The project is conceptualized to 'Geo Web-Based Facility Mapping for Zone-2 in Greater Visakhapatnam Municipal Corporation- GVMC in Visakhapatnam, India'. The main objective is to share the spatial data to the public to help them find the information about the nearest Hospital, ATM, Educational institutions, petrol filling stations, and supermarkets by providing both web map services and web coverage services using QGIS Cloud.
The paper is aimed at an investigation of the effects of the mover eccentricity on the PM eddy current loss of tubular-linear synchronous machines (T-LSMs) equipped with quasi-Halbach magnetized PMs. The study is initiated by an analytical prediction of the no-load spatial repartition of the flux density taking into account the non-coaxiality of the mover and stator, and its validation by a 3D finite element analysis (FEA). Then a special attention is paid to an analytical formulation of the PM eddy current loss in presence of eccentricity.
The dataset consists of vessel tracking data in the form of AIS observations in the Baltic Sea during years 2017-19. The AIS observations have been enriched with vessel metadata such as power, max speed and draft. The data has been collected for master’s thesis work and the data has been splitter into training and validation sets. The AIS observations do not cover all months of the collection period.
Along with the increasing use of unmanned aerial vehicles (UAVs), large volumes of aerial videos have been produced. It is unrealistic for humans to screen such big data and understand their contents. Hence methodological research on the automatic understanding of UAV videos is of paramount importance.
The dataset has been colected around the Valencia Seaport. This dataset contains AIS raw data from January, February, March 2017. This dataset has been used in the testing of the Seaport Data Space proposed in the journal article titled "Seaport Data Space for Improving Logistic Maritime Operations".
Dataset of GPS, inertial and WiFi data collected during road vehicle trips in the district of Porto, Portugal. It contains 40 trip datasets collected with a smartphone fixed on the windshield or dashboard, inside the road vehicle. The dataset was collected and used in order to develop a proof-of-concept for "MagLand: Magnetic Landmarks for Road Vehicle Localization", an approach that leverages magnetic anomalies created by existing road infrastructure as landmarks, in order to support current vehicle localization system (e.g. GNSS, dead reckoning).
Pedestrian detection has never been an easy task for computer vision and automotive industry. Systems like the advanced driver assistance system (ADAS) highly rely on far infrared (FIR) data captured to detect pedestrians at nighttime. The recent development of deep learning-based detectors has proven the excellent results of pedestrian detection in perfect weather conditions. However, it is still unknown what is the performance in adverse weather conditions.
Research on damage detection of road surfaces has been an active area of research, but most studies have focused so far on the detection of the presence of damages. However, in real-world scenarios, road managers need to clearly understand the type of damage and its extent in order to take effective action in advance or to allocate the necessary resources. Moreover, currently there are few uniform and openly available road damage datasets, leading to a lack of a common benchmark for road damage detection.
