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Electrical power quality (PQ) is a crucial competitive and developing factor to all economic activities. The economic impact resulting from a bad PQ would be drastic on all consumers. Computers, uninterruptible and switched power supplies (UPS), and fluorescent lamps/tubes are examples of nonlinear loads that have the consumption of a nonsinusoidal current, which cause disturbances in the power supply system (that may be severe or not). This study discusses residential generic power circuitry analysis and simulation, under nonlinear loads, in connection with undergraduate electrical engineering education. It briefly reviews some of the basic techniques, and presents a software tool that has been found to be very useful in the context. The tool has an easy-to-use, friendly interface, and can be used to teach design techniques or as a laboratory support to study the applicability of known methods to real situations. The students can perform simulations with their own data on Microsoft TM Windows 1 -based platforms. ß

This paper reviews instrumented hip joint replacements, instrumented femoral replacements and instrumented femoral fracture stabilizers. Examination of the evolution of such implants was carried out, including the detailed analysis of 16 architectures, designed by 8 research teams and implanted in 32 patients. Their power supply, measurement, communication, processing and actuation systems were reviewed, as were the tests carried out to evaluate their performance and safety. These instrumented implants were only designed to measure biomechanical and thermodynamic quantities in vivo, in order to use such data to conduct research projects and optimize rehabilitation processes. The most promising trend is to minimize aseptic loosening and/or infection following hip or femoral replacements or femoral stabilization procedures by using therapeutic actuators inside instrumented implants to apply controlled stimuli in the bone-implant interface.

Frequently, the vineyards in the Douro Region present multiple grape varieties per parcel and even per row. An automatic algorithm for grape variety identification as an integrated software component was proposed that can be applied, for example, to a robotic harvesting system. However, some issues and constraints in its development were highlighted, namely, the images captured in natural environment, low volume of images, high similarity of the images among different grape varieties, leaf senescence, and significant changes on the grapevine leaf and bunch images in the harvest seasons, mainly due to adverse climatic conditions, diseases, and the presence of pesticides. In this paper, the performance of the transfer learning and fine-tuning techniques based on AlexNet architecture were evaluated when applied to the identification of grape varieties. Two natural vineyard image datasets were captured in different geographical locations and harvest seasons. To generate different datasets for training and classification, some image processing methods, including a proposed four-corners-in-one image warping algorithm, were used. The experimental results, obtained from the application of an AlexNet-based transfer learning scheme and trained on the image dataset pre-processed through the four-corners-in-one method, achieved a test accuracy score of 77.30%. Applying this classifier model, an accuracy of 89.75% on the popular Flavia leaf dataset was reached. The results obtained by the proposed approach are promising and encouraging in helping Douro wine growers in the automatic task of identifying grape varieties.