Magnetoresistive (MR) sensors have been identified as promising candidates for the development of high-performance magnetometers due to their high sensitivity, low cost, low power consumption, and small size. The rapid advance of MR sensor technology has opened up a variety of MR sensor applications. These applications are in different areas that require MR sensors with different properties. Future MR sensor development in each of these areas requires an overview and a strategic guide. An MR sensor roadmap (non-recording applications) was therefore developed and made public by the Technical Committee of the IEEE Magnetics Society with the aim to provide an research and development (R&D) guide for MR sensors intended to be used by industry, government, and academia. The roadmap was developed over a three-year period and coordinated by an international effort of 22 taskforce members from ten countries and 17 organizations, including universities, research institutes, and sensor companies. In this paper, the current status of MR sensors for non-recording applications was identified by analyzing the patent and publication statistics. As a result, timescales for MR sensor development were established and critical milestones for sensor parameters were extracted in order to gain insight into potential MR sensor applications (non-recording). Five application areas were identified, and five MR sensor roadmaps were established. These include biomedical applications, flexible electronics, position sensing and human-computer interactions, non-destructive evaluation and monitoring, and navigation and transportation. Each roadmap was analyzed using a logistic growth model, and new opportunities were predicted based on the extrapolated curve, forecast milestones, and professional judgment of the taskforce members. This paper provides a framework for MR sensor technology (non-recording applications) to be used for public and private R&D planning, in order to provide guidance into likely MR sensor applications, products, and services expected in the next 15 years and beyond.

Protection devices are extensively utilized in direct current (DC) systems to ensure their normal operation and safety. However, series arc faults that establish current paths in the air between conductors introduce arc impedance to the system. Consequently, they can result in a decrease of current, and thus conventional protection devices may not be triggered. Undetected series arc faults can cause malfunctions and even lead to fire hazards. Therefore, a series arc-fault detection system is essential to DC systems to operate reliably and efficiently. In this paper, a series arc-fault detection system based on arc time-frequency signatures extracted by a modified empirical mode decomposition (EMD) technique and using a support vector machine (SVM) algorithm in decision making is proposed for DC systems. The oscillatory frequencies from the arc current are decomposed by the EMD with an analysis of the Hurst exponent ( ${H}$ ) to reject interference from the power electronics noise. ${H}$ analyzes the trend of a signal and the intrinsic oscillations of the signal are those with values of ${H}$ larger than 1/2. Comparing to traditional filters or wavelet transforms, this method does not require knowledge of the frequency range of the interference which varies from system to system. The capability and applicability of the proposed technique are validated in a photovoltaic system. The effectiveness of arc-fault detection is significantly improved by this technique because it can acquire sufficient and accurate arc signatures and it does not need to predefine various thresholds.

Smart living is a trending and connected lifestyle that envisions efficient and sustainable energy utilization, stable and reliable power supply, intelligent and coordinated transportation and mobility, and personalized and cost-effective healthcare. Its realization needs the Internet of Things (IoT). IoT is a compelling platform connecting trillions of sensors and collecting data for connectivity and analytics. It is more advanced than traditional monitoring systems where limited sensors and wired communication can merely collect fragmented data in the application domains. Spintronic sensors with the superb measuring ability and multiple unique advantages can be one of the critical sensing devices supporting the IoT and enabling smart living. In this paper, we review successful applications of individual spintronic sensors in electrical current sensing, transmission and distribution lines monitoring, vehicle detection, and biodetection that can help to fulfill the promises of smart living in energy management, power delivery, transport, and healthcare. The wireless spintronic sensor networks (WSSNs) working at the massive interconnected network level are proposed and illustrated to provide pervasive monitoring systems, which facilitate the intelligent surveillance and management over building, power grid, transport, and healthcare. The database of collected information will be of great use to policy making in public services and city planning. This paper provides insights for realizing smart living through the integration of IoT with spintronic sensor technology.