To improve the precision of intelligent sugarcane harvesting, and to meet the requirements of high precision and low complexity for use with embedded devices, a lightweight model called MobileNet v2-YOLO v5s for the real-time detection of sugarcane stem nodes in complex natural environments was developed. In this study, images of sugarcane stem nodes in a complex natural environment were collected and a dataset containing 12,600 images was constructed using a data extension process. The MobileNet network was introduced to replace the backbone of the YOLO v5s algorithm and the improved algorithm was used to train the MobileNet-YOLO v5s sugarcane stem node identification model. In experiments aiming to verify the advantages of the lightweight model, MobileNet v2-YOLO v5s achieved the best combination of high precision and low complexity. Its AP was decreased by only 0.8%, while its complexity was reduced by 40% compared to YOLO v5s. It also had a fast detection speed of 4.4 ms on a Dell workstation P7920. Therefore, 11 other models were selected for comparative experiments to demonstrate the superiority of MobileNet v2-YOLO v5s. Finally, TensorRT accelerated optimization tests, execution tests, and real-time detection tests were performed on Jetson Nano. The results showed that the optimised MobileNet v2-YOLO v5s outperformed YOLO v5s in terms of identification, lightweight and detection speed on embedded devices. Overall, MobileNet v2-YOLO v5s model meets the requirements of embedded devices and can provide a visual identification method for intelligent sugarcane harvesting.

Human gait exhibits stable contralateral coupling, making healthy-side motion a viable predictor for affected-limb kinematics. Leveraging this property, this study develops FusionTCN–Attention, a causality-preserving temporal model designed to forecast contralateral hip and knee trajectories from unilateral IMU measurements. The model integrates dilated temporal convolutions with a lightweight attention mechanism to enhance feature representation while maintaining strict real-time causality. Evaluated on twenty-one subjects, the method achieves hip and knee RMSEs of 5.71° and 7.43°, correlation coefficients over 0.9, and a deterministic phase lag of 14.56 ms, consistently outperforming conventional sequence models including Seq2Seq and causal Transformers. These results demonstrate that unilateral IMU sensing supports low-latency, stable prediction, thereby establishing a control-oriented methodological basis for unilateral prediction as a necessary engineering prerequisite for future hemiparetic exoskeleton applications.

Retired lithium-ion batteries retain considerable residual value for second-life energy storage applications, but significant variations in health conditions and complex operating scenarios make accurate remaining useful life (RUL) prediction challenging. To address the limited availability of capacity measurements and the poor adaptability of conventional models to dynamically fluctuating degradation trajectories, a hybrid RUL prediction framework integrating Wavelet Packet Energy Entropy (WPEE), a Fractional-Order Grey Model (FGM), and an Unscented Kalman Filter (UKF) is proposed. WPEE extracted from discharge voltage signals is employed as a degradation indicator, while a Box–Cox transformation enhances its correlation with capacity. An Adaptive Mutation Particle Swarm Optimization (AMPSO) algorithm is used to determine the optimal fractional-order parameter, and the optimized FGM is incorporated into the UKF state-transition process for recursive state correction. Validation was conducted using four retired lithium-ion cells and two series-connected battery packs with different health conditions at prediction starting points of 20, 25, and 30 cycles. The results show that the proposed method effectively tracks degradation evolution, with RUL prediction errors within 7 cycles for retired cells and within 6 cycles for battery packs. Across all 18 prediction cases, FGM–UKF achieved an overall mean AE of 3.111 cycles, lower than those of FGM (5.111 cycles), GM(1, 1) (4.000 cycles), and LR (4.278 cycles). These results demonstrate the effectiveness and robustness of the proposed framework for lifetime assessment in second-life battery energy storage systems.