Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks

This work addresses the problem of vehicle identification through non-overlapping cameras. As our main contribution, we introduce a novel dataset for vehicle identification, called Vehicle-Rear, that contains more than three hours of high-resolution videos, with accurate information about the make, model, color and year of nearly 3,000 vehicles, in addition to the position and identification of their license plates. To explore our dataset we design a two-stream CNN that simultaneously uses two of the most distinctive and persistent features available: the vehicle's appearance and its license plate. This is an attempt to tackle a major problem: false alarms caused by vehicles with similar designs or by very close license plate identifiers. In the first network stream, shape similarities are identified by a Siamese CNN that uses a pair of low-resolution vehicle patches recorded by two different cameras. In the second stream, we use a CNN for OCR to extract textual information, confidence scores, and string similarities from a pair of high-resolution license plate patches. Then, features from both streams are merged by a sequence of fully connected layers for decision. In our experiments, we compared the two-stream network against several well-known CNN architectures using single or multiple vehicle features. The architectures, trained models, and dataset are publicly available at https://github.com/icarofua/vehicle-rear

Restauração de serviços em nuvem óptica: uma abordagem tolerante à degradação de banda e ao atraso de restauração

Este artigo, aborda o problema de restauração de serviços na nuvem  com infraestrutura óptica na presença de falha em um único enlace. O algoritmo proposto, denominado R3D, leva em consideração dois parâmetros especificados em classes de serviço (tolerância ao atraso na restauração e degradação de banda passante) para fazer melhor uso dos recursos ópticos disponíveis durante o processo de restauração. Resultados, obtidos através de simulação, demonstram melhorias significativas na capacidade de restauração de serviços sem impactar de forma negativa a probabilidade de bloqueio

Restauração de serviços em nuvem óptica: uma abordagem tolerante à degradação de banda e ao atraso de restauração

Este artigo, aborda o problema de restauração de serviços na nuvem  com infraestrutura óptica na presença de falha em um único enlace. O algoritmo proposto, denominado R3D, leva em consideração dois parâmetros especificados em classes de serviço (tolerância ao atraso na restauração e degradação de banda passante) para fazer melhor uso dos recursos ópticos disponíveis durante o processo de restauração. Resultados, obtidos através de simulação, demonstram melhorias significativas na capacidade de restauração de serviços sem impactar de forma negativa a probabilidade de bloqueio

The influence of passenger load, driving cycle, fuel price and different types of buses on the cost of transport service in the BRT system in Curitiba, Brazil

This study analyses the influence of passenger load, driving cycle, fuel price and four different types of buses on the cost of transport service for one bus rapid transit (BRT) route in Curitiba, Brazil. First, the energy use is estimated for different passenger loads and driving cycles for a conventional bi-articulated bus (ConvBi), a hybrid-electric two-axle bus (HybTw), a hybrid-electric articulated bus (HybAr) and a plug-in hybrid-electric two-axle bus (PlugTw). Then, the fuel cost and uncertainty are estimated considering the fuel price trends in the past. Based on this and additional cost data, replacement scenarios for the currently operated ConvBi fleet are determined using a techno-economic optimisation model. The lowest fuel cost ranges for the passenger load are estimated for PlugTw amounting to (0.198–0.289) USD/km, followed by (0.255–0.315) USD/km for HybTw, (0.298–0.375) USD/km for HybAr and (0.552–0.809) USD/km for ConvBi. In contrast, the coefficient of variation (Cv role= presentation style= box-sizing: inherit; display: inline; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative; \u3eCvCv) of the combined standard uncertainty is the highest for PlugTw (Cv role= presentation style= box-sizing: inherit; display: inline; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative; \u3eCvCv: 15–17%) due to stronger sensitivity to varying bus driver behaviour, whereas it is the least for ConvBi (Cv role= presentation style= box-sizing: inherit; display: inline; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative; \u3eCvCv: 8%). The scenario analysis shows that a complete replacement of the ConvBi fleet leads to considerable higher cost of transport service on the BRT route, amounting to an increase by 64% to 139%, depending on the bus fleet composition. Meanwhile, the service quality is improved resulting in 42% up to 64% less waiting time for passengers at a bus stop