Miracle-dz/UniFAR-FTUs huggingface dataset:
Potential Hazard-Aware Adaptive Shared Control for Human-Robot Cooperative Driving in Unstructured Environment

Research on the shared control system for human-in-the-loop cooperative driving has grown steadily in the past decade. However, most proposed methodologies were focused on structural roads such as highway rather than the unstructured environment. This paper presents a novel potential hazard-aware shared steering approach for human-robot cooperative driving in unstructured environment. First, we propose a hierarchical Gaussian risk field (HGRF) to evaluate the potential hazard of the predicted path. Then an adaptive control authority allocation module is developed to engage the control in real-time. The control authority will be fully owned by the human driver when the vehicle drives in a safe manner. However, in the situation where the hazard level is predicted to be high, the authority of the human driver decrease, and the automation actively assists the driver by dynamically sharing the control authority to enhance safety and performance. The proposed methodology is experimentally verified with a steer-by-wire car-like mobile robot. The results show that our proposed approach can effectively engage and cooperatively control the vehicle in hazard cases, ensuring driving safety.
Design and Control of a Humanoid Avatar Head with Realtime Face Animation

Human-robot interaction is composed of multiple aspects that together create a single experience. In this paper we present a humanoid head that is designed for human-robot interaction as a tele-operated robot meant to be controlled as a remote Avatar. A real-time audio signal processing algorithm is presented that uses an abstracted representation of speaking to provide users with a clear sense of presence during an interaction. We avoid using any video feeds of real human faces, nor creating artificial skins, which emphasizes the capabilities and intent of our robot to be a friendly and unique contribution to the emerging field of physical humanoid avatars.
A longitudinal study on Italian reactions to the different narratives of Covid-19

—This work investigates how individuals in Italy perceived the COVID-19 outbreak and all its implications in real-life. We unveil the most discussed narratives on Twitter and measure how users’ interests, sentiment and emotions have evolved over time and across the several aspects of the pandemic. Our analysis shows that while the overall sentiment is negative, Italians have shown upbeat responses to the pandemic, especially in regards to the vaccination campaign. The emotion analysis reveals that while fear progressively decreased after the first wave of the pandemic, the overall anger has remained constant but gradually turned into various narratives.
Mobility-aware Task Offloading in Fog-Assisted Networks

In a fog-computing assisted Internet of Things network, end-devices typically offload computation and storage-intensive tasks to fog devices. It is primarily done to meet the latency requirements of tasks, and QoS requirements of the network. In addition to providing localized computing and storage services, the fog network also needs to support end-device mobility while handling offloaded tasks, especially, to mimic the ubiquitous availability of the cloud. Most of the existing works in this direction either recommend task migration or offloading tasks by predicting the device's location. Both these approaches are shown to have their respective limitations, and, thus a mobility-aware task offloading scheme is crucial to meet end-device task requirements. In this paper, we present an approach to handle the mobility of end-devices for effectively handling offloaded tasks. The proposed mechanism is simple, effective, and is not constrained by a device's location, thereby lowering the costs associated with mobility. Especially, the proposed scheme entirely eliminates the cost induced during migration, since effective task offloading can lessen the necessity to attempt task migrations. The simulation result of the proposed scheme reduces execution latency by 44%, saves upto 68% of network usage and 62% of computational cost at the cloud compared to the state-of - the-art.
A cross-prediction, hidden-state-augmented approach for Dynamic Occupancy Grid filtering

Accurate modeling of complex dynamic environments is a fundamental requirement in robotics and automotive applications. While grid-mapping approaches used to be limited to static settings, methods for dynamic occupancy grids have recently been developed, tracking spatial occupancy at a sub-object level, in every cell. In this paper, we present a generic dynamic occupancy grid tracker, which filters cell states and infers dynamics of the scene through the interaction of a grid-based and a particle-based model. These are set to represent different parts of the scene, and optimize particle allocation only to relevant areas, their predictions being fused accordingly. New hidden variables in the filtering process permit to address previously mishandled situations, like concurrent state predictions or specific filtering sensitivity. The presented method has been implemented, optimized on a GPU and tested on real-road conditions, embedded on an experimental vehicle.
Investigation of Oscillations of the Piezoelectric Actuator with Two-Directional Polarization

The recent trends in robotics and mechatronics necessitate for developing small-size multi-DOF motion systems. Piezoelectric actuators are one of the feasible solutions of this problem. An investigation of the contact point multicomponent oscillations of the beam type piezoelectric actuator with two-directional polarizations under different excitation regimes is presented in the paper. Polarization of the actuator has perpendicular directions on the first and second half of the actuator. Two-directional polarization of the actuator is used to achieve flexural oscillations of the actuator in two perpendicular planes. Superposition of longitudinal and flexural modes allows achieving oscillations of contact point in different planes. Electrodes of the actuator are divided into sections, and several excitation schemes are used to control a trajectory of the contact point movement. Numerical simulation and experimental study of piezoelectric actuator oscillations were performed. Elliptical motions of the contact point were obtained in different planes. Dependencies of contact point oscillation trajectories from excitation voltage phase and amplitude are determined at different excitation regimes.
Online Recognition Method for Target Maneuver in UAV Autonomous Air Combat

In unmanned aerial vehicle (UAV) autonomous air combat, target maneuver online recognition is beneficial to predict the tactical intention of the target, which is of great significance to air combat situational awareness and decision-making assistance. However, the target trajectory data acquired by our airborne sensors may contain one or more maneuvers. The existing research methods mainly focus on the recognition of single maneuver trajectory which has been segmented according to maneuver segments or the segmentation of target maneuver trajectory by introducing human experience. The above methods can not meet the requirements of online recognition of the unknown continuous multi-segment maneuvers trajectory in autonomous air combat. In this paper, the online recognition problem of target maneuver is transformed into three classification problems, which are maneuver switch subsequence recognition problem, maneuver switch point localization problem and single maneuver recognition problem. The cascaded classification networks based on Long Short-Term Memory (LSTM) temporal feature extraction is used to map maneuver trajectory to maneuver category sequence. The algorithm proposed in this paper is tested by randomly selecting target trajectories, which can automatically segment the trajectories and recognize the correct categories. The average sequence similarity between the predicted maneuver category sequence and the real maneuver category sequence after segmentation and recognition on the test set is above 0.9. The feasibility and effectiveness of the proposed algorithm are verified.
Deep Reinforcement Learning for Autonomous Vehicles Collaboration at Unsignalized Intersections

As conservative intersection management, signalized intersection has a significant bottleneck in improving traffic efficiency when it comes to connected autonomous vehicles (CAVs). In this paper, to make the intersection management more fine-grained, a decentralized conflict-free coordination scheme is tailed for CAVs at intersections without traffic signals. First, the problem of multiple vehicles navigation through an unsignaled intersection is formulated as a Partially Observable Stochastic Game (POSG). Second, we propose a cooperative multi-agent proximal optimization algorithm (CMAPPO) to make driving-decision for each CAV agent and achieve collaboration in a distributed manner. Finally, simulations are carried out on SUMO to evaluate the proposed method. The results show that the CMAPPO has significant effectiveness in solving the multi-vehicle coordination at intersections.
Performance Improvement in Kalman Filters Due to Cooperation for Multi-agent Systems Under Identical Noise

In this paper, we consider cooperative Kalman filtering with information exchange between agents for a discrete-time multi-agent system. The aim of the paper is to evaluate efficacy of the cooperation through comparing precision of the cooperative Kalman filtering with that of Kalman filtering without information exchange, an existing estimation method. The precision of these Kalman filters is mainly evaluated by the traces of a priori error covariance matrices, which are solutions to the associated matrix Riccati difference equations. We show that when an identical process noise is added to each agent, the information exchange improves the precision of Kalman filtering, and the precision improvement increases as the number of agents increases.
A 28 GHz Band Dual-Polarized LTCC Filtering Antenna with Extracted-pole Unit

This paper proposes a dual-polarized filtering antenna with extracted-pole unit (EPU) using LTCC substrate. The EPU realizes the high skirt characteristic of the bandpass filter with transmission zeros (TZs) located near the passband without cross coupling. The filtering antenna with EPU is designed and fabricated in 28 GHz band for 5G Band-n257 (26.5-29.5 GHz). The measured $S$11 is less than −10.6 dB in Band-n257, and the isolation between two ports for dual polarization is greater than 20.0 dB. The measured peak antenna gain is 4.0 dBi at 28.8 GHz and the gain is larger than 2.5 dBi in Band-n257. The frequency characteristics of the measured antenna gain shows the high skirt characteristic out of band, which is in good agreement with electromagnetic (EM)-simulated results.
Effects of Beam Misalignment on Heterogeneous Cellular Networks with Mm-Wave Small Cells

This paper studies the effects of millimeter-wave (mm-wave) beam alignment errors on the downlink achievable rate of a heterogeneous network (HetNet), which consists of sub-6 GHz macro-cells and mm-wave small-cells. The alignment error is modeled as a function of the underlying mm-wave link parameters. The conventional maximum biased received power criterion, where the bias is used for mm-wave small-cells, is adopted for cell associations. By varying the value of the bias factor, we investigate the changes in the downlink rate coverage probability. Our simulation results indicate that high values (of the order of 30 dB) for the bias, while beneficial in the case of perfect alignment, are actually disadvantageous for the low-rate users in the case of imperfect beam alignment. The low-rate users are better served by a moderate value (of the order of 20 dB) of the bias when the beam alignment errors are accounted for. We also show that the above disparity can be narrowed down by increasing by mm-wave base station (BS) antennas $a$ nd/or the mm-wave BS density.
Securable Networks for Nonintrusive Sensor Fusion

Fault detection and diagnostics (FDD) and energy scorekeeping for power systems can be performed nonintrusively with current and voltage sensing at a utility panel. The key problem remains that some important electromechanical behavior, including vibration and acoustic signatures, cannot be easily detected in the power stream, and a small set of additional sensors may be necessary for automated analysis. A nonintrusive power monitor can serve as a central point for collating or “fusing” sensor data from remote sensors, providing a correlated picture of remote sensor data with power consumption. A local, physically secured intranet eases the cybersecurity challenge of a facility by avoiding conventional Internet of Things (IoT) solutions. This article demonstrates a Wi-Fi-based implementation of an FDD intranet on the microgrids of several marine vessels. Connectivity tests presented in this work show that Wi-Fi is a viable link medium for an FDD intranet in shipboard environments. In addition, this work demonstrates the ability to track pump pressure, air conditioning (A/C) compressor loading state, and fan imbalance using fused nonintrusive sensing. Cyclostationary signal processing techniques identified a faulty relief valve in a vacuum pump that evaded traditional analysis. Four sensor network strategies are presented for these scenarios. This work concludes that synchronized power and vibration data provide complementary fault detection ability.
Dynamic Trajectory-Based Traffic Dispersion Method for Intersection Traffic Accidents in an Intelligent and Connected Environment

Intersection traffic accidents (ITAs) are one of the major causes of nonrecurrent congestion in urban road networks because the intersection is the bottleneck of road traffic. Although there has been a proliferation of studies that confirm connected automated vehicle (CAV) technology demonstrates the potential to improve traffic mobility and safety performance at intersections, studies that handle ITAs using CAV technology are scarce. To fill this gap, this study proposes an intersection control system to coordinate vehicle trajectories and ensure operational efficiency and safety under the condition of typical ITAs. A route allocation judgment method based on the position of obstacles is proposed to avoid the accident vehicle. A spatiotemporal grid-based trajectory coordination model is proposed by formulating the route allocation. We utilize the . NET Framework to secondarily develop a Simulation of Urban Mobility-based simulation environment that examines the proposed method. The experimental results indicate that the presented method outperforms the signal control method in terms of reducing delay per vehicle and rescue time and increasing throughput. Consequently, we concluded that the proposed method could handle ITAs under an intelligent and connected environment and improve the safety and robustness of intelligent intersections. It is reasonably believed that the technique has potential applicability in autonomous intersection management.
The influence of late arriving energy on spatial impression

New subjective experiments using sound fields simulated in an anechoic room confirm that spatial impression in concert halls is composed of at least two distinct aspects: apparent source width and listener envelopment. Previously published research has shown that apparent source width is related to the relative level of early lateral reflections. This new work demonstrates that listener envelopment is related to the level, direction of arrival, and temporal distribution of late arriving reflections. It is further shown that increased amounts of listener envelopment decrease the subject’s sensitivity to changes in the apparent source width of the sound field. Listener envelopment is related to objective acoustical measures, and the implications for concert hall design are discussed.
Access Delay Minimization for Scalable Videos in Cache-Enabled Multi-UAV Networks

Utilizing unmanned aerial vehicles (UAVs) as edge caching devices to provide scalable video services to ground users is a promising endeavor, where the caching placement should balance the trade-off between video quality and video diversity. In this paper, we investigate the scalable video coding (SVC)-based layered caching scheme in cache-enabled multi-UAV networks, where specified layers of video are cached at different UAV s. We aim to minimize the aggregate video access delay of all users, via joint design of layered caching placement and UAV deployment as well as user association to provide video services with different qualities. A mixed-integer non-convex optimization problem is formulated which is arduous to solve directly, we decomposed the original problem into two subproblems and proposed corresponding algorithms, i.e., penalty successive convex approximation (P-SCA) based user association optimization algorithm and penalty difference-of-convex (P-DC) programming based UAV deployment and layered caching placement algorithm. An efficient iterative algorithm is proposed wherein we alternatively optimize two subproblems until the algorithm converges. Simulation results show that the proposed algorithm can achieve considerable benefits compared with other benchmark schemes in terms of video access delay.
Signal Classification with Linear Phase Modulation for RIS-Assisted Near-Field Localization

Reconfigurable intelligent surface (RIS), one core element in 6G, opens a new opportunity to design near-field (NF) localization since a signal's propagation distance can be different depending on the reflected points on large RIS. For the design of NF localization to be effective, signals reflected on distinct RIS points should be profiled without interfering with the others, called signal classification (SC). In this paper, we propose a simple yet novel SC technique, called linear phase modulation (LPM), where sequences of distant RIS elements' phases are linearly modulated with different rates. Along with the conventional or-thogonal frequency division multiplexing waveform, LPM makes it possible to classify the reflected signals by distant RIS elements as well as estimate their propagation distances using a two-dimensional Fourier transform (2D-FT) technique. It exploits one more signal dimension for performance improvement than conventional one-dimensional FT (1D-FT) based SC. Through analytic and numerical studies, we verify the effectiveness of the proposed SC using LPM by comparing its localization accuracy with several benchmarks designed based on 1D-FT.
Corrected: On Confident Policy Evaluation for Factored Markov Decision Processes with Node Dropouts

In this work we investigate an importance sampling approach for evaluating policies for a structurally time-varying factored Markov decision process (MDP), i.e. the policy's value is estimated with a high-probability confidence interval. In particular, we begin with a multi-agent MDP controlled by a known policy but with unknown transition dynamics. One agent is then removed from the system - i.e. the system experiences node dropout - forming a new MDP of the remaining agents, with a new state space, action space, and new transition dynamics. We assume that the effect of removing an agent corresponds to the marginalization of its factor in the transition dynamics. The reward function may likewise be marginalized, or it may be entirely redefined for the new system. Robust policy importance sampling is then used to evaluate candidate policies for the new system, and estimated values are presented with probabilistic confidence bounds. This computation is completed with no observations of the new system, meaning that a safe policy may be found before dropout occurs. The utility of this approach is demonstrated in simulation and compared to Monte Carlo simulation of the new system.
Influence of Speed and Other External Factors on the Functioning of Automatic Emergency Braking During Unexpected Interactions With Pedestrians

Today’s conventionally manufactured vehicles have a range of driving aids that help keep the vehicle safe. The operation of these features depends primarily on the proper functioning of sensors, which continuously monitor surroundings to detect obstacles. However, these sensors are not always reliable. The aim of this article is to investigate the actual limits of automatic emergency braking (AEB) when a pedestrian enters the roadway, which in many cases is unpredictable, sudden, and not automatically controlled. The expected high efficiency of AEB is significantly affected by both the speed of the vehicle and weather conditions. The conclusion of this study, therefore, is used to develop a discussion on urban speed limits and their impacts on vulnerable road users.
Estimation of Darkness factor from Low-light Images based on Adaptive Neuro-fuzzy Inferencing Technique

An efficient adaptive neuro fuzzy inferencing system (ANFIS) based low-light image darkness factor estimation technique is proposed in this paper. The proposed work is divided into three major phases: data processing, feature identification, and neuro-fuzzy implementation. The developed ANFIS technique uses three different approaches for implementation, namely, fuzzy c-means, grid partitioning, and subtractive clustering. The models are trained and tested on publicly available Flickr 8k and CAS+PEAL datasets. The performance of the proposed technique is evaluated using eight standard performance metrics Which demonstrates the ability of all the approaches. The comparative results shows that subtractive clustering based ANFIS model outperforms the other two approaches.
Multi-Objective Optimization Strategy Applied in Data Centers Electrical Subsystems

Data center infrastructures must have high availability, low cost, and high energy efficiency. However, these objectives are often conflicting. For instance, an additional Uninterruptible Power Supply (UPS) improves the system availability but may jeopardize the total cost and the energy consumption. This paper presents a strategy based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II), a multi-objective evolutionary optimization algorithm, to improve the design of electrical data center architectures. To show the applicability of the proposed strategy, we present a comparative study between the brute force algorithm and proposed strategy based on NSGA-II, as it has shown promising results in multi-objective problems 1, 2 and 3. When setting complex electrical infrastructure data center models, the results showed that applying the proposed strategy reduces the runtime 961 times and achieves Pareto optimal curvature with a difference of approximately 1%.