This paper addresses the issue of stiffness in the upper body and lack of coordination between the upper and lower body during humanoid robot walking. An improved humanoid robot reinforcement learning algorithm incorporating an LSTM framework is proposed to optimize full-body coordinated movement. Based on the Humanoid-Gym framework, a novel reward mechanism is designed, taking into account the detailed evaluation of arm movement and the collaborative control between the arms and thighs. The reinforcement learning model adopts an Actor-Critic architecture, integrating the LSTM framework into the network to enhance the feature extraction and dynamic modeling capabilities. Finally, experiments were conducted using the Hi ROBOT humanoid platform to validate the proposed model. The proposed LSTM network algorithm is compared with the original network, GRU, CNN, and other networks, demonstrating the superiority of the model. Compared with other networks, the performance improves by approximately $3.4 \%$ in terms of reward metric, and the model reaches the performance level of the original network after 40k training steps, as opposed to 60 k steps. It also maintains a fast convergence rate. Additionally, the optimized algorithm results in better gait arm swing and leg coordination, with smoother and more coordinated movement, closest to the human walking pattern.

In IoT devices, True Random Number Generators (TRNGs) play an increasingly important role, and advanced TRNGs must possess high throughput, low resource overhead, and high stability. In this article, we propose a fine-grained entropy extraction circuit based on Self-Timed Ring (STR), which can change the entropy extraction capability by varying the stages of STRs to extract randomness from different entropy sources. Importantly, the throughput of the proposed TRNG can be automatically adjusted according to the frequency of the entropy source, adapting to user requirements. The proposed TRNG is validated on Xilinx Spartan-6, Xilinx Artix-7, and Xilinx Virtex-6 FPGA development boards. It utilizes a three-stage Ring Oscillator (RO) and a five-stage RO for entropy extraction, requiring only 53 LUTs, 32 DFFs, and 62 registers. The generated random numbers of the TRNG, without any post-processing, achieve excellent results in NIST SP 800-22, NIST SP 800-90B, robustness test, universality test, AIS-31, and TEST U01, demonstrating a throughput of 280 Mbps.

When traditional path planning algorithms are used for path searching for Automated Guided Vehicles (AGVs) in static environments, they usually encounter the difficulties of excessive search nodes, high memory consumption, and long running time. To tackle these problems, this paper proposes a tangent point search algorithm based on quadtree grid environment modeling. The algorithm establishes a weighted graph by extracting key cell points and then uses the Dijkstra algorithm for path planning. In the scenarios of different map sizes, A* algorithm, Rapidly-exploring Random Trees (RRT) algorithm, Dijkstra algorithm, and a Dijkstra algorithm with a tangent point search (DA+TPS) were simulated and analyzed. The results show that among the four algorithms, the DA+TPS has the shortest route length and minimum search time. The comparison demonstrates that the proposed method can effectively reduce the number of search nodes, speed up the search process, and reduce redundant nodes in the path, thus reducing the number of turns for AGVs.

The current nonlinear optimization of visual SLAM back-end has disadvantages such as slow optimization speed and poor optimization effect. To overcome these problems, this paper improves the traditional Levenberg-Marquardt algorithm (L-M) based on a framework of Bundle Adjustment (BA) nonlinear optimization. Firstly, the radius and expansion multiplier of the trust region are formulated and a threshold value is set; secondly, the trust region after each iteration is restricted with the pre-defined range for the purpose of improving nonlinear optimization; finally, a comparative analysis is conducted by setting up a comparison experiment with the traditional L-M algorithm, and It is concluded that the improved L-M algorithm can reduce the number of iterations by 16 times and time performance was reduced by an average of 62.30%, which shorens the optimization time, improves the optimization efficiency and has better robustness.