Miracle-dz/UniFAR-FTUs huggingface dataset:
Concept for a Real-Time IoT-Architecture for Collision Avoidance in Smart Cities based on the 5G Mobile Technology

Road traffic significantly increased in the last decades. The avoidance of accidents and the continuity of traffic flow are significant challenges for road mobility. In this paper, a real-time Internet of Things (IoT) architecture is presented for these current road traffic challenges. The presented architecture takes advantage of the emerging 5G mobile technology and relies on infrastructure components so that the presented architecture can be integrated into city infrastructure to become smarter and provide benefits to citizens. A collision warning service is being implemented to warn road users of imminent collisions. The parallel traffic flow optimization will also enable the prioritization of highlighted traffic partici-pants, such as emergency vehicles. By prioritizing pedestrians, bicycles, e-bikes and buses, for example, it will also be possible to achieve specific traffic and climate policy goals. An according smartphone app allows the resulting advantages to be used in the short term. Furthermore, additional actuators such as displays and signal transmitters will integrate all road users, regardless of the specific app usage. By using 5G, these components will also be implemented quickly, flexibly and reliably as infrastructure IoT devices. The paper concludes with a presentation of a research project in which these solutions are currently being implemented.
Optical multi-camera UAV positioning system via ArUco fiducial markers

In this paper, a multi-camera positioning system for Unmanned Aerial Vehicles (UAVs) via ArUco fiducial markers is proposed. Alternatively to the commonly used Global Navigation Satellite System (GNSS) technology, or to the positioning services provided by the mobile networks, the proposed system makes use of a set of optical cameras and a set of ArUco fiducial markers embedded in the UAV's frame. This offers a simple and scalable system in which a variable amount of cameras can detect any number of UAVs. A static analysis of the system has been carried out, in which a set of cameras mounted on the ground look at the target from different distances. The collected results exhibit a promising performance in terms of the precision of the system. Moreover, the effect of a weighted average between different camera estimates has been investigated, showing that it effectively compensates for the effect of the increasing distance between cameras and a target.
Impact of Band Nonparabolicity and Interface Broadening effects on Electronic Structure and Rabi-Coupling Frequency in THz Quantum Cascade Lasers

Modeling of quantum cascade lasers requires precise determination of the emission frequency and current density for which the electronic band structure must be accurately solved. In this work, we explore the effect of the fourteenth-order k·p conduction band nonparabolicity and interface broadening due to inter-diffusion and fluctuations of the band edges on the electronic structure and Rabi-coupling oscillation frequency of a THz quantum cascade laser.
On Retrofitting Provenance for Transparent and Fair Software - Drivers and Challenges

There have been ongoing discussions about how to ensure transparency and fairness in software that utilise artificial intelligence (AI). However, transparency and fairness are not limited to AI. Modern (non-AI) software is often constructed in a black box fashion. This means, components and services provide some functionality, but details on how this is achieved are hidden. Common software development and design principles like encapsulation and information hiding promote this. Engineers often only look inside the black boxes when they need to fix problems, e.g., when tracing bugs or vulnerabilities. The demand for transparency has created a need to open those black boxes also to non-engineers. For instance, businesses need to demonstrate regulation compliance, and end users want to understand how systems make fair decisions that affect them. However, adding provenance (i.e., the ability to gather information about data and algorithms used in systems) to existing systems is invasive and costly, and current approaches to collect provenance data are not designed to expose data to end users. We argue that this requires “provenance retrofitting”, i.e., adding provenance capabilities to systems mechanically, and exposing provenance data through standard language and service application programming interfaces (APIs). This could facilitate an infrastructure that supports transparency, which then can in turn be used to create feedback mechanisms for users that in the long term can improve the fairness of software. In this paper we discuss drivers, objectives, key challenges and some possible approaches to provenance retrofitting.
Quick Repair of Semantic Errors for Debugging

Current automatic program repair (APR) approaches typically require a high-quality test suite and can take considerable time. Developers need to fix program problems during development from within an IDE. They generally do not have a high-quality test suite or a lot of time to find and fix problems. We explore a new approach to APR that is more practical and can be used within an IDE. Our prototype system, ROSE, is invoked while debugging and does not require a test suite. Our initial evaluations confirm its effectiveness and utility.
Learn-able Evolution Convolutional Siamese Neural Network for Adaptive Driving Style Preference Prediction

We propose a framework for detecting user driving style preference with multimodal signals, to adapt autonomous vehicle driving style to drivers’ preferences in an automatic manner. Mismatch between the automated vehicle driving style and the driver’s preference can lead to more frequent takeovers or even disabling the automation features. We collected multi-modal data from 36 human participants on a driving simulator, including eye gaze, steering grip force, driving maneuvers, brake and throttle pedal inputs as well as foot distance from pedals, pupil diameter, galvanic skin response, heart rate, and situational drive context. Based on the data, we constructed a data-driven framework using convolutional Siamese neural networks (CSNNs) to identify preferred driving styles. The model performance has significant improvement compared to that in the existing literature. In addition, we demonstrated that the proposed framework can improve model performance without network training process using data from target users. This result validates the potential of online model adaption with continued driver-system interaction. We also perform an ablation study on sensing modalities and present the importance of each data channel.
People Counting with Carry-on RFID Tags

People counting tracks the number of people entering or exiting any given space in real-time, which helps make intelligent business decisions and improve quality of service (QoS) for a range of commercial applications, e.g., retail analytics, queue management, and occupancy monitoring. Existing CV-based and CSI-based solutions suffer from problems ranging from blind zones and privacy breaches to low robustness and complicated system calibration. In this paper, we propose a new solution to the counting problem with carry-on RFID tags. The basic idea behind the curtain is that when a person is under an RFID reader's coverage zone, his/her carry-on tags can be read by this reader at the same time. We can classify the tags based on the records of reading profiles. Hence, this problem can be boiled down to a clustering problem. To achieve this goal, we build a feature vector for each tag by gathering its reading profile, specify the distance between two feature vectors by combining the Hausdorff distance and the Euclidean distance, and perform a maximum density clustering algorithm to get the distributions of each tag. Afterward, we design an outlier detection algorithm to screen out the cluster centers from the non-cluster ones. The number of cluster centers is treated as the number of people. We implement the RFID-based counting model in a commodity RFID system. Extensive experimental results show that our approach is superior to existing solutions under different scenarios.
A Novel Message Packing Strategy for Time-Sensitive Networking

The rapid development of automobile intelligent driving has led to a gradual increase in data traffic and the number of ECUs, thus raising the requirements for high-bandwidth and low-latency for automotive electrical/electronic (E/E) network architectures. Time-Sensitive Networking (TSN) is used in intelligent automobiles and is served as the backbone network because it can provide real-time communication with high capacity. To achieve the high-bandwidth transmission of data traffic, message packing is one of the prospective strategies. However, the state-of-the-art message packing methods mainly consider the single bus type rather than the hybrid bus type. In this paper, we exploit a heuristic message packing strategy (MA) for real-time traffic based on the multi-domain Electrica/Electronic (E/E) network architecture. Experiment results show that the MA method can significantly reduce bandwidth utilization.
Imaging Using Mems Electron Microscope

The article presents imaging results performed using a MEMS electron microscope setup. Three different electron detectors were developed and tested. The detectors integrated with a scanning octupole system were placed inside the JEOL JSM IT-100 SEM sample chamber. Using the SEM electron beam, the test images were obtained, confirming the usefulness of all three detectors. Moreover, a comparison between images obtained using the JEOL microscope and MEMS EM setup is presented.
SWAM: Driver Distraction Recognition Based on Attention Mechanism

With an increasing number of vehicles, drivers, and various multimedia streams, distracted driving has been more prevalent than ever, leading to tremendous fatalities, injuries, and costs. Many efforts have been made to alleviate the threat caused by driver distraction, where methods based on Convolutional Neural Network (CNN) turned out to perform well. As one of the properties of CNN, however, the local perceptive field may serve as a limitation sometimes. Therefore, we propose a novel method named SWAM, which applies Shifted Windows to the Attention Mechanism for vision-based driver distraction recognition. Specifically, the self-attention mechanism is utilized to capture the long-distance dependency, so as to identify different kinds of driver manual distraction. In addition, techniques such as shifted windows and transfer learning are applied to boost performance. The experiments illustrate that our method worked out remarkably on AUC Distracted Driver V2, achieving up to an accuracy of 93.97%.
Learning and Adapting Behavior of Autonomous Vehicles through Inverse Reinforcement Learning

The driving behavior of autonomous vehicles has a significant impact on safety for all traffic participants. Unlike current traffic participants, autonomous vehicles in the future will also need to adhere to safety standards and defined risk properties in order to achieve a high level of public acceptance. At the same time, successful autonomous vehicles must be able to interact with human drivers in mixed traffic in a way that enables traffic to flow. In this paper, we present a hybrid approach to trajectory planning that learns and adapts human driving behavior using inverse reinforcement learning. The proposed approach performs a large-scale simulation with HighD real-world scenarios to learn human driving behavior and domain-specific traffic-flow characteristics. The analysis of the work focuses on the influence of risk-taking, which provides information about driving style safety. The results show insights into the risk behavior of trajectory planning approaches compared to human risk assessment. The comparison to human trajectories is intended to ensure comparability and accurate classification of risk-taking. We recommend a hybrid method for adapting driving behavior, in order to maintain the explainability and safety of the trajectory planning algorithm.
FPGA-based Acceleration of Lidar Point Cloud Processing and Detection on the Edge

Edge nodes such as Intelligent Transportation System Stations are becoming increasingly relevant in the context of automated driving as they provide connected vehicles with additional information to support their automated driving functions. However, the power budget for these edge nodes is limited and data has to be processed in real-time to be of use to automated driving functions. In this work, we present a system for processing raw lidar data in real-time on an FPGA, resulting in a significant reduction in power consumption compared to conventional hardware. Our approach leads to a 42.4% reduction in power consumption while maintaining the quality of the results. Processing two 128-layer surround-view lidar point clouds takes 522 ms per frame and an average power consumption of 39.3 W for the CPU and 34.5W for the FPGA. Our optimizations surpass the state-of-the-art by up to 193 times.
The Emergence of Memristive Devices in Microwave Circuits: A Review of Progress and Potential Application

The emergence of memristive devices has sparked significant interest in microwave circuits due to their potential to revolutionize traditional microwave circuits. This review paper provides a comprehensive overview of the historical background and important results of memristors. Additionally, we present recent progress and potential applications of memristive devices in microwave passive circuits, such as phase shifters and filters. Furthermore, we showcase experimental results obtained using memristors, including the implementation of circuits such as amplifiers, phase shifters, and tunable amplifiers. These results demonstrate the effectiveness and versatility of memristors in microwave circuits’ applications. Overall, this paper highlights the key findings and implications of memristor research in the field of microwave circuits, providing an insight into the exciting possibilities for future development.
Improving Fairness in Coexisting 5G and Wi-Fi Network on Unlicensed Band with URLLC

To meet the growing need of mobile traffic with Ultra-Reliable and Low Latency Communication (URLLC) requirement, 5G New Radio (NR) is extending from licensed band to unlicensed band on which Wi-Fi has already been operated, resulting in coexisting NR/Wi-Fi network. Existing works have made great efforts on throughput and latency of coexisting NR/Wi-Fi network. However, excessive NR requests offloaded from licensed band lead to unfair utilization of unlicensed band, which further causes unsatisfaction on URLLC and performance degradation of Wi-Fi. In this paper, we propose a novel Reinforcement Learning based Transmission Revoking Approach (RL-TRA) to address this problem aiming at fairer utilization of unlicensed band restrained by URLLC. Firstly, we formulate the coexistence problem of NR/Wi-Fi as integer non-linear programming and show its NP-hardness. Secondly, we decompose the problem into three sub-problems, namely redundancy determining, request scheduling, and transmission revoking. The former two sub-problems are solved with our proposed method to satisfy URLLC requirement. We further propose a novel transmission revoking mechanism when tackling transmission revoking sub-problem, aiming at maintaining fairness of coexisting NR/Wi-Fi network. Finally, simulation results verify the effectiveness of RL-TRA. By using our method, the fairness is improved by 16.5% averagely with only 1.77% loss on success rate of URLLC requests compared with baselines.
Stable Clustering in VANET Using Federated Deep Reinforcement Learning

In a vehicular ad-hoc network (VANET), vehicles can communicate with each other to exchange critical infor-mation. However, communications among vehicles may suffer from unstable transmission paths or high messaging overhead owing to the high mobility of vehicles. Vehicle clustering is an effective approach for VANET management. This paper presents a distributed clustering algorithm based on federated deep reinforcement learning. The proposed algorithm can improve the stability of vehicle-to-vehicle connections in a rapidly changing environment. The results show that our algorithm can improve the stability of clustering by reducing the number of role and cluster changes for vehicles.
Meta-Learning Based Runtime Adaptation for Industrial Wireless Sensor-Actuator Networks

IEEE 802.15. 4-based industrial wireless sensor-actuator networks (WSANs) have been widely deployed to connect sensors, actuators, and controllers in industrial facilities. Configuring an industrial WSAN to meet the application-specified quality of service (QoS) requirements is a complex process, which involves theoretical computation, simulation, and field testing, among other tasks. Since industrial wireless networks become increasingly hierarchical, heterogeneous, and complex, many research efforts have been made to apply wireless simulations and advanced machine learning techniques for network configuration. Unfortunately, our study shows that the network configuration model generated by the state-of-the-art method decays quickly over time. To address this issue, we develop a MEta-learning based Runtime Adaptation (MERA) method that efficiently adapts network configuration models for industrial WSANs at runtime. Under MERA, the parameters of the network configuration model are explicitly trained such that a small number of optimization steps with only a few new measurements will produce good generalization performance after the network condition changes. Experimental results show that MERA achieves higher prediction accuracy with less physical measurements, less computation time, and longer adaptation intervals compared to a state-of-the-art baseline.
EAGLE: Heterogeneous GNN-based Network Performance Analysis

Performance analysis is of great importance for management and optimization of space-terrestrial integrated networks (STINs). Traditional approaches to network performance analysis are often based on idealized assumptions that are deviated from the real network environment. This leads to the fact that these models are usually inefficient and restricted in real-world STINs with complicated behavior and even dynamic capacity. In this paper, we propose a network performance analysis approach EAGLE based on heterogeneous graph neural networks. Firstly, we propose a powerful computer network representation model that can preserve all of the information in computer networks. It represents different components of computer networks as a set of heterogeneous nodes and edges, and finally constructs a heterogeneous graph. Then, we obtain the topological representation for the routers in the network through a bandwidth-aware network embedding model. Based on this heterogeneous graph, we propose a heterogeneous GNN model to accurately predict network KPIs because it can completely capture the rich topological and attribute information of computer networks. Experimental results demonstrate that EAGLE can accurately model different networks, and outperforms both traditional methods and the latest neural network-based methods.
Past, present, and futures of non destructive techniques

Recent progress in manufacturing techniques has led to a wide range of new materials and components. Advance in material development has resulted in a wide range of new kind of materials as carbon composite and carbon fiber reinforced. The need, already consolidated, to investigate its properties (mechanical, electrical, thermal, etc.) arises and characterize the possible innovations by industrial sector such as aeronautics and aerospace, through the use of analysis techniques non-destructive (NDT). Common NDT techniques can be classified into seven major categories: visual (visual inspection), penetrating radiation (X-ray, and neutron imaging), magnetic-electrical (magnetic particle), mechanical vibration (ultrasonic, acoustic emission, and tapping), chemical/electrochemical (chemical spot testing), thermal (infrared thermography) and other optical methods (interferometry, holography, and shearography). More traditional non-destructive techniques (NDT) are not always effective as expected, and there is therefore a long-term need to use innovative NDT methods capable of provide reliable and quantitative information regarding damage status and integrity of materials. In the present work we focus on the development of specific tools of NDT for assuring the quality related to the fabrication of industrial components. The potential tool that will be take into consideration for inspection and detection of defects will mainly based on the following two main methods: ‐full-field optical techniques (such as profilometry, interferometry, sherography and thermography); ‐Laser ultrasound testing (LUT); The integrated and combinated use of different NDT (thermography, shearography and ultrasound) was necessary for a complete characterization of some types of composite materials.
Intrusion Detection Based on Statistical Analysis for RPL-based Internet of Things

The Internet of Things (IoT) is an emerging field of computer networks, in which resource-constrained objects connect to the Internet by using a wide range of technologies. Due to the features of sensors used in the IoT networks and the insecure nature of the Internet, IoT is vulnerable to many internal routing attacks. A novel distributed hybrid intrusion detection framework is proposed in this paper, which is composed of anomaly-based and signature-based intrusion detection modules for detecting routing attacks in the RPL-based IoT networks. According to the commonality of these attacks, we propose an anomaly detection method based on information entropy and deploy it in the border router of the network. A lightweight module is deployed in client nodes to collect and report information to the border router. The abnormal nodes will be further checked by the signature module to determine the attack type. We have fully implemented the proposed approach in the Contiki IoT operating system and extensively evaluated it through Cooja emulation in comparison with two representative schemes. The results of our experiments show the feasibility and superiority of our proposal in terms of detection accuracy, detection time, energy consumption and memory overhead.