Providing a Periodic Control Solution for Balance Control While Standing Using a Pendulum-Based Approach

The stability of standing in humans is a complex process that leads to maintaining the upright position against external disturbances. Balance control during standing is of vital importance for humans in daily life. An issue that is still not clearly understood is which control mechanism the central nervous system uses to maintain stability. In the rehabilitation of standing function, the coordination pattern between the angles of the leg joint of a healthy person should be restored. For example, one of the rehabilitation methods is functional electrical stimulation. In the work that was mainly done in the control of standing balance with functional electrical stimulation, the problem of the optimal pattern using the phase space was not mentioned at all, and a series of predetermined desired curves were assigned to the joints, and the controller only used these curves. followed, while the origin of these curves are not real patterns. Therefore, the main goal of this project is to design a periodic controller based on phase space. In such a way that a mapping related to standing is detected first, then a feedback controller is designed so that it is activated only when the system state space curves find a significant distance from the detected mapping, then the feedback controller is activated, and it adjusts the control signal so that the system state space curves come close to the detected mapping

Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images

In this study, the main objective is to develop an algorithm capable of identifying and delineating tumor regions in breast ultrasound (BUS) and mammographic images. The technique employs two advanced deep learning architectures, namely U-Net and pretrained SAM, for tumor segmentation. The U-Net model is specifically designed for medical image segmentation and leverages its deep convolutional neural network framework to extract meaningful features from input images. On the other hand, the pretrained SAM architecture incorporates a mechanism to capture spatial dependencies and generate segmentation results. Evaluation is conducted on a diverse dataset containing annotated tumor regions in BUS and mammographic images, covering both benign and malignant tumors. This dataset enables a comprehensive assessment of the algorithm's performance across different tumor types. Results demonstrate that the U-Net model outperforms the pretrained SAM architecture in accurately identifying and segmenting tumor regions in both BUS and mammographic images. The U-Net exhibits superior performance in challenging cases involving irregular shapes, indistinct boundaries, and high tumor heterogeneity. In contrast, the pretrained SAM architecture exhibits limitations in accurately identifying tumor areas, particularly for malignant tumors and objects with weak boundaries or complex shapes. These findings highlight the importance of selecting appropriate deep learning architectures tailored for medical image segmentation. The U-Net model showcases its potential as a robust and accurate tool for tumor detection, while the pretrained SAM architecture suggests the need for further improvements to enhance segmentation performance