Despite the growing global interest in 3D printed carbon fiber reinforced polymers, most of the applications are still limited to high-performance sectors due to the low effectiveness–cost ratio of virgin carbon fibers. However, the use of recycled carbon fibers in 3D printing is almost unexplored, especially for thermoset-based composites. This paper aims to demonstrate the feasibility of recycled carbon fibers 3D printing via UV-assisted direct ink writing. Pyrolyzed recycled carbon fibers with a sizing treatment were firstly shredded to be used as a reinforcement of a thermally and photo-curable acrylic resin. UV-differential scanning calorimetry analyses were then performed to define the material crosslinking of the 3D printable ink. Because of the poor UV reactivity of the resin loaded with carbon fibers, a rheology modifier was added to guarantee shape retention after 3D printing. Thanks to a customized 3D printer based on a commercial apparatus, a batch of specimens was successfully 3D printed. According to the tensile tests and Scanning Electron Microscopy analysis, the material shows good mechanical properties and the absence of layer marks related to the 3D printing. These results will, therefore, pave the way for the use of 3D printed recycled carbon fiber reinforced polymers in new fields of application.
Real-time detecting moving objects in infrared video sequences may be particularly challenging because of the characteristics of the objects, such as their size, contrast, velocity and trajectory. Many proposed algorithms achieve good performances but only in the presence of some specific kinds of objects, or by neglecting the computational time, becoming unsuitable for real-time applications. To obtain more flexibility in different situations, we developed an algorithm capable of successfully dealing with small and large objects, slow and fast objects, even if subjected to unusual movements, and poorly-contrasted objects. The algorithm is also capable to handle the contemporary presence of multiple objects within the scene and to work in real-time even using cheap hardware. The implemented strategy is based on a fast but accurate background estimation and rejection, performed pixel by pixel and updated frame by frame, which is robust to possible background intensity changes and to noise. A control routine prevents the estimation from being biased by the transit of moving objects, while two noise-adaptive thresholding stages, respectively, drive the estimation control and allow extracting moving objects after the background removal, leading to the desired detection map. For each step, attention has been paid to develop computationally light solution to achieve the real-time requirement. The algorithm has been tested on a database of infrared video sequences, obtaining promising results against different kinds of challenging moving objects and outperforming other commonly adopted solutions. Its effectiveness in terms of detection performance, flexibility and computational time make the algorithm particularly suitable for real-time applications such as intrusion monitoring, activity control and detection of approaching objects, which are fundamental task in the emerging research area of Smart City.
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