Search

This article studies the control problem for a class of stochastic nonlinear time-delay systems with uncertain output functions. Under the appropriate assumptions, a stabilization controller is explicitly constructed by applying the adding a power integrator method. Then, using the Lyapunov-Krasovskii functionals to address time-delay, it is proven that the designed controller can guarantee the closed-loop system to be globally asymptotically stable (GAS) in probability. Finally, two simulations show that the control strategy is effective and can be applied to the actual system.

This article investigates the fixed-time synchronization problem of a class of delayed complex-valued inertial neural networks (CVINNs). The analysis does not rely on the traditional reduced-order transformation, but constructing Lyapunov functions directly focused on the original system. Based on the direct method and the separation method, different control strategies are proposed, under which the addressed CVINNs can achieve synchronization perfectly in a fixed time. The corresponding synchronization criteria in terms of matrix inequalities are derived, which are more concise and easier to verify than algebraic inequalities conditions, and the estimation of the settling times. The direct method makes full use of some innovative inequalities in the complex field; the exponential parameters in the designed controllers are independent. Finally, in order to fully support the theoretical results, based on two typical activation functions, the proposed theoretical results are numerically validated and compared.

This paper addresses adaptive fuzzy event-triggered controller design for nonstrict nonlinear systems. Firstly, for estimating the unmeasurable states, the high-gain fuzzy state observer is designed. By using backstepping technique, the adaptive fuzzy controller is designed. A new switching threshold event-triggered mechanism is given to decide when the controller needs to be updated and the Zeno behavior is avoided. Stability analysis shows that the tracking error can be arbitrarily small and all variables of the closed-loop system remain bounded. At last, the effectiveness of our control strategy is illustrated through simulation.

This paper studies the delay bounds problem of second-order multi-agent systems with input and communication delays under the directed graph. Firstly, the systems with input delay only are studied and the delay margin of input delay is given. Then the critical value of communication delay is considered for the fixed input delay within the proposed scope. Then, the input delay and communication delay are analyzed simultaneously from geometric perspective. The explicit geometric description of the consensus crossing curves is obtained. Then the consensus regions can be completely determined.

This brief proposes a sampled-data practical tracking control scheme for nonlinear time-delay systems. A novel technique lemma is firstly proposed to effectively estimate the trajectory growth between two successive sampling points. The output feedback domination approach, as a powerful tool, is utilized in this brief. After constructing appropriate observer as well as sampled-data controller, the desired tracking accuracy can be realized. Finally, we present an electromechanical system simulation example to verify the effectiveness of the proposed control approach.

In this article, adaptive event-triggered finite-time control is explored for uncertain nonlinear systems with time delay. First, to handle the time-varying state delays, the Lyapunov-Krasovskii function is used. Fuzzy-logic systems are used to deal with the unknown nonlinearities of the system. Notice that compared to the reporting achievements, our proposed virtual control laws are derivable by using the novel switch function, which avoids "singularity hindrance" problem. Moreover, the dynamic event-triggered controller is designed to reduce the communication pressure and we prove that the controller is Zeno free. Our proposed control strategy ensures that the tracking error is arbitrarily small in finite time and all variables of the closed-loop system remain bounded. Finally, to show the effectiveness of our control strategy, the simulation results are given.

The freshness of status information is of great importance for time-critical Internet-of-Things (IoT) applications. A metric measuring status freshness is the Age of Information (AoI), which captures the time elapsed from the status being generated at the source node (e.g., a sensor) to the latest status update. However, in intelligent IoT applications such as video surveillance, the status information is revealed after some computation-intensive and time-consuming data processing operations, which would affect the status freshness. In this article, we propose a novel metric, Age of Processing (AoP), to quantify such status freshness, which captures the time elapsed of the newest received processed status data since it is generated. Compared with AoI, AoP further takes the data processing time into account. Since an IoT device has limited computation and energy resources, the IoT device can choose to offload the data processing to the nearby edge server under constrained status sampling frequency. We aim to minimize the average AoP in a long-term process by jointly optimizing the status sampling frequency and processing offloading policy. We first formulate this online problem as an infinite-horizon constrained Markov decision process (CMDP) with an average reward criterion. We then transform the CMDP problem into an unconstrained Markov decision process (MDP) by leveraging a Lagrangian method, and accordingly propose a Lagrangian transformation framework for the original CMDP problem. Furthermore, we integrate the framework with a perturbation-based refinement mechanism for achieving the optimal policy of the CMDP problem. Our investigation shows that to minimize the average AoP: 1) for processing offloading: the policy exploits good channel state to offload processing to the edge server and 2) for status sampling: the waiting time presents a threshold structure. Extensive numerical evaluations show that the proposed algorithm outperforms the benchmarks, with an average AoP reduction up to 30%.

The apparent increase in magnetic field measurement has led to a growing demand for new sensing technologies. However, a long-lasting challenge unaddressed is how to achieve ultrahigh sensitive magnetic field measurement in both amplitude and orientation. Here, we demonstrate a compact-in-size and simple-to-implement fiber-optic magnetometer for highly sensitive vector magnetic field measurement. The sensor is based on magnetic nanoparticles coated tilted fiber Bragg grating inscribed in a commercial single mode fiber. The sensing mechanism stems from the backward coupled cladding mode resonances with narrow bandwidth (Q factor > 104), which is highly sensitive to slight surface refractive index perturbation. The switching of the magnetic field direction on the surface of fiber induces redistribution of coated magnetic nanoparticles with spatial anisotropy density and scattering by formation of magnetic chains hereafter the effective index is changed, which in turn modulates the cladding mode resonance with wavelength shift and amplitude attenuation. Through calculating the peak-to-peak intensity of target cladding modes of TFBG, the external magnetic field was precisely measured with an intensity sensitivity of 0.39 dB/mT and a direction sensitivity of 0.038 dB/rad. The inherent core mode can be used as a reference to get rid of temperature crosstalk and light source disturbance. The proposed sensor is compact in size and easy to be used in hard-to-reach place, showing good potentials for industrial applications.

We study the economic interactions among sellers and buyers in online markets. In such markets, buyers have limited information about the product quality, but can observe the sellers' reputations which depend on their past transaction histories and ratings from past buyers. Sellers compete in the same market through pricing, while considering the impact of their heterogeneous reputations. We consider sellers with limited as well as unlimited capacities, which correspond to different practical market scenarios. In the unlimited seller capacity scenario, buyers prefer the seller with the highest reputation-price ratio. If the gap between the highest and second highest seller reputation levels is large enough, then the highest reputation seller dominates the market as a monopoly. If sellers' reputation levels are relatively close to each other, then those sellers with relatively high reputations will survive at the equilibrium, while the remaining relatively low reputation sellers will get zero market share. In the limited seller capacity scenario, we further consider two different cases. If each seller can only serve one buyer, then it is possible for sellers to set their monopoly prices at the equilibrium while all sellers gain positive market shares; if each seller can serve multiple buyers, then it is possible for sellers to set maximum prices at the equilibrium. Simulation results show that the dynamics of reputations and prices in the longer-term interactions will converge to stable states, and the initial buyer ratings of the sellers play the critical role in determining sellers' reputations and prices at the stable state.

For many Internet of Things (IoT) applications, the freshness of status information is of great importance, and age of information (AoI) is a newly proposed metric to quantify the freshness of system status. However, in many cases, the original raw data collected by IoT devices needs to be preprocessed in real-time to extract the hidden effective information, which is usually computationally intensive and time consuming. To this end, we promote an edge computing assisted approach and aim to reduce the AoI by flexibly offloading the raw IoT data to the edge server for information preprocessing. We consider that the IoT devices can opportunistically collect extra energy through energy harvesting for sustainable operations, and propose a novel timely system status update model that consists of multiple IoT devices with energy harvesting and edge-assisted information preprocessing. The objective is to minimize the system-wide average AoI under a fixed energy cost budget. To tackle the key challenges due to the unpredictability of the stochastic energy harvesting process and the long-term energy constraints, we propose a Lyapunov-based average AoI Minimization (LAoIM) algorithm to derive an approximate optimal solution, and further quantify the performance gap from the optimal solution. Extensive numerical evaluations demonstrate that LAoIM can take full advantages of local and edge computation resources and achieve superior performance gain over existing schemes.

With the rapid development of high-speed and heavy-load electrified railway, the peak impact and the regenerative braking energy content of traction load become increasingly significant, which has become an important problem affecting the construction and operation benefits of electrified railway. On the basis of comprehensively solving the power quality problems of electrified railway, a multi-application strategy based on the railway static power conditioner (RPC) with the energy storage system (ESS) is proposed in this paper to improve the economic benefits of the traction system. Through the implementation of the peak clipping and the recuperation and utilization of regenerative braking energy of traction load with the guidance of such strategy, the comprehensive economic benefits of the traction system can be improved. By analyzing the mathematical relationship between the comprehensive economic benefits and the parameters such as peak clipping rate and regenerative braking energy utilization rate of the system, an optimization model with the goal of maximizing the comprehensive economic benefits is established. Therefore, the optimal configuration capacity of the ESS is obtained. Finally, the experiments are carried out in MATLAB. The results show that, with the multi-application strategy, the peak load clipping and the recuperation and utilization of regenerative braking energy can be simultaneously realized. It is also demonstrated that the comprehensive economic benefits and environmental benefits can be improved while improving energy efficiency.

This article presents a low-power, high-gain, high-linearity phased-array transmit (Tx) beamformer chip in a 45-nm RFSOI process. An architecture employing a power amplifier with neutralization, a 180° active phase shifter and 11/22/45/90° passive phase shifter result in high linearity and low power consumption. The measured gain is 22 dB with a 3-dB bandwidth of 57.5–65.5 GHz and an ${\mathrm{ OP}}_{1\,{\mathrm{ dB}}}$ and ${\mathrm{ OP}}_{\mathrm{ sat}}$ of 7.5–10 dBm and 9.5–11.5 dBm at 57–64 GHz, respectively. A peak power-added efficiency (PAE) of 14.6% and 17.6% at 60 GHz is achieved at $P_{1\,{\mathrm{ dB}}}$ and $P_{\mathrm{ sat}}$ , respectively. The measured rms phase and gain errors are $ and ${\mathrm{ OP}}_{1\,{\mathrm{ dB}}}$ . Complex modulation measurements using a 400 MBaud 64-QAM waveform show an error vector magnitude (EVM) of 5% (−26 dB) at an average power of 5 dBm and with a dc power consumption of only 52 mW, resulting in a linear PAE of 6.1%. Also, 18–20 Gbps data rates are demonstrated with an EVM < 5% (64-QAM) and < 9% (16-QAM) at 60 GHz. To the author’s knowledge, this work achieves state-of-the-art linearity and efficiency for 60-GHz communication systems.

In this article, the problem of output feedback control for a class of stochastic nonlinear systems in the presence of nondifferentiable measurement function and input saturation is studied. A novel power-auxiliary system is introduced to handle the adverse effects of input saturation. What is more, the common growth assumptions of nonlinear terms can be eliminated by a key lemma. Then, an output feedback controller is constructed to ensure that all the signals in the closed-loop system are globally bounded almost surely. Finally, a simulation shows that the control strategy is effective.

This article presents a 5G 37–42-GHz $8\times 8$ phased array. The array is based on $2\times 2$ SiGe transmit/receive (TRX) beamformer chips in the SiGe technology with 6 bits of phase control and 8 bits of gain control. A detailed study is presented, showing the effects of array-level amplitude and phase errors on the radiated error vector magnitude (EVM) of 64-element arrays. The array scans to ±60° in the azimuth plane and ±50° in the elevation plane with low sidelobes. The measured peak effective isotropic radiated power (EIRP) is 51 dBm at Psat with a 3-dB bandwidth of 36–41.5 GHz. A 39-GHz communication system is also demonstrated along with a high-pass filter and an integrated upconverter/downconverter and achieves a local oscillator (LO) and image rejection level of 50 dBc, meeting FCC requirements of

In this article, the passivity-based control problem is addressed for hidden Markov jump systems with singular perturbations and partially unknown probabilities. The hidden Markov model (HMM) with partially unknown probabilities is introduced, where the partially unknown probabilities may exist in either the transition probability matrix of Markov chain, the observation probability matrix of observed signal, or in both of them. Thus, the underlying HMM is more general than the one in some existing works. By using this HMM, some passivity analysis criteria are established for Markov jump singularly perturbed systems with partial unknown probabilities. Based on these criteria, a unified method is presented for the design of controllers to ensure the passivity of the system. A numerical example and an armature controlled dc motor model are given to illustrate the efficiency of the proposed method.

This article presents a 20–44-GHz image-rejection receiver in GlobalFoundries 22-nm fully-depleted silicon-in-insulator (FD-SOI). The receiver includes a wideband LNA with a three-pole high-pass rejection filter coupled to a double-balanced mixer with a wideband LO driver and an intermediate frequency (IF) amplifier. At an IF of 16 GHz, the image band at dc −12 GHz is filtered by the LNA response and is greater than 75 dB over the entire 20–44-GHz range. The receiver results in a gain of 24–28.5 dB and a noise figure (NF) of 3.3–5 dB at 20–44 GHz and achieves an error vector magnitude (EVM) of 3.6%–3.7% at the carrier frequency of 28 and 39 GHz with 2-Gbaud symbol rate at 64-QAM waveform. To the best of the authors’ knowledge, this is the first wideband image-rejection receiver that covers the entire millimeter-wave 5G band based on GF 22-nm CMOS FD-SOI.

This article presents a packaged SiGe BiCMOS reflectometer for 0.01–26-GHz two-port vector network analyzers (VNAs). The reflectometer chip is composed of a resistive bridge coupler and two wideband heterodyne receivers for coherent magnitude and phase detection. In addition, a high-linearity receiver channel is designed to accommodate ~20 dBm of RF input power to the reflectometer. External sources are used to provide stimulus over the entire bandwidth, and a high-linearity shunt 50- $\Omega $ switch is placed at the input of each reflectometer chip to provide a matched condition for better measurement accuracy. The measured dynamic range (DR) of the heterodyne receiver is 129±3 dB at 0.01–26 GHz at an IF resolution bandwidth (RBW) of 10 Hz. The reflectometer chip is packaged on a low-cost printed circuit board (PCB) and connectorized for repeatable measurements. Several passive and active device-under-test (DUTs), such as filters and amplifiers, are measured in single- and dual-port complex $S$ -parameter setup at 0.01–26 GHz. The single-chip reflectometer results in excellent agreement with commercially available test equipment and with minimal magnitude and phase difference. Several definitions (mean deviation and error vector) are used to quantify the measurement accuracy. A measured $S_{21}$ of −80 dB is obtained using high-rejection filters. An in-depth DR analysis of possible limiting factors in the measurement setup is also presented. This article shows that silicon-based highly integrated reflectometers can be used in low-cost VNAs with excellent DR and accuracy.

For transmission based on long-distance vortex waves, the partial receiving scheme which uses a limited angular aperture receiving and demultiplexing multi-beam can solve the difficulty of the conventional whole beam receiving scheme due to the divergence of the vortex beam. But the atmospheric turbulence is rarely considered in analyzing the stability of the radio vortex (RV) communication system based on the partial angular aperture receiving (PAAR) scheme. Here we first introduce atmospheric turbulence into the RV communication system based on the PAAR scheme. Moreover, in order to compare the effects of turbulence on the PAAR scheme and the whole angular aperture receiving (WAAR) scheme, a new turbulence attenuation degree D model is proposed, which represents the stability of the RV communication system in the atmospheric turbulence environment. Simulation results indicate that the difference of D values between PAAR scheme and WAAR scheme does not exceed the order of 0.01 when the range of refractive index structure constant Cn2 is 10-17m-2/3 - 10-12m-2/3 and the distance is 90m-120m. When the range of Cn2 is 10-13m-2/3 - 10-12m-2/3 and the distance is 90m-120m, D value of PAAR scheme is always smaller than that of WAAR scheme. These demonstrations suggest that the RV communication system using PAAR scheme is more stable than that using WAAR scheme in the strong atmospheric turbulence environment.

Owing to the capability of providing a certain phase gradient on the interface between two media, metasurfaces have shown great promise for altering the directions of outgoing electromagnetic (EM) waves arbitrarily. With the suitable arrangement of particles on metasurfaces, anomalous reflection and refraction have been observed in wide frequency ranges. To completely control the propagation of EM waves, both phase and amplitude profiles are required in some applications. Herein, we propose a new type of metasurface with both phase and amplitude modulations, which is composed of C-shaped particles and can generate and control multiple beams using amplitude and phase responses simultaneously. An addition theorem of complex reflection coefficients is presented to acquire various states of multiple beams reflected from designed metasurfaces. Meanwhile, the intensities of multiple beams can be separately modulated as desired benefitting from the independent controls of phase and amplitude profiles. All the experimental results have good agreements with the numerical simulations. The presented method opens a new way to form and manipulate multiple beams using metasurfaces, which can find potential applications in beam shaping, radar detection systems, and high-quality holography.

A lossy-mode resonance (LMR) refractive index (RI) sensor with a few layer transition metal dichalcogenides (TMDs) is proposed. Contrasting with traditional surface plasmon resonance (SPR) sensors, the LMR sensor can support both transverse magnetic (TM) and transverse electric (TE) polarizations, while the traditional SPR sensors can only operate for TM polarization. Through theoretical calculation and simulation, we find that the proposed LMR sensor can realize higher sensitive and narrower full width half maximum (FWHM), and the most attractive character is figure-of-merit (FOM) with 1582 RIU−1, which is a significant improvement compared to the existing LMR sensors and is even 40% higher than the best graphene-based SPR sensors. We trust that the proposed LMR sensor can be extensively applied in optical sensing technology, chemical biomolecular detection, and so on.

This paper is concerned with the problem of global output-feedback control for a class of stochastic nonlinear time-delay systems in the presence of input saturation and unmeasurable states. As a distinctive feature, the growth assumptions imposed on the drift and diffusion terms are proven to be unnecessary, which can be removed through a technical lemma. In addition, by introducing an auxiliary system whose order is the same as the considered system, and using Lyapunov–Krasovskii functional, the adverse effects generated by input saturation and time-varying delay are eliminated. Then, based on state-observer and backstepping recursive design, an output-feedback controller is constructed to render the closed-loop system be globally bounded almost surely. Particularly, instead of converging to an arbitrarily small neighborhood of zero as in related results, the tracking error is ensured to be tuned by the design parameters and an input saturation error in the mean quartic sense. Finally, a stochastic chemical reactor system is established and shown to demonstrate the effectiveness of the proposed scheme.

The issue of missing values (MVs) has appeared widely in real-world datasets and hindered the use of many statistical or machine learning algorithms for data analytics due to their incompetence in handling incomplete datasets. To address this issue, several MV imputation algorithms have been developed. However, these approaches do not perform well when most of the incomplete tuples are clustered with each other, coined here as the Clustered Missing Values Phenomenon , which attributes to the lack of sufficient complete tuples near an MV for imputation. In this paper, we propose the Order-Sensitive Imputation for Clustered Missing values (OSICM) framework, in which missing values are imputed sequentially such that the values filled earlier in the process are also used for later imputation of other MVs. Obviously, the order of imputations is critical to the effectiveness and efficiency of OSICM framework. We formulate the searching of the optimal imputation order as an optimization problem, and show its NP-hardness. Furthermore, we devise an algorithm to find the exact optimal solution and propose two approximate/heuristic algorithms to trade off effectiveness for efficiency. Finally, we conduct extensive experiments on real and synthetic datasets to demonstrate the superiority of our OSICM framework.

To improve the electrical performance of thin-film transistors with an atomic-layer-deposited Al2O3 dielectric/InOx channel, O2 plasma surface treatments of the InOx back channel are explored in comparison with thermal annealing. It is demonstrated that the O2 plasma treatment can enhance the device performance much more efficiently than thermal annealing, exhibiting an extremely low-thermal budget. Furthermore, it is revealed that the plasma treatment at a higher temperature can improve the device performance much faster than that at a lower temperature. For a 4 min O2plasma treatment at 200 °C, superior electrical characteristics are achieved, such as a field-effect mobility of 11 cm2V−1s−1, a threshold voltage of 0.9 V, a subthreshold swing of 0.38 V/dec, and good gate bias stress stabilities. This is ascribed to faster passivation of oxygen vacancies, removal of more C residues, and weaker surface damage and smaller surface roughness of the InOx back channel in comparison with long plasma treatments at lower temperatures.

Research on cyborg intelligent insects requires the experiment platform to collect data from the distributed sensing models and process the multi-modal signals in real-time. To overcome these issues, we present a novel data model, naming as the Hybrid-synchronizedrealtime (HSR) data model, which can synchronize the hybrid raw data channels by a meta data integration method and process them with a fixed priority scheduling algorithm. Real animal experiments show that the insect-machine experiment platform based on the HSR data model can fully satisfy the requirements of cyborg intelligent insect research, especially in dealing with the technical challenges of data synchronization, real-time processing and hybrid data integration. It provides an efficient approach to implement the experiment platform and thus aid the frontier research on cyborg insects.