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Forest fire is becoming one of the most significant natural disasters at the expense of ecology and economy. In this article, we develop an effective SqueezeNet based asymmetric encoder-decoder U-shape architecture, Attention U-Net and SqueezeNet (ATT Squeeze U-Net), mainly functions as an extractor and a discriminator of forest fire. This model takes attention mechanism to highlight useful features and suppress irrelevant contents by embedding Attention Gate (AG) units in the skip connection of U-shape structure. In this way, salient features are emphasized so that the proposed method could be competent at forest fire segmentation tasks with a small number of parameters. Specifically, we first replace classical convolution layer by a depthwise one and engage a Channel Shuffle operation as a feature communicator in the Fire module of classical SqueezeNet. Then, this modified SqueezeNet is employed as a substitution of the encoder of Attention U-Net and a corresponding DeFire module designed is combined into the decoder as well. Finally, to classify true fire, we take use of a fragment of the encoder in ATT Squeeze U-Net. The experimental results of modified SqueezeNet integrated Attention U-Net show that a competitive accuracy at 0.93 and an average prediction time at 0.89 second per image are achieved for reliable real-time forest fire detection.

Fabric-based flexible electronics have promising applications in biomedicine, soft robots, and human–machine interfaces. However, fabrication of flexible electronics on fabrics in a high throughput and scalable manner without significantly sacrificing the benefits of fabrics is still a challenge. To address this, a laser direct writing (LDW)-based technique is developed for the mask-free fabrication of flexible electronics on fabrics. Carboxymethyl cellulose (CMC) is chosen as the precursor for the carbon electrode formed by LDW on the fabrics because CMC is water soluble, which is convenient to be processed and able to be mixed with inorganic precursors to form composites. Flexible pressure sensor based on LDW-carbonized CMC (CCMC) has a sensitivity of −0.25 kPa–1 within 5 kPa, response time of 0.5 s, and detectable limit of 140 Pa. The specific capacitance of the LDW prepared Mo x O y /CCMC electrode is 12.8 mF/cm2. The LDW Mo x O y /CCMC composite-based flexible all-solid-state supercapacitor has a capacitance of 1.095 mF/cm2, with a high flexibility and mechanical durability.

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