Search Results (13)

In this study, we present the custom development and implementation of a mobile application designed specifically to experiment with Malus’s law, leveraging the integration of a LEGO EV3 and the light sensor of a mobile device. Unlike previous studies that utilize pre-designed mobile applications, our approach focuses on creating a tailored solution that meets the unique requirements of this optical experiment. Using MIT App Inventor, we developed a customized interface that allows for the rotation of polarizers controlled by motors connected to a LEGO EV3 while simultaneously measuring the light intensity using the mobile’s light sensor. The block-based programming in App Inventor facilitates the application of programming concepts in creating physics experiments in a straightforward manner. This innovative approach not only facilitates the understanding of fundamental optical concepts but also integrates accessible technology to enrich the educational experience in physics, offering a customizable solution adaptable to various educational settings. Preliminary results indicate a significant improvement in students’ comprehension of optical polarization principles, demonstrating the effectiveness of our approach. Full article

Monitoring lake area changes with a higher spatial and temporal resolution can facilitate a more detailed analysis of climate-related changes in the Tibetan Plateau. In the Landsat era, optical remote sensing observation with water body index-based methods mainly contributed to alpine lake investigation. However, monitoring the seasonal or monthly change of a lake area is challenging since optical data are easily contaminated by the high cloud cover in the Tibetan Plateau. To cope with this, we generated new time series datasets including Sentinel-1 Synthetic Aperture Radar (SAR) and the Landsat-8 Operational Land Imager (OLI) observations. Meanwhile, we presented an improved deep learning model with spatial and channel attention mechanisms. Based on these datasets, we compared several deep learning models and found that the CloudNet+ had better performance. Taking this architecture as a baseline, we added spatial and channel attention mechanisms to generate our AttCloudNet+ for extracting the lake area. The results revealed that AttCloudNet+ had a better performance compared with the CloudNet+ and other CNNs (e.g., DeepLabv3+, UNet). For the accuracy of the lakeshore prediction, results from AttCloudNet+ demonstrated closer distance to the truth-value than other models. The obtained mean RMSE and MAE were 21.6 and 16.6 m, respectively. In contrast, the mean RMSE and MAE of the DeepLabv3+ were 99.5 and 76.0 m, while the corresponding RMSE and MAE for UNet were 91.1 and 64.9 m. In addition, we found our AttCloudNet+ was more robust than UNet and DeepLabv3+ because AttCloudNet+ is less influenced by the input optical images compared with DeepLabv3+ and UNet. By combining the results from different seasons and satellite sensors, we are capable of generating the complete lake area seasonal dynamics of the 15 largest lakes. The mean correlation coefficient (R²) between our seasonal lake area time series and the water level of LEGOS is 0.81, which is much better than the previous study (0.25). This indicates that our method can be used to monitor lake area seasonal variation, which is important for understanding regional climate change in the Tibetan Plateau and other similar areas. Full article

In order to enhance the sensitivity of elastomers, pores were integrated into their structure. These pores facilitate the adjustment of thickness in response to external pressure variations, thereby improving the sensitivity of pressure sensors. Pores were introduced by emulsifying immiscible polydimethylsiloxane (PDMS) and water with a surfactant. By controlling the water content in the PDMS and water emulsion, we controlled the size, density, uniformity, and spatial distribution (2D or 3D) of the pores within the PDMS matrix. The presence of these pores significantly improved the sensitivity of PDMS under low external pressure conditions compared to high pressures. Specifically, porous PDMS exhibited approximately 10-times greater sensitivity under low-pressure conditions than non-porous PDMS. The effectiveness of porous PDMS was demonstrated through dynamic loading and unloading detection of a small Lego toy and monitoring of human heartbeats. These results highlight the efficacy of our pressure sensor based on porous PDMS, which is fabricated through a simple and cost-effective process using a PDMS and water emulsion. This approach is highly suitable for developing the ability to detect applied pressures or contact forces. Full article

A proper and complete formation in technology (science, communications, programming, robotics, Computational Thinking, etc.) must be imparted at all educational levels for a lifelong education. However, students may lose motivation or interest due to the complexity and abstraction of some of the concepts imparted. In line with this, the work at hand looks to improve the interest and commitment of students by presenting the programming concepts and contents in a practical way. The teaching–learning process is based on the development of projects about robotics, which are adapted for courses and groups of different educational levels. The Design Thinking methodology is used to impart the content. This methodology allows the students to experiment, design and test different solutions for a given problem, increasing their motivation and interest, promoting creativity, and making the students conscious of their learning process. Two different projects are considered, a simulated one based on a sensor network to localise and track a robot in a closed area for vocational education students, and an experimental one about constructing a robot with several capabilities using Lego Mindstorms for compulsory secondary education students. The results obtained over three different groups of students are analysed and compared, and show that the methodology and projects selected can be adopted and adapted for different educational levels, increasing the proficiency of the students, their development, motivation and self-learning despite the difficulty and complexity of some concepts related to computer science. Full article

A new three-phase downhole flow measurement methodology is developed based on measurements of speed of sound at different locations along the well, where the pressure is greater than the bubble-point pressure at the first location and smaller at the second location. A bulk velocity measurement is also required at the second location. The fluid at the first location is a mixture of two phases, but becomes a mixture of three phases at the second location due to the liberation of gas from the oil phase. The flow equations are first solved for two-phase flow at the first location to obtain the first phase fraction, water-in-liquid ratio, and then this information is fed into the flow equations after adjustment to the local pressure and temperature conditions to solve for three-phase flow at the second location to obtain the second phase fraction, namely the liquid volume fraction. These two phase fractions along with the bulk velocity at the second location are sufficient to calculate the three-phase flow rates. The methodology is fully explained and the analytical solutions for three-phase flow measurement is explicitly provided in a step-by-step process. A Lego-like approach may be used with various sensor technologies to obtain the required measurements, although distributed acoustic sensing systems and optical flowmeters are ideal to easily and efficiently adopt the current methodology. This game-changing new methodology for measuring downhole three-phase flow can be implemented in existing wells with an optical infrastructure by adding a topside optoelectronics system. Full article

This article is a continuation and extension of research on a new approach to the classification and recognition of EEG signals. Their goal is to control the mobile robot through mental commands, using a measuring set such as Emotiv Epoc Flex Gel. The headset, despite its relative advancement, is rarely found in this type of research, which makes it possible to search for its advanced and innovative applications. The uniqueness of the proposed approach is the use of an EMG measuring device located on the biceps, i.e., MyoWare Muscle Sensor. This is to verify pure mental commands without additional muscle contractions. The participants of the study were asked to imagine the forearm movement that was responsible for triggering the movement command of the LEGO Mindstorms EV3 robot. The change in direction of movement is controlled by artifacts in the signal caused by the blink of an eyelid. The measured EEG signal was subjected to meticulous analysis by an expert system containing a classic classification algorithm and an artificial neural network. It was supposed to recognize mental commands, as well as detect artifacts in the form of blinking and change the direction of the robot’s movement. In addition, the system monitored the analysis of the EMG signal, detecting possible muscle tensions. The output of the expert algorithm was a control signal sent to the mobile robot. Full article

This paper presents a new approach to the issue of recognition and classification of electroencephalographic signals (EEG). A small number of investigations using the Emotiv Epoc Flex sensor set was the reason for searching for original solutions including control of elements of robotics with mental orders given by a user. The signal, measured and archived with a 32-electrode device, was prepared for classification using a new solution consisting of EEG signal integration. The new waveforms modified in this way could be subjected to recognition both by a classic authorial software and an artificial neural network. The properly classified signals made it possible to use them as the signals controlling the LEGO EV3 Mindstorms robot. Full article

LEGO Mindstorms robots are widely used as educational tools to acquire skills in programming complex systems involving the interaction of sensors and actuators, and they offer a flexible and modular workbench to design and evaluate user–machine interaction prototypes in the robotic area. However, there is still a lack of support to interoperability features and the need of high-level tools to program the interaction of a robot with other devices. In this paper, we introduce Legodroid, a new Java library enabling cross-programming LEGO Mindstorms robots through Android smartphones that exploits their combined computational and sensorial capabilities in a seamless way. The library provides a number of type-driven coding patterns for interacting with sensors and motors. In this way, the robustness of the software managing robot’s sensors dramatically improves. Full article

C-arm X-ray imaging is commonly applied in operating rooms for guiding orthopedic surgeries. Augmented Reality (AR) with C-arm X-ray images during surgery is an efficient way to facilitate procedures for surgeons. However, the accurate calibration process for surgical AR based on C-arm is essential and still challenging due to the limitations of C-arm imaging systems, such as instability of C-arm calibration parameters and the narrow field of view. We extend existing methods using a depth camera and propose a new calibration procedure consisting of calibration of the C-arm imaging system, and 3D/2D calibration of an RGB-D camera and C-arm system with a new method to achieve reliable data and promising accuracy and, at the same time, consistent with standard surgical protocols. For the calibration procedure, we apply bundle adjustment equations with a 3D designed Lego multi-modal phantom, in contrast to the previous methods in which planar calibration phantoms were applied. By using our method, the visualization of the X-ray image upon the 3D data was done, and the achieved mean overlay error was 1.03 mm. The evaluations showed that the proposed calibration procedure provided promising accuracy for AR surgeries and it improved the flexibility and robustness of existing C-arm calibration methods for surgical augmented reality (using C-arm and RGB-D sensor). Moreover, the results showed the efficiency of our method to compensate for the effects of the C-arm movement on calibration parameters. It was shown that the obtained overlay error was improved for the non-zero rotation movement of C-arm by using a virtual detector. Full article

The constructivist approach is interested in creating knowledge through active engagement and encourages students to build their knowledge from their experiences in the world. Learning through digital game making is a constructivist approach that allows students to learn by developing their own games, enhancing problem-solving skills and fostering creativity. In this context two tools, Create@School App and the Project Management Dashboard (PMD), were developed to enable students from different countries to be able to adapt their learning material by programming and designing games for their academic subjects, therefore integrating the game mechanics, dynamics, and aesthetics into the academic curriculum. This paper focuses on presenting the validation context as well as the evaluation of these tools. The Hassenzahl model and AttrakDiff survey were used for measuring users’ experience and satisfaction, and for understanding emotional responses, thus providing information that enables testing of the acceptability and usability of the developed apps. After two years of usage of code-making apps (i.e., Create@School and its pre-design version Pocket Code), the pupils processed knowledge from their academic subjects spontaneously as game-based embedded knowledge. The students demonstrated creativity, a practical approach, and enthusiasm regarding making games focused on academic content that led them to learning, using mobile devices, sensors, images, and contextual information. This approach was widely accepted by students and teachers as part of their everyday class routines. Full article

This publication presents a complete description of a technological solution system for the physical and cognitive rehabilitation of elderly people through a biofeedback system, which is combined with a Lego robot. The technology used was the iOS’s (iPhone Operating System) Objective-C programming language and its XCode programming environment; and SQLite in order to create the database. The biofeedback system is implemented by the use of two biosensors which are, in fact, a Microsoft band 2 in order to register the user’s heart rate and a MYO sensor to detect the user’s arm movement. Finally, the system was tested with seven elderly people from La Santa y Real Casa de la Misericordia nursing home in Bilbao. The statistical assessment has shown that the users are satisfied with the usability of the system, with a mean score of 79.29 on the System Usability Scale (SUS) questionnaire. Full article

In this paper, a remote lab for experimenting with a team of mobile robots is presented. Robots are built with the LEGO Mindstorms technology and user-defined control laws can be directly coded in the Matlab programming language and validated on the real system. The lab is versatile enough to be used for both teaching and research purposes. Students can easily go through a number of predefined mobile robotics experiences without having to worry about robot hardware or low-level programming languages. More advanced experiments can also be carried out by uploading custom controllers. The capability to have full control of the vehicles, together with the possibility to define arbitrarily complex environments through the definition of virtual obstacles, makes the proposed facility well suited to quickly test and compare different control laws in a real-world scenario. Moreover, the user can simulate the presence of different types of exteroceptive sensors on board of the robots or a specific communication architecture among the agents, so that decentralized control strategies and motion coordination algorithms can be easily implemented and tested. A number of possible applications and real experiments are presented in order to illustrate the main features of the proposed mobile robotics remote lab. Full article

This paper presents a sensor fusion framework that improves the localization of mobile robots with limited computational resources. It employs an event based Kalman Filter to combine the measurements of a global sensor and an inertial measurement unit (IMU) on an event based schedule, using fewer resources (execution time and bandwidth) but with similar performance when compared to the traditional methods. The event is defined to reflect the necessity of the global information, when the estimation error covariance exceeds a predefined limit. The proposed experimental platforms are based on the LEGO Mindstorm NXT, and consist of a differential wheel mobile robot navigating indoors with a zenithal camera as global sensor, and an Ackermann steering mobile robot navigating outdoors with a SBG Systems GPS accessed through an IGEP board that also serves as datalogger. The IMU in both robots is built using the NXT motor encoders along with one gyroscope, one compass and two accelerometers from Hitecnic, placed according to a particle based dynamic model of the robots. The tests performed reflect the correct performance and low execution time of the proposed framework. The robustness and stability is observed during a long walk test in both indoors and outdoors environments. Full article