High-Speed and High-Temperature Calorimetric Solid-State Thermal Mass Flow Sensor for Aerospace Application: A Sensitivity Analysis.

A high-speed and high-temperature calorimetric solid-state thermal mass flow sensor (TMFS) design was proposed and its sensitivity to temperature and airflow speed were numerically assessed. The sensor operates at 573.15 Kelvin (300 °C), measuring speeds up to 265 m/s, and is customized to be a transducer for an aircraft Air Data System (ADS). The aim was to enhance the system reliability against ice accretion on pitot tubes' pressure intakes, which causes the system to be inoperative and the aircraft to lose protections that ensure its safe operation. In this paper, the authors assess how the distance between heater and thermal sensors affects the overall TMFS sensitivity and how it can benefit from the inclusion of a thermal barrier between these elements. The results show that, by increasing the distance between the heater and temperature sensors from 0.1 to 0.6 mm, the sensitivity to temperature variation is improved by up to 80%, and that to airspeed variation is improved by up to 100%. In addition, adding a thermal barrier made of Parylene-N improves it even further, by nearly 6 times, for both temperature and air speed variations.

Fast real-time SDRE controllers using neural networks.

This paper describes the implementation of fast state-dependent Riccati equation (SDRE) control algorithms through the use of shallow and deep artificial neural networks (ANN). Several ANNs are trained to replicate an SDRE controller developed for a satellite attitude dynamics simulator (SADS) to display the technique's efficacy. The neural controllers have reduced computational complexity compared with the original SDRE controller, allowing its execution at a significantly higher rate. One of the neural controllers was validated using the SADS in a practical experiment. The experimental results indicate that the training error is sufficiently small for the neural controller to perform equivalently to the original SDRE controller.

Evaluation of multi-class support-vector machines strategies and kernel adjustment levels in hand posture recognition by analyzing sEMG signals acquired from a wearable device.

One-vs-One (OVO) and One-vs-All (OVA) are decomposition methods for multi-class strategies used to allow binary Support-Vector Machines (SVM) to transform a given k-class problem into pairwise small problems. In this context, the present work proposes the analysis of these two decomposition methods applied to the hand posture recognition problem in which the sEMG data of eight participants were collected by means of an 8-channel armband bracelet located on the forearm. Linear, Polynomial and Radial Basis Function kernels functions and its adjustments level were implemented combined to the strategies OVO and OVA to compare the performance of the SVM when mapping posture data into the classification spaces spanned by the studied kernels. Acquired sEMG signals were segmented considering 0.16 s e 0.32 s time windows. Root Mean Square (RMS) feature was extracted from each time window of each posture and used for SVM training. The present work focused in investigating the relationship between the multi-class strategies combined to kernels adjustments levels and SVM classification performance. Promising results were observed using OVA strategy which presents a reduced number of binary SVM implementation achieved a mean accuracy of 97.63%.

Filtragem de sinais Raman de tecidos biológicos para reduçäo de ruído e background / Filter of Raman signals of biological tissue for background and noise reduction

A redução do tempo de aquisição do sinal e processamento de espectros Raman obtidos à partir de tecidos biológicos é um passo importante para se tornar os sistemas laboratoriais uma ferramenta clínica. Neste trabalho é apresentado a extração de fluorescência e ruído de um espectro Raman de uma coronária humana.