MPADA: Open source framework for multimodal time series antenna array measurements

This paper presents an open-source framework for collecting time series S-parameter measurements across multiple antenna elements, dubbed MPADA: Multi-Port Antenna Data Acquisition. The core of MPADA relies on the standard SCPI protocol to be compatible with a wide range of hardware platforms. Time series measurements are enabled through the use of a high-precision real-time clock (RTC), allowing MPADA to periodically trigger the VNA and simultaneously acquire other sensor data for synchronized cross-modal data fusion. A web-based user interface has been developed to offer flexibility in instrumentation, visualization, and analysis. The interface is accessible from a broad range of devices, including mobile ones. Experiments are performed to validate the reliability and accuracy of the data collected using the proposed framework. First, we show the framework's capacity to collect highly repeatable measurements from a complex measurement protocol using a microwave tomography imaging system. The data collected from a test phantom attain high fidelity where a position-varying clutter is visible through coherent subtraction. Second, we demonstrate timestamp accuracy for collecting time series motion data jointly from an RF kinematic sensor and an angle sensor. We achieved an average of 11.8 ms MSE timestamp accuracy at a mixed sampling rate of 10 to 20 Hz over a total of 16-minute test data. We make the framework openly available to benefit the antenna measurement community, providing researchers and engineers with a versatile tool for research and instrumentation. Additionally, we offer a potential education tool to engage engineering students in the subject, fostering hands-on learning through remote experimentation.

Microwave lymphedema assessment using deep learning with contour assisted backprojection

This paper presents a microwave imaging based method for detection of lymphatic fluid accumulation in lymphedema patients. The proposed algorithm uses contour information of the imaged limb surface to approximate the wave propagation velocity locally to solve the eikonal equation for implementing the adjoint imaging operator. This modified backprojection algorithm results in focused imagery close to the limb surface where lymphatic fluid accumulation presents itself. Next, a deep neural network based on U-Net architecture is employed to identify the location and extent of the lymphatic fluid. Simulation studies with various upper and lower arm profiles are used to compare the proposed contour assisted backprojection imaging with the baseline approach that assumes homogeneous media. The empirical results of the simulation experiments show that the proposed imaging method significantly improves the ability of the deepnet model to identify the location and the volume of the body fluid.