This paper focuses on the research background and status of the system design of RAFID access control based on raspberry pie, the construction of hardware circuits, the working principle of each module, the design of ...
This paper focuses on the research background and status of the system design of RAFID access control based on raspberry pie, the construction of hardware circuits, the working principle of each module, the design of software programs and the process of physical debugging. The main reason for setting up the topic is to change the original human guard. Although the original human guard was flexible, but the irreversible personnel mobilization could not be quickly adjusted, resulting in management loopholes. The modern access control system not only reduces labor costs, but also makes flow management more standardized. In this paper, through the detection of face feature information and RFID card identification code, the data collected is transmitted to the raspberry pie through the trigger module (photo module and RFID module), and the raspberry dispatch database or photo library is compared and verified. If the match is successful, it will also be transferred to the computer to display the measured results, and then let the driver module operate to open the valve. This raspberry pie design raspberry pie as the core module, the function of RFID plus face recognition is the innovation of this access system, as long as one of the two recognition successes can open the door valve. This design, through the face recognition principle, RFID recognition principle, WiFi communication methods and other aspects of mastery and analysis, has planned and designed to complete the convergence between various modules, and finally completed the design, to achieve access control systems of two recognition modes.
The increased interest in automated and precise vehicle damage detection tasks has influenced the advances of deep learning and computervision technologies. This research presents a novel method for automatic vehicle...
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ISBN:
(数字)9798331527549
ISBN:
(纸本)9798331527556
The increased interest in automated and precise vehicle damage detection tasks has influenced the advances of deep learning and computervision technologies. This research presents a novel method for automatic vehicle damage assessment based on YOLOv8, one of the latest and powerfull detection models. The model's aim is to assist users in inspecting vehicles in cases where different category of damage such as broken lights, door and others are present. It is done by accurately identifying and classifying the damages without depending on manual effort which is produce issues like inaccurate and time consuming. For eight image damage categories extensive dataset has been prepared, that was used for modeling. To achieve good-quality models, the Roboflow tool was employed for the purpose of picture resizing and augmentation, which assures high-quality training and therefore enhances models. As the main tool for damage detection, YOLOv8 has also been trained over 5000 epochs to detect more complex images. The cumulated metrics include mean Average Precision, precision, and recall. From the data collected, it was established that the YOLOv8 model has been efficient in detecting damages in vehicles with enhanced ability than there was in previous versions of the model. This automated solution is particularly promising for use in the insurance and automobile damage assessment industries as it makes damage assessments more accurate and faster. There are future applications for this model and also made suggestions including increasing the dataset to account for more damage types and using techniques like transfer learning to increase the accuracy of the system.
In this post, we discuss how to boost quality when faced with high loads and rapid diminishing. In order to improve bandwidth utilization and delay efficiency, a methodology was presented in the Transactions of the IE...
In this post, we discuss how to boost quality when faced with high loads and rapid diminishing. In order to improve bandwidth utilization and delay efficiency, a methodology was presented in the Transactions of the IEEE conference on Connectivity (ICC), that employed either a sensible path or a relayed path depending on rate comparability. Unfortunately, since direct route or intermediary path dependability are less reliable amid dimming circumstances, speed decreases. was demonstrated to outperform due to higher communication stability offered by the secondary relay line in terms of typical capacity and latency metrics. Despite being superior to the direct way, only utilises the broadcast path as a supplementary alternative path, thus it is unable to benefit significantly from the relay method's 4 percentage. The collaborating Proposed method we progress in this editorial, called extremely rapid relay-based conscientious, researchers estimate the transceiver ratio of the starting point, reroute, and citation links and seems to be using broadcaster remains a central to consistently select between the relay path and the successful and prosperous for feedback and control and delay achievements.
Capturing urban areas and infrastructure for automated analysis processes becomes ever more important. Laserscanning and photogrammetry are used for scanning the environment in highly detailed resolution. In this work...
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ISBN:
(纸本)9789897584886
Capturing urban areas and infrastructure for automated analysis processes becomes ever more important. Laserscanning and photogrammetry are used for scanning the environment in highly detailed resolution. In this work, we present techniques for the semantic classification of 3D point clouds from mobile mapping scans of road environments and the detection of road markings. The approach renders 3D point cloud input data into images for which U-Net as an established image recognition convolutional neural network is used for the semantic classification. The results of the classification are projected back into the 3D point cloud. An automated extraction of vector data is applied for detected road markings, generating detailed road marking maps. Different approaches for the vector data generation are used depending on the type of road markings, such as arrows or dashed lines. The automatically generated shape files created by the presented process can be further used in various GIS applications. Our results of the implemented out-of-core techniques show that the approach can efficiently be applied on large datasets of entire cities.
With millions of users39; End-to-End encryption has been converted into an ordinary characteristic in popular mobile chat relevance39;s (apps). Most imperative chat apps have added end-to-end encryption characteri...
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With the growing adoption of photovoltaic power systems, ensuring the reliable and efficient operation of photovoltaic panels is critical. However, defects such as cracks, dirt, and failures are common during long-ter...
With the growing adoption of photovoltaic power systems, ensuring the reliable and efficient operation of photovoltaic panels is critical. However, defects such as cracks, dirt, and failures are common during long-term use. Manual inspection is subjective and resource intensive. Recent computervision and machine learning techniques present new opportunities for automated diagnosis. This paper reviews photovoltaic panel defect detection using deep learning. Two main approaches exist: visible light and infrared imaging. Earlier works employed object detection with lower precision. Newer methods use semantic segmentation for pixel-level accuracy. However, most focus on single modalities and datasets. Transfer learning across diverse data could improve generalization. Few works estimate defect areas, needed for automated analysis. This paper proposes a dual-modality semantic segmentation approach with knowledge transfer learning. It shares representations across visible and infrared datasets via distillation. A lightweight network reduces parameters and computations for deployment. Soft targets provide latent features transferable to new data. An algorithm estimates defect area proportions to assess hazard levels. The multi-modal transferable learning system aims to advance automated, accurate photovoltaic panel defect diagnosis. Key innovations include tackling insufficient data via shared knowledge, reducing model complexity, improving generalization, and pioneering defect severity assessment. This can facilitate intelligent photovoltaic inspection to ensure system health, safety and longevity.
In this paper, we propose a novel point clouds based 3D object detection method for achieving higher-accuracy of autonomous driving. Different types of objects on the road has a different shape. A LiDAR sensor can pro...
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ISBN:
(纸本)9789897584886
In this paper, we propose a novel point clouds based 3D object detection method for achieving higher-accuracy of autonomous driving. Different types of objects on the road has a different shape. A LiDAR sensor can provide a point cloud including more than ten thousand points reflected from object surfaces in one frame. Recent studies show that hand-crafted features directly extracted from point clouds can achieve nice detection accuracy. The proposed method employs YOLOv4 as feature extractor and gives Normal-map as additional input. Our Normal-map is a three channels bird's eye view image, retaining detailed object surface normals. It makes the input information have more enhanced spatial shape information and can be associated with other hand-crafted features easily. In an experiment on the KITTI 3D object detection dataset, it performs better than conventional methods. Our method can achieve higher-precision 3D object detection and is less affected by distance. It has excellent yaw angle predictability for the object, especially for cylindrical objects like pedestrians, even if it omits the intensity information.
The study shows how factors internal to the sales forecasting process can introduce bias. Black Friday is upon us, and retailers everywhere stock up on inventory at dramatic discounts. The products aren39;t accessib...
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Prediction of Approval of Consumer Personal Loans Applications using different Machine Learning (ML) Algorithms and convert it into a web app by using stream lit library in a very simple and efficient way. Out of the ...
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We show for the first time that a multilayer perceptron (MLP) can serve as the only scene representation in a real-time SLAM system for a handheld RGB-D camera. Our network is trained in live operation without prior d...
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ISBN:
(纸本)9781665428125
We show for the first time that a multilayer perceptron (MLP) can serve as the only scene representation in a real-time SLAM system for a handheld RGB-D camera. Our network is trained in live operation without prior data, building a dense, scene-specific implicit 3D model of occupancy and colour which is also immediately used for tracking. Achieving real-time SLAM via continual training of a neural network against a live image stream requires significant innovation. Our iMAP algorithm uses a keyframe structure and multi-processing computation flow, with dynamic information-guided pixel sampling for speed, with tracking at 10 Hz and global map updating at 2 Hz. The advantages of an implicit MLP over standard dense SLAM techniques include efficient geometry representation with automatic detail control and smooth, plausible filling-in of unobserved regions such as the back surfaces of objects.
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