Machine Learning

The rapid evolution of wireless technology has led to the proliferation of small, low-power IoT devices, often constrained by traditional battery limitations, resulting in size, weight, and maintenance challenges. In response, ambient radio frequency (RF) energy harvesting has emerged as a promising solution to power IoT devices using RF energy from the environment. However, optimizing the placement of energy harvesters is crucial for maximizing energy reception. This paper employs machine learning (ML) techniques to predict areas with high power intensity for RF energy harvesting.

These datasets are gathered from an array of six gas sensors to be used for the odor recognition system. The sensors those used to create the data set are; Df-NH3, MQ-136, MQ-135, MQ-8, MQ-4, and MQ-2.

odors of different 10 samples are taken from these six sensors 

1- Natural Air

2- Fresh Onion

3- Fresh Garlic

4- Fresh Lemon

5- Tomato

6- Petrol

7- Gasoline

8- Coffee 1,2

9- Orange

10- Colonia Perfume 

Safety of the Intended Functionality (SOTIF) addresses sensor performance limitations and deep learning-based object detection insufficiencies to ensure the intended functionality of Automated Driving Systems (ADS). This paper presents a methodology examining the adaptability and performance evaluation of the 3D object detection methods on a LiDAR point cloud dataset generated by simulating a SOTIF-related Use Case.

This dataset accompanies a research paper on leveraging Machine Learning (ML) techniques for regression to predict the optimum DC bias in direct current in optical orthogonal frequency division multiplexing (DCO-OFDM).  The dataset comprises a set of features to facilitate the prediction of the required DC bias to mitigate the impact of clipping distortion at the transmitter. MATLAB software was utilized for modelling the DCO-OFDM transmission and generating the research dataset.