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Performing classification on embedded systems, rather than transmitting data, allows rapid perception and decision\u2010making critical for time\u2010sensitive applications such as search and rescue, hazardous environment exploration, and military operations. To fully capitalize on these systems\u2019 potential, specialized deep learning solutions are needed that balance accuracy and computational efficiency for time\u2010sensitive inference. This article introduces the widened attention\u2010enhanced atrous convolution\u2010based efficient network (WACEfNet), a new convolutional neural network designed specifically for real\u2010time visual classification challenges using resource\u2010constrained embedded devices. WACEfNet builds on EfficientNet and integrates innovative width\u2010wise feature processing, atrous convolutions, and attention modules to improve representational power without excessive overhead. 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