Miracle-dz/UniFAR-FTUs huggingface dataset:
Bandwidth Enhancement of a Comb-Line Array Antenna for mm-Wave Automotive Radar Applications

Bandwidth enhancement of a comb-line array antenna for millimeter (mm)-wave automotive radar applications is proposed. The proposed antenna consists of 12 radiating elements, 13 parasitic elements and 12 slots, which staggered up and down on the feeding line. By adjusting the width and the length of each radiating element, the antenna achieves a low sidelobe. Meanwhile, a wide impedance bandwidth is obtained by adding parasitic elements. The proposed antenna is fabricated and measured. The simulation and measurement are in good agreement. Experimental results show that the antenna has a 10-dB impedance bandwidth over 75.1-80.3 GHz with a low sidelobe level of -18.9 dB.
Artificial Neural Network (ANN)-Aided Signal Demodulation in a SiPM-Based VLC System

This paper describes a new type of data-driven signal demodulation technique using an artificial neural network (ANN) in a silicon photomultiplier (SiPM) based visible light communication (VLC) system. SiPMs can potentially be used to create the most sensitive optical receiver in VLC since it contains a large array of microcells and each microcell is capable of detecting individual photons. However, after each photon is detected, the associated microcell needs a period to recover. This means that the photon counting rate is not linearly related to the intensity of the light received by the SiPM and this effect causes a unique form of signal distortion when the transmission data rate becomes high. In this paper, an ANN aided data detection method is studied when the received signal is influenced by the SiPM nonlinearity. Both a single neuron based linear classifier and an ANN based non-linear classifier are introduced and explained in detail. The bit error rate (BER) results suggest that the ANN based receiver can significantly reduce the negative impacts caused by the SiPM nonlinearity for a wide range of irradiance levels.
Pinning Control of Multi-agent Systems with Periodical Switching Topology

This paper focuses on the pinning synchornisation problem of complex networks with periodical switching topology. We show that without assuming each possible topology contains a spanning tree, the system can achieve synchornisation if the combination of them can form a complete graph that has a spanning tree. The condition of synchonisation behaviour is analysed by constructing a Lyapunov function and averaging theory. Also, a practical application of an unmanned aerial vehicle system is provided for validation. Finally, the impacts of switching patterns of topology on the switching interval threshold are analysed via numerical simulations.
A Unique Approach to Malware Detection Using Deep Convolutional Neural Networks

Detecting malwares has always been a challenging task for malware analysts due to ever-changing technologies. To tackle advances in modern malware, which even have the potential to take entire organisations down within seconds, we resort to Machine and Deep Learning (ML/DL) to improve the detection process. This helps detect malware efficiently and accurately, without manual intervention, which often leads to overlooking advanced or obfuscated malwares. The objective of this paper is to train and validate various deep learning neural networks, such as convolutional and recurrent networks, while visualising the malware for better analysis at the same time. We also perform a comparative analysis based on various performance metrics. We were able to achieve over 98% validation accuracy using CNN. RNN yielded poor results. Although better accuracy has been achieved using other networks, this paper mainly focuses on a novel technique of visualising malware and using CNNs for detection.
Deterministic Learning-based Generalizable Trajectory Tracking Control for Permanent-Magnet Synchronous Motors Driven Two-Axis X-Y Table

In this paper, based on the deterministic learning theory and the model of permanent-magnet synchronous motors (PMSMs) driven two-axis X-Y table, a radial basis function neural network (RBFNN) learning control generalization rule for the non-repetitive trajectory tracking control of the system is proposed. Because of its excellent approximation capability, adaptability and learning capability for uncertain systems, RBFNN is used to approximate the model of X-Y table. Aiming to improve the generalization capability of deterministic learning theory, a generalization rule of deterministic learning theory for X-Y table is proposed. Based on the proposed generalizable deterministic learning control scheme, the tracking accuracy of the X-Y table is improved and the effectiveness of the generalization rules are verified through corresponding experiments.
Planetary Circulations: 1. Barotropic Representation of Jovian and Terrestrial Turbulence

Abstract We seek the formative processes of the planetary circulations of Jupiter and Saturn. The study concentrates on examining whether processes known to control the terrestrial circulation, namely, two-dimensional turbulence and baroclinic instability, can produce Jovian circulations under Jovian conditions. The first numerical model involves a spherical barotropic vorticity equation subjected to a stochastic representation of baroclinic processes. The resulting solutions suggest that a strong affinity exists between the Jovian and terrestrial circulations. This leads to a reevaluation of terrestrial circulation theory from the broader perspective of parameter space. The solutions in the Jovian regime support the hypothesis that a variation of the Rhines effect—an interaction of the two-dimensional turbulence cascade and Rossby wave propagation—creates the pseudoaxisymmetry and scale Lβ=π(2U/β)½ of the bands (U is the rms zonal velocity, and β the northward gradient of the Coriolis force). The anisotr.
Reproduction of roller painting operations by humanoid robot for observational learning

The aging population reduces the availability of skilled workers in various industries, such as construction. To handle this problem, unskilled workers can be trained. Among training methods, learning from demonstration allows to acquire motor skills. However, expert trainers are unavailable to perform demonstrations at any time. Hence, we aim to use a humanoid robot as a demonstrator and consider roller painting as the target construction task. We propose a method for the robot to reproduce roller painting operations based on human demonstration. The roller pose and contact wrench are measured from the demonstration using a marker-less method. The measurements combined with compliance control allow to control the robot to reproduce the demonstration. During reproduction, the robot adjusts the stiffness using compliance control. A comparison between the reproduced and original operations shows that the robot achieves comparable performance to the human demonstrator in roller painting.
A Dexterous, Adaptive, Affordable, Humanlike Robot Hand: Towards Prostheses with Dexterous Manipulation Capabilities

In this work, we present a 16 degree of freedom (DoF) humanlike, adaptive robotic hand specifically designed to achieve increased dexterity and the execution of dexterous manipulation tasks with minimal cost. Three Dynamixel XM430-W350-R actuate the hand via a combination of direct driven and tendon driven systems. The unique, coupled, two degree-of-freedom thumb mechanism is capable of achieving a complex trajectory for the thumb, providing increased thumb dexterity over traditional underactuated designs. This can facilitate the execution of improved in-hand manipulation tasks while minimising the control effort required for operation. The effectiveness of the proposed device was validated through kinematic, grasping, and manipulation experiments, including the Kapandji test, as well as force exertion experiments. It was found that the hand achieved a Kapandji score of 9 out of 11, and peak contact forces of 19.43 N for pinch grasps and 35.24 N for power grasps. The device was manufactured using the Selective Laser Sintering (SLS) process with nylon and molding using silicone rubber. The device has a weight of 603 g and costs approximately $1,000 USD to manufacture.
A Magneto-Electric Dipole Antenna Array With Flat-Top Radiation Patterns

Flat-top beam patterns are widely used in wireless communication as a spatial filter to enhance signals from a specific direction and suppress interference from other directions. In this paper, a new magneto-electric (ME) dipole antenna array with flat-top radiation patterns is presented. The proposed antenna has a simple structure with a pair of planar patches working as an electric dipole and six vertically oriented metallic vias working as a magnetic dipole. Slots on the planar patch could form equivalent magnetic currents which would realize flat-top radiation pattern and enhance the beam-width performance in E-plane. Furthermore, flat-top property in H plane is implemented by the nonuniform $1 \times 6$ antenna array. The proposed antenna array is found to cover an operating frequency band from 24.2 GHz to 27.7 GHz (relative bandwidth 9.03%), and a stable flat-top radiation pattern can be maintained from 24.8 GHz to 25.8 GHz. The 3-dB beam-widths are 265° and 31° in E-plane and H-plane, respectively. Details of the antenna design and obtained results are presented in the paper.
Whole-Body Control and Estimation of Humanoid Robots with Link Flexibility

This article presents a whole-body controller for humanoid robots affected by concentrated link flexibility. We characterize the link flexibility by introducing passive joints at the concentration of deflections, which separate the flexible links into two or more rigid bodies. In this way, we extend our robot model to take link deflections into account as underactuated extra degrees of freedom, allowing us to design a whole-body controller capable to anticipate deformations. Since in a real scenario, the deflection is not directly measurable, we present an observer aiming at estimating the flexible joint state, namely position, velocity, and torque, only considering the measured contact force and the state of actuated joint. We validate the overall approach in simulations with the humanoid robot TALOS, whose hip is mechanically flexible due to a localized mechanical weakness. Furthermore, the paper compares the proposed whole-body control strategy with state-of-the-art approaches. Finally, we analyze the performance of the estimator in the case of different values of hip elasticity.
A Simple Ultra-Wideband Transmit-Array With True Time Delay Lines

A new ultra-wideband transmit-array element has been demonstrated. The unit cell is based on two monopole-loop antennas mirrored along a reflector and connected together by a strip line. By varying the length of the strip line, distance delay up to 2. 5λ0 can be easily realized to compensate the spatial phase delay. Based on this TA unit cell, a 20×20-element TA surface is constructed and adopts the true-time-delay compensation method to achieve wide gain bandwidth. With 0.5 F/D ratio, the proposed design shows 106%bandwidth and 56% maximum aperture efficiency.
Breaking the Nyquist Law Using Machine-Learning Empowered Interpolation Method in Planar Near-Field Antenna Measurements

This work presents a machine-learning empowered interpolation method to break the Nyquist sampling law for the planar near-field antenna measurements. In this way, the measurement time can be reduced significantly. Specifically, the proposed method uses a complete dataset X2 to interpolate the incomplete dataset X1 to realize the data supplementation and the reconstruction of the antenna far-field pattern, with less initial measurement data and time cost. This method uses K-means classification and Voronoi cell to cluster the initial dataset X1 and accomplish the deep and shallow interpolation. Then, the truncation error of the interpolated planar near-field data can be reduced after using the Gerchberg-Papoulis (GP) algorithm. Since the proposed method uses a complete dataset X2 with small data size to achieve the interpolation process, the sampling interval of X1 can be larger than half-wavelength, and accordingly the sampling time can be further reduced without sacrificing the reconstruction accuracy within the confidence area.
Bayesian Receiver Design for Asynchronous Massive Connectivity

In this paper, we consider asynchronous massive connectivity, where massive low-power and low-rate devices with sporadic activity patterns connect to a multi-antenna access point (AP) in an asynchronous manner. Asynchronous transmission minimizes the amount of coordination between the devices and the AP, and thereby simplifies the transmitter design, yet at the cost of a more challenging receiver design. Specifically, asynchronous transmission results in inter-symbol interference (ISI) since the sampling at AP generally cannot match with the symbol intervals of uncoordinated devices. To enable reliable reception, we develop a turbo approximate message passing (TAMP) algorithm that consists of a channel-signal decomposition (CSD) module and a delay learning (DL) module. The CSD carries out sparse matrix factorization to estimate the channels and the ISI corrupted signals of active devices, and the DL is designed to estimate the delay of each active user and resolve the corresponding ISI based on the Bayesian principle. To refine the delay estimation, we further divide the DL module into symbol-level delay learning (SDL) and sub-symbol-level delay learning (sub-SDL) submodules. In particular, the sub-SDL estimates the residue delays (obtained by taking modulo of the symbol interval) and then finely compensates the ISI. Due to the continuity and randomness of time delay, the receive signal constellation consists of lines and curves instead of discrete points, even if the transmit signal constellation is discrete. To reduce the complexity of soft demodulation, we introduce a truncation and projection based approximation method to simplify the related message calculation. Numerical results demonstrate the superior performance of the proposed TAMP algorithm. Particularly, the TAMP algorithm is able to approach the single-user bound with known user delay.
Dynamic Bipedal Turning through Sim-to-Real Reinforcement Learning

For legged robots to match the athletic capabilities of humans and animals, they must not only produce robust periodic walking and running, but also seamlessly switch between nominal locomotion gaits and more specialized transient maneuvers. Despite recent advancements in controls of bipedal robots, there has been little focus on producing highly dynamic behaviors. Recent work utilizing reinforcement learning to produce policies for control of legged robots have demonstrated success in producing robust walking behaviors. However, these learned policies have difficulty expressing a multitude of different behaviors on a single network. Inspired by conventional optimization-based control techniques for legged robots, this work applies a recurrent policy to execute four-step, 90° turns trained using reference data generated from optimized single rigid body model trajectories. We present a training framework using epilogue terminal rewards for learning specific behaviors from pre-computed trajectory data and demonstrate a successful transfer to hardware on the bipedal robot Cassie.
AGV Semantic Attack Detection Based on Hidden Markov Model

Automatic Guided Vehicle (AGV) semantic attack is now becoming an important research topic in digital workshops. Unfortunately, the conventional attack detections could trigger the wrong attack detection alarms, because of the unexpected wireless radio frequency interference. This paper investigates a new method for AGV semantic attack detection based on Hidden Markov Model (HMM). Our method is able to discriminate the typical wireless retransmission in presence of environment interference or in the context of AGV semantic attack by comparing the hidden state transition probability with the predefined threshold. Our model has been evaluated on our developed platform. The extensive experimental results and analysis prove the effectiveness of our method.
A Wideband High-Isolation Omnidirectional Antenna for In-Band Full Duplex Application

A wideband, high isolation, omnidirectional antenna for the IBFD application is proposed. The antenna is composed of a crossed loop antenna as the RX antenna, and a 2×2 monopole array as the TX antenna. To achieve high isolation, the crossed loop is excited with 90° phase procession. Based on the near-field cancellation technology, a high TX/RX isolation of more than 60 dB is achieved in the frequency range of 1.89 to 3.73 GHz. Besides, good omnidirectional radiation patterns are achieved across the entire frequency band.
Communication-Aware Formation Control of AUVs With Model Uncertainty and Fading Channel via Integral Reinforcement Learning

Most formation approaches of autonomous underwater vehicles (AUVs) focus on the control techniques, ignoring the influence of underwater channel. This paper is concerned with a communication-aware formation issue for AUVs, subject to model uncertainty and fading channel. An integral reinforcement learning (IRL) based estimator is designed to calculate the probabilistic channel parameters, wherein the multivariate probabilistic collocation method with orthogonal fractional factorial design (M-PCM-OFFD) is employed to evaluate the uncertain channel measurements. With the estimated signal-to-noise ratio (SNR), we employ the IRL and M-PCM-OFFD to develop a saturated formation controller for AUVs, dealing with uncertain dynamics and current parameters. For the proposed formation approach, an integrated optimization solution is presented to make a balance between formation stability and communication efficiency. Main innovations lie in three aspects: 1) Construct an integrated communication and control optimization framework; 2) Design an IRL-based channel prediction estimator; 3) Develop an IRL-based formation controller with M-PCM-OFFD. Finally, simulation results show that the formation approach can avoid local optimum estimation, improve the channel efficiency, and relax the dependence of AUV model parameters.
Low Thickness Transmitarray Unit-Cell for Wideband Millimeter-wave Applications

Wideband millimeter-wave transmitarray antennas draw many interests in modern wireless communications when high speed connections for massive traffic are required. This paper introduces a unit-cell structure for wideband transmitarray antennas. The profile of the unit-cell is low with the total thickness of $\lambda_{0}/7.7$. The unit-cell structure has three metal layers printed on two identical substrates. The designed structure is based on polarization rotation technique to provide eight phase states with a phase step of 45° within a wide -1dB transmission bandwidth from 21 GHz to 31.5 GHz.
A Highly Isolated Millimeter-wave Encapsulated Antenna for Radar Sensing

In this paper, a group of millimeter-wave antennas with high isolation are packaged for radar sensing. There are three patch antennas encapsulated in a compact aperture with the dimension of 15×15 mm2, where two of them are employed for signal reception and the left one is for signal transmission. Although the two antennas for reception are closely placed, the isolation between them is enhanced to more than 20 dB by introducing an H-shaped decoupling structure. In addition, we design a coplanar waveguide (CPW) transition between the feed probe and connector for the convenience of measurement. For demonstration, the proposed encapsulated millimeter-wave antenna is fabricated and measured. The measured results shows that the common impedance bandwidth (reflection coefficient < - 10 dB) of the three antennas are wider than 1 GHz and the gains are higher than 5 dBi. As a result, the proposed design has a potential for the application in smart home.
Night Vision Enhancement for License Plate Recognition based on Deep Learning

License plate recognition technology plays an important role in traffic management. It is widely used in parking, high-speed and road traffic management. The existing license plate recognition system is easy to be disturbed by the external environment, and the detection performance is poor in the night scene. This paper investigates the problem of license plate recognition in night vision scenarios and license plate tilt. This paper proposes a license plate recognition method with night vision enhancement, which can detect and recognize license plates under extremely poor lighting conditions. The method first uses a night vision enhancement module, Recursive Encoder-Decoder Network (RED-Net), and a set of non-reference loss functions designed for properties of the image. And then the License Plate Location and Recognition (LPLR) system is used to get the license plate number. The algorithm is tested on 75k images of the simulated CCPD night dataset. The testing result that the accuracy of the license plate recognition algorithm after night vision enhancement can reach 72.29% increase 65.5% than without night vision enhancement.
Passivity-based multi-contact balancing of humanoid robots based on power-conserved transformations

We present a method to control multi-contact balance for humanoid robots. Our method is based on physical transformations, which conserve the power generated by fully actuated control inputs. We calculate several controlled wrenches to determine contact sets obtained from the quasi-static balance of a humanoid model and superimpose the wrenches after transforming them around the corresponding contact sets. The superimposed input dynamically stabilizes the whole-body motions. We experimentally validated the effectiveness of this control method with the humanoid robot E2-DR.
A Consensus-Based Charging Control Strategy for Electric Vehicles Participating in Performance-Based Regulation Markets

With the continuous expansion of the scale of electric vehicles, the application value behind electric vehicles (EV) as mobile energy storage units is increasing. In this paper, a consensus algorithm-based control strategy for electric vehicle clusters to participate in the secondary frequency regulation of the power grid is proposed adjust the power of the electric vehicle clusters to participate in the secondary regulation of the power market. The algorithm can make the regulation power of each electric vehicle cluster track the upper-layer regulation signal, and control the state of charge (SOC) of the electric vehicle battery on this basis, so that the regulation capacity of EVs can be efficiently utilized. Through simulation analysis, the effectiveness of the consistent frequency modulation power control proposed in this paper is verified, which can alleviate the fluctuation problem of grid frequency.
Message Passing Enhanced Distributed Kalman Filter for Cooperative Localization

This letter proposes a message passing enhanced distributed Kalman filter (MP-KF) for cooperative localization (CL). By simplifying the factor graph (FG) model of the traditional belief propagation (BP) algorithm, MP-KF replaces part of the message passing (MP) process in BP with the distributed Kalman filtering. According to the analysis, the computational complexity of MP-KF is lower than that of the traditional BP estimator. The results based on the experimental data set verify the effectiveness and advantages of MP-KF, it outperforms KF-based methods by fully exploiting the correlation inside a CL system, and is better than the BP-based methods by avoiding the performance loss caused by data smoothing. Results also show that MP-KF is a cost-effective approach for CL systems with an acceptable real-time performance, which is suitable for practical CL systems.