This paper presents a novel low power security system based on magnetic anomaly detection by using Tunneling Magnetoresistance (TMR) magnetic sensors. In this work, a smart light has been developed, which consists of TMR sensors array, detection circuits, a micro-controller and a battery. Taking the advantage of low power consumption of TMR magnetic sensors, the smart light powered by Li-ion battery can work for several months. Power Spectrum Density of the obtained signal was analyzed to reject background noise and improve the signal to noise ratio effectively by 1.3 dB, which represented a 30% detection range improvement. Also, by sending the signals to PC, the magnetic fingerprints of the objects have been configured clearly. In addition, the quick scan measurement has been also performed to demonstrate that the system can discriminate the multiple objects with 30 cm separation. Since the whole system was compact and portable, it can be used for security check at office, meeting room or other private places without attracting any attention. Moreover, it is promising to integrate multiply such systems together to achieve a wireless security network in large-scale monitoring.

Simulation platforms are critical and indispensable tools for application developments of unmanned aerial vehicles (UAVs) because the UAVs are generally costly, have certain requirements for the test environment, and need professional licensed operators. Thus, developers prefer (or have) to test their applications on simulation platforms before implementing them on real machines. In the past decades, a considerable number of simulation platforms for robots have been developed, which brings convenience to developers, but also makes them hard to choose a proper one as they are not always familiar with all the features of platforms. To alleviate this dilemma, this paper provides a survey of open-source simulation platforms and employs the simulation of a multi-copter UAV swarm as an example. The survey covers seven widely used simulators, including Webots, Gazebo, CoppeliaSim, ARGoS, MRDS, MORSE, and USARSim. The paper outlines the requirements for multi-copter UAV swarms and shows how to select an appropriate platform. Additionally, the paper presents a case study of a UAV swarm based on Webots. This research will be beneficial to researchers, developers, educators, and engineers who seek suitable simulation platforms for application development, (not only multi-copter UAV swarms but also other types of robots), which further helps them to save expenses for testing, and speed up development progress.

Tunneling magnetoresistive (TMR) magnetic sensors are broadly applied in various fields. However, to further improve the limit of detection (LOD), it is highly desirable to realize and calculate the noise limits of such sensor systems. Thus, techniques for noise elimination can be developed. In this paper, we propose an equivalent magnetic noise model to describe the LOD of a TMR magnetometer by considering intrinsic noise sources in the TMR sensing element and readout electronics. According to the model, the contribution of each noise source can be directly obtained. Therefore, the LOD of the magnetometer can be established theoretically. Moreover, the predicted detectivity of 390 pT/√Hz is also demonstrated by the experimental results. The model is applicable to all kinds of TMR sensors with a “Wheatstone bridge” configuration.

This research utilized an 8-fluxgate array, based on the magnetic gradient tensor method (MGT), to perform positioning trials within a controlled aquatic environment. The experiment featured five designated locations and targeted marginal magnetic signals of approximately 2-3 nT, which are resolvable by the sensors. The standard deviation of the positioning outcomes ranged from 0.0724 to 0.0764, while the root mean square error (RMSE) was confined within 0.0921 to 0.1468. At a detection distance of about 6 m, the discrepancies in the x and y axes were comparatively minor, whereas the z-axis exhibited a more significant deviation in positioning. By comparing with STAR, which is also based on the MGT algorithm, and incorporating the averaging method from their approach, we have optimized the original MGT algorithm. This enhancement has led to improved positioning accuracy, with the RMSE reduced to a range of 0.0219 to 0.0734, resulting in superior localization performance.