Miracle-dz/UniFAR-FTUs huggingface dataset:
A Transformed-based Ka-band GaN Low-Noise Amplifier MMIC

This paper reports the design of a broadband GaN low noise amplifier (LNA) microwave monolithic integrated circuit (MMIC). A novel transformer structure is proposed to implement the gm-boosting as well as bandwidth extension in the LNA. The designed LNA shows >20dB gain, <2dB noise figure (NF) and >14.5dBm output 1dB compression point (OP1dB) within a wide working frequency range from 20 to 41GHz, which fully covers the Ka-band. In addition, the gain and NF fluctuation is less than 2dB and 0.7dB, respectively. The die size of the LNA MMIC is 1.6×1mm2. This work verifies the feasibility of the transformer-based LNA design in GaN MMIC platform.
Drive Signal Switching based Discontinuous PWM for Suppression of Zero Sequence Circulating Current in Parallel Inverters

In order to suppress the zero sequence circulating current (ZSCC) in parallel inverters, a discontinuous PWM (DPWM) scheme based on drive signal switching is proposed in this paper. It can effectively reduce the ZSCC in parallel inverters while reduced switching losses and smaller output current ripples can also be simultaneously achieved. According to the voltage sector in which the reference voltage vector is located, the drive signals generated by the proposed scheme for one of the three phases of two inverters will be switched in turn. The effectiveness of proposed method is finally analyzed and verified via tests on a prototype inverter.
Energy-efficient Silicon Optical Phased Array with Ultra-sparse Nonuniform Spacing

We experimentally demonstrate an ultra-sparse 120-channel silicon optical phased array with a large aperture size of 6 mm × 5 mm. A 162° field of view was achieved with a total power consumption of 0.47 W and thermo-optic power efficiency of 3.1 mW/π.
A unified synergistic retrieval of clouds, aerosols, and precipitation from EarthCARE: the ACM-CAP product

Abstract. ACM-CAP provides a synergistic best-estimate retrieval of all clouds, aerosols, and precipitation detected by the atmospheric lidar (ATLID), cloud-profiling radar (CPR), and multi-spectral imager (MSI) aboard EarthCARE (Earth Cloud Aerosol and Radiation Explorer). While synergistic retrievals are now mature in many contexts, ACM-CAP is unique in that it provides a unified retrieval of all hydrometeors and aerosols. The Cloud, Aerosol, and Precipitation from mulTiple Instruments using a VAriational TEchnique (CAPTIVATE) algorithm allows for a robust accounting of observational and retrieval errors and the contributions of passive and integrated measurements and for enforcing physical relationships between components (e.g. the conservation of precipitating mass flux through the melting layer). We apply ACM-CAP to EarthCARE scenes simulated from numerical weather model forecasts and evaluate the retrievals against “true” quantities from the numerical model. The retrievals are well-constrained by observations from active and passive instruments and overall closely resemble the bulk quantities (e.g. cloud water content, precipitation mass flux, and aerosol extinction) and microphysical properties (e.g. cloud effective radius and median volume diameter) from the model fields. The retrieval performs best where the active instruments have a strong and unambiguous signal, namely in ice clouds and snow, which is observed by both ATLID and CPR, and in light to moderate rain, where the CPR signal is strong. In precipitation, CPR's Doppler capability permits enhanced retrievals of snow particle density and raindrop size. In complex and layered scenes where ATLID is obscured, we have shown that making a simple assumption about the presence and vertical distribution of liquid cloud in rain and mixed-phase clouds allows improved assimilation of MSI solar radiances. In combination with a constraint on the CPR path-integrated attenuation from the ocean surface, this leads to improved retrievals of both liquid cloud and rain in midlatitude stratiform precipitation. In the heaviest precipitation, both active instruments are attenuated and dominated by multiple scattering; in these situations, ACM-CAP provides a seamless retrieval of cloud and precipitation but is subject to a high degree of uncertainty. ACM-CAP's aerosol retrieval is performed in hydrometeor-free parts of the atmosphere and constrained by ATLID and MSI solar radiances. While the aerosol optical depth is well-constrained in the test scenes, there is a high degree of noise in the profiles of extinction. The use of numerical models to simulate test scenes has helped to showcase the capabilities of the ACM-CAP clouds, aerosols, and precipitation product ahead of the launch of EarthCARE.
Phase patterns of dispersive waves from moving localized sources

A general approach is proposed within which the phase structure of wave perturbations caused by a moving localized source can be described based on the wave dispersion law alone. Applying this approach, a simple analytical expression for the phase surfaces is obtained. It is used to study the details of phase patterns of gravity-capillary waves, the structure of wave trains in the ocean in the wake of a moving tropical hurricane, and the system of lee waves in Earth's atmosphere.
Experiments with bioradars in an automotive environment

In this study three different bioradars were tested inside a vehicle to verify if they are able to measure respiratory rate and heart rate in an automotive environment. Raw data recordings and vital sign measurements were obtained, and after using low-complexity methods to extract the vital sign measurements from the raw data, these were compared to a ground truth. The best performance in respiratory rate measurement was achieved by an Impulse-Response Ultra Wide Band radar, with an average of 7.18 % mean relative error when the vehicle is stationary, and the best performance in heart rate measurement was achieved by a Frequency Modulated Continuous Wave radar, with an average mean relative error of 3.27 % when the vehicle is stationary.
DOD-DOA Estimation using MIMO Antenna Arrays with Manifold Extenders

With increasing demands on capacity and height-ened user density, it is imperative to propose approaches that exploit and utilise all possible degrees of freedom (DoF) of a MIMO system. This directly translates to increased channel estimation accuracy. With this motivation, in this paper, a novel MIMO antenna array system with a receiver of $N$ antennas that includes a joint estimation of Direction-of-Arrival (DOA) and Direction-of-Departure (DOD) of the desired user in the presence of other multiple access users, is presented. This is achieved by using the concept of the “Manifold Extender” which increases the dimensionality of the observation space of the received vector-signals, and thus increases the DoF from $N$ to $NN_{\text{ext}}$ where $N_{\text{ext}}$ is the extended dimension. The performance of the receiver is evaluated against the existing spatial-only and virtual receivers by computer simulation studies.
Design of a global sliding-mode controller for a motor drive with bounded control

A global sliding-mode controller can ensure sliding behaviour throughout an entire response for applications of a motor drive. The desired specifications, input disturbances and uncertainties of parameters are considered in the design, and the range of allowable reference input (RARI) is then estimated under the constraint on control activities and the sliding mode requirement; conversely, the designers can specify the demands on the capability of a servo driver to achieve a desired performance. To verify the proposed scheme, we performed experiments on its implementation in a DC brushless servo system. The advantages of the proposed scheme were indicated by comparison with the conventional sliding-mode scheme; the coherence of the theoretical estimates and the experimental results were tabulated for further validation of the proposed scheme.
A Ka-band High Gain Wideband Low Noise Amplifier in $.18-\mu \mathrm{m}\ \text{SiGe}$ BiCMOS

This paper presents a design approach for Ka-band low noise amplifier (LNA) with both high gain and wide bandwidth. With a five-stage cascode structure, emitter degeneration is used in the first stage to implement the simultaneous power and noise matching and positive-feedback network is adopted to enhance the gain in the next four stages. By using stagger tuning technique, wide bandwidth can be achieved. For demonstration, a Ka-band LNA is designed and fabricated in a 0.18 $\boldsymbol{\mu}\mathbf{m}\ \mathbf{SiGe}$ BiCMOS process. It achieves a 3-dB bandwidth from 31.6 GHz to 38.2 GHz with a maximum gain of 42.9 dB. The circuit operates from a 2.5 V supply with a DC power consumption of 60 mW.
Design Method of Coupled Inductor for Multi-Phase Coupled Interleaving Boost Converter

Currently, Interleaving Boost Converters are widely used to achieve high efficiency and minimize the volume of existing Boost Converter. This paper proposes a method for designing a coupled inductor applied to a multiphase interleaving boost converter, which is a more efficient topology, making up for the shortcomings of the existing multiphase interleaving boost converter. It also explains how to derive self-inductance, mutual inductance and coupling coefficients of coupled inductor using the 3D Maxwell analysis tool to determine the availability of the designed coupled inductor and explains how to verify whether the core is saturated.
Low-Profile Wideband Magneto-Electric Dipole Antenna with AMC

This paper proposes a magneto-electric (ME) dipole antenna with low profile and wideband characteristics based on artificial magnetic conductor (AMC) structures. The zero-phase reflection surface is composed of two AMC structures, which can reduce the profile of proposed antenna to $0.124\lambda_{0}\ (\lambda_{0}$ is the free-space wavelength of center frequency). The profile height is reduced by 32.5% when compared to the typical ME dipole antenna in the same dielectric substrate. Furthermore, the proposed AMC structure has the similar characteristic to the electromagnetic bandgap (EBG) structure, which can suppress the propagation of surface wave and lessen the coupling impact between the antenna in the array. By simulation, the impedance bandwidth of this antenna is 39.1 % (from 8.25 to 12.16GHz) for $\mathrm{S}_{11} < -10\text{dB}$. Additionally, the ME dipole antenna has a stable gain about 6.3dBi and the beam-width of $\pm 46^{\circ}$ in the band. In the operating frequency, the $1\times 8$ one-dimensional array composed of ME dipole antennas can scan $\pm 53^{\circ}$, which is suitable for use in a X-band phased array.
A Machine-learning Based Energy Management System for Microgrids with Distributed Energy Resources and Storage

An Energy Management System (EMS) for a microgrid system was developed to fulfill consumer load demand by maximizing distributed energy resource (DER) usage. A MATLAB model of different scenarios of the community was created to simulate the performance of the EMS algorithm. Extreme Learning Machine was used as the machine learning model for solar resource and load forecasting. A case study was conducted to validate the performance of the system. The integration of the EMS control resulted in an increase in the self-consumption of solar PV generation and a decrease in the grid dependence of the system compared to the scenario without EMS based on a system that uses a smaller battery capacity.
UAV Positioning with Joint NOMA Power Allocation and Receiver Node Activation

This paper proposes reinforcement learning (RL)-based solutions for unmanned aerial vehicle (UAV) data offloading in B5G mmWave-enabled communications. This is particularly useful for ad-hoc transmission scenarios within environments experiencing connectivity issues with the main servicing network as in disaster-stricken areas. Double deep Q-network and multiarmed bandit-based algorithms are proposed to tackle the joint problem of UAV-positioning and Rx-node activation and power allocation for data offloading in downlink NOMA transmissions. Numerical simulations are performed to ensure the proposed RL-based algorithms can adequately provide high data transfer rates, along with random and exhaustive search solutions as benchmarks for lower and upper bounds on the achievable sum-rate levels.
Low-Complexity Multi-symbol Output Complex-Valued Neural Network for Nonlinear Equalization in 100G Coherent Photonic-assisted W-band Fiber-wireless Integrated Communication

A low-complexity multi-symbol complex-valued NN nonlinear-equalizer is proposed and experimentally demonstrated in coherent photonics-assisted MMW communication. Effective nonlinear compensation is demonstrated for 100Gbps 16-QAM photonic-assisted W-band signal after fiber-wireless integrated transmission, while the computational complexity is reduced by up to 78.1% compared with single-output NN.
A Comparison of Feature Selection and Feature Extraction in Network Intrusion Detection Systems

Internet of Things (loT) has been playing an important role in many sectors, such as smart cities, smart agriculture, smart healthcare, and smart manufacturing. However, loT de-vices are vulnerable to cyber-attacks, which may result in security breaches and data leakages. To effectively prevent these attacks, a variety of machine learning-based network intrusion detection methods for loT networks have been developed, which often rely on either feature extraction or feature selection techniques for reducing the dimension of input data before being fed to machine learning models. This aims to make the detection complexity low enough for real-time operations, which is partic-ularly vital in intrusion detection systems. This paper provides a comprehensive comparison between these two methods in terms of various performance metrics, namely, precision rate, recall rate, detection accuracy, as well as runtime complexity, in the presence of UNSW-NB15 dataset. Note that such a comparison between feature selection and feature extraction methods has been overlooked in the literature. Furthermore, based on this comparison, we provide a useful guideline on selecting a suitable intrusion detection type for each specific scenario.
Comparative Study of the Π-Core Doubly Salient PM Machines Having Different Stator Core Segments and Armature Winding Configurations

This paper discusses the influence of stator iron core shape and armature winding distribution on the torque characteristic of the FI-core doubly salient permanent magnet machines (II-core DSPMMs) in detail. In this paper, the structures of four different motors are firstly described in detail. Thereafter, by right of the finite element technique, all investigated machines are optimized by parametric scanning for maximum torque output for a fair comparison. Then, some key performances are compared and discussed, for example, open-circuit phase flux linkage, phase back-EMF, cogging torque, and electromagnetic torque. The results show that the design of the focusing tooth (FC) is similar to the dual armature (DA) in terms of boosting the output torque of FI-core DSPMM. Compared to the conventional FI-core DSPMM, the FI-core DSPMM with FC and DA design (II-core FC-DA-DSPMM) has a sharp average torque increase of124.7%, and its torque ripple decreases from 4.8% to 2.8%. In addition, the FI-core FC-DA-DSPMM has better PM utilization and inherent fault-tolerant feature.
Many-to-Many Matching User Association Scheme in Ultra-Dense Millimeter-wave Networks

Millimeter-wave (mmWave) communication has been regarded as one of the most promising means to improve system throughput in the fifth-generation (5G) era. However, there exists signal blockage problem in mmWave network. We consider the combination of mmWave communication with ultradense network (UDN) and virtual cell structure. UDN allows multiple small base stations (SBSs) to be deployed within a limited area. user-centric virtual cell structure enables users to associate multiple mmWave SBSs simultaneously by using coordinated multi-point (CoMP) transmission technology. Therefore, we formulate user association problem for ultra-dense mmWave networks to maximize system throughput while guaranteeing quality of service (QoS) requirements of users. To solve this nonlinear binary integer programming optimization problem, a novel user association scheme based on the many - to-many two-sided matching game with externalities is proposed. To characterize the properties of proposed scheme, we prove that it converges to the two-sided exchange stability within a limited number of iterations. Simulation results show that proposed scheme significantly outperforms other schemes in improving user rate and system throughput while guaranteeing QoS requirements of users.
Thermal Modeling and Analysis of Axial Flux Permanent Magnet Machine with PCB Stator

It is necessary to consider temperature rise when an axial flux permanent magnet machine (AFPMM) is designed since the excessive rise in temperature damages the machine’s performance. An AFPMM with a coreless printed circuit board (PCB) stator is designed and studied in this paper. The studies mainly focus on the thermal modeling and analysis of windings for the incomplete heat dissipation conditions of the PCB stator inside the machine. Firstly, the three-dimensional (3D) electromagnetic field model of the AFPMM is established by the finite element method (FEM). Then the bidirectional coupled simulation between the electromagnetic field model and the thermal model is realized. Finally, the temperature rise of the PCB stator is reduced by increasing the linewidth of the effective conductors to optimize the design of the AFPMM. Measured performances of the manufactured prototype verify the validity of the proposed bidirectional coupled thermal model.
Influence of Stator Tooth-tip Shape on Power Factor of Surface-Mounted Permanent-Magnet Field-Modulation Motor

The power factor of surface-mounted permanent-magnet field-modulation (SMPMFM) motor is studied in this paper. By simply deriving the power factor expression, it is found that the q-axis inductance will affect the motor power factor, mainly including the tooth-tip leakage inductance and the slot leakage inductance. Hence, the variations of these inductance characteristics with the tooth-tip shape are analyzed, and the influence of tooth-tip shape on power factor of the analyzed SMPMFM motor is investigated by using the FEA. It is proved that when a straight-tooth structure without pole shoe is adopted, a high-power factor can be realized in the analyzed SMPMFM motor.
An Improved Stage-Combined Belief Propagation Decoding of Polar Codes

Belief Propagation (BP) algorithm is an alternative decoding method to Successive Cancellation (SC) decoding of polar codes with an advantage of higher throughput and lesser latency. BP algorithm is performed over the factor graph of a polar code, and its throughput is oppositely proportional to the number of stages of the factor graph. The stage-combining idea can be applied to the factor graph of a polar code to halve the number of stages, and thus to double the decoding throughput. By this idea, every two adjacent stages of a factor graph are combined and processed in a single step. In this paper, we propose a modified stage-combined BP decoding method that is able to improve the Bit-Error Rate (BER) performance of the decoding algorithm. This improvement is almost 0.5 dB at low to intermediate SNRs, and much more considerable at high SNRs, since the undesired phenomenon of error floor is mitigated as a result of the proposed stage-combining mechanism.
Native and non‐native clear speech production.

Previous work established that both native and non‐native clear speech increase intelligibility for native and proficient non‐native listeners. However, non‐native clear speech enhanced intelligibility less than native clear speech. In this study, we examine native and non‐native conversational and clear speech productions with an eye on the differences in hyperarticulation strategies that may underlie a smaller clear speech intelligibility benefit. Results revealed a slower conversational speaking rate for non‐native talkers compared with native talkers. Clear speaking rate was similar for both groups suggesting a limit in the speaking rate decrease in intelligibility enhancing clear speech. The durational distinction between tense and lax vowels was increased less in non‐native speech due to the smaller speaking rate decrease in clear speech. Analyses of the stop voice onset time (VOT) showed that non‐native talkers lengthened the voiced category in the negative VOT direction, while the voiceless catego.
Managing Scores of Crowdsourcing Workers Using Blockchain

In recent years, crowdsourcing, in which various workers provide computing and human resources for requested jobs through networks, has attracted wide attention as an efficient matching system between service users and workers. In crowdsourcing, a service provider needs to evaluate each worker on the basis of their contribution in order to fairly reward workers, so the provider is required to manage the private data of workers, e.g., trajectories and behaviors of workers. However, in crowdsourcing, a single service provider manages all the private data of workers, so there is a possibility that this data might be leaked or manipulated when the provider is attacked by malicious users. Moreover, employees of a service provider might abuse the private data of workers. To solve these problems, we propose managing the private data of workers by using the blockchain for crowdsourcing. In particular, we focus on a type of crowdsourcing in which workers have a direct contract with the service provider providing a service to users, e.g., Uber Eats. Moreover, we also propose a method for evaluating worker scores that uses an entropy weight to weight each evaluation criterion of workers on the basis of the distribution of each criterion. Through a computer simulation using data of cycling conditions obtained by the motion sensor of smartphones attached to bicycles, we evaluate the effectiveness of the proposed system when applied to worker evaluation for a food delivery service, e.g., Uber Eats.
Decimeter Level Indoor Tracking Using a Single Access Point

Tracking is promising in various applications, such as elderly care and security monitoring. Prior works require multiple access points (AP) deployed in the tracking region or to utilize elaborately selected multipaths in an indoor environment to achieve tracking. However, this prevents their wide adoption because scenarios like homes typically only have a single AP, and using multipaths is vulnerable to unexpected interferences due to the complex indoor environment. This paper proposes a tracking system using only a single AP. The insight behind the proposed system is to mine the multi-dimensional channel parameters from the direct path to realize tracking. Therefore, we develop a model that establishes the correlation between the target motion and channel parameters, including the direct path's angle of arrival (AoA) and angle of departure (AoD). Then, we can formulate the tracking problem as the state transition with the observations. Since the nonlinear observation equation, we use particle filters to solve the problem accurately. Finally, we validate the proposed system via simulation using ray-tracing software. The simulation results show that the median tracking error is 0.21 m based on a 20 dB signal-to-noise ratio (SNR), comparable to previous state-of-the-art systems.