Miracle-dz/UniFAR-FTUs huggingface dataset:
A Game Theoretic Approach for Safe and Distributed Control of Unmanned Aerial Vehicles

This paper presents a distributed methodology to produce collision-free control laws for an Unmanned Aerial Vehicle (UAV) fleet. We use a game theoretic framework, where UAVs accommodate for individual and fleet goals, while respecting safety requirements. The method combines Control Barrier Functions (CBFs) and a primal-dual algorithm for Nash equilibrium (NE) seeking in generalized games. Feedback is introduced by Model Predictive Control (MPC) and we analyze its stability properties. The combination of these tools allows for a distributed, collision-free pointwise equilibrium solution, despite the agents' coupling, due to common target tracking and the collision avoidance constraints. Our algorithmic results are supported theoretically and our method's efficacy is demonstrated via extensive numerical simulations.
Neuroevosp Arse: A Biologically Plausible Framework for Efficient

SNNs have shown great potential in terms of power efficiency and event-driven processing during inference. To fully utilize their low power consumption and further improve their efficiency, researchers have explored pruning methods to create sparse SNNs without redundant connections after training. However, parameter redundancy still poses a challenge for SNNs during training. Inspired by the dynamic rewiring process observed in the human brain, we propose an efficient NeuroEvoSparse framework for SNNs, called NeuroEvoSparse-SNNs, to enable sparse SNN training from the beginning. In this framework, the pruning and regeneration of synaptic connections in SNNs evolve dynamically during learning, while maintaining a specific level of structural sparsity. This allows NeuroEvoSparse-SNNs to explore all potential parameters over time and find the optimal sparse connectivity. In summary, our proposed NeuroEvoSparse framework is a promising approach for training sparse SNNs from scratch. By incorporating biologically plausible evolutionary mechanisms and addressing the gap in expressiveness between sparse and dense training, this framework effectively learns SNNs with sparse structures while minimizing accuracy loss. This work has significant implications for lightweight training and inference of SNNs, which can lead to low power consumption and minimal memory usage in various applications.
Studying the Travel Direction Patterns in Urban Mobility: A Case Study of Taxis

Travel direction is important information in urban mobility and reflects the trend of urban travel. Understanding travel direction patterns in urban mobility is crucial for urban planning, transportation management, and resource allocation. Through analyzing the travel direction distribution and direction entropy of three taxi trip datasets from Chengdu, New York City and Beijing, this research examines the relationship between travel behavior and city structure by analyzing travel direction patterns in urban mobility. The analysis reveals that travel direction distributions are influenced by city morphology, indicating a connection with urban shape and temporal factors. Moreover, spatial heterogeneity in travel direction entropy is across different city regions, highlighting varying levels of mobility chaos. Additionally, the analysis reveals temporal variations in travel direction entropy during weekdays, and weekends, reflecting temporal regularities in mobility. These findings contribute to a better understanding of population movement dynamics and inform urban planning and transportation management strategies.
Reinforcement Learning-based Operational Decision-Making in the Process Industry Using Multi-View Data

Owing to the frequent fluctuations encountered in raw material characteristics and operational conditions in the process industry, traditional data-driven approaches prove inadequate in adapting the adjustment of operational variables. Furthermore, the potential of multi-view data, including images, audio, and sensor data, remains underexploited in industrial processes. This study proposes an operational decision-making method based on feedstock-guided multi-view actor-critic (FMAC-ODM) using multi-view data to address these issues. This method utilizes the idea of reinforcement learning (RL) for enhanced decision-making. First, the problem of optimizing operational variables is reformulated into a continuous RL problem to acquire an improved decision-making policy aligned with the current operational conditions. Subsequently, the inclusion of feedstock properties in the state space is implemented to provide essential guidance for the decision-making process. Finally, in pursuit of a comprehensive understanding and bolstering the precision of the decision-making strategy, multi-view data sourced from the industrial site is harnessed as a surrogate for human observation. The effectiveness of the proposed decision-making method is substantiated through its practical application in the industrial flotation process.
Effect of Reaction Time on Epoxidation Efficiency of Waste Cooking Oil

Finding novel sources of primal matter for use in the synthesis of biopolymer groups has become of utmost importance in recent years. Currently, waste materials with a high reuse possibility are being sought after in accordance with green chemistry and a circular economy. The valorization of waste cooking oil into valuable products has gained considerable attention as a sustainable approach. One of the most routes to obtain a bio-sourced co-polymer is the epoxidation, which is known for its long reaction time. The main aspect of our study is to investigate how varying the reaction time affects the characteristics of the obtained product, namely epoxidized waste cooking oil (EWCO) and to explore if is it possible to obtain a good oxirane number with a short reaction time. Based on the results of this study, the optimal conditions were the ratio: 1: 2: 2.4; double bonds: $\mathrm{H}_{2}\mathrm{O}_{2}$: formic acid at a temperature of $6 5^{\circ}\mathrm{C}$; and 2 hours as an optimum reaction time which results to a maximum oxirane oxygen of 6.42%.
Data Privacy and Confidentiality in Healthcare Applications of IoT-Enabled Wireless Sensor Networks

In this paper, a comprehensive framework for protecting sensitive healthcare information in Internet of Things (IoT)-enabled Wireless Sensor Network (WSN) is presented. Sensitive medical data is protected by the proposed architecture's usage of secure data transmission protocols, encryption, access control, and authentication. It also looks at how the General Data Protection Regulation (GDPR) and other data privacy regulations might affect IoT-enabled healthcare infrastructure. The study results indicate a need for more privacy and security education and implementation throughout the healthcare, technology, government, and user communities. The need of teaching healthcare professionals and patients about the dangers of sharing personal information online and the importance of managing data ethically is discussed. New loopholes and dangers can only be patched if security is regularly assessed, audited, and improved. This research paper sheds light on the problems of privacy and confidentiality in WSN healthcare applications enabled by the Internet of Things. The effort aims to safeguard and defend healthcare IoT adoption and enhance patient care by offering a complete framework that emphasizes regulatory compliance and appropriate data management.
Current Balancing of Interleaved Boost PFC Converter With Auxiliary Winding Coupled Inductor

This paper proposes the current imbalance detection and balancing strategy for the interleaved boost power factor correction (PFC) converter with an auxiliary winding coupled inductor (AWCI). To detect the current imbalance, the auxiliary winding is implemented to the coupled inductor. Then, the current balancing is achieved by using a voltage information of the auxiliary winding. Compared to the traditional current balancing schemes for interleaved converters, the proposed strategy has advantages of simple and intuitive because the complicated phase current reconstruction process is not required. Theoretical evaluation and controller design procedure of the proposed balancing method are analyzed. To validate the proposed method, the 4-kW boost PFC converter prototype has been built and tested. Both simulation and experimental results verify the theoretical analyses of the proposed method and the stability of the current balancing controller design procedure. The experimental results confirm the effectiveness of the proposed balancing strategy in practical current imbalance conditions.
Phenology-based crop classification from multi-frequency dual-pol SAR data utilizing Gaussian Processes

This paper proposes a novel multivariate Gaussian Process Regression (GPR) approach for multi-class crop classification. Backscatter information from E-SAR L- and C-band dual-polarimetric data acquired during the AGRISAR 2006 campaign were used to train and validate the proposed Gaussian Process Classifier (GPC) model. The model’s accuracy was assessed separately for mono-temporal and multi-temporal data for both frequencies. The mono-temporal analysis reflected the inter-crop misclassifications because of high crop phenology dynamics. The overall multi-temporal accuracy of the proposed model for both L- and C-band was 96% with Kappa Score (κ) = 0.95. Finally, the optimized classifier model was utilized to generate the classification maps of the major crop types.
Demonstration of Staggered Ambiguous SAR Mode for Ship Monitoring Using TerraSAR-X

Maritime surveillance employing synthetic aperture radar (SAR) necessitates the simultaneous achievement of a wide swath and high resolution, enabling effective monitoring of vast areas with enhanced detection capabilities. However, conventional SAR modes encounter limitations as widening the swath compromises azimuth resolution. Ambiguous SAR modes, such as the staggered ambiguous mode utilizing continuous variation of short pulse repetition intervals and low pulse repetition frequency mode, surmount this constraint by enabling the imaging of a wide swath with high resolution. Both modes ensure remarkable detection performance even for small ships without relying on digital beamforming or multiple receive apertures. This paper presents the demonstration of the staggered ambiguous mode through experimental data acquired by the TerraSAR-X satellite over the North Sea. Despite technical limitations inherent in the SAR instrument, a swath of 110 km was successfully imaged with an azimuth resolution of 2.2 m. A probability of detection greater than 0.8 was achieved for small ships measuring 21 m × 6 m. Range ambiguities of the ships were identified based on their characteristics and position, and subsequently rejected. The detected ships were validated with maritime positioning data obtained from their automatic identification system. These findings provide compelling evidence and rationale for adopting the ambiguous SAR modes in current and future SAR systems and missions.
MBC-UBEM-IDS: A Two-Step Intrusion Detection System for Imbalanced Network Traffic

Intrusion Detection System is a type of tool capable of effectively identifying malicious network traffic. However, existing intrusion detection methods face the challenge of class imbalance, making it difficult to correctly classify the minority class. To address this issue, a two-step intrusion detection system is proposed, employing a multi-branch convolutional neural network in the first step and combining undersampling algorithm with One-Versus-Others strategy in the second step. The performance of the proposed method has been evaluated on the NSL-KDD dataset, demonstrating superior performance compared to other methods.
Assessing the Impact of Non-Compliant Users Response to System-Optimal Dynamic Traffic Assignment

In the present work, we address a pseudo - System Optimum Dynamic Traffic Assignment optimization problem on road networks relying on trajectory control over a portion of the flows and limited knowledge on user response. The fractions of controlled flow moving between each origin-destination couple are defined as “compliant”, while the remaining portions, consisting of users free to make their own individual choices, are defined as “non-compliant”. The objective is to globally improve the state of the network by controlling a varying sub-set of compliant traffic flows. A Godunov discretization of the Lighthill-Williams-Richards model coupled with a triangular fundamental diagram is employed as the flow dynamics model. At junctions, a multi-class solver is applied which requires a class-density-weighted aggregate distribution matrix and incoming links priorities. On one hand, the selfish response of non-compliant users to changing traffic conditions is computed at each time step by updating the class related turn ratios accordingly to a discrete-choice multinomial Logit model to represent users imperfect information. On the other hand, the control action is actuated by varying the flow rates over a pre-computed set of routes while the coupled optimization problem takes into account an a priori fixed distribution of users at the nodes. We show how the effectiveness of the resulting finite horizon optimal control problem degrades by not considering the dynamic response of non-compliant users and how it varies according to the fraction of compliant ones.
Orbital Lymphoproliferative Disorder Diagnosis with Incomplete Multimodal Images based on Self-/Cross-Representation and Hypergraph Ensemble

Orbital lymphoproliferative disorders (OLPDs) are complex orbital mass-like lesions ranging from benign to malignant. Precise preoperative diagnosis of OLPDs holds profound importance in facilitating timely and effective patient management. Recent studies have shown that exploiting multimodal images can boost the performance in identifying different orbital lesions. However, one or several imaging modalities are sometimes missing in practical applications, which has not yet been properly addressed in existing studies. To this end, we propose a novel OLPD diagnostic method with incomplete multimodal images based on self-/cross-representation and hypergraph ensemble. Specifically, in the first stage, we develop a self-representation network to extract unimodal features and a cross-representation network to impute missing features. In the second stage, by using unimodal features as input, we construct a hypergraph for each modality to make unimodal diagnosis; while for multimodal diagnosis we conduct a multi-view grouping fusion method to reduce the semantic gap between multimodal features and fuse multiple unimodal hypergraphs as multimodal hypergraph to perform multimodal diagnosis. In the third stage, we propose an ensemble strategy that incorporates unimodal diagnosis and multimodal diagnosis to accomplish the final decision. Extensive experiments demonstrate that the proposed model outperforms the state-of-the-art approaches.
Neural Network Observer for Lateral Vehicle Model with Varying Sampled and Delayed Output

This research offers a neural network adaptive observer (NNAO) architecture for nonlinear lateral vehicle dynamics with variable sampled delayed output. A radial basis function (RBF) neural network is used to approximate the system's unknown part, and a new weight updating mechanism is provided. A closed-loop output predictor is used to offer inter-sample output estimate while dealing with variable samples, and a closed-loop integral compensation is used to deal with variable delay. The convergence of the proposed observer is proved using Lyapunov function and small gain arguments. Simulation tests confirm the NNAO's estimate algorithm's accuracy in estimating yaw rate, longitudinal speed, and particularly the excellent performance of the estimation of lateral speed.
Automatic Sleep Stage Classification by CNN-Transformer-LSTM using single-channel EEG signal

Sleep stage classification plays a crucial role in diagnosing sleep disorders and understanding sleep physiology. In recent years, automated models based on machine learning and deep learning have gained attention for sleep stage classification. This paper uses the single-channel EEG signal to present an automatic sleep stage classification system using a combination of Convolutional Neural Network (CNN), Transformer, and Long Short-Term Memory (LSTM) models. Experimental evaluation of the ISRUC sleep datasets S1 and S3 demonstrates the effectiveness of the proposed model. It achieves accuracies of 80.37% and 82.40%, respectively, achieving competitive performance compared to state-of-the-art models.
MOGA-Based Design and Control of a DC-DC Buck Converter with Voltage and Current Loops

This paper presents a comprehensive study on the design, stability, and control of a DC-DC buck converter, employing simulation analysis using MATLAB/SIMULINK. The control system encompasses dual loops, one for voltage and another for current regulation. Voltage and current control are achieved through the application of proportional-integral (PI) controllers. To optimize the performance of these controllers, a Multi-Objective Genetic Algorithm (MOGA) is employed to determine the optimal PI controller gains. Various objective functions for MOGA are rigorously tested to ascertain the most effective performance for the system. The simulation results corroborate the stability and efficiency of the DC-DC buck converter, validating its robust operation under various conditions.
Construction of a Traditional Chinese Medicine Dao Yin Science Knowledge Graph Based on Neo4j*

Construct a Knowledge Graph based on the characteristics of Dao Yin Science in Traditional Chinese Medicine to facilitate users in querying, learning, and disseminating the culture of health maintenance in Traditional Chinese Medicine, and to enhance their health literacy. Using the Bert-CRF model, with manual proofreading, we conducted named entity recognition on literature related to Dao Yin practice, established relationships between entities based on the characteristics of the subject and built a Knowledge Graph for Dao Yin Science using Neo4j. Incorporating 3152 relevant documents, the constructed knowledge graph contains 2262 entity nodes, 5108 relationship data, 7 types of entity attribute categories, and 7 types of relationships. The knowledge graph can be searched using the Cypher query language of Neo4j. This Knowledge Graph visualizes the knowledge of Dao Yin Science in Traditional Chinese Medicine and can be employed to build applications and web platforms for knowledge retrieval, intelligent question answering, and exercise recommendation. It holds practical value for developing related artificial intelligence devices and fostering innovation in the fields of computer science and automation. Moreover, it can be utilized for in-depth data mining and uncovering new research directions.
A Rhythm-Specific ECG Signal Quality Assessment Framework for Robust Cardiac Health Monitoring of AI-based Arrhythmia Classifer

Electrocardiogram (ECG) is an essential approach for monitoring cardiovascular diseases (CVDs). However, ECG signals obtained from wearable devices are often corrupted by noise and artifacts, particularly electrode movement (EM) artifacts that are hard to eliminate due to their similarity to ectopic heartbeats and result in significant false positive rates. Signal quality assessment (SQA) can mitigate this effect by identifying and filtering out noise-contaminated ECGs. Prior SQA studies relied on human defined signal quality standards and unsuitable for AI-based arrhythmia classifiers. Therefore, in this work, a rhythm-specific SQA framework for AI-based arrhythmia classifier is proposed and provides auxiliary reliability analysis of the classification results. Evaluated on the MIT-BIH and NSTB ECG datasets, it achieves 92.2%/98.5% noise detection sensitivity and specificity, and reduces up to 80.7%/55.4% false alarm rate for ‘SVEB’ and ‘VEB’ classification in noisy ECG signals. Cooperated with the AI-based arrhythmia classifier, this SQA method is suitable for application in wearable ECG devices and ensure robust cardiac health monitoring in daily life.
Optimal Linear Attack in Cyber-Physical Systems with Periodic Detection

Security issues are of significant importance for cyber-physical systems (CPS), where the attack design is a major concern. Most related studies on attack design implicitly consider that the control period and detection period are the same. However, the two periods could be different in practical systems with remote detection such as supervisory control and data acquisition (SCADA) systems, which could lead to new vulnerabilities for attackers. In this paper, we consider the design of innovation-based linear attack strategies for CPS when the control period and detection period are inconsistent. Specifically, we propose an attack framework that consists of attack strategies for detection and non-detection instants under the period discrepancy. On this basis, we design the optimal stealthy innovation-based linear attack strategies for state estimation and LQG control to maximize the estimation error or control cost, respectively. Simulations are given to demonstrate the effectiveness of the proposed attack strategies.
Minimally Invasive Subdermal Wireless EEG Sensor Arrays for Home Monitoring of Epilepsy

Currently, the principal diagnostic method for the large patient population, like those with epilepsy, is based on short term stays in a hospital. Therefore, it is increasingly recognized that for many patients, it can be of paramount importance to be able to record neural activity continuously over weeks and months since a clinically informative assessment could vastly improve clinical diagnostics and patient care. We propose a scalable approach for a multielectrode, minimally invasive wireless EEG device for long term neurological event monitoring in home and mobile use. At the core of the proposed technology is a monolithic wireless sensor, a millimeter-wide, less than quarter millimeter thick multielectrode polymer strip, several of which can be inserted in a doctor’s office into subdermal space using a syringe type of carrier for recording brain signals over large areas. A tiny radio chip is integrated in the strip which is externally powered and transmits EEG signals through the scalp to an external receiver. In a first step, we demonstrate the capability of the 500 µm × 800 µm four-channel custom ASIC to record gamma band EEG signals in saline when integrated onto the multielectrode strip.
MGDTI: Graph Transformer with Meta-Learning for Drug-Target Interaction Prediction

Drug-target interaction (DTI) prediction is of great importance for drug discovery and development. With the rapid development of biological and chemical technologies, computational methods for DTI prediction are becoming a promising strategy. However, there are few methods which explore solving the cold-start problem in DTI prediction scenarios due to most of existing methods require modeling under the existing interaction that can’t effectively capture information from new drugs and new targets which have few interactions in existing literature. In this paper, we propose a graph transformer method based on meta-learning named MGDTI to fill the gap. In particular, we employ drug-drug similarity and target-target similarity as additional information for network to mitigate the scarcity of interactions. Besides, we trained our model via meta-learning to be adaptive to cold-start tasks. Moreover, we introduced graph transformer to prevent over-smoothing by capturing long-range dependencies. Comparison results on the benchmark dataset demonstrate that our proposed MGDTI is effective in the DTI prediction.