This paper presents a type-2 fuzzy inference tree designed for a differential wheeled mobile robot that navigates in indoor environments. The proposal consists of a controller designed for obstacle avoidance, a controller for path recovery and goal reaching, and a third controller for the real-time selection of behaviors. The system takes as inputs the information provided for a 2D laser range scanner, i.e., the distance of nearby objects to the robot, as well as the robot position in space, calculated from mechanical odometry. The real performance is evaluated through metrics such as clearance, path smoothness, path length, travel time and success rate. The experimental results allow us to demonstrate an appropriate performance of our proposal for the navigation task, with a higher efficiency than the reference methods taken from the state of the art.

The purpose of this research is to develop an efficient model for human pose estimation (HPE). The main limitations of the study include the small size of the dataset and confounds in the classification of certain poses, suggesting the need for more data to improve the robustness of the model in uncontrolled environments. The methodology used combines MediaPipe for the detection of key points in images with a CNN1D model that processes preprocessed feature sequences. The Yoga Poses dataset was used for the training and validation of the model, and resampling techniques, such as bootstrapping, were applied to improve accuracy and avoid overfitting in the training. The results show that the proposed model achieves 96% overall accuracy in the classification of five yoga poses, with accuracy metrics above 90% for all classes. The implementation of the CNN1D model instead of traditional 2D or 3D architectures accomplishes the goal of maintaining a low computational cost and efficient preprocessing of the images, allowing for its use on mobile devices and real-time environments.

In the field of image processing, noise represents an unwanted component that can occur during signal acquisition, transmission, and storage. In this paper, we introduce an efficient method that incorporates redescending M-estimators within the framework of Wiener estimation. The proposed approach effectively suppresses impulsive, additive, and multiplicative noise across varied densities. Our proposed filter operates on both grayscale and color images; it uses local information obtained from the Wiener filter and robust outlier rejection based on Insha and Hampel’s tripartite redescending influence functions. The effectiveness of the proposed method is verified through qualitative and quantitative results, using metrics such as PSNR, MAE, and SSIM.

In the self-driving vehicles domain, steering control is a process that transforms information obtained from sensors into commands that steer the vehicle on the road and avoid obstacles. Although a greater number of sensors improves perception and increases control precision, it also increases the computational cost and the number of processes. To reduce the cost and allow data fusion and vehicle control as a single process, this research proposes a data fusion approach by formulating a neurofuzzy aggregation deep learning layer; this approach integrates aggregation using fuzzy measures $\mu$ as fuzzy synaptic weights, hidden state using the Choquet fuzzy integral, and a fuzzy backpropagation algorithm, creating a data processing from different sources. In addition, implementing a previous approach, a self-driving neural model is proposed based on the aggregation of a steering control model and another for obstacle detection. This was tested in an ROS simulation environment and in a scale prototype. Experimentation showed that the proposed approach generates an average autonomy of $95\%$ and improves driving smoothness by $9\%$ compared to other state-of-the-art methods.

This article proposes a method for the classification of different types of cardiac arrhythmias through a deep learning model that utilizes recurrence graphs constructed through a fuzzy intuitionistic clustering technique. The utilization of recurrence graphs is predicated on their capacity to encapsulate the pertinent information of the ECG signal into a succinct graphical representation, thereby facilitating the analysis and identification of aberrant patterns. To substantiate the efficacy of the proposed method, ten deep learning models with disparate configurations were trained, and the outcomes were contrasted with those of existing methodologies in the literature. The findings demonstrate an exceptional performance, with an accuracy of 98%, underscoring the promise of recurrence graphs and convolutional neural networks in signal analysis.