Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Cloud Security with Elliptic has Curve Referring to Two-Phase Cryptography

The objective of this research is to present an innovative security architecture designed to safeguard user data within a cloud context. This state-of-the-art security framework incorporates digital signature methods, access control mechanisms, and encryption and decryption processes. Introducing a novel key generation procedure utilizing elliptic curve cryptography ensures the generation of highly secure keys. Furthermore, we introduce a unique Identity-based Elliptic Curve Access Control technique (Id-EAC) to regulate data access provided by users across various data types. In addition, to enhance user data security in the cloud environment, we propose a two-phase encryption and decryption technique based on binary values. Lastly, to ensure data integrity within this security framework, we propose a Lightweight Virtual Signature Algorithm built around a modulo function. The security architecture guarantees users high levels of data safety, convenience, and integrity. Experimental findings demonstrate that the techniques proposed in this framework outperform current methods in terms of both performance and security.
Research on Information Tracing Method of Wind Turbine Full Life Cycle Based on Trusted Identification

Aiming at the problems of complexity of each link in the whole life cycle of wind turbine, insecurity of data transmission, low transparency of information and information islands, a set of trusted identification method for the traceability of the whole life cycle of wind turbine is proposed. Based on the analytic hierarchy process (AHP) to assess the importance of key components of wind turbines, an identification data model centered on key components was constructed. A blockchain technology based storage scheme for identification data was designed. Adopting the method of combining private blockchain and consortium blockchain, the full life cycle information traceability model of wind turbine is established. Finally, the whole life cycle information traceability system of wind turbine based on trusted identification is built, which verifies the feasibility and effectiveness of the proposed method.
An efficient approach to escalate the speed of training convolution neural networks

Deep neural networks excel at image identification and computer vision applications such as visual product search, facial recognition, medical image analysis, object detection, semantic segmentation, instance segmentation, and many others. In image and video recognition applications, convolutional neural networks (CNNs) are widely employed. These networks provide better performance but at a higher cost of computation. With the advent of big data, the growing scale of datasets has made processing and model training a time-consuming operation, resulting in longer training times. Moreover, these large scale datasets contain redundant data points that have minimum impact on the final outcome of the model. To address these issues, an accelerated CNN system is proposed for speeding up training by eliminating the noncritical data points during training alongwith a model compression method. Furthermore, the identification of the critical input data is performed by aggregating the data points at two levels of granularity which are used for evaluating the impact on the model output. Extensive experiments are conducted using the proposed method on CIFAR-10 dataset on ResNet models giving a 40% reduction in number of FLOPs with a degradation of just 0.11% accuracy.
Face Detection with Landmark using YOLOv8

The advent of deep learning combined with computer vision has brought forth unparalleled advancements in facial detection and landmark identification. One pivotal player in this transformation has been the YOLO (You Only Look Once) series, setting new milestones in object detection methodologies. Our research is centered on harnessing the YOLOv8 model to optimize face detection processes. We incorporate the OpenCV library for image processing, enhancing detection fidelity through adjustable parameters like confidence and intersection over union (IoU) thresholds. A standout feature of our methodology is its innate ability to adjust to diverse image proportions. This is achieved by innovatively resizing and padding input images, which not only maintains consistency in detection but also augments accuracy. The proposed technique not only demarcates the face but also pinpoints facial landmarks, thus offering a comprehensive spatial map for each detected face. Preliminary results on benchmark datasets underscore the model's dual advantage of speed and precision. Our approach promises not only improved face detection but also paves the way for its amalgamation into expansive facial recognition and analytic platforms.
Cellular Connectivity for Advanced Air Mobility: Use Cases and Beamforming Approaches

Advanced air mobility (AAM) is a rapidly growing segment of the transport industry, which encompasses a range of innovative use cases, technologies, and aerial vehicles. For example, urban air mobility, one of the driving use cases of AAM, employs electrical vertical takeoff and landing aircraft to offer efficient and on-demand air transportation for goods as well as human passengers. Key stakeholders, such as original equipment manufacturers, prospective AAM operators and communications service providers are exploring suitable technologies to meet various service, safety, and sustainability requirements. In this article, we discuss the most important AAM use cases and associated requirements from a cellular connectivity perspective and summarize the Third Generation Partnership Project (3GPP) standardization activities addressing these requirements. While previous works have investigated the role of cellular connectivity for command and control of uncrewed aerial vehicles, other types of AAM vehicles, such as flying buses or flying taxis likely impose more demanding requirements due to higher flying altitudes and demands for mobile broadband services for crew members and passengers. Therefore, we evaluate the performance of beamforming alternatives that are applicable for providing cellular connectivity in AAM scenarios. The results show the benefits of 3GPP standardized and to-be-standardized features, such as codebook-based beamforming, multi-user multiple-input multiple-output systems, and user equipment directional antennas in cellular networks with mixed terrestrial and aerial users.
Research on Cotton Supply Chain Quality Tracing Scheme Based on Blockchain Smart Contracts

The issue of quality traceability has been a persistent challenge in the current cotton supply chain, impeding the industry’s development. The lack of transparent and timely information transmission hampers effective regulation of cotton quality, thereby significantly impacting both the quality of cotton products and enterprises’ brand image. To address this problem, this paper proposes an Ethernet blockchain and smart contract-based platform for quality traceability in the cotton supply chain, enabling efficient management with complete transparency. We have developed five smart contracts and eight algorithms, providing comprehensive implementation, testing, and validation details for their integration into the cotton supply chain system. This approach ensures secure and authentic dissemination of quality information throughout the cotton supply chain while mitigating issues related to isolated product information.
Motion Vehicle Tracking Based on Multi-Branch Depth Fusion

To address the challenge of varying target poses across different viewpoints, which result in issues such as target loss and tracking errors during object tracking, a motion model optimization algorithm based on multi-branch deep fusion has been proposed. First, an end-to-end sequence-level training method combining sampling and argmax tracker is added by improving the Transformer Tracking model to better capture moving targets. Second, a pixel-level feature fusion box refinement module is also introduced to effectively utilize spatial information and improve prediction accuracy. Experiments demonstrate that the method attains a 75.8% and 85.7% area under the curve and normalized accuracy, respectively, on the LaSOT vehicle portion of the dataset. Furthermore, the method has the capability to carry out real-time tracking at 31 fps.
The Brief Assessment of Performances of Electrical Components Grids

In this study, we explore the vital importance of electrical measurements in the smart grid setting, especially for data-driven applications. We highlight the close connection between data quality and smart grid data mining and provide a novel fuzzy evaluation technique for multivariate electrical measurement quality evaluation in smart grids. This approach entails determining relevant quality dimensions for smart grid data and providing novel membership functions designed to assess data quality according to certain desired attributes linked to each dimension. Using actual power and current data from the IEEE 14-bus power flow analysis, we perform assessments to verify the effectiveness of the suggested membership functions. Especially, our method is adaptable as it can assess the integrity of a single smart grid dataset including electrical readings of different power system parameters, including binary parameters like the state of the interrupter. It distinguishes itself from previous techniques by assessing measurement consistency. Additionally, our proposed method demonstrates sensitivity regarding the false data distribution in a given database.
Reduced Switch Converter Photovoltaic Powered WPS

In this study WPS (water pumping system) use an isolated photovoltaic (PV) panel is used to power a PMSM using a 4-switch, 3-phase inverter under the direction of an integrative FOC method from FIS. The traditional 4-switch 3-ph converter replaces the standard 6-switch 3-ph converter by substituting semiconductors for four of the electrical valves on the legs. This lowers the inverter's changeover losses, enhancing effectiveness under various operating circumstances. The P&O rate standard generating technique generates a benchmark rate needed for the FOC technique. The operation of PMSM is enhanced by the FOC scheme's integration to the FIS frequency regulator, which replaces the traditional PI controller. This The structures that are with as well as the controls (PI and FIS) of the Afs architecture are subjected to a comparison, & the finding are brought up in this work. produced using the MATLAB Simulink is programme. Every result is produced as an image plotted against time.
Precision Printed Circuit Board Probing Into W-Band

Performing accurate measurements of surface-mount components using printed circuit boards (PCBs) is not trivial when extending to frequencies as high as 110 GHz. To obtain accurate measurement results for these scenarios, several factors must be considered. These factors include proper calibration techniques, choosing a suitable PCB material, and PCB fabrication tolerances, among others. This paper demonstrates how successful mm-wave measurements can be performed with softboard PCBs by utilizing a multi-line thru-reflect-line (mTRL) calibration. A commercially available PCB material is used for all characterizations. Measurements are shown to yield high quality S-parameter results for several device test cases.
Research on Depth Information Acquisition of Array Camera Based on Depth Map Fusion

People use both eyes to acquire stereoscopic information, and computer vision can acquire depth information by taking pictures with a camera, which has high application value in the intelligent era. Aiming at the shortcomings of binocular stereo vision technology that can not deal with the occlusion problem, the array camera containing three visible cameras is used for scene information and the acquisition of disparity map and the fusion of depth map are mainly studied, the research includes the camera calibration of multi-camera, stereo correction, stereo matching, depth map fusion and so on, and puts forward a depth map fusion algorithm. The experimental results show that, Using array cameras has the advantage of multiple cameras, combined with the depth map fusion algorithm, the final depth result is more accurate and can effectively deal with the occlusion scene.
Enhancing Grid Resilience Through Real-Time Power Line Disturbance Detection and Control in Smart Systems

The traditionally stable and gradually evolving power transmission and distribution sector now stands at the intersection of the energy, telecommunications, and information technology industries. This convergence is compelling the industry to adapt and innovate, paving the way for the development of a modernized utility network through Smart Grids. While Smart Grids have emerged as an essential component for the 21st-century energy landscape, their implementation has also brought forth certain limitations related to power quality disturbances. In this research paper, we have explored the different types of power disturbances that affect smart grids and examine their impact on the overall performance of the grid. To gain insight into the impact of these disturbances, a comprehensive analysis has been conducted using MATLAB-Simulink simulations. Using various analytical methods and simulation tools, this paper aims to evaluate a cascading effect of disturbances on the overall performance of the grid. Our study also provides valuable insights into the consequences of power disturbances in smart grids, such as how these disturbances can cause damage to equipment, loss of power, and other problems also providing crucial information regarding the development of effective mitigation strategies and improving network resilience in the face of these challenges.
A Road Damage Detection Method Based on Improved YOLOV8s

In order to solve the problems of diverse road damage scales, complex backgrounds, and inconspicuous damage in road damage detection, this paper proposes a road damage detection method based on improved YOLOV8s. First, the LSK module is added after the C2f module of the backbone network to solve the problem of high similarity between road damage targets and multi-scale damage. Then, a small target detection layer is added to improve the accuracy of small target detection. Finally, Replacing the positioning loss function of the network model with Wise-IoU enables it to dynamically adjust its receptive field, making the model pay more attention to ordinary quality anchor boxes, and improving the model’s ability to position targets. Experimental results show that compared with the YOLOv8s model, the mAP value is increased by 2.7%. Compared with other algorithms, the method proposed in this article performs better on small and mild damage detection.
Robust subspace clustering via joint reweighted Frobenius norm and Lq norm minimization

Subspace clustering minimization is a common problem in machine learning and image segmentation. Nevertheless, the classical nuclear norm exacerbates the impact of significant singular values. This paper presents a novel approach for clustering incomplete noise observation data by introducing a joint reweighted Frobenius norm and norm model. Subsequently, we present a new algorithm for resolving the model. In addition, we incorporate a weight matrix to flexibly modify the influence of significant singular values. The two datasets ultimately confirm that the Joint Inexact Splitting Method (JISM) exhibits superior clustering segmentation efficacy and ensures algorithm convergence. Furthermore, JISM has good performance in restoring block occlusion of any size.
Node Importance Assessment Method Based on Double Deep Q-Network

The assessment of node importance in complex networks is a current research focus in network science. The accuracy of such evaluations has a direct impact on the security and development of real-world complex networks in different sectors. This paper presents a method for assessing the importance of nodes, Dws. It combines the double deep Q-Network as a core model with entropy weighting and reordering techniques. Comparing Dws with five classical methods for importance identification, two simulated experiments are conducted using infectious disease models for node ranking and key node identification in six real-world complex networks. The strong discriminative ability, robustness, and applicability of Dws in appraising the ranking of node importance and pinpointing crucial nodes is substantiated by the results.
Improved DN-DETR for Safety helmet wearing Detection

Safety helmet wearing detection is a hot research topic in industrial production and other fields. Many models based on YOLO have made significant improvements to this task in the past. However, transformer-based DETR models are rare, and the DETR model suffers from slow convergence speed and low accuracy in detecting small objects. Therefore, a new object detection model, CDN-DETR, which combines Res2Net with DN-DETR, is proposed. The improved Res2Net is used to replace the backbone network of the DN-DETR model. To address the issue of poor training results for the attention detection module, Smooth-L1 and GIOU loss functions are linearly fused, enabling the model to converge to higher precision. To address the lack of negative sample noise training in DN-DETR, a positive-negative sample contrastive denoising training algorithm is used. Experimental results show that the improved CDN-DETR model can achieve better performance with the same number of parameters. Compared to the unimproved DN-DETR, the proposed CDN-DETR has improved the recognition accuracy by 1.8% on the Safety helmet wearing detect dataset.
Hybrid LIDAR Point Cloud Compression Based on Projection Compensation

Lidar point cloud data is being widely used in the fields of autonomous driving, virtual reality, 3D reconstruction, etc., but its huge data volume poses some challenges for its storage and transmission. Compression of point cloud data is necessary. Existing point cloud compression methods have shown different superiorities in compression rate, compression time, compression loss, and other compression-related properties. One of the methods, based on plane fitting, is able to achieve fast compression of point cloud data, but at the cost of large information loss. To address this, we propose a projection-compensated approach that can reduce the information loss of the point cloud while achieving fast compression. In addition, we use a projection method for non-planar fitted points, which further reduces the information redundancy and improves the compression effect. Compared with plane fitting and commonly used octree and prediction tree based point cloud compression methods, our method is close to the optimal octree method in terms of direct information loss, and is better than the prediction tree and plane fitting methods, and the proposed method outperforms the other three methods in terms of application accuracy and compression time with the same compression rate.
Spectral Clustering Algorithm based on Natural Nearest Neighbor and Density Stratification

Spectral clustering algorithm is easy to misjudge data points in low-density areas as noise points or classify them into the same cluster, and the process of spectral decomposition is easy to lose the information of data points with low density. In order to solve this problem, this paper proposes a spectral clustering algorithm based on natural neighbors and density stratification (SCND). A simple and efficient similarity measure is designed. The local density of samples describes the data distribution structure, and k-means algorithm is used to classify the whole dataset to obtain multiple groups of classification results. Davies-Bouldin index is used to select the best evaluation result as the density level of the dataset. According to the density level difference between samples, the similarity based on natural neighbors is scaled, and the similarity matrix closer to the potential structure of the dataset is obtained. Finally, the Laplace matrix is calculated, and some of its feature vectors are clustered in two stages to achieve effective clustering of multi-density datasets. The experimental results show that compared with other excellent clustering algorithms, the proposed SCND method has good clustering effect on multi-density datasets and balanced distribution datasets.
Exploring Advanced Edge Techniques in AI to Develop High-Performance Information Retrieval Systems

Information retrieval (IR) is an essential aspect of modern-day generation, especially with the fast increase and expansion of the net and its related technologies. IR aims to broaden systems that can correctly and appropriately retrieve applicable statistics from considerable data, assisting customers in their search and records accumulating wishes. AI has performed a tremendous function in advancing IR techniques, making systems more innovative and efficient in retrieving data. However, with the ever-growing extent of facts on the net and the want for real-time admission to statistics, there may be a growing desire for side computing in IR systems. Area computing refers to the processing and garage of records at the brink of the community instead of sending them to a critical area for processing. This method gives numerous advantages, including reduced latency, stepped-forward information privacy and safety, and efficient use of community bandwidth. In recent years, researchers have explored superior side strategies in AI to broaden high-overall performance IR systems. These techniques include aspect caching, facet gadget mastering, and edge-based statistics filtering, Part caching entails storing frequently accessed data at the community's edge, reducing the need to retrieve information from a critical server. This technique can improve IR systems' performance by reducing latency and community congestion.
Supporting Internet of Things Devices with DNNS

Deep neural network powered smart metres have become revolutionary technologies in several industrial areas as a result of the current upsurge in IoT and DNNs. nevertheless, dealing with information from IoT devices using DNNs results in substantial delay and usage of energy due to their complexity, which is characterised by a large number of parameters and data-processing procedures. Methodologies have been created to solve these issues and allow real-time DNN deployments on devices with restricted resources. This work provides a thorough analysis of various methods, focusing on the trade-off between simulation dimension, preciseness of classification, processing velocity, and fuel economy. A brief introduction of DNNs sets the scene for the evaluation at the outset. The accessible tools for the installation of DNNs using low-resource hardware devices are then covered in detail. The topic then shifts into dataflow mapping techniques and memory tree architectures, which are crucial for maximising DNN adoption on limited technology. Additionally, several model optimisation methods like compression and trimming are investigated in depth. This paper offers real-world case examples that demonstrate the viability of using DNNs in Internet of Things (IoT) applications in spite of theoretical reflections. Finally, the paper offers helpful advice for designing and delivery of future-oriented software and hardware systems geared for practical Internet of Things (IoT) apps by delving deeply into gaps in knowledge and future prospects.