Fast, cheap, and scalable magnetic tracker with an array of magnetoresistors

We present the hardware of a cheap multi-sensor magnetometric setup where a relatively large set of magnetic field components is measured in several positions by calibrated magnetoresistive detectors. The setup is developed with the scope of mapping the (inhomogeneous) field generated by a known magnetic source, which is measured as superimposed to the (homogeneous) geomagnetic field. The final goal is to use the data produced by this hardware to reconstruct position and orientation of the magnetic source with respect to the sensor frame, simultaneously with the orientation of the frame with respect to the environmental field. Possible applications of the setup are shortly discussed, together with a synthetic description of the data elaboration and analysis.Comment: 10 pages, 7 figures, 30 ref

Hybrid inertial-manipulator based position tracking system for ultrasound imaging application

In medical field, ultrasound imaging is one of the imaging modalities that needs position tracking system (PTS) in enlarging field of view (FoV) of an image. The enlarged FoV will result easier scanning procedure, and produce more accurate and comprehensive results. To overcome the weakness of commercially available PTSs which suffer from interference and occlusion, many researchers proposed improved PTSs. However, the improved PTSs focused on the portability and compact design, neglecting the vertical scanning aspect which is also important in ultrasound imaging. Hence, this research presents the development of hybrid inertial-manipulator based PTS for 3-dimensional (3D) ultrasound imaging system which capable of measuring the horizontal and vertical scanning movements. The proposed PTS uses the combination of inertial measurement unit sensor and manipulator. The research involves design and evaluation processes for the PTS. Once the design process of the PTS is completed, forward kinematics is calculated using Denavit-Hartenberg conversion. The next step is to evaluate the accuracy and repeatability of the output of the designed PTS by comparing with five sets of reference trajectory of ABB robot. A comparison of the accuracy for the proposed PTS with three other available PTSs is done using the horizontal movement’s error. The experimental results showed high repeatability of position output reading of the designed PTS with standard deviation of 0.27 mm in all different movements and speeds. The proposed PTS is suitable to be used in ultrasound imaging as the error is less than 1.45 mm. Furthermore, the proposed PTS can measure the vertical scanning movement which is neglected in all the previous works, thus fulfilling the main objective of the research

Intelligent Power Aware Algorithms for Traffic Sensors

The Internet of Things (IoT) is reshaping our world. Soon our world will be based on smart technologies. According to IHS Markit forecasts, the number of connected devices will grow from 15.4 billion in 2015 to 30.7 billion in 2020. Forrester Research predicts that fleet management and the transportation sectors lead others in IoT growth. This may come as no surprise, since the infrastructure (roadways, bridges, airports, etc.) is a prime candidate for sensor integration, providing real-time measurements to support intelligent decisions. The energy cost required to support the anticipated enormous number of predicted deployed devices is unknown. Currently, experts estimate that 2 to 4% of worldwide carbon emissions can be attributed to power consumption in the information and communication industry [1]. This thesis presents several algorithms to optimize power consumption of an intelligent vehicle counter and classifier sensor (iVCCS) based on an event-driven methodology wherein a control block orchestrates the work of various components and subsystems. Data buffering and triggered vehicle detection techniques were developed to reduce duty cycle of corresponding components (e.g., microSD card, magnetometer, and processor execution). A sleep mode is also incorporated and activated by an artificial intelligence-enabled, reinforcement learning algorithm that utilizes the field environment to select proper processor mode (e.g., run or sleep) relative to traffic flow conditions. Sensor life was extended from 48 hours to more than 200 days when leveraging 2300 mAh battery along with algorithms and techniques introduced in this thesis

FULLY AUTONOMOUS SELF-POWERED INTELLIGENT WIRELESS SENSOR FOR REAL-TIME TRAFFIC SURVEILLANCE IN SMART CITIES

Reliable, real-time traffic surveillance is an integral and crucial function of the 21st century intelligent transportation systems (ITS) network. This technology facilitates instantaneous decision-making, improves roadway efficiency, and maximizes existing transportation infrastructure capacity, making transportation systems safe, efficient, and more reliable. Given the rapidly approaching era of smart cities, the work detailed in this dissertation is timely in that it reports on the design, development, and implementation of a novel, fully-autonomous, self-powered intelligent wireless sensor for real-time traffic surveillance. Multi-disciplinary, innovative integration of state-of-the-art, ultra-low-power embedded systems, smart physical sensors, and the wireless sensor network—powered by intelligent algorithms—are the basis of the developed Intelligent Vehicle Counting and Classification Sensor (iVCCS) platform. The sensor combines an energy-harvesting subsystem to extract energy from multiple sources and enable sensor node self-powering aimed at potentially indefinite life. A wireless power receiver was also integrated to remotely charge the sensor’s primary battery. Reliable and computationally efficient intelligent algorithms for vehicle detection, speed and length estimation, vehicle classification, vehicle re-identification, travel-time estimation, time-synchronization, and drift compensation were fully developed, integrated, and evaluated. Several length-based vehicle classification schemes particular to the state of Oklahoma were developed, implemented, and evaluated using machine learning algorithms and probabilistic modeling of vehicle magnetic length. A feature extraction employing different techniques was developed to determine suitable and efficient features for magnetic signature-based vehicle re-identification. Additionally, two vehicle re-identification models based on matching vehicle magnetic signature from a single magnetometer were developed. Comprehensive system evaluation and extensive data analyses were performed to fine-tune and validate the sensor, ensuring reliable and robust operation. Several field studies were conducted under various scenarios and traffic conditions on a number of highways and urban roads and resulted in 99.98% detection accuracy, 97.4782% speed estimation accuracy, and 97.6951% classification rate when binning vehicles into four groups based on their magnetic length. Threshold-based, re-identification results revealed 65.25%~100% identification rate for a window of 25~500 vehicles. Voting-based, re-identification evaluation resulted in 90~100% identification rate for a window of 25~500 vehicles. The developed platform is portable and cost-effective. A single sensor node costs only $30 and can be installed for short-term use (e.g., work zone safety, traffic flow studies, roadway and bridge design, traffic management in atypical situations), as well as long-term use (e.g., collision avoidance at intersections, traffic monitoring) on highways, roadways, or roadside surfaces. The power consumption assessment showed that the sensor is operational for several years. The iVCCS platform is expected to significantly supplement other data collection methods used for traffic monitoring throughout the United States. The technology is poised to play a vital role in tomorrow’s smart cities

Magnetometer Modeling and Validation for Tracking Metallic Targets

With the electromagnetic theory as basis, we present a sensor model for three-axis magnetometers suitable for localization and tracking as required in intelligent transportation systems and security applications. The model depends on a physical magnetic dipole model of the target and its relative position to the sensor. Both point target and extended target models are provided as well as a target orientation dependent model. The suitability of magnetometers for tracking is analyzed in terms of local observability and the Cramér Rao lower bound as a function of the sensor positions in a two sensor scenario. The models are validated with real field test data taken from various road vehicles which indicate excellent localization as well as identification of the magnetic target model suitable for target classification. These sensor models can be combined with a standard motion model and a standard nonlinear filter to track metallic objects in a magnetometer network