Miracle-dz/UniFAR-FTUs huggingface dataset:
Complex Medium Permittivity Retrieval Based on Curvature Calculation

This paper concerns the estimation of the refractive index of a complex medium from tomographic Synthetic Aperture Radar (TomoSAR) data. It is based on curvature calculation which is due the assumption of vacuum propagation velocity through the medium during the signal focusing process. A mathematical modelling is developed so as to depict the propagation of electromagnetic waves in a complex medium. It takes into account properties such as thickness, wave propagation velocity and refractive index. Furthermore, the developed modelling technique is used to calculate the curvature and compare it with the curvature observed in Tomographic Synthetic Aperture Radar (TomoSAR) image. The results clearly illustrate that the refractive index can be accurately estimated with a minimal error rate of 6%.
SolarNet- An efficient multi-model ensemble approach for Solar Power Forecasting based on Artificial Intelligence

Solar energy has become a critical topic in the field of electrical power generation, and it is expected to remain so soon due to the world’s finite supply of fossil fuels and experts’ predictions of an energy crisis in the coming decades. Solar energy is gaining traction as a viable alternative, but solar electricity generation is heavily dependent on weather conditions, which poses certain challenges. Extensive use of solar energy is required to provide a reliable supply of electricity, and precise prediction of solar power is one solution to this problem. Accurately predicting solar power generation is critical for energy grid management and renewable energy planning. In this study, we introduce a new solar power forecasting approach called SolarNet, which utilizes deep learning techniques. The SolarNet model combines a multi-model ensemble approach that incorporates a 1D hybrid Convolutional Neural Network (Dilated Convolution), an attention-based Bidirectional LSTM, and a machine learning approach. We evaluated the performance of the SolarNet model using real-world solar power generation data and compared it to traditional forecasting methods in terms of Mean Absolute Error (MAE), Coefficient of Determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results of our forecasts have been examined using several models, including feature engineering-based machine learning models, LSTM, BiLSTM, and gated recurrent units (GRU). The results demonstrate that SolarNet outperforms traditional methods in both accuracy and reliability. Our study contributes to the advancement of efficient and dependable solar power forecasting methods.
Centralized Dynamic Vehicle Routing with Macroscopic Fundamental Diagram

Optimizing urban mobility through dynamic vehicle routing and centralized control mechanisms is a complex task with a high computational cost. Macroscopic fundamental diagram (MFD) provides a practical tool for traffic state control at a macroscopic level, aiming to achieve system optimum. This paper introduces a centralized dynamic vehicle routing system based on MFD for connected vehicles. Its objective is to minimize travel time delays for individual vehicles and the entire city. The system divides the city into multiple regions and calibrates the MFD for each region. Connected vehicles monitor the macroscopic traffic state in real time and adjust the weight of link travel time accordingly. This enables the system to dynamically search for the most efficient routes, maintain region traffic close to capacity, maximize throughput, and alleviate network congestion. The system's performance is evaluated on both a synthetic grid network and a real-world city network, demonstrating significant mobility benefits. Results show a potential doubling of region capacity and up to a 60% reduction in travel time delays. Additionally, the paper analyzes the influence of connected vehicle market penetration rates and network congestion levels on mobility benefits using microscopic traffic simulations.
Secure Turbo Code Design Based on Chaotic Interleavers

This paper is a proposal for a secure turbo code design based on chaotic interleavers, employing general symmetric unimodal maps. This interleaving process introduces randomness and confusion, making it more challenging for unauthorized parties to decode the encoded data. Secure turbo codes based on chaotic interleavers are a promising approach to physical layer security.
Distributed Adaptive Cooperative Control for Virtually Coupled Train Set with State Constraints and Input Saturation

Virtual coupling (VC) is an emerging problem, which could control multiple trains as a virtually coupled train set (VCTS) via train automation and communication. This paper proposes a novel adaptive control method for the VCTS. Firstly, a VCTS dynamics model is constructed by analyzing the dynamic evolution of VCTS in a real-world environment, taking into account input saturation, uncertain parameters and unknown disturbances. Next, we construct a nonlinear state-dependent function (NSDF) to cope with the tracking interval and speed constraints. And a novel adaptive control protocol is designed, which can simultaneously deal with uncertain resistance parameters and unknown disturbances, while enabling the train to track the preceding train stably and efficiently. Based on Lyapunov stability theory, the strategy stability is analyzed. Simulation results are also used to confirm the feasibility and effectiveness of the proposed control method.
Modeling Perception Performance in Microscopic Simulation of Traffic Flows Including Automated Vehicles

Mixed traffic with automated and human-driven vehicles interacting with one another will soon become a common reality. Microscopic traffic simulation can preemptively help assess the impact on the traffic flow dynamics as long as the tools adequately capture the differences on how automated driving systems (ADSs) drive compared to humans. In this work a modeling approach that captures differences in perception performance is proposed. While human drivers perceive through their senses and cognitive processes, ADS perceive the driving context through on-board sensors, connectivity features and software. The perception performance is described in terms of accuracy, precision, detection range, and detection delay. The model for perception is implemented in SUMO and a simulation test in a platoon shows the acceleration response affected by up to 35 % for perception errors of $\approx 10 \%$ which by extension will affect the traffic flow dynamics. The proposed modeling approach for perception contributes to the robustness of microscopic traffic simulation and the modeling of heterogeneous mixed traffic.
Modeling Interaction-Aware Driving Behavior using Graph-Based Representations and Multi-Agent Reinforcement Learning

Modeling the driving behavior of traffic partici-pants in highly interactive traffic situations, such as roundabouts, poses a significant challenge due to the complex interactions and the variety of traffic situations. To address this task, we propose a combination of graph-based representations of the environment with Multi-Agent Reinforcement Learning (MARL). By utilizing a graph-based representation of the local environment of each vehicle, our approach efficiently accounts for road structures and a varying number of surrounding vehicles interacting with each other. Building upon this representation, MARL enables us to learn a driving policy based on a minimal set of principles: drivers want to move along the road while avoiding collisions and maintaining comfortable accelerations. Sharing the learned policy among all agents allows us to leverage Proximal Policy Optimization (PPO), a policy gradient Reinforcement Learning (RL) algorithm. To evaluate our proposed model, we conduct experiments in a roundabout scenario from the INTERACTION dataset and compare it to a model learned via Behavior Cloning (BC). The results demonstrate that our proposed model is capable of maneuvering through dense traffic, indicating that our graph-based representation is well suitable for modeling and understanding complex road layouts and interactions between agents.
A Text Prompt-Based Approach for Zero-Shot Corner Case Object Detection in Autonomous Driving

Deep learning-based object detection is a critical technology for autonomous driving, as it enables vehicles to perceive and react to the environment. However, most models are heavily dependent on annotated data, which can limit their ability to detect rare or unusual objects, also known as corner cases. In this paper, we apply a state-of-the-art text-prompted object detection model to handle this problem. This model leverages a text-image multimodal cross-attention mechanism to incorporate semantic information from natural language. Therefore, it can detect objects under limited data and has strong open-set detection and zero-shot generalization capabilities. We generated text prompts based on the prior distribution of objects and performed joint detection with images. Our experimental results on the CODA dataset demonstrate that our proposed approach significantly outperforms baseline models in terms of mean average precision (mAP) and mean average recall (mAR) scores. Our work presents a new detection approach that can contribute to the development of safer and more intelligent autonomous driving systems.
Sensor Fusion-Based Localization Framework for Autonomous Vehicles in Rural Forested Environments

One major hurdle for the deployment of autonomous vehicles in rural environments is achieving accurate localization in areas with tree-canopied roads or outdated point cloud maps. The presence of limited visibility and high variability renders standalone sensor localization unreliable in such situations. To tackle these issues, this paper presents a sensor fusion-based localization framework that integrates data from GNSS, LiDAR, INS, and vehicle odometry. The proposed approach uses a loosely-coupled Extended Kalman Filter for sensor fusion and a weighted gate approach for accurate state estimations. Compared to a state-of-the-art technique, the proposed method achieves a reduction of around 71% in maximum lateral deviations. This method successfully enables a safe and reliable localization in challenging scenarios that are frequently found in the rural and inter-urban sectors.
SMOTE-LOF and Borderline-SMOTE Performance to Overcome Imbalanced Data and Outliers on Classification

Dealing with data imbalance and outliers is an important challenge in data classification. The aim of this study is to improve classification performance by reducing the effects of class imbalance and the presence of outliers in the dataset. SMOTE-LOF combines the SMOTE oversampling method with the Local Outlier Factor (LOF) to create a synthetic sample that also accounts for potential outliers. Meanwhile, Borderline-SMOTE identifies "borderline" samples in the minority class and then creates synthetic samples along the border between the majority and minority classes. In this study, experiments were conducted using classification algorithms such as Naïve Bayes, and Support Vector Machine on datasets that are imbalanced and contain outliers. The datasets used in this research include Pima Indians, Haberman, Glass, and Rainfall. This research scenario includes a comparison with previous research that has been done regarding SMOTE-LOF and Borderline-SMOTE on the Rainfall dataset. The results showed that on the three datasets, Borderline-SMOTE outperformed SMOTE-LOF on all three classifiers with an average accuracy of 4-6%, precision of 2-4%, recall of 5-10%, and F1 score of 5-6%. When the technique was applied to the Rainfall dataset, the results showed a 10-25% increase in accuracy. The outcomes consistently demonstrate that, when applied to the Pima Indians, Haberman, and Glass datasets, Borderline-SMOTE improves the performance of several classification algorithms. Better accuracy, precision, recall, and F1 score are evidence of this when compared to the application of the SMOTE-LOF technique.
An Intention-Based Multi-Modal Trajectory Prediction Framework for Overtaking Maneuver

Understanding the intention of other traffic participants and anticipating their future trajectories can help autonomous vehicle make decisions in advance. The proposed intention-based multi-modal trajectory prediction framework (IMTPF) aims to predict the complex overtaking trajectories of human-driven rear vehicle on highways to assist autonomous vehicles in decision-making. The framework uses a modified LSTM encoder-decoder and a novel social pooling operation to model spatial interactions. Nine motions are defined to show the multi-modal nature of rear vehicle during overtaking, and Time-to-Collision (TTC) is used to measure the risk. The trajectory prediction performance is analyzed based on capturing the rear vehicle's overtaking intention. The experimental results demonstrate that the proposed method outperforms other trajectory prediction methods.
Length of Stay Prediction for Diabetes Mellitus Patients on Indonesia Health Insurance Data using Logistic Regression and Process Mining

Diabetes Mellitus has a substantial impact on Indonesians, affecting 88.51% of those aged 44-94, as revealed by the Indonesia Health Insurance program. The urgency of this health concern is underscored by the International Diabetes Federations alarming statistic: one person succumbs to Diabetes Mellitus every five seconds, demanding immediate attention to diagnosis and risk reduction. This study focuses on predicting the Length of Stay (LOS) for Diabetes Mellitus patients, utilizing Logistic Regression and various machine learning models, particularly Random Forest. Concurrently, Process mining, encompassing Process Discovery and Conformance Checking, elevates model quality. Key factors influencing patient LOS, including the healthcare facilitys location, patient age, and arrival time, are unveiled. Logistic Regression exhibits commendable accuracy at 0.76 with rapid prediction times of 0.00082 seconds, while Random Forest excels with an impressive accuracy of 0.88 and a prediction time of 0.07 seconds. Specific medical procedures, such as those involving 'Diabetes and mild Nutritional/Metabolic Disorders,' reveal prolonged sojourn times, averaging 41.2 days. Best conformance-checking results emerge with a dependency threshold of 0.6 and a loop-two threshold of 32, yielding a Precision of 0.97, Trace Fitness of 0.66, and Generalization of 0.60. The finding of this study underscores the pivotal role of model selection and class distribution in LOS prediction. Despite Random Forest superior performance, Logistic Regression demonstrates notable accuracy and efficiency. Analysis of sojourn times underscores the importance of efficient healthcare services, particularly in cases involving 'Diabetes and Mild Nutritional/Metabolic Disorders. ' Adjusting threshold values significantly impacts model accuracy, with lower threshold values potentially leading to overfitting.
Semantic Segmentation of Railway Infrastructure Based on Virtual Model Synthetic Data

The application of digitalization in railway infrastructure is steadily increasing, and it accelerates the process of automating the asset management and inspection process of railway infrastructure. In this study, a point cloud segmentation method for railway infrastructure is proposed based on virtual model synthetic data and improved dynamic graph convolutional network (DGCNN). First, virtual data of railway infrastructure is created and inserted into real data for blending and augmentation, which is used to train point cloud segmentation neural networks. Acceptable segmentation results are achieved by using improved dynamic graph convolutional neural networks to train the augmented data and using the real data for point cloud segmentation. It is shown that the dataset augmented with virtual data achieves a 3.38% improvement in the accuracy of the final point cloud segmentation over that without augmentation, and the segmentation accuracy using the improved DGCNN network improves by 2.97% over that of the DGCNN network without improvement. This work could effectively improve the effect of 3D point cloud segmentation model to achieve accurate and efficient digitization of railway infrastructure.
Power Theft Detection in Residential Distribution Grids using Dirichlet Process Mixture Modelling

In developing nations like India, distribution companies annually face a loss of billions of dollars due to electricity theft. Conventional manual inspection of power theft can be very time-consuming and ineffective. Hence, advanced methods need to be developed to accurately detect electricity theft. The technique proposed in this work is based upon irregular-scenario classification, feature extraction and clustering to identify suspicious customers. The proposed methodology makes use of Bayesian Non-Parametric Dirichlet Process Mixture Modelling (BNP-DPMM) which is an advanced clustering algorithm and eliminates the problem of having a fixed number of clusters. A rigorous analysis is carried out and the proposed technique successfully group irregular and regular customers in very distinct clusters. For verification, another analysis on actual Smart meter data is also carried out and the technique is able to again successfully categorize different types of customers in distinct clusters.
User-Centric Green Light Optimized Speed Advisory with Reinforcement Learning

We address Green Light Optimized Speed Advisory (GLOSA), an application in the field of Intelligent Transportation Systems (ITS) for improving traffic flow and reducing emissions in urban areas. The aim of this study is to improve GLOSA, both by including traffic condition information, more specifically queue length, into the calculation of an optimal speed as well as by applying Reinforcement Learning (RL). We incorporate rule-based classic GLOSA and RL-based GLOSA in a common comparable simulation environment. In doing so, performance is also examined considering action frequency in order to create a user-centric GLOSA system for settings of non-automated driving. Results show that incorporating queue information positively influences the performance of both, RL-agents and classic GLOSA systems. Both algorithms achieve the best results at the lowest investigated action frequency of an update every second. As the frequency decreases, the improvement compared to the baseline without any GLOSA diminishes. However, the decline is more pronounced for the RL-agent, so the classic GLOSA algorithm delivers better results on average when the action frequency reaches five seconds. We make the source code of this work available under: github.com/urbanAIthi/GLOSA_RL.
The Importance of Kinematic Configurations for Motion Control of Driving Simulators

This paper describes how the kinematic configuration of a driving simulator's motion system affects the rendered inertial motion. The specific force and rotational rate equations between the point where the motion is applied (Motion Reference Point (MRP)), and the point in which the driver perceives the motion (Cueing Reference Point (CRP)), are derived for three kinematic configurations: (i) a hexapod, (ii) a hexapod with an xy-drive and a yaw-drive below, and (iii) the same system as (ii), but with the yaw-drive on top. The rotational rate equations show that having a yaw-drive on top greatly complicates the motion control. Furthermore, simulation results show that, regardless of the yaw-drive location, the difference between MRP and CRP becomes noticeable for large yaw-drive excitations. For such driving simulators, the positional offset between MRP and CRP can therefore not be ignored, complicating the motion control.
A Thorough Probe into Enhancing Energy Harvesting via Piezo-Modules in Metrorail Stations

In the current global focus on sustainable and eco-friendly energy sources, energy harvesting has emerged as a significant area of interest. This research provides a detailed explanation of the design process for an energy harvesting prototype with piezo electric modules and the power conditioning circuit employed to optimize energy capture. The study primarily aims to explore efficient energy generation and capture methods. The electric energy generated by these modules through direct strain or ambient vibration is influenced by factors such as the number of piezoelectric transducers, the harvesting circuit, applied load, and arrangement scheme. The design and testing of the model were intended for scalability and increased power generation. This research assesses the effectiveness of various piezoelectric module connections for power generation in the 17-station of MRT line-6 project of Bangladesh. It comprehensively explores the performance of piezoelectric modules in different configurations like series, parallel, and series-parallel combined. Results show significant potential in these combinations, producing impressive power outputs under an average 60kg pressure: 0.1674mW, 0.242mW, and 0.2223mW for series, parallel, and series-parallel setups, respectively. Rigorous data analysis leads to the clear conclusion of outstanding performance. These findings are crucial for understanding the use of piezoelectric modules in generating power for transportation systems and suggest opportunities for further research in this domain.
Adversarial Generation of Safety-Critical Lane-Change Scenarios for Autonomous Vehicles

Autonomous vehicles must undergo a comprehensive performance evaluation before being deployed in real-world traffic, and testing in safety-critical scenarios is essential for identifying scenarios that autonomous vehicles cannot handle. Given the rarity of safety-critical scenarios, it is necessary to investigate how to systematically generate these scenarios. In this paper, we propose an adversarial method to efficiently generate safety-critical scenarios through deep reinforcement learning. We first formulate the typical lane-change scenarios based on the Markov Decision Process and then train the background vehicles to aggressively interfere with the autonomous vehicle under test and create some risky situations. We also propose a reasonableness reward to avoid the extreme adversarial behavior of the background vehicles, making the scenarios reasonable and informative for the autonomous vehicle testing. Simulation results show that the generated scenarios are more critical in terms of safety than those in the naturalistic environment, significantly degrading the performance of the vehicle under test and providing a basis for improving the model.
Coded Aperture compressive high dynamic range modulation for laser communication

Laser communication technology plays an important role in the aerospace application, such as deep-space exploration relay communication, satellite-to-ground quantum communication, gravitational wave measurement and so on. All of those are based on the establishment of satellite-to-ground high-speed laser communication link, which needs to maintain high speed, high precision and high sensitivity. Also that is an enormous challenge in scenarios like high dynamic range, low signal-to-noise ratio(SNR), high-speed movement, high-precision tracking, etc. This paper proposes a method of dynamic range compressive modulation with coded aperture to improve the accuracy, rate and sensitivity of signal acquisition in high-speed laser communication links. Furthermore, the method can improve effectively the performance of Acquisition, Tracking and Pointing (ATP). Finally, the proposed method was verified by the simulation experiment with well-designed region of interest(ROI) modulation matrix, which provides the more opportunity and possibility for its wide application.