Electronics

Existing methods for recognizing motion intent in lower limb rehabilitation robots focus on spatial feature extraction while neglecting movement continuity, thus failing to extract temporal features. This paper proposes a movement intention recognition model based on a CNN-LSTM parallel dual-stream spatio-temporal neural network, taking surface electromyography (sEMG) signals as the core data. This model concurrently extracts temporal and spatial features from sEMG signals, integrating dual-dimensional information to comprehensively explore deep signal characteristics. By overcoming the limitations of traditional single-feature extraction, it significantly enhances recognition accuracy. Experimental results from movement intention recognition studies involving multiple subjects demonstrate an average recognition accuracy of 97%, providing reliable technical support for precise intent recognition and human–robot collaborative control in lower limb rehabilitation robots.

Filter pruning is an effective approach for improving the inference efficiency of neural networks and is particularly attractive for on-device artificial intelligence (AI) applications. However, many existing methods fail to accurately identify redundant filters due to limited modeling of inter-filter dependencies. A filter pruning method based on nuclear norm analysis is proposed to quantify filter independence and guide structured pruning. By analyzing the layer-wise distribution of independence scores, a principled trade-off between pruning rate and accuracy preservation is achieved. In most evaluation scenarios, the proposed method achieves 75–95% parameter reduction and 70–80% FLOPs reduction, while substantially higher compression ratios (up to 99%) can be obtained for more redundant network architectures, with consistent performance trends observed across multiple accuracy-related metrics. Furthermore, deployment on an RK3588 neural processing unit (NPU) demonstrates substantial reductions in memory consumption and inference latency, confirming the practical effectiveness of the method for mobile and edge AI applications.

This paper proposes an AI-based trading framework that integrates supervised price forecasting with reinforcement learning (RL)-based decision-making. The objective is to enhance both profitability and risk management in cryptocurrency trading by equipping RL agents with forward-looking market information and risk-aware incentives. The proposed methodology follows a two-stage design. First, a univariate long short-term memory (LSTM) model generates 72 bitcoin price forecasts. These predictions are used to compute future technical indicators, which are combined with current market indicators to construct an enriched, forward-looking state representation. Second, an RL agent is trained in this environment using a novel long-term reward function that incorporates transaction costs, drawdown penalties, volatility penalties, and delayed rewards to promote stable and sustainable trading behavior. Four state-of-the-art RL algorithms (PPO, SAC, TD3, and A2C) are systematically evaluated over randomized 180-day episodes using hourly bitcoin data. The results demonstrate that the proposed agent consistently outperforms conventional buy-and-hold and moving average crossover strategies, achieving an average profit ratio of 32% and a Sharpe ratio of 1.34. These findings highlight the novelty and effectiveness of combining mid-term price forecasts, enriched technical states, and risk-aware RL training for robust cryptocurrency trading.