Skin-Penetrating Interfaces for Millimeter-Wave Non-Invasive Glucose Concentration Estimation: Numerical Analysis.

Millimeter-wave (MMW) glucose concentration estimation possesses a great advantage of non-invasiveness. The long history of investigation, however, has not yet reached practical applications because of its insufficient accuracy and stability. To solve these problems, this paper proposes two high skin-penetration interfaces, which we name equivalent quarter-wavelength interface and equivalent Brewster's-angle interface. We analyze their scattering characteristics in a frequency range of 60 - 90 GHz. Analysis results show that both the interfaces suppress the body-surface scattering, allowing the MMWs to penetrate through body surface into tissues to extract information on blood-glucose concentration with higher sensitivity, e.g., with 147-times enhancement of phase changes. These interfaces can be an important step toward realizing non-invasive blood glucose concentration estimation.

Robust Symbol Detection Based on Quaternion Neural Networks in Wireless Polarization-Shift-Keying Communications.

Quaternion neural networks (QNNs) form a class of neural networks constructed with quaternion numbers. They are suitable for processing 3-D features with fewer trainable free parameters than real-valued neural networks (RVNNs). This article proposes symbol detection in wireless polarization-shift-keying (PolSK) communications by employing QNNs. We demonstrate that quaternion plays a crucial role in the symbol detection of PolSK signals. Existing artificial-intelligence communication studies mainly focus on RVNN-based symbol detection in digital modulations having constellations in complex plane. However, in PolSK, information symbols are represented as the state of polarization, which can be mapped on the Poincare sphere and thus its symbols have a 3-D data structure. Quaternion algebra offers a unified representation to process 3-D data with rotational invariance and, therefore, it keeps the internal relationship among three components of a PolSK symbol. Hence, we can expect that QNNs learn the distribution of received symbols on the Poincare sphere with higher consistency to detect the transmitted symbols more efficiently than RVNNs. We compare PolSK symbol detection accuracy of two types of QNNs, RVNN, existing methods such as least-square and minimum-mean-square-error channel estimations, as well as detection knowing perfect channel state information (CSI). Simulation results including symbol error rate show that the proposed QNNs outperform the existing estimation methods and that they reach better results with two to three times fewer free parameters than the RVNN. We find that QNN processing will bring practical use of PolSK communications.

Proposal of higher-order tensor independent component analysis for signal separation in multiple-input multiple-output respiration/heartbeat remote sensing.

This paper proposes higher-order tensor independent component analysis (HOT-ICA). HOT-ICA is a tensor ICA that makes effective use of the relationships among the axes of a separating tensor. We deal with multiple-target signal separation in a multiple-input multiple-output (MIMO) radar system to detect respiration and heartbeat. Numerical physical experiments demonstrate the significance of the HOT-ICA which keeps the tensor structure unchanged to fully utilizes the high-dimensionality of the separation tensor.