Background: This paper presents an electronic stethoscope model for cardiac auscultation, that adds innovative functionality to the conventional stethoscope, nominated "Electronic Stethoscope". Among the Electronic Stethoscope functionalities, can be cited the volume adjust in order to facilitate the hearing of the heart pulses, the graphic presentation of the heart beat sound waveforms, from a monitor, and the store of the cardiac auscultation data in a memory card for future analysis. It is believed that this additional functions, nonexistent in conventional stethoscopes, can contribute in the identification of pathology anomalies by the doctors and medicine students, growing the diagnoses trusting rate. Beyond that, the stored data in the equipment can be visualized and listened, without the necessity of the patient presence, characteristic that enables the build of a database, for example, of indicative signals of pathologies that could be used in class and practicing. With this equipment it is also possible to make adjusts on amplitude and length of the graphics (zoom), for a better details visualization. Undesirable ambient sounds can also be mitigated by the use of low-pass digital filters of IIR type implemented on the stethoscope software. This equipment has also a graphic resource for the identification of patient's heart rate similar of the gestational ultrasound equipment. Results of interviews with doctors and medicine students shows that the equipment have practical applicability, either in clinic or classroom, and extremely intuitive mode of operation, which would require no prior specific training.
Electric companies face flow control and inventory obstacles such as reliability, outlays, and time-consuming tasks. Convolutional Neural Networks (CNNs) combined with computational vision approaches can process image classification in warehouse management applications to tackle this problem. This study uses synthetic and real images applied to CNNs to deal with classification of inventory items. The results are compared to seek the neural networks that better suit this application. The methodology consists of fine-tuning several CNNs on Red–Green–Blue (RBG) and Red–Green–Blue-Depth (RGB-D) synthetic and real datasets, using the best architecture of each domain in a blended ensemble approach. The proposed blended ensemble approach was not yet explored in such an application, using RGB and RGB-D data, from synthetic and real domains. The use of a synthetic dataset improved accuracy, precision, recall and f1-score in comparison with models trained only on the real domain. Moreover, the use of a blend of DenseNet and Resnet pipelines for colored and depth images proved to outperform accuracy, precision and f1-score performance indicators over single CNNs, achieving an accuracy measurement of 95.23%. The classification task is a real logistics engineering problem handled by computer vision and artificial intelligence, making full use of RGB and RGB-D images of synthetic and real domains, applied in an approach of blended CNN pipelines.
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