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
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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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.
Aiming at the accurate and effective coaxiality measurement for twist drill with irregular surface, an optical measurement mechanism is proposed in this paper. First, A high-precision rotation instrument based on four...
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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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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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Histopathological staining is a technique widely used to highlight biological states or pharmacological activities in uman tissue. A quantitative analysis of the resulting images can produce biomarkers for diseases or...
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ISBN:
(纸本)9781467390378
Histopathological staining is a technique widely used to highlight biological states or pharmacological activities in uman tissue. A quantitative analysis of the resulting images can produce biomarkers for diseases or even other specific conditions, thus providing valuable information for diagnosis and prognosis. Since biomarkers require measurements to be made in an objective and consistent way, software systems are employed to provide this quantitative analysis. For measurements to be reproducible, the same methods must be available across different laboratories. In this paper, we present a tool that allows users to perform quantitative analyses over the web, thus providing an efficient environment not only for individual cases to be evaluated, but also for users to share a common ground when making measurements. The classification method used by the tool to segment stained pixels is performed by a similarity function based on the polynomial version of the Mahalanobis distance, which is nonlinear and provides very robust classification for m-dimensional feature spaces. Furthermore, the similarity function can be generalized in the tool, so that images can be classified by reusing parameters of previous cases. The results of our web-based approach were compared with established ground-truth data sets, producing sensitivity, specificity and fitness values of 97.09%, 98.70%, and 97.90%.
The exponential growth of text data on the World Wide Web as well as on databases off line created a critical need for efficient text summarizers that significantly reduce its size while maintaining its integrity. In ...
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ISBN:
(纸本)9781467377898
The exponential growth of text data on the World Wide Web as well as on databases off line created a critical need for efficient text summarizers that significantly reduce its size while maintaining its integrity. In this paper, we present a new multigraph-based text summarizer method. This method is unique in that it produces a multi-edge-irregular-graph that represents words occurrence in the sentences of the target text. This graph is then converted into a symmetric matrix from which we can produce the ranking of sentences and hence obtain the summarized text using a threshold. To test our method performance, we compared our results with those from the most popular publicly available text summarization software using a corpus of 1000 samples from 6 different applications: health, literature, politics, religion, science and sports. The simulation results show that the proposed method produced better or comparable summaries in all cases. The proposed method is fast and can be implement for real time summarization.
In this paper,an irregular displacement-based lensless wide-field microscopy imaging platform is presented by combining digital in-line holography and computational pixel super-resolution using multi-frame *** samples...
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In this paper,an irregular displacement-based lensless wide-field microscopy imaging platform is presented by combining digital in-line holography and computational pixel super-resolution using multi-frame *** samples are illuminated by a nearly coherent illumination system,where the hologram shadows are projected into a complementary metal-oxide semiconductor-based imaging *** increase the resolution,a multi-frame pixel resolution approach is employed to produce a single holographic image from multiple frame observations of the scene,with small planar *** are resolved by a hybrid approach:(i)alignment of the LR images by a fast feature-based registration method,and(ii)fine adjustment of the sub-pixel information using a continuous optimization approach designed to find the global optimum *** method for phase-retrieval is applied to decode the signal and reconstruct the morphological details of the analyzed *** presented approach was evaluated with various biological samples including sperm and platelets,whose dimensions are in the order of a few *** obtained results demonstrate a spatial resolution of 1.55 μm on a field-of-view of<30 mm^(2).
Fiber tracking is a mature technique that aids neurosurgeons by providing the location of neuronal fibers in a patient's brain. Although the interactivity of tools have been typically limited, the exploitation of ...
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Fiber tracking is a mature technique that aids neurosurgeons by providing the location of neuronal fibers in a patient's brain. Although the interactivity of tools have been typically limited, the exploitation of the parallel nature of fiber tracking is rapidly changing this landscape. The parallel environment offered by GPUs and modern CPUs enables fiber tracking to be executed very quickly on consumer-grade computers. The adjustment of fiber tracking parameters can currently be made on the fly, and the new results can be both computed and displayed in real-time on the screen. In order to further improve the interactivity of fiber tracking tools, this article proposes the application of histogram equalization, a traditional digital imageprocessing procedure, to the fiber tracking parameters. The non-linear scale obtained by this process allows the user to interactively explore sensitive regions of the parameter space. Results show that this scale can be computed in real-time and that it can be cleanly integrated into fiber tracking tools.
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