Vehicle detection in aerial images plays a key role in surveillance, transportation control and traffic monitoring. It forms an important aspect in the deployment of autonomous Unmanned Aerial System (UAS) in rescue a...
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ISBN:
(纸本)9789898565471
Vehicle detection in aerial images plays a key role in surveillance, transportation control and traffic monitoring. It forms an important aspect in the deployment of autonomous Unmanned Aerial System (UAS) in rescue and surveillance missions. In this paper, we propose a two-stage algorithm for efficient detection of cars in aerial images. We discuss how sophisticated detection technique may not give the best result when applied to large scale images with complicated backgrounds. We use a relaxed version of HOG (Histogram of Oriented Gradients) and SVM (Support Vector Machine) to extract hypothesis windows in the first stage. The second stage is based on discriminatively trained part-based models. We create a richer model to be used for detection from the hypothesis windows by detecting and locating parts in the root object. Using a two-stage detection procedure not only improves the accuracy of the overall detection but also helps us take complete advantage of the accuracy of sophisticated algorithms ruling out it's incompetence in real scenarios. We analyze the results obtained from Google Earth dataset and also the images taken from a camera mounted beneath a flying aircraft. With our approach we could achieve a recall rate of 90% with a precision of 94%.
Sleep is a complex physiological process evaluated through various modalities recording electrical brain, cardiac, and respiratory activities. We curate a large polysomnography dataset from over 14,000 participants co...
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Sleep is a complex physiological process evaluated through various modalities recording electrical brain, cardiac, and respiratory activities. We curate a large polysomnography dataset from over 14,000 participants comprising over 100,000 hours of multi-modal sleep recordings. Leveraging this extensive dataset, we developed SleepFM, the first multi-modal foundation model for sleep analysis. We show that a novel leave-one-out approach for contrastive learning significantly improves downstream task performance compared to representations from standard pairwise contrastive learning. A logistic regression model trained on SleepFM’s learned embeddings outperforms an end-to-end trained convolutional neural network (CNN) on sleep stage classification (macro AUROC 0.88 vs 0.72 and macro AUPRC 0.72 vs 0.48) and sleep disordered breathing detection (AUROC 0.85 vs 0.69 and AUPRC 0.77 vs 0.61). Notably, the learned embeddings achieve 48% top-1 average accuracy in retrieving modality clip pairs from 90,000 candidates. This work demonstrates the value of holistic multi-modal sleep modeling to fully capture the richness of sleep recordings. SleepFM is open source and available at https://***/rthapa84/sleepfmcodebase. Copyright 2024 by the author(s)
The problem with telecommunications companies today is that transactional data is more extensive than existing source tables. This makes business reporting less efficient and overwhelms query processing results in dat...
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The problem with telecommunications companies today is that transactional data is more extensive than existing source tables. This makes business reporting less efficient and overwhelms query processing results in data warehouses so that they do not meet business requirements. The fast and complex evolution of the digital world must be scalable to the data warehouse process, so that the authors implement it in the data warehouse using massive parallel processing (MPP) with the Greenplum database, so that business users can get reports faster and more optimally. This case study explains how the MPP system implements and measures the performance of the Greenplum database by performing complex queries in the data warehouse with parallel processing. Therefore, this case study analyzes whether the use of MPP systems can measure the scalability of throughput and the response time in the data warehouse so that system performance in the Greenplum database remains stable for daily, weekly, and monthly operations.
A high-resolution time-frequency distribution is applied to the study of violin vibrato. Our analysis indicates that the frequency modulation induced by the motion of the stopped finger on the string is accompanied by...
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In the realm of cybersecurity, the evolution of network attacks necessitates innovative approaches for threat prediction and mitigation. Traditional methods often fall short in identifying novel attack patterns or ada...
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One of the challenges in database security is timely detection of an insider attack. This gets more challenging in the case of sophisticated/expert insiders. Behavioral-based techniques have shown promising results in...
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We study a generalization nested relational databases which are managing temporal variation of complex objects with imprecise information in heterogeneous databases environment. It combines the research in nested rela...
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ISBN:
(纸本)9781932415605
We study a generalization nested relational databases which are managing temporal variation of complex objects with imprecise information in heterogeneous databases environment. It combines the research in nested relational databases, temporal databases, and corporate data warehousing for database applications. We define and extend a set of temporal relational aggregate operators over nested historical relational data model. Properties and computations of these aggregate functions are showed. Finally, we discuss the computational complexities for computing some temporal relational aggregate operators over nested historical relational database model.
Students generally experience difficulties in learning human body anatomy due to constraints to visualize the body anatomy from 2D into 3D image. This research aims to develop a human anatomy learning system using aug...
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Students generally experience difficulties in learning human body anatomy due to constraints to visualize the body anatomy from 2D into 3D image. This research aims to develop a human anatomy learning system using augmented reality technology. By using this system, it is expected that students can easily understand the anatomy of the human body using a 3D image visualization. The method used in this system is augmented reality marker on mobile computing platform. The marker is captured by taking a picture. Then, the captured image is divided into pieces and the pattern is matched with images stored in the database. In this research, we use Floating Euphoria Framework and combine it with the SQLite database. Augmented reality anatomy system of the human body has features that can interactively display the whole body or parts of the human organs. To evaluate the usefulness of the application, we tested the augmented reality anatomy system with high school students and medical students for learning the anatomy of the human body. The results show that the human anatomy learning system with interactive augmented reality visualization helps students learn human anatomy more easily.
This study examines the efficiency of summative assessment in schools utilizing the *** assessment platform. The objectives of this study were (1) to describe staff efficiency on summative assessment using *** in scho...
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Lung cancer is one of the most deadly and ubiquitous forms of cancer globally. Early detection can make a significant difference in survival rates, prognosis, etc. Background The present study compares the performance...
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