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Magnetic detection electrical impedance tomography (MDEIT) is an imaging modality that aims to compute the cross-sectional distribution of the conductivity of a volume from the magnetic flux density surrounding the object. Owing to the Biot-Savart law, the MDEIT inverse problem is inherently ill-conditioned making image reconstruction highly susceptible to the effects of noise and numerical errors. Appropriate priors or penalties are needed to facilitate reconstruction and to restrict the search space to a specific solution set. The images have the sparsity property and sharp variations. Consequently, this paper presents an approach involving a combination of the L¹ and total variation norm penalties, the former to suppress spurious background signals and enforce sparsity and the latter to preserve local smoothness and piecewise constancy in the MDEIT reconstructed images. The primal dual-interior point method (PD-IPM) for minimizing the joint L¹--TV penalty was used in the paper. The method was validated by using MDEIT simulated data and experimental data in comparison with the performances of the L², L¹ and total variation norm penalty-based approaches. The results show that the joint L¹--TV regularized algorithm preserves sparsity property, local smoothness and piecewise constancy, leading to improvements in the localization of the reconstructed images in MDEIT. [ABSTRACT FROM AUTHOR]

In order to remotely monitor the activities of heartbeat, respiration and body movement, this article designed and implemented a single-channel nonorthogonal in-phase/quadrature (I/Q) RF sensor, which works at the central frequency of 2.4 GHz. The designed RF sensor is based on a printed circuit board (PCB) antenna. Taking into account low cost, simple structure and easy fabrication, the antenna is designed and implemented with a special broad-band microstrip-tocoplanar strip line Balun structure by combining the transmitter with the receiver on a PCB to form one single antenna system. The gain of the sensor is 7.7 dB. The return loss is -22.7 dB at 2.4 GHz and the directionality of the antenna is satisfactory for monitoring vital signs. It has been validated that the designed sensor can detect the respiration, heartbeat and body movement accurately in comparison with the signals acquired by respiration sensor and pulse sensor. [ABSTRACT FROM AUTHOR]

Because arterial pulse waves contain vital information related to the condition of the cardiovascular system, considerable attention has been devoted to the study of pulse waves in recent years. Accurate acquisition is essential to investigate arterial pulse waves. However, at the stage of developing equipment for acquiring and analyzing arterial pulse waves, specific pulse signals may be unavailable for debugging and evaluating the system under development. To produce test signals that reflect specific physiological conditions, in this paper, an arterial pulse wave generator has been designed and implemented using a field programmable gate array (FPGA), which can produce the desired pulse waves according to the feature points set by users. To reconstruct a periodic pulse wave from the given feature points, a method known as piecewise Gaussian-cosine fitting is also proposed in this paper. Using a test database that contains four types of typical pulse waves with each type containing 25 pulse wave signals, the maximum residual error of each sampling point of the fitted pulse wave in comparison with the real pulse wave is within 8%. In addition, the function for adding baseline drift and three types of noises is integrated into the developed system because the baseline occasionally wanders, and noise needs to be added for testing the performance of the designed circuits and the analysis algorithms. The proposed arterial pulse wave generator can be considered as a special signal generator with a simple structure, low cost and compact size, which can also provide flexible solutions for many other related research purposes. [ABSTRACT FROM AUTHOR]