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This study proposes an automated data-labeling model that combines a pseudo-labeling algorithm with waveform segmentation based on Long Short-Term Memory (LSTM) to effectively label time-series data in smart agriculture. This model aims to address the inefficiency of manual labeling for large-scale data generated by agricultural systems, enhancing the performance and scalability of predictive models. Our proposed method leverages key features of time-series data to automatically generate labels for new data, thereby improving model accuracy and streamlining data processing. By automating the labeling process, we reduce dependence on manual labeling, which is often labor-intensive and prone to errors in large datasets. This approach enables the efficient preparation of labeled data for applications such as anomaly detection, pattern recognition, and predictive modeling in smart agriculture. Experimental results demonstrate that the automated labeling model achieves 89% accuracy in agricultural environments and reduces data processing time by 30%. Future research will focus on extending the model’s applicability to diverse agricultural settings, enhancing generalization performance, and improving real-time processing capabilities, thereby advancing intelligent and sustainable smart agriculture systems.

This study is motivated by the critical need for accurate and durable ride height sensors in modern automotive systems, particularly as these sensors play a crucial role in maintaining vehicle stability and safety. Current sensors’ susceptibility to environmental damage and inherent nonlinearities highlight the necessity for improved sensor designs and nonlinearity correction algorithms. To address these issues, this study proposes a novel sensor system that integrates a ball joint and a Hall sensor to enhance the accuracy and durability of ride height sensors and develops a Real-time Nonlinearity Correction and Calibration (RNCC) Algorithm for real-time correction. The RNCC algorithm combines Model Reference Adaptive Control (MRAC), Kalman filtering, and nonlinear optimization techniques to correct sensor nonlinearity in real-time, enabling precise position measurement. Experimental results demonstrate that the RNCC algorithm can calibrate sensors more quickly and accurately than existing methods and is effective in handling outlier data. This research suggests the potential for the RNCC algorithm to be applied in various real-time control applications and contribute to improving automotive suspension systems.

Fog screens offer a great advantage to be used as scattering screens in three-dimensional displays. In the absence of a fixed rigid form, fog screens work as screens without any physical obstruction. The density of water droplets can be regulated to adjust the screen’s transparency and scattering level. This study proposes a method for crosstalk reduction between adjacent viewing windows by decreasing the divergence of view of the projector, with concentration lenses. By applying the Scheimpflug principle, we also reduce the keystone effect, which occurs when the fog screen is slanted with respect to the optical axis. In this study, we have realized a tabletop multiview fog display without any structures on the table and established that the proposed method is feasible for decreasing crosstalk effectively.

Digital holographic microscopy (DHM) is a valuable technique for investigating the optical properties of samples through the measurement of intensity and phase of diffracted beams. However, DHMs are constrained by Lagrange invariance, compromising the spatial bandwidth product (SBP) which relates resolution and field of view. Synthetic aperture DHM (SA-DHM) was introduced to overcome this limitation, but it faces significant challenges such as aberrations in synthesizing the optical information corresponding to the steering angle of incident wave. This paper proposes a novel approach utilizing deep neural networks (DNNs) for compensating aberrations in SA-DHM, extending the compensation scope beyond the numerical aperture (NA) of the objective lens. The method involves training a DNN from diffraction patterns and Zernike coefficients through a circular aperture, enabling effective aberration compensation in the illumination beam. This method makes it possible to estimate aberration coefficients from the only part of the diffracted beam cutoff by the circular aperture mask. With the proposed technique, the simulation results present improved resolution and quality of sample images. The integration of deep neural networks with SA-DHM holds promise for advancing microscopy capabilities and overcoming existing limitations.

Abstract It is well known that wearing virtual reality (VR) and augmented reality (AR) devices for long periods can cause visual fatigue and motion sickness due to vergence-accommodation conflict (VAC). VAC is considered the main obstacle to the development of advanced three-dimensional VR and AR technology. In this paper, we present a novel AR high-density super-multiview (HDSMV) display technique capable of eliminating VAC in wide range. The designed binocular time-sequential AR HDSMV projection, which delivers 11 views to each eye pupil, is experimentally demonstrated, confirming that VAC is eliminated over a wide-range of viewer’s focus distance. It is believed that the proposed time-sequential AR HDSMV method will pave the way for the development of VAC-free AR technology.

Recently, quantitative and repetitive inspections of the old urban area were conducted because many structures exceed their designed lifetime. The health of a building can be validated from the condition of the outer wall, while the slant angle of the wall widely serves as an indicator of urban regeneration projects. Mostly, the inspector directly measures the inclination of the wall or partially uses 3D point measurements using a static light detection and ranging (LiDAR). These approaches are costly, time‐consuming, and only limited space can be measured. Therefore, we propose a mobile mapping system and automatic slant map generation algorithm, configured to capture urban environments online. Additionally, we use the LiDAR‐inertial mapping algorithm to construct raw point clouds with gravity information. The proposed method extracts walls from raw point clouds and measures the slant angle of walls accurately. The generated slant angle map is evaluated in indoor and outdoor environments, and the accuracy is compared with real tiltmeter measurements.

LiDAR sensors are extensively used in autonomous vehicles and their optimal use is a critical concern. In this paper, we propose an embedded software architecture for multiple LiDAR sensors, i.e., a fusion system that acts as an embedded system for processing data from multiple LiDAR sensors. The fusion system software comprises multiple clients and a single server. The client and server are connected through inter-process communication. Multiple clients create processes to process the data from each LiDAR sensor via a multiprocessing method. Our approach involves a scheduling method for efficient multiprocessing. The server uses multithreading to optimize the internal functions. For internal communication within the fusion system, multiple clients and a single server are connected using the socket method. In sequential processing, the response time increases in proportion to the number of connected LiDAR sensors. By contrast, in the proposed software architecture, the response time decreases in inverse proportion to the number of LiDAR sensors. As LiDAR sensors become increasingly popular in the field of autonomous driving, the results of this study can be expected to make a substantial contribution to technology development in this domain.

The nature of trions and their interaction with light has remained a puzzle. The composition and dispersion of polaritons involving trions provide insights into this puzzle. Trions and excitons in doped two-dimensional (2D) materials are not independent excitations but are strongly coupled as a result of Coulomb interactions. When excitons in doped 2D materials are also strongly coupled with light inside an optical waveguide, the resulting polariton states are coherent superpositions of exciton, trion, and photon states. We realize these exciton-trion-polaritons by coupling an electron-doped monolayer of 2D material MoSe_{2} to the optical mode in a photonic crystal waveguide. Our theoretical model, based on a many-body description of these polaritons, reproduces the measured polariton energy band dispersion and Rabi splittings with excellent accuracy. Our work sheds light on the structure of trion states in 2D materials and also on the indirect mechanism by which they interact with light.

Color digital holography (DH) has been researched in various fields such as the holographic camera and holographic microscope because it acquires a realistic color object wave by measuring both amplitude and phase. Among the methods for color DH, the phase-shifting DH has an advantage of obtaining a signal wave of objects without the autocorrelation and conjugate noises. However, this method usually requires many interferograms to obtain signals for all wavelengths. In addition, the phase-shift algorithm is sensitive to the phase-shift error caused by the instability or hysteresis of the phase shifter. In this paper, we propose a new method of color phase-shifting digital holography with monitoring the phase-shift. The color interferograms are recorded by using a focal plane array (FPA) with a Bayer color filter. In order to obtain the color signal wave from the interferograms with unexpected phase-shift values, we devise a generalized phase-shifting DH algorithm. The proposed method enables the robust measurement in the interferograms. Experimentally, we demonstrate the proposed algorithm to reconstruct the object image with negligibly small conjugate noises.

A vehicular network is composed of an in-vehicle network (IVN) and Internet of Vehicles (IoV). IVN exchanges information among in-vehicle devices. IoV constructs Vehicle-to-X (V2X) networks outside vehicles and exchanges information among V2X elements. These days, in-vehicle devices that require high bandwidth is increased for autonomous driving services. Thus, the spread of data for vehicles is exploding. This kind of data is exchanged through IoV. Even if the Ethernet backbone of IVN carries a lot of data in the vehicle, the explosive increase in data from outside the vehicle can affect the backbone. That is, the transmission efficiency of the IVN backbone will be reduced due to excessive data traffic. In addition, when IVN data traffic is transmitted to IoV without considering IoV network conditions, the transmission efficiency of IoV is also reduced. Therefore, in this paper, we propose an IoV access gateway to controls the incoming data traffic to the IVN backbone and the outgoing data traffic to the IoV in the network environment where IVN and IoV are integrated. Computer simulations are used to evaluate the performance of the proposed system, and the proposed system shows better performance in the accumulated average transmission delay.

BACKGROUND: Exposure of agricultural workers in rice paddies to the insecticide chlorantraniliprole and its subsequent potential health risks were investigated during two scenarios (mixing/loading and hand-held spraying). The exposure factors, such as the outer dosimeter, inner dosimeter, gauze, and nitrile gloves, were calculated using whole-body dosimetry to measure dermal exposure. The inhalation exposure was determined using a fiberglass filter which is set with an Institute of Occupational Medicine (IOM) sampler. A recovery test was performed to evaluate the accuracy of the analytical method. RESULTS: The exposure amounts of various matrices were calculated from extraction volume and concentration of the target compound. The dermal exposure to chlorantraniliprole was 0.6 mg [0.001% of the total active ingredient (a.i.)] for mixing and loading, and 28.6 mg (0.066% of the total a.i.) for application. The inhalation exposure to chlorantraniliprole was 7.2 μg (1.3%, 1.2 × 10−5% of the total applied a.i.) for mixing and loading, and 1.9 μg (0.006%, 4.4 × 10−6% of the total applied a.i.) for application. The most exposed part of the body was the hand (90.4%) during mixing and loading, whereas the primary sites during application were the thighs (32.8%) and shins (22.6%). For mixing and loading, the amount of actual dermal exposure was 5.5 μg day−1 and that of actual inhalation exposure was 21.9 μg day−1. By contrast, in the application, the amounts of actual dermal and actual inhalation exposures were 34 178.7 and 5.9 μg day−1, respectively. CONCLUSIONS: The risk assessment results demonstrated that the risk of chlorantraniliprole exposure in rice paddies was low during application than during mixing and loading. [ABSTRACT FROM AUTHOR]

The article offers information on the concept of precarity. Topics discussed include the global expansion of precarious work and its connection with social problems; precarious employment systems; and expansion of the concept of precarity to incorporate wide range of security, citizenship, and social life.

We demonstrate that a spin degree of freedom can introduce additional texture to higher order topological insulators (HOTIs), manifesting in novel topological invariants and phase transitions. Spin-polarized mid-gap corner states of various multiplicities are predicted for different HOTI phases, and novel bulk-boundary correspondence principles are defined based on bulk invariants such as total and spin corner charge. Those are shown to be robust to spin-flipping perturbations. Photonic realizations of spin-linked topological phases are demonstrated in engineered systems using pseudospin. [ABSTRACT FROM AUTHOR]

A valley plasmonic crystal for graphene surface plasmons is proposed. We demonstrate that a designer metagate, placed within a few nanometers of graphene, can be used to impose a periodic Fermi energy landscape on graphene. For specific metagate geometries and bias voltages, the combined metagate-graphene structure is shown to produce complete propagation band gaps for the plasmons, and to impart them with nontrivial valley-linked topological properties. Sharply curved domain walls between differently patterned metagates are shown to guide highly localized plasmons without any reflections owing to suppressed intervalley scattering. Our approach paves the way for nonmagnetic and dynamically reconfigurable topological nanophotonic devices. [ABSTRACT FROM AUTHOR]

We present a many-body theory of exciton-trion polaritons (ETPs) in doped two-dimensional semiconductor materials. ETPs are robust coherent hybrid excitations involving excitons, trions, and photons. In ETPs, the 2-body exciton states are coupled to the material ground state via exciton-photon interaction, and the 4-body trion states are coupled to the exciton states via Coulomb interaction. The trion states are not directly optically coupled to the material ground state. The energy-momentum dispersion of ETPs exhibit three bands. We calculate the energy band dispersions and the compositions of ETPs at different doping densities using Green's functions. The energy splittings between the polariton bands, as well as the spectral weights of the polariton bands, depend on the strength of the Coulomb coupling between the excitons and the trions, which in turn depends sensitively on the doping density. The doping density dependence of the ETP bands and the charged nature of the trion states could enable novel electrical and optical control of ETPs. [ABSTRACT FROM AUTHOR]