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. Due to a highly novel type of visual sensors used in event-based vision, only a few datasets aimed at visual navigation tasks are publicly available.


In order to obtain the constants of our PID temperature controller, it was necessary to identify the system. The identification of the system allows us, through experimentation, to find the representation of the plant to be able to control it.

The first data with name "data_2.mat" represent the open loop test, where the sampling frequency is 100 [Hz], this data was useful to find the period of the pulse train generator, which is twice the slowest sampling time analyzed between the high pulse and low pulse of the input. 


The LEDNet dataset consists of image data of a field area that are captured from a mobile phone camera.

Images in the dataset contain the information of an area where a PCB board is placed, containing 6 LEDs. Each state of the LEDs on the PCB board represents a binary number, with the ON state corresponding to binary 1 and the OFF state corresponding to binary 0. All the LEDs placed in sequence represent a binary sequence or encoding of an analog value.


Design and fabrication outsourcing has made integrated circuits vulnerable to malicious modifications by third parties known as hardware Trojan (HT). Over the last decade, the use of side-channel measurements for detecting the malicious manipulation of the chip has been extensively studied. However, the suggested approaches mostly suffer from two major limitations: reliance on trusted identical chip (e.i. golden chip); untraceable footprints of subtle hardware Trojans which remain inactive during the testing phase.



1. Movie "movie_S1.avi" demonstrates the normalized absolute element values of dynamic influence matrices and its scaled matrices by comb drive torque along the amplitude. The dynamic influence matrices are plotted according to the comb drive frequency component, index n, and the input frequency component, index m. The absolute values of the elements of the matrix are normalized by its maximum element. The maximum element is drawn in white and the normalized elements less than 10e−6 of the maximum element are depicted in black.


This dataset is in support of my following Research papers  

Preprint  (Make sure you have read Caution) :

  • Novel ß Transtibial Prosthetic 9-DoF Artificial Leg Adaptive Controller - Part I*


This dataset is in support of my 3 research papers 'Comparative Analysis of 72 Flyback Transformers on 5τ Non-linear Battery with Loss Functions - Part I', 'Comparative Analysis of 72 Flyback Transformers on 5τ Non-linear Battery with Loss Functions - Part II' and 'Comparative Analysis of 72 Flyback Transformers on 5τ Non-linear Battery with Loss Functions - Part III'.

Preprint : 


This dataset provides GPS, IMU, and wheel odometry readings on various terrains for the Pathfinder robot which is a lightweight, 4-wheeled, skid-steered, custom-built rover testbed platform. Rover uses a rocker system with a differential bar connected to the front wheels. Pathfinder is utilized with slick wheels to encounter more slippage. The IMU incorporated on the rover is an ADIS-16495 with 50Hz data rate. Pathfinder's quadrature encoders with 47,000 pulses/m resolution are used for wheel odometry readings with 10Hz data rate.