Complexity of Interlocking Polyominoes

Polyominoes are a subset of polygons which can be constructed from integer-length squares fused at their edges. A system of polygons P is interlocked if no subset of the polygons in P can be removed arbitrarily far away from the rest. It is already known that polyominoes with four or fewer squares cannot interlock. It is also known that determining the interlockedness of polyominoes with an arbitrary number of squares is PSPACE hard. Here, we prove that a system of polyominoes with five or fewer squares cannot interlock, and that determining interlockedness of a system of polyominoes including hexominoes (polyominoes with six squares) or larger polyominoes is PSPACE hard.Comment: 18 pages, 15 figure

Regional mapping of crops under agricultural nets using Sentinel-2

Geography and Environmental Studie

COMPOSITE KERNEL FEATURE ANALYSIS FOR CANCER CLASSIFICATION

Computed tomographic (CT) colonography, or virtual colonoscopy, is a promising technique for screening colorectal cancers by use of CT scans of the colon. Current CT technology allows a single image set of the colon to be acquired in 10-20 seconds, which translates into an easier, more comfortable examination than is available with other screening tests. Currently, however, interpretation of an entire CT colonography examination is time-consuming, and the reader performance for polyp detection varies substantially. To overcome these difficulties while providing a high detection performance of polyps, researchers are developing computer-aided detection (CAD) schemes that automatically detect suspicious lesions in CT colonography images. The overall goal of this study is to achieve a high performance in the detection of polyps on CT colonographic images by effectively incorporating an appearance-based object recognition approaches into a model-based CAD scheme. Our studies are focused in developing a fast kernel feature analysis that can efficiently differentiate polyps from false positives and thus improve the detection performance of polyps. We have developed a novel method of selecting kernel functions that are appropriate for the given data set and then use their linear combination in the construction of Kernel Gram matrix which can then used for efficient reconstruction of feature space. The main contribution of this work lies in providing a Composite kernel Matrix that involves appearance-based approach to improve kernel feature analysis for the classification of texture-based features. We evaluated our proposed kernel feature analysis on texture-based features that were extracted from the polyp candidates generated by our shape-based CAD scheme

Integration of remote sensing and GIS in studying vegetation trends and conditions in the gum arabic belt in North Kordofan, Sudan

The gum arabic belt in Sudan plays a significant role in environmental, social and economical aspects. The belt has suffered from deforestation and degradation due to natural hazards and human activities. This research was conducted in North Kordofan State, which is affected by modifications in conditions and composition of vegetation cover trends in the gum arabic belt as in the rest of the Sahelian Sudan zone. The application of remote sensing, geographical information system and satellites imageries with multi-temporal and spatial analysis of land use land cover provides the land managers with current and improved data for the purposes of effective management of natural resources in the gum arabic belt. This research investigated the possibility of identification, monitoring and mapping of the land use land cover changes and dynamics in the gum arabic belt during the last 35 years. Also a newly approach of object-based classification was applied for image classification. Additionally, the study elaborated the integration of conventional forest inventory with satellite imagery for Acacia senegal stands. The study used imageries from different satellites (Landsat and ASTER) and multi-temporal dates (MSS 1972, TM 1985, ETM+ 1999 and ASTER 2007) acquired in dry season (November). The imageries were geo-referenced and radiometrically corrected by using ENVI-FLAASH software. Image classification (pixel-based and object-based), post-classification change detection, 2x2 and 3x3 pixel windows and accuracy assessment were applied. A total of 47 field samples were inventoried for Acacia senegal tree’s variables in Elhemmaria forest. Three areas were selected and distributed along the gum arabic belt. Regression method analysis was applied to study the relationship between forest attributes and the ASTER imagery. Application of multi-temporal remote sensing data in gum arabic belt demonstrated successfully the identification and mapping of land use land cover into five main classes. Also NDVI categorisation provided a consistent method for land use land cover stratification and mapping. Forest dominated by Acacia senegal class was separated covering an area of 21% and 24% in the year 2007 for areas A and B, respectively. The land use land cover structure in the gum arabic belt has obvious changes and reciprocal conversions between the classes indicating the trends and conditions caused by the human interventions as well as ecological impacts on Acacia senegal trees. The study revealed a drastic loss of Acacia senegal cover by 25% during the period of 1972 to 2007.The results of the study revealed to a significant correlation (p ≤ 0.05) between the ASTER bands (VNIR) and vegetation indices (NDVI, SAVI, RVI) with stand density, volume, crown area and basal area of Acacia senegal trees. The derived 2x2 and 3x3 pixel windows methods successfully extracted the spectral reflectance of Acacia senegal trees from ASTER imagery. Four equations were developed and could be widely used and applied for monitoring the stand density, volume, basal area and crown area of Acacia senegal trees in the gum arabic belt considering the similarity between the selected areas. The pixel-based approach performed slightly better than the object-based approach in land use land cover classification in the gum arabic belt. The study come out with some valuable recommendations and comments which could contribute positively in using remotely sensed imagery and GIS techniques to explore management tools of Acacia senegal stands in order to maintain the tree component in the farming and the land use systems in the gum arabic belt

Spatial heterogeneity in forested landscapes: an examination of forest fragmentation and suburban sprawl in the Florida Parishes of Louisiana

Forest fragmentation refers to the spatial distribution of forests in a landscape. Forest fragmentation drastically alters forest composition, habitat quality, genetic flow and many other ecological processes associated with forested ecosystems. This research examined spatial patterns and rates of forest fragmentation during the 1991-2001 period for a region in southeast Louisiana known as the Florida Parishes. Following classification of 1991 and 2001 Landsat data into forest and non-forest classes, spatial patterns were examined using Fragstats 3.3 spatial analysis software. Spatial statistics such as patch density, perimeter to area ratios, core area indices, edge density, and various landscape continuity indices were used to assess patterns and trends of forest fragmentation in landscapes throughout the region. A variety of patch, core and edge metrics indicated increasing forest fragmentation in a majority of the landscapes examined. Values of various landscape continuity indices were also found to suggest significant increases in forest fragmentation in a majority of landscapes. The correlation of various forest fragmentation metrics with metrics associated with suburban sprawl was shown to be relatively weak by low R2 values. These findings may suggest that suburban sprawl was not the only factor affecting the spatial arrangement of forests in the Florida Parishes during the study period. The results of this research facilitate an increased understanding of the current trends of forest land-cover fragmentation in the Florida Parishes and the potential influences of these trends on related ecological processes

Motion planning in 2D and 3D with rotation

Imperial Users onl

Cartographie des éricacées (Kalmia angustifolia, Ledum) en forêts d'épinette noire (Picea mariana) cas de la Côte-Nord

L'établissement et la croissance de l'épinette noire en régénération après une coupe en forêts boréales sont fréquemment affectés par la prolifération de plantes éricacées telles le Kalmia angustifolia . La compétition éricacées-épinette noire est fréquente au point d'entraîner une baisse significative du potentiel forestier dans la forêt boréale de l'est du Canada. Il est toujours difficile de proposer des scénarios sylvicoles qui garantissent la résilience des peuplements propices à l'envahissement. Ceci découle du manque de connaissance des impacts de l'aménagement sur la dynamique des éricacées à l'echelle du paysage ; ainsi que du manque de compréhension des mécanismes écologiques qui font qu'une pessière coupée se transforme en pessière à éricacées et non en pessière dense.L'objectif général de cette étude est de cartographier la distribution spatiale des éricacées au niveau régional. Après l'acquisition des données et de leur prétraitement, une interprétation experte a permis de produire les polygones pour l'entraînement des algorithmes de classification pour les images IKONOS. Deux séries de polygones (couverture de surface et strate arborescente) découlent de ces opérations. Chaque serie est associée à l'une des deux stratifications et contient toutes les classes thématiques de cette stratification. Une première segmentation fut appliquée sur une mosaïque de sept images IKONOS pour créer des objets spatiaux. Ces objets ont ensuite étés assignés à une classe thématique en faisant appel à la logique floue disponible dans le logiciel eCognition. Deux types de cartes thématiques (strate arborescente et couverture du sol) sont créés à l'aide des sites d'entraînement issus de la photo-interprétation experte.Les résultats furent validés à l'aide des placettes de sondage terrain (précision globale de 80 % pour les deux thématiques). 70% de l'étendue des cartes produites sur la mosaïque IKONOS furent ensuite utilisée pour son application à la classification de l'image Landsat-TM qui couvre toute la zone d'étude. Le 30% non utilisé des cartes de la mosaïque IKONOS ont servi à valider les résultats cartographiques de la classification de l'image Landsat-TM (précision globale de 88.0% pour la carte arborescente et de 78.4% pour la carte de la couverture du sol).Les méthodes et cartes résultantes seront utiles pour la gestion de la ressource forestière, en particulier pour la productivité de l'épinette noire dans les régions nordiques

Characterisation and monitoring of forest disturbances in Ireland using active microwave satellite platforms

Forests are one of the major carbon sinks that significantly contribute towards achieving targets of the Kyoto Protocol, and its successors, in reducing greenhouse (GHG) emissions. In order to contribute to regular National Inventory Reporting, and as part of the on-going development of the Irish national GHG reporting system (CARBWARE), improvements in characterisation of changes in forest carbon stocks have been recommended to provide a comprehensive information flow into CARBWARE. The Irish National Forest Inventory (NFI) is updated once every six years, thus there is a need for an enhanced forest monitoring system to obtain annual forest updates to support government agencies and forest management companies in their strategic decision making and to comply with international GHG reporting standards. Sustainable forest management is imperative to promote net carbon absorption from forests. Based on the NFI data, Irish forests have removed or sequestered an average of 3.8 Mt of atmospheric CO2 per year between 2007 and 2016. However, unmanaged and degraded forests become a net emitter of carbon. Disturbances from human induced activities such as clear felling, thinning and deforestation results in carbon emissions back into the atmosphere. Funded by the Department of Agriculture, Food and the Marine (DAFM, Ireland), this PhD study focuses on exploring the potential of data from L-band Synthetic Aperture Radar (SAR) satellite based sensors for monitoring changes in the small stand forests of Ireland. Historic data from ALOS PALSAR in the late 2000s and more recent data from ALOS-2 PALSAR-2 sensors have been used to map forest areas and characterise the different disturbances observed within three different regions of Ireland. Forest mapping and disturbance characterisation was achieved by combining the machine learning supervised Random Forests (RF) and unsupervised Iterative Self-Organizing Data Analysis (ISODATA) classification techniques. The lack of availability of ground truth data supported use of this unsupervised approach which forms natural clusters based on their multi-temporal signatures, with divergence statistics used to select the optimal number of clusters to represent different forest classes. This approach to forest monitoring using SAR imagery has not been reported in the peer-review literature and is particularly beneficial where there is a dearth of ground-based information. When applied to the forests, mapped with an accuracy of up to 97% by RF, the ISODATA technique successfully identified the unique multi-temporal pattern associated with clear-fells which exhibited a decrease of 4 to 5 decibels (dB) between the images acquired before and after the event. The clustering algorithm effectively highlighted the occurrence of other disturbance events within forests with a decrease of 2±0.5dB between two consecutive years, as well as areas of tree growth and afforestation. A highlight of the work is the successful transferability of the algorithm, developed using ALOS PALSAR, to ALOS-2 PALSAR-2 data thereby demonstrating the potential continuity of annual forest monitoring. The higher spatial and radiometric resolutions of ALOS-2 PALSAR-2 data have shown improvements in forest mapping compared to ALOS PALSAR data. From mapping a minimum forest size of 1.8 ha with ALOS PALSAR, a minimum area of 1.1 ha was achieved with the ALOS-2 PALSAR-2 images. Moreover, even with some different backscatter characteristics of images acquired in different seasons, similar signature patterns between the sensors were retrieved that helped to define the cluster groups, thus demonstrating the robustness of the algorithm and its successful transferability. Having proven the potential to monitor forest disturbances, the results from both the sensors were used to detect deforestation over the time period 2007-2016. Permanent land-use changes pertaining to conversion of forests to agricultural lands and windfarms were identified which are important with respect to forest monitoring and carbon reporting in Ireland. Overall, this work has presented a viable approach to support forest monitoring operations in Ireland. By providing disturbance information from SAR, it can supplement projects working with optical images which are generally limited by cloud cover, particularly in parts of northern, western and upland Ireland. This approach adds value to ground based forest monitoring by mapping distinct forests over large areas on an annual basis. This study has demonstrated the ability to apply the algorithm to three different study areas, with a vision to operationalise the algorithm on a national scale. The main limitations experienced in this study were the lack of L-band SAR data availability and reference datasets. With typically only one image acquired per year, and discrepancies and omissions existing within reference datasets, understanding the behaviour of certain cluster groups representing disturbances was challenging. However, this approach has addressed some issues within the reference datasets, for example locating areas for which a felling licence was granted but where trees were never cut, by providing detailed systematic mapping of forests. Future satellites such as Tandem-L, SAOCOM-2A and 2B, P-band BIOMASS mission and ALOS-4 PALSAR-3 may overcome the issue of limited SAR image acquisitions provided more images per year are available, especially during the summer months

An investigation of the mechanism of information reduction

Contemporary theories of skill acquisition emphasise qualitative changes in processing as expertise is acquired (Anderson 1981; Cheng 1985; Newell 1990; 1998; Lewis 2001). In particular, describing this qualitative change as a switch from calculating the answer to the retrieval of the solutions from memory, is popular (Campitelli and Gobet, 2005; Logan 1988,2002; Nosofsky & Palmeri, 1997; Palmeri, 1997, 1999). Against this background, Haider and Frensch (1996, 1999a, 1999b, 2002) have recently identified some of the quantitative changes that may also occur with practice, changes which they term Information Reduction (IR). They demonstrated that people could 'reduce' to processing task-relevant segments of a stimulus, without instruction to do so. Further they found this effect was not stimulus-specific, transferring to novel item sets. This latter point was particularly troublesome for any theory reliant on the retrieval of exemplars from memory, since such a strategy will become unsuccessful for novel items. The work presented in this thesis further explored the factors that may playa role in IR. Study 1 both replicated the basic effect and also found reduction when the visual regularity of the stimulus was varied. Study 2 realised a new target search task (TST) in which IR also developed, generalising the strategy beyond the original alphabet arithmetic task (AAT). The third study of the thesis investigated further attributes that could inhibit or facilitate reduction. The final study determined the regularity of task-redundancy necessary for IR to take place. The results are discussed in terms of the residual processing of task irrelevant items and the overall part IR must play in skill acquisition