Miracle-dz/UniFAR-FTUs huggingface dataset:
Opportunistic Use of In-Vehicle Wireless Networks for Vulnerable Road User Interaction

In-vehicle wireless networks (e.g., Wi-Fi, Bluetooth) are experiencing a faster market penetration than dedicated V2V technologies and are compatible with non-V2X devices. In this paper, we assess whether commodity in-vehicle networks can leverage opportunistic V2X communication to nodes outside of the vehicle, particularly with Vulnerable Road Users. We characterize the radiation pattern and performance of communication links in the 2.4 GHz band between in-car wireless networks and a wireless-enabled bicycle in two representative interaction scenarios (i.e. parallel and perpendicular vehicle-VRU trajectories) using both production hardware (i.e. built-in WiFi hotspot) and dedicated measurement equipment. Empirical results show that (i) the signal propagation to the outside of the vehicle is strongly affected (up to 20 dB) by the vehicle elements (e.g. pillars) and by the placement of the wireless system inside the car, and (ii) the communication performance (in terms of RSSI, IRT, Throughput) is also impaired by the spatial arrangement of vehicle and VRU, and other time-varying phenomena (e.g., human body and bicycle shadowing). We conclude that the in-car system performance allows supporting a wide range of safety and infotainment applications (e.g., IRT under 300 ms) even at large TX-RX distances, and that the placement of an in-car wireless system should be tailored according to the target application.
Adaptive Partition Migration for Irregular Graph Algorithms on Elastic Resources

Component-centric graph programming models allow distributed graph algorithms to be composed, and executed in an iterative manner on commodity clusters and Clouds. Graphs are partitioned and statically placed on a fixed number of machines before execution. However, many graph algorithms have an irregular execution behavior across partitions in different iterations, which causes resource under-utilization. We propose wo strategies, First Fit Decreasing with Migration Planning (FFDMP) and MinMax, for adaptive partition placement onto an elastic number of Cloud resources for such irregular algorithms. For each iteration, our strategies decide the number of hosts and the placement of partitions on them to balance the compute load, and enact this by migrating partitions between hosts at iteration boundaries. Unlike others, our strategies actively consider the time and cost penalties for moving partitions between hosts, and reduce the overall cost of execution while mitigating any increase in makespan. We implement these strategies on our GoFFish subgraph-centric graph processing platform, and evaluate them for performing Breadth First Search (BFS) on large real-world graphs with 10^7–10^9 edges. Our results show that the proposed strategies reduce the median resource cost by 13–38% when compared to a static placement, increase the median makespan by 1–33%, which is strictly bound by a given time budget, and also out-perform existing baseline scheduling algorithms from literature.
Emergency Vehicle Traversal using DSRC/WAVE based Vehicular Communication

The response time of emergency vehicles (EVs) determines the outcome of many emergencies, thus improving the traversal time of EVs is of paramount importance. Vehicular communication is a key enabler of such an improvement. This paper studies two EV traversal algorithms, focusing mainly on their communication aspects. Fast moving dense traffic is modeled, and the traversal algorithms are implemented on top of the dedicated short-range communication (DSRC) / wireless access in vehicular environments (WAVE) protocol stack, while accounting for channel impairments such as path loss and fading. Algorithms that highlight the required packet transfers for the traversal and for safe lane changes are presented, and simulated in the VEINS framework for different traffic conditions. Simulation results show that the suitability of the EV traversal algorithms defer depending on the speed distribution of the vehicles. Further insights drawn from the simulation are utilized to fine tune the EV traversal algorithms and to decrease the traversal time further.
Beamspace Local LMMSE: An Efficient Digital Backend for mmWave Massive MIMO

We explore an all-digital architecture for a mmWave massive MIMO cellular uplink in which the number of users scales with the number of antenna elements at the base station. We consider the design of multiuser detection strategies after a spatial DFT, which concentrates the energy of each user onto a few DFT bins in “beamspace. ” In this paper, we propose and investigate a local LMMSE receiver that exploits this property, using a small window in beamspace to demodulate each user. The proposed architecture is computationally efficient: the required window size depends on load factor (the number of users divided by the number of antenna elements) and does not scale with the number of elements. We also show that adaptive implementations of such local LMMSE receivers naturally extend to provide implicit channel estimation.
A Quantitive Description Method of Vascular basing on Unsupervised Learning towards Operation Skills Assessment of Endovascular Surgery

In the field of rapidly developing endovascular technique and technology, accurate assessment of surgical operation is essential for improving the efficiency of endovascular surgery and the performance of endovascular surgery robots. Existing methods of assessment have taken into consideration of a variety of indicators such as path length of operation, operation time and so on. The indicators that have been considered all come from surgeon’s operation itself. However, the characteristics of specific patients’ blood vessels are not considered for objective assessment. So in this paper, operating difficulty of different blood vessels was described for operation through the aortic arch by machine learning k-means models. Then clustering results were verified with external and internal metrics. Based on this study, difficulty levels of blood vessels can be taken as an important indicator for surgeons’ endovascular operation evaluation in the future research.
A Reading Assistant System of Chinese Text for Persons with Central Visual Field Loss

Macular disease patients who lost central vision must use their peripheral vision to read slowly. There are some assistive methods proposed to improve their reading experience. Sequence word display methods like RSVP are suited to improve the reading performance of low vision people because it can reduce crowding effect, limit the visual span and do not need saccades. However, the performance of these methods in peripheral reading is not satisfactory. This study is aimed to design a reading assistant system that uses the new display method by fixed gaze to improve patient reading performance and test the effects of the new reading assistant software called NRSVP produced improvement in Chinese peripheral reading speed. Firstly, the software was designed to transform the display style of text in daily life. This software uses OCR and word segmentation technology to pre-process Chinese text, and then display the text word by word in NRSVP mode on the screen to help the central field vision loss patients to read quickly. Then the performance of the NRSVP was tested. Participants were asked to read the text by NRSVP and natural reading mode with a central-scotoma simulator. We analysed the reading rate (characters/min) of the participants simulated as central loss vision. There was an improvement in measures of oral reading speed (90%) with NRSVP than the natural reading mode. These findings illustrate the potential benefits of NRSVP as a reading aid method for the patients who loss central vision.
DiCeS: Detecting Communities in Network Streams over the Cloud

We consider the problem of uncovering communities in complex real-world networks whose nodes and their respective associations originate in streams of data. Although community detection has received much attention in centralized settings, the prevalence of online social networks has resulted in unprecedented volumes of data whose handling calls for novel streaming approaches. Moreover, bursty production of network interactions necessitates cloud-enabled techniques that can both deal with diverse data rates and deploy more computing resources on the fly for improved performance yields. We propose a distributed streaming community detection approach termed DiCeS, and implement it as a cloud application. While seeking communities, the novelty of our approach is at balancing the incoming load to a cluster of computing nodes and adjusting the cluster processing capacity in an elastic manner. We also provide fault tolerance by ensuring that temporarily suspended or failed nodes are restored and all edges of the network stream ultimately received their due processing. Lastly, DiCeS is interactive regarding i) updating the target communities, and ii) obtaining results on demand. Our experimental results demonstrate that DiCeS does handle the edges of real-world network streams at impressive rates, allows for near-linear scaling, and outperforms previous non-distributed approaches. While using ground-truth communities for a wide range of large real-word networks, we also show that DiCeS attains improved accuracy if compared to earlier centralized algorithms.
Road Model Design Based on Reward Function in Traffic Light Control

Traffic congestion has become serious across the world since the recent two centuries. Among existing control methods, Deep Q-learning Network (DQN) based reinforcement learning achieved superior performance over state-of-art methods. Reward function is a crucial part of reinforcement learning. Different terms in reward function guide the agent attaining different goals. In traffic light control problem, the reward function could be either too simple to perform well or too complicated to find the best parameters. It influents the performance of the algorithm and bring the challenge in the standardized design of the control algorithm. Amid at this problem, we proposed a road model based reward function and applied it in DQN algorithm. The road structure was introduced and modelled, then from this model we then took factors which can represent different car flow and different road situation. Also, they were easily obtained from the real world. Those factors were applied to design the reward function. Experiments with simulation dataset showed the efficiency of the road model based reward function in the DQN algorithm.
Guidance-based Two-Dimensional Haptic Contour Rendering for Accessible Photography

Touching the faces of loved ones on a photograph can be a strong means to invoke associated pleasant memories to visually-impaired users, as seeing is to sighted users. Inspired by this long-term goal of multimodal accessible photography, we present in this paper a two-dimensional contour rendering algorithm based on haptic guidance using force feedback for tactile graphics. We begin with a basic algorithm for one contour and find its optimal guidance parameters by a user study. This basic algorithm is then applied to rendering geometric primitives, and its perceptual performance is evaluated by a shape identification experiment. We extend the basic algorithm to cover multiple contours and apply it to rendering portrait photographs. Major facial features are extracted from photographs, and their contours are rendered by our algorithms. It is demonstrated that users can accurately recognize emotional expressions on the faces, suggesting the high potential of our approach for enhancing accessible photography.
Optimal Threshold-Based Distributed Control Policies for Persistent Monitoring on Graphs

We consider the optimal multi-agent persistent monitoring problem defined by a team of cooperating agents visiting a set of nodes (targets) on a graph with the objective of minimizing a measure of overall node state uncertainty. The solution to this problem involves agent trajectories defined both by the sequence of nodes to be visited by each agent and the amount of time spent at each node. We propose a class of distributed threshold-based parametric controllers through which agent transitions from one node to the next are controlled by thresholds on the node uncertainty. The resulting behavior of the agent-target system is described by a hybrid dynamic system. This enables the use of Infinitesimal Perturbation Analysis (IPA) to determine on-line optimal threshold parameters through gradient descent and thus obtain optimal controllers within this family of threshold-based policies. Simulations are included to illustrate our results and compare them to optimal solutions derived through dynamic programming.
PointLaneNet: Efficient end-to-end CNNs for Accurate Real-Time Lane Detection

Lane detection is crucial for driving assistance systems such as lane keeping assist (LKA) and lane departure warning (LDW). In the conventional method, image processing operations such as edge detection and the Hough transform are utilized to recognize lane lines. Currently, the deep learning method for lane detection is dominated by semantic segmentation algorithms. Image pixels are classified by the deep neural network, and the lane line information is extracted by clustering and other post-processing methods. However, these methods have problems such as large models and unintuitive training. This paper proposes an end-to-end lane detection algorithm that can obtain the coordinates of lane line points directly from the drive scene, and can be deployed on the embedded platform NVIDIA PX2. The algorithm satisfies the requirements of high precision and real-time performance in autonomous driving.
Monitoring Neural Activity during Motion-Force Control Task Using Functional Near-Infrared Spectroscopy

Real-time monitoring of neural activity during haptic tasks helps in enhancing scientific understanding of brain-motor relations. Considering the limitations of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) measurements, in this paper, we address the feasibility of functional near-infrared spectroscopy (fNIRS) for monitoring neural activity during a motion-force control task. The task difficulty is modulated by changing the allowable trajectory and force tolerance. Cortical hemodynamic signals of 14 participants were recorded from the bilateral prefrontal, sensorimotor and occipital cortices. Results show that the brain activation over these brain regions and the functional connectivity between these regions increased when the motion-control became more challenging. In contrary, the activation and functional connectivity tended to decrease with the increase of the force-control difficulty. This study indicates the sensitivity of using fNIRS to differentiate difficulty levels in haptic tasks, and supports the potential of fNIRS in realistic haptic interaction.
Lifetime Optimization and Connectivity Control in Asymmetric Networks*

The problem of lifetime maximization and connectivity control in an asymmetric network is considered in this work. It is assumed that the network is modeled by a weighted directed graph, and that the information flow structure is centralized. The generalized algebraic connectivity notion is adopted as the network connectivity measure, and is formulated here as an implicit function of the transmission powers used by the nodes to communicate with their neighbors. An optimization problem is presented to maximize the network lifetime while ensuring that the degree of connectivity of the network is above a prespecified threshold and that transmission powers of the nodes are limited to a predefined range. The mixed interior point-exterior point technique is utilized to convert the problem into a sequential unconstrained optimization. In order to solve each subproblem numerically in the new framework, the subgradient technique with backtracking line search is utilized. It is shown that the proposed solution asymptotically converges to the global optimum of the original optimization problem. Simulations confirm the efficacy of the proposed technique.
Deep Intersection Classification Using First and Third Person Views

We explore the problem of intersection classification using monocular on-board passive vision, with the goal of classifying traffic scenes with respect to road topology. We divide the existing approaches into two broad categories according to the type of input data: (a) first person vision (FPV) approaches, which use an egocentric view sequence as the intersection is passed; and (b) third person vision (TPV) approaches, which use a single view immediately before entering the intersection. The FPV and TPV approaches each have advantages and disadvantages. Therefore, we aim to combine them into a unified deep learning framework. Experimental results show that the proposed FPV-TPV scheme outperforms previous methods and only requires minimal FPV/TPV measurements.
A Time-efficient Test Rig for Thermal Characterisation of Electric Machines and Manufacturing Process Development

This paper presents a DC motorette test rig with a closed-loop temperature control that significantly reduces the run time of a thermal test. Testing only a segment of a stator, this rig is developed to compare, monitor and improve winding and impregnation manufacturing processes, which is an original and novel application of the rig. The hardware setup, instrumentation of a test sample, parameter settings for the test and a software algorithm to control the temperature are described. The sensitivity and repeatability of the test are quantified. Results from a Design of Experiments to study a trickle impregnation process using the test rig are presented. Last but not least, it is shown that, even with the introduced changes, the test results can still be used for calibration of the thermal modelling of a stator segment, the traditional aim of the DC motorette testing.
Chamfering Max-Separable Lyapunov Functions to Accept Non-ISS in Interconnected Systems

This paper aims to unify the construction of Lyapunov functions for interconnected systems comprising integral input-to-state stable (iISS) and input-to-state stable (ISS) subsystems. The sum-separable Lyapunov functions reported for interconnected iISS systems in the literature successfully apply to ISS and linear systems since stable linear systems are ISS, and ISS systems are iISS. However, the sum-separable Lyapunov functions remain seriously complicated and they do not agree with popular Lyapunov functions even if systems are ISS or linear. This paper resolves the complexity and disagreement by proposing a framework of chamfering the corner of the max-separable Lyapunov function which was popular for ISS systems, but could not alone accommodate iISS subsystems which are not ISS.
A Comparison of Lateral Dynamic Models for Tractor-Trailer Systems

In the literature, researchers studying the lateral dynamics of tractor-trailer systems have each developed their own reduced-order lateral dynamic model, each with their own assumptions, state representations, and derivation methods. Little to no work has been performed to compare the accuracy of these models to each other, nor to validate their results against high-fidelity multi-body simulations or real truck data. The purpose of this paper is to identify several reduced-order lateral dynamic models for tractor-trailer systems present in the current literature, simulate their estimated states through common driving maneuvers, and then compare their estimates against reference data from a high-fidelity multi-body dynamic model. From this comparison, we identify the reduced-order model that maintains the least error from the reference data as the best representation of a real tractor-trailer system. The results of our comparison will be useful to researchers interested in using a reduced-order dynamic model from the literature as the basis for articulation angle estimation, model-predictive control, or adaptive control algorithms for autonomous tractor-semitrailer systems.
Deterministic Modeling of 5G Millimeter-Wave Communication in an Underground Mine Tunnel

This paper presents broadband simulations and measurements of Millimeter-Waves (mm-wave) propagation in a rugged underground mine environment. Mathematical formulation was carried out in the framework of Uniform Theory of Diffraction (UTD) to develop a deterministic Ray-Tracing (RT) model under Line-Of-Sight (LOS) condition. The developed theoretical model was then validated experimentally in Frequency Domain (FD) and Time Domain (TD). A significant agreement between simulations and measurements is achieved in both domains. The rough surfaces of the mine are modeled deterministically as groups of diffracting wedges having random dimensions (heights, angles) being transversely oriented throughout the gallery. Acute (inferior to 30°) and obtuse (superior to 120°) wedges angles are found to have significant effects on the overall propagation performance. In the mm-wave band, the UTD diffraction phenomenon is evident and must be considered in the design of underground mine channels. In fact, the presented model is found to be capable of predicting the complex multipath of underground mine channels, due to the ray-optical behavior at mm-wave bands.
Phantom Sensation: When the phantom escapes the bounds of the actuators and the end-point is sensed in the air

The Phantom Sensation is a case of spatiotemporal interaction. In this paper, we study the influence of the duration of a stimulation on the position of the perceived end-point of the illusion. We find that the position of the end-point is highly dependent on the duration of the stimulation. Three experiments have been conducted. Participants held an object delivering phantom sensations of different durations. We show that for durations ranging from 0.25 to 2.5s, the direction of the illusory motion is well perceived (experiment 1). We secondly show that for increased durations (> 0.75s), the perceived endpoint of the illusion can be located outside of the hand, inside the object held (experiment 2). We finally show that for increased durations (> 1.5s), the perceived end-point can be located even further, outside of the object held, i.e. beyond the intra-actuator space (experiment 3).
Real-Time Pose Graph SLAM based on Radar

This work presents a real-time pose graph based Simultaneous Localization and Mapping (SLAM) system for automotive Radar. The algorithm constructs a map from Radar detections using the Iterative Closest Point (ICP) method to match consecutive scans obtained from a single, front-facing Radar sensor. The algorithm is evaluated on a range of realworld datasets and shows mean translational errors as low as 0.62 m and demonstrates robustness on long tracks. Using a single Radar, our proposed system achieves state-of-the-art performance when compared to other Radar-based SLAM algorithms that use multiple, higher-resolution Radars.