Miracle-dz/UniFAR-FTUs huggingface dataset:
Vision-Guided UAV Landing on a Swaying Ocean Platform in Simulation

This paper presents a new method for the autonomous landing of an unmanned aerial vehicle (UAV) on a moving ocean platform in simulation. The method uses GNSS data and a lightweight boat detection algorithm to guide the UAV toward the unmanned surface vehicle (USV). Once the USV is within the view of the down-looking camera, the UAV estimates its relative position by detecting a set of ArUco markers on the USV landing pad. During the descent process, to compensate for the heave motion of the USV caused by waves, the system utilizes data from a buoy equipped with a wave sensor. Simulation experiments demonstrate the reliability and effectiveness of the proposed approach.
AI-based Network Flooding Attack Detection in SDN using Multiple Learning Models and Controller

The management of the classic IP networks, which are still commonly used today, has grown challenging. The complexity of the network is increased because IT operators must access the network devices independently and use vendor-specific instructions to create any high-level network policies, such as Quality of Service (QoS) or routing policy. A networking paradigm called Software-Defined Networking (SDN) offers a centralized method of network management and control. SDN offers flexibility and programmability in managing and controlling network infrastructure. In this study, we present a framework for simulating an SDN environment using Mininet and Ryu controller to evaluate network policies and security mechanisms. The framework incorporates modules for simulating network traffic using hping3, collecting traffic data, and applying AI algorithms for detecting network flooding attacks. The collected traffic data is pre-processed and used to train AI models capable of identifying various types of network flooding attacks. The best-performing model is then deployed in the controller for realtime attack detection. By employing this framework, network administrators can enhance their understanding of SDN behavior and develop effective strategies for securing their networks against network flooding attacks.
AIC-based Distributed Photovoltaic Data Compression Sensing Method

In view of the high-speed data transmission requirements of the new generation of power line carrier communication in distributed photovoltaic information access, data compression sensing is required to improve. In this paper, we study an AIC-based distributed photovoltaic data compression sensing method. Based on the adaptive characteristics of K-singular value decomposition (K-SVD) algorithm, the data sparsity is dynamically estimated to realize the adaptive dynamic adjustment of data acquisition. AIC algorithm is used to reconstruct data, which greatly improves the accuracy of data reconstruction. Information aggregation based on compressed sensing is constructed to achieve high efficiency data aggregation under the condition of low sampling rate. Simulation results show the superior performance of the proposed algorithm in the data transmission volume and energy consumption.
Detecting Anomalous Behaviours Based on System Logs: A Dynamic Graph Perspective

System logs provide rich information about the current system’s state. To make use of system logs, various existing work extracts a causal graph from the log events, and then applies graph-based machine learning algorithms to detect the anomalous behaviors. One limitation of these work is that they treat the graph in a static way, neglecting the dynamic nature of anomalous behaviors. In this paper, we propose to detect anomalous behaviors based on system logs using dynamic graph modeling. Our method consists of several key steps, including negative sampling which helps to balance the dataset, edge encoding which encodes the spatial information for each behavior, and sequence modeling which further models the temporal information. Experimental evaluations on real datasets demonstrate the effectiveness of the proposed approach.
Research on Lightweight Model-Based Leather Defect Edge Inference Mechanism

Real-time and accurate detection of production defects is one of the most critical aspects of the production process. This paper proposes a model lightweight-based edge inference mechanism for leather defect detection. By collecting thousands of samples of leather defects and creating a self-made dataset. Then train the model through the YOLO algorithm. Constraining the size of the channel factor γ and adding an L1 regular term about the γ to the target equation, so that it can be thinned and automatically pruned during training process. The pruning model is retrained on the self-made dataset by the weight of the pruning model, and the gradient descent algorithm is used to fine-tune the model to restore the accuracy, and then deployed on the edge device to perform inference detection on the real-time production picture of leather. The detection results are transmitted to the industrial control host computer for storage and screening, achieving real-time detection, classification, and display of defects in leather production. Compared with traditional defect detection systems, this paper achieves faster and more effective leather defect detection in the production process.
An Automated System for Epilepsy Detection Using EEG Brain Signals Based on Tunable Q-Factor Wavelet Transform and Local Binary Transitions Pattern Approach

To address the problem that hard to determine the parameters of Tunable Q-Factor Wavelet Transform (TQWT) and easy to have redundant features due to TQWT characteristics, this paper proposed a new Tunable Q-Factor Wavelet Transform-Local Binary Transitions Pattern (TQWT-LBTP) method for feature extraction in the machine learning classification of epilepsy from EEG signals. Firstly, determining Q-factor parameter and decomposition layer of TQWT based on energy, the energy of different Q-factor and layer were calculated and retained the sub-bands that the energy was larger than 10. Then, Local Binary Transitions Pattern (LBTP) method was used to descend dimension. By using Local Binary Transitions Pattern (LBTP) method, a small feature vector size of only 24 were got at first. Then, Random Forest classifier calculated high importance features and could further reduce the feature vector dimension. Bonn Epilepsy EEG datasets were used for test in this paper and 7 tasks were tested. The proposed method could achieve more than 98% accuracy results for 7 tasks classification of epileptic EEG set (set E) against any other single set (set A, B, C, or D), also against the combination of the normal sets.
Breast tumor classification based on fluorescence hyperspectral imaging

Rapid frozen sectioning is a commonly used method for obtaining intraoperative pathological diagnosis during breast surgery due to its short turnaround time. This technique enables quick identification of lesion type and pathological grading. However, the process involves several steps including frozen sectioning, staining, and inspection, taking up to an hour to obtain a pathological report. This can still cause significant delays for doctors and patients during surgery, impeding timely diagnosis and intraoperative decision-making. Hyperspectral data can provide valuable auxiliary information for medical pathological diagnosis and enable detection of finer spectral features in histopathology. In this study, fluorescence hyperspectral imaging was utilized to analyze HE-stained and unstained pathological sections of breast tumor tissue. The collected hyperspectral fluorescence data underwent preprocessing operations, such as derivative spectra and data standardization, to minimize the impact of environmental and system noise and enhance the signal-to-noise ratio of hyperspectral data. Subsequently, a three-dimensional convolutional neural network (3D-CNN) classification method that combines spatial structure and spectral information was proposed. The method achieved classification accuracies of 98% and 95% for HE-stained and unstained sections, respectively. These findings demonstrate the potential of using unstained tissue sections for pathological auxiliary diagnosis and provide theoretical support for the use of hyperspectral imaging technology in rapid intraoperative diagnosis of breast tumors.
Data Cataloging based SINDy Approach for Weather Forecasting

In the present era, climatic change is a crucial phenomenon that poses a great threat to human and animal life. Accurately simulating and forecasting the temperature of a particular geographical location have been a great conundrum throughout history particularly in tropical regions due to the sophisticated and multi-faceted oceanic transactions. Nonetheless, a smattering number of machine learning techniques such as random forests, deep learning algorithms, neural networks, support vector machines, stacked autoencoders have been exploited to forecast the weather and other volatile field variables with encouraging prospects. These algorithms require dense training data and also consumes large CPU time to perform future weather predictions. This article proposes a data cataloging based Sparse regression of Nonlinear Dynamics (SINDy) method for weather prediction. It differs from the existing prediction techniques by formulating an elevated time series data from the actual measured dataset. By adopting this empirical prediction method, the governing relationship between the input and output data is attained in simple mathematical form rather than complex functions. The MATLAB simulations of temperature and relative humidity confirm the superiority of the proposed approach in aspects of performance criteria (Mean square error).
Detection of Obstructive Sleep Apnea Based on ECG System Dynamics, Hybrid Signal Processing and Neural Networks

This study aims to develop a new approach for detecting OSA automatically with ECG system dynamics disparity. First, tunable quality factor (Q-factor) wavelet transform (TQWT), variational mode decomposition (VMD) and phase space of three-dimension (3D) are combined for the feature extraction, which can carry clinically relevant information about the anomalies present in the OSA ECG recordings. Second, dynamic modeling and identification of ECG systems are carried out using neural networks. By constructing on a bank of dynamical estimators, normal and OSA ECG signals will be classified based on their differences in dynamics. Finally, a physionet apnea-ECG database consisting of 70 overnight recordings from 70 participants is used to evaluate the outcomes. By using a cross-validation scheme of 10 folds, experimental results indicate that neural network based classifier along with proposed features achieves higher accuracy, sensitivity, and specificity values of 98.27%, 97.68%, and 98.63%, respectively. Results validate that the proposed method may serve as an alternative to PSG to detect OSA automatically in clinical settings.
Prediction Method of APU Performance Parameters based on Neural Network

Accurate prediction of the Exhaust Gas Temperature (EGT) of the Aircraft Auxiliary Power Unit (APU) can effectively monitor the future operating status of the APU and prevent safety accidents from occurring. An APU performance parameter prediction method that incorporates Atrous Convolutional Neural Network (ACNN), Bi-directional Long Short-Term Memory (Bi-LSTM) and Attention mechanism is proposed. By combining the characteristics of different networks, the purpose of improving the accuracy of EGT prediction is achieved. ACNN is introduced to extract features from the input parameters, and then the Bi-LSTM network is used to learn and train the extracted feature parameters and combine with the Attention mechanism to give different weights to different feature parameters to achieve parameter prediction. The experimental results show that for single-step and multi-step prediction of EGT, the prediction accuracy of the proposed model is significantly improved compared with other prediction models, providing a certain reference for APU performance change trend prediction.
A Dynamics Perspective of Defense-Intrusion Game

Defense-intrusion games are fascinating in both nature and the artificial world. In these games, the purpose of defender is to protect the target from being attacked by the intruder, and if possible, capture the intruder. This article solves the defense-intrusion game with considering the bio-inspired model adopted by two agents-a defender and an intruder, and obtains the attack and interception strategies. The intruder usually need to avoid the defender, so we provide attack strategy based on setting the intruder's emergency maneuver distance, and compare it with the strategy based on artificial potential field method proposed in our previous work. For the defender, we design a radical interception strategy and a conservative interception strategy according to different circumstances. Numerical simulations are conducted to illustrate the effectiveness of the proposed strategies. The results are consistent with natural observations and disclose some natural laws in the defense-intrusion scenario. This paper will provide a reference for the research of intrusion prevention related to marine robots.
HABARALA - A Comprehensive Solution for Food Security and Sustainable Agriculture through Alternative Food Resources and Technology

Hunger is a pressing global issue affecting up to 828 million people of the world's population, and the problem is projected to worsen without intervention. Sri Lanka is one country that continues to suffer from rising food prices and insecurity, affecting millions of people. To combat this challenge, this proposed solution “Habarala”, is a novel technological solution that uses Deep Learning, Augmented Reality, and Machine Learning to provide alternative plant identification, recipe suggestions, plant recommendations based on climate conditions, space evaluation, 3D modeling, and personalized product recommendations. “Habarala” aims to contribute to the global effort to eradicate hunger and promote sustainable agriculture, revolutionize food production and distribution, improve food security, and enhance the quality of life for millions of people in Sri Lanka and beyond. The proposed method for plant identification, plant and product recommendation, and area evaluation has shown promising results with an accuracy range of 84% – 99%.
Measurement of work group diversity

Whilst research on work group diversity has proliferated in recent years, relatively little attention has been paid to the precise definition of diversity or its measurement. One of the few studies to do so is Harrison and Klein’s (2007) typology, which defined three types of diversity – separation, variety and disparity – and suggested possible indices with which they should be measured. However, their typology is limited by its association of diversity types with variable measurement, by a lack of clarity over the meaning of variety, and by the absence of a clear guidance about which diversity index should be employed. In this thesis I develop an extended version of the typology, including four diversity types (separation, range, spread and disparity), and propose specific indices to be used for each type of diversity with each variable type (ratio, interval, ordinal and nominal). Indices are chosen or derived from first principles based on the precise definition of the diversity type. I then test the usefulness of these indices in predicting outcomes of diversity compared with other indices, using both an extensive simulated data set (to estimate the effects of mis-specification of diversity type or index) and eight real data sets (to examine whether the proposed indices produce the strongest relationships with hypothesised outcomes). The analyses lead to the conclusion that the indices proposed in the typology are at least as good as, and usually better than, other indices in terms of both measuring effect sizes and power to find significant results, and thus provide evidence to support the typology. Implications for theory and methodology are discussed.
OCR Text Region Detection Based on Chinese Character Features in Natural Scenes

With the widespread application of multimedia and network technologies, natural scene data such as images and videos are becoming increasingly abundant. The text information contained in images and videos is an important part of understanding multimedia content. The detection and recognition of text information in images and videos is of great research significance in fields such as video retrieval and visual assistance systems. This paper analyzed and studied text detection in multimedia data in natural scenes, then proposed an OCR text detection method based on Chinese character features. The experimental results show that this method has good performance in detecting Chinese characters in natural scenes.
Unsupervised clustering using supervised support vector machines

The goal of clustering analysis is to partition objects into groups, such that members of each group are more “similar” to each other than the members of other groups. In this paper we describe methods for using (supervised) SVM classifiers to perform unsupervised clustering and partially supervised clustering (projection). The former is accomplished by iteratively changing some of the label information and redoing the SVM training, the latter has no label alteration, but requires a set specified as the positives (a bag model, so partially supervised)
Coordination of Vehicle Platoons of Random Lengths at Highway Bottlenecks with Capacity Breakdown

This paper considers the coordination among platoons of connected and autonomous vehicles (CAVs) at mixed-autonomy bottlenecks. CAV platooning is an emerging technology with a significant potential for energy saving and throughput improvement. However, interaction between CAV platoons and non-CAV traffic has not been sufficiently understood and addressed. This paper studies such interaction under two critical factors, viz. random platoon lengths and bottleneck capacity breakdown, both of which may lead to throughput loss. We formulate a temporal cell transmission model that explicitly captures the above two elements and can easily incorporate a class of interplatoon coordination strategies. We design a coordination strategy that ensures an invariant set of the traffic state disjoint with the breakdown regime. We use a Lyapunov drift-based approach to show that the proposed strategy stabilizes the traffic if and only if the system is stabilizable, which implies throughput maximization. We finally validate our results in a standard micro-simulation environment.
Hearing from Within a Sound: A Series of Techniques for Deconstructing and Spatialising Timbre

We present a series of compositional techniques for deconstructing and spatialsing timbre in an immersive audio environment. These techniques aim to engulf a spectator within a given abstract timbre, by highlighting said timbre’s distinct spectral and gestural characteristics through our approach to sound spatialisation. We have designed these techniques using both additive synthesis, and time-frequency analysis and resynthesis, building upon analytical methods such as the discrete Fourier transform and the joint time-frequency scattering transform. These spatialisation techniques can be used to deconstruct a sound into subsets of spectral and gestural information, which can then be independently positioned in unique locations within an immersive audio environment. We here survey and evaluate how perceptibly cohesive and aesthetically nuanced a timbre remains after deconstruction and spatialisation, when applied in both live performance and studio production contexts. In accordance with their varying design, each spatialisation technique engenders a unique aesthetic experience, affording a listener various means through which to hear from within a sound.
Enhanced Classification of Epileptogenic and Nonepileptogenic EEG Signals using ANN-FDM

EEG signals, which show the electrical activity of the brain, are considered to be the most significant physiological signals in the diagnosis of epilepsy. Manual EEG signal interpretation, however, is labour-intensive, time-consuming, and prone to error. Automatic identification of epileptic focus using computer technology and signal processing has been developed as an adjunct diagnostic tool to improve the accuracy of epilepsy diagnosis and the success rate of epilepsy surgery. As a result, it is crucial to create a system that can swiftly tell apart ordinary EEG data from generalised and focal epileptic seizures. This study introduces a new technique for identifying the brain areas affected by focal and non-focal epilepsy, in order to help patients suffering from epilepsy resulting from abnormal brain electrical activity. The method employs a deep neural network and involves recording EEG signals from the brain, followed by decomposing them into sub-bands using the Fourier Decomposition Method. Next, features are calculated from each sub-band and a very effective features are analyzed further. The final classification of the EEG signals into focal and non-focal epilepsy is then performed. The system achieved the highest classification accuracy of $ 93.0\%$ with the lowest training and validation loss of $ 4.3\%$ and $ 13.3\%$, respectively, when using 10 sub-bands. The lowest accuracy was $\mathbf{9 0 . 0 \%}$ with training and validation loss of $ 2.9\%$ and $ 26.7\%$, respectively, when using 20 sub-bands. Using the minimum of 5 sub-bands resulted in a classification accuracy of $ 92.0\%$. The results obtained imply that the suggested approach may, in actual applications, prove to be a helpful tool for physicians in determining the cause of epilepsy.
Application and Research of One-dimensional convolutional neural network in Intelligent Stratification in Oilfield

At present, most of the domestic oilfield development is in the mid-to-late stages, so it is necessary to delineate the stratigraphy more accurately and quickly and effectively. The traditional manual stratification method is based on logging curves, which is costly in terms of time, manpower and material resources and relies on the subjective experience of researchers, leading to a decrease in the accuracy of oilfield stratification and thus negatively impacting oil and gas exploration. In recent years, convolutional neural networks have been used to classify data in a variety of fields such as medical, agricultural, and industrial, all of which have made important progress. Therefore, this paper introduces a 1DCNN (one-dimensional convolutional neural network) for intelligent stratification of oil field strata, which can be achieved by learning and training the features of existing data (natural gamma data, acoustic time difference data, resistance data, natural potential data, and mud content data). The experimental dataset contains a total of 133 sets of oil well data, each set includes 5 feature dimensions, and is randomly divided into training and test sets in the ratio of 7:3. The experiments compare the classification results of 1DCNN with both decision tree and Adaboost algorithms, and the results show that the automatic stratification of strata using 1DCNN has good results with an accuracy of 81%.