Classification and Regression folders each have a 'zz_published_results'folder. You can either generate your own results again using the Or you can move these folders'contents up to either the Classification or Regression folder and onlyrun the analysis code in sf_analysis.


10 Use cases of containers sway speed along the X-axis during loading and unloading procedures using a quay crane in Klaipeda containers terminal.


The file includes 10 use cases of container sway speed including the spreader. 

Data samples: dataX_1, dataX_3, dataX_5, dataX_10, provide sway speed for the X-axis during the container unloading procedures from a ship, while other samples provide the opposite procedures.

Data sample called Y provides the time-stamp. 


This is the data for paper "Environmental Context Prediction for Lower Limb Prostheses with Uncertainty Quantification" published on IEEE Transactions on Automation Science and Engineering, 2020. DOI: 10.1109/TASE.2020.2993399. For more details, please refer to 


Seven able-bodied subjects and one transtibial amputee participated in this study. Subject_001 to Subject_007 are able-bodied participants and Subject_008 is a transtibial amputee.


Each folder in the file has one continuous session of data with the following items: 

1. folder named "rpi_frames": the frames collected from the lower limb camera. Frame rate: 10 frames per second. 

2. folder named "tobii_frames": the frames collected from the on-glasses camera. Frame rate: 10 frames per second. 

3. labels_fps10.mat: synchronized terrain labels, gaze from the eye-tracking glasses, GPS coordinates, and IMU signals. 

3.1 cam_time: the timestamps for the videos, GPS, gazes, and labeled terrains (unit: second). 10Hz

3.2 imu_time: the timestamps for the IMU sensors (unit: second). 40Hz.

3.3 GPS: the GPS coordinates (latitude, longitude)

3.4 rpi_FrameIds, tobii_FrameIds: the frame ID for the lower-limb and on-glasses cameras respectively. The ids indicate the filenames in "rpi_frames" and "tobii_frames" respectively. 

3.5 rpi_IMUs, tobii_IMUs: the imu signals from the two devices. Columns: (accel_x,accel_y,accel_z,gyro_x,gyro_y,gyro_z)

3.6 terrains: the type of terrains the subjects are current on. Six terrains: tile, brick, grass, cement, upstairs, downstairs. "undefined" and "unlabelled" can be regarded as the same kind of data that needs to be deprecated.


The following sessions were collected during busy hours (many pedestrians were around):







The following sessions were collected during non-busy hours (few pedestrians were around):









The other sessions were collected without specific collecting hours (e.g. busy or non-busy). 

For the following sessions, the data collection devices were not optimized (e.g. non-optimal brightness balance). Thus, we recommend to use these sessions as training or validation dataset but not as testing data.








Analysis Data - Sample


The "Dynamic Scenes" Dataset is provided for testing visual loop closure detection algorithms in highly dynamic scenes. It has a strong background in some crucial applications such as autonomous driving systems.


Our state of arousal can significantly affect our ability to make optimal decisions, judgments, and actions in real-world dynamic environments. The Yerkes-Dodson law, which posits an inverse-U relationship between arousal and task performance, suggests that there is a state of arousal that is optimal for behavioral performance in a given task. Here we show that we can use on-line neurofeedback to shift an individual's arousal from the right side of the Yerkes-Dodson curve to the left toward a state of improved performance.


We hope you find this dataset useful. For help please see the provided readme file, the article by Faller et al. (2019) in PNAS, the preprint by Faller et al. (2018) on BioRxiv, the conference paper by Faller et al. (2016) at IEEE SMC and/or the tutorials for the Matlab toolboxes EEGLAB and BCILAB. Thank you very much.


Indoor location systems based on ultra-wideband (UWB) technology have become very popular in recent years following the introduction of a number of low-cost devices on the market capable of providing accurate distance measurements. Although promising, UWB devices also suffer from the classic problems found when working in indoor scenarios, especially when there is no a clear line-of-sight (LOS) between the emitter and the receiver, causing the estimation error to increase up to several meters.


A solenoid-operated valve with DC resistance of about 10.3 Ω was exposed to 50 % RH, its rated temperature of 80 °C, and powered at its rated voltage of 12 VDC. (Note: the valve was unrated with regards to relative humidity, RH.) Using Fourier transform infrared (FTIR) spectroscopy, the insulation material was found to be a combination of polystyrene and polyethylene terephthalate. The wire gauge was not disclosed in the valve information sheet.


Mobile Brain-Body Imaging (MoBI) technology was deployed at the Museo de Arte Contemporáneo (MARCO) in Monterrey, México, in an effort to collect Electroencefalographic (EEG) data from large numbers (N = ~1200) of participants and allow the study of the brain’s response to artistic stimuli, as part of the studies developed by University of Houston (TX, USA) and Tecnológico de Monterrey (MTY, México).


The dataset can be used for Brain MRI study for academic purpose only. Undersampling Masks can be used for random undersampling at different sampling rates.