A triangular lattice dispersion compensating photonic crystal fiber is presented in this paper. The fiber produces high birefringence and operates at fundamental mode only. The full vector finite element method with a perfectly matched absorbing layer boundary condition is applied to investigate the guiding properties of the proposed fiber. The designed fiber demonstrates that it is possible to obtain a very large negative dispersion of $-9486.1\mathrm{ps}/(\mathrm{nm}·\mathrm{km})$ at 1550 nm wavelength with a negative dispersion more than $-7000\mathrm{ps}/(\mathrm{nm}·\mathrm{km})$ over the entire C-band (1530–1565 nm), which is suitable for broadband dispersion compensation. The birefringence is about $4.13\times {10}^{-2}$ at 1550 nm wavelength, which is also very high. All these properties make this fiber very suitable in the area of broadband dispersion compensation and polarization-maintaining applications.

A triangular lattice photonic crystal fibre is presented in this paper for residual dispersion compensation. The fibre exhibits a flattened negative dispersion of −992.01 ± 6.93 ps/(nm-km) over S+C+L wavelength bands and −995.83 ± 0.42 ps/(nm-km) over C-band. The birefringence is about 4.4 × 10⁻² at the excitation wavelength of 1550 nm which is also very high. Full vector finite element method (FEM) with a perfectly matched absorbing layer (PML) boundary condition is applied to numerically investigate the guiding properties of this PCF. The fibre operates at fundamental mode only. All these properties endorse this fibre as a suitable candidate for compensating residual dispersion and polarization maintaining applications.

This study addresses the classic control problem of stabilizing an inverted pendulum on a moving cart, a challenge in control theory and robotics due to its inherent instability and highly nonlinear dynamics. We explore the application of Q-learning, a model-free reinforcement learning algorithm, and its efficacy in deriving an optimal control policy for the system without precise system models. Our approach utilizes Q-learning's capacity for stabilizing a pendulum in an upright position on the top of the horizontally moving cart within a certain boundary. Our strategy adapts to a dynamic environment while showcasing its robustness in developing control policies for complex systems. This research bridges classical control theory with reinforcement learning techniques, contributing to the domain by demonstrating the versatility and potential of machine learning in control tasks.

As wheelchair technology evolves and embraces a more prominent role in assistive technology, the onset of intelligent control systems necessitates a comprehensive review from an engineering perspective. In this work, we analyze the development and the emerging trends in intelligent wheelchair control. A specific focus is provided on classifying and comparing model-driven and data-driven control methodologies. In this review, findings from a range of past contributions are examined, including conventional control theories, rule-based systems, and modern data-driven approaches that include supervised, unsupervised, and reinforcement learning control algorithms. The analysis indicates that while model-driven methods offer interpretability, data-driven techniques—in particular those leveraging machine learning—provide for a superior adaptability for navigating complex and dynamic environments. We further highlight key supporting systems found in sensors, actuators, and human-machine interfaces. Additionally, the important functionalities such as autonomous navigation and obstacle avoidance methods are identified. Our findings point to some future objectives that need to be addressed. For example, energy efficiency, robustness in unpredictable settings, computational requirements, and associated demands when utilizing data-driven methods. One of the highlighted fields of study in this work is the integration of reinforcement learning and sensor fusion, which may hold some promising results for future wheelchair technologies.

A hexagonal slotted porous core tube lattice cladding photonic crystal fiber (SPC-TLC-PCF) is suggested to simultaneously achieve a very high birefringence and low transmission loss. At the operating frequency of 1 THz the proposed SPC-TLC-PCF yields a birefringence of 9.06×10−2 and effective material loss of 0.047 cm−1. The fiber operates at fundamental mode only and exhibits a small positive dispersion less than 1.6 ps/THz/cm over a band of 250 GHz (0.85 THz to 1.1 THz). Moreover, bending loss is around 1.61×10−7 cm−1 at 1 THz and confinement loss is also pretty small. These favorable attributes endorse this SPC-TLC-PCF much useful for polarization maintaining applications and sensing applications in THz regime.