Facial landmark detection (FLD) is a field of study in computer vision that specializes in detecting and tracking key points from human faces. There are many applications, such as detecting a human's head pose (po...
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The web applications security systems often use the authentication strategy and credentials to assess the identity of the user. Based on the credentials, the system is able to claim the identity of the user. Also, the...
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The field of Optical Character Recognition (OCR) consists of techniques that are mainly focused on document image analysis. Aside from generating significant speedups of everyday procedures, OCR has a considerable rol...
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The market of Medical Devices has an annual growth rate perspective of 5.4% in the 2022 - 2028 period. The growth rate is mainly supported by the increased usage of devices for chronic diseases prevention and for remo...
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Data collected from the environment in computerengineering may include missing values due to various factors, such as lost readings from sensors caused by communication errors or power outages. Missing data can resul...
Data collected from the environment in computerengineering may include missing values due to various factors, such as lost readings from sensors caused by communication errors or power outages. Missing data can result in inaccurate analysis or even false alarms. It is therefore essential to identify missing values and correct them as accurately as possible to ensure the integrity of the analysis and the effectiveness of any decision-making based on the data. This paper presents a new approach, the Gap Imputing Algorithm (GMA), for imputing missing values in time series data. The Gap Imputing Algorithm (GMA) identifies sequences of missing values and determines the periodic time of the time series. Then, it searches for the most similar subsequence from historical data. Unlike previous work, GMA supports any type of time series and is resilient to consecutively missing values with different gaps distances. The experimental findings, which were based on both real-world and benchmark datasets, demonstrate that the GMA framework proposed in this study outperforms other methods in terms of accuracy. Specifically, our proposed method achieves an accuracy score that is 5 to 20% higher than that of other methods. Furthermore, the GMA framework is well suited to handling missing gaps with larger distances, and it produces more accurate imputations, particularly for datasets with strong periodic patterns.
Sensors are the foundation to facilitate smart cities, smart grids, and smart transportation, and distance sensors are especially important for sensing the environment and gathering information. Researchers have devel...
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Timely pest detection and identification is critical as part of modern agriculture. Halyomorpha Halys is a prevalent pest with proven harmful impacts on numerous crops and agricultural regions. The paper proposes an e...
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This paper introduces the VibroWear architecture, a solution with a modular design at both hardware and software levels, that integrates an energy-aware engine in order to increase operational autonomy. By providing m...
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The representation of relevant information from volume data sets is a challenging task due to the high complexity of the structures and spatial features found in such data. The challenge is to represent such structure...
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ISBN:
(数字)9798350364293
ISBN:
(纸本)9798350364309
The representation of relevant information from volume data sets is a challenging task due to the high complexity of the structures and spatial features found in such data. The challenge is to represent such structures and features in a way that makes them easy to visually perceive without causing information overload in the resulting images. To this end, we propose a straightforward means of highlighting various regions from isosurfaces found in volume data, such that the visual perception of important surface details is improved. We use an approach based on curvature analysis to determine variations of the isosurface shape, allowing the accentuation of meaningful surface regions. We show that, while the resulting surface accents alone are enough to improve the display of surface details, combining our method with local illumination significantly contributes to a raised level of perception of the surface shape, as well as to the generation of more comprehensive representations of the underlying data. We present our results through illustrative images of medical CT volumes and perform an evaluation using several state-of-the-art no-reference image quality assessment methods. Additionally, our technique does not require precomputation and is easy to incorporate into existing volume rendering engines.
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