Data, Signal and Image Processing and Applications in Sensors II.

A vast and ever-growing amount of data in various domains and modalities is readily available, being the rapid advance of sensor technology one of its main contributor [...].

Data, Signal and Image Processing and Applications in Sensors.

With the rapid advance of sensor technology, a vast and ever-growing amount of data in various domains and modalities are readily available [...].

Deep Learning Techniques for Grape Plant Species Identification in Natural Images.

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.

Using Android Tablets to develop handwriting skills: A case study.

A system to support the teaching and learning of handwriting skills is proposed. It is composed of two components: the hardware component (e.g., Android Tablet); and the software component. The software component as two modules: the server and the client. A teacher chooses what exercises/games a child should do directly in the Android table or using the server, from the existing ones in the system. A child does the exercises/games by logging into the system in the Android Tablet. Automatic feedback about the correctness of the answers is provided by the system. Data (number of tries, time spent, etc.) are automatically grabbed and processed to be presented to the teachers and parents. Registered parents can see the results and follow their children' s "academic life", by logging into the server side of the system. We found a significant improvement in the development of handwriting skills in the children throughout the academic year, and improvements were also more present when comparing children who had have contact with the system with children who did not have this contact. Educators, children, School Boards, City Town Hall and the Educational Community are unanimous in stating that the implementation of this system was a real success.