We propose the concept of and experimentally demonstrate a metasurface-based smart table for wireless power transfer. The proposed structure supports propagating surface modes that can be excited by the near field of wireless devices. As a result, if two devices are placed anywhere on a metasurface table, a substantial enhancement of the effective near-field coupling can be achieved. This enhancement leads to a demonstrated power-transfer efficiency of 80% for the receiver placed in any location with respect to the transmitter on the table. The proposed concept enables a variety of applications from consumer electronics to charging electric cars.

The paper presents an approach to noninvasively measure pulse wave delay and velocity by means of two multiplexed fiber-optic Fabry-Perot interferometric sensors interrogated by means of spectral interferometry. The pulse wave signals were measured with 1 kHz sampling rate. The obtained optical path difference resolutions are on the order of tens picometers, which allowed the pulse wave signal features as small as 100 pm to be clearly observed in the demodulated waveforms. The pulse wave velocity between different arteries was measured, demonstrating good agreement with the known data, where applicable. The paper also presents a mathematical model based on Cramer-Rao approach, describing the fundamental resolution limits of the pulse wave delay measurement. The derived mathematical model is not limited to fiber-optic pulse wave sensors and can be used for estimating the performance of other systems, such as photoplethysmographic and ultrasound. The reported work paves the way to development of novel all-optical non-invasive systems for medical diagnostics and vital signs monitoring, including portable devices.

Pulse wave (PW) measurement is a highly prominent technique, used in biomedical diagnostics. Development of novel PW sensors with increased accuracy and reduced susceptibility to motion artifacts will pave the way to more advanced healthcare technologies. This paper reports on a comparison of performance of fiber optic pulse wave sensors, based on Fabry–Perot interferometer, fiber Bragg grating, optical coherence tomography (OCT) and singlemode-multimode-singlemode intermodal interferometer. Their performance was tested in terms of signal to noise ratio, repeatability of demodulated signals and suitability of demodulated signals for extraction of information about direct and reflected waves. It was revealed that the OCT approach of PW monitoring provided the best demodulated signal quality and was most robust against motion artifacts. Advantages and drawbacks of all compared PW measurement approaches in terms of practical questions, such as multiplexing capabilities and abilities to be interrogated by portable hardware are discussed.

Smartphone-based interrogation of a fiber Bragg grating sensor is, to the best of our knowledge, reported for the first time. The smartphone flashlight LED was used as a light source and a transmissive diffraction grating projected the CFBG spectra on the smartphone camera. In order to efficiently couple light from the smartphone LED to the fiber with CFBG, multimode fiber was used for inscription. Interrogation setup consists of a smartphone and low-cost off-the-shelf available components. Measurement principle was illustrated through the fiber strain caused by applied longitudinal force. Attained measurement sensitivity and resolution were validated via comparison with commercial spectrometer and theoretical results based on Cramer-Rao approach. Also, to the best of our knowledge, the influence of modal noise on the smartphone-based fiber optic sensor interrogation system performance is considered for the first time.

Smartphone-based optical spectrometers allow the development of a new generation of portable and cost-effective optical sensing solutions that can be easily integrated into sensor networks. However, most commonly the spectral calibration relies on the external reference light sources which have known narrow spectral lines. Such calibration must be repeated each time the fiber and diffraction grating holders are removed from the smartphone and reattached. Moreover, the spectrometer wavelength scale can drift during the measurement because of the smartphone temperature fluctuations. The present work reports on a novel spectral self-calibration approach, based on the correspondence between the light wavelength and the hue features of the spectrum measured using a color RGB camera. These features are caused by the nonuniformity of camera RGB filters’ responses and their finite overlap, which is a typical situation for RGB cameras. Thus, the wavelength scale should be externally calibrated only once for each smartphone spectrometer and can further be continuously verified and corrected using the proposed self-calibration approach. An ability of the plug-and play operation and the temperature drift elimination of the smartphone spectrometer was experimentally demonstrated. Conducted experiments involved interrogation of optical fiber Fabry-Perot interferometric sensor and demonstrated a nanometer-level optical path difference resolution.

The paper is aimed at improving the efficiency of signal processing for intermode fiber-optic interferometers. To do so, we propose to use the MUSIC algorithm. It is shown that the use of traditional methods for estimating the number of signal components leads to poor operation of the MUSIC algorithm when applied to intermode interference signals. The possibility of using machine learning to estimate the number of signal components was investigated. The advantage of the proposed signal processing for demodulating the signals of an intermode interferometer over the Fourier transform has been experimentally demonstrated on the examples of simultaneous strain and curvature measurement, as well as pulse-wave sensing. The results can be also applied for processing signals of other optical-fiber sensors and multi-component signals of a different nature, for example, optical coherence tomography and radar signals.