This paper proposes a novel fuzzy-adaptive extended Kalman filter (FAEKF) for the real-time attitude estimation of agile mobile platforms equipped with magnetic, angular rate, and gravity (MARG) sensor arrays. The filter structure employs both a quaternion-based EKF and an adaptive extension, in which novel measurement methods are used to calculate the magnitudes of system vibrations, external accelerations, and magnetic distortions. These magnitudes, as external disturbances, are incorporated into a sophisticated fuzzy inference machine, which executes fuzzy IF-THEN rules-based adaption laws to consistently modify the noise covariance matrices of the filter, thereby providing accurate and robust attitude results. A six-degrees of freedom (6 DOF) test bench is designed for filter performance evaluation, which executes various dynamic behaviors and enables measurement of the true attitude angles (ground truth) along with the raw MARG sensor data. The tuning of filter parameters is performed with numerical optimization based on the collected measurements from the test environment. A comprehensive analysis highlights that the proposed adaptive strategy significantly improves the attitude estimation quality. Moreover, the filter structure successfully rejects the effects of both slow and fast external perturbations. The FAEKF can be applied to any mobile system in which attitude estimation is necessary for localization and external disturbances greatly influence the filter accuracy.

This review paper presents an overview of depth cameras. Our goal is to describe the features and capabilities of the introduced depth sensors in order to determine their possibilities in robotic applications, focusing on objects that might appear in applications with high accuracy requirements. A series of experiments was conducted, and various depth measuring conditions were examined in order to compare the measurement results of all the depth cameras. Based on the results, all the examined depth sensors were appropriate for applications where obstacle avoidance and robot spatial orientation were required in coexistence with image vision algorithms. In robotic vision applications where high accuracy and precision were obligatory, the ZED depth sensors achieved better measurement results.

Accurate and reliable measurement of the electrical impedance spectrum is an essential requirement in order to draw relevant conclusions in many fields and a variety of applications; in particular, for biological processes. Even in the state-of-the-art methods developed for this purpose, the accuracy and efficacy of impedance measurements are reduced in biological systems, due to the regular occurrence of parameters causing measurement errors such as residual impedance, parasitic capacitance, generator anomalies, and so on. Recent observations have reported the necessity of decreasing such inaccuracies whenever measurements are performed in the ultra-low frequency range, as the above-mentioned errors are almost entirely absent in such cases. The current research work proposes a method which can reject the anomalies listed above when measuring in the ultra-low frequency range, facilitating data collection at the same time. To demonstrate our hypothesis, originating from the consideration of the determinant role of the measuring frequency, a physical model is proposed to examine the effectiveness of our method by measuring across the commonly used vs. ultra-low frequency ranges. Validation measurements reflect that the range of frequencies and the accuracy is much greater than in state-of-the-art methods. Using the proposed new impedance examination technique, biological system characterization can be carried out more accurately.

The utilization of stereo cameras in robotic applications is presented in this paper. The use of a stereo depth sensor is a principal step in robotics applications, since it is the first step in sequences of robotic actions where the intent is to detect and extract windows and obstacles that are not meant to be painted from the surrounding wall. A RealSense D435 stereo camera was used for surface recording via a real-time, appearance-based (RTAB) mapping procedure, as well as to navigate the painting robot. Later, wall detection and the obstacle avoidance processes were performed using statistical filtering and a random sample consensus model (RANSAC) algorithm.

Bioimpedance spectrum (BIS) measurements have a great future in in vitro experiments, meeting all the requirements for non-destructive and label-free methods. Nevertheless, a real basic research can provide the necessary milestones to achieve the success of the method. In this paper a self-developed technology-based approach for in vitro assays is proposed. Authors invented a special graphene-based measuring plate in order to assess the high sensitivity and reproducibility of introduced technique. The design of the self-produced BIS plates maximizes the detection capacity of qualitative changes in cell culture and it is robust against physical effects and artifacts. The plates do not influence the viability and proliferation, however the results are robust, stable and reproducible regardless of when and where the experiments are carried out. In this study, physiological saline concentrations, two cancer and stem cell lines were utilized. All the results were statistically tested and confirmed. The findings of the assays show, that the introduced BIS technology is appropriate to be used in vitro experiments with high efficacy. The experimental results demonstrate high correlation values across the replicates, and the model parameters suggested that the characteristic differences among the various cell lines can be detected using appropriate hypothesis tests.

This paper presents the use of the ZED depth sensor in a robot-based painting application. The use of a stereo depth sensor is a very important factor in robotic applications, since it is both the initial and the essential step in a sequence of robotic operations, where the goal is to detect and extract the useful surface and objects or the obstacle on a wall that is not intended for painting. The ZED depth sensor was used for surface recording and navigation of our painting robot. Later, wall extraction was performed using simple image processing and morphological operations in a surface extraction algorithm. The goal was to use well-known, simple, and proven image processing operations in the algorithm to ensure both reliable and smooth operation of the robot’s vision system in an industrial environment. The experiments showed that the developed algorithm detects and extracts the wall successfully under various depth measurement conditions.