This is a data set for Radio Frequency fingerprinting, which is a kind of identification of wireless devices based on their intrinsic physical features. The data set is composed by GSM bursts collected from 12 GSM mobile phones while transmitting. The samples have been collected using a Software Defined Radio with a sample rate at 20 MS/s. The content information has been removed from the bursts to remove the risk of bias due to content. The data set is in MATLAB format.
A reliable and extensive set of public WiFi fingerprinting radio map databases for researchers to implement, evaluate and compare Wi-Fi RSSI indoor localization schemes, the radio map database contains RSSI information from more than 400 APs collected during the conducted experiment on different days with the support of lab mates and volunteers.
This dataset containg 1900+ images divided into fresh oranges and rotten oranges. In an orange packing factory, a video was recorded, by placing the camera parallel and above the oranges conveyor. The video was captured for 10 minutes with a quality of Ultra High Definition (4K) with 60 frames per second and a High Dynamic Range feature. The video was changed from High Dynamic Range to Standard Dynamic Range by the use of Splice - Video Editor & Maker software. The video is inserted to developed algorithm operating video processing on it and creating the frames.
In this project, an LSTM-based Model Predictive Controller (LSTM-MPC), with 200 neurons of each layer, is designed to have a highly efficient control on the temperature. The resulted dataset is attached, for further considerations.
In emerging vehicular networks, delay-sensitive tasks can be processed in real time by offloading to
the edge computing servers. Unlike the legacy scenarios, in this paper a novel data offloading/delivery
decision making framework is proposed, where users have the option to divide their task into several
portions and partially offload their data to a complex multi-access edge computing (MEC) environment,
consisting of several MEC servers located on road side units (RSUs), base stations (BSs), and unused
20 falls and 16 daily living activities were performed by 17 volunteers with 5 repetitions while wearing 6 sensors (3.060 instances) that attached to their head, chest, waist, wrist, thigh and ankle.
In practical media distribution systems, visual content usually undergoes multiple stages of quality degradation along the delivery chain, but the pristine source content is rarely available at most quality monitoring points along the chain to serve as a reference for quality assessment. As a result, full-reference (FR) and reduced-reference (RR) image quality assessment (IQA) methods are generally infeasible. Although no-reference (NR) methods are readily applicable, their performance is often not reliable.
In practical media distribution systems, visual content usually undergoes multiple stages of quality degradation along the delivery chain, but the pristine source content is rarely available at most quality monitoring points along the chain to serve as a reference for quality assessment. As a result, full-reference (FR) and reduced-reference (RR) image quality assessment (IQA) methods are generally infeasible. Although no-reference (NR) methods are readily applicable, their performance is often not reliable.
Buildings are essential components of urban areas. While research on the extraction and 3D reconstruction of buildings is widely conducted, information on fine-grained roof types of buildings is usually ignored. This limits the potential of further analysis, e.g., in the context of urban planning applications. The fine-grained classification of building roof type from satellite images is a highly challenging task due to ambiguous visual features within the satellite imagery.
The dataset is generated from the ice-cream factory simulation environmen that is composed of six modules (Mixer, Pasteurizer, Homogenizer, Aeging Cooling, Dynamic Freezer, and Hardening). The values of analog sensors for level and temperature are modified using three anomaly injection options: freezing value, step change and ramp change. The dataset is composed of 1000 runs, out of which 258 were executed without anomalies.
Link to github: https://github.com/vujicictijana/MIDAS
