulalle, vaikutti koko hiomon kapasiteettiin ja rejektilinjan massan laatuun. Koeajojen tuloksena havaittiin, että kytkentämuutolla pystyttiin nostamaan koko hiomon kapasiteettia ja rejektilinjan akseptin laatu parani.

Reducción del vector de características de reconocimiento facial

In this present work, we are proposing a characteristics reduction system for a facial biometric identification system, using transformed domains such as discrete cosine transformed (DCT) and discrete wavelets transformed (DWT) as parameterization; and Support Vector Machines (SVM) and Neural Network (NN) as classifiers. The size reduction has been done with Principal Component Analysis (PCA) and with Independent Component Analysis (ICA). This system presents a similar success results for both DWT-SVM system and DWT-PCA-SVM system, about 98%. The computational load is improved on training mode due to the decreasing of input’s size and less complexity of the classifier.