Time-resolved electromagnetic near-field scanning is vital for antenna measurement and addressing complex electromagnetic interference and compatibility issues. However, the swift acquisition of high-resolution spatio...
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ISBN:
(数字)9798350360394
ISBN:
(纸本)9798350360400
Time-resolved electromagnetic near-field scanning is vital for antenna measurement and addressing complex electromagnetic interference and compatibility issues. However, the swift acquisition of high-resolution spatiotemporal data remains challenging due to physical constraints, such as moving the probe position and allowing sufficient time for sampling. This paper introduces a novel hybrid approach that combines Kriging for sparse spatial measurement, compressed sensing (CS) for sparse temporal sampling, and dynamic mode decomposition (DMD) for a comprehensive analysis of dual-sparse sampling electromagnetic near-field data. CS optimizes sparse sampling in the time domain, capitalizing on the inherent sparsity within electromagnetic radiated signals, resulting in reliable representation of time-domain signals and reducing the required time samples. Latin hypercube sampling guides the probe position, facilitating sparse measurement in the space domain. DMD extracts meaningful insights from the resulting sparse spatiotemporal data, producing sparse dynamic modes and temporal evolution information. Subsequently, Kriging is employed to infer missing spatial measurements for each sparse dynamic mode. Finally, the entire spatiotemporal signals are reconstructed based on interpolated dynamic modes and temporal evolution information. Validation of the proposed method is demonstrated with an example using crossed dipole antennas as the device under test. The Kriging-CS-DMD framework effectively reconstructs electromagnetic fields with precision while concurrently reducing the measurement workload in both the time and space domains. This methodology holds promise for various applications, including space-time-modulated electronic devices.
A navigation system is an essential tool designed to assist users in determining and following a route from one location to another. Navigation systems are typically categorized into two types: outdoor navigation syst...
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ISBN:
(数字)9798350365191
ISBN:
(纸本)9798350365207
A navigation system is an essential tool designed to assist users in determining and following a route from one location to another. Navigation systems are typically categorized into two types: outdoor navigation systems designed for open areas and indoor navigation systems used within enclosed spaces. However, transitioning between indoor and outdoor environments has limitations, often requiring users to switch applications, such as using GPS for outdoor navigation and a different application for indoor navigation. Therefore, we propose the integration of indoor and outdoor navigation based on Augmented Reality. The development of AR navigation system begins with the creation of 3D assets of the Politeknik Elektronika Negeri Surabaya (PENS) campus, which is the site of our research including three buildings for indoor navigation and the connecting roads between the buildings for outdoor navigation. The development of this navigation system uses the Immersal SDK as a library for Spatial Mapping, Localization and System Integration. Several features are included, such as indoor-outdoor navigation, multilevel floor navigation, and zero additional devices. The system testing results are based on user testing, integration testing, and multilevel floor testing. From User Testing with PIECES Framework, 32 respondents expressed satisfaction with the proposed system. Integration Testing showed that the system could navigate between indoor and outdoor environments. And Multilevel Floor Testing demonstrated that the system could navigate within buildings with multiple floors.
Face recognition technology has many uses in the distribution of photographs, from detecting persons in pictures to automatically grouping pictures based on people's faces. Several methods have been proposed for f...
Face recognition technology has many uses in the distribution of photographs, from detecting persons in pictures to automatically grouping pictures based on people's faces. Several methods have been proposed for face detection, including deep learning methods like Convolutional Neural Networks and Histogram of Oriented Gradients. This study analyzes the suitability of CNN and HOG algorithms for recognizing faces with higher accuracy and speed. The accuracy of the algorithms implemented and the standard of quality of the images determine how well face recognition technology performs in the distribution of photographs. Further, this study also contributes to present effectiveness and impact on storing and retrieving images as well as its advantages and disadvantages in the context of image distribution applications.
With the rapid development of recycling and remanufacturing technologies, disassembly line balancing problems (DLBP) have drawn great attention. Considering the limitation of disassembly by humans or robots alone, thi...
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Sentence matching is to compare the relevance of two paragraphs of sentences. The current mainstream approach is a deep learning-based approach, usually through the attention mechanism to interact with sentence pairs,...
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The rapid development of quantum computing poses a significant threat to the security of current cryptographic systems, including those used in User Equipment (UE) for mobile communications. Conventional cryptographic...
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In the last decade, the increasing popularity of image sharing applications over the web has led to a huge rising in the size of the personal image collections. While conventional content-based image retrieval systems...
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ISBN:
(数字)9798350309249
ISBN:
(纸本)9798350309256
In the last decade, the increasing popularity of image sharing applications over the web has led to a huge rising in the size of the personal image collections. While conventional content-based image retrieval systems suffer from the commonly acknowledged semantic difference between low-level image features and high-level semantics, using textual information associated with images could be a good alternative. Therefore, in order to facilitate the navigation through these collections, and extracting meaningful information from them rapidly and accurately, semantic clustering of images based on textual information could help performing such an important task. In this study, we present a comparative study of several semantic similarity metrics for image datasets clustering. In particular, we evaluate the performance of eight measures namely Path, Resnik, Wu-Palmer, Lin, Jiang-Conrath, Leacock-Chodorow, Li, and Wpath. We conduct our experiments on three public datasets. The experimental results revealed that Resnik and Wpath Similarity measures whith accuracy (78% and 77.67% respectively) outperform the remaining metrics and yield more coherent and fast clustering solutions.
The optimal deadlock avoiding, deadlock recovery, as well as deadlock detection in Petri nets are the NP-hard problems. For this reason, heuristic algorithms for finding the approximate solutions of such problems are ...
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Autonomous vehicles are a key element of the automotive industry, where the impact of the human factor on the condition of the vehicle and driving is minimized. An important element is the analysis of vehicular condit...
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The development and growth of the shared economy in the smart cities are significantly influenced by technology. An online community-based ecosystem that enables peer-to-peer trade between buyers and sellers as well a...
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