Miracle-dz/UniFAR-FTUs huggingface dataset:
Cardiovascular magnetic resonance imaging: Error sources in phase contrast flow measurements

Phase contrast velocity encoded flow measurement was first introduced 30 years ago and have been subject to constant evolution since. The method has been validated repeatedly to be suitable for numerous clinical applications. It is capable to visualize and quantify dynamic phenomena such as blood flow in any part of the human body without being invasive or using ionizing radiation. Recent publications even suggest that velocity encoded imaging should be used as a gold standard in the flow measurements of great arteries. Despite of numerous possibilities, the method remains underused. However, phase contrast flow imaging carries some challenges that are limiting clinical applicability. First of all, magnetic resonance imaging is expensive and time consuming imaging modality. Currently velocity encoded flow imaging can be seen as a secondary modality which is used if necessary after alternative methods such as ultrasound. Also, phase contrast flow imaging includes numerous physical and physiological error sources affecting the accuracy of flow measurements. The understanding of these error sources is important to be able to estimate the accuracy of obtained results. An overview on these error sources is presented in this work. Essential theoretical basis to understand the physics underlying the error mechanisms are presented and means to minimize the error are addressed. The dimensions of human arteries are varying considerably along the flow track beyond the ventricles. Changing geometrical dimension and individual characteristics of cardiac valves are giving a raise to several spatially alternating flow phenomena such as acceleration artefact and voxel dephasing. In this work a research is presented where optimal measurement plane to quantify stroke volume is studied. The importance of optimal measurement plane is studied in both healthy controls and patients with accelerated flow velocities. In the work it was found that optimal location to measure arterial flow is approximately 2 cm distal from the aortic or pulmonal valve in case of accelerated flow. In controls no such relation was found between the measurement plane and the stroke volume. We are also considering the amount of hardware velocity offset in our measurement system and discussing how it affects to the results.
Design and Implementation of a Smart Traffic Light System with Libyan License Plate Recognition on FPGA

The primary purpose of conducting this research is to create, test, and apply a traffic light system based on traffic density that operates in real-time with red light violation detection and automatic Libyan license plate recognition. A group of infrared sensors positioned on each intersection roadway are used to measure traffic density. It uses radio frequency identification technology to prioritize emergency cars. The system detects red light violations using passive infrared sensors and then captures the violating vehicle image, then the image is handed to the automatic license plate recognition system which processes the image to localize the license plate region and recognize the digits and characters. The suggested traffic light system is designed and simulated using Verilog Hardware Description Language and tested on Cyclone IV GX field-programmable gate array. The automatic license plate recognition system is implemented using machine learning and image processing algorithms.
MMT: Mixed-Mask Transformer for Remote Sensing Image Semantic Segmentation

Remote sensing image semantic segmentation is a crucial step in the intelligent interpretation of remote sensing. Most of the current approaches are based on the attention mechanism to enhance long-range representations. However, these works ignore the key problem of foreground–background imbalance, and their performances encounter a bottleneck. In this article, we introduce mask classification into remote sensing image interpretation for the first time and propose a novel mixed-mask Transformer (MMT) for remote sensing image semantic segmentation. Specifically, we propose a mixed-mask attention mechanism, a simple but effective module, which assists the network to learn more explicit intraclass and interclass correlations by capturing long-range interdependent representations. In addition, a progressive multiscale learning strategy (MSL) is proposed to solve the problem of large-scale-varied targets in remote sensing images, which integrates semantic and visual representations of different scale targets by efficiently utilizing large-scale feature maps in Transformer. Experimental results show that the proposed MMT exceeds the existing alternative approaches and achieves state-of-the-art performance on three semantic segmentation datasets.
A Hierarchical Intrusion Detection Model Combining Multiple Deep Learning Models With Attention Mechanism

In order to ensure the security of computer systems and networks, it is very important to design and implement intrusion detection systems that can detect and mitigate network attacks and threats. Deep learning has great advantages in processing complex, high-dimensional and large-scale traffic data. Therefore, intrusion detection system based on deep learning method has better detection effect. Through the analysis of the research status, this paper finds that there are some problems in the existing intrusion detection system. To solve the problems of low detection accuracy, structure to be optimized and high false positive rate, this paper presents a hierarchical intrusion detection model which combines multiple deep learning models with attention mechanism. The advantages of this hierarchical model include: Firstly, the SCDAE model is used to extract the features of traffic data and reduce noise; Secondly, CNN is used to extract spatial features of network traffic data from the spatial dimension; Thirdly, BiLSTM is able to fully consider the relationship between the front and back features, so that the temporal features of network traffic data can be mined; Fourthly, a Self-Attention mechanism is added to weight the output of each time step to sum up and retain the important information in it. Thus, a CNN-BiLSTM-Attention model is constructed; Finally, the Softmax classifier is used to obtain the classification results. To verify the effectiveness of the proposed model, four time-sensitive and representative datasets are selected for experiments and five classical detection models are compared in this paper. The experimental results show that the classification accuracy of the proposed model reaches 93.26 % and the false positive rate reaches 7.53%.
A summary of research-based assessment of students' beliefs about the nature of experimental physics

Within the undergraduate physics curriculum, students' primary exposure to experimental physics comes from laboratory courses. Thus, as experimentation is a core component of physics as a discipline, lab courses can be gateways in terms of both recruiting and retaining students within the physics major. Physics lab courses have a wide variety of explicit and/or implicit goals for lab courses, including helping students to develop expert-like beliefs about the nature and importance of experimental physics. To assess students' beliefs, attitudes, and expectations about the nature of experimental physics, there is currently one research-based assessment instrument available—the Colorado Learning Attitudes about Science Survey for Experimental Physics (E-CLASS). Since its development, the E-CLASS has been the subject of multiple research studies aimed at understanding and evaluating the effectiveness of various laboratory learning environments. This paper presents a description of the E-CLASS assessment and a.
Comparative Analysis of Covid Detection using Chest X-rays by SVM-PCA and Deep Learning Techniques

In the medical field, it is very crucial to obtain the results accurately in less amount of time. nowadays the covid cases have been increasing gradually and as a reason, the time of computations has to be decreased. In this paper, an SVM-based covid detection model on chest x-rays has been proposed with a relatively small dataset by preprocessing the images with a pooling operation and performing principal component analysis.
The Golden Modulation for Massive IoT

Massive access for the Internet of Things (IoT) is a hot research topic for current and future systems, such as 6G. The main contribution of this paper is the design and evaluation of a novel set of waveforms, called Golden Modulation (GM), which departs conceptually from linear frequency modulated signals, e.g., as used in LoRa, and allows a huge increase in the number of parallel channels, compared to the state of the art mentioned above. The basic modulation principles relying on spectrum spreading via direct Zadoff-Chu sequence modulation are presented. The multiuser interference level is evaluated and shown to be small, even under uncoordinated conditions. Overall, GM paves the way for the deployment of Low Power Wide Area Networks with truly massive capacity, or for the upgrade of existing ones.
Blowup of solutions for the initial boundary value problem of the 3‐dimensional compressible damped Euler equations

The blowup phenomenon of solutions is investigated for the initial boundary value problem of the 3‐dimensional compressible damped Euler equations. It is shown that if the initial functional associated with a general test function is large enough, the solutions of the damped Euler equations will blow up on finite time. Hence, a class of blowup conditions is established. Moreover, the blowup time could be estimated.
Random Vortex Method for Geometries with Unsolvable Schwarz-Christoffel Formula

In this research we have implemented the Random Vortex Method to calculate velocity fields of fluids inside open cavities in both turbulent and laminar flows. the Random Vortex Method is a CFD method (in both turbulent and laminar fields) which needs the Schwarz-Christoffel transformation formula to map the physical geometry into the upper half plane. In some complex geometries like the flow inside cavity, the Schwarz-Christoffel mapping which transfers the cavity into the upper half plane cannot be achieved easily. In this paper, the mentioned mapping function for a square cavity is obtained numerically. Then, the instantaneous and the average velocity fields are calculated inside the cavity using the RVM. Reynolds numbers for laminar and turbulent flows are 50 and 50000, respectively. In both cases, the velocity distribution of the model is compared with the FLUENT results that the results are very satisfactory. Also, for aspect ratio the cavity (α) equal 2, the same calculation was done for Re=50 and 50000. The advantage of this modelling is that for calculation of velocity at any point of the geometry, there is no need to use meshing in all of the flow field and the velocity in a special point can be obtained directly and with no need to the other points.
An Improvement of Power Demand Prediction Method using Weather Information and Machine Learning: A Case of a Clinic in Japan (II)

In Japan, power generation from renewable energy sources has been promoted since the Great East Japan Earthquake in 2011. For this reason, the installing of renewable energy is increasing in Japan. However, the amount of power generated by renewable energies is influenced depending on natural conditions. To ensure a stable supply of electricity, it is important to keep a balance between supply and demand. Therefore, research and development of a demand response is becoming increasingly important. Hospitals and clinics, which are among the most energy-consuming types of medical facilities using renewable energy systems, need to predict an electricity demand to consider carrying out a demand response. This paper proposes a method to improve the accuracy of an electricity demand prediction for a clinic. A neural network is used as a prediction method, and the predictors consist of day of the week and temperature data by Japan Meteorological Agency. As a result, it is clarified that the proposed method is close to $\sqrt{\pi/2}\approx 1.253$, which is the value to be evaluated when the error is normally distributed.
A Lightweight Pipeline Defect Detection Method via Structural Reparameterization Technique and Ghost Convolution

To address the inference speed problem of pipeline defect detection, this paper proposes a single-stage lightweight pipeline defect detection method based on the reparameterization technique and ghost convolution. Initially, the baseline model selected for this study is You Only Look Once (YOLO)v5. To further enhance the learning ability without increasing model inference time, the reparameterization convolution (RepConv) is used instead of the $3 \times 3$ convolution of the original model. Subsequently, the ghost convolution (GhostConv) is utilized to build the ghost module, which substantially reduces the model's complexity to achieve the trade-off between accuracy and speed. Finally, an efficient channel attention (ECA) is employed in the backbone network and feature fusion stages to improve the model's feature extraction and aggregation capability. After actual experimental tests, the proposed improvement method reduces about 30% in parameters, reduces about 34% in floating point operations (FLOPs), and improves about 1% in accuracy.
Revolutionizing Surgery: A Review of the Advancements and Challenges in AI and Robotic Surgery

With the integration of robotics and artificial intelligence (AI), the area of surgery has experienced a considerable revolution recently. This review examines the developments and difficulties in robotic and AI surgery, emphasizing the effects on patient safety and surgical outcomes. Surgery planning has been transformed by AI, empowering surgeons to provide individualized care and make well-informed decisions and prevent deaths. Several surgical specialties have also adopted robotic surgery on a large scale, improving accuracy, precision, and lowering the risk of complications and increased hygiene. However issues like high costs, a lack of accessibility, and the requirement for considerable training continue to be important obstacles to the widespread use of AI and robotic surgery. The review’s conclusion looks at potential future directions for this field’s study and development.
Detection of DDoS Attacks in IoT Devices

Detection of DDoS attacks is one of the challenging tasks so that only legal users can use the proper resources. In this paper, the detection of attacks using various machine learning classifiers is discussed [1]. By utilising the three most pertinent feature selection techniques (chi-squared, RFE (recursive feature elimination), and reliefF), the most significant features were extracted from a publicly accessible NSL KDD dataset. Then, from the entire merged feature set, we selected the most relevant features by using a hybrid method. The dataset acquired using the hybrid technique underwent instance filtering to separate DDoS instances because all anomalous cases do not constitute DDoS instances, and the dataset was given the name train DDoS. This train DDoS dataset was uploaded into the weka tool and discretized using the discretise tool. Finally, the detection rates of the dataset were calculated by applying four machine learning classifiers (Naive Bayes, Decision Table, SVM (support vector machine) and Random Forest) and the detection rates were plotted and compared. The hybrid method produced the best detection rate of 99.97% and an average detection rate of 95.18% using the same set of classifiers as the train set, which had a best detection rate of 99.99% and an average detection rate of 86.41%. Thus, in comparison to other methods, the hybrid method has the best detection rate [2].
Structured Matrix Methods Computing the Greatest Common Divisor of Polynomials

Abstract This paper revisits the Bézout, Sylvester, and power-basis matrix representations of the greatest common divisor (GCD) of sets of several polynomials. Furthermore, the present work introduces the application of the QR decomposition with column pivoting to a Bézout matrix achieving the computation of the degree and the coeffcients of the GCD through the range of the Bézout matrix. A comparison in terms of computational complexity and numerical effciency of the Bézout-QR, Sylvester-QR, and subspace-SVD methods for the computation of theGCDof sets of several polynomials with real coeffcients is provided.Useful remarks about the performance of the methods based on computational simulations of sets of several polynomials are also presented.
SpatialHD: Spatial Transformer Fused with Hyperdimensional Computing for AI Applications

Brain-inspired computing methods have shown remarkable efficiency and robustness compared to deep neural networks (DNN). In particular, HyperDimensional Computing (HDC) and Vision Transformer (ViT) have demonstrated promising achievements in facilitating effective and reliable cognitive learning. This paper proposes SpatialHD, the first framework that combines spatial transformer networks (STN) and HDC. First, SpatialHD exploits the STN, which explicitly allows the spatial manipulation of data within the network. Then, it employs HDC to operate over STN output by mapping feature maps into high-dimensional space, learning abstracted information, and classifying data. In addition, the STN output is resized to generate a smaller input feature map. This further reduces computing complexity and memory storage compared to HDC alone. Finally, to test the model’s functionality, we applied spatial HD for image classification, utilizing the MNIST and Fashion-MNIST datasets, using only 25% of the dataset for training. Our results show that SpatialHD improves accuracy by ≈ 8% and enhances efficiency by approximately 2.5x compared to base-HDC.
Brain Tumor Detection via Deep Learning Framework on MRI Images

In medical imaging and diagnostics, identifying brain tumors is crucial. The objective of this study is the automated diagnosis of brain tumors using Magnetic Resonance Imaging (MRI) pictures, and the suggestion of a deep learning approach utilizing the YOLOv7 neural network model is presented. Initially a dataset had been collected which consists of 300 images. The image datasets are labelled in such a way that the area of tumor is correctly annotated. The dataset is then expanded in size as a result of the data augmentation approaches that are used. The model is trained on 80% of the photos, then tested on the remaining 20%. On a sizable dataset of brain MRI images, the suggested approach is assessed, and its performance is compared to that of earlier YOLO algorithm versions. The results demonstrate that the proposed approach of using YOLO7 achieves high accuracy in brain tumor detection, outperforming previous versions by a significant margin. The proposed model of YOLOv7 achieves a mAP score of 94%. These results prove that the YOLOv7 model is capable to detect the brain tumor along with its location which proves, that the model has potential in finding tumors using MRI.
WeightLock: A Mixed-Grained Weight Encryption Approach Using Local Decrypting Units for Ciphertext Computing in DNN Accelerators

With the wide use of NVM-based DNN accelerators for higher computing efficiency, the long data retention time essentially causes a high risk of unauthorized weight stealing by attackers. Weight encryption is an effective method, but existing ciphertext computing accelerators cannot achieve high encryption complexity and flexibility. This paper proposes WeightLock, a mixed-grained hardware-software co-design approach based on local decrypting units (LDUs). This work proposes a key-controlled cell-level hardware design for higher granularity and two weight selection schemes for higher flexibility. The simulation results show that the accuracy of VGG-8 and ResNet-18 in the Cifar-10 classification drops from 80% to only 10% even if 80% of keys are leaked. This shows >20% higher key leakage tolerance and >17x longer retraining latency protection, compared with the prior state-of-the-art hardware and software approaches, respectively. The area cost of the encryption function is negligible, with ~600x, 2.2x, and 2.4x reduction from the state-of-the-art cell-wise, column-wise, and 1T4R structures, respectively.
Transport optimal de mesures positives : modèles, méthodes numériques, applications

L'objet de cette these est d'etendre le cadre theorique et les methodes numeriques du transport optimal a des objets plus generaux que des mesures de probabilite. En premier lieu, nous definissons des modeles de transport optimal entre mesures positives suivant deux approches, interpolation et couplage de mesures, dont nous montrons l'equivalence. De ces modeles decoule une generalisation des metriques de Wasserstein. Dans une seconde partie, nous developpons des methodes numeriques pour resoudre les deux formulations et etudions en particulier une nouvelle famille d'algorithmes de "scaling", s'appliquant a une grande variete de problemes. La troisieme partie contient des illustrations ainsi que l'etude theorique et numerique, d'un flot de gradient de type Hele-Shaw dans l'espace des mesures. Pour les mesures a valeurs matricielles, nous proposons aussi un modele de transport optimal qui permet un bon arbitrage entre fidelite geometrique et efficacite algorithmique.
Studies on the trapped-mode resonant properties in asymmetric terahertz metamaterial

Artificial metamaterials with appropriate design can exhibit unique electromagnetic phenomena which do not exist in natural materials. Some studies have shown that the method of breaking the geometric symmetry is capable to modify the electromagnetic responses. Here, we simulated and measured the transmission spectra of period arrays of subwavelength double-bar structure. The obtained results show the trapped-mode resonance with Fano-shaped spectrum can be induced in terahertz metamaterial with asymmetric double-bar structure, accompanied with a metamaterial induced transparency window between two resonant dips. And the bar spacing and lattice constant have great impact on the coupling strength concerned with the transparency position and spectral lineshape. We attribute there are two mechanisms together determine the coupling pattern between the bar array and the terahetz wave, the coupling between the bars of the same unit cell and the coupling between the bars of the neighbouring cells. Our obtained results indicate that such metamaterial with very simple configuration could also provide the potential application in the field of terahertz slow-light devices, amplitude and phase modulators.
Research on Crop Fruit Segmentation Method Based on Point Cloud

The precise segmentation of point cloud is the basic work of 3D reconstruction, which has important value in scene understanding fields such as unmanned driving, crop phenotype measurement, fruit grading, picking robots and postharvest sorting. At present, there are many effective remedies to improve the speed and accuracy of segmentation on simple scenes. However, due to the complexity of real-world and agricultural environment and the limitations of 3D acquisition devices, it is still challenging to efficiently segment the target object from the chaotic real background. In this paper, we provide a comprehensive review of point cloud segmentation techniques, and classify point cloud segmentation methods based on different mathematical principles to provide insight to inspire further research. To address the problem that point cloud segmentation algorithms are difficult to evaluate, a quantitative evaluation scheme is proposed by combining segmentation precision, recall and F-score. Finally, we analyze the advantages and disadvantages of algorithms for application in the agricultural field through fruit point cloud segmentation experiments. At the same time, potential future research directions is inspired in this rapidly developing field.