Two-Dimensional Position Tracking Using Gradient Magnetic Fields.

In this work, a two-dimensional (2D) position-detection device using a single axis magnetic sensor combined with orthogonal gradient coils was designed and fabricated. The sensors used were an induction coil and a GMR spin-valve sensor GF807 from Sensitec Inc. The field profiles generated by the two orthogonal gradient coils were analyzed numerically to achieve the maximum linear range, which corresponded to the detection area of the tracking system. The two coils were driven by 1-kHz sine wave currents with a 90° phase difference to generate the fields with uniform gradients along the x- and y-axis in the plane of the tracking stage. The gradient fields were detected by a single-axis sensor incorporated with a digital dual-phase lock-in detector to retrieve the position information. A linearity correction algorithm was used to improve the location accuracy and to extend the linear range for position sensing. The mean positioning error was found to be 0.417 mm, corresponding to the relative error of 0.21% in the working range of 200 mm × 200 mm, indicating that the proposed tracking system is promising for applications requiring accurate control of the two-dimensional position.

Detection of Surface and Subsurface Flaws with Miniature GMR-Based Gradiometer.

The eddy-current (EC) testing method is frequently utilized in the nondestructive inspection of conductive materials. To detect the minor and complex-shaped defects on the surface and in the underlying layers of a metallic sample, a miniature eddy-current probe with high sensitivity is preferred for enhancing the signal quality and spatial resolution of the obtained eddy-current images. In this work, we propose a novel design of a miniature eddy-current probe using a giant magnetoresistance (GMR) sensor fabricated on a silicon chip. The in-house-made GMR sensor comprises two cascaded spin-valve elements in parallel with an external variable resistor to form a Wheatstone bridge. The two elements on the chip are excited by the alternating magnetic field generated by a tiny coil aligned to the position that balances the background output of the bridge sensor. In this way, the two GMR elements behave effectively as an axial gradiometer with the bottom element sensitive to the surface and near-surface defects on a conductive specimen. The performance of the EC probe is verified by the numerical simulation and the corresponding experiments with machined defects on metallic samples. With this design, the geometric characteristics of the defects are clearly visualized with a spatial resolution of about 1 mm. The results demonstrate the feasibility and superiority of the proposed miniature GMR EC probe for characterizing the small and complex-shaped defects in multilayer conductive samples.

Quasi-Static Current Measurement with Field-Modulated Spin-Valve GMR Sensors.

A non-contact current measurement device comprised of a GMR sensor and a ferrite ring core was investigated. The sensor chip employed a high-sensitivity spin-valve full-bridge GMR sensor of which the direct output has non-negligible hysteresis and a limited linear range. By applying an AC modulation current to modulate the output of the GMR sensor, the hysteresis was reduced, and the linear range was over ±0.5 A. The resolution for DC and quasi-static current measurement was 0.1 mA at a 10 Hz bandwidth. The output in proportion to the measured current was obtained either by demodulating the current-sensitive AC signal or by employing the filtered output of the intrinsically nonlinear spin-valve response. The proposed current sensing scheme is suitable for quasi-static current measurement from DC to over 100 Hz.

An Efficient CNN-Based Method for Intracranial Hemorrhage Segmentation from Computerized Tomography Imaging.

Intracranial hemorrhage (ICH) resulting from traumatic brain injury is a serious issue, often leading to death or long-term disability if not promptly diagnosed. Currently, doctors primarily use Computerized Tomography (CT) scans to detect and precisely locate a hemorrhage, typically interpreted by radiologists. However, this diagnostic process heavily relies on the expertise of medical professionals. To address potential errors, computer-aided diagnosis systems have been developed. In this study, we propose a new method that enhances the localization and segmentation of ICH lesions in CT scans by using multiple images created through different data augmentation techniques. We integrate residual connections into a U-Net-based segmentation network to improve the training efficiency. Our experiments, based on 82 CT scans from traumatic brain injury patients, validate the effectiveness of our approach, achieving an IOU score of 0.807 ± 0.03 for ICH segmentation using 10-fold cross-validation.