Miracle-dz/UniFAR-FTUs huggingface dataset:
Ranging-aided Aerobridge Navigation Using Dual Quaternion Based Multiplicative Extended Kalman Filter

The autopilot of aerobridge has attracted considerable attention recently since human error and high labor costs. To estimate the pose of an aerobridge, this paper proposes a navigation algorithm with the aid of pairwise distances and inertial measurements. We first locate the joints of the aerobridge with the distance measurement. Then the pose of the aerobridge is calculated using its kinematic in closed-form and the associated covariance is derived. A dual quaternion based multiplicative extended Kalman filter is designed to fuse inertial measurements with the estimated pose, which produces a smooth pose. To verify the precision of this algorithm, a simulation is constructed based on Robotics Toolbox and the experiment results are positive.
A Computation Offloading Mechanism for Multimedia Services in Green Smart Community

Edge computing can provide quality assurance for multimedia services. In order to protect the environment, many communities have begun to use green energy, so we add this status to the formulation of the computation offloading mechanism. In this paper, we investigate the computation offloading mechanism for multimedia services in green smart community. First, we introduce a dynamic adjustment mechanism for unit cost of local energy consumption, which makes our algorithm more flexible. Then, we propose a computation offloading mechanism, which aims to decouple the optimization problem and minimize the system cost. We model the computational task offloading decision problem as a distributed potential game, and solve the resource allocation problem using mathematical methods and theories such as Lagrange multiplier method, KKT condition, gradient descent, bisection method, and we also design a CBPA (Computation resource and bandwidth and transmission power allocation) algorithm to optimize edge computing. Finally, simulation results show that the computation offloading mechanism proposed in this paper is effective.
Impact of Residual Hardware Impairment on the IoT Secrecy Performance of RIS-Assisted NOMA Networks

Non-orthogonal multiple access (NOMA) technology is expected to effectively improve the spectrum efficiency of fifth-generation and later wireless networks. As a new technology, reconfigurable Intelligent surfaces (RIS) can achieve high spectral and energy efficiency with a low cost in wireless networks. These are achieved by integrating a great quantity of low-cost passive reflective units (RUs) on the plane. In this article, in order to meet the needs of high efficiency, low power consumption, and wide coverage, we combine RIS-assisted NOMA technology with the internet of things (IoT). Because in the actual wireless communication system, the residual hardware impairment (RHI) characteristics of the actual transceiver equipment will have an important impact on system secrecy performance. Therefore, the study will propose a single eavesdropper RIS-assisted downlink NOMA system with RHI (E-RHI-RIS-NOMA). The study will also investigate the impact of RHI on the physical layer security (PLS) performance of the system and the closed-form expression of the user’s secrecy outage probability (SOP) is derived. Finally, the simulation results show that 1) the main factors affecting the SOP are the quantity of RUs in RIS, the transmit SNR, and the target data rate, 2) it is proved that the hardware impairment of the transceiver harms the system’s secrecy outage performance while the severity of the impact of RHI on the system performance depends on the transmit SNR and target data rate. Moreover, RHI at different nodes has a different influence on system secrecy performance. 3) the system performance of RIS relying on NOMA is improved compared with orthogonal multiple access (OMA) and conventional NOMA.
Multiscale Deep Neural Network for Obstructive Sleep Apnea Detection Using RR Interval From Single-Lead ECG Signal

The detection of obstructive sleep apnea (OSA) based on single-lead electrocardiogram (ECG) is better suited to the noninvasive needs and hardware conditions of wearable mobile devices. From previous ECG-based OSA detection methods, we can find that deep learning methods have shown great potential and advantages. However, due to the nonstationarity of sympathetic nerve signals and the complex characteristics of heart rate variability (HRV), the neural network under a single scale cannot effectively capture the features of HRV. In this study, an OSA detection method based on a multiscale dilation attention 1-D convolutional neural network (MSDA-1DCNN) and a weighted-loss time-dependent (WLTD) classification model were proposed. The introduction of dilated convolution effectively balanced the relationship between model parameters and performance. Attention mechanism technology modified the multiscale features after fusion and improved the weight of features under important channels. In the final classification part of the network, the combination of weighted cross-entropy loss function and hidden Markov model effectively alleviated the problem of data imbalance and improved the classification accuracy of the classifier. In segment identification, the accuracy, sensitivity, and specificity of the proposed method are 89.4%, 89.8%, and 89.1%, respectively; as for individual identification, the accuracy of that achieved 100%. The results demonstrated that the method proposed in this study can identify sleep apnea accurately.
IFIX: Fixing Concurrency Bugs While They Are Introduced

Concurrency bugs are notoriously hard to identify and fix. A systematic way of avoiding concurrency bugs is to design and implement a locking policy that consistently guards all shared variables. Concurrency bugs thus can be viewed as the result of an illy-designed or poorly implemented locking policy. The trouble is that the locking policy is often not documented, which makes debugging concurrency bugs clueless. We argue that it is too late to debug concurrency bugs after programming is done and we instead detect and fix them while they are being implemented. In this work, we propose an approach named IFIX which flags potential concurrency bugs and recommends fixes while the bugs are introduced. The key idea is to automatically conjecture what the intended locking policy is based on static analysis and recommend fixes accordingly. The recommended fixes are present to the programmer promptly and the user feedback (i.e., whether the certain recommendation is selected) is used to refine the conjectured locking policy and consequently future fixes. IFIX is evaluated on 43 concurrent programs, and through a user study with 30 programmers. The experiment results and user feedback show that IFIX is efficient, accurate and user-friendly.
Using Magnetic Control of DC-DC SIMO Converters in LED Driver Applications

This work presents magnetic control (MC) applied to Single Inductor and Multiple Output SIMO DC-DC converters in parallel configuration, for use in LED lighting drivers. The proposed MC is performed using a Variable Inductor (VI) to extend the operating range of the converter in Discontinuous Conduction Mode (DCM). In order to evaluate the method experimentally, a three-channel-output SIMO converter prototype for RGB lighting control has been implemented. This work demonstrates that using MC allows the converter to extend its operation in DCM for a wider range of input voltage, compared with using a fixed inductance value, as well as using other conventional control methods such as duty cycle and frequency control of DC-DC converters.
Dual-Mode Power Control of a Battery Supported Hybrid Power Generation System Based on Maximum Versoria Criterion

The intermittency management plays a key role in enabling increased penetration of renewable energy sources (RESs) in modern power systems. In this context, this work presents the control of a battery supported hybrid power generation system (HPGS), integrating wind and solar renewable resources. The charging discharging of the battery energy storage (BES) for intermittency management is indirectly monitored through control of grid currents. The current control is performed for two operating modes: constant grid power control mode and variable grid power control mode. A maximum Versoria criterion based adaptive scheme facilitates the generation of reference grid currents. Moreover, the grid interactive system compensates for the reactive power requirement as well as harmonics and unbalance currents of loads, connected at point of common intersection (PCI) with the HPGS. An electromechanical wind energy conversion is performed using a doubly fed induction generator (DFIG). A position sensor-less rotor phase locked loop (RPLL) based scheme and a drift-free perturb and observe scheme (DFPO) are utilized for extracting peak powers from DFIG and solar photovoltaic (PV) array, respectively. The HPGS is modelled in MATLAB platform and obtained results demonstrate the appropriate working of the presented control schemes.
Millimeter-Wave Dielectric Slab-Based Chip-to-Chip Interconnect Network Allowing for Relaxed Assembly Tolerances

An on-chip, low-loss, and wideband transition to dielectric slab waveguide is proposed in this article, for millimeter-wave interchip communication network applications. The transition design features a three-terminal circuit with one on-chip planar microstrip line and two off-chip dielectric slab-waveguide ports. It presents an add-drop attribute, which can be employed to realize the traditional bus and backbone network topologies. Prototypes presented operate on a thin-film layer imitating the back-end-of-line layers of a chip. Measurements are performed for an end-to-end connection and a hexagonal ring interconnect network at 170 and 100 GHz, respectively. In addition, the proposed transition is shown to enable an equal signal power distribution scheme in a linear array of receivers. To provide much needed relaxation against mechanical tolerances and thermal expansion effects in multiport circuits based on the ceramic dielectric waveguide, a novel low-loss and mechanically flexible connection between two dielectric slab waveguides is presented. The design of this connection, operating from 80 to 100 GHz, is verified by measurements. Technical merits, packaging attributes, and limitations of the proposed single hierarchy interchip interconnect network are discussed.
Consumer acceptability, sensory properties and expert quality judgements of Australian Cabernet Sauvignon and Shiraz wines.

Background and Aims: This study aimed to determine what sensory attributes most drive consumer and expert acceptance for Cabernet Sauvignon and Shiraz wines. Methods and Results: The sensory attributes of a set of commercial wines were quantified by a trained panel. A subset was assessed blind for liking by 203 consumers and for quality by 67 winemakers. For the total group of consumers, wines with low levels of ‘bitterness’, ‘hotness’, ‘metallic’, ‘smoky’ and ‘pepper’ were preferred. In addition, four consumer clusters were identified, each with different sensory drivers of preference, with the attributes ‘red berry’, ‘floral’, ‘caramel’ and ‘vanilla’ aroma, ‘acidity’, ‘green’ flavour and astringency being of importance in distinguishing the different clusters' acceptance scores. The winemakers' quality scores had little relationship with consumer response, although both groups gave low ratings to wines with Brettanomyces-related flavour. Conclusions: A relatively small set of sensory attributes were of greatest importance to consumer liking, and these generally dominate varietal differences. Winemakers' quality concepts do not closely align with those of the consumers. Significance of the Study: This study identifies sensory properties of red wines which could be maximised as well as those which should be reduced, allowing producers to better meet consumers' preferences.
Restaurant Reviews Analysis Model Based on Machine Learning Algorithms

In recent years, the emergence of a variety of social media has dramatically enriched people's lives and generated a massive amount of data, which has provided great opportunities and challenges for the application of building a data-driven machine learning analysis model. Due to its high commercial value, customer review data are continuously collected and analyzed by various merchants or networks. Through the analysis of consumers' dining experience and the evaluation of the restaurant's food and service, the restaurant can continuously adjust its business strategy, which is of high economic value to the restaurant. As for consumers, they can avoid unpleasant dining experience and have a better experience of local specialty food by looking at people's reviews. The analysis model based on machine learning algorithms and big data can efficiently and accurately extract the critical letters in customer reviews, predict customer behavior, and proofread the accuracy of reviews to achieve a win-win situation for restaurants and consumers, to strive for the maximum economic benefits.
A bibliometric network analysis of Deep Learning publications applied into legal documents

Deep Learning has been gradually adopted as the main methodology to perform Natural Language Processing tasks on legal documents. In this work we provide a bibliometric network analysis of Deep Learning publications (formerly Neural Networks) applied to the analysis of legal documents. Our study includes a total sample of 138 works published between 1987 and 2020 that used DL as primary methodology. We focused on three specific objectives: identification of the journals with more publications on the subject, a co-authorship network analysis, and an examination of the most cited works. Our results show that the publications are concentrated in a small number of specialized journals on the topic, and consequently, the number of works that use DL methodologies in the legal context is smaller compared to other areas. The co-authorship network analysis reveals four broad clusters of researchers that are time-dependent concentrated into Connectionism (1), Neural Networks (1) and Deep Learning (2). Our analysis of highly cited publications delivered 10 articles with two particular authors that centralize the network. Finally, we have found that collaboration between groups of researchers from different areas is minimal, showing a window of opportunity to increase interdisciplinary research, particularly between computer and legal research groups.
Resilience of Grid-Connected Wind Farm Using a Flexible Asynchronous AC Link

This paper presents an integration of wind farm (WF) into the utility grid through the flexible asynchronous AC link (FASAL) to achieve better resilience and mitigate power fluctuations. An equivalent aggregated squirrel cage induction generator (SCIG) based WF of 200 MW connected to a 150 kV utility grid through FASAL is considered for performance evaluation under LLLG fault conditions using PSCAD/EMTDC based simulation. Moreover, a comparison of the performance of the FASAL system and AC link for the connection of WF is made under grid fault conditions. Under LLLG fault, the AC link experiences significantly high fluctuations and overshoots in real and reactive power, electromagnetic torque, which causes stress on the wind turbine and the gearbox. It is found that the fluctuations and overshoots are small in the real and reactive power, electrical frequency of WF, and the electromagnetic torque in the case of the FASAL system. Thus, the FASAL shows better resilience as it reaches the normal operating condition faster with fewer oscillations. Moreover, the adverse effects of the LLLG fault on the wind turbine and the gearbox are also found small.
Robust Symbol Detection in Large-Scale Overloaded NOMA Systems

We a framework for the design of low-complexity and high-performance receivers for multidimensional overloaded non-orthogonal multiple access (NOMA) systems. The framework is built upon a novel compressed sensing (CS) regularized maximum likelihood (ML) formulation of the discrete-input detection problem, in which the $\ell _{0}$ -norm is introduced to enforce adherence of the solution to the prescribed discrete symbol constellation. Unlike much of preceding literature,. g. , (Assaf et al., 2020, Yeom et al., 2019, Nagahara, 2015, Naderpour and Bizaki, 2020, Hayakawa and Hayashi, 2017, Hayakawa and Hayashi, 2018, and Zeng et al., 2020), the method is not relaxed into the $\ell _{1}$ -norm, but rather approximated with a continuous and asymptotically exact expression without resorting to parallel interference cancellation (PIC). The objective function of the resulting formulation is thus a sum of concave-over-convex ratios, which is then tightly convexified via the quadratic transform (QT), such that its solution can be obtained via the iteration of a simple closed-form expression that closely resembles that of the classic zero-forcing (ZF) receiver, making the method particularly suitable to large-scale set-ups. By further transforming the aforementioned problem into a quadratically constrained quadratic program with one convex constraint (QCQP-1), the optimal regularization parameter to be used at each step of the iterative algorithm is then shown to be the largest generalized eigenvalue of a pair of matrices which are given in closed-form. The method so obtained, referred to as the Iterative Discrete Least Square (IDLS), is then extended to address several factors of practical relevance, such as noisy conditions, imperfect channel state information (CSI), and hardware impairments, thus yielding the Robust IDLS algorithm. Simulation results show that the proposed art significantly outperforms both classic receivers, such as the linear minimum mean square error (LMMSE), and recent CS-based state-of-the-art (SotA) alternatives, such as the sum-of-absolute-values (SOAV) and the sum of complex sparse regularizers (SCSR) detectors. It is also shown via simulations that the technique can be integrated with existing iterative detection-and-decoding (IDD) methods, resulting in accelerated convergence.
Speed Sensorless Vector Controlled PMSM Drive with A Single Current Sensor

This paper presents the current-estimation and speed estimation for Permanent Magnet Synchronous Motor (PMSM) drive. The speed-sensorless is built using the Fictitious Quantity based Model Reference Adaptive System (Y-MRAS) for Vector Controlled PMSM drive. It is developed using reference-voltage, actual-currents in the reference-model, and actual-currents in the adjustable-model. The two-phase stationary reference frame currents are estimated using q-axes reference current and single current sensor information from A-phase. The current estimation technique is independent of machine parameters. The presented methods apply to all kinds of PMSM machines. These methods are mathematically modeled and simulated in MATLAB/SIMULINK platform and verified in Typhoon-HIL.
Road Damage Detection Using YOLO with Smartphone Images

Deep learning-based technology is a good key to unlock the object detection tasks in our real world. By using deep neural networks, we could break a problem that is dangerous and very time-consuming but has to be done every day like detecting the road state. This paper describes the solution using YOLO to detect the various types of road damage in the IEEE BigData Cup Challenge 2020. Our YOLOv5x based-solution is light-weight and fast, even it has good accuracy. We achieved an F1 score of 0.58 using our ensemble model with TTA, and it could be an adequate candidate for detecting real road damage in real-time.
Real-time Interpolation Method For Sparse LiDAR Point Cloud Using RGB Camera

LiDAR (Light Detection and Ranging) sensor-based mapping and navigation is one of the fundamental techniques for achieving autonomous driving capabilities in urban scenarios. LiDARs can generate long-distance omni-directional measurements of its surrounding that is crucial for object detection and obstacle avoidance for autonomous vehicles. Currently, the most popular LiDAR sensors used for the application of autonomous driving are 360-degree rotating multi-layer type sensor. These are expensive for general use and mainly suffer from poor vertical resolution. In this study, we propose a method of LiDAR point cloud interpolation in real-time using information from an RGB camera. We propose a method to treat sparse point cloud as a depth image which enables us to apply depth enhancement methods to the point cloud. Additionally, we propose a new depth enhancement method with image segmentation for point cloud and compare its accuracy with existing methods. From the results, we present the usefulness of introducing depth image and applying the new depth enhancement method for LiDAR point cloud.
An Efficient Topology for Inductive Wireless Power Transfer in Vehicular Application

In this paper, a current fed DC-DC converter topology is proposed for electrical vehicle battery charging of a wireless power transfer system. The proposed configuration is composed of a conventional current source inverter (CSI) interfaced to a diode bridge rectifier through a suitably modified tank circuit. It will be shown that an addition of a capacitor in the primary side transmitter loop results in the relieving of the CSI from supplying the reactive power demand while catering only to the active power demand of the load. Furthermore, this reduces the voltage stress of the CSI switches and thereby leads to reduced switching power losses, which further enhances the overall power conversion efficiency. The operating principle, mathematical analysis and design guidelines of the proposed converter are presented. An experimental verification using the laboratory developed prototype rated for 260 W is carried out and the resultes are presented. The obtained efficiency from the laboratory prototype is approximately 86.22% with a coupling coefficient of 17.88%.
LegalOps: A Summarization Corpus of Legal Opinions

We present a new, large-scale corpus for training and evaluating text summarization systems on legal opinions, called LegalOps. The corpus includes ~14K opinions together with their summaries from U.S. Federal Courts, e.g. the Supreme Court and Federal Appeals Courts. The aim of this paper is to provide a novel data source of sufficient variety that it will advance theoretical work on modeling the particular patterns within legal discourse, but also of sufficient size that it will provide a new challenging testbed for state-of-the-art automatic summarization models.
Knowledge Graph Enhanced Event Extraction in Financial Documents

Event extraction is a classic task in natural language processing with wide use in handling large amount of yet rapidly growing financial, legal, medical, and government documents which often contain multiple events with their elements scattered and mixed across the documents, making the problem much more difficult. Though the underlying relations between event elements to be extracted provide helpful contextual information, they are somehow overlooked in prior studies.We showcase the enhancement to this task brought by utilizing the knowledge graph that captures entity relations and their attributes. Beyond straightforward feature engineering attempts, we propose a first event extraction framework that embeds a knowledge graph through a Graph Neural Network and integrates the embedding with regular features, all at document-level. Specifically, for extracting events from Chinese financial announcements, our method outperforms the state-of-the-art method by 5.3% in F1-score. Specifically, our method demonstrates its effectiveness in improving the performance of multiple events extraction. Besides, we make the lawsuit event dataset public to facilitate relevant research.