Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Tracking of Articulated Vehicles in Self-Driving Applications Using Phantom Observations

One of the critical challenges in a self-driving system addressed by the perception module is the tracking of actors in the vicinity of an autonomous vehicle. This is a difficult and complex task, that entails inferring higher-order states of the actors (such as velocity, acceleration, heading, and so on) as well as their tracking through time. The task is particularly challenging in the case of vehicles that have a non-rigid shape, such as articulated vehicles where two or more rigid parts are connected by hitch points. In addition, if we consider a highway case where a large number of such vehicles can be present, the task becomes even more demanding as all surrounding actors need to be tracked very efficiently in real-time. While there exist accurate vehicle kinematic models that account for articulation, applying them directly to tracking of a large number of articulated vehicles can be difficult due to the complexity and high latency cost of such models when used in the update step of the tracker. We focus on this problem and propose an approach that allows for the accurate tracking of articulated vehicles in a very efficient manner. In particular, we achieve this by tracking each rigid part independently and applying tracker updates using both actual observations as well as articulated phantom observations. These phantom observations are computed by considering the articulated kinematic model and the state of the neighboring connected part, which allows for efficient and kinematically consistent update steps. We evaluated the proposed approach on large-scale real-world data collected on highways in Texas, and compared it to a number of state-of-the-art baselines. The results strongly indicate the benefits of using phantom updates for accurate and efficient tracking of articulated vehicles.
Entity Linking Supported Multimodal Data: Fusing Text and Image features for Higher Accuracy

To address the problem of the low accuracy of traditional text-only data entity linking methods, this paper proposes a new multimodal entity-linking model that leverages the richness and complementarity of multi-modal information by effectively integrating text and image characteristics to enhance the accuracy of entity linking. The proposed method uses Bert model and the CNN-RNN model to stratify the image and text characteristics that contain the references, respectively; then the mechanism of co-attention and the method of gate fusion are imported to learn the correlation between text and images automatically; and the weight and importance of the characteristics are adjusted to achieve accurate alignment and interaction between the text and the images. Finally, the cosine similarity are used to measure the similarities between the candidate entities and mentions. Furthermore, experimental research is proposed with the RMEL and WMEL Multimodal Entity Linking Dataset.The results show that the proposed method outperforms other entity-linked models.
Ion-Selective Analysis of Water Quality in the contexts of Plant Production, Biological Life Support Systems and Space Exploration

ION-SELECTIVE ANALYSIS OF WATER QUALITY IN THE CONTEXTS OF PLANT PRODUCTION, BIOLOGICAL LIFE SUPPORT SYSTEMS AND SPACE EXPLORATION Cody G. Thompson University of Guelph, 2015 Advisor: Professor M.A. Dixon The present work explores the use of ion-selective sensors for nutrient sensing in waters relevant to plant production, biological life support systems (BLSS) and space exploration. Ion-selective detection capabilities are essential in managing environments and life support systems for humans on long-duration space missions. HPLC remains the most effective method for ion-selective analysis of water samples however the technology is large, expensive, relatively difficult to operate and requires consumable materials, making it unfavourable for use in space. Single ion-selective sensors are commercially available such as ion-selective electrodes or ionselective field effect transistors and prototype technologies are emerging such as ion-selective optrodes. Ion-selective optrodes were selected to develop an online ion-detection system for K, Ca, Na and NO− 3 , using optrode sensors obtained from collaborators at the Canadian Space Agency and the National Optics Institute. They were chosen based on their small mass, compact design, simple operation, and insensitivity to electric-field interference. During characterization trials, several issues were identified using optrodes in nutrient solutions: material selection of system components is critical as contamination influences optical properties of the sensors; fabrication practices are critical and optrodes mechanically coated in a clean environment perform best; biofouling quickly impairs optrode performance; and light exposure degrades optrode dyes. Based on these results a prototype Ion-Selective Optrode System (ISOS) was developed. The ISOS was tested in the context of robotic-science exploration of Saturn’s moon Titan on the remote lake Laguna Negra, Chile. The ISOS successfully detected Ca and Na ions in surface water samples. Light-plant-nutrient interactions were also studied and the dietary habits of Lactuca sativa cv. New Red Fire were monitored in response to blue (440 nm) and red (660 nm) light environments. Results indicate light quality influences the uptake of certain nutrients. Plants under blue light consumed more nitrogen. The uptake of certain metal ions (Zn and Fe) also appear favoured by blue or red light treatments repsectively. These results have significant implications for recirculating nutrient systems inherent in BLSS. Dedicated to my parents, the continuous love and support of whom, give my words meaning.
With Fused Point Cloud Height and Intensity Information improved the Loop Closure Detection for LeGO-LOAM

Simultaneous Localization and Mapping (SLAM) is primarily divided into two components: the front-end odometry and the back-end optimization. During the measurement process, errors in feature extraction and noise effects lead to an accumulation of discrepancies between the estimated position by the front-end odometry and the actual position over time. Therefore, loop closure detection is introduced for back-end optimization. It corrects accumulated errors by identifying previously visited landmarks or positions in the scene. This paper builds upon the LeGO-LOAM-SC SLAM algorithm, which combines scan context and lightweight ground optimization LiDAR Odometry and Mapping (LeGO-LOAM). We integrate point cloud intensity information into the global descriptor to mitigate feature loss and consequent misalignment caused by the use of a single descriptor. Loop closure detection is performed using the fused global descriptor. When a loop frame is detected, the relative pose between the current frame and the history is added as a constraint to GTSAM for graphical optimization and point cloud map construction. Experiments conducted on the publicly available KITTI dataset’s sequence 05 demonstrate that the proposed enhanced algorithm outperforms existing methods in terms of performance.
Foresighted Decisions for Inter-Vehicle Interactions: An Offline Reinforcement Learning Approach

Adaptive decision-making capability is essential for autonomous driving systems that require interaction with other drivers and is concerned with safeness and efficiency. Although several attempts have taken, e.g., model-based online reinforcement learning, the online learning process is impractical, and it is challenging to build a model reflecting a multi-agent environment. This study aims to create an autonomous driving agent who can make adaptive decisions using foresighted future information among other drivers. To endow this functionality into the agent, we introduce episodic future thinking (EFT) to a partially observable Markov decision process (POMDP) and mathematically define it. We adopt the offline learning paradigm, enabling an agent to leverage foresighted information preprocessed from the previously collected dataset in a training phase. Consequently, we benchmark the state-of-the-art offline reinforcement learning algorithm and compare the performance between vanilla and EFT-based reinforcement learning.
Effect of Substrate Resistivity on Spiral Inductor in Radio Frequency Bands

SOI(Silicon-on-Insulator)technology, considering its development and benefits compared to silicon Bulk technology, is considered very promising to meet the increased demands of the wireless telecommunications market in terms of low cost, CMOS compatibility, low substrate losses, and low power consumption system-on-chip manufacturing capability. Thus, best-in-class Si-based substrates, additionally high performance RF components are in high demand. The porous silicon (PSi) substrate holds great promise for emerging wireless communication integrated circuits demanding the elimination significant attenuation at high frequencies in the bulk silicon substrate. It is within this context that, the work aims to demonstrate the applicability of the porous silicon substrate for the design of RF circuits operating in radio frequency band. An inductor is integrated, and a comparison between the performances of this RF band inductor in terms of S-parameters (insertion losses) and of quality factor on the substrates studied is developed.
Air pollution detection and Monitoring with the smart sensor interface on the IoT platform

Air pollution has devastating impacts on the well-being of people, creatures, and ecosystems and the stability of the environment. The proportion of contaminants and strange circumstances in an area may be precisely monitored by an air pollution monitoring system, which has been recommended to mitigate and control air pollution. Furthermore, numerous IoT-related technologies have been created to analyze and monitor different metrics of air quality to lessen air pollution's negative effects. This Internet of Things (IoT)-based approach aims to track pollution in various locations, focusing on urban areas, utilizing a network of interconnected sensors. It's proposed that different stations be located in other parts of cities. Stations update the IoT cloud with data regularly. A smart sensor is a device that can collect data from its surrounding environment and analyze it using onboard computing capabilities after detecting a particular input. This article suggests an Internet of Things (IoT)-enabled smart sensor Interface (IoT-SSI) system for monitoring the air quality in densely populated places like shopping complexes. The sensors like MQ-2, MQ-4, MQ-5, MQ-6, MQ-7, MQ-8, and MQ-135, Temperature, and Humidity sensors are embedded to monitor the air quality. The platform's sensor networks use advanced processing utilizing multiple-input-single-output neural networks to get gas concentration values that account for temperature and humidity. The realtime readings from the sensor fusion model give a vast amount of data collecting and digitally providing services on these capabilities. In addition, it looks at various scenarios where IoT air quality monitoring technologies were tested and successfully passed in shopping complexes.
Image rain removal algorithm based on conditional diffusion model

Rainfall image restoration is one of the research hotspots in the field of computer vision. For raindrops attached to the camera lens or glass, it will hinder the visualization of the complete image and also lead to image degradation. Most of the existing methods for converting raindrop-degraded images into clean images use generative adversarial neural networks (GANs) to solve this problem. At present, the denoising probability diffusion model has been widely used in various fields. Therefore, this paper proposes a raindrop image restoration algorithm based on conditional diffusion model, which improves the U-Net network used for noise prediction in diffusion model. The model is verified and evaluated on the raindrop removal dataset, and has strong generalization ability for raindrop image restoration in the real world.
An Efficiency Framework for Task Allocation Based on Reinforcement Learning

In response to the challenges of task allocation for Unmanned Aerial Vehicles (UAVs) in large-scale scenarios, this paper proposes an improved task allocation method based on Particle Swarm Optimization (PSO) algorithm and reinforcement learning, named PSO-DQN. We first design an innovational clustering method based on the K-Means algorithm, which combines the target importance with distance to reduce the computation complexity. The PSO-DQN method we formulated serves to allocate targets from UAV swarms to specific target clusters while simultaneously mapping individual targets within a cluster to UAVs. The experimental results demonstrate that the superior task allocation accuracy of the PSO-DQN method when compared to traditional approaches. The framework we proposed also proves that it can significantly reduce the complexity of computation of task allocation for UAV swarms in large-scale scenarios.
Application of Mobile Health in Mental Health Interventions

Mobile health services may effectively mitigate the obstacles to mental health treatment due to their ability to meet people where they are and provide instant assistance. M-Health allows for modifying and facilitating psychological support for well-being when the likelihood of success is greatest. The potential translation advantages for boosting mental health must be acknowledged. Yet, insufficient delivery of mental health technologies in the present world shows that novel means of increasing the accessibility to healthcare measures are necessary. Exploratory methods like randomized trials are used to improve and modify m-health services. Evidence gathered from this method may be used to develop and improve m-health programs. The data will be utilized to establish the specific locations and types of m-health operations required to integrate accurate therapy into ordinary practice. This study proposes and discusses Mobile Mental Health Interventions (MMHI), which consider issues during an MRI and provide a manual for psychiatric intervention developers. In comparison to existing methods, the suggested MMHI performs well in simulation
Proposing an External Rotor In-Hub PM Assisted Synchronous Reluctance Motor for an E-Bike

In recent years, permanent magnet (PM) motors have commercialized light electric vehicles. Due to the high cost of rare-earth PMs and environmental side effects, the structures without or with the low number of PMs that maintain the application constraints can be a good choice. The synchronous reluctance motors (SynRM) can be a proper candidate for this application. Choosing a good value for the number of flux barriers and their end position can reduce the torque ripple. To, assist the synchronous reluctance motor in improving its capabilities, especially torque density and power factor, low number of PMs can be inset in the flux barriers. This paper proposes designing and optimizing an external rotor permanent magnet in-wheel PM-assisted SynRM (PMaSynRM) for an electric bicycle application. The rotor two-barriers and three-barriers structures with different PMs, i.e., rare-earth and Ferrite and without PM (SynRM) are comprehensively investigated by finite element analysis (FEA) in both skewed and non-skewed stator in terms of average torque and torque ripple. Finally, the design concepts are validated with a prototype machine.
A comparative study on the application of Convolutional Neural Networks for wooden panel defect detection

Deep learning operates on wide artificial intelligence applications and services, that boost automation, performing analytical and physical tasks without human intervention. The research explores four popular Convolutional Neural Network (CNN) architectures, namely MobileNet, MobileNetV2, ResNet-50 and VGG-19, and compares their performance in detecting defects in wooden panels. The study uses a dataset of images of wooden panels containing various types of defects, such as knots, cracks, and holes. The images are preprocessed, and the CNN models are trained and tested using various metrics, including Accuracy, F1-score, Precision (P), and Recall (R). The results show that all the CNN architectures can effectively detect wooden panel defects that performs in achieving higher Accuracy, F1-score, Precision, and Recall. The findings of this study provide insights into the potential application of CNNs for wooden panel defect detection, which can help improve the quality control process in the wood industry. This study presents a comparative analysis of the application of CNN for detecting defects in wooden panels.
GATFormer: A Graph-based Transformer for Long-Term Forecasting of Traffic Overcrowding

Urban traffic forecasting is a critical issue in modern cities. In recent years, there has been a growing interest in using data from automated fare collection (AFC) systems to analyze passenger movement patterns and identify and predict travel behaviors. Urban transportation networks can be optimised using this analysis and by implementing machine learning algorithms. Accurately forecasting traffic flows (e.g., for identifying congested stations) is important for enhancing passenger satisfaction and safety. However, most existing methods analyze only station-level data for short-term flow forecasting, failing to consider the complex interconnected relations across the transportation network and the impact of long-term trends. In this paper, we propose a novel approach, GATFormer, that combines Graph Attention Networks (GAT) with a sequence-to-sequence attention mechanism to predict long-term overcrowding at traffic nodes (e.g., congestion at stations) and providing information to both transport network managers for policy decision making and to passengers for traffic guidance. Our method includes two parts: anticipation of both where and when overcrowding will take place. The proposed method is applied to real subway AFC data from both Suzhou and Hangzhou, China. The experimental results show that the model outperforms other baselines in long-term overcrowded station prediction.
Estimated State-Based Optimal Path Planning and Control System for Lane-Keeping of Semi-Trailer Trucks

In this paper, a novel lane-keeping path planner and steering wheel angle controller are proposed for semi-trailer trucks. These vehicles exhibit different dynamic charac-teristics compared to conventional passenger vehicles due to the presence of an articulation angle and variations in the weight of the trailer. To account for these unique characteristics, a hitch angle estimator is utilized to generate an optimal lane-keeping path. Furthermore, an optimal steering controller based on Linear Quadratic Regulator (LQR) is designed utilizing the dynamics model of the vehicle, and the estimated mass of each unit from the mass estimator is used to improve the control performance. The performance and robustness of the proposed lane keeping system is validated in simulations with trailer weight variations.
Advancements in CNN Architectures for Computer Vision: A Comprehensive Review

Over time, Convolutional Neural Networks (CNNs) have established themselves as robust and influential tool in a variety of computer vision tasks, including image classification and object detection. The comprehensive review paper presents an in-depth analysis and comparison of recent advancements in CNN architectures for computer vision applications by providing a thorough examination of 21 state-of-the-art CNN architectures, such as LeNet-5, AlexNet, ZFNet, VGGNet, NiN, GoogleNet (Inception v1), ResNet, DCGAN, Inception v2/v3, SqueezeNet, MobileNet v1, Xception, ResNeXt, DenseNet, ShuffleNet v1, SENet, Inception v4, MobileNet v2, ShuffleNet v2, MobileNet v3, and GhostNet. Each architecture is critically evaluated based on its design principles, architectural components, suitability for various computer vision tasks, applications where these architectures have shown exceptional performance and discussions of their advantages and limitations. This review aims to provide guidance to both researchers and practitioners in the selection of the most suitable architecture for addressing specific computer vision challenges. The insights gleaned from this analysis can contribute to the ongoing research and development of CNN architectures, propelling the advancement of computer vision systems and encouraging the creation of systems that are not only more robust but also more efficient.
Imbalance Label Enhancement with Adaptive Neighbors

Label distribution learning (LDL) is gaining popularity due to its more generalized capacity to deal with label ambiguity. The cost of collecting label distribution information for data, as opposed to logical labels, is significantly higher. Thus, it is suggested to apply label enhancement to extract label distributions from logical labels. In this paper, we propose a novel label enhancement method by using adaptive neighbors. Based on the local connectivity, we first assign adaptive neighbors to each instance, then the smoothness constraint assigns similar label distributions to similar instances. Second, we solve the class-imbalance problem by assigning weights to instances. Finally, comparison experiments on 13 datasets show that our method fits the ground-truth label distributions better.
A walking assistance robot based on bionic traction characteristics

With the continuous development of social modernization, the research of various types of mobility aids is in full swing. However, most of the assistive devices on the market at present have the characteristics of single function, weak adaptability and low reliability, which makes it impossible for people to establish a trusting relationship with assistive devices and break through psychological barriers when traveling. The wearable walker uses the principles of ergonomics, kinematics, dynamics, bionics and other principles to design a walker for use in families and communities to help the elderly with lower limb movement and walking dysfunction caused by neurological diseases. Our research interests mainly include assisted exoskeletons, rehabilitation assistive devices, human softening belt design, fall protection system, rehabilitation evaluation system, and provide a complete rehabilitation diagnosis and treatment plan to help the elderly help rehabilitate walkers and gradually recover their walking ability. The implementation of the project involves computer vision, natural language processing, and multi-module collaborative analysis, which will help drive further research and development of walk-assist robots.
Routing Protocols in VANET, Study and Evaluation

Vehicular Ad Hoc Network (VANET) is a significant area of research in data applications. Its primary goal is to enhance traffic efficiency and address various challenges while offering improved services to customers. One notable advantage of VANET is its potential to alleviate traffic congestion and enhance road safety by minimizing accidents. However, as with any wireless network lacking infrastructure, the routing task becomes particularly challenging, particularly when dealing with mobile nodes. The mobility of vehicles in VANET leads to rapid changes in network topology, resulting in intermittent connections between source and destination nodes. This paper aims to assess the performance of different routing protocols by analyzing them and proposing various VANET routing protocol scenarios. The simulations conducted using NS3 simulator and SUMO demonstrate the significant impact of routing protocols on VANET network performance.