Točna 3D rekonstrukcija zasnovana na rotirajućoj platformi i telecentričnoj viziji

This paper presents a camera+telecentric lens that is able to obtain 3D information. We designed and implemented a method which can register and integrate 3D information captured from different viewpoints to build a complete 3D object model. First, a geometric model of a camera+telecentric lens is established. Then a calibration process using a planar checkerboard is developed and implemented. The object is placed on a rotation stage in front of a stationary camera. Normally the rotation axis is considered to be aligned with camera frame. In the description presented in this paper, the rotation matrix and translation vector of the rotation axis are calibrated. At the same time, a three-dimensional reconstruction system based on contour extraction of objects with dimensions less than 50 mm in diameter is developed. Finally, an analysis of the uncertainty model parameters and performance reconstruction of 3D objects are discussed.Članak prestavlja sustav koji se sastoji od kamere i telecentrične leće koji omogućavaju dobivanje 3D informacije o objektu. Dizajnirana je i implementirana metoda koja može registrirati i integrirati 3D informacije iz različitih točaka gledišta, kako bi se izgradio potpuni 3D model. Na početku, uspostavlja se geometrijski model kamere i telecentrične leće. Nakon toga koristi se razvijena metoda kalibracije zasnovana na šahovskoj ploči te se objekt postavlja na rotirajuću platformu ispred stacionarne kamere. Također, pretpostavlja se da je os rotacije poravnta s koordinantim sustavom kamere. U ovome članku kalibriraju se rotacijska matrica i translacijski vektor rotacijske osi. Razvijen je i sustav 3D rekonstrukcija zasnovan na izlučivanju kontura objekta dimenzija manjih od 50 mm u promjeru. Na kraju, provedena je i analiza nesigurnosti parametara modela kao i točnost rekonstrukcije 3D modela

A New Approach for Motor Imagery Classification Based on Sorted Blind Source Separation, Continuous Wavelet Transform, and Convolutional Neural Network

Brain-Computer Interfaces (BCI) are systems that allow the interaction of people and devices on the grounds of brain activity. The noninvasive and most viable way to obtain such information is by using electroencephalography (EEG). However, these signals have a low signal-to-noise ratio, as well as a low spatial resolution. This work proposes a new method built from the combination of a Blind Source Separation (BSS) to obtain estimated independent components, a 2D representation of these component signals using the Continuous Wavelet Transform (CWT), and a classification stage using a Convolutional Neural Network (CNN) approach. A criterion based on the spectral correlation with a Movement Related Independent Component (MRIC) is used to sort the estimated sources by BSS, thus reducing the spatial variance. The experimental results of 94.66% using a k-fold cross validation are competitive with techniques recently reported in the state-of-the-art

Auto-Regression Model-Based Off-Line PID Controller Tuning: An Adaptive Strategy for DC Motor Control

Brushed (B) and Brushless (BL) DC motors constitute the cornerstone of mechatronic systems regardless their sizes (including miniaturized), in which both position and speed control tasks require the application of sophisticated algorithms. This manuscript addresses the initial step using time series analysis to forecast Back EMF values, thereby enabling the elaboration of real-time adaptive fine-tuning strategies for PID controllers in such a control system design problem. An Auto-Regressive Moving Average (ARMA) model is developed to estimate the DC motor parameter, which evolves in time due to the system’s imperfection (i.e., unpredictable duty cycle) and influences the closed-loop performance. The methodology is executed offline; thus, it highlights the applicability of collected BDC motor measurements in time series analysis. The proposed method updates the PID controller gains based on the Simulink ™ controller tuning toolbox. The contribution of this approach is shown in a comparative study that indicates an opportunity to use time series analysis to forecast DC motor parameters, to re-tune PID controller gains, and to obtain similar performance under the same perturbation conditions. The research demonstrates the practical applicability of the proposed method for fine-tuning/re-tuning controllers in real-time. The results show the inclusion of the time series analysis to recalculate controller gains as an alternative for adaptive control

A Study of Movement Classification of the Lower Limb Based on up to 4-EMG Channels

The number and position of sEMG electrodes have been studied extensively due to the need to improve the accuracy of the classification they carry out of the intention of movement. Nevertheless, increasing the number of channels used for this classification often increases their processing time as well. This research work contributes with a comparison of the classification accuracy based on the different number of sEMG signal channels (one to four) placed in the right lower limb of healthy subjects. The analysis is performed using Mean Absolute Values, Zero Crossings, Waveform Length, and Slope Sign Changes; these characteristics comprise the feature vector. The algorithm used for the classification is the Support Vector Machine after applying a Principal Component Analysis to the features. The results show that it is possible to reach more than 90% of classification accuracy by using 4 or 3 channels. Moreover, the difference obtained with 500 and 1000 samples, with 2, 3 and 4 channels, is not higher than 5%, which means that increasing the number of channels does not guarantee 100% precision in the classification