A Review of Vision-Based Assistive Systems for Visually Impaired People: Technologies, Applications, and Future Directions

Visually impaired individuals rely heavily on accurate and timely information about obstacles and their surrounding environments to achieve independent living. In recent years, significant progress has been made in the development of assistive technologies, particularly vision-based systems, that enhance mobility and facilitate interaction with the external world in both indoor and outdoor settings. This paper presents a comprehensive review of recent advances in assistive systems designed for the visually impaired, with a focus on state-of-the-art technologies in obstacle detection, navigation, and user interaction. In addition, emerging trends and future directions in visual guidance systems are discussed.

A New Self-organizing Interval Type-2 Fuzzy Neural Network for Multi-Step Time Series Prediction

This paper proposes a new self-organizing interval type-2 fuzzy neural network with multiple outputs (SOIT2FNN-MO) for multi-step time series prediction. Differing from the traditional six-layer IT2FNN, a nine-layer network is developed to improve prediction accuracy, uncertainty handling and model interpretability. First, a new co-antecedent layer and a modified consequent layer are devised to improve the interpretability of the fuzzy model for multi-step predictions. Second, a new transformation layer is designed to address the potential issues in the vanished rule firing strength caused by highdimensional inputs. Third, a new link layer is proposed to build temporal connections between multi-step predictions. Furthermore, a two-stage self-organizing mechanism is developed to automatically generate the fuzzy rules, in which the first stage is used to create the rule base from empty and perform the initial optimization, while the second stage is to fine-tune all network parameters. Finally, various simulations are carried out on chaotic and microgrid time series prediction problems, demonstrating the superiority of our approach in terms of prediction accuracy, uncertainty handling and model interpretability.