Velocity estimation of moving ships using C-band SLC SAR data

A new algorithm for the velocity vector estimation of moving ships using Single Look Complex (SLC) SAR data in strip map acquisition mode is proposed. The algorithm exploits both amplitude and phase information of the Doppler decompressed data spectrum, with the aim to estimate both the azimuth antenna pattern and the backscattering coefficient as function of the look angle. The antenna pattern estimation provides information about the target velocity; the backscattering coefficient can be used for vessel classification. The range velocity is retrieved in the slow time frequency domain by estimating the antenna pattern effects induced by the target motion, while the azimuth velocity is calculated by the estimated range velocity and the ship orientation. Finally, the algorithm is tested on simulated SAR SLC data.

Dynamic Analysis and Pattern Visualization of Forest Fires

This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns.

Feature Discretization with Relevance and Mutual Information Criteria

Feature discretization (FD) techniques often yield adequate and compact representations of the data, suitable for machine learning and pattern recognition problems. These representations usually decrease the training time, yielding higher classification accuracy while allowing for humans to better understand and visualize the data, as compared to the use of the original features. This paper proposes two new FD techniques. The first one is based on the well-known Linde-Buzo-Gray quantization algorithm, coupled with a relevance criterion, being able perform unsupervised, supervised, or semi-supervised discretization. The second technique works in supervised mode, being based on the maximization of the mutual information between each discrete feature and the class label. Our experimental results on standard benchmark datasets show that these techniques scale up to high-dimensional data, attaining in many cases better accuracy than existing unsupervised and supervised FD approaches, while using fewer discretization intervals.

Exploiting the bin-class histograms for feature selection on discrete data

In machine learning and pattern recognition tasks, the use of feature discretization techniques may have several advantages. The discretized features may hold enough information for the learning task at hand, while ignoring minor fluctuations that are irrelevant or harmful for that task. The discretized features have more compact representations that may yield both better accuracy and lower training time, as compared to the use of the original features. However, in many cases, mainly with medium and high-dimensional data, the large number of features usually implies that there is some redundancy among them. Thus, we may further apply feature selection (FS) techniques on the discrete data, keeping the most relevant features, while discarding the irrelevant and redundant ones. In this paper, we propose relevance and redundancy criteria for supervised feature selection techniques on discrete data. These criteria are applied to the bin-class histograms of the discrete features. The experimental results, on public benchmark data, show that the proposed criteria can achieve better accuracy than widely used relevance and redundancy criteria, such as mutual information and the Fisher ratio.

Magma flow pattern in dykes of the Azores revealed by ansiotropy of magnetic susceptibility

The localization of magma melting areas at the lithosphere bottom in extensional volcanic domains is poorly understood. Large polygenetic volcanoes of long duration and their associated magma chambers suggest that melting at depth may be focused at specific points within the mantle. To validate the hypothesis that the magma feeding a mafic crust, comes from permanent localized crustal reservoirs, it is necessary to map the fossilized magma flow within the crustal planar intrusions. Using the AMS, we obtain magmatic flow vectors from 34 alkaline basaltic dykes from São Jorge, São Miguel and Santa Maria islands in the Azores Archipelago, a hot-spot related triple junction. The dykes contain titanomagnetite showing a wide spectrum of solid solution ranging from Ti-rich to Ti-poor compositions with vestiges of maghemitization. Most of the dykes exhibit a normal magnetic fabric. The orientation of the magnetic lineation k1 axis is more variable than that of the k3 axis, which is generally well grouped. The dykes of São Jorge and São Miguel show a predominance of subhorizontal magmatic flows. In Santa Maria the deduced flow pattern is less systematic changing from subhorizontal in the southern part of the island to oblique in north. These results suggest that the ascent of magma beneath the islands of Azores is predominantly over localized melting sources and then collected within shallow magma chambers. According to this concept, dykes in the upper levels of the crust propagate laterally away from these magma chambers thus feeding the lava flows observed at the surface.

Pattern of mycobacterial antigen detection in leprosy

Immunohistochemistry reaction (Peroxidase anti-peroxidase - PAP) was carried out on fifty-two skin biopsies from leprosy patients with the purpose to identify the antigenic pattern in mycobacteria and to study the sensitivity of this method. Five different patterns were found: bacillar, granular, vesicular, cytoplasmatic and deposits, classified according to the antigenic material characteristics. Deposits (thinely particulate material) appeared more frequently, confirming the immunohistochemistry sensitivity to detect small amounts of antigens even when this material is not detected by histochemical stainings.

Spatial metrics and Landsat data for urban landuse change detection: case of Addis Ababa, Ethiopia.

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Multi-scale vegetation classification using earth observation data of the Sundarban mangrove forest, Bangladesh.

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Dynamic Analysis and Pattern Visualization of Forest Fires

This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns.

p53 immunostaining pattern in Brazilian patients with hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is an important type of cancer etiologically related to some viruses, chemical carcinogens and other host or environmental factors associated to chronic liver injury in humans. The tumor suppressor gene p53 is mutated in highly variable levels (0-52%) of HCC in different countries. OBJECTIVE: The objective of the present study was to compare the frequency of aberrant immunohistochemical expression of p53 in HCC occurring in cirrhotic or in non-cirrhotic patients as well as in liver cell dysplasia and in adenomatous hyperplasia. We studied 84 patients with HCC or cirrhosis. RESULTS: We detected p53 altered immuno-expression in 58.3% of patients in Grade III-IV contrasting to 22.2% of patients in Grade I-II (p = 0.02). Nontumorous areas either in the vicinity of HCC or in the 30 purely cirrhotic cases showed no nuclear p53 altered expression, even in foci of dysplasia or adenomatous hyperplasia. No significant difference was found among cases related to HBV, HCV or alcohol. CONCLUSION: The high frequency of p53 immunoexpression in this population is closer to those reported in China and Africa, demanding further studies to explain the differences with European and North American reports.

The histopathology of cutaneous leishmaniasis due to Leishmania (Leishmania) mexicana in the Yucatan peninsula, Mexico

Localized Cutaneous Leishmaniasis (LCL) known as "chiclero's ulcer" in southeast Mexico, was described by SEIDELIN in 1912. Since then the sylvatic region of the Yucatan peninsula has been documented as an endemic focus of LCL. This study of 73 biopsies from parasitological confirmed lesions of LCL cases of Leishmania (Leishmania) mexicana infection was undertaken: 1) to examine host response at tissue level; and 2) to relate manifestations of this response to some characteristics of clinical presentation. Based on Magalhães' classification we found that the most common pattern in our LCL cases caused by L. (L.) mexicana was predominantly characterized by the presence of unorganized granuloma without necrosis, (43.8%). Another important finding to be highlighted is the fact that in 50/73 (68.5%) parasite identification was positive. There was direct relation between the size of the lesion and time of evolution (r s = 0.3079, p = 0.03), and inverse correlation between size of the lesion and abundance of amastigotes (r s = -0.2467, p = 0.03). In view of the complexity of clinical and histopathological findings, cell-mediated immune response of the disease related to clinical and histopathological features, as so genetic background should be studied.

On the Sensor Classification Scheme of Robotic Manipulators

This paper analyzes the signals captured during impacts and vibrations of a mechanical manipulator. The Fourier Transform of eighteen different signals are calculated and approximated by trendlines based on a power law formula. A sensor classification scheme based on the frequency spectrum behavior is presented.

Towards a classification scheme for musical sounds

Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 2013

The pattern of immune cell infiltration in chromoblastomycosis: involvement of macrophage inflammatory protein-1 alpha/CCL3 and fungi persistence

Chromoblastomycosis (CR) is a subcutaneous chronic mycosis characterized by a granulomatous inflammatory response. However, little is known regarding the pattern of leukocyte subsets in CR and the pathways involved in their recruitment. The objective of this study was to assess the cellular subsets, chemokine, chemokine receptors and enzymes in CR. The inflammatory infiltrate was characterized by immunohistochemistry using antibodies against macrophages (CD68), Langerhans'cells (S100), lymphocytes (CD3, CD4, CD8, CD45RO, CD20 and CD56) and neutrophils (CD15). The expression of MIP-1alpha (Macrophage inflammatory protein-1alpha), chemokine receptors (CXCR3 and CCR1) and enzymes (superoxide dismutase-SOD and nitric oxide synthase-iNOS) was also evaluated by the same method. We observed an increase in all populations evaluated when compared with the controls. Numbers of CD15+ and CD56+ were significantly lower than CD3+, CD4+, CD20+ and CD68+ cells. Statistical analysis revealed an association of fungi numbers with CD3, CD45RO and iNOS-positive cells. Furthermore, MIP-1alpha expression was associated with CD45RO, CD68, iNOS and CXCR3. Our results suggest a possible role of MIP-1alpha and fungi persistence in the cell infiltration in CR sites.

Impact of RIFLE Classification in Liver Transplantation

Acute renal failure (ARF) is common after orthotopic liver transplantation (OLT). The aim of this study was to evaluate the prognostic value of RIFLE classification in the development of CKD, hemodialysis requirement, and mortality. Patients were categorized as risk (R), injury (I) or failure (F) according to renal function at day 1, 7 and 21. Final renal function was classified according to K/DIGO guidelines. We studied 708 OLT recipients, transplanted between September 1992 and March 2007; mean age 44 +/- 12.6 yr, mean follow-up 3.6 yr (28.8% > or = 5 yr). Renal dysfunction before OLT was known in 21.6%. According to the RIFLE classification, ARF occurred in 33.2%: 16.8% were R class, 8.5% I class and 7.9% F class. CKD developed in 45.6%, with stages 4 or 5d in 11.3%. Mortality for R, I and F classes were, respectively, 10.9%, 13.3% and 39.3%. Severity of ARF correlated with development of CKD: stage 3 was associated with all classes of ARF, stages 4 and 5d only with severe ARF. Hemodialysis requirement (23%) and mortality were only correlated with the most severe form of ARF (F class). In conclusion, RIFLE classification is a useful tool to stratify the severity of early ARF providing a prognostic indicator for the risk of CKD occurrence and death.