Web API and Mushup dataset from ProgrammableWeb

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Driving behavior plays a vital role in maintaining safe and sustainable transport, and specifically, in the area of traffic management and control, driving behavior is of great importance since specific driving behaviors are significantly related with traffic congestion levels. Beyond that, it affects fuel consumption, air pollution, public health as well as personal mental health and psychology. Use of Smartphone sensors for data acquisition has emerged as a means to understand and model driving behavior. Our aim is to analyze driving behavior using on Smartphone sensors’ data streams.

Instructions: 

The datasets folder include .csv files of sensor data like Accelerometer, Gyroscope, etc. This data was recorded in live traffic while driver was executing certain driving events. The travel time for each one way trip was approximately 5kms - 20kms. The smartphone position was fixed horizontally in the vehicles utility box. Vehicle type used for data recording was LMV.

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There is an unmet need for quick, physically small, and cost-effective office-based techniques that can measure bone properties without the use of ionizing radiation. The present study reports application of a neural network classifier to the processing of previously collected data on very low power radiofrequency propagation through the wrist with the goal to detect osteoporotic/osteopenic conditions. Our approach categorizes the data obtained for two dichotomic groups. Group 1 included 27 osteoporotic/osteopenic subjects with low BMD (DXA T score below - 1) measured within one year.

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5G technologies have enabled new applications on a heterogeneous and distributed infrastructure edge which unifies hardware, network and software aimed at digital enabling. Based on the requirements of Industry 4.0, this infrastructure is developed using the cloud and fog computing sharing model, which should meet the needs of service level agreements in a convenient and optimized way, requiring an orchestration mechanism for the dynamic resource allocation.

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The Internet of Things (IoT) is reshaping our connected world, due to the prevalence of lightweight devices connected to the Internet and their communication technologies. Therefore, research towards intrusion detection in the IoT domain has a lot of significance. Network intrusion datasets are fundamental for this research, as many attack detection strategies have to be trained and evaluated using these datasets.

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This dataset comprises sensory data of in and out miniature vehicle (mobile sink) movement in the agriculture fields. The dataset is collected from the miniature vehicle using a 9-axis Inertial Measurement Unit (IMU) sensor (MPU-9250) placed on the top of the vehicle. Though the vehicle is small but designed to handle all the hurdles of the agricultural land, such as rough and muddy surface. This dataset aims to facilitate appropriate path planning in the agricultural field for the automatic cultivation of seeds, manure spread, and nutrients insertion.

Instructions: 

The dataset contains Multivariate Time Series (MTS) of the miniature vehicle’s in and out movement in the agricultural field. The miniature vehicle collects the sensory data of the Inertial Measurement Unit (IMU) sensor (MPU-9250) deployed on it. MPU-9250 is a 9-axis sensor used for recording the linear and angular motion of the vehicle in the jerking condition due to the uneven surface of the farmland. MPU-9250 comprises a 3-axis accelerometer, 3-axis gyroscope, and 3-axis magnetometer. These sensors are connected to a NodeMCU with an attached SD card, which stores the data. The sensory data is collected from sixteen different agricultural fields at a sampling rate of 5 Hz for 5 minutes each. Therefore, each field produces 1500 instances of tri-axial sensors (accelerometer, gyroscope, and magnetometer). Hence, the total instances we have collected is 1500 X 16 =24000.

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This dataset was prepared to estimate the winding temperature of a BLDC motor for a variable load and speed profile. It contains two files. The first one is the measurement results for the motor without cooling, while the second one is the measurement results after installing an additional cooling fan on the shaft. The data included in the files are time stamp, winding temperature, casing temperature, speed, current, power loss, mean and standard deviation of the measured quantities for 14400 data records.

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The dataset is divided into two sub-folders - 'source' and 'target'. The 'source' folder has a total of 4,080 images of Chest X-rays. The 'target' folder has a total of 4,080 Dual-Energy subtracted images corresponding to the images present in 'source' folder.

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Detailed documentation is provided in the following link: https://github.com/hmchuong/ML-BoneSuppression

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Another raw ADS-B signal dataset with labels, the dataset is captured using a BladeRF2 SDR receiver @ 1090MHz with a sample rate of 10MHz

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This dataset is used for i) analyzing the influence of process information on monitoring signals through signal processing methods; ii) training and testing models of tool monitoring and tool wear prediction especially for cutting conditions with large variations including cutting parameters, material and geometry of cutting tools, and workpiece materials, and also cutting conditions with continuous changes. This data set includes monitoring signals collected from machining process of sidewalls and closed pockets. The sidewall machining belongs to the cutting process with fixed cutting conditions; the closed pocket machining belongs to the cutting process of continuously varying cutting conditions for the reason that the tool path of closed pocket includes line, arc, full cutting and non-full cutting. Although cutting parameters are given fixed in the arc tool path area, the actual cutting parameters (such as feed, cutting width) are constantly changing due to the change of cutting geometry.

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