Miracle-dz/UniFAR-FTUs huggingface dataset:
ATopNet: Robust Visual Localization for AMR Navigation

This article tackled some adverse influences on the localization problem in challenging indoor corridor/aisle environments. We proposed Automatic Topological Localization Network (ATopNet), suitable for autonomous mobile robots (AMRs) based on low-resolution RGB vision. We provided experimental evidence that ATopNet enables AMRs to conduct autonomous routing along indoor corridors with sufficient spatial precision and accuracy that allows AMRs to conduct the following manipulation works. To effectively enhance the performance of the deep Convolutional Neural Network (ConvNet) for visual localization of AMR, we developed automatic training augmentation schemes for the pose, illumination, speed, and occlusion enhancements. We derived the kinematic theories correlating view-frames and observer’s poses, based on which thousands of pose-explicit images can be automatically generated and annotated for ConvNet training. In addition, the strategy of recruiting a conditional Generative Adversarial Network for illumination image generation was proposed. The impact of vehicle speed on localization performance when trained on static images was also tested and discussed. A series of experiments were conducted to verify the efficacy of the proposed methods. We demonstrated ATopNet performing real-time robot localization with a Raspberry Pi4+NCS2. Another prominent feature of ATopNet is that all the procedures are fully automated and require minimal manual operation.
A Material Law Based on Neural Networks and Homogenization for the Accurate Finite Element Simulation of Laminated Ferromagnetic Cores in the Periodic Regime

Electromagnetic fields and eddy currents in thin electrical steel laminations are governed by the laws of magnetodynamics with hysteresis. If the lamination is large with respect to its thickness, field and current distributions are accurately resolved by solving a 1-D finite element magnetodynamic problem with hysteresis across half the lamination thickness. This 1-D model is able to deliver mesoscopic information to be used, after appropriate homogenization, in the macroscopic modeling of an electrical machine or transformer. As each evaluation of such a homogenized model implies a finite element simulation at the mesoscale, a monolithic coupling might be very time-consuming. This article proposes an alternative approach, assuming a periodic excitation of the system, where the parameters of a parametric homogenized material law are determined in each finite element with a neural network. The local material law can then be used as a conventional constitutive relationship in a 2-D or 3-D modeling, with a massive speed-up with respect to the monolithic coupling.
Amplitude Distributions of Mobile Fading Channels: Impact on Communication Performances

In this paper, we consider several kinds of fading channels which are used to describe the multipath effect, including Rayleigh, Rice, Nakagami, Nakagami-Hoyt, and $\kappa - \mu$. The communication performance indicators for each kind of channel, such as instantaneous signal-to-noise rate (SNR), outage probability (OP), and average error rate are analyzed and some novel expressions are derived. Among them, the OP is derived by calculating the cumulative distribution function (CDF) of the instantaneous SNR, and the average error rate is derived from the probability density function (PDF) of the instantaneous SNR with integral operation, by using the direct method or the moment generating function method. The most obvious result is that the increase of channel parameters $m, \kappa, \mu$, and q leads to lower OP and lower average error rate. Moreover, the same results can be obtained by taking the same special case of different generalized fading channels.
Traffic Flow Estimation using Machine Learning and 4G/5G Radio Frequency Counters

As the demand for vehicles continues to outpace construction of new roads, it becomes imperative to implement strategies that improve the utilization of existing transport infrastructure. Traffic sensors are crucial in helping us gain valuable insights and inform city planning; however, as a result of the cost and lead time associated with their installation and maintenance, municipalities look toward cheaper solutions. Fortunately, 4G/5G cellular networks offer one such alternative, due to their ubiquitous nature and widespread deployment. In this paper, we present a novel method for traffic flow estimation using standardized radio frequency counters obtained from roadside cellular masts. The problem is cast as a supervised regression task, where we further apply transfer learning to compensate for the lack of geographic diversity in our training data. The results are very promising and, unlike competing solutions, our approach utilizes aggregate radio frequency data that is inherently privacy-preserving, readily available on both 4G and 5G bands, and scales globally without any additional network impact.
A Novel Scheme to Mitigate the RNTI-FA in Blind Detection of 5G Polar Codes

Polar codes have been selected as the channel coding scheme for 5G new radio physical downlink control channel (NR PDCCH). In 5G NR PDCCH, blind detection is performed by user equipment (UE) to obtain its intended information. During blind detection, false alarm (FA) may happen which means that the UE can decode successfully even there is no intended information. FA can be caused by many factors. In this paper, we focus on the FA caused by radio network temporary identifier (RNTI), we call it RNTI-FA. RNTI-FA may happen even in high SNR range and results in abnormal network status. We firstly investigate the causes of RNTI-FA. Then a novel RNTIFA mitigate scheme, based on the Hamming distance metric, is proposed to reduce the RNTI-FA. The simulation results show that the RNTI-FA rate can be reduced from $10^{-1}$ to $10^{-5}$ or lower with negligible performance loss in the medium-to-high SNR range.
Multimodal Fusion-GMM based Gesture Recognition for Smart Home by WiFi Sensing

With the thriving development of communication systems and the ubiquity of commodity WiFi devices, wireless intelligent sensing has attracted increasingly attention owing to its importance in Human-Computer Interaction (HCI) where hand gesture recognition has been widely studied these years. However, most of gesture recognition approaches have a limited performance owing to the background noises. In this paper, we present a multimodal fusion-Gaussian mixed model (GMM) based gesture recognition scheme by exploiting channel state information (CSI) of WiFi signals. To this end, we firstly design two theoretical underpinnings, including a sensing model and a recognition model. Firstly, the sensing model is established to investigate the impact of gestures on the propagation properties of WiFi signals and quantify the correlation between the CSI dynamics and various gestures. Secondly, the recognition model is used to exploit physical gesture-induced signal changes to infer potential gestures information. Then, we implement the proposed scheme on a set of WiFi devices and evaluate it in both laboratory and corridor environments. The experiment results show that the proposed scheme can achieve average recognition accuracies of 96% and 94% in these two scenarios, respectively. This shows promise for future ubiquitous hands-free gesture-based interaction with mobile devices.
ResNet-Based Top-N Transmit Antenna Selection Algorithm for Massive MIMO Systems

Antenna selection has attracted more and more attention in massive multiple input multiple output (MIMO) systems to balance the performance and computational complexity. In this paper, we design a top-N transmit antenna selection algorithm for massive MIMO systems. This algorithm can reach the optimization rather the sub-optimization which traditional algorithms usually achieve. Specifically, we build a top $-N -$ output ResNet which selects the top-N optimal schemes to improve the accuracy compared to the single output network. And then we design a N-to-one selection algorithm that acquires the optimal scheme from the top-N optimization. With the help of the top-N antenna selection algorithm, our proposed algorithm reaches 95% accuracy with only 2.23% complexity compared to exhaustive-search-based antenna selection.
Multidimensional Pulsewidth Modulation for Cascaded Split-Source Inverter

Cascaded split-source inverter (CSSI) is a single-stage modular multilevel structure. This article proposes the multidimensional pulsewidth modulation (MD-PWM) for this topology. All multilevel modulations are a particular version of this approach. The principles of this geometrical method are based on selecting the optimal points and determining the switching sequences and time intervals. By considering the operational principles of CSSI, this method is modified to accurately control the voltage of dc links, charge inductors with constant duty cycles, control dc and ac sides of cells independently, and reduce the reverse recovery effects of input diodes. MD-PWM, with a low computational burden, can be implemented for CSSI with any number of voltage levels. The output power can be distributed equally or unequally among cells, and both buck and boost operation modes are realized for each cell. Also, this method is extended for the operation of the converter with unequal input sources. For an asymmetric topology with N cells, harmonics are located at multiples of N times switching frequency. Thus, this strategy eliminates low-frequency distortions from the output voltage of asymmetric CSSI. The simulation and experimental results verify the performance and accuracy of the proposed technique and mathematical relations.
A foreknowledge perception method of multi-stages machining accuracy in aviation turbine shafts based on hidden Markov model and Neural networks*

In the field of aerospace, aviation turbine shafts with the slender and thin-walled shape are important parts in aero engines. The machining accuracy of aviation shafts has been playing a crucial role in the whole assembly process. Focused on the multi-stages machining accuracy of aviation shafts, this paper provides a foreknowledge perception and decision method in aviation shafts manufacture. In this paper, in-site machining process concerning aviation turbine shafts has been analyzed as well as extracting the key detection features. Based on the proposed hierarchical criterion of aviation shaft qualification states, this paper establishes a hidden Markov model of machining quality conforming to the actual aviation shaft machining conditions by collecting the industrial in-site data. Furthermore, a data-driven model of aviation shaft multi-process inspection data generated by qualification state hidden Markov model is established by means of neural network model. Consequently, the foreknowledge perception of subsequent process machining accuracy can be realized by the inspection data of the current machining process, laying the foundation for the prediction of machining accuracy of multiple processes in aviation turbine shafts manufacture.
Laser Power Efficiency of Partial Histogram Direct Time-of-Flight LiDAR Sensors

On-chip capacity for storing temporal photon data in direct time-of-flight (dToF) lidar sensors is limited. This has prompted the development of various partial histogram approaches to reduce the amount of data stored on-chip. The aim of this paper is to inform sensor design by providing a taxonomy of these approaches, models for evaluating their impact on system laser power and identification of additional trade-offs which must be considered. All published on-chip partial histogram lidar approaches to-date are reviewed and two main categories are established: zooming and sliding. A means of evaluating any specific configuration based on its histogram reduction ratio (HRR) is also established. To quantitatively evaluate partial histogram approaches, a model to determine the minimum number of required laser cycles is developed. Both zooming and sliding are compared to an ideal baseline using this model, in order to establish a laser power penalty benchmark for each approach. These are evaluated over a range of real-world design conditions for two contrasting designs: short-range indoor and long-range outdoor. In general, a sliding approach is found to be the most laser power-efficient for long-range outdoor applications, while a zooming approach becomes increasingly more effective under low ambient conditions. Power efficient cycle-scaled variations on the conventional zooming and sliding approaches are introduced. These are shown to consistently reduce the laser power penalty across all tested design conditions. It is also shown that a cycle-scaled sliding histogram approach can be adopted to reduce the required on-chip histogram storage capacity by half, with almost no additional laser power penalty. Finally, a qualitative discussion of zooming and sliding compares additional key design considerations such as sensitivity to motion artefacts.
Sensor Optimization and Influencing Factor Analysis of Gas Content by Capacitive Method

This paper studies and designs a double-ring capacitive sensor gas content detection system, in order to solve the problems of low detection accuracy of traditional gas-liquid two-phase flow gas content and the influence of factors such as inclination angle and flow pattern in actual measurement. The sensor is designed and optimized based on COMSOL simulation software, the influence of electrode width and spacing on the sensitivity uniformity index is analyzed, and the optimized structural parameters of the sensor are obtained. Simulation and experimental results show that the double-loop capacitive sensor measurement system has good linearity and a small average relative error (0.4%). The inclination angle of the pipe and the flow pattern will have a certain influence on the measurement of the sensor.
Synthesizing Radar Detections of Target Objects for Unmanned Vehicle Behavioral Simulation

The behavior of an unmanned Ego Vehicle-such as an Autonomous Vehicle (AV)-in a scenario where a field experiment is too risky or time-consuming to conduct is better studied in simulation; e.g. when a mobile vehicle cuts in front of the ego vehicle, perhaps colliding with it. In simulation, we may synthesize detections of the mobile target vehicle to determine the response of the ego motion planning stack. When the field test is too laborious to conduct-such as one involving two ego vehicles operating synchronously in each other’s vicinity-we may use the first ego vehicle’s perception output to simulate those of the second.In this paper, we pose more precisely and solve the above problem pertaining to the synthesis of target detections from an ego radar. We focus primarily on the radar because as a position and velocity sensor, it allows a wider variety of synthesis problems to be solved compared to position sensors such as camera and lidar; nonetheless, much of our approach is readily applicable to these position sensors as well. To facilitate efficient simulation, we develop a simple geometrical model of the ego radar’s Field of View (FOV) that neglects the physical details of radar wave propagation. We illustrate the results of our approach using AVs as ego vehicles; nonetheless, our methods are agnostic to the robot motion model, as we demonstrate in a synchronized motion simulation between an AV and an Unmanned Aerial Vehicle (UAV/drone). We conclude with a critique of our approach and how future work would address its limitations.
A Convex Optimization Approach to Satellite Selection for Global Navigation Satellite System (GNSS) Receivers

This paper presents a new approach to satellite selection for Global Navigation Satellite System (GNSS) receivers. With the advent of a large number of navigation satellites, it is desirable for GNSS receivers to have efficient means of selecting an optimal subset (from ones in view) to compute a position solution. A principled approach using a formulation closely related to the theory of optimal experiment design is demonstrated. The resulting problem is a Boolean optimization problem which has combinatorial complexity. Convex relaxation is used to reduce this complexity and frame the problem as one of semidefinite programming (SDP), which is a convex optimization problem. In almost all cases considered in the experiments, the proposed method provides selections that are exactly the same as that of the best possible selections found by brute force search. Computational times for the proposed technique are in the order of tens of milliseconds regardless of subset size. Furthermore, the present approach is extensible in that it can accommodate constraints depending on the application at hand or on prior knowledge. The method is illustrated on a real-world multi-constellation GNSS dataset.
Study of Mixed Social World on the Social World of Virtual and Real World

In recent years, the social world of virtual reality has become more popular in education, metaverse, entertainment, design, and real-life applications with simultaneous localization and mapping. This is based on an augmented reality (AR) smartphone that supports simultaneous localization and mapping with its two cameras, RGB camera and depth camera, to sense 3D real world. In this study, the average depth data of point clouds on the real 3D objects captured by the depth camera are used to determine whether there are 3D dynamic objects for social world of virtual and real world. When the average depth data change exceeds the threshold of the average depth data, a 3D dynamic object can be detected. This threshold is also adopted to adjust 3D scanning speed and to update Virtual Reality (VR) representation. We conduct detailed experimental analysis as well as verification and completely analyze physical errors for integrating virtual and real worlds.
Stability of Linear Hyperbolic Distributed Parameter Systems Based on Event-Triggered Control

This paper investigates the stability for a class of linear hyperbolic distributed parameter systems based on event-triggered control. Lyapunov technique is used to obtain the stable feedback gain and boundary conditions of the system. Sufficient conditions have been established for the exponential stability of the system under linear boundary condition and dissipative boundary condition. In addition, the existence of positive upper bound between two triggering times is proved. A numerical example illustrates the proposed methods.
A Fault Detection and Isolation Method Based on Supervised Nonnegative Matrix Factorizations

Standard nonnegative matrix factorization (NMF) is a powerful algorithm for data analysis and processing. In this paper, we improve the standard NMF and propose a supervised NMF algorithm that uses labeled samples for model training. The new NMF can identify the label of samples and achieve clustering. Using the clustering property of the supervised NMF algorithm, fault detection and isolation (FDI) is developed when both fault-free samples and all types of faulty samples are available for model training. Finally, the effectiveness of the proposed FDI approach is verified by an experiment on FDI of the penicillin fermentation process (PFP).
A 22nm FD-SOI CMOS 2-way D-band Power Amplifier Achieving PAE of 7.7% at 9.6dBm OP1dB and 3.1% at 6dB Back-off by Leveraging Adaptive Back-Gate Bias Technique

This work presents a 2-way 3-stage D-band Power Amplifier (PA) in 22nm FD-SOI technology. A dynamic 3-stage bias scaling technique is proposed for this PA. It is based on leveraging the back-gate terminal in CMOS FD-SOI technology to optimize the power consumption of each stage adaptive to the input power of the PA, and hence improve its overall PAE in the linear range, i.e. at OP1dB and power back-off. The PA has been fabricated on a die with the core area of 0.16mm 2. Small signal and large signal characteristics of the PA chip have been measured. At 1V supply voltage and frequency of 135GHz, it provides 14.2dB power gain with 20GHz and 52GHz 3-dB and 6-dB bandwidth, respectively. Measurement results show this PA achieves 7.7% and 3.1% PAE at 9.6 dBm OP1dB and 6dB back-off that features >1.5X and >2X improvement, respectively, compared to the state-of-the-art D-band PAs in CMOS technologies.
Research on Features of Learning Engagement Based on Random Forest

Learning engagement is an important factor affecting the classification of learning effects. In order to solve the problem of accurate classification of learning effect in small sample data set of professional courses, this paper uses the Random Forest algorithm to analyze the learning engagement on the classification of learning effect. Firstly, the data of 173 students in the course of digital transmission principle and system from 2019 to 2021 are taken as samples to abstract the learning features including behavioral and cognitive engagement. Seven machine learning algorithms, including Random Forest and Gradient Boosting Decision Tree (GBDT), are used to train and learn to evaluate the accuracy of the learning effect model. Among them, the classification accuracy of random forest algorithm and GBDT can reach 80.77% and 89%, respectively. Finally, the feature importance of Random Forest is used to analyze the factors affecting the learning effect. The importance of behavioral engagement accounts for 41%, and cognitive engagement accounts for 59%. The results show that taking learning engagement as the evaluation model and index system of learning effect can guide the course teaching scientifically and reasonably.
Vehicle Width Detection Based on Millimeter-Wave LFMCW Radar for Autonomous Driving

With the development of autonomous driving and intelligent transportation system, the outline size detection of a vehicle has been very critical in the field of road traffic. In our paper, we propose a vehicle width detection based on millimeter-wave frequency-modulated continuous wave radar for autonomous and assisted driving. First, the three-dimensional fast Fourier transform is performed on radar echo signals to get the range-velocity-angle (RVA) spectrum. Then, Order Statistic constant false alarm rate (OS-CFAR) is employed to obtain the information of vehicle scatters. To improve the angular resolution, the root multiple signal classification method is used. Finally, to improve measurement accuracy and reduce the complexity in training effectively, we propose a random forest method based on important features selection so that the width of vehicles can be calculated more precisely. Numerical results derived from real-world datasets show that the proposed calculation scheme of vehicle width achieves good accuracy.
Iterative Learning Control for the Active Error Correction of Polygon Mirror Based Laser Scanning

Polygon mirror (PM) based scanners suffer from scan line wobbles caused by facet variant pyramidal errors. The active error correction method uses a fast steering mirror (FSM) to adjust the laser reflection angle actively such that the wobble error can be compensated on the scan surface. This method requires the FSM to follow a fast varying and repetitive reference, which is challenging for the traditional feedback and feedforward control due to limited dynamic bandwidth and model accuracy. This paper proposes to use iterative learning control (ILC) for controlling the FSM, which significantly improves the correction effectiveness and reduces the line wobble for PM scanning systems.