Electromagnetic reflections can alter the RF signal strength at the receiving node. This

alteration could result in the enhancement of the received signal. The work is primarily based upon passive

reflecting walls and their effect on the received signal strength under various operating conditions. The

work could further be utilized for energy harvesting purposes for the mobile phone charging applications.

The operational frequency range in this research is from 469:5MHz to 773:5MHz. The results show


Parasitic infections have been recognised as one of the most significant causes of illnesses by WHO. Most infected persons shed cysts or eggs in their living environment, and unwittingly cause transmission of parasites to other individuals. Diagnosis of intestinal parasites is usually based on direct examination in the laboratory, of which capacity is obviously limited.

Last Updated On: 
Thu, 02/24/2022 - 15:33
Citation Author(s): 
Duangdao Palasuwan, Thanarat H. Chalidabhongse, Korranat Naruenatthanaset, Thananop Kobchaisawat, Kanyarat Boonpeng, Nuntiporn Nunthanasup, Nantheera Anantrasirichai

This dataset is a collection of images and their respective labels containing multiple Indian coins of different denominations and their variations. The dataset only contains images of one side of each coin (Tail side) which contains the denomination value.

The samples were collected with the help of a mobile phone while the coins were placed on top of a white sheet of A4-sized paper.


We propose a novel high-resolution dataset named, “Dataset for Indian Road Scenarios (DIRS21)” for developing perception systems for advanced driver assistance systems.


To study the driver's behavior in real traffic situations, we conducted experiments using an instrumented vehicle, which comprises:

(i) a camera, installed above the vehicle's side window and oriented toward the driver, and (ii) a Mobile Digital Video Recorder (MDVR).


The accompanying dataset for the CVSports 2021 paper: DeepDarts: Modeling Keypoints as Objects for Automatic Scoring in Darts using a Single Camera

Paper Abstract:


The recommended way to load the labels is to use the pandas Python package:

import pandas as pd

labels = pd.read_pickle("labels.pkl")

See github repository for more information: https://github.com/wmcnally/deep-darts


The dataset consists of two classes: COVID-19 cases and Healthy cases 


Unzip the dataset


An offline handwritten signature dataset from two most popular scripts in India namely Roman and Devanagari is proposed here. 


Writer identification dataset availability on Indic scripts is a major issue to carry forward research in this domain. Devanagari and Roman are two most popular and widely used scripts of India. We have a total of 5433 signatures of 126 writers, out of which 3929 signatures from 80 writers in Roman script and 1504 signatures from 46 writers in Devanagari scripts. Script-wise per writer 49 signatures from Roman and 32 signatures from Devanagari are considered making an average of 43 signatures per writer on whole dataset. We have reported a benchmark results on this dataset for writer identification task using a lightweight CNN architecture. Our proposed method is compared with state-of-the-art handcrafted feature based method such as gray level co-occurrence matrix (GLCM), Zernike moments, histogram of oriented gradients (HOG), local binary pattern (LBP), weber local descriptor (WLD), gabor wavelet transform (GWT) and it outperforms. In addition, few well known CNN arechitechture is also compared with the proposed method and it shows comparable performance. 

User guidance: The images are available in .jpg format with 24 bit color. The dataset is freely available for research work. Cite the following paper while using the dataset

Sk Md Obaidullah, Mridul Ghosh, Himadri Mukherjee, Kaushik Roy and Umapada Pal “Automatic Signature-based Writer Identification in Mixed-script Scenarios”, in 16th International Conference on Document Analysis and Recognition (ICDAR 2021), Lussane, Switzerland, 2021


 Lung segmentation is essential in developing AI-assisted diagnosis methods. Here is the result of lung segmentation using morphological operation, and it has been used in our study. It contains 7053 CT slices in .jpg format. And the original dataset can be seen via  the Kaggle link https://www.kaggle.com/hgunraj/covidxct


Indian Regions Soil Image Database (IRSID) : A dataset for classification of Indian soil types