To address the possible lack or total absence of pulses from particle detectors during the development of its associate electronics, we propose a model that can generate them without losing the features of the real ones. This model is based on artificial neural networks, namely Generative Adversarial Networks (GAN). This dataset contains the pulses of Na-22 and Cs-137 and the Python code to generate new synthetic pulses.
The UBFC-Phys dataset is a public multimodal dataset dedicated to psychophysiological studies. 56 participants followed a three-step experience where they lived social stress through a rest task T1, a speech task T2 and an arithmetic task T3. During the experience, the participants were filmed and were wearing a wristband that measured their Blood Volume Pulse (BVP) and ElectroDermal Activity (EDA) signals. Before the experience started and once it finished, the participants filled a form allowing to compute their self-reported anxiety scores.
Please find more details about the UBFC-Phys dataset's organization in the READ_ME file.
If you use this dataset, please cite the following paper:
R. Meziati Sabour, Y. Benezeth, P. De Oliveira, J. Chappé, F. Yang. "UBFC-Phys: A Multimodal Database For Psychophysiological Studies Of Social Stress", IEEE Transactions on Affective Computing, 2021.
As an alternative to classical cryptography, Physical Layer Security (PhySec) provides primitives to achieve fundamental security goals like confidentiality, authentication or key derivation. Through its origins in the field of information theory, these primitives are rigorously analysed and their information theoretic security is proven. Nevertheless, the practical realizations of the different approaches do take certain assumptions about the physical world as granted.
The data is provided as zipped NumPy arrays with custom headers. To load an file the NumPy package is required.
The respective loadz primitive allows for a straight forward loading of the datasets.
To load a file “file.npz” the following code is sufficient:
import numpy as np
measurement = np.load(’file.npz ’, allow pickle =False)
header , data = measurement [’header ’], measurement [’data ’]
The dataset comes with a supplementary script example_script.py illustrating the basic usage of the dataset.
The MIMOSigRef-SD dataset was created with the goal to support the research community in the design and development of novel multiple-input multiple-ouotput (MIMO) transceiver architectures. It was recorded using software radios as transmitters and receivers, and a wireless channel emulator to facilitate a realistic representation of a variety of different channel environments and conditions.
The MIMOSigRef-SD dataset is provided in the form of 8 individual TAR files. Each file represents one of the 8 modulation schemes utilized in our dataset. Within each file, the data is organized in a similar format: Naming of the contained folders represents the modulation scheme and order, channel environment, MIMO configuration, and type of MIMO. An example of such naming is: 16QAM – Vehicular B – (TX2 – RX1) – Spatial Diversity. This provides easy access to the information of interest.
YonseiStressImageDatabase is a database built for image-based stress recognition research. We designed an experimental scenario consisting of steps that cause or do not cause stress; Native Language Script Reading, Native Language Interview, Non-native Language Script Reading, Non-native Language Interview. And during the experiment, the subjects were photographed with Kinect v2. We cannot disclose the original image due to privacy issues, so we release feature maps obtained by passing through the network.
- Subject Number (01~50)
- Data acquisition phase
(Native Language Script Reading, Native Language Interview, Non-native Language Script Reading, Non-native Language Interview)
- Data (*.npy, the filename is set to the time the data was acquired; YYYYMMDD_hhmmss_ms)
In the case 'Non-native_Language_Interview' data of subject 26, it was not acquired due to equipment problems.
If you use YonseiStressImageDatabase in a scientific publication, we would appreciate references to the following paper:
Copyright © 2019 AI Hub, Inc., https://aihub.or.kr/
AI data provided by AI Hub was built as part of a business National Information Society Agency's 'Intelligent information industry infrastructure construction project' in Korea, and the ownership of this database belongs to National Information Society Agency.
Specialized field AI data was built for artificial intelligence technology development and prototype production and can be used for research purposes in various fields such as intelligent services and chatbots.
This file is the related program and data of a deep interpolation convnet for bearing fault classification under complex conditions
Anxiety affects human capabilities and behavior as much as it affects productivity and quality of life. It can be considered as the main cause of depression and suicide. Anxious states are easily detectable by humans due to their acquired cognition, humans interpret the interlocutor’s tone of speech, gesture, facial expressions and recognize their mental state. There is a need for non-invasive reliable techniques that perform the complex task of anxiety detection.
Download Zip file and extract it.
The ADAB database (The Arabic handwriting Data Base) was developed to advance the research and development of Arabic on-line handwritten systems. This database is developed in cooperation between the Institut fuer Nachrichtentechnik (IfN) and Research Groups in Intelligent Machines, University of Sfax, Tunisia. The text written is from 937 Tunisian town/village names. A pre-label assigned to each file consists of the postcode in a sequence of Numeric Character References, which stored in the UPX file format.
Download Zip file and extract it.
A pre-label assigned to each file consists of the postcode in a sequence of Numeric Character References, which stored in the UPX file format.
An InkML file including trajectory information and a plot image of the word trajectory are also generated.
Additional information about the writer can also be provided.
The PD-BioStampRC21 dataset provides data from a wearable sensor accelerometry studyconducted for studying activity, gait, tremor, and other motor symptoms in individuals with Parkinson's disease (PD).In addition to individuals with PD, the dataset also includes data for controls that also went through the same study protocol as the PD participants. Data were acquired using lightweight MC 10 BioStamp RC sensors (MC 10 Inc, Lexington, MA), five of which were attached to each participant for gathering data over a roughly two day interval.
Users of the dataset should cite the following paper:
Jamie L. Adams, Karthik Dinesh, Christopher W. Snyder, Mulin Xiong, Christopher G. Tarolli, Saloni Sharma, E. Ray Dorsey, Gaurav Sharma, "A real-world study of wearable sensors in Parkinson’s disease". Submitted.
where an overview of the study protocol is also provided. Additional detail specific to the dataset and file naming conventions is provided here.
The dataset is comprised of two main components: (I) Sensor and UPDRS-assessment-task annotation data for each participant and (II) demographic and clinical assessment data for all participants. Each of these is described in turn below:
I) Sensor and UPDRS-assessment-task annotation data:
For each participant the sensor accelerometry and UPDRS-assessment-task annotation data are provided as a zip file, for instance, ParticipantID018DataPDBioStampRC.zip for participant ID 018. Unzipping the file generates a folder with a name matching the participant ID, for example, 018, that contains the data organized as the following files. Times and timestamps are consistently reported in units of milliseconds starting from the instant of the earliest sensor recording (for the first sensor applied to the participant).
a) Accelerometer sensor data files (CSV) corresponding to the five different sensor placement locations, which are abbreviated as
1) Trunk (chest) - abbreviated as "ch"
2) Left anterior thigh - abbreviated as "ll"
3) Right anterior thigh - abbreviated as "rl"
4) Left anterior forearm - abbreviated as "lh"
5) Right anterior forearm - abbreviated as "rh"
Example file name for accelerometer sensor data files:
E.g. ch_ID018Accel.csv, ll_ID018Accel.csv, rl_ID018Accel.csv, lh_ID018Accel.csv, and rh_ID018Accel.csv
File format for the accelerometer sensor data files: Comprises of four columns that provide a timestamp for each measurement and corresponding triaxial accelerometry relative to the sensor coordinate system.
Column 1: "Timestamp (ms)" - Time in milliseconds
Column 2: "Accel X (g)" - Acceleration in X-direction (in units of g = 9.8 m/s^2)
Column 3: "Accel Y (g)" - Acceleration in Y-direction (in units of g = 9.8 m/s^2)
Column 4: "Accel Z (g)" - Acceleration in Z-direction (in units of g = 9.8 m/s^2)
b) Annotation file (CSV). This file provides tagging annotations for the sensor data that identify, via start and end timestamps, the durations of various clinical assessments performed in the study.
Example file name for annotation file:
File format for the annotation file: Comprises of four columns
Column 1: "Event Type" - List of in-clinic MDS-UPDRS assessments. Each assessment comprises of two queries - medication status and MDS-UPDRS assessment body locations
Column 2: "Start Timestamp (ms)" - Start timestamp for the MDS-UPDRS assessments
Column 3: "Stop Timestamp (ms)" - Stop timestamp for the MDS-UPDRS assessments
Column 4: "Value" - Responses to the queries in Column 1 - medication status (OFF/ON) and MDS-UPDRS assessment body locations (E.g. RIGHT HAND, NECK, etc.)
II) Demographic and clinical assessment data
For all participants, the demographic and clinical assessment data are provided as a zip file "Clinic_DataPDBioStampRCStudy.zip". Unzipping the file generates a CSV file named Clinic_DataPDBioStampRCStudy.csv.
File format for the demographic and clinical assessment data file: Comprises of 19 columns
Column 1: "ID" - Participant ID
Column 2: "Sex" - Participant sex (Male/Female)
Column 3: "Status" - Participant disease status (PD/Control)
Column 4: "Age" - Participant age
Column 5: "updrs_3_17a" - Rest tremor amplitude (RUE - Right Upper Extremity)
Column 6: "updrs_3_17b" - Rest tremor amplitude (LUE - Left Upper Extremity)
Column 7: "updrs_3_17c" - Rest tremor amplitude (RLE - Right Lower Extremity)
Column 8: "updrs_3_17d" - Rest tremor amplitude (LLE - Right Lower Extremity)
Column 9: "updrs_3_17e" - Rest tremor amplitude (Lip/Jaw)
Column 10 - Column 14: "updrs_3_17a_off" - "updrs_3_17e_off" - Rest tremor amplitude during OFF medication assessment (ordering similar as that from Column 5 to Column 9)
Column 15 - Column 19: "updrs_3_17a_on" - "updrs_3_17e_on" - Rest tremor amplitude during ON medication assessment
For details about different MDS-UPDRS assessments and scoring schemes, the reader is referred to:
Goetz, C. G. et al. Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov Disord 23, 2129-2170, doi:10.1002/mds.22340 (2008)