The S3 dataset contains the behaviour (sensors, statistics of applications, and voice) of 21 volunteers interacting with their smartphones for more than 60 days. The type of users is diverse, males and females in the age range from 18 until 70 have been considered in the dataset generation. The wide range of age is a key aspect, due to the impact of age in terms of smartphone usage. To generate the dataset the volunteers installed a prototype of the smartphone application in on their Android mobile phones.

 

Instructions: 

The data set is compressed into a zip file. Please unzip this file in the desired place and inside the main folder, you will find the file Readme.md with the instructions and the details of the database.

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WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.

Instructions: 

A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images

Email the authors at ushasi@iitb.ac.in for any query.

 

Classes in this dataset:

Airplane

Baseball Diamond

Buildings

Freeway

Golf Course

Harbor

Intersection

Mobile home park

Overpass

Parking lot

River

Runway

Storage tank

Tennis court

Paper

The paper is also available on ArXiv: A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images

 

Feel free to cite the author, if the work is any help to you:

 

``` @InProceedings{Chaudhuri_2020_EoC, author = {Chaudhuri, Ushasi and Banerjee, Biplab and Bhattacharya, Avik and Datcu, Mihai}, title = {A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images}, booktitle = {http://arxiv.org/abs/2008.05225}, month = {Aug}, year = {2020} }

 

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The Ways To Wear a Mask or a Respirator Database (WWMR-DB) is a test database that can be used to compare the behavior of current mask detection systems with images that most closely resemble the real case. It consists of 1222 images divided into 8 classes, depicting the most common ways in which masks or respirators are worn:

- Mask Or Respirator Not Worn

- Mask Or Respirator Correctly Worn

- Mask Or Respirator Under The Nose

- Mask Or Respirator Under The Chin

- Mask Or Respirator Hanging From An Ear

- Mask Or Respirator On The Tip Of The Nose

Instructions: 

For any question, please send an email to antonio.marceddu@polito.it.

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This data is for the portfolio

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This dataset contains (1) the Simulink model of a three-phase photovoltaic power system with passive anti-islanding protections like over/under current (OUC), over/under voltage (OUV), over/under frequency (OUF), rate of change of frequency (ROCOF), and dc-link voltage and (2) the results in the voltage source converter and the point of common coupling of the photovoltaic system during islanding operation mode and detection times of analyzed anti-islanding methods.

Instructions: 

The anti-islanding protection relays are included in the "Relay Protection Bus B20 (20 kV)" subsystem.

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This dataset was used for OFDM Signal Real-Time Modulation Recognition Based on Deep Learning and Software-Defined Radio, which provides additional details and description of the dataset. We generate 6 modulated OFDM baseband signals with header modulation and payload modulation as BPSK+BPSK, BPSK+QPSK, BPSK+8PSK, QPSK+BPSK, QPSK+QPSK, QPSK+8PSK, respectively. The SNR range of each signal is from -10 dB to +20 dB at intervals of 2 dB. There are 4096 pieces of data generated for each signal type under a specific SNR and each piece of data has 1024 samples.

Instructions: 

This dataset was used for OFDM Signal Real-Time Modulation Recognition Based on Deep Learning and Software-Defined Radio, which provides additional details and description of the dataset. We generate 6 modulated OFDM baseband signals with header modulation and payload modulation as BPSK+BPSK, BPSK+QPSK, BPSK+8PSK, QPSK+BPSK, QPSK+QPSK, QPSK+8PSK, respectively. The SNR range of each signal is from -10 dB to +20 dB at intervals of 2 dB. There are 4096 pieces of data generated for each signal type under a specific SNR and each piece of data has 1024 samples. That is, 6×16×4096 = 393216 pieces in total.

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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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A new generation of computer vision, namely event-based or neuromorphic vision, provides a new paradigm for capturing visual data and the way such data is processed.
Event-based vision is a state-of-art technology of robot vision. It is particularly promising for use in both mobile robots and drones for visual navigation tasks.

Instructions: 

Instructions

The dataset consists of 21 data sequences recorded in 12 agricultural scenarios in the autumn season. The following <..sequence_id..>'s are available:

  • 01_forest -- Closed loop, Forest trail, No wind, Daytime
  • 02_forest -- Closed loop, Forest trail, No wind, Daytime
  • 03_green_meadow -- Closed loop, Meadow, grass up to 30 cm, No wind, Daytime
  • 04_green_meadow -- Closed loop, Meadow, grass up to 30 cm, Mild wind, Daytime
  • 05_road_asphalt -- Closed loop, Asphalt road, No wind, Nighttime
  • 06_plantation -- Closed loop, Shrubland, Mild wind, Daytime
  • 07_plantation -- Closed loop, Asphalt road, No wind, Nighttime
  • 08_plantation_water -- Random movement, Sprinklers (water drops on camera lens), No wind, Nighttime
  • 09_cattle_farm -- Closed loop, Cattle farm, Mild wind, Daytime
  • 10_cattle_farm -- Closed loop, Cattle farm, Mild wind, Daytime
  • 11_cattle_farm_feed_table -- Closed loop, Cattle farm feed table, Mild wind, Daytime
  • 12_cattle_farm_feed_table -- Closed loop, Cattle farm feed table, Mild wind, Daytime
  • 13_ditch -- Closed loop, Sandy surface, Edge of ditch or drainage channel, No wind, Daytime
  • 14_ditch -- Closed loop, Sandy surface, Shore or bank, Strong wind, Daytime
  • 15_young_pines -- Closed loop, Sandy surface, Pine coppice, No wind, Daytime
  • 16_winter_cereal_field -- Closed loop, Winter wheat sowing, Mild wind, Daytime
  • 17_winter_cereal_field -- Closed loop, Winter wheat sowing, Mild wind, Daytime
  • 18_winter_rapeseed_field -- Closed loop, Winter rapeseed, Mild wind, Daytime
  • 19_winter_rapeseed_field -- Closed loop, Winter rapeseed, Mild wind, Daytime
  • 20_field_with_a_cow -- Closed loop, Cows tethered in pasture, Mild wind, Daytime
  • 21_field_with_a_cow -- Closed loop, Cows tethered in pasture, Mild wind, Daytime

Each sequence contains the following separately downloadable files:

  • <..sequence_id..>_data.tar.gz -- entire date sequence in raw data format (AEDAT2.0 - DVS, images - RGB-D, point clouds in pcd files - LIDAR, and IMU csv files with original sensor timestamps). Timestamp conversion formulas are available.
  • <..sequence_id..>_rosdata.tar.gz -- main sequence in ROS bag format. All sensors timestamps are aligned with DVS with an accuracy of less than 1 ms.
  • <..sequence_id..>_rawcalib_data.tar.gz -- recorded fragments that can be used to perform the calibration independently (intrinsic, extrinsic and time alignment).
  • <..sequence_id..>_video.mp4 -- provides an overview of the sequence data (for the DVS and RGB-D sensors).

The contents of each archive are described below...

Raw format data

The archive <..sequence_id..>_data.tar.gz contains the following files and folders:

+ meta-data/ - all the useful information about the sequence
| + meta-data.md - detailed information about the sequence,
| | sensors, files, and data formats
| + cad_model.pdf - sensors placement
| + <...>_timeconvs.json - timestamp conversion formulas
| + ground-truth/ - movement ground-truth data,
| | calculated using 3 different Lidar-SLAM algorithms
| | (Cartographer, HDL-Graph, LeGo-LOAM)
| + calib-params/ - intrinsic and extrinsic calibration parameters
+ recording/ - main sequence
+ dvs/ - DVS events and IMU data
+ lidar/ - Lidar point clouds and IMU data
+ realsense/ - Realsense camera RGB, Depth frames, and IMU data
+ sensorboard/ - environmental sensors data
(temperature, humidity, air pressure)

ROS Bag format data

Will be published later...

Calibration data

The <..sequence_id..>_rawcalib_data.tar.gz archive contains the following files and folders:

+ imu_alignments/ - IMU recordings of the platform lifting/releasing before
| and after the main sequence
| (can be used for custom timestamp alignment)
+ solenoids/ - IMU recordings of the solenoid vibrations before
| and after the main sequence
| (can be used for custom timestamp alignment)
+ lidar_rs/ - Lidar vs Realsense camera extrinsic calibration
| by placing a spherical object (ball) in the field of view of both sensors
+ dvs_rs/ - DVS and Realsense camera intrinsic and extrinsic
calibration frames (checkerboard pattern)

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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.

Instructions: 

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

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