This work deals with an adaptive disturbance rejection controller which combines the equivalent control based integral sliding mode control (ISMC) strategy and a Mamdani fuzzy inference system (FIS) for trajectory tracking of a quadrotor vehicle subject to Dryden-type wind gusts. The FIS carries out an on-line estimation of the time constant of a low-pass filter (LPF) to extract the equivalent control from the discontinuous control signal. This strategy dynamically estimates the disturbances and uncertainties acting on the plant. The estimated disturbances are compensated via a feedback control law making the system insensitive to these undesired effects. Finally, numerical simulations to evaluate the proposed controller performing a simulated quadrotor flying inspection task are presented.
This paper presents a control strategy for blood glucose regulation in the presence of an unknown time-varying delayed input. The proposed control scheme is based on an observer-based linear parameter varying (LPV) control with an $H$ criterion, designed for the Bergman minimal model (BMM) for type 1 diabetes mellitus patients. This control strategy regulates glucose levels in response to meal intake. A novel LPV system representation is used to mitigate the inherent conservativeness of the nonlinear system. The observer employs an event-triggered (ET) strategy to avoid unnecessary signal updates, enabling the estimation of the current state vector despite the delayed input, even when the delay is relatively long. Consequently, the control law ensures good performance despite a long unknown delay. The observer-based control scheme is validated under various meal disturbances and unknown delay.
Anemia, characterized by a deficiency in red blood cells (RBCs) or hemoglobin (HGB), poses significant global public health challenges. Accurate diagnosis and classification of anemia types, crucially dependent on hemoglobin (HGB) level estimation, is essential for effective management and treatment planning. This study investigates the prevalence, severity, and demographic associations of various anemia types among 364 adult patients using publicly available data from complete blood count (CBC) tests. The dataset includes 11 CBC attributes pivotal for anemia diagnosis, encompassing HGB levels and RBC counts. To address the complexities inherent in CBC data, such as variations in normal ranges based on age and gender, and challenges in data interpretation across diverse patient demographics, machine learning techniques are employed. Specifically, projected layers of long short-term memory networks (PLSTM) are utilized. These techniques require robust methodologies for data preprocessing, feature selection, and model optimization based on coefficient of determination R2, prioritizing realism and the preservation of critical medical information. This approach ensures accurate predictive modeling of anemia types based on hemoglobin (HGB) levels, classified as mild, moderate, or severe according to World Health Organization (WHO) guidelines. Insights derived from this study, leveraging PLSTM over traditional LSTM networks, contribute to enhancing diagnostic accuracy and developing personalized treatment strategies for anemia. This underscores the role of machine learning in tailoring approaches to complex medical datasets, thereby advancing healthcare outcomes.
The estimation of three abdominal tissue thicknesses, namely subcutaneous fat (SAT), muscle tissue (MT), and visceral fat (VT), was performed using radio frequency (RF) signal measurement and machine learning algorithms. The scattering parameter data were collected from simulations of a planar multilayered model of the human abdomen, with parametric analysis on each layer's thickness. Various models, incorporating combinations of several frequency bands and 120 data points, were investigated. The results showed that polynomial regression (PR) and ridge polynomial regression (RiPR) using scattering parameters from frequency bands of 300 MHz, 400 MHz, 500 MHz, and 1 GHz performed the best among other models. PR (300 MHz, 400 MHz, 500 MHz, and 1 GHz) achieved RMSE values of 0.71 mm for SAT estimation, 1.2 mm for MT estimation, and 1.17 mm for VT estimation. RiPR (300 MHz, 400 MHz, 500 MHz, and 1 GHz) achieved RMSE values of 0.35 mm for SAT estimation, 1.57 mm for MT estimation, and 1.7 mm for VT estimation. However, these results indicated a high error percentage for VT estimation, as VT had a mean thickness of only 3 mm. The results also showed that the deeper the tissue, the more prone the estimation was to error. This is due to the nature of electromagnetic reflection and transmission within a multilayered medium, which affects the scattering parameter measurement. While the models perform well for upper tissue estimation, further improvements in the model, feature selection, and physical solutions are necessary to enhance the accuracy of deeper tissue estimation.
The increase in information technologies has generated the need to implement new methodologies to secure the exchange of information. This paper proposes a cryptographic algorithm based on the use of quantum differential geometry operators. These operators exhibit mathematical behaviors similar to quantum behavior, with the advantage that specialized equipment is not required to perform the encryption process. The algorithm is composed of two phases: In the first phase, a subkey is generated through an addition and multiplication process in Z_16 based on a user-defined key. In the second phase, three types of fundamental quantum operators are employed: creation, annihilation, and crossing, which are sequentially connected. The specific selection and configuration of these operators are based on their ability to avoid generating correlation between the data. The method proposed in this work is implemented using grayscale images, obtaining the Peak Signal-to-Noise Ratio (PSNR), the Structural Similarity Index Measure (SSIM), and the Correlation Value, in order to evaluate the fidelity and integrity of the recovered images.
