Titlebook: Intelligent Transport Systems, From Research and Development to the Market Uptake; Second EAI Internati Joao Carlos Ferreira,Ana Lúcia Mart

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书目名称Intelligent Transport Systems, From Research and Development to the Market Uptake读者反馈学科排名

Constant

minion

With Whom Transport Operators Should Partner? An Urban Mobility and Services Geolocation Data Analysmobility data is used to identify mobility patterns of different segments of passengers and points-of-interest data is used to analyse the type of services that are likely to concentrate around public transport stations. The results allowed to identify the potential city services to establish partne

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Intermodal Routing Model for Sustainable Transport Through Multi-objective Optimizationde of the network - undefined destination. The mathematical formulation of the model leads to a multi-objective mixed binary linear program, and a classical scalarization method is performed to solve the problem. There is a lack of intermodal routing models in literature and specifically no multi-ob

TRACE

A Low Latency SCAN-Flip Polar Decoder for 5G Vehicular Communication improve the performance of our SCAN-Flip decoder further. Simulation results show that the proposed decoder has a much smaller average number of iterations than that of SCF at equivalent frame error rate. At equivalent max number of iterations, the error-correction performance of SCAN-Flip outperfo

实现

灯丝

Joao Carlos Ferreira,Ana Lúcia Martins,Vitor Monte

Interferons

先驱

ilitation. Consequently, the development of both metaverse and 6G will progress in tandem, hand in hand, offering local services in hospitals. From a value perspective, this paper contributes to the development of the 6G and metaverse in the hospital vertical by understanding the needs, capabilities

Substance-Abuse

João Carlos Ferreira,Ana Lúcia Martins,Frederica Gonçalves,Rui Maia through extensive feature selection, model testing, tuning, and edge optimization, we propose two novel Long Short-Term Memory (LSTM) neural networks. The first, uses 4 EEG sensors and can classify AD and Frontotemporal Dementia from cognitively normal (CN) subjects. The second, requires 3 EEG sens