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A theoretical analysis of guided-wave acoustooptic (AO) Bragg cells generated by a single surface acoustic wave (SAW) in PE:LiNbO3/LiNbO3- and ZnO/GaAs/AlGaAs-based structures is presented. A rigorous model for SAW propagation in multilayered structures is utilized. The AO interaction is studied by a generalized multimode equation of the coupled-mode theory. Applications in high performance configurations for wide-band optical communication and signal processing is shown.

A leaky mode propagation method, an analytical numerical method, has been developed for finding radiation loss of grating-assisted codirectional couplers. With this method accurate results of the radiation loss, coupling efficiency and coupling length have been obtained, showing that the oscillatory variation of radiation loss is a complex behavior. The comparison between results of this method and transfer matrix method are discussed.

We present the theoretical investigation, design, and simulation of a novel guided-wave optical processor for L-band-transmission beam forming in a linear array of phased active antennas. The proposed configuration includes two contradirectional surface acoustic-wave transducers, and it is based on a Y-cut X-propagating Ti:LiNb[O.sub.3] planar waveguide supporting the lowest-order modes of both polarizations (T[E.sub.0] and T[M.sub.0]) at the free-space wavelength [Lambda] = 0.85 [[micro]meter]. A detailed comparison between the processor we propose and other optical and electronic architectures reported in the literature is carried out, exhibiting a number of significant advantages in terms of weight, total chip size, and power consumption, when the number of antenna elements is greater than 50. Keywords: Acousto-optics, phased arrays, guided waves

Thermoelectric technology can directly harvest the waste heat into electricity, which is a promising field of green and sustainable energy. In this aspect, flexible thermoelectrics (FTE) such as wearable fabrics, smart biosensing, and biomedical electronics offer a variety of applications. Since the nanofibers are one of the important constructions of FTE, inorganic thermoelectric fibers are focused on here due to their excellent thermoelectric performance and acceptable flexibility. Additionally, measurement and microstructure characterizations for various thermoelectric fibers (Bi-Sb-Te, Ag2Te, PbTe, SnSe and NaCo2O4) made by different fabrication methods, such as electrospinning, two-step anodization process, solution-phase deposition method, focused ion beam, and self-heated 3ω method, are detailed. This review further illustrates that some techniques, such as thermal drawing method, result in high performance of fiber-based thermoelectric properties, which can emerge in wearable devices and smart electronics in the near future. [ABSTRACT FROM AUTHOR]

We propose a novel approach to measure photosynthetically active radiation (PAR) in the form of photosynthetic photon flux density with an inexpensive, small multi-channel spectrometer sensor, with integrated optical filters and analog-to-digital converter. Our experiments prove that the combination of eight spectral channels with different optical sensitivities, such as the sensorchip in use (AS7341, ams), derive the PAR with an accuracy of 14 μ m − 2 s − 1 . Enabled by the sensor architecture, additional information about the light quality can be retrieved which is expressed in the reduced light quality index. A calibration method is proposed, and exemplary measurements are performed. Moreover, the integration in a solar-powered wireless sensor node is outlined, which enables long-term field experiments with high sensor densities and may be used to obtain important indexes, such as the gross primary production. [ABSTRACT FROM AUTHOR]

This review article is focused on the analysis of the state of the art of sensors for guided ultrasonic waves for the detection and localization of impacts for structural health monitoring (SHM). The recent developments in sensor technologies are then reported and discussed through the many references in recent scientific literature. The physical phenomena that are related to impact event and the related main physical quantities are then introduced to discuss their importance in the development of the hardware and software components for SHM systems. An important aspect of the article is the description of the different ultrasonic sensor technologies that are currently present in the literature and what advantages and disadvantages they could bring in relation to the various phenomena investigated. In this context, the analysis of the front-end electronics is deepened, the type of data transmission both in terms of wired and wireless technology and of online and offline signal processing. The integration aspects of sensors for the creation of networks with autonomous nodes with the possibility of powering through energy harvesting devices and the embedded processing capacity is also studied. Finally, the emerging sector of processing techniques using deep learning and artificial intelligence concludes the review by indicating the potential for the detection and autonomous characterization of the impacts. [ABSTRACT FROM AUTHOR]

Pure, mixed and doped metal oxides (MOX) have attracted great interest for the development of electrical and electrochemical sensors since they are cheaper, faster, easier to operate and capable of online analysis and real-time identification. This review focuses on highly sensitive chemoresistive type sensors based on doped-SnO2, RhO, ZnO-Ca, Smx-CoFe2−xO4 semiconductors used to detect toxic gases (H2, CO, NO2) and volatile organic compounds (VOCs) (e.g., acetone, ethanol) in monitoring of gaseous markers in the breath of patients with specific pathologies and for environmental pollution control. Interesting results about the monitoring of biochemical substances as dopamine, epinephrine, serotonin and glucose have been also reported using electrochemical sensors based on hybrid MOX nanocomposite modified glassy carbon and screen-printed carbon electrodes. The fundamental sensing mechanisms and commercial limitations of the MOX-based electrical and electrochemical sensors are discussed providing research directions to bridge the existing gap between new sensing concepts and real-world analytical applications. [ABSTRACT FROM AUTHOR]

Systems experiencing high-rate dynamic events, termed high-rate systems, typically undergo accelerations of amplitudes higher than 100 g-force in less than 10 ms. Examples include adaptive airbag deployment systems, hypersonic vehicles, and active blast mitigation systems. Given their critical functions, accurate and fast modeling tools are necessary for ensuring the target performance. However, the unique characteristics of these systems, which consist of (1) large uncertainties in the external loads, (2) high levels of non-stationarities and heavy disturbances, and (3) unmodeled dynamics generated from changes in system configurations, in combination with the fast-changing environments, limit the applicability of physical modeling tools. In this paper, a deep learning algorithm is used to model high-rate systems and predict their response measurements. It consists of an ensemble of short-sequence long short-term memory (LSTM) cells which are concurrently trained. To empower multi-step ahead predictions, a multi-rate sampler is designed to individually select the input space of each LSTM cell based on local dynamics extracted using the embedding theorem. The proposed algorithm is validated on experimental data obtained from a high-rate system. Results showed that the use of the multi-rate sampler yields better feature extraction from non-stationary time series compared with a more heuristic method, resulting in significant improvement in step ahead prediction accuracy and horizon. The lean and efficient architecture of the algorithm results in an average computing time of 25 μ s, which is below the maximum prediction horizon, therefore demonstrating the algorithm's promise in real-time high-rate applications. [ABSTRACT FROM AUTHOR]

In this study, the response of fiber Bragg gratings (FBGs) embedded in cast aluminum parts under thermal and mechanical load were investigated. Several types of FBGs in different types of fibers were used in order to verify general applicability. To monitor a temperature-induced strain, an embedded regenerated FBG (RFBG) in a cast part was placed in a climatic chamber and heated up to 120 ∘ C within several cycles. The results show good agreement with a theoretical model, which consists of a shrink-fit model and temperature-dependent material parameters. Several cast parts with different types of FBGs were machined into tensile test specimens and tensile tests were executed. For the tensile tests, a cyclic procedure was chosen, which allowed us to distinguish between the elastic and plastic deformation of the specimen. An analytical model, which described the elastic part of the tensile test, was introduced and showed good agreement with the measurements. Embedded FBGs - integrated during the casting process - showed under all mechanical and thermal load conditions no hysteresis, a reproducible sensor response, and a high reliable operation, which is very important to create metallic smart structures and packaged fiber optic sensors for harsh environments. [ABSTRACT FROM AUTHOR]

Semantic segmentation of room maps is an essential issue in mobile robots' execution of tasks. In this work, a new approach to obtain the semantic labels of 2D lidar room maps by combining distance transform watershed-based pre-segmentation and a skillfully designed neural network lidar information sampling classification is proposed. In order to label the room maps with high efficiency, high precision and high speed, we have designed a low-power and high-performance method, which can be deployed on low computing power Raspberry Pi devices. In the training stage, a lidar is simulated to collect the lidar detection line maps of each point in the manually labelled map, and then we use these line maps and the corresponding labels to train the designed neural network. In the testing stage, the new map is first pre-segmented into simple cells with the distance transformation watershed method, then we classify the lidar detection line maps with the trained neural network. The optimized areas of sparse sampling points are proposed by using the result of distance transform generated in the pre-segmentation process to prevent the sampling points selected in the boundary regions from influencing the results of semantic labeling. A prototype mobile robot was developed to verify the proposed method, the feasibility, validity, robustness and high efficiency were verified by a series of tests. The proposed method achieved higher scores in its recall, precision. Specifically, the mean recall is 0.965, and mean precision is 0.943. [ABSTRACT FROM AUTHOR]

This review summarizes the leading advancements in porous silicon (PSi) optical-biosensors, achieved over the past five years. The cost-effective fabrication process, the high internal surface area, the tunable pore size, and the photonic properties made the PSi an appealing transducing substrate for biosensing purposes, with applications in different research fields. Different optical PSi biosensors are reviewed and classified into four classes, based on the different biorecognition elements immobilized on the surface of the transducing material. The PL signal modulation and the effective refractive index changes of the porous matrix are the main optical transduction mechanisms discussed herein. The approaches that are commonly employed to chemically stabilize and functionalize the PSi surface are described. [ABSTRACT FROM AUTHOR]