Performance of microstrip antennas in the presence of EBG in an indoor localization system

Indoor localization systems in nowadays is a huge area of interest not only at academic but also at industry and commercial level. The correct location in these systems is strongly influenced by antennas performance which can provide several gains, bandwidths, polarizations and radiation patterns, due to large variety of antennas types and formats. This paper presents the design, manufacture and measurement of a compact microstrip antenna, for a 2.4 GHZ frequency band, enhanced with the use of Electromagnetic Band-Gap (EBG) structures, which improve the electromagnetic behavior of the conventional antennas. The microstrip antenna with an EBG structure integrated allows an improvement of the location system performance in about 25% to 30% relatively to a conventional microstrip antenna.

Differential expression of the eukaryotic release factor 3 (eRF3/GSPT1) according to gastric cancer histological types

Background: There are now several lines of evidence to suggest that protein synthesis and translation factors are involved in the regulation of cell proliferation and cancer development. Aims: To investigate gene expression patterns of eukaryotic releasing factor 3 (eRF3) in gastric cancer. Methods: RNA was prepared from 25 gastric tumour biopsies and adjacent non-neoplastic mucosa. Real time TaqMan reverse transcription polymerase chain reaction (RT-PCR) was performed to measure the relative gene expression levels. DNA was isolated from tumour and normal tissues and gene dosage was determined by a quantitative real time PCR using SYBR Green dye. Results: Different histological types of gastric tumours were analysed and nine of the 25 tumours revealed eRF3/GSPT1 overexpression; moreover, eight of the 12 intestinal type carcinomas analysed overexpressed the gene, whereas eRF3/GSPT1 was overexpressed in only one of the 10 diffuse type carcinomas (Kruskal-Wallis Test; p , 0.05). No correlation was found between ploidy and transcript expression levels of eRF3/GSPT1. Overexpression of eRF3/GSPT1 was not associated with increased translation rates because the upregulation of eRF3/GSPT1 did not correlate with increased eRF1 levels. Conclusions: Overexpression of eRF3/GSPT1 in intestinal type gastric tumours may lead to an increase in the translation efficiency of specific oncogenic transcripts. Alternatively, eRF3/GSPT1 may be involved in tumorigenesis as a result of its non-translational roles, namely (dis)regulating the cell cycle, apoptosis, or transcription.

Differential expression of CDC25 phosphatases splice variants in human breast cancer cells

Background: CDC25 phosphatases control cell cycle progression by activating cyclin dependent kinases. The three CDC25 isoforms encoding genes are submitted to alternative splicing events which generate at least two variants for CDC25A and five for both CDC25B and CDC25C. An over-expression of CDC25 was reported in several types of cancer, including breast cancer, and is often associated with a poor prognosis. Nevertheless, most of the previous studies did not address the expression of CDC25 splice variants. Here, we evaluated CDC25 spliced transcripts expression in anti-cancerous drug-sensitive and resistant breast cancer cell lines in order to identify potential breast cancer biomarkers. Methods: CDC25 splice variants mRNA levels were evaluated by semi-quantitative RT-PCR and by an original real-time RT-PCR assay. Results: CDC25 spliced transcripts are differentially expres-sed in the breast cancer cell lines studied. An up-regulation of CDC25A2 variant and an increase of the CDC25C5/C1 ratio are associated to the multidrug-resistance in VCREMS and DOXOR breast cancer cells, compared to their sensitive counterpart cell line MCF-7. Additionally, CDC25B2 tran-script is exclusively over-expressed in VCREMS resistant cells and could therefore be involved in the development of certain type of drug resistance. Conclusions: CDC25 splice variants could represent interesting potential breast cancer prognostic biomarkers.

Differential expression of GSPT1 GGCn alleles in cancer

The human eukaryotic release factor 3a (eRF3a), encoded by the G1 to S phase transition 1 gene (GSPT1; alias eRF3a), is upregulated in various human cancers. GSPT1 contains a GGCn polymorphism in exon 1, encoding a polyglycine expansion in the N-terminal of the protein. The longer allele, GGC12, was previously shown to be associated to cancer. The GGC12 allele was present in 2.2% of colorectal cancer patients but was absent in Crohn disease patients and in the control group. Real-time quantitative RT-PCR analysis showed that the GGC12 allele was present at up to 10-fold higher transcription levels than the GGC10 allele (P < 0.001). No GSPT1 amplifications were detected, and there was no correlation between the length of the alleles and methylation levels of the CpG sites inside the GGC expansion. Using flow cytometry, we compared the levels of apoptosis and proliferation rates between cell lines with different genotypes, but detected no significant differences. Finally, we used a cytokinesis-block micronucleus assay to evaluate the frequency of micronuclei in the same cell lines. Cell lines with the longer alleles had higher frequencies of micronuclei in binucleated cells, which is probably a result of defects in mitotic spindle formation. Altogether, these findings indicate that GSPT1 should be considered a potential proto-oncogene.

A class of singular first order differential equations with applications in reaction-diffusion

Agências Financiadoras: FCT e MIUR

An Evaluation of Local Feature Combiners for Robot Visual Localization

In the last decade, local image features have been widely used in robot visual localization. To assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image to those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, we compare several candidate combiners with respect to their performance in the visual localization task. A deeper insight into the potential of the sum and product combiners is provided by testing two extensions of these algebraic rules: threshold and weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance. The voting method, whilst competitive to the algebraic rules in their standard form, is shown to be outperformed by both their modified versions.

Optimization of magneto-electro-elastic composite structures using differential evolution

Magneto-electro-elastic structures are built from materials that provide them the ability to convert in an interchangeable way, magnetic, electric and mechanical forms of energy. This characteristic can therefore provide an adaptive behaviour to a general configuration elastic structure, being commonly used in association with any type of composite material in an embedded or surface mounted mode, or by considering the usage of multiphase materials that enable achieving different magneto-electro-elastic properties. In a first stage of this work, a few cases studies will be considered to enable the validation of the model considered and the influence of the coupling characteristics of this type of adaptive structures. After that we consider the application of a recent computational intelligence technique, the differential evolution, in a deflection profile minimization problem. Studies on the influence of optimization parameters associated to the problem considered will be performed as well as the adoption of an adaptive scheme for the perturbation factor. Results are also compared with those obtained using an enhanced particle swarm optimization technique. (C) 2013 Elsevier Ltd. All rights reserved.

2D PCA-Based localization for mobile robots in unstructured environments

In this paper a new PCA-based positioning sensor and localization system for mobile robots to operate in unstructured environments (e. g. industry, services, domestic ...) is proposed and experimentally validated. The inexpensive positioning system resorts to principal component analysis (PCA) of images acquired by a video camera installed onboard, looking upwards to the ceiling. This solution has the advantage of avoiding the need of selecting and extracting features. The principal components of the acquired images are compared with previously registered images, stored in a reduced onboard image database, and the position measured is fused with odometry data. The optimal estimates of position and slippage are provided by Kalman filters, with global stable error dynamics. The experimental validation reported in this work focuses on the results of a set of experiments carried out in a real environment, where the robot travels along a lawn-mower trajectory. A small position error estimate with bounded co-variance was always observed, for arbitrarily long experiments, and slippage was estimated accurately in real time.

Global localization with non-quantized local image features

In the field of appearance-based robot localization, the mainstream approach uses a quantized representation of local image features. An alternative strategy is the exploitation of raw feature descriptors, thus avoiding approximations due to quantization. In this work, the quantized and non-quantized representations are compared with respect to their discriminativity, in the context of the robot global localization problem. Having demonstrated the advantages of the non-quantized representation, the paper proposes mechanisms to reduce the computational burden this approach would carry, when applied in its simplest form. This reduction is achieved through a hierarchical strategy which gradually discards candidate locations and by exploring two simplifying assumptions about the training data. The potential of the non-quantized representation is exploited by resorting to the entropy-discriminativity relation. The idea behind this approach is that the non-quantized representation facilitates the assessment of the distinctiveness of features, through the entropy measure. Building on this finding, the robustness of the localization system is enhanced by modulating the importance of features according to the entropy measure. Experimental results support the effectiveness of this approach, as well as the validity of the proposed computation reduction methods.

Pentagonal parch-excited sectorized antenna for localization systems

A pentagonal patch-excited sectorized antenna (SA) suitable for 2.4-2.5 GHz localization systems was studied and developed. The integration of six patch-excited structures converges into a sectorized antenna called Hive5 that provides gain improvement compared to a patch antenna, maximum variation of 3 dB beam width over the radiation pattern and circular polarization (CP). This antenna is presented and analyzed taking into account the tap length and the flare angle. The proposed antenna in combination with a RF-Switch provides a cost effective solution for localization based on Wireless Sensor Networks (WSN) and will be used for implementing angle of arrival (AoA) techniques combined with RF fingerprinting techniques.

Enhanced PCA-based localization using depth maps with missing data

In this paper a new method for self-localization of mobile robots, based on a PCA positioning sensor to operate in unstructured environments, is proposed and experimentally validated. The proposed PCA extension is able to perform the eigenvectors computation from a set of signals corrupted by missing data. The sensor package considered in this work contains a 2D depth sensor pointed upwards to the ceiling, providing depth images with missing data. The positioning sensor obtained is then integrated in a Linear Parameter Varying mobile robot model to obtain a self-localization system, based on linear Kalman filters, with globally stable position error estimates. A study consisting in adding synthetic random corrupted data to the captured depth images revealed that this extended PCA technique is able to reconstruct the signals, with improved accuracy. The self-localization system obtained is assessed in unstructured environments and the methodologies are validated even in the case of varying illumination conditions.

Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches

In the last decade, local image features have been widely used in robot visual localization. In order to assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image with those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, in this paper we compare several candidate combiners with respect to their performance in the visual localization task. For this evaluation, we selected the most popular methods in the class of non-trained combiners, namely the sum rule and product rule. A deeper insight into the potential of these combiners is provided through a discriminativity analysis involving the algebraic rules and two extensions of these methods: the threshold, as well as the weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. Furthermore, we address the process of constructing a model of the environment by describing how the model granularity impacts upon performance. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance, confirming the general agreement on the robustness of this rule in other classification problems. The voting method, whilst competitive with the product rule in its standard form, is shown to be outperformed by its modified versions.