Transmission of sub-terahertz signals over a fiber-FSO-5†G NR hybrid system with an aggregate net bit rate of 227.912 Gb/s.

Transmission of sub-terahertz (sub-THz) signals over a fiber-free-space optical (FSO)-fifth-generation (5 G) new radio (NR) hybrid system is successfully realized. It is a promising system that utilizes multiple media of optical fiber, optical wireless, and 5 G NR wireless to achieve a 227.912-Gb/s record-high aggregate net bit rate. The system concurrently transmits a 59.813-Gb/s net bit rate in the 150-GHz sub-THz frequency, 74.766-Gb/s in the 250-GHz sub-THz frequency, and 93.333-Gb/s in the 325-GHz sub-THz frequency through the fiber-FSO-wireless convergence, including 25-km single-mode fiber, 100-m FSO, and 30-m/25-m/20-m sub-THz-wave transmissions. This system achieves sufficiently low bit error rates (< hard-decision forward error correction (FEC) threshold of 3.8 × 10-3 at 16 and 20 Gbaud symbol rates; < soft-decision FEC threshold of 2 × 10-2 at 28 Gbaud symbol rate) and clear and distinct constellation diagrams, meeting the demands of 5 G NR communications in the sub-THz band. The development of fiber-FSO-5 G NR hybrid system represents a substantial development in the field of advanced communications. It has the ability to enhance the way we communicate in the future.

Micrometer Backstepping Control System for Linear Motion Single Axis Robot Machine Drive.

In order to cut down influence on the uncertainty disturbances of a linear motion single axis robot machine, such as the external load force, the cogging force, the column friction force, the Stribeck force, and the parameters variations, the micrometer backstepping control system, using an amended recurrent Gottlieb polynomials neural network and altered ant colony optimization (AACO) with the compensated controller, is put forward for a linear motion single axis robot machine drive system mounted on the linear-optical ruler with 1 um resolution. To achieve high-precision control performance, an adaptive law of the amended recurrent Gottlieb polynomials neural network based on the Lyapunov function is proposed to estimate the lumped uncertainty. Besides this, a novel error-estimated law of the compensated controller is also proposed to compensate for the estimated error between the lumped uncertainty and the amended recurrent Gottlieb polynomials neural network with the adaptive law. Meanwhile, the AACO is used to regulate two variable learning rates in the weights of the amended recurrent Gottlieb polynomials neural network to speed up the convergent speed. The main contributions of this paper are: (1) The digital signal processor (DSP)-based current-regulation pulse width modulation (PWM) control scheme being successfully applied to control the linear motion single axis robot machine drive system; (2) the micrometer backstepping control system using an amended recurrent Gottlieb polynomials neural network with the compensated controller being successfully derived according to the Lyapunov function to diminish the lumped uncertainty effect; (3) achieving high-precision control performance, where an adaptive law of the amended recurrent Gottlieb polynomials neural network based on the Lyapunov function is successfully applied to estimate the lumped uncertainty; (4) a novel error-estimated law of the compensated controller being successfully used to compensate for the estimated error; and (5) the AACO being successfully used to regulate two variable learning rates in the weights of the amended recurrent Gottlieb polynomials neural network to speed up the convergent speed. Finally, the effectiveness of the proposed control scheme is also verified by the experimental results.

Precision Motion Control of a Linear Permanent Magnet Synchronous Machine Based on Linear Optical-Ruler Sensor and Hall Sensor.

The linear optical-ruler sensor with 1um precision mounted in the linear permanent magnet synchronous machine (LPMSM) is used for measuring the mover position of LPMSM in order to enhance the precision of a measured mover position. Due to nonlinear friction and uncertainty effects, linear controllers are very hard to achieve good mover positioning of LPMSM. The proposed adaptive amended Elman neural network backstepping (AAENNB) control system is adopted for controlling the LPMSM drive system to bring about the mover positioning precision of LPMSM. Firstly, a backstepping scheme is posed for controlling the tracing motion of the LPMSM drive system. The proposed backstepping control system, which is applied in the mover position of the LPMSM drive system, possesses better dynamic control performance and robustness to uncertainties for the tracing trajectories. Because of the LPMSM with nonlinear and time-varying dynamic characteristics, an adaptive amended Elman neural network uncertainty observer (AAENNUO) is posed to estimate the required lumped uncertainty. According to the Lyapunov stability theorem, on-line parameter training methodology of the amended Elman neural network (AENN) can be derived by use of adaptive law. The error estimated law is proposed to compensate for the observed error induced by the AENN with adaptive law. Furthermore, to help improve convergence and to obtain better learning performance, the mended particle swarm optimization (PSO) algorithm is utilized for adjusting the varied learning rate of the weights in the AENN. At last, these experimental results, which show better performance, are verified by the proposed control system.

Novel application of continuously variable transmission system using composite recurrent Laguerre orthogonal polynomials modified PSO NN control system.

Because the V-belt continuously variable transmission system spurred by permanent magnet (PM) synchronous motor has much unknown nonlinear and time-varying characteristics, the better control performance design for the linear control design is a time consuming procedure. In order to overcome difficulties for design of the linear controllers, the composite recurrent Laguerre orthogonal polynomials modified particle swarm optimization (PSO) neural network (NN) control system which has online learning capability to come back to the nonlinear and time-varying of system, is developed for controlling PM synchronous motor servo-driven V-belt continuously variable transmission system with the lumped nonlinear load disturbances. The composite recurrent Laguerre orthogonal polynomials NN control system consists of an inspector control, a recurrent Laguerre orthogonal polynomials NN control with adaptation law and a recouped control with estimation law. Moreover, the adaptation law of online parameters in the recurrent Laguerre orthogonal polynomials NN is originated from Lyapunov stability theorem. Additionally, two optimal learning rates of the parameters by means of modified PSO are posed in order to achieve better convergence. At last, comparative studies shown by experimental results are illustrated to demonstrate the control performance of the proposed control scheme.

A Novel Range-Extended Strategy for Fuel Cell/Battery Electric Vehicles.

The range-extended electric vehicle is proposed to improve the range anxiety drivers have of electric vehicles. Conventionally, a gasoline/diesel generator increases the range of an electric vehicle. Due to the zero-CO2 emission stipulations, utilizing fuel cells as generators raises concerns in society. This paper presents a novel charging strategy for fuel cell/battery electric vehicles. In comparison to the conventional switch control, a fuzzy control approach is employed to enhance the battery's state of charge (SOC). This approach improves the quick loss problem of the system's SOC and thus can achieve an extended driving range. Smooth steering experience and range extension are the main indexes for development of fuzzy rules, which are mainly based on the energy management in the urban driving model. Evaluation of the entire control system is performed by simulation, which demonstrates its effectiveness and feasibility.

Resonance modes, cavity field enhancements, and long-range collective photonic effects in periodic bowtie nanostructures.

The discovery of single-molecule sensitivity via surface-enhanced Raman scattering on resonantly excited noble metal nanoparticles has brought an increasing interest in its applications to the molecule detection and identification. Periodic gold bowtie nanostructures have recently been shown to give a large enhancement factor sufficient for single molecule detection. In this work, we simulate the plasmon resonance for periodic gold bowtie nanostructures. The difference between the dipole and the quadrupole resonances is described by examining the magnitude and phase of electric field, the bound surface charge, and the polarization. The gap size dependence of the field enhancement can be interpreted by considering cavity field enhancement. Also, additional enhancement is obtained through the long-range collective photonic effect when the bowtie array periodicity matches the resonance wavelength.

Modulation of optical focusing by using optimized zone plate structures.

The focusing properties of the optimized zone plate structures which have upper and lower zones with different thicknesses are studied by the three-dimensional finite-difference time-domain method. Two kinds of materials are chosen, including silver representing metal and BK7 glass representing dielectric. An optimization algorithm is applied to tune the parameters of zone plate structures. Several optimized zone plate structures with smaller circular-shape focus are presented. By using the angular spectrum representation method, we found that the cases with smaller focal sizes have larger high-k components; however, the intensities of side lobes also become larger in comparison with the main beam. It is also found that the phase differences between different spatial field components can have the influences on focusing properties. A special case with two focuses is shown by changing the cost function of the same optimization algorithm. Our findings suggest that the optimized zone plate structures can reconstruct the light intensity distribution and have a great potential for the applications in imaging, lithography, and data storage.