Low Cost Precise Navigation in Urban Area with Multi-Constellation GNSS and Inertial-Aiding

2018 ◽  
Author(s):  
Yu Wang ◽  
Olivier Julien
Keyword(s):  
Urban Area ◽  
Low Cost

scholarly journals An Improved Handover Algorithm for LTE-A Femtocell Network

2020 ◽  
pp. 558-565
Author(s):  
Olusegun O. Omitola ◽  
◽  
Viranjay M. Srivastava
Keyword(s):  
Quality Of Service ◽  
Urban Area ◽  
Cellular Networks ◽  
Low Cost ◽  
Mobile Cellular ◽  
Femtocell Network ◽  
Mobile Cellular Networks ◽  
And Performance ◽  
Handover Algorithm

Femtocells have been regarded as low-power and low cost devices for enhancing the capacity and performance of mobile cellular networks. Apart from forming a two-tier network with the macrocell to offload traffic from the macrocell, femtocells can be deployed in an urban area to achieve more data rate with better Quality of Service (QoS). However, this is at the expense of increased frequency of the handover of the UEs from one femtocell to another femtocell. Selecting a particular femtocell for handover is a serious challenge in a femtocell/macrocell deployment environment. Similarly, managing the resulting handovers can be extremely difficult. Thus, this study presents an algorithm to improve handover in LTE-A femtocell network. The complexity of the algorithm was determined and the performance by comparing it with existing algorithm in terms of number of handovers and the ratio of target femtocells. The results have shown that the proposed algorithm outperformed the existing algorithm.

scholarly journals URBAN DATA COLLECTION USING A BIKE MOBILE SYSTEM WITH A FOSS ARCHITECTURE

2017 ◽  
Vol XLII-4/W2 ◽  
pp. 3-9 ◽  
Author(s):  
A. Abdul Jabbar ◽  
I. Aicardi ◽  
N. Grasso ◽  
M. Piras
Keyword(s):  
Air Quality ◽  
Urban Area ◽  
3D Model ◽  
Time Synchronization ◽  
Low Cost ◽  
Laser Scanner ◽  
Digital Camera ◽  
Quality Parameter ◽  
Raspberry Pi ◽  
Safety Level

European community is working to improve the quality of the life in each European country, in particular to increase the quality air condition and safety in each city. The quality air is daily monitored, using several ground station, which do not consider the variation of the quality during the day, evaluating only the average level. In this case, it could be interesting to have a “smart” system to acquire distributed data in continuous, even involving the citizens. On the other hand, to improve the safety level in urban area along cycle lane, road and pedestrian path, exist a lot of algorithms for visibility and safety analysis; the crucial aspect is the 3D model considered as “input” in these algorithms, which always needs to be updated. <br><br> A bike has been instrumented with two digital camera as Raspberry PI-cam. Image acquisition has been realized with a dedicated python tool, which has been implemented in the Raspberry PI system. Images have been georeferenced using a u-blox 8T, connected to Raspberry system. GNSS data has been acquired using a specific tool developed in Python, which was based on RTKLIB library. Time synchronization has been obtained with GNSS receiver. Additionally, a portable laser scanner, an air quality system and a small Inertial platform have been installed and connected with the Raspberry system. <br><br> The system has been implemented and tested to acquire data (image and air quality parameter) in a district in Turin. Also a 3D model of the investigated site has been carried. In this contribute, the assembling of the system is described, in particular the dataset acquired and the results carried out will be described. different low cost sensors, in particular digital camera and laser scanner to collect easily geospatial data in urban area.

scholarly journals UAV-BASED HYPERSPECTRAL DATA ANALYSIS FOR URBAN AREA MAPPING

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 621-626
Author(s):  
N. A. Suran ◽  
H. Z. M. Shafri ◽  
N. S. N. Shaharum ◽  
N. A. W. M. Radzali ◽  
V. Kumar
Keyword(s):  
Urban Area ◽  
Low Cost ◽  
Hyperspectral Data ◽  
Pollution Monitoring ◽  
Support Vector ◽  
Land Resource ◽  
Easy Method ◽  
Spectral Angle Mapper ◽  
Aerial Vehicle ◽  
Artificial Neural Network Ann

Abstract. A recent development in low-cost technology such as Unmanned Aerial Vehicle (UAV) offers an easy method for collecting geospatial data. UAV plays an important role in land resource surveying, urban planning, environmental protection, pollution monitoring, disaster monitoring and other applications. It is a highly adaptable technology that is continuously changing in innovative ways to provide greater utility. Thus, this study aimed to evaluate the capability of UAV-based hyperspectral data for urban area mapping. In order to do the mapping, Artificial Neural Network (ANN), Support Vector Machine (SVM), Maximum Likelihood (ML) and Spectral Angle Mapper (SAM) were used to classify the urban area. The classifications involved seven classes: concrete, aluminium, flexible pavement, clay tile, interlocking block, tree and grass. Then, the overall accuracies obtained from ANN, SVM, ML and SAM for 0.3 m spatial resolution images were 92.33%, 85.86%, 83.41% and 46.55% with the kappa coefficient of 0.91, 0.83, 0.80 and 0.38 respectively. Thus, the classification results showed that the powerful and intelligent ANN algorithm produced the highest accuracy compared to the other three algorithms. Overall, mapping of urban area using UAV-based hyperspectral data and advanced algorithms could be the way forward in producing updated urban area maps.

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