Aiming at the problem that Wireless Capsule Endoscopy （WCE） image classification models are only for a single disease or limited to a specific organ， and are difficult to adapt to clinical needs， a WCE image classification model based on improved ConvNeXt-T（ConvNeXt Tiny） was proposed. Firstly， a Simple parameter-free Attention Module （SimAM） was introduced during the model’s feature extraction process to make the model focus on the key areas of WCE images， so as to capture the detailed features such as the boundaries and textures of lesion areas accurately. Secondly， a Global Context Multi-scale Feature Fusion （GC-MFF） module was designed. In the module， global context modeling capability of the model was firstly optimized through Global Context Block （GC Block）， and then the shallow and deep multi-scale features were fused to obtain WCE images features with more representation ability. Finally， the Cross Entropy （CE） loss function was optimized to address the problem of large intra-class differences among WCE images. Experimental results on a WCE dataset show that the proposed model has the accuracy and F1 value increased by 2.96 and 3.16 percentage points， respectively， compared with the original model ConvNeXt-T； compared with Swin-B （Swin Transformer Base） model， which has the best performance among mainstream classification models， the proposed model has the number of parameters reduced by 67.4% and the accuracy and F1 value increased by 0.51 and 0.67 percentage points， respectively. The above indicates that the proposed model has better classification performance and can assist doctors in making accurate diagnosis of digestive tract diseases effectively.

Real-time error detections are needed in highly reliable systems. This paper presented three online models with self-checking method for built-in error detection. The error detection model adopted two pipes in the Field Programmable Gate Array (FPGA). By comparing whether the current configuration information and FPGA configuration memory of the original information were consistent, the model can detect errors in real-time, and by comparing their configuration data, it also can locate the error where logic blocks have undergone an Single Event Upset (SEU). The testing result shows that the method proposed in this article has better performance than that of online Built-In Self-Test unit (BIST).