Due to magnetic flux concentrator (MFC) can cluster and aggregate magnetic field flux, so it can improve the GMR biosensor sensitivity, and the uniformity of the magnetic field in GMR biosensor detection area plays a great significance on GMR biosensor performance. Therefore, this paper employs the COMSOL software to study the characteristics of magnetic field generated by the magnetic flux concentrator. The influence of various parameters (length, thickness, etc.) of the magnetic flux concentrator on the uniformity of the magnetic field in GMR biosensor detection area had been investigated. The simulation results have certain theoretical guidance for high sensitivity GMR biosensors design.
KEYWORDS: Magnetism, Solar concentrators, Microsoft Foundation Class Library, Biosensors, Sensors, Design and modelling, Nanoparticles, Resistance, Cancer detection, Finite element methods
Due to GMR biosensor has the advantages of miniaturization, low-power consumption and rapid detection, so it has a widely application in bacterial, viral and cancer biomarker detection. However, the current GMR biosensor has relatively low detection sensitivity in biotarget detection, that limiting the development in biomedical detection applications rapidly. As we know, the magnetic flux concentrator (MFC) can improve detection sensitivity and resolution of the GMR sensor. So, a new micro-funnel-shaped MFC is proposed for GMR biosensor and analyzed by finite element method (FEM). The MFC shape, size and distribution of magnetic labels were investigated. The simulation results indicate that the signal gain can be increased by 30% with the hypotenuse of MFC. The distribution of magnetic labels was arranged horizontally along the X-axis can generate high magnetic field intensity and the strongest magnetic gain. The simulation results has certain theoretical guidance for high sensitivity GMR biosensors design.
Micropatterned Co-based amorphous ribbon meanders were prepared by MEMS technology on the flexible PET substrate. Different holders with varying arch heights were fabricated through 3D printing technology and used for carrying out tensile stresses. The giant magnetoimpedance (GMI) ratio was investigated under different tensile stress inductions (0~88 MPa). The results indicate that the GMI ratio of the flexible ribbon meanders is significantly impacted due to presence of tensile stress. As tensile stress increases, the GMI ratio first increases and then decreases. The maximum GMI ratio of 28.38% is achieved at a tensile stress level of 11 MPa. Additionally, the peak field gradually approaches zero as stress increases. When tensile stress increases from 0 MPa to 88 MPa, the peak field is shifted by 10.59 Oe.
Hand detection plays an important role in human–computer interaction. Because of the convenient and natural advantages of hands, hand detection is increasingly used in virtual reality, remote control, and other fields. However, since the complex background and the diversity of hand postures, the YOLOv4 algorithm for hand detection suffers from low accuracy and robustness. Therefore, A YOLOv4-HAND network, improved from YOLOv4, is proposed to solve the problem. We first use the dilation convolution to build the feature enhancement pyramid that enables the network to expand semantic information. Second, for better detection of different hand scales, we design a multiscale attention module to capture the correlation of channel information within different scales. Third, we design a head that incorporates a spatial attention module to compensate for the network’s lack of spatial contextual location information correlation. Finally, we use soft nonmaximum suppression to reduce the impact of occlusion. The results show that the YOLOv4-HAND detection network can achieve 83.22% and 93.95% mAP on the publicly available datasets Oxford hand and Egohands datasets. Compared with the most recent method, the YOLOv4-HAND network effectively improves the accuracy of hand detection for practical applications.
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