Enhanced short wavelength response in laser-scanned-photodiode image sensor using an a-SiC : H/a-Si : H tandem structure

We report in this paper the recent advances we obtained in optimizing a color image sensor based on the laser-scanned-photodiode (LSP) technique. A novel device structure based on a a-SiC:H/a-Si:H pin/pin tandem structure has been tested for a proper color separation process that takes advantage on the different filtering properties due to the different light penetration depth at different wavelengths a-SM and a-SiC:H. While the green and the red images give, in comparison with previous tested structures, a weak response, this structure shows a very good recognition of blue color under reverse bias, leaving a good margin for future device optimization in order to achieve a complete and satisfactory RGB image mapping. Experimental results about the spectral collection efficiency are presented and discussed from the point of view of the color sensor applications. The physics behind the device functioning is explained by recurring to a numerical simulation of the internal electrical configuration of the device.

Image and color recognition using amorphous silicon p-i-n photodiodes

Large area hydrogenated amorphous silicon single and stacked p-i-n structures with low conductivity doped layers are proposed as monochrome and color image sensors. The layers of the structures are based on amorphous silicon alloys (a-Si(x)C(1-x):H). The current-voltage characteristics and the spectral sensitivity under different bias conditions are analyzed. The output characteristics are evaluated under different read-out voltages and scanner wavelengths. To extract information on image shape, intensity and color, a modulated light beam scans the sensor active area at three appropriate bias voltages and the photoresponse in each scanning position ("sub-pixel") is recorded. The investigation of the sensor output under different scanner wavelengths and varying electrical bias reveals that the response can be tuned, thus enabling color separation. The operation of the sensor is exemplified and supported by a numerical simulation.

Evaluation of the respiratory motion effect in small animal PET images with GATE Monte Carlo simulations

The rapid growth in genetics and molecular biology combined with the development of techniques for genetically engineering small animals has led to increased interest in in vivo small animal imaging. Small animal imaging has been applied frequently to the imaging of small animals (mice and rats), which are ubiquitous in modeling human diseases and testing treatments. The use of PET in small animals allows the use of subjects as their own control, reducing the interanimal variability. This allows performing longitudinal studies on the same animal and improves the accuracy of biological models. However, small animal PET still suffers from several limitations. The amounts of radiotracers needed, limited scanner sensitivity, image resolution and image quantification issues, all could clearly benefit from additional research. Because nuclear medicine imaging deals with radioactive decay, the emission of radiation energy through photons and particles alongside with the detection of these quanta and particles in different materials make Monte Carlo method an important simulation tool in both nuclear medicine research and clinical practice. In order to optimize the quantitative use of PET in clinical practice, data- and image-processing methods are also a field of intense interest and development. The evaluation of such methods often relies on the use of simulated data and images since these offer control of the ground truth. Monte Carlo simulations are widely used for PET simulation since they take into account all the random processes involved in PET imaging, from the emission of the positron to the detection of the photons by the detectors. Simulation techniques have become an importance and indispensable complement to a wide range of problems that could not be addressed by experimental or analytical approaches.

Convex Total Variation Denoising of Poisson Fluorescence Confocal Images With Anisotropic Filtering

Fluorescence confocal microscopy (FCM) is now one of the most important tools in biomedicine research. In fact, it makes it possible to accurately study the dynamic processes occurring inside the cell and its nucleus by following the motion of fluorescent molecules over time. Due to the small amount of acquired radiation and the huge optical and electronics amplification, the FCM images are usually corrupted by a severe type of Poisson noise. This noise may be even more damaging when very low intensity incident radiation is used to avoid phototoxicity. In this paper, a Bayesian algorithm is proposed to remove the Poisson intensity dependent noise corrupting the FCM image sequences. The observations are organized in a 3-D tensor where each plane is one of the images acquired along the time of a cell nucleus using the fluorescence loss in photobleaching (FLIP) technique. The method removes simultaneously the noise by considering different spatial and temporal correlations. This is accomplished by using an anisotropic 3-D filter that may be separately tuned in space and in time dimensions. Tests using synthetic and real data are described and presented to illustrate the application of the algorithm. A comparison with several state-of-the-art algorithms is also presented.

Towards an efficient and robust foot classification from pedobarographic images

O documento em anexo encontra-se na versão post-print (versão corrigida pelo editor).

Fatty liver characterization and classification by ultrasound

Steatosis, also known as fatty liver, corresponds to an abnormal retention of lipids within the hepatic cells and reflects an impairment of the normal processes of synthesis and elimination of fat. Several causes may lead to this condition, namely obesity, diabetes, or alcoholism. In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis from ultrasound images. The features are selected in order to catch the same characteristics used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The algorithm, designed in a Bayesian framework, computes two images: i) a despeckled one, containing the anatomic and echogenic information of the liver, and ii) an image containing only the speckle used to compute the textural features. These images are computed from the estimated RF signal generated by the ultrasound probe where the dynamic range compression performed by the equipment is taken into account. A Bayes classifier, trained with data manually classified by expert clinicians and used as ground truth, reaches an overall accuracy of 95% and a 100% of sensitivity. The main novelties of the method are the estimations of the RF and speckle images which make it possible to accurately compute textural features of the liver parenchyma relevant for the diagnosis.

On cluster analysis of complex and heterogeneous data

3rd SMTDA Conference Proceedings, 11-14 June 2014, Lisbon Portugal.

Embedded platform for ECG biometric recognition

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações

Denoising of LSFCM images with compensation for the photoblinking/photobleaching effects

Fluorescence confocal microscopy images present a low signal to noise ratio and a time intensity decay due to the so called photoblinking and photobleaching effects. These effects, together with the Poisson multiplicative noise that corrupts the images, make long time biological observation processes very difficult.

Modelling and segmentation of the vocal tract during speech production by using deformable models in magnetic resonance images

The first and second authors would like to thank the support of the PhD grants with references SFRH/BD/28817/2006 and SFRH/PROTEC/49517/2009, respectively, from Fundação para a Ciência e Tecnol ogia (FCT). This work was partially done in the scope of the project “Methodologies to Analyze Organs from Complex Medical Images – Applications to Fema le Pelvic Cavity”, wi th reference PTDC/EEA- CRO/103320/2008, financially supported by FCT.

Selective recognition in potentiometric transduction of amoxicillin by molecularly imprinted materials

The indiscriminate use of antibiotics in foodproducing animals has received increasing attention as a contributory factor in the international emergence of antibiotic- resistant bacteria (Woodward in Pesticide, veterinary and other residues in food, CRC Press, Boca Raton, 2004). Numerous analytical methods for quantifying antibacterial residues in edible animal products have been developed over years (Woodward in Pesticide, veterinary and other residues in food, CRC Press, Boca Raton, 2004; Botsoglou and Fletouris in Handbook of food analysis, residues and other food component analysis, Marcel Dekker, Ghent, 2004). Being Amoxicillin (AMOX) one of those critical veterinary drugs, efforts have been made to develop simple and expeditious methods for its control in food samples. In literature, only one AMOX-selective electrode has been reported so far. In that work, phosphotungstate:amoxycillinium ion exchanger was used as electroactive material (Shoukry et al. in Electroanalysis 6:914–917, 1994). Designing new materials based on molecularly imprinted polymers (MIPs) which are complementary to the size and charge of AMOX could lead to very selective interactions, thus enhancing the selectivity of the sensing unit. AMOXselective electrodes used imprinted polymers as electroactive materials having AMOX as target molecule to design a biomimetic imprinted cavity. Poly(vinyl chloride), sensors of methacrylic acid displayed Nernstian slopes (60.7 mV/decade) and low detection limits (2.9×10-5 mol/L). The potentiometric responses were not affected by pH within 4–5 and showed good selectivity. The electrodes were applied successfully to the analysis of real samples.

New sensing materials of molecularly-imprinted polymers for the selective recognition of Chlortetracycline

As a result of the stressful conditions in aquaculture facilities there is a high risk of bacterial infections among cultured fish. Chlortetracycline (CTC) is one of the antimicrobials used to solve this problem. It is a broad spectrum antibacterial active against a wide range of Gram-positive and Gram-negative bacteria. Numerous analytical methods for screening, identifying, and quantifying CTC in animal products have been developed over the years. An alternative and advantageous method should rely on expeditious and efficient procedures providing highly specific and sensitive measurements in food samples. Ion-selective electrodes (ISEs) could meet these criteria. The only ISE reported in literature for this purpose used traditional electro-active materials. A selectivity enhancement could however be achieved after improving the analyte recognition by molecularly imprinted polymers (MIPs). Several MIP particles were synthesized and used as electro-active materials. ISEs based in methacrylic acid monomers showed the best analytical performance according to slope (62.5 and 68.6 mV/decade) and detection limit (4.1×10−5 and 5.5×10−5 mol L−1). The electrodes displayed good selectivity. The ISEs are not affected by pH changes ranging from 2.5 to 13. The sensors were successfully applied to the analysis of serum, urine and fish samples.

Automatic diagnosis of liver steatosis by ultrasound using autoregressive tissue characterization

Liver steatosis is mainly a textural abnormality of the hepatic parenchyma due to fat accumulation on the hepatic vesicles. Today, the assessment is subjectively performed by visual inspection. Here a classifier based on features extracted from ultrasound (US) images is described for the automatic diagnostic of this phatology. The proposed algorithm estimates the original ultrasound radio-frequency (RF) envelope signal from which the noiseless anatomic information and the textural information encoded in the speckle noise is extracted. The features characterizing the textural information are the coefficients of the first order autoregressive model that describes the speckle field. A binary Bayesian classifier was implemented and the Bayes factor was calculated. The classification has revealed an overall accuracy of 100%. The Bayes factor could be helpful in the graphical display of the quantitative results for diagnosis purposes.

Informal learning recognition through a cloud ecosystem

Learning and teaching processes, like all human activities, can be mediated through the use of tools. Information and communication technologies are now widespread within education. Their use in the daily life of teachers and learners affords engagement with educational activities at any place and time and not necessarily linked to an institution or a certificate. In the absence of formal certification, learning under these circumstances is known as informal learning. Despite the lack of certification, learning with technology in this way presents opportunities to gather information about and present new ways of exploiting an individual’s learning. Cloud technologies provide ways to achieve this through new architectures, methodologies, and workflows that facilitate semantic tagging, recognition, and acknowledgment of informal learning activities. The transparency and accessibility of cloud services mean that institutions and learners can exploit existing knowledge to their mutual benefit. The TRAILER project facilitates this aim by providing a technological framework using cloud services, a workflow, and a methodology. The services facilitate the exchange of information and knowledge associated with informal learning activities ranging from the use of social software through widgets, computer gaming, and remote laboratory experiments. Data from these activities are shared among institutions, learners, and workers. The project demonstrates the possibility of gathering information related to informal learning activities independently of the context or tools used to carry them out.

TRAILER Project (Tagging, Recognition, Acknowledgment of Informal Learning Experiences) a methodology to make learners' informal learning activities visible to the institutions

The application of information technologies (specially the Internet, Web 2.0 and social tools) make informal learning more visible. This kind of learning is not linked to an institution or a period of time, but it is important enough to be taken into account. On the one hand, learners should be able to communicate to the institutions they are related to, what skills they possess, whether they were achieved in a formal or informal way. On the other hand the companies and educational institutions need to have a deeper knowledge about the competencies of their staff. The TRAILER project provides a methodology supported by a technological framework to facilitate communication about informal learning between businesses, employees and learners. The paper presents the project and some of the work carried out, an exploratory analysis about how informal learning is considered and the technological framework proposed. Whilst challenges remain in terms of establishing the meaningfulness of technological engagement for employees and businesses, the continuing transformation of the social, technological and educational environment is likely to lead to greater emphasis for the effective exploitation of informal learning.