Due to their rapid reproduction, development, and similar genetic structure to humans, zebrafish are popular animals in various biological research. Their small size and body transparency make them convenient to exami...
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Speed bumps are vertical raisings of the road pavement used to force drivers to slow down to ensure greater safety in traffic. However, these obstacles have disadvantages in terms of efficiency and safety, where the p...
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Speed bumps are vertical raisings of the road pavement used to force drivers to slow down to ensure greater safety in traffic. However, these obstacles have disadvantages in terms of efficiency and safety, where the presence of speed bumps can affect travel time and fuel consumption, cause traffic jams, delay emergency vehicles, and cause vehicle damage or accidents when not properly signaled. Due to these factors, the availability of geolocation information for these obstacles can benefit several applications in Intelligent Transportation System (ITS), such as Advanced Driver Assistance Systems (ADAS) and autonomous vehicles, allowing to trace more efficient routes or alert the driver of the presence of the obstacle ahead. Speed bump detection applications described in the literature employ cameras or inertial sensors, represented by accelerometers and gyroscopes. While camera-based solutions are mature with evaluation in different contextual conditions, those based on inertial sensors do not offer multi-contextual analyses, being mostly simple applications of proof of concept, not applicable in real-world scenarios. For this reason, in this work, we propose the development of a reliable speed bump detection model based on inertial sensors, capable of operating reliably in contextual variations: different vehicles, driving styles, and environments in which vehicles can travel to. For the model development and validation, we collect nine datasets with contextual variations, using three different vehicles, with three different drivers, in three different environments, in which there are three different surface types, in addition to variations in conservation state and the presence of obstacles and anomalies. The speed bumps are present in two different pavement types, asphalt and cobblestone. We use the collected data in experiments to evaluate aspects such as the influence of the placement of the sensors for vehicle data collection and the data window size. Afterwar
While considering a mirror and light rays coming either from a point source or from infinity,the reflected light rays may have an envelope,called a caustic *** this paper,we study developable surfaces as *** caustic s...
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While considering a mirror and light rays coming either from a point source or from infinity,the reflected light rays may have an envelope,called a caustic *** this paper,we study developable surfaces as *** caustic surfaces,described in a closed form,are also developable surfaces of the same type as the original mirror *** provide efficient,algorithmic computation to find the caustic surface of each of the three types of developable surfaces(cone,cylinder,and tangent surface of a spatial curve).We also provide a potential application of the results in contemporary free-form architecture design.
In this paper, we present a structured literature mapping of the state-of-the-art of vehicular perception methods and approaches using inertial sensors. An in-depth investigation and classification were performed empl...
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As a popular evolutionary algorithm, artificial bee colony (ABC) algorithm has been successfully applied into the threshold-based image segmentation problem. Based on our analysis, we find that the Otsu segmentation f...
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Spine MRI images generally have the characteristics of low contrast and much noise. Because the variable shape of the spine edge, the traditional spine image segmentation method requires a lot of preprocessing and can...
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The demand for a variety of situational data from the traffic environment and its participants has intensified with the development of applications in Intelligent Transport Systems (ITS). Among these data, the road su...
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ISBN:
(数字)9781728182865
ISBN:
(纸本)9781728182872
The demand for a variety of situational data from the traffic environment and its participants has intensified with the development of applications in Intelligent Transport Systems (ITS). Among these data, the road surface type classification is one of the most important and can be used in the entire ITS domain. For its widespread application, it is necessary to employ a robust technology for the generation of raw data and to develop of a reliable and stable model to process these data in order to produce the classification. The developed model must operate correctly in different vehicles, under different driving styles and in different environments in which a vehicle can travel. In this work we employ inertial sensors, represented by accelerometers and gyroscopes, which are a safe, non-polluting, and low-cost alternative, ideal for large-scale use. We collect nine datasets with contextual variations, including three different vehicles, with three different drivers, in three different environments, in which there are three different road surface types, in addition to variations in the conservation state and presence of anomalies and obstacles such as potholes and speed bumps. After data collection, these data were used in experiments to evaluate various aspects, such as the influence of the vehicle data collection point, the analysis domain, the model input features, and the data window. Afterwards we evaluated the learning and generalization capacity of the models for unknown contexts. In a third step, the data were used in three Deep Neural Network (DNN) models: LSTM-based, GRU-based, and CNN-based. Through a multi-aspect and multi-contextual analysis, we considered the CNN-based model as the best one, which obtained an average accuracy between the data collection placements of 94.27% for learning and 92.70% for validation, classifying the road surface between asphalt, cobblestone or dirt road segments.
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to thei...
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We analyze a database containing medical data of patients who suffered hemorrhagic stroke. From the original database we built the corresponding comorbidities network, and we analyzed this network using a graph theore...
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ISBN:
(数字)9781728147932
ISBN:
(纸本)9781728147949
We analyze a database containing medical data of patients who suffered hemorrhagic stroke. From the original database we built the corresponding comorbidities network, and we analyzed this network using a graph theoretical approach. Our main tool was to analyze the spectrum of the normalized Laplacian matrix of the network.
Ongoing and future climate change driven expansion of aeroallergen-producing plant species comprise a major human health problem across Europe and elsewhere. There is an urgent need to produce accurate, temporally dyn...
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