Aneto

Ascending Aneto: Conquering the Pyrenees’s Pinnacle The adventure began with anticipation as we gathered our gear, crampons and ice axes, essential for the challenge ahead. From the starting elevation of 1700 meters, we embarked on our ascent to 2140 meters, finding sanctuary at the Renclusa Refuge. Dinner was our prelude to rest, yet sleep proved elusive. At 9 PM, I nestled into my bunk, but the altitude, the cold, and a symphony of nocturnal noises (someone snoring) allowed for only two hours of light slumber.

My routine

My Daily Routine Sports and Physical Activities Every day, I make sure to prioritize my physical well-being. My routine includes approximately 30 minutes of biking, which can vary depending on whether I’m working remotely or other factors. According to the World Health Organisation adults aged between 18-64 years should do at least 150–300 minutes of moderate-intensity aerobic physical activity, so I always like to do some cardio, moreover it makes me feel good.

My setup

Setup My coding setup consists of a dual-monitor arrangement and a high-performance PC featuring a GeForce 3080, an i7-12700KF, 64GB RAM 3200MHz, and a 2TB SSD. This configuration allows me to efficiently handle large datasets and run resource-intensive algorithms, enhancing my productivity.

3DP-TP-00 | 3D segmentation and object shape checking based on RGB-D sensor with CloudCompare

3D objects quality control.

3DP-TP-01 | Monocular localization with iterative PnL

Iterative estimation of a camera extrinsic parameters.

Autoencoder

Creation of pre-trained autoencoder to learn the initial condensed representation of unlabeled datasets. This architecture consists of 3 parts: Encoder: Compresses the input data from the train-validation-test set into a coded representation which is typically smaller by several orders of magnitude than the input data. Latent Space: This space contains the compressed knowledge representations and is thus the most crucial part of the network. Decoder: A module that helps the network to “decompress” the knowledge representations and reconstruct the data from their coded form.

Skin Cancer Detection using semantic segmentation

Skin Cancer Detection Tool README.md Overview The objective of this project is to build a Skin Cancer Detection Tool. The tool that we are creating is a segmentation model of spots (moles, melanomas, etc…) on microscopic images of the skin. To create this tool we will have to train a semantic segmentation AI model. The data that we use for that training is from The International Skin Imaging Collaboration. File Descriptions: data.

Perception 3D