Research of pressure sensor chip utilizing novel electrical circuit with bipolar-junction transistor-based (BJT) piezosensitive differential amplifier with negative feedback loop (PDA-NFL) for 5 kPa differential range was done. The significant advantages of developed chip PDA-NFL in the comparative analysis of advanced pressure sensor analogs, which are using the Wheatstone piezoresistive bridge, are clearly shown. The experimental results prove that pressure sensor chip PDA-NFL with 4.0x4.0 mm2 chip area has sensitivity S = 11.2 ± 1.8 mV/V/kPa with nonlinearity of 2KNLback = 0.11 ± 0.09 %/FS (pressure is applied from the back chip side) and 2KNLtop = 0.18 ± 0.09 %/FS (pressure is applied from the top chip side). All temperature characteristics have low errors, because the precision elements balance of PDA-NFL electric circuit was used. Additionally, the burst pressure is 80 times higher than the working range.

To solve the autonomous navigation problem in complex environments, an efficient motion planning approach is newly presented in this paper. Considering the challenges from large-scale, partially unknown complex environments, a three-layer motion planning framework is elaborately designed, including global path planning, local path optimization, and time-optimal velocity planning. Compared with existing approaches, the novelty of this work is twofold: 1) a novel heuristic-guided pruning strategy of motion primitives is proposed and fully integrated into the state lattice-based global path planner to further improve the computational efficiency of graph search, and 2) a new soft-constrained local path optimization approach is proposed, wherein the sparse-banded system structure of the underlying optimization problem is fully exploited to efficiently solve the problem. We validate the safety, smoothness, flexibility, and efficiency of our approach in various complex simulation scenarios and challenging real-world tasks. It is shown that the computational efficiency is improved by 66.21\% in the global planning stage and the motion efficiency of the robot is improved by 22.87\% compared with the recent quintic B\’{e}zier curve-based state space sampling approach. We name the proposed motion planning framework E$ \mathbf{^3} $MoP, where the number 3 not only means our approach is a three-layer framework but also means the proposed approach is efficient in three stages.

We investigate the benefits of feature selection, nonlinear modelling and online learning when forecasting financial time series. We combine sequential updating with continual learning, specifically transfer learning. We perform feature representation transfer through clustering algorithms that determine the analytical structure of radial basis function networks we construct. These networks achieve lower mean-square prediction errors than kernel ridge regression models, which arbitrarily use all training vectors as prototypes. We also demonstrate quantitative procedures to determine the very structure of the networks. Finally, we conduct experiments on the log-returns of financial time series and show that these online transfer learning models outperform a random-walk baseline. In contrast, the offline learning models struggle to do so.

Machine Learning (ML) is increasingly applied in industrial manufacturing, but often performance is limited due to insufficient training data. While ML models can benefit from collaboration, due to privacy concerns, individual manufacturers cannot share data directly. Federated Learning (FL) enables collaborative training of ML models without revealing raw data. However, current FL approaches fail to take the characteristics and requirements of industrial clients into account. In this work, we propose a FL system consisting of a process description and a software architecture to provide \acrfull{flaas} to industrial clients deployed to edge devices. Our approach deals with skewed data by organizing clients into cohorts with similar data distributions. We evaluated the system on two industrial datasets. We show how the FLaaS approach provides FL to client processes by considering their requests submitted to the Industrial Federated Learning (IFL) Services API. Experiments on both industrial datasets and different FL algorithms show that the proposed cohort building can increase the ML model performance notably.

A document by Liang Gao . Click on the document to view its contents.

This paper analyzes the global stability of synchronous reference frame phase-locked loops (SRF-PLLs), from a large signal viewpoint. First, a large signal model of SRF-PLL is accurately established, without applying any linearization method. Then, According to the phase portrait tool, the global stability of SRF-PLL is discussed in the nonlinear frame. Compared with existing methods, the proposed analysis, not relying on small signal model and linearization method, provides a global discussion of SRF-PLL stability. Some novel discoveries are as follow: 1) SRF-PLL has infinite equilibrium points, including stable points and saddle points; 2) Although saddle points are unstable in local regions, there still exists two special lines for each saddle point, and SRF-PLL converges to a certain saddle point when the initial states are on its special lines. 3) These special lines of saddle points divide the global region of SRF-PLL into infinite small regions. A Lyapunov-based discussion proves that SRF-PLL converges to different stable points, when the initial states are in the different small regions. The experiment results have been verified the global stability analysis of SRF-PLL.

This work presents a methodology for the joint calibration and synchronization of two arrays of microphones and loudspeakers. The problem is modeled as estimation of the rigid motion of one array with respect to the other, as well as estimation of the synchronization mismatch between the two. The proposed method uses dedicated signals emitted by the loudspeakers of the two arrays to compute a set of time of arrival (TOA) estimates. Through a simple transformation, estimated TOAs are converted into a set of linearly independent time difference of arrival (TDOA) measurements, which are modeled by a system of nonlinear equations in the unknown parameters of interest. A maximum likelihood estimate is then given as the solution to a nonlinear weighted least squares (NWLS) problem, which is optimized applying a parallelizable variant of Particle Swarm Optimization (PSO). In this paper, we also derive the Cramer-Rao lower bound (CRLB), and benchmark it against the proposed method in a series of Monte Carlo (MC) simulations. Results show that the proposed method attains high-performance comparable to the CRLB.

Abstract: In this paper, we propose a novel resource management scheme that jointly allocates the transmit power and computational resources in a centralized radio access network architecture. The network comprises a set of computing nodes to which the requested tasks of different users are offloaded. The optimization problem minimizes the energy consumption of task offloading while takes the end-to-end-latency, i.e., the transmission, execution, and propagation latencies of each task, into account. We aim to allocate the transmit power and computational resources such that the maximum acceptable latency of each task is satisfied. Since the optimization problem is non-convex, we divide it into two sub-problems, one for transmit power allocation and another for task placement and computational resource allocation. Transmit power is allocated via the convex-concave procedure. In addition, a heuristic algorithm is proposed to jointly manage computational resources and task placement. We also propose a feasibility analysis that finds a feasible subset of tasks. Furthermore, a disjoint method that separately allocates the transmit power and the computational resources is proposed as the baseline of comparison. A lower bound on the optimal solution of the optimization problem is also derived based on exhaustive search over task placement decisions and utilizing Karush–Kuhn–Tucker conditions. Simulation results show that the joint method outperforms the disjoint method in terms of acceptance ratio. Simulations also show that the optimality gap of the joint method is less than 5%.

Efficient and compact frequency converters are essential for frequency stabilization of terahertz sources. In this paper, we present a 3.5-THz, x6-harmonic, integrated Schottky diode mixer operating at room temperature. The designed frequency converter is based on a single-ended, planar Schottky diode with a sub-micron anode contact area defined on a suspended 2-um ultra-thin GaAs substrate. The dc-grounded anode pad was combined with the radio frequency E-plane probe, which resulted in an electrically compact circuit. At 200 MHz intermediate frequency, a mixer conversion loss of about 59 dB is measured resulting in a 40 dB signal-to-noise ratio in a phase-locking application. Using a quasi-static diode model combined with electromagnetic simulations, good agreement with the measured results was obtained. Harmonic frequency converters without the need of cryogenic cooling will help in the realization of highly sensitive space and air-borne heterodyne receivers.

We propose a blind joint equalization algorithm for M-QAM signals based on a widely linear filtering approach. The proposed scheme jointly compensates receiver IQ imbalance and polarization mixing, along with carrier recovery, followed by transmitter IQ imbalance compensation. We first investigate the proposed scheme’s tolerance to transceiver IQ Imbalance, polarization mixing, phase noise and frequency offset through numerical simulations for 32 GBd PM-16QAM and PM-64QAM signals and compare its performance with the conventional digital processing algorithms. Further, with the proposed algorithm, we experimentally demonstrate the improvement in Q2 value to up to ~ 1.22 dB for a 32 GBd PM-16QAM and ~ 3.72 dB for a 16 GBd PM-64QAM signal with a phase imbalance of 9o. We show that the MSE convergence of the proposed joint equalizer is much faster than conventional DSP algorithms. Deployment of such an equalizer in optical communication systems is beneficial due to its improved tolerance to multiple impairments, albeit with increased complexity.

To minimize the curtailment of renewable generation and incentivize grid-scale energy storage deployment, a concept of combining stationary and mobile applications of battery energy storage systems built within renewable energy farms is proposed. A simulation-based optimization model is developed to obtain the optimal design parameters such as battery capacity and power ratings by solving a multi-objective optimization problem that aims to maximize the economic profitability, the energy provided for transportation electrification, the demand peak shaving, and the renewable energy utilized. Two applications considered for the stationary energy storage systems are the end-consumer arbitrage and frequency regulation, while the mobile application envisions a scenario of a grid-independent battery-powered electric vehicle charging station network. The charging stations receive supplies from the energy storage system that absorbs renewable energy, contributing to a sustained DC demand that helps with revenues. Representative results are presented for two operation modes and different sets of weights assigned to the objectives. Substantial improvement in the profitability of combined applications over single stationary applications is shown. Pareto frontier of a reduced dimensional problem is obtained to show the trade-off between design objectives. This work could pave the road for future implementations of the new form of energy storage systems.

Modeling the electromagnetic response of carbon nanotube (CNT) reinforced composites is inherently a three dimensional (3D) multi-scale problem that is challenging to solve in real-time for nondestructive evaluation applications. This article presents a fast and accurate full-wave electromagnetic solver based on a multi-layer dyadic Green’s function approach. In this approach, we account for the effects of the dielectric slab, where the CNTs are embedded, without explicitly discretizing its interfaces. Due to their large aspect ratios, the CNTs are modeled as arbitrary thin wires (ATWs), and the method of moment (MoM) formulation with distributed line impedance is used to solve for their coupled currents. The accuracy of the inhouse solver is validated against commercial method of moment (MoM) and finite element method (FEM) solvers over a broad range of frequencies (from 1 GHz to 10 THz) and for a wide range of dielectric slab properties. Examples of 100nm long vertical and horizontal CNTs embedded in a 1 μm thick lossy dielectric substrate are presented. The in-house solver provides more than 50 ✕ speed up while solving the vertical CNT, and more than 570 ✕ speed up while solving the horizontal CNT than a commercial MoM solver over the GHz to THz frequency range.

Unmanned aerial vehicle (UAV) systems are of increasing interest to academia and industry due to their mobility, flexibility and maneuverability, and are an effective alternative to various uses such as surveillance and mobile edge computing (MEC). However, due to their limited computational and communications resources, it is difficult to serve all computation tasks simultaneously. This article tackles this problem by first proposing a scalable aerial computing solution, which is applicable for computation tasks of multiple quality levels, corresponding to different computation workloads and computation results of distinct performances. It opens up the possibility to maximally improve the overall computing performance with limited computational and communications resources. To meet the demands for timely video analysis that exceed the computing power of a UAV, we propose an aerial video streaming enabled cooperative computing solution namely, UAVideo, which streams videos from a UAV to ground servers. As a complement to scalable aerial computing, UAVideo minimizes the video streaming time under the constraints on UAV trajectory, video features, and communications resources. Simulation results reveal the substantial advantages of the proposed solutions. Besides, we highlight relevant directions for future research.

The paper presents MEMS pressure sensor chip utilizing novel electrical circuit with bipolar-junction transistor-based (BJT) differential amplifier with negative feedback loop (PDA-NFL). Pressure sensor chips with two circuits have been manufactured and tested: the first chip uses circuit with vertical n-p-n (V-NPN) BJTs and the second – circuit with horizontal p-n-p (L-PNP) BJTs. The demonstrated approach allows for increase of pressure sensitivity while keeping the same chip size. It also can be used for chip size reduction and increase of pressure overload capability while maintaining the same pressure sensitivity. Significant reduction of both noise and temperature instability of output signal has been demonstrated using transistor amplifier with negative feedback loop.

Type-IV wind turbines can experience torsional vibrations in the drivetrain structure. This can lead to additional stress on turbine components and a quality reduction of the power delivered to the grid. The vibrations are mostly induced by fast variations of the electromagnetic torque, which depends on the control of a back-to-back converter. A number of studies have already presented methods to mitigate the drivetrain vibrations. However, the research was dedicated to cases when the converter, interfacing a wind turbine to the grid, operates based on a grid-following control. A wind turbine can be also interfaced to a grid-forming converter. In this case, a back-to-back converter control creates a strong link between the electromagnetic torque and grid dynamics, so the abovementioned problem remains relevant. Therefore, this paper presents a solution to damp torsional vibrations in the direct drive of a Type-IV wind turbine, interfaced to the electrical power grid with a voltage source converter based on a grid-forming control. The damping of the drivetrain vibrations relies on the input shaping method implemented using a zero-vibration filter. Simulation results prove the effectiveness of the method to damp drivetrain vibrations during grid frequency variations. In addition to that, damping impact on system behavior with respect to other parameters is analyzed and its mitigation is discussed.

This paper deals with a new smooth line-switching method that facilitates the network reconfiguration of islanded networks. Its distinct features include the ability to handle network asymmetries and to minimize the line current during the switching action. This is attained by developing a three-phase sensitivity-based method to determine the operating set-points of distributed generators (DGs) that minimize the current of the candidate line participating in the switching action. These set-points correspond to the positive-sequence powers as well as the negative- and zero-sequence currents of DGs. Furthermore, the network constraints such as voltage limits and power limits of DGs are always satisfied. Simulations are performed in a balanced 33-bus islanded network as well as in the unbalanced IEEE 8500-node network to evaluate the performance of the proposed method.

This paper investigates the seamless mapping of wireless mmWave phase-levels to the optical phase-levels in a seamless digital mmWave-to-photonic converter. We report a non-linear mapping of phase-levels from the wireless to the optical domain which results in digital symbol degeneracy. Our work provides various optimum design guidelines to make such seamless converters practically feasible in digital communication links. The latency and the design complexity of such a seamless converter are expected to be appreciably lower than those of a conventional converter, thereby making it more suitable for 6G communication systems. Although the wireless operating frequency considered in this paper belongs to the mmWave band, all the contributions presented are equally applicable and relevant in the THz wireless band as well.

In this paper, we present a novel design framework of synthetic radial aperture focusing for 3-dimensional (3-D) transrectal ultrasound imaging (TRUS-rSAF), in which multiple transmittance/reception events at different scanning angles are synthesized to reconstruct a radial plane in the target volume, securing high spatial resolution and texture uniformity. A theory-based design approach has not been available to push the envelope of the 3-D rSAF technique. Herein, a closed-form analytical description of the TRUS-rSAF method is presented for the first time, effectively delineating spatial resolution and grating lobe positions in the radial dimension of a TRUS transducer. We demonstrate a solid optimization workflow based on the theoretical foundation to improve its spatiotemporal resolution, grating lobe artifacts, and signal-to-noise ratio (SNR). A specific design criterion was considered to outperform a clinical 3-D TRUS imaging (TRUS-REF), where each radial plane is reconstructed with a single transmittance/reception event using a motorized actuator. The optimized TRUS-rSAF method significantly enhanced spatial resolution up to 50 % over the TRUS-REF method while providing clinically-effective temporal resolution (2-8 volumes per second) with negligible grating lobe artifacts. The results indicate that the proposed design approach would enable a novel TRUS imaging solution in clinics.