Given the lack of investments in surveillance in remote places, this paper presents a prototype that identifies vehicles in irregular conditions, notifying a group of people, such as a network of neighbors, through a low-cost embedded system based on the Internet of things (IoT). The developed prototype allows the visualization of the location, date and time of the event, and vehicle information such as license plate, make, model, color, city, state, passenger capacity and restrictions. It also offers a responsive interface in two languages: Portuguese and English. The proposed device addresses technical concepts pertinent to image processing such as binarization, analysis of possible characters on the plate, plate border location, perspective transformation, character segmentation, optical character recognition (OCR) and post-processing. The embedded system is based on a Raspberry having support to GPS, solar panels, communication via 3G modem, wi-fi, camera and motion sensors. Tests were performed regarding the vehicle’s positioning and the percentage of assertiveness in image processing, where the vehicles are at different angles, speeds and distances. The prototype can be a viable alternative because the results were satisfactory concerning the recognition of the license plates, mobility and autonomy.

This paper presents the main characteristics and discusses the rationale for the cooperative mechanisms implemented in CODES -a Web-based environment designed to support cooperative music prototyping. The CODES environment aims to provide actual cooperation, social knowledge construction, argumentation and negotiation among the different actors of musical prototypes design activities. A brief description of CODES and its original concept of "music prototyping" is initially presented, characterizing this activity as non-technical products design. This is followed by concepts and descriptions of CODES cooperation mechanisms, illustrating how they increase awareness of other users and their intentions in the context of group activities for non-technical design. We present the design and prototypical implementation of CODES as well as some encouraging results from preliminary qualitative tests.

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Pump sizing is the process of dimensional matching an impeller and stator to provide a satisfactory performance test result and good service life during the operation of progressive cavity pumps. In this process, historical data analysis and dimensional monitoring are done manually, consuming a large number of man-hours and requiring a deep knowledge of progressive cavity pump behavior. This paper proposes the use of graph neural networks in the construction of a prototype to recommend interference during the pump sizing process in a progressive cavity pump. For this, data from different expert applications were used in addition to individual control excel spreadsheets to build the database used in the prototype. From the pre-processed data, complex network techniques and the betweenness centrality metric were used to calculate the degree of importance of each order confirmation, as well as to calculate the dimensionality of the rotors. Using the proposed model a mean squared error of 0.28 was obtained for the cases where there were recommendations for order confirmations. Based on the results obtained, it was realized that there is a similarity with the dimensional defined by design engineers during the pump sizing process and this approach can be used to validate the project definitions.

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