Traditionally, current transformers are often used for current measurement in low voltage (LV) electrical networks. They have a large physical size and are not designed for use with power electronic circuits. Semiconductor-based current sensing devices such as the Hall sensor and Giant Magnetoresistive (GMR) sensor are advantageous in terms of small size, high sensitivity, wide frequency range, low power consumption, and relatively low cost. Nevertheless, the operational characteristics of these devices limit their current measurement range. In this paper, a design based on using counteracting magnetic field is introduced for extending the GMR current measurement range from 9 A (unipolar) to ±45 A. A prototype has been implemented to verify the design and the linear operation of the circuit is demonstrated by experimental results. A microcontroller unit (MCU) is used to provide an automatic scaling function to optimize the performance of the proposed current sensor.

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Abstract-Waves arise in many physical phenomena which have applications such as describing the voltage along a transmission line and medical imaging modality of elastography. In this paper, estimating the parameters for two forms of lossy wave equations, which correspond to multi-mode and multi-dimensional waves, are tackled. By exploiting the linear prediction property of the noise-free signals, an iterative quadratic maximum likelihood (IQML) approach is devised for accurate parameter estimation. Simulation results show that the estimation performance of the proposed IQML algorithms can attain the optimal benchmark, namely, Cramér-Rao lower bound, at sufficiently high signal-to-noise ratio and/or large data size conditions.

Matching and retrieval of motion sequences has become an important research area in recent years, due to the increasing availability and popularity of motion capture data. The main challenge in matching two motion sequences is the diversity of the captured motions, including variable length, local shifting, local and global scaling. Most existing methods employ Dynamic Time Warping (DTW) or Uniform Scaling to handle these problems. In this paper, we propose a novel content-based method for matching of this human motion captured data. We convert the matching problem of motion capture data into a transportation problem. To solve this problem efficiently, we employ Earth Mover's Distance (EMD) as the matching framework. To penalize any strayed matching, we provide a ground distance that works similar to SakoeChiba band of DTW. Empirical results obtained are encouraging.