A simple and scalable method was developed for the fabrication of wearable strain and bending sensors, based on high aspect ratio (length/thickness ∼10(3)) graphite nanobelt thin films deposited by a modified Langmuir-Blodgett technique onto flexible polymer substrates. The sensing mechanism is based on the changes in contact resistance between individual nanobelts upon substrate deformation. Very high sensor response stability for more than 5000 strain-release cycles and a device power consumption as low as 1 nW were achieved. The device maximum stretchability is limited by the metal electrodes and the polymer substrate; the maximum strain that could be applied to the polymer used in this work was 40%. Bending tests carried out for various radii of curvature demonstrated distinct sensor responses for positive and negative curvatures. The graphite nanobelt thin flexible films were successfully tested for acoustic vibration and heartbeat sensing.

Nowadays, medical diagnostics using images has a considerable importance in many areas of medicine. It promotes and makes easier the acquisition, transmission and analysis of medical images. The use of digital images for diseases evaluations or diagnostics is still growing up and new application modalities are always appearing. This paper presents a methodology for a semi-automatic segmentation of the coronary artery tree in 2D X-Ray angiographies. It combines a region growing algorithm and a differential geometry approach. The proposed segmentation method identifies about 90% of the main coronary artery tree.

We report the design and fabrication of microreactors and sensors based on metal nanoparticle-decorated carbon nanotubes. Titanium adhesion layers and gold films were sputtered onto Si/SiO2substrates for obtaining the electrical contacts. The gold layers were electrochemically thickened until 1 μm and the electrodes were patterned using photolithography and wet chemical etching. Before the dielectrophoretic deposition of the nanotubes, a gap 1 μm wide and 5 μm deep was milled in the middle of the metallic line by focused ion beam, allowing the fabrication of sensors based on suspended nanotubes bridging the electrodes. Subsequently, the sputtering technique was used for decorating the nanotubes with metallic nanoparticles. In order to test the as-obtained sensors, microreactors (100 μL volume) were machined from a single Kovar piece, being equipped with electrical connections and 1/4′′ Swagelok-compatible gas inlet and outlets for controlling the atmosphere in the testing chamber. The sensors, electrically connected to the contact pins by wire-bonding, were tested in the 10−5to 10−2 W working power interval using oxygen as target gas. The small chamber volume allowed the measurement of fast characteristic times (response/recovery), with the sensors showing good sensitivity.

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This work proposes a new methodology for automatically validating the internal lighting system of an automotive, i.e., assessing the visual quality of an instrument cluster (IC) from the point of view of the user. Although the evaluation of the visual quality of a component is a subjective matter, it is highly influenced by some photometric features of the component, such as the light intensity distribution. The methodology proposed here uses this last photometric feature to classify regions in images of instrument cluster components as homogenous or not, while also taking into account the user subjective evaluation. In order to achieve that, we acquired a set of 107 IC component images, and preprocessed them. These same components were evaluated by a user to identify their non-homogenous regions. Then, for each component region, we extracted a set of homogeneity descriptors. These descriptors were associated with the results of the user evaluation, and given to two machine learning algorithms. These algorithms were trained to identify a region as homogenous or not, and showed that the proposed methodology obtains precision above 95%.

High Content Screening (HCS) combines high throughput techniques with the ability to generate cellular images of biological systems. The objective of this work is to evaluate the performance of predictive models using CNN to identify the number of cells present in digital contrast microscopy images obtained by HCS. One way to evaluate the algorithm was through the Mean Squared Error metric. The MSE was 4,335.99 in the A549 cell line, 25,295.23 in the Huh7 and 36,897.03 in the 3T3. After obtaining these values, different parameters of the models were changed to verify how they behave. By reducing the number of images, the MSE increased considerably, with the A549 cell line changing to 49,973.52, Huh7 to 79,473.88 and 3T3 to 52,977.05. Correlation analyzes were performed for the different models. In lineage A549, the best model showed a positive correlation with R = 0.953. In Huh7, the best correlation of the model was R = 0.821, it was also a positive correlation. In 3T3, the models showed no correlation, with the best model having R = 0.100. The models performed well in quantifying the number of cells, and the number and quality of the images interfered with this predictive ability.

High Content Screening (HCS) combines high throughput techniques with the ability to generate cellular images of biological systems. The objective of this work is to evaluate the performance of predictive models using CNN to identify the number of cells present in digital contrast microscopy images obtained by HCS. One way to evaluate the algorithm was through the Mean Squared Error metric. The MSE was 4,335.99 in the A549 cell line, 25,295.23 in the Huh7 and 36,897.03 in the 3T3. After obtaining these values, different parameters of the models were changed to verify how they behave. By reducing the number of images, the MSE increased considerably, with the A549 cell line changing to 49,973.52, Huh7 to 79,473.88 and 3T3 to 52,977.05. Correlation analyzes were performed for the different models. In lineage A549, the best model showed a positive correlation with R = 0.953. In Huh7, the best correlation of the model was R = 0.821, it was also a positive correlation. In 3T3, the models showed no correlation, with the best model having R = 0.100. The models performed well in quantifying the number of cells, and the number and quality of the images interfered with this predictive ability.

Neste artigo,é apresentada uma metodologia automática para a validação fotométrica em sistemas de iluminação interna veicular. Nessa metodologia, propõe-se um método para extração de descritores de homogeneidade de cada região de avaliação. A percepção visual humana, representada pela avaliação do usuário,é usada para classificar as regiões em homogêneas e não-homogêneas. Dois algoritmos de aprendizado de máquina (Redes neurais e Support Vector Machine) são usados para a classificação de regiões visando identificar quais as melhores configurações de descritores irá representar a percepção do usuário em relaçãoà homogeneidade da iluminação dos sistemas de interação com o motorista. Resultados experimentais mostram que a metodologia proposta consegue diferenciar regiões homogêneas de não-homogêneas com precisão superiorà 90%. Palavras-chave-Homogeneidade, segmentação, classificação de padrões, avaliação com usuário, aprendizado de máquina.