The permittivity of blood glucose is not a strong function of its concentration in microwave or millimeter-wave frequencies. Measuring glucose concentrations remains a challenge, particularly in the presence of interference caused by the ambient leaky waves. In this paper, however, we demonstrate that a near-linear correlation between the glucose concentration and the blood permittivity was noticeably observed at a whispering gallery mode resonance. Method: the proposed sensor was a vacuum suction aspirator partially wounded with a turn of the Goubau line. This arrangement enabled a fixed cylindrical volume of a skin tissue bump or glucose/water solution to be formed and used as a whispering gallery resonator for in-vivo and ex-vivo measurements. Results: in the in-vivo study, a near-linear correlation between the glucose levels and the S21 parameters was noticeably observed at the fundamental whispering gallery resonance (i.e., at 2.18 GHz). In the ex-vivo study, a similar correlation was observed between the concentration of a glucose/water solution and the S21 parameters 56.6 GHz. Conclusion: the results of both investigations were consistent not only with the invasive measurements using the Accu-check^TM, but also with the conclusion drawn by some other research groups who have successfully measured blood glucose concentrations at millimeter-wave frequencies.

In this work, we have realized Zenneck’s style of wireless power transfer over a seawater surface. Method: The problem can be thought of as a surface electromagnetic wave propagating along an interface between a low-loss non-polar medium and seawater. A 10 m long water-filled plastic duct was connected to two separated water tanks, with one tank identified as the transmitting end and the other the receiving end. At the transmitting end, the water tank was excited with a 100 kV plasma from a spark gap transmitter at 44 MHz. At the receiving end, surface power was harvested in an open-circuit manner with the help of a suspended monopole antenna. Results: Without any antenna, no power was received at the receiving end. However, when two monopole antennas were individually connected to the transmitter and the receiver, a power was noticeably detected and successfully delivered to multiple loads even if the water’s conductivity was low. As the salinity level increased from 0 to 5 g/L, the transmission efficiency was increased from 10% to 99%. Consistent with Marconi’s law of transmission, the transmission distance leading to the first maximum efficiency was found to be approximately proportional to square of the antenna heights. Conclusion: A vertically mounted monopole antenna enables power to be wirelessly transmitted along the interface between a low-loss dielectric medium and seawater.

Accurate fault diagnosis of electrical machines is essential for operational reliability and safety. Handcrafted features remain attractive in practice because of their interpretability and computational efficiency. However, this traditional approach relies on individual feature vectors and is limited in preserving inherent temporal dynamics. To address this issue, a label-consistent input structuring based on the second stage sliding window (SSSW) method is proposed. This approach retains the benefits of handcrafted features and arranges feature vectors into temporally coherent sequences while ensuring label consistency. A hyperparameter optimization scheme is incorporated with a long short-term memory classifier to reduce manual tuning. Performance evaluations demonstrate the robustness of the proposed SSSW method across diverse operating conditions and input signal configurations. These include single-phase currents, multi-phase currents, vibration, and fused current-vibration signals. Notably, the proposed approach achieves classification accuracy of up to 100% and demonstrates stable learning behavior. Experimental verification on a laboratory scale permanent magnet synchronous motor testbed further validates the proposed method under realistic measurement noise and interference conditions. Finally, combining handcrafted features with the proposed SSSW input structuring method provides a practical, scalable solution for reliable electrical machine fault diagnosis.

This chapter proposes an innovative method for digitizing computer numerical control (CNC) machines. This research promotes the transformation from traditional factories to smart factories that serve sustainable manufacturing. The chapter provides a detailed digitization process of three CNC machines in the pilot phase and leverages the results to connect seven CNC machines currently in operation at a company. During the pilot phase, current sensors, human–machine interface (HMI) screens, and relays receiving signals from the CNC machine light towers were added to each machine. In addition, a control box containing a central programmable logic controller (PLC) was developed to collect data and send control signals to the HMIs. A computer server was utilized to send control signals, read data from the PLC, and display critical values on a central dashboard. Based on the success of the pilot phase, a real project with several upgrades was implemented at a manufacturing company. The most significant upgrade focused on production management, which includes labor time tracking, machine operation monitoring, drawing management, and production data statistics by machine groups and other related features. The successful digitization of CNC machines enables the company to rapidly transition from traditional manufacturing to smart manufacturing with more efficient and sustainable production. This research is an essential step in the company’s innovation journey that aligns with Industry 4.0 by shifting human-based production to data and equipment-driven processes.