Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Complexity Heuristics for Multi-Objective Sensor Placement in Traffic Networks

Monitoring the traffic state of the road network is very important for a plethora of reasons, such as the prevention of traffic congestion and the development of estimation and control policies. In order to efficiently obtain high-quality information on traffic, the sensors must be installed at optimal locations in the road network under study. This problem is known as the Network Sensor Location Problem (NSLP). In this work, a multi-objective NSLP is proposed for the installation of a pre-defined number of sensors to maximise (i) the covered traffic flow volume and (ii) the minimum distance between candidate links for sensor installation, while taking into account pre-installed sensors. We reformulate the problem into a single-objective mixed-integer linear program (MILP) that yields the optimal sensor locations. In addition, we propose four low-complexity heuristics for the solution of the problem. The performance of the proposed algorithms is evaluated for the traffic network of the Republic of Cyprus under real-life conditions and traffic data. Results show that the four low-complexity approaches yield a different trade-off between execution speed and solution quality.
Extraction of vehicle behaviors at intersections

Simulation of realistic urban environments is essential for the validation of decision-making algorithms in automated driving. To populate such spaces with independent agents that execute trajectories and that correctly interact with each other it is necessary to identify all possible behaviors that may arise from a set of real users trajectories. Using a two-step clustering process on trajectories with different duration, first on the position vector and then on the longitudinal velocity and acceleration, a number of behavior profiles can be identified per trajectory in intersections. To speed up the maneuver identification, the position clustering is executed on the dissimilarity matrix created using the dynamic time warp (DTW) measure between trajectories. As for the second clustering, the use of both longitudinal velocity and acceleration allows the representation of specific behaviors for each possible path. In both procedures the k-means clustering with DTW dissimilarity measure was used, combined with the Davies- Boudin score to define the optimal number of clusters. The final result provides the identification of behaviors in different geometric dispositions independently from map information and keeping the explainability property concerning each cluster.
Design and Analysis of a Compact 28 GHz Textile-Based Antenna for Wireless Body Area Network

This research paper presents a novel design of a 28 GHz textile-based antenna for Wireless Body Area Network (WBAN) applications, with Panama textile material as the substrate. The proposed antenna’s design capitalizes on the unique properties of Panama fabric, which offers both flexibility and durability, making it suitable for integration into clothing. Computer Simulation Technology (CST) software was utilized to simulate and design this antenna. The physical dimension of the Panama substrate is 9mm x 6mm x 1.04 mm. Copper conductors are used to design the antenna and the ground plane. -32.56 dB is the antenna’s reflection coefficient at 28 GHz. With a radiation efficiency of 81%, the antenna has a gain of 5.2 dBi. In the CST software, a three-layer human body facsimile was created in order to assess the antenna’s effectiveness on the human body. The results were analyzed while the antenna was 6mm from the human body facsimile. This antenna performs admirably in free space and on the human body.
A Design of Homogeneous Multi-Core SoC with General-Purpose Floating-Point Processors

In the era of big data and artificial intelligence, SoC design for computationally intensive applications are a huge challenge, especially for general-purpose processors where the underlying architecture is single-core in nature. This paper exploits customized general-purpose floating-point processors to build a homogeneous multi-core SoC. The general-purpose floating-point processor supports the IEEE 754 single-precision floating-point standard and has effective circuit implementation of matrix operations. The SoC integrates a micro-controller compatible with Microblaze instruction set and a floating-point arithmetic module composed of various functional IPs, which makes the presented homogeneous multi-core SoC programmable and flexible. The homogeneous multi-core SoC is built using a three-layer network-on-chip, and the general-purpose floating-point processor is mounted on the network-on-chip node. Finally, matrix multiplication mapping is implemented on this homogeneous multi-core SoC for performance evaluation.
Detection of Moving Objects Based on Efficient Particle Tracking in 1.5D LiDAR Range Images

For autonomous driving it is essential to know the state of dynamic objects in the ego vehicle's vicinity. While learning-based approaches achieve remarkable results in object detection benchmarks, they still struggle to detect unknown object classes, which are not well represented in existing datasets. Occupancy grids in combination with particle tracking have the potential to close this gap for moving objects, as they have only little assumptions about their shape or appearance. Our previous work has shown that particle tracking can be performed very efficiently without occupancy grids but with 1D range images as they implicitly store free space, occupancy and occlusion information. Due to the efficiency of the approach, it is possible to make extensive reasoning about the available free space information, which has been shown to be important in order to distinguish false-positive ghost motion from actual motion. Since the 1D range image only stores the closest obstacle for each azimuth angle of the LiDAR sensor, this approach is not able to detect all moving objects in the sensor's field of view. We propose to use 1.5D range images in combination with an adapted particle tracking to avoid this limitation. This data structure is able to store a variable number of obstacles per azimuth angle. We show that the detection performance is improved as also distant and partially occluded moving objects can be perceived.
Applications of Deep Reinforcement Learning in Wireless Networks-A Recent Review

Deep Reinforcement Learning (DRL) techniques have gained substantial attention in recent years for future wireless networks. They can overcome the ever-increasing challenges of wireless environment and limitations of traditional techniques. Some applications of DRL for future wireless and mobile networks are network optimization, network control, power management, mobile edge computation (MEC), offloading, edge cachings, network slicing and many others. This paper mainly addresses network optimization specifically energy optimization, and secondly resource allocation. A recent literature review is presented by looking at research so far achieved, DRL methods used, and future research directions.
Research on multi-task learning method based on causal features

Multi-task deep learning has gradually evolved from general layer parameter sharing to connecting and switching between specific layers. However, due to the limited knowledge of task relationships and hierarchies, it is difficult to reasonably set up shared modules in the network, which leads to insufficient model accuracy and generalization ability. To address this problem, a multi-task learning method based on the combination of causal learning and adaptive strategy is given here: at the feature level adopts causal discovery, causal feature reflux to extract stable task features required for model training to improve the accuracy and generalization ability of the model; and then adaptive learning strategy is used to dynamically learn the shared network modules between tasks, which avoids inaccuracy of the manual settings. The experimental results and analyses validate the effectiveness of the approach through rich experiments on the NYU_V2.
Study on Adaptive Cruise Control Systems of Passenger Vehicles and Solutions Based on V2X Technologies

Adaptive Cruise Control (ACC) systems play a crucial role in enhancing automotive safety. However, ACC systems suffer from limitations such as inadequate single-vehicle perception. Current research predominantly focuses on target recognition algorithms, overlooking the technological pathways for enhancing ACC system performance through vehicle-to-vehicle and vehicle-to-infrastructure cooperative technologies. This paper provides a comprehensive review of ACC technology’s development and current state, encompassing ACC products from automotive manufacturers, relevant standards, regulations, and associated research theories while summarizing the existing issues in contemporary ACC technology. Furthermore, this paper explores ACC technology solutions based on these cooperative and networked approaches by analyzing the current status and advantages of vehicle-to-vehicle cooperation and vehicle-to-infrastructure communication technology (V2X). The study reveals that by integrating vehicle-to-vehicle cooperation and vehicle-to-infrastructure communication technologies, it is possible to cost-effectively improve the reliability and safety of ACC systems in scenarios such as cornering, driving on wet or slippery surfaces, and encountering stationary obstacles. This research provides theoretical support for the development of ACC systems, further enhancing road traffic safety and operational efficiency.
An Optimized Resource Allocation Method Based on Deep Reinforcement Learning for the Ad Hoc Network of TACS

The ad hoc network is suggested to be used for train autonomous circumambulate system (TACS) due to its high survivability and low latency of communication. The resource allocation is critical to reducing interference within the network. In this paper, we propose a multi-dimensional resource allocation method to maximize network capacity and minimize the latency of data transmission. Models to analyze the capacity and latency of multi-path transmission are set up, which consider the location of nodes, the interference among concurrent multi-path transmission, the periodicity of communication and the waiting of data at each hop, etc. The multi-path transmission resource allocation based on deep reinforcement learning (MTRARL) algorithm is proposed to obtain a set of communication paths and the corresponding resource allocation scheme to optimize the capacity and latency of train-to-train (T2T) and train-to-ground (T2G) communication. Simulation results show that the proposed method can support the communication of 4 trains in a cluster. The minimum bandwidth and the maximum latency of communication for trains in a cluster are 17.8 Mbps and 9.1 ms, which satisfy the requirements of TACS.
Agent Guidance in Autonomous Mobility on Demand Systems: An Approach Utilizing Priority Double Deep-Q-Networks

Autonomous Mobility-on-Demand (AMoD) systems have become an integral part of modern urban life, reshaping transportation dynamics. However, the challenge of orchestrating multiple vehicles to maintain a dynamic equilibrium between supply and demand in a multi-agent environment remains a critical concern. To address this challenge, we propose a novel Multiagent Deep Reinforcement Learning framework called Priority Double Deep-Q-Network (Pr-DDQN) with a new cooperative reward mechanism to optimize the repositioning route of vacant vehicles within the AMoD system. Through rigorous experimentation using a city-scale dataset comprising 48,000 requests on a weekday in Chicago, we assess the scalability and efficiency of our approach. Comparative results demonstrate that Pr-DDQN outperforms existing methods, showcasing superior performance across key metrics, including Service Rate, Satisfaction Index, and Repositioning Time. These findings underscore the efficacy of our approach in enhancing the operational efficiency and overall performance of AMoD systems.
An Infrastructure-Enabled-Perception System for High-Precision Vehicle Perception

An Infrastructure Enabled Perception (IEP) System is put forward for high-precision vehicle perception. It enables wide perception range and high precision perception, and maintains robust against vehicle states. Experiments are carried out to evaluate the performance of the proposed IEP system in terms of vehicle localization accuracy and speed tracking accuracy. The results show that applying the enhanced IEP system can achieve cm-level precision in vehicle detection and tracking while maintaining robustness.
Perceived Trust of Paylater and Relaxation Shopping: Gender as A Moderator

This study aims to examine the impact of perceived trust and ease of use online features of Paylater on relaxation shopping behaviours. Besides, this study has examined gender as a moderator between perceived trust and ease of use of online features of Paylater on relaxation shopping behaviours. The respondents were sampled from millennials who had already utilised and experienced Paylater. The sampling technique has utilised the purposive sampling technique. Statistical results of 251 millennials who participated show that perceived trust has a significant relationship with relaxation in shopping behaviour. No evidence exists that ease of use has a positive and significant relationship with relaxation in shopping behaviour. We found that gender does not play an important role in moderating the relationship between perceived trust and ease of use online features of Paylater on relaxation shopping behaviours. We conclude that choosing the right online feature on the online commerce platform drives consumer shopping. We suggested the online systems developer design the ease of use functionality and ensure that online shopping apps provide convenience to consumers.
The Effect of Stator/Rotor Bifurcations on a PMaSynRM Torque Ripple

In the operation of PMaSynRM, the generated torque ripple can lead to the production of acoustic noise and vibration. It is important to take this into consideration to minimize any potential negative effects on the systems, especially in electric vehicle applications. The study investigates the torque produced by a PMaSynRM and highlights the effect of geometry bifurcation on torque ripple and average torque. This paper presents a motor design with different bifurcation shapes of stators, such as the circle, triangle, and gear shapes, and the rotor, which can be trapezium or triangle-shaped. Eleven design patterns with different width and height were analyzed for each one, and the performance of the motor in different patterns were compared and the best one was selected based on the 2D finite element method FEM. The simulation results indicated that when the stator teeth notch is circular in shape and the rotor pole groove is trapezium-shaped, the electromagnetic torque is reduced by 5.27%, and the torque ripple is minimized by 63.58% compared to the base design.
Enhancing Realistic Floating Car Observers in Microscopic Traffic Simulation

We present a system that enables a realistic detection of traffic participants using Floating Car Observers (FCOs) directly within microscopic simulations. Point clouds are utilized to transform the two-dimensional simulation into a three-dimensional environment. In this environment, vehicles can be equipped with up to four camera sensors. Employing computer vision strategies, we identify which traffic participants would be detected by real-world sensor systems. We utilize the resulting system to generate datasets. In this process, various vehicles move within a simulation, recording which traffic participants would be recognized by the approach. Additionally, for each step of the simulation, an image from the current simulation and the position vectors of the traffic participants are documented. We employ the dataset to train neural networks, enabling them to replicate the results achieved using the CV method. The trained Vision Transformer and ResNet architectures achieve accuracies of up to 90%. Compared to the CV approach, the neural networks facilitate as much as 18-fold speed enhancement. We have made the source code, datasets, and trained models openly accessible at: github.com/urbanAIthi/SUMO_FCO.
A Data-Driven Long-Term Prediction Method of Mandatory and Discretionary Lane Change Based on Transformer

Current common choices of data-driven lane change predicting targets are left lane change, right lane change and car-following. However, to achieve accurate long-term lane change prediction, the motivation behind drivers' lane changes must also be considered. Motivated by this necessary consideration, we propose a Lane Change Attention Model (LCAM). Unlike past data-driven lane change prediction models, LCAM applied mandatory lane change (MLC), discretionary lane change (DLC) and lane-keeping (LK) as predicted driving states instead of left lane change, right lane change and car-following. This approach expands the prediction horizon of LCAM, as left/right lane changes and car-following are merely external manifestations of the process of achieving strategic driving targets. By imitating human drivers' prediction and attention mechanisms towards surrounding vehicles and road information while driving, LCAM applies Transformer architecture to solve the lane change prediction problem. Considering the specificity of lane change prediction, we specially design a route information embedding module. With its contribution, LCAM achieves accurate long-term lane change prediction.
Layout similarity based key information extraction framework for structural images

Image key information extraction is an important technique in automatic digital image recognizers. To deal with complex image layout, vague image semantics and image defects, we propose a layout similarity-based model to extract image key information under certain image distortion situations. Our model uses perceptual hash algorithm (PHA) to detect visually similar images and uses a template matching based algorithm to solve distortion problems when PHA cannot find a match. The framework is tested on a dataset with more than 17,000 images and we demonstrate that our model can reach 99.5% accuracy on the original dataset, significantly outperforming Chargrid model and a parity-based model. We also compare the performance of different models on images with artificially overlaid boxes, which is commonly seen on screenshots. Results show that our model still performs much better than the parity-based model.
Integrated Signal and Bus Lane Control: An Intersection-Level Approach

This paper proposes a novel passenger- oriented intelligent intersection-level control named “Integrated Signal and Bus Lane Control” (ISBLC), which aims to balance between the optimization of passenger throughput and the reliability of public transport operations by dynamically adjusting traffic signals and assigning dedicated bus lanes to upstream lanes of the intersection, based on the observed traffic conditions, as well as the Vehicle-to- Infrastructure (V2I) communication. The method utilizes reinforcement learning, which allows the system to learn and adapt to continuously changing traffic conditions in real-time, while enabling the proposed scheme to continuously improve its decisions based on the consequences of previous actions, leading to more efficient traffic management. Findings suggest that ISBLC, when compared to traditional traffic signal and bus prioritization approaches, shows excellent results both in terms of transit reliability and level of service of adjacent road traffic, in a wide range of demand profiles.
A new transformer-based axis temperature alarm system

The current axis temperature alarm system mainly determines the current alarm level based on the axle temperature status or characteristics detected by a single monitoring station, which has a problem: the characteristic data detected by the monitoring station is not related to the previously detected characteristic data. In this study, temperature rise, column temperature rise difference and vehicle temperature rise difference are taken as the feature data, and the attention mechanism model with mask is used to judge the current alarm level by combining the feature data measured by the previous monitoring station. The simulation results show that the optimized system has a discrimination agreement rate of 87.2% for the alarm level, which is a 13.5% increase in accuracy over the previous method.
Optimal Planning on a Single-Route Transit System with Modular Buses

This paper considers optimal planning of modular buses on a single route to minimize the average passenger travel time. Modular vehicles consist of individual units that can attach and detach from other modules at any stop, allowing for increased flexibility. As the passenger demand and the travel time between stops are stochastic variables, we use a Markov decision process to model the system. The problem of minimizing passenger travel time is converted to optimally planning the actions of all the vehicles, which include stopping at a stop, skipping the stop, and attaching or detaching from other modules. We develop a cost formulation to capture the impact of these actions on passenger travel time, and propose a control policy that selects an action with the minimum cost. To evaluate our proposed policy, we compare it in simulation with two other policies: when vehicles serve every stop without attaching or detaching, and a previously proposed rule-based attaching/detaching policy. Our proposed policy significantly outperforms both of these strategies.
Adaptive Observer-Based Synchronization For a Class of Fractional-Order Nonlinear Systems Subject to Unknown Inputs

The synchronization of a fractional-order drive-response configuration system is addressed in this study. A slave fractional order adaptive observer system is designed in order to synchronize with a particular master fractional chaotic system whose dynamical model is exposed to unknown inputs data. Based on the stability theorem of Lyapunov, a fractional-order adaptation law is derived to estimate the unknown inputs,the observation and estimation errors stability is analyzed. To validate the benefits of the novel synchronization approach an application example to fractional-order Chua system is presented.
Feature-Attentive Multimodal Emotion Analyzer with CLIP

Sentiment analysis has evolved into a multidimensional field with the advent of various data modalities, such as text and images. Traditional methods often struggle to effectively integrate these diverse data types. This paper introduces a model based on Contrastive Language-Image Pre-Training (CLIP), named the Feature-Attentive Multimodal Emotion Analyzer with CLIP (FAMEAC), which is a novel framework designed to address the challenges of multimodal sentiment analysis. By utilizing state-of-the-art algorithms such as Vision Transformer (ViT), CLIP, and robustly optimized bidirectional encoder representations from Transformers approach (RoBERTa), the FAMEAC model excelled in feature extraction from both text and image modalities. An attention-based layer was incorporated for feature fusion, enabling the model to capture intricate relationships between different modalities. Experimental results derived from Twitter datasets reveal that the proposed model outperforms existing methodologies in both accuracy and robustness. The FAMEAC model holds promise for various applications, ranging from commercial advertising to social science research, while also raising important ethical considerations. This research serves as a seminal contribution to the burgeoning field of multimodal sentiment analysis, establishing a novel paradigm that holds significant promise for guiding future scholarly investigations.