convolutional neural network

1.The spectrum of the dataset is obtained by applying force to the tactile sensor based on Chirped Bragg gratings.

2.The applied force ranges from 0N to 10N on the sensing pad of 4cm×4cm.

3.The folder name (x, y) represents the specific coordinates of the point at which the force is applied, and the xN name of the subfolder represents the xN force applied at that point.

4.A total of 120 spectral data were collected in each applied force state.

5.The first column of each spectrum is wavelength and the second column is intensity.

Miniature mobile robots in multi-robotic systems require robust environmental perception for successful navigation, especially when operating in real-world environments. One of the sensors that have recently become accessible for miniature mobile robots due to their size and cost-effectiveness is a multi-zone time-of-flight (ToF) sensor. The object of classification in the dataset is a miniature mobile robot on a sand-like terrain with rocks. The dataset was acquired with the ST VL53L5CX ToF sensor.

This dataset consists of the training and the evaluation datasets for the LiDAR-based maritime environment perception presented in our journal publication "Maritime Environment Perception based on Deep Learning." Within the datasets, LiDAR raw data are processed using Deep Neural Networks (DNN). In the training dataset, we introduce the method for generating training data in Gazebo simulation. In the evaluation datasets, we provide the real-world tests conducted by two research vessels, respectively.

INDIA is the second-largest fruit and vegetable exporter in the world after China. It ranked first in the production of Bananas, Papayas, and Mangoes. Public datasets of fruits are available but they are limited to general fruit classes and failed to classify the fruits according to the fruit quality. To overcome this problem, we have created a dataset named FruitsGB (Fruits Good/Bad) dataset.