vision based runway identification using 'marked or unmarked terrain' image sequences captured from a fixed wing unmanned aerial vehicle through onboard stereovision sensor is presented in this paper. An innov...
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vision based runway identification using 'marked or unmarked terrain' image sequences captured from a fixed wing unmanned aerial vehicle through onboard stereovision sensor is presented in this paper. An innovative convolutional neural netwok (CNN) based YOLO-v8 object detection algorithm is used to detect the runway during approach segment of UAv. This deep learning algorithm detects the region of interest in real time and in a computationally efficient manner. The captured unknown road segment or runway image frames are processed and examined for width, length, level and smoothness aspects to qualify as a suitable runway for UAv landings. Also, it is ensured that there are no obstacles, patches or holes on the detected road or runway. Runway start and end threshold lines and regions, touchdown point and runway edge lines are considered as the region of interest. imageprocessingalgorithms are applied on the captured runway or road images to detect strong features in the region of interest. Feature detector based imageprocessing algorithm with stereo vision constraint is used to establish the relation between unmanned aerial vehicle's center of gravity and detected runway feature points imageprocessingalgorithms like hough line detection, RANSAC, Oriented FAST and Rotated BRIEF (ORB), median filters, morphological methods are applied to extract terrain features. Based on the detected runway orientation and position with respect to UAv position. An automatic landing manoeuvre is performed by UAv autopilot to land the UAv on intended touchdown point on runway computed through detected feature points.
This work compares two different approaches to imageprocessing algorithm implementation in Zynq Zybo and Zedboard Field Programmable Gate Array (FPGA) boards. There are three main phases for the study, namely, Hardwa...
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Type 2 Diabetes mellitus (T2DM) patients are experiencing diabetic foot problems that heavily burden healthcare systems around the globe. Although there have been challenges with diagnosing and treating these problems...
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Type 2 Diabetes mellitus (T2DM) patients are experiencing diabetic foot problems that heavily burden healthcare systems around the globe. Although there have been challenges with diagnosing and treating these problems using traditional approaches, the advent of machine learning technology signals the beginning of a new age in diabetic foot care, with the promise of improved precision and customized treatment plans. Machine learning is a beneficial tool for extracting essential insights from large, complicated datasets to improve the accuracy of diabetic foot diagnosis and therapeutic planning. This research aims to employ artificial intelligence to build a decision support system that will use clinical and demographic variables to predict the likelihood that individuals with mild, moderate, or severe peripheral neuropathy may develop diabetic foot syndrome in T2DM. Real-time processing of clinical information is made possible by a customized stacked ensemble model, which offers immediate peripheral neuropathy risk prediction with low computing latency. The system's capacity to transform raw patients' data into valuable insights in milliseconds supports quick clinical decision-making. Additionally, comparison and testing have been conducted on four deep learning algorithms: ResMLP, LSTM, DNN, and 1D-CNN. The predictions produced by the classifiers have been interpreted using three explainers: LIME, Eli5, and SHAP. Using the mutual information feature selection technique, the final stack reached a maximum accuracy of 99%. The three most significant markers that helped predict the onset of diabetic foot syndrome were area under pressure, vpt right, and vpt left;the encouraging findings point to the possibility of predicting diabetic foot condition with a decision system.
The quantity of data in any field of activity is increasing at an exponential rate on an annual basis. images are no exception to this rule. In such circumstances, the importance of image analysis and p...
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Parameter-efficient transfer learning (PETL) methods show promise in adapting a pre-trained model to various downstream tasks while training only a few parameters. In the computer vision (Cv) domain, numerous PETL alg...
Embedded systems typically require the transmission of significant amounts of data to small-scale CPUs for applications such as radar signal processing, imageprocessing, and embedded AI. Ensuring data integrity durin...
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ISBN:
(纸本)9798350377217;9798350377200
Embedded systems typically require the transmission of significant amounts of data to small-scale CPUs for applications such as radar signal processing, imageprocessing, and embedded AI. Ensuring data integrity during transmission is typically managed using Cyclic Redundancy Check (CRC) algorithms. However, achieving real-time CRC calculation and data storage poses challenges, often necessitating large FIFO memories and multiple clock domains. These additional resources involve a greater hardware complexity. This paper presents an approach aimed at synchronizing the CPU frequency with data transmission. This enables having a single clock domain and a reduction of power consumption. Using hardware/software co-design, it is possible to achieve real-time data storage and CRC calculation without data loss and with a low power consumption.
Weeds are unwanted plants that grow with crops and usually removed by spraying herbicides or by manual labour. Herbicides being sprayed mostly do not reach their target because of the focus on a very wide area. This a...
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Weeds are unwanted plants that grow with crops and usually removed by spraying herbicides or by manual labour. Herbicides being sprayed mostly do not reach their target because of the focus on a very wide area. This also tends to harm the environment, and other living organisms. Manual labour is time-consuming and expensive and it is continuously managed and monitored. The autonomous robotics and imageprocessing tasks can be completed with precision and ease in agriculture. With imageprocessing, plants and weeds can be classified. Methods like scale invariant feature transforms (SIFT), speeded-up robust features (SURF), and ensemble learning, neural networks can be incorporated into identifying the difference. We can easily classify weeds and crops from images of plantations leveraging machine learning algorithms, artificial vision analysis systems, among others. Deep learning methods like convolutional neural network (CNN), rectified linear units (ReLU) and SoftMax (for classification) are focused in this paper.
This work focuses on detecting defects in welding seams using the most advanced You Only Look Once (YOLO) algorithms and transfer learning. To this end, the authors prepared a small dataset of images using manual weld...
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This work focuses on detecting defects in welding seams using the most advanced You Only Look Once (YOLO) algorithms and transfer learning. To this end, the authors prepared a small dataset of images using manual welding and compared the performance of the YOLO v5, v6, v7, and v8 methods after two-step training. Key findings reveal that YOLOv7 demonstrates superior performance, suggesting its potential as a valuable tool in automated welding quality control. The authors' research underscores the importance of model selection. It lays the groundwork for future exploration in larger datasets and varied welding scenarios, potentially contributing to defect detection practices in manufacturing industries. The dataset and the code repository links are also provided to support our findings.
Tree root systems are crucial for providing structural support and stability to trees. However, in urban environments, they can pose challenges due to potential conflicts with the foundations of roads and infrastructu...
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Tree root systems are crucial for providing structural support and stability to trees. However, in urban environments, they can pose challenges due to potential conflicts with the foundations of roads and infrastructure, leading to significant damage. Therefore, there is a pressing need to investigate the subsurface tree root system architecture (RSA). Ground-penetrating radar (GPR) has emerged as a powerful tool for this purpose, offering high-resolution and nondestructive testing (NDT) capabilities. One of the primary challenges in enhancing GPR's ability to detect roots lies in accurately reconstructing the 3-D structure of complex RSAs. This challenge is exacerbated by subsurface heterogeneity and intricate interlacement of root branches, which can result in erroneous stacking of 2-D root points during 3-D reconstruction. This study introduces a novel approach using our developed wheel-based dual-polarized GPR system capable of capturing four polarimetric scattering parameters at each scan point through automated zigzag movements. A dedicated radar signal processing framework analyzes these dual-polarized signals to extract essential root parameters. These parameters are then used in an optimized slice relation clustering (OSRC) algorithm, specifically designed for improving the reconstruction of complex RSA. The efficacy of integrating root parameters derived from dual-polarized GPR signals into the OSRC algorithm is initially evaluated through simulations to assess its capability in RSA reconstruction. Subsequently, the GPR system and processing methodology are validated under real-world conditions using natural Angsana tree root systems. The findings demonstrate a promising methodology for enhancing the accurate reconstruction of intricate 3-D tree RSA structures.
Studying circumstellar environments is crucial for understanding exoplanets and stellar systems. Instruments like SPHERE can extract information about these environments by leveraging advanced image reconstruction met...
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