This data set is from a recent biological molecular communication (MC) testbed and provides a set of experimental measurement data.In particular, the MC testbed is realized using Escherichia coli (E. coli) bacteria that express the light-driven proton pump gloeorhodopsin (GR) from Gloeobacter violaceus (G.

Instructions: 

Package:

- Comprehensive description of the data set and experimental procedure: supplement.pdf

- Publication: A Molecular Communication Testbed Based on Proton Pumping Bacteria: Methods and Data, IEEE Transactions on Molecular, Biological, and Multi-Scale Communications

- Signals for 18 experiments of types "Dark Adaption", "Control Response", "Long On-Off Response", and "Bit Response": SignalData.zip

- Signals for 12 noise measurements: NoiseData.zip

- Matlab script for visualization: example_DisplayData.m

 

Citing:

- Reference to the experimental system: Biological Optical-to-Chemical Signal Conversion Interface: A Small-Scale Modulator for Molecular Communications, https://ieeexplore.ieee.org/document/8467351

- Reference to this data set: A Molecular Communication Testbed Based on Proton Pumping Bacteria: Methods and Data, IEEE Transactions on Molecular, Biological, and Multi-Scale Communications

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PURPOSE

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This data package is provided as supplementary material of the paper:

“A GPU Approach to Efficiently Reusing Constraint Proofs”, by M. Chen, G. Denaro and M. Pezzè, 

submitted for evaluation to IEEE Transactions on software engineering, Nov. 2019.

 

STRUCTURE

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The data package consists of 24 files:

- a file named _README.txt: this file

- a file named _programs.txt

- 22 files named <program_name>.sexpr

 

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This dataset comes up as a benchmark dataset for machines to automatically recognizing the handwritten assamese digists (numerals) by extracting useful features by analyzing the structure. The Assamese language comprises of a total of 10 digits from 0 to 9. We have collected a total of 516 handwritten digits from 52 native assamese people irrespective of their age (12-86 years), gender, educational background etc. The digits are captured in .jpeg format using a paint mobile application developed by us which automatically saves the images in the internal storage of the mobile.

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751 Views

This work presents a methodology of constructing three models respectively without blades, with straight blades and with curved blades, coupled for artificial simulated fog-haze environment with computational fluid dynamics (CFD), to predict the impact of the rotating blades on the flow velocities in the enclosed environment by simulation. Atmospheric flow characteristics and variation of flow velocities were analyzed, and the influences of different rotating blades on flow velocities were compared to get the related simulation results in three models. 

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114 Views

An accurate and reliable image-based quantification system for blueberries may be useful for the automation of harvest management. It may also serve as the basis for controlling robotic harvesting systems. Quantification of blueberries from images is a challenging task due to occlusions, differences in size, illumination conditions and the irregular amount of blueberries that can be present in an image. This paper proposes the quantification per image and per batch of blueberries in the wild, using high definition images captured using a mobile device.

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1831 Views

Attempts to prevent invasion of marine biofouling on marine vessels are demanding. By developing a system to detect marine fouling on vessels in an early stage of fouling is a viable solution. However, there is a  lack of database for fouling images for performing image processing and machine learning algorithm.

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1066 Views

This dataset contains the files provided in support of the paper entitled 'Isolating Change Propagation in Software Architectures' authored by Andrew Leigh, Michel Wermelinger and Andrea Zisman of the Open University.

If you have any questions please contact: andrew.leigh@open.ac.uk the files have been shared to allow other researchers to reproduce the work. The data set includes a README file that explains the file purpose and contents in detail.

Instructions: 

Please refer to:

1. The paper entitled 'Isolating Change Propagation in Software Architectures' by Andrew Leigh, Michel Wermelinger and Andrea Zisman;

2. README.txt enclosed within the dataset.

Please note: the paper has not yet been published. These instructions will be updated to include a URL and reference upon publication.

Please contact andrew.leigh@open.ac.uk for any questions/enquiries.

 

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81 Views

Chinese Hotel Review Dataset

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473 Views

The dataset consists of 60285 character image files which has been randomly divided into 54239 (90%) images as training set 6046 (10%) images as test set. The collection of data samples was carried out in two phases. The first phase consists of distributing a tabular form and asking people to write the characters five times each. Filled-in forms were collected from around 200 different individuals in the age group 12-23 years. The second phase was the collection of handwritten sheets such as answer sheets and classroom notes from students in the same age group.

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The modified CASIA dataset is created for research topics like: perceptual image hash, image tampering detection, user-device physical unclonable function and so on. 

Instructions: 

 

"training" folder: 160 images are categorized into the "training" folder, in which, "au" folder contains the original (authentic) 160 images without any modifications; "au_cp_**" folder contains those 160 images that undergoes content-preserving (cp) operations. For example, "au_cp_gamma" means those images inside are obtained by appling gamma corrections to the authentic images in the "au" folder; The "tampered" folder is the tampered version of the "au" folder correspondingly. 

"Au_ani_0001" in "au" folder: authentic image, animal category, index 0001;

"ani00008_ani00011_105" in "tampered" folder: tamper image "Au_ani_0008" by applying partial contents of image "Au_ani_0011".  Here "Au_ani_0008" and "Au_ani_0011" are all from "au" folder. 

 

"testing" folder: Another 240 images are categorized into the "testing" folder. The naming rules of the sub-folders and the images are same as the "training" folder. Inside, "tampered_with_cp" is the  D_tampered_cp as introduced in the above Abstract. "testing/tampered_with_cp/gamma" folder indicate s the authentic images are applied both tampering operation and gamma correction. 

contains

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2367 Views

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