In this work we addressed a key challenge in realizing multiferroics-based reconfigurable magnetic devices, which is the ability to switch between distinct collective magnetic states in a reversible and stable manner with a control voltage. Three possible non-volatile switching mechanisms have been demonstrated, arising from the nature of the domain states in pervoskite PZN-PT crystal that the ferroelectric polarization reversal is partially coupled to the ferroelastic strain. Electric impulse non-volatile control of magnetic anisotropy in FeGaB/PZN-PT and domain distribution of FeGaB during the ferroelectric switching have been observed, which agrees very well with simulation results. These approaches provide a platform for realizing electric impulse non-volatile tuning of the order parameters that are coupled to the lattice strain in thin-film heterostructures, showing great potentials in achieving reconfigurable, compact, light-weight and ultra-low-power electronics.

Numerous indoor localization techniques have been proposed recently to meet the intensive demand for location based service, and Wi-Fi fingerprint-based approaches are the most popular and inexpensive solutions. Among them, one of the main trends is to incorporate the built-in sensors of smartphone and to exploit crowdsourcing potentials. However the noisy built-in sensors and multi-tasking limitation of underline OS often hinder the effectiveness of these schemes.In this work, we propose a passive crowdsourcing CSI-based indoor localization scheme, C 2 IL. Our scheme C 2 IL only requires the locating-device (e.g., a phone) to have a 802.11n wireless connection, and it does not rely on inertial sensors only existing in some smartphones. C 2 IL is built upon our innovative method to accurately estimate the moving distance purely based on 802.11n Channel State Information (CSI). Our extensive evaluations show that the moving distance estimation error of our scheme is within 3% of the actual moving distance regardless of varying speeds and environment. Relying on the accurate moving distance estimation as constraints, we are able to construct a more accurate mapping between RSS fingerprints and location. To address the challenges of collecting fingerprints, a crowdsourcingbased scheme is designed to gradually establish the mapping and populate the fingerprints. In C 2 IL, we design a trajectory clustering-based localization algorithm to provide precise realtime indoor localization and tracking. We developed and deployed a practical working system of C 2 IL in a large office environment. Extensive evaluation results indicate that our scheme C 2 IL provides accurate localization with error 2m at 80% at very complex indoor environment with minimal overhead.

This paper proposes a decentralized demand management approach to reduce the energy bill of industrial park and improve its economic gains. A demand management model for industrial park considering the integrated demand response of combined heat and power (CHP) units and thermal storage is firstly proposed. Specifically, by increasing the electricity outputs of CHP units during peak-load periods, not only the peak demand charge but also the energy charge can be reduced. The thermal storage can efficiently utilize the waste heat provided by CHP units and further increase the flexibility of CHP units. The heat dissipation of thermal storage, thermal delay effect, and heat losses of heat pipelines are considered for ensuring reliable solutions to the industrial park. The proposed model is formulated as a multi-period alternating current (AC) optimal power flow problem via the second-order conic programming formulation. The alternating direction method of multipliers (ADMM) algorithm is used to compute the proposed demand management model in a distributed manner, which can protect private data of all participants while achieving solutions with high quality. Numerical case studies validate the effectiveness of the proposed demand management approach in reducing peak demand charge, and the performance of the ADMM-based decentralized computation algorithm in deriving the same optimal results of demand management as the centralized approach is also validated. Index Terms--Alternating direction method of multipliers (ADMM), combined heat and power (CHP) unit, demand management, industrial park, integrated demand response (IDR), thermal storage.

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In order to solve pressure measurement problems in the fields of aerospace, petroleum and chemical industry, mobile and military industry, a oil-filled isolated piezoresistive high pressure sensor has been developed with the range of 0~100 MPa, and was able to work reliably under high temperature of above 200 °C. Based on MEMS (Micro Electro-Mechanical System) and SIMOX (Separation by Implantation of Oxygen) technology, the piezoresistive sensor chip has been developed. By high temperature packaging process, the oil-filled isolated high pressure sensor was fabricated with the sensor chip and corrugated diaphragm. The experimental results showed that the oil-filled isolated high pressure sensor had good performances under high temperature of 200 °C, such as linearity error of 0.07%FS, repeatability error of 0.04%FS, hysteresis error of 0.03%FS.