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Author |
Angel Sappa (ed) |
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Title |
ICT Applications for Smart Cities |
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Year |
2022 |
Publication |
ICT Applications for Smart Cities |
Abbreviated Journal |
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Volume |
224 |
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Keywords |
Computational Intelligence; Intelligent Systems; Smart Cities; ICT Applications; Machine Learning; Pattern Recognition; Computer Vision; Image Processing |
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Abstract |
Part of the book series: Intelligent Systems Reference Library (ISRL)
This book is the result of four-year work in the framework of the Ibero-American Research Network TICs4CI funded by the CYTED program. In the following decades, 85% of the world's population is expected to live in cities; hence, urban centers should be prepared to provide smart solutions for problems ranging from video surveillance and intelligent mobility to the solid waste recycling processes, just to mention a few. More specifically, the book describes underlying technologies and practical implementations of several successful case studies of ICTs developed in the following smart city areas:
• Urban environment monitoring
• Intelligent mobility
• Waste recycling processes
• Video surveillance
• Computer-aided diagnose in healthcare systems
• Computer vision-based approaches for efficiency in production processes
The book is intended for researchers and engineers in the field of ICTs for smart cities, as well as to anyone who wants to know about state-of-the-art approaches and challenges on this field. |
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September 2022 |
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Springer |
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Angel Sappa |
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ISRL |
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978-3-031-06306-0 |
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MSIAU; MACO |
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Admin @ si @ Sap2022 |
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3812 |
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Author |
Victoria Ruiz; Angel Sanchez; Jose F. Velez; Bogdan Raducanu |
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Title |
Waste Classification with Small Datasets and Limited Resources |
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Book Chapter |
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Year |
2022 |
Publication |
ICT Applications for Smart Cities. Intelligent Systems Reference Library |
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224 |
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Pages |
185-203 |
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Abstract |
Automatic waste recycling has become a very important societal challenge nowadays, raising people’s awareness for a cleaner environment and a more sustainable lifestyle. With the transition to Smart Cities, and thanks to advanced ICT solutions, this problem has received a new impulse. The waste recycling focus has shifted from general waste treating facilities to an individual responsibility, where each person should become aware of selective waste separation. The surge of the mobile devices, accompanied by a significant increase in computation power, has potentiated and facilitated this individual role. An automated image-based waste classification mechanism can help with a more efficient recycling and a reduction of contamination from residuals. Despite the good results achieved with the deep learning methodologies for this task, the Achille’s heel is that they require large neural networks which need significant computational resources for training and therefore are not suitable for mobile devices. To circumvent this apparently intractable problem, we will rely on knowledge distillation in order to transfer the network’s knowledge from a larger network (called ‘teacher’) to a smaller, more compact one, (referred as ‘student’) and thus making it possible the task of image classification on a device with limited resources. For evaluation, we considered as ‘teachers’ large architectures such as InceptionResNet or DenseNet and as ‘students’, several configurations of the MobileNets. We used the publicly available TrashNet dataset to demonstrate that the distillation process does not significantly affect system’s performance (e.g. classification accuracy) of the student network. |
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September 2022 |
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ISRL |
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978-3-031-06306-0 |
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LAMP |
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Admin @ si @ |
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3813 |
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Author |
L. Calvet; A. Ferrer; M. Gomes; A. Juan; David Masip |
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Title |
Combining Statistical Learning with Metaheuristics for the Multi-Depot Vehicle Routing Problem with Market Segmentation |
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Journal Article |
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2016 |
Publication |
Computers & Industrial Engineering |
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CIE |
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94 |
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93-104 |
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Multi-Depot Vehicle Routing Problem; market segmentation applications; hybrid algorithms; statistical learning |
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Abstract |
In real-life logistics and distribution activities it is usual to face situations in which the distribution of goods has to be made from multiple warehouses or depots to the nal customers. This problem is known as the Multi-Depot Vehicle Routing Problem (MDVRP), and it typically includes two sequential and correlated stages: (a) the assignment map of customers to depots, and (b) the corresponding design of the distribution routes. Most of the existing work in the literature has focused on minimizing distance-based distribution costs while satisfying a number of capacity constraints. However, no attention has been given so far to potential variations in demands due to the tness of the customerdepot mapping in the case of heterogeneous depots. In this paper, we consider this realistic version of the problem in which the depots are heterogeneous in terms of their commercial o er and customers show di erent willingness to consume depending on how well the assigned depot ts their preferences. Thus, we assume that di erent customer-depot assignment maps will lead to di erent customer-expenditure levels. As a consequence, market-segmentation strategiesneed to be considered in order to increase sales and total income while accounting for the distribution costs. To solve this extension of the MDVRP, we propose a hybrid approach that combines statistical learning techniques with a metaheuristic framework. First, a set of predictive models is generated from historical data. These statistical models allow estimating the demand of any customer depending on the assigned depot. Then, the estimated expenditure of each customer is included as part of an enriched objective function as a way to better guide the stochastic local search inside the metaheuristic framework. A set of computational experiments contribute to illustrate our approach and how the extended MDVRP considered here di ers in terms of the proposed solutions from the traditional one. |
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PERGAMON-ELSEVIER SCIENCE LTD |
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CIE |
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0360-8352 |
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OR;MV; |
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Admin @ si @ CFG2016 |
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2749 |
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Author |
Josep Llados; J. Lopez-Krahe; Enric Marti |
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Title |
A Hough-based method for hatched pattern detection in maps and diagrams. |
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Miscellaneous |
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Year |
1999 |
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Proceedings of the International Conference on Document Analysis and Recognition. |
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Bangalore-India |
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DAG @ dag @ LlM1999b |
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1 |
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Author |
Felipe Lumbreras; Ramon Baldrich; Maria Vanrell; Joan Serrat; Juan J. Villanueva |
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Title |
Multiresolution colour texture representations for tile classification |
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Miscellaneous |
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Year |
1999 |
Publication |
Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes |
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ADAS;CIC |
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ADAS @ adas @ LBV1999a |
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3 |
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Author |
Daniel Ponsa; A.F. Sole; Antonio Lopez; Cristina Cañero; Petia Radeva; Jordi Vitria |
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Title |
Regularized EM |
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Miscellaneous |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes |
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invisible;ADAS;OR;MILAB;MV |
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ADAS @ adas @ PSL1999 |
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4 |
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Author |
David Guillamet; Jordi Vitria |
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Title |
Using Eigenspace analysis of color distributions for object recognition |
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Miscellaneous |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes |
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OR;MV |
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BCNPCL @ bcnpcl @ GuV1999a |
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5 |
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Author |
A. Pujol; Felipe Lumbreras; Javier Varona; Juan J. Villanueva |
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Title |
Template matching through invariant eigenspace projection. |
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Miscellaneous |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. |
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ADAS @ adas @ PLV1999 |
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6 |
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Josep Llados; Felipe Lumbreras; Javier Varona |
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A multidocument platform for automatic reading of identity cards. |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. |
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ADAS @ adas @ LLV1999 |
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Author |
A. Martinez; Jordi Vitria |
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Learning mixture models with the EM algorithm and genetic algorithms |
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1998 |
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CVC Technical Report #23 |
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OR;MV |
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BCNPCL @ bcnpcl @ MaV1998a |
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8 |
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Author |
A.F. Sole; Antonio Lopez; Cristina Cañero; Petia Radeva; J. Saludes |
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Title |
Crease enhancement diffusion |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes |
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ADAS @ adas @ SLC1999 |
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9 |
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Author |
Javier Varona; A. Pujol; Juan J. Villanueva |
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Visual tracking in application domains. |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. |
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ISE @ ise @ VPV1999 |
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Antonio Lopez; David Lloret; Joan Serrat |
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Creaseness measures for CT and MR image registration. |
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1998 |
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CVPR’98 , IEEE Computer Society, pgs.694–699 |
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Creases are a type of ridge/valley structures that can be characterized by local conditions. Therefore, creaseness refers to local ridgeness and valleyness. The curvature K of the level curves and the mean curvature kM of the level surfaces are good measures of creaseness for 2-d and 3-d images, respectively. However, the way they are computed gives rise to discontinuities, reducing their usefulness in many applications. We propose a new creaseness measure, based on these curvatures, that avoids the discontinuities. We demonstrate its usefulness in the registration of CT and MR brain volumes, from the same patient, by searching the maximum in the correlation of their creaseness responses (ridgeness from the CT and valleyness from the MR). Due to the high dimensionality of the space of transforms, the search is performed by a hierarchical approach combined with an optimization method at each level of the hierarchy |
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Santa Barbara, USA. |
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ADAS @ adas @ LLS1998a |
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Antonio Lopez; Felipe Lumbreras; Joan Serrat |
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Creaseness form level set extrinsec curvature. |
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1998 |
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5th European Conference on Computer Vision (ECCV’98), Lecture Notes in Computer Science,vol 1407, pgs. 156–169 |
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Freiburg, Germany. |
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ADAS @ adas @ LLS1998b |
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Xavier Roca; Jordi Vitria; Maria Vanrell; Juan J. Villanueva |
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Visual behaviours for binocular navigation with autonomous systems. |
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Miscellaneous |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes |
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OR;ISE;CIC;MV |
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BCNPCL @ bcnpcl @ RVV1999a |
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13 |
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| Permanent link to this record |
