Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fast Classification of Virtual 5G Core Network Elements

To adapt to the smooth evolution from 5G to 6G and realize the accurate management of 5G core network elements, this paper proposes a resource management system integrating collection, maintenance, and management. Based on 5GC network elements information, several feasible neural network models are constructed to realize real-time classification of 5G virtual elements in the live network. Through our experiments, the paper proves the selected algorithm to be reasonable, convenient and efficient.
Unlocking Emotions Through Heat: Facial Emotion Recognition via Thermal Imaging

Facial emotion recognition involves the analysis of facial expressions to identify and classify human emotions. As there is a relationship between facial temperature changes and emotions, this study focuses on facial emotion recognition using thermal imaging. The primary goal of this work is to enhance the efficiency, robustness, and accuracy of emotion recognition systems to improve human-computer interaction, mental health monitoring, personalized education, marketing effectiveness, and security. The proposed Convolution Neural Network (CNN) architecture is designed to recognize human emotions by classifying facial images into six distinct categories (happiness, sadness, anger, fear, disgust, and surprise). With an average detection accuracy of 0.9857, the proposed model showcases its effectiveness in emotion prediction. The findings indicate that our proposed system outperforms YOLOv5 and YOLOv5-NMS models across all emotions, showcasing superior results.
Wireless Networking for 5G And 6G using Millimeter-Wave Technological Innovation

Over the last forty years, technology for wireless communication has advanced quickly since the debut of the first phase of cellphone telephony. Fifth generation (5G) technology, which is soon to be available, promises to provide very high data speeds, greatly decreased latency, and increased spectral efficiency. This is accomplished via the innovative addition to the mobile communication infrastructure known as the millimeter-wave spectrum. The rise of data-intensive applications and a vast wireless network will increase the need for sixth-generation (6G) connectivity as we move beyond 2030. By intending to cover the whole surface of the Earth and even reach into close to outer space, 6G offers a significant improvement above 5G. Millimeter-wave technology will play a significant role in the deployment of both the 5G and future 6G technologies in order to meet their planned goals for network efficacy and connectivity. We give an overview of the key millimeter-wave enabling methods in this paper. We also briefly discuss each component's needs to enable future 6G connections.
Handwritten Digit Recognition Method Based on DTW and SVM

Handwritten digit recognition is a classic and fundamental problem in the field of machine learning. In recent years, with the development of deep learning theory, deep neural networks have become the main solution to this problem, achieving higher recognition accuracy. However, there are still challenges related to interpretability, lack of theoretical guidance in network structure design, overfitting, and high reliance on data. In this paper, we propose a method for handwritten digit recognition based on the combination of Dynamic Time Warping (DTW) and Support Vector Machine (SVM). Firstly, we utilize the Radon transform to convert the two-dimensional images into multiple one-dimensional data, achieving rotational invariance of the images. Then, we apply DTW technique to align the signals, making the recognition algorithm insensitive to local and global deformations of the images. Based on this, we construct a Gaussian kernel function by considering both alignment distance and deformation distance of the signals. Finally, we employ the well-established SVM technique to accomplish the task of handwritten digit recognition. Experimental results demonstrate that our proposed algorithm achieves state-of-the-art recognition rate, with better generalization performance, validating the effectiveness of our method.
Threat intelligence analysis based on multi-dimensional feature topic clustering and knowledge graph

In the Internet era, network security experts need to collect and analyze security threats from open source security information in real time, and convert them into data containing high-value information to help security engineers respond quickly or prevent potential network threats. Aiming at massive security information, this paper designs and implements a threat intelligence analysis system based on multi-dimensional feature topic clustering and knowledge graph. Taking the keywords based on TextRank, topic words based on LDA and entity words based on named entity recognition as features, a topic clustering model based on hierarchical clustering is proposed. The knowledge graph is generated based on semantic role annotation to quickly understand the threat information, causality and time dependencies in the topic.
Multi-domain Landslide and Sandstorm Target Detection

Conventional methodologies rooted in deep learning have been harnessed for the purpose of landslide target detection. These methods entail the utilization of pre-trained target detection models to discern objects within high spatial resolution satellite remote sensing imagery, thereby facilitating augmented decision-making processes and emergency response. However, it is imperative to note that the deployment context of these object detection models does not consistently align with the actual conditions of landslide scenarios.In the context of this research, we embark on a pioneering endeavor by amalgamating the domains of landslide and sandstorm detection, resulting in the creation of a comprehensive dataset encompassing both phenomena. This novel dataset spans multiple domains and encompasses a total of 4735 images, comprising 4095 labels for landslide and 6145 labels for sandstorms.Within the domain of model algorithm development, this study introduces refinements to the architecture of the YOLO V5S model. Specifically, a novel network structure is proposed, fusing the principles of the "naive convolutional network" (VanillaNet) with a Multi-Head Dynamic Attention Detection Head. The VanillaNet, serving as a minimalist backbone network, incorporates an array of non-linear enhancement strategies that notably elevate the model’s capabilities. In parallel, the MultiHead Dynamic Attention mechanism is incorporated into the model’s architecture to enhance the capacity to discern image features across three critical dimensions: scale, spatial context, and contextual awareness.The research further benchmarks its proposed model architecture against a YOLOV5s baseline, contemplating the integration of two alternative backbone networks, VanillaNet and Swin Transformer. A series of experimentation ensues, facilitated by the introduction of the Dynamic Head, and comprehensive comparisons are drawn with state-of-the-art (SOTA) models such as YOLOV8s and YOLO-NAS series models. The experimental results unequivocally demonstrate that the model formulated through the fusion of naive CNN and dynamic multi-head attention, as posited in this study, yields a substantial 9.2 percentage point enhancement in mAP@50 when contrasted against YOLO-NAS_S, as well as an impressive 18.6 percentage point improvement in mAP@[. 5:. 95] when juxtaposed with YOLOV8.
Fault Diagnosis of Underwater Robot Propulsion Based on ResNet18 Network

In response to the complex situation and low recognition efficiency of underwater robot thruster faults, this paper proposes a fault diagnosis method based on the Resnet18 network. This residual network can input the collected one-dimensional and multidimensional signals, and use a residual structure to greatly reduce the computational complexity of the network model. At the beginning of the fault, the fault status and category can be detected in a timely manner. After experimental analysis, This network model can achieve high accuracy in the “HaiZhe” AUV fault dataset in a short period of time.
Complete Hierarchy Modelling of Motor Functions: A Design and Management Strategy

We show a system for organizing and controlling the execution of different manual tasks by physiologically generated figures. Our approach employs a tiered collection of popular because of its low to produce mechanically locomotion programs. As a result, software components are concentrated on areas where they're many needed: momentum plans with shorter time spans are produced using increased products, because when kinematic plans with longer intervals are produced using rougher configurations. Our paradigm can be applied to a variety of motor abilities, as well as those requiring exact coordination between the movements of the limbs and legs. We show tools for actions like stepping out of a chair, ascending steps using a railing, and slithering onto an upper floor. Without the necessity of gesture recognition data, every single one of the movements are generated using just a black-box game bryo engine from high-ranking user instructions.
Ensuring Resident Well-Being: IoT Solution for Shelter Home Security and Welfare

The Internet of Things (IoT) is a popularly growing technology that has merged into our routine life and has improvised our living magnificently, whose impact can be noticed everywhere. This paper describes how exactly an advanced technology can be employed to ensure smart monitoring of the shelter homes using IoT Technology along with inbuilt sensors. The paper reviews various IoT sensor technology, used within multiple wireless devices for providing smart shelter home solutions which cater to the real-time monitoring and control of various functions remotely. The study suggests smart wireless technologies use, for real-time tracking, digital exchange -imparting maximum benefit with a cost-effective approach. Utilize wireless technologies 4G and 5G for high data rate transmission and to attain minimal response time delays. The paper results that amalgamation of IoT technologies integrated with real-time tracking and digital exchange to extract maximum benefit with a cost-effective approach. The paper thus concluded, IoT Technology with sensor-enabled wireless devices empowers users to customize it, and remotely monitor the events thus rebuilding it into an economical structure of automated shelter homes and assisting in convenient and progressive living.
An improved YOLOX autonomous UAV-based system for monitoring photovoltaic panel failures

Addressing the prevalent difficulty in detecting anomalies in photovoltaic panels, such as hot spots or incidental surface contamination, this research propounds an autonomous UAV monitoring system. This system synergizes cutting-edge UAV inspection methodologies with an augmented YOLOX model. The foremost stage of the research accentuates the evolution of a sophisticated detection model, innovating beyond the conventional YOLOX framework. This progression incorporates the PVT-v2 network, lauded for its superior feature extraction. Further precision in the model’s diagnostic ability is achieved by embedding the CBAM, refining the network’s analytical focus on pertinent data. The revision in the label allocation strategy fortifies the network training, making it more precise and streamlined. The incorporation of the SIoU loss function accelerates the network’s convergence rate, effectively counteracting complications of misdiagnoses and recognition inaccuracies. The subsequent segment of the research pivots towards the enhancement of a segmentation algorithm via an advanced iteration of DeeplabV3+. This adaptation leverages the MobileNetV2 architecture, adjusting the downsampling multiplier to 8, thereby fine-tuning the algorithm’s performance. The strategy of employing the Dice loss function significantly reduces segmentation errors, especially those triggered by photovoltaic panel reflections. Experimental assessments reveal a substantial improvement in the mAP of the customized detection models, outstripping the benchmarks of not just the original YOLOX models but also other established frameworks like YOLOv5 series, Deformable DETR-R50, DAB-DETR-R50, and EfficientDet series. For segmentation, the enhanced “E” models surpass traditional accuracy thresholds of models like DeeplabV3+ and PSPnet. These empirical results confirm the robustness, reliability, and practicality of the proposed UAV system fortified with the improved YOLOX model for meticulous photovoltaic panel fault monitoring.
Examining 6G Infrastructure Capabilities: Paving the Way for Future Connectivity

6G structures are anticipated to be a major driver for the smart data society, providing functionality that is better to 5G and satisfying innovative features and apps. In this piece, we outline utilization situations and specifications for non - linear and non per third and sophisticated 6G networks as well as our view of what 6G will be. That can provide all-encompassing and limitless wireless communication, we show a massive and independent structured cabling that combines land, air, sea, and space communications. For network interfaces and creative vapour style, we also address artificial intelligence and machine learning. The Internet of Graphene, microwave frequency connectivity, very highly directional arrays myriad, numerous different, large erudite textures and holograms directional antennas, orbital rotational frequency - division, laser and that can be seen comms, nanotech, genomic messaging, and Ethereum - based frequency hopping are a few of the systems we identify as enticing for the 6F I environment.
Research on Community Discovery Method of Complex Network Based on Density Peak Clustering and Its Application

Nowadays, examples of complex networks are everywhere, such as biological networks, transportation networks and power networks. Community discovery is a basic task of complex network analysis. In complex network analysis, analyzing community structure and extracting information are crucial research tasks. The existing research starts with the density peak clustering algorithm and obtains a variety of methods to divide complex network communities, but the sensitivity of cutoff distance dc is not considered in the research of density peak clustering algorithm. Based on this, this paper proposes a new method of density peak clustering in complex network communities (GWO-DPC-Node2vec) based on grey wolf optimization. Firstly, the grey wolf optimization algorithm is iterated to find the optimal dc value of the density peak species silhouette coefficient. Compared with the classical algorithm, the stability and superiority of the improved density peak clustering algorithm are proved. Then, the nodes in the network are transformed into vectors through Node2vec, and the grey wolf optimization algorithm is run to search the cutoff distance with the maximization of Q value of modularity as the optimization goal. The searched dc results are applied to the density peak clustering algorithm, so that the division results can be obtained. Finally, the stability and superiority of the complex network community discovery method proposed in this paper are demonstrated by real network data sets.
Deepfakes: A Review on Technologies, Applications and Strategies

Deepfakes are artificial intelligence-generated multimedia, primarily videos or audio, that effectively edit or invent information by superimposing one person's likeness or voice onto that of another. They use deep learning techniques such as GAN s to generate hyper-realistic simulations that blur the line between reality and fiction. Deepfakes encompass everything from political satire to possible concerns in the fields of disinformation, privacy invasion, and cybersecurity. Understanding deepfakes is critical in an age when manipulated media may influence public opinion and undermine confidence. The primary objective of this paper is to provide a holistic overview of deepfake technology, shedding light on both its creative potential and inherent risks. Through a systematic exploration of the evolution, applications, techniques, and detection strategies, we aim to equip readers with an informed perspective on this ever-evolving field. By synthesizing existing knowledge and insights, this paper intends to contribute to the ongoing discourse surrounding deepfakes, enabling stakeholders to make informed decisions, develop effective countermeasures, and anticipate the future trajectory of this technology. Ultimately, our work strives to foster awareness and preparedness in an age where discerning the truth has never been more vital.
Equivalent Circuit for Inverter fed Induction Motor

This paper describes a method to determine the instantaneous input current, output power, input power factor and efficiency of sine PWM inverter fed induction motor using equivalent circuit. This method can be used for inverter fed induction motor with any PWM technique. The possible variation in all the parameters of induction motor equivalent circuit is also investigated and is compared with experimental results.
5G in Smart Cities: Enhancing Urban Futures for Sustainable Growth

This essay delves into the concept of “Smart Cities,” outlining its numerous benefits. It elucidates the diverse applications and services essential for transforming a city into a Smart City, underscoring the pivotal role of Information and Communication Technology (ICT) in their realization. Furthermore, it scrutinizes the challenges posed by the deployment of Smart Cities for contemporary ICT systems. The study investigates the promised attributes of 5G technology and its potential as a linchpin for the efficient implementation of urban smart cities. Additionally, it provides a catalog of 5G-enabled Smart City use cases, emphasizing how 5G will serve as the bedrock for the Internet of Things (IoT), fostering the evolution of smart cities.
A Progressive Real-Time System Performance Defect Diagnosis Framework Based on Java Virtual Machine Run-time Data

Effective diagnosis of real-time system performance defects is of great significance in practical scenarios. Traditional performance defect detection methods are limited in localization and time-cost, which restrict the use of real applications. To address the above problem, this paper proposes a progressive real-time system performance defect diagnosis framework based on Java Virtual Machine (JVM) run-time data. This framework first conducts the fast diagnosis to derive the preliminary performance evaluation results based on the status data from the operating system during the running period of a real-time system. secondly, conducts the deep diagnosis to analyze the cause of defects in depth based on the stack data from the JVM run-time area. Finally, utilizes a deep-learning method to capture the potential relationships of stack data in the JVM run-time area and learns the disposal policies for the detection task. In addition, we instantiate the framework to a real-time scenario in the field of civil aviation e-commerce tickets and design the core data structure and functions list of the prototype diagnostic system for the sales of civil aviation e-commerce tickets.
A Tensor Dataflow Modeling Framework with Fine-grained Hardware Description

In recent years, deploying tensor computation tasks on spatial accelerators has been proven to effectively enhance execution speed and efficiency. To effectively deploy tensor applications on spatial accelerators, a series of dataflow modeling frameworks have been proposed, which can quickly evaluate dataflow for efficient design space exploration. However, these frameworks lack a precise description of the hardware structure, which leads to an invalid and incomplete description of the constrained dataflow design space, and the optimal dataflow searched for a given accelerator is often invalid or non-optimal. In this paper, we first propose a hardware description, which models the array structure, storage structure, and interconnect network structure in detail, and a set of workload and dataflow descriptions, to deploy tensor operations on the proposed hardware description. Based on this, we further propose a dataflow evaluation framework to evaluate performance metrics such as latency, data reuse, and process element utilization of the dataflow. Compared to the state-of-the-art evaluation framework, we achieved a reduction in evaluation error of 4.5% for latency.
Research on 3D Point Cloud Object Detection Method Based on PointNet Model

Three-dimensional object detection technology in autonomous driving systems is an important component of the environment perception module, which relies on sensors such as LiDAR to measure the distance of the surrounding environment and generate three-dimensional point cloud information. For autonomous driving tasks, three-dimensional object detection and tracking are critical tasks that require the use of the object’s historical trajectory as input. Currently, scholars at home and abroad are conducting research on three-dimensional point cloud object detection. This paper proposes a method for processing the geometric features of point clouds based on the PointNet model. After extracting the geometric features of point cloud information, the feature vector is used as input, and PointNet is used to process the feature information for three-dimensional point cloud object detection, which has high accuracy and speed.
Dual Active Bridge Converter for Electric Vehicle Charging Infrastructure in Dual-Load Scenarios Achieving Zero Voltage Switching

This paper introduces a modified Dual Active Bridge (DAB) topology combined with an LLC resonant converter for bidirectional power transfer and multiple output voltage capabilities, focusing on its application within Electric Vehicle (EV) charging infrastructure. This converter, featuring a two-stage power conversion system, offers significant advantages of high-power applications, including high efficiency, reduced voltage stress, cost-effectiveness, and the distinct Zero Voltage Switching (ZVS) property, setting it apart from isolated and non-isolated DC-DC converters. The paper delves into a detailed analysis of the converter, elucidating its operational principles, soft-switching behaviour, steady-state characteristics, output attributes, and control mechanisms. A simulation involving a dual active proposed topology with a power rating of 50 kilowatts has been conducted utilizing the MATLAB/Simulink software platform, and the resultant findings have been duly observed and documented.
A Multi-scale Features Fusion Method for Concrete Crack Segmentation

Cracks are a major defect in concrete bridges, and accurate segmentation is crucial for their detection. The complex topology, low contrast, and noisy background make crack detection discontinuous and insufficient in detecting small branches. Thus, crack detection remains a challenging task. This paper proposes a model that integrates semantic and detail features, specifically designed for multi-branch cracks. The model consists of two components: the high-level semantic extraction part and the shallow detail fusion part. The semantic extraction part is based on the U-Net, and optimized with the Efficient Channel Attention (ECA). We introduce a Max-pooling Efficient Channel Attention Block between the encoder and decoder to aggregate larger spatial information and preserve more texture information of cracks. Subsequently, we establish the detail part to enhance the detection accuracy of small branches. This is achieved by connecting shallow multi-scale features, which retain important detail information from different global morphologies, thereby better constraining the topological continuity of the segmentation. Experimental results on our custom dataset, our method has surpassed the state-of-the-art TransUNet model. Compared to the baseline model. Our approach achieved improvements of 0.009 and 0.147 in F1 scores on both datasets.