Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Artificial Intelligence Approach for Early Stroke Prediction

This research aims to explore the application of Machine Learning (ML) models, specifically ensemble methods, in conjunction with Genetic Algorithms (GA) for the early prediction of strokes using real-world data from Kaggle. The primary focus is on enhancing the performance of the stacking ensemble method.Stacking leverages the strengths of various ML models while mitigating their weaknesses, creating a meta-model that aggregates predictions from base models. We conducted extensive testing involving multiple meta-models and compared their performance. The Multi-Layer Perceptron (MLP) meta-model, with tuned parameters, outperformed others. It was trained on selected dataset features and data from base models selected via GA. Subsequently, we assessed its performance using an independent test set. We employed various metrics for evaluation, including accuracy, F1 score, recall, precision, AUC-ROC, and Matthews Correlation Coefficient (MCC). Our assessment also encompassed measuring time complexity by calculating execution time and prediction time. The results underscore the superior performance of this meta-model across all metrics, including accuracy (99.69%), F1-score (99.68%), recall (99.78%), precision (99.57%), AUC-ROC (99.69%), MCC (99.38%), execution time (2.20 minutes), and prediction time (0.0020 seconds).
Machine Learning_Based Paradigm for Diagnosis of Gestational Diabetes

One of the issues related to pregnancy is gestational diabetes mellitus (GDM). The risk of exploration in the GDM is lowered with early detection. The study aims to develop a machine learning-based paradigm for the detection of the GDM. Four distinct classifiers-logistic regression, decision tree, gradient boosting, and support vector machine were used. Nine factors of the clinical setting are distinguished in each case. A data set called PIMA for diabetes is used as the data source. The challenge of the existence of missing values is solved by substituting the mean and median values alternatively. Therefore, three scenarios have been applied to the dataset. The performance of the classifiers was assessed by measuring accuracy, sensitivity, specificity, and precision. Results yielded reliable outcomes for the proposed paradigm. In specific, the best results were obtained for the gradient boosting classifier by substituting the missing value by the median value. Results demonstrate the effectiveness of the paradigm.
Effects of Region of Interest Location on Osteoarthritis Detection Using Deep Feature Learning

Knee OsteoArthritis (OA) is a common degenerative joint disorder requiring early detection for effective treatment. In this study, using a sequential approach assisted by the BoneFinder algorithm, we propose a method for the early classification of knee OA by examining the impact of several extracted Regions of Interest (ROIs) from the tibial bone. First, the knee joint region is segmented from plain radiography of the knee. Secondly, a sub-contour detection technique is employed to extract distinct sub-regions from the tibial bone. Then, seven specific ROIs are selected on each sub-contour. Thirdly, a Stacked Sparse Autoencoder (SSAE) is employed to extract informative features from the selected ROIs. Finally, the extracted features are then used to train a Support Vector Machine with Radial Basis Function (SVM-RBF) classifier. The proposed ROI extraction as well as the selected SSAE enable improving notably the classification results.
Features Extraction on Independent Component Analysis Computed on Interictal Magnetoencephalography to Characterize Epileptic Sources

For pharmacoresistant patients in epilepsy one solution is to resect the brain area responsible for seizures, the epileptogenic zone (EZ). To define the location and the extension of the EZ, several neurophysiological techniques can be used, either non-invasive (magnetoencephalography, MEG, or electroencephalography, EEG) or invasive (stereoencephalography, SEEG). In particular, MEG has been shown to be a useful tool in presurgical diagnosis, but it allows almost only recording of interictal abnormalities which can bear complex relations to the EZ.We propose here a new approach based on the extraction of features from independent component analysis (ICA) computed on the MEG signals in 7 subjects. For each subject we computed 100 ICs on continuous MEG data. These components were labelled by an expert as epileptic or non-epileptic. We test whether different features could individually discriminate the epileptic components from other non-epileptic signals.We show in this preliminary study that some features were significantly separating epileptic from non-epileptic components. But we observed a strong inter-patient variability in feature performance. In particular, automatic extraction of spikes proved to be a difficult issue.
JACO: JAva Code Layout Optimizer Enabling Continuous Optimization without Pausing Application Services

Many Java applications in data centers suffer from severe processor pipeline frontend bottlenecks, which can be mitigated by profile-guided code layout optimizations (PGCLO). To maximize optimization opportunities, state-of-the-art PGCLO solutions adopt continuous optimization to ensure that the code layout consistently matches ever-changing application control flow characteristics. However, existing continuous optimizations inevitably pause the application to execute the new code completely, which leads to high response latency and significantly deteriorates user experience.In this paper, we propose JACO, a novel profile-guided Java code layout optimizer, enabling continuous optimization without pausing application services. The key idea of JACO is to enable the execution of both the old and new code simultaneously rather than completely switching to the new code. In particular, JACO is composed of three components: (1) A lightweight profiler captures the control flow information of the application and then generates an optimized function order. (2) A control flow switcher generates new code based on optimized function order and switches the application to execute the new code without pausing the application services. (3) A selective code reclaimer only frees the memory occupied by the inactive old code. We evaluated JACO on both open-source applications and real-world applications from a world-leading company. JACO achieved up to a 16.36% performance improvement for real-world applications. The state-of-the-art approach introduces up to 37.93x latency overhead that will interrupt application services, while JACO only introduces a negligible 7% latency overhead.
Mono6D++: Learning Point Cloud Visibility for 3D Prior-based Vehicle 6D Pose Estimation

Point cloud visibility is a crucial attribute for 3D tasks as it links the visible object points to a given viewpoint. In this paper, we address the problem of point cloud visibility for monocular vehicle 6D pose estimation. To this end, a network, dubbed Mono6D++, is introduced which jointly predicts vehicle poses and the associated points visibility. Our method mainly consists of: 1) a multi-model feature extraction module and 2) a fusion unit for learning the pose- and visibility-specific representations. Consequently, the proposed method significantly outperforms the baseline approaches. Mono6D++ is capable of handling heavily occluded, truncated and/or appearance-ambiguous vehicles.
FeFET based Logic-in-Memory design methodologies, tools and open challenges

Data-centric applications such as Artificial Intelligence and IoT are putting stringent performance and energy efficiency constraints on hardware implementations of computing architectures. Computing in Memory paradigm appears as a viable approach to to address such constraints and ferroelectric FETs (FeFETs) push this paradigm at a finer grain by enabling the design of true non-volatile logic gates, by implementing tight combination of memory and logic called Logic-in-Memory (LiM). From the basic non-volatile logic gate design up to the application-level evaluation, several challenges have to be addressed.In this paper, we present a methodology to design complex operations such as cryptographic operations using FeFET, integrate them into a complete computing architecture and evaluate its benefits. Current challenges related to logic synthesis and tools for synthesis of these LiM structures will also be discussed.
Allocation Method of Daily Carbon Emission Quota and Dispatch Strategies in Parks Considering Typical Operating Scenarios

This paper proposes an allocation method of daily carbon emission quota and the dispatch strategy in industrial parks considering typical operational scenarios. Firstly, data preprocessing is performed on the original electrical load of each feeder flowing into the park, and daily operation characteristics of each feeder are generated. Then, the K-means method is used to cluster the daily operational characteristics, where the Elbow Method is applied to determine the optimal number of clusters, thereby obtaining the typical daily operational scenarios for each feeder. Furthermore, the daily carbon emission quota for different operating scenarios of each feeder in the next year is allocated according to its proportion in the annual carbon emission. Finally, a dispatch optimization model of the park, considering demand response with the aid of flexible loads, is established, where the comfort level and the change rate of operating cost are introduced as objectives. This study provides a new method for carbon emission quota allocation in daily scheduling of park, and offers a powerful tool for the achievement of dual carbon goals in park.
LiDAR Odometry for Autonomous Mining Trucks Utilizing Massif Registration

SLAM has been employed in the construction of unknown and complex environments in autonomous driving. However, special challenges exist in open-pit mining scenario: uneven terrains cause inaccurate sensing observation, especially LiDAR point cloud and IMU data; open-pit mines cannot provide valid geometric feature points and loop closure constraints, which leads to cumulative error. To address these problems, we present a LiDAR point cloud registration algorithm, including ground normal correction (GNC), 2D projection of massif alignment (PMA), and 3D massif alignment (MA), which calculates a more accurate pose in odometry. Our method exploits GNC to correct the potential deviation of roll and pitch angle by RANSAC, then PMA is performed to retrieve the 2D transform matrix. Finally, MA is introduced to implement 3D registration based on the prior pose from previous steps. We also present an extensive evaluation in real-world open-pit mines by constructing a SLAM front-end odometry, including the result of mapping and comparison of trajectories. Using GNSS data as ground truth, our proposed method shows higher accuracy on odometry without expensive time costs.
Preprocessing Technique for Oral Lesion Classification using U-NET Segmentation

This research aims to use deep learning techniques to segment oral lesions in medical images for use as a preprocessing step in a classification model. Due to the complexity of oral lesions with undefined margins and dynamic shapes, and the limited amount of data certified by dentists, this approach was found to be underfitting and unsatisfactory. To improve the accuracy of the model, a new approach was proposed to segment interferences such as teeth from the images. This allows the model to better focus on the oral lesions. To achieve this goal, we implemented U-net models with different additional Convolutional Neural Networks (CNN) backbones, including DenseNet 121, EfficientNet B3, VGG 19, ResNet 18, SE-ResNet 18, ResNeXt 50, Inception V3, Mobilenet V2 and SE-ResNeXt 50. A segmentation model was trained with five classes of oral lesions: leukoplakia, pseudomembranous candidiasis, lichen planus, ulcer, and other white lesions. The results showed that DenseUNet and EfficientUNet achieved the highest validation and Intersection over Union (IoU) scores of 98% and 92%, respectively. Our proposed approach effectively segmented the interferences from the images, demonstrating the success of these models in handling the approach. Subsequently, a CNN model of DenseNet 121 was employed for classification. The training accuracy achieved 99.1%, while the validation and test accuracies reached 86.1% and 75.5%, respectively.
Optical Methods to Study the Composition of Human Tissue-Based Hydrogels for 3D Bioprinting and Regenerative Medicine

This work used optical methods (Raman scattering spectroscopy and FTIR spectroscopy) to study the samples of collagen-containing hydrogels for 3D bioprinting at the different production process steps. Integrity of essential organic components has been shown in the hydrogels manufactured from Lioplast® freeze-dried human bone tissue.
Study on stability evaluation of karst cave roof with strength reduction FEM

The safety factor of karst cave roof is defined as the ratio of actual shear strength parameter to critical failure shear strength parameter in this paper. Stability of karst cave roof was analyzed with strength reduction technique, elasto-plastic FEM and dichotomy. Then, not only the safety factor of karst cave roof with specific physics meaning can be obtained, but the overall failure process and the location of failure surface may also be determined at the same time, So the present method is obviously superior to those traditional methods. The calculated results of a few engineering examples prove that the proposed method is effective in analyzing the stability of karst cave under the pile tip.
Synthetic Agricultural Load Data Generation Using TimeGANs

In power system applications, accessing real data is one of the main challenges. Load modeling is an example that can be hampered by a lack of real data. This paper proposes synthetic data generation as a solution to deal with insufficient datasets. To this end, Timeseries Generative Adversarial Networks (TimeGANs) are proposed to create synthetic electricity data for an agricultural load. Center pivot irrigation system data is targeted, which does not exhibit the same trends as residential or commercial utility data, and information is presented in the lower dimensions. Obtained results show that TimeGANs can leverage the lower dimensional information to produce synthetic data with the same characteristics as the actual data. The proposed method can be utilized for modeling different electric loads.
A Steep Slope Sub-10nm Armchair Phosphorene Nanoribbon FET with Intrinsic Cold Contact

The thermionic limit of conventional MOSFET hinders steep slope switching, requiring at least 60 mV gate voltage to modulate the current at room temperature. In this work, we adopted a multiscale simulation approach to investigate sub 10 nm gate length cold source field effect transistors (CS FET) based on edge-oxidized armchair phosphorene nanoribbons (APNR-O). We showed that the presence of narrow DOS in APNR-O could filter out the high energy electrons, thus bringing the subthreshold swing (SS) down to a minimum value of 40 mV/decade and an average value of 58 mV/decade for a four-decade increase in drain current (IDS). An on current (ION) of 1418.5 µA/µm and ION/IOff ratio of 2.1 × 106 was achieved by the proposed device. Low switching energy (PDP) and fast switching speed (τ) of ∼0.15 fJ/µm and ∼0.35 ps, respectively, were predicted in this work. These results indicate that the APNR-O-based FET can simultaneously fulfill the requirements of the International Roadmap for Devices and Systems (IRDS) both for high-performance (HP) and low-power (LP) devices in 2028. Hence, the APNR-O can be a good channel material for more than Moore devices.
Optimization Operation Strategy of Distributed Energy Aggregator in VPP

Under the background of high penetration of distributed energy resource (DER) and gradual deepening of marketization reform of the electricity market, this paper analyzes the bidding strategy of distributed energy resource aggregators (DERs) participating in the day-ahead (DA) and real-time (RT) market. This problem can be expressed by a stochastic robust optimization model to solve their optimal returns in the power market, in which stochastic scenarios are used to simulate the uncertainty of market price and wind power generation output in the power market. By using confidence intervals to handle the uncertainty of the charging demand for electric vehicles (EVs), and solving the stochastic robust optimization model, bidding strategies for purchasing and selling electricity in the DA market and RT market at different periods were obtained. The effectiveness of this model has been verified through practical operational data examples.
A Novel Read Scheme Using GIDL Current to Suppress Read Disturbance in 3-D nand Flash Memories

In this article, a novel read scheme using gate-induced drain leakage (GIDL) current is proposed to suppress read disturbance in 3-D NAND flash memories. The proposed read scheme is demonstrated through the technology computer-aided design (TCAD) simulation. In the selected string, GIDL current is not generated due to the low potential difference. On the other hand, in the unselected string, holes are accumulated under the selected cell and potential of the selected cell is enhanced due to the GIDL current before the read operation. The potential differences between the selected and adjacent cells are decreased and the maximum electric field ( $E_{m}$ ) is decreased. Therefore, soft programming in the selected cell during the read operation is decreased and hot carrier injection (HCI) between selected and adjacent cells is reduced completely. The proposed read scheme can be applied to high stacked word-line layers over 200 layers.
Enhancing Facial Expression Synthesis through GAN with Multi-Scale Dilated Feature Extraction and Edge-Enhanced Facial Features*

Affective computing aims to facilitate effective communication between humans and machines. Many affective computing systems use machine learning models trained on labeled data, like images and videos, to recognize emotions. Among these, the Generative Adversarial Network (GAN)-based expression GAN (ExprGAN) stands out as it can generate faces displaying various expressions. However, the generated faces often lack clarity in crucial facial features, such as the eyes and lips, which are essential for defining facial expressions. To address this issue, a novel feature extraction block is proposed. This module incorporates two parallel channels with multi-scale dilated convolution to mimic the human visual system and extract multi-scale facial features from the facial images. Additionally, an unsharp masking filter is integrated into the pre-processing stage to enhance the quality of facial expression features, making them sharper and clearer. The proposed model can generate faces with six distinct expressions: Anger, Disgust, Fear, Happiness, Sadness, and Surprise. To evaluate, a Convolutional Neural Network (CNN)-based expression classifier, a Principal Component Analysis (PCA)-based face classifier, and the FID (Frechet Inception Distance) score are used for comparison. The results demonstrate that the proposed model outperforms the existing ExprGAN, providing better quality and more expressive faces, which can be beneficial for improving human-machine interaction.
Finite-Set Model Predictive Control and p-q Theory Applied on Shunt Active Filter in an Off-Grid Microgeneration System

This paper deals with a finite-set model predictive control applied to a shunt active power filter. The instantaneous power theory is used to generate the compensation signals and reduce the execution time of the algorithm, which allows increasing the sampling frequency used since this is a critical condition for the type of controller considered. The model predictive control presents good transient response and tracking of high-frequency reference signals. This enables the proposed control algorithm to drive a shunt active filter in a three-phase four-wire off-grid microgeneration system under nonlinear loads. Simulation results demonstrate the effectiveness and good performance obtained with the proposed strategy.
OpenDSS and Typhoon HIL Co-Simulation for Real-Time Evaluation of a Distribution Network

Studies that require the analysis of large-scale distribution system models are usually performed using phasor based simulation packages. However, phasor based simulations can only perform steady-state analysis. With the advent of distributed generators (DGs), it is necessary to evaluate the impact of smart inverter controls on the distribution network in real-time. This paper evaluates the co-simulation platform between Typhoon Hardware-in-Loop (HIL) and OpenDSS by subjecting the inverter to various faults. First, the placement and sizing of DGs are determined using the Whale Optimization Algorithm (WOA). Next, the grid-tied photovoltaic (PV) inverter is simulated in Typhoon HIL and interfaced with the distribution network modeled in OpenDSS. The co-simulation platform is evaluated for its accuracy by comparing the results with those obtained from OpenDSS. Open Circuit faults are applied across the inverter terminals and their impact on the distribution network is analyzed in real-time. Additionally, the application of PV inverter control to improve the voltage profile of the distribution network is studied. Simulations carried out on the IEEE 13 bus unbalanced distribution network demonstrate the effectiveness of the co-simulation platform.
Improved U-Net with Global Attention Block for Hyperspectral Image Classification

Hyperspectral image (HSI) classification aims to predict the class of each pixel. The deep learning methods represented by convolutional neural network (CNN), fully convolutional network (FCN) and vision transformer (ViT) have achieved great success in HSI classification. However, CNN and FCN methods have the problem of limited receptive field, capturing global features requires the accumulation of multi layers of the network. ViT introduced self-attention mechanism to solve this problem, but the performance of ViT with limited train samples is not better than CNN. To solve the above problems, this paper introduces the global attention block (GAB) for feature extraction of HSI. GAB can be embedded in the existing CNN and FCN. In addition, this paper improves U-net for HSI classification, and combines with GAB to form GAB-UFCN. Experiment results of the proposed GAB-UFCN on three popular HSI datasets show better performance compared with the state-of-the-art methods.
GNSS/INS Navigation System Based on Lie Group Right Invariant Kalman Filter

Invariant Kalman filter is a novel Lie group-based method to solve the state estimation challenges of unmanned systems. It has garnered widespread attention because its error dynamics are not dependent on the current state, enhancing its filter consistency. In this paper, the GNSS/INS integrated navigation system is built on the right invariant Kalman filter, and its right invariant error propagation model and observation equation are derived. We further used the real-flight data of the fixed-wing UAV for offline validation. Compared with the nonlinear complementary filter algorithm based on manifold (NCF) and the standard error state EKF algorithm(ES-EKF), our method ensures a notably swifter pose convergence and superior accuracy.