Design, Construction, and Validation of an Experimental Electric Vehicle with Trajectory Tracking.

This research presents an experimental electric vehicle developed at the Tecnológico Nacional de México Celaya campus. It was decided to use a golf cart-type gasoline vehicle as a starting point. Initially, the body was removed, and the vehicle was electrified, meaning its engine was replaced with an electric one. Subsequently, sensors used to measure the vehicle states were placed, calibrated, and instrumented. Additionally, a mathematical model was developed along with a strategy for the parametric identification of this model. A communication scheme was implemented consisting of four slave devices responsible for controlling the accelerator, brake, steering wheel, and measuring the sensors related to odometry. The master device is responsible for communicating with the slaves, displaying information on a screen, creating a log, and implementing trajectory tracking techniques based on classical, geometric, and predictive control. Finally, the performance of the control algorithms implemented on the experimental prototype was compared in terms of tracking error and control input across three different types of trajectories: lane change, right-angle curve, and U-turn.

Low-Cost Sensor for Lycopene Content Measurement in Tomato Based on Raspberry Pi 4.

Measuring lycopene in tomatoes is fundamental to the agrifood industry because of its health benefits. It is one of the leading quality criteria for consuming this fruit. Traditionally, the amount determination of this carotenoid is performed using the high-performance liquid chromatography (HPLC) technique. This is a very reliable and accurate method, but it has several disadvantages, such as long analysis time, high cost, and destruction of the sample. In this sense, this work proposes a low-cost sensor that correlates the lycopene content in tomato with the color present in its epicarp. A Raspberry Pi 4 programmed with Python language was used to develop the lycopene prediction model. Various regression models were evaluated using neural networks, fuzzy logic, and linear regression. The best model was the fuzzy nonlinear regression as the RGB input, with a correlation of R2 = 0.99 and a mean error of 1.9 × 10-5. This work was able to demonstrate that it is possible to determine the lycopene content using a digital camera and a low-cost integrated system in a non-invasive way.

Power Losses Models for Magnetic Cores: A Review.

In power electronics, magnetic components are fundamental, and, unfortunately, represent one of the greatest challenges for designers because they are some of the components that lead the opposition to miniaturization and the main source of losses (both electrical and thermal). The use of ferromagnetic materials as substitutes for ferrite, in the core of magnetic components, has been proposed as a solution to this problem, and with them, a new perspective and methodology in the calculation of power losses open the way to new design proposals and challenges to overcome. Achieving a core losses model that combines all the parameters (electric, magnetic, thermal) needed in power electronic applications is a challenge. The main objective of this work is to position the reader in state-of-the-art for core losses models. This last provides, in one source, tools and techniques to develop magnetic solutions towards miniaturization applications. Details about new proposals, materials used, design steps, software tools, and miniaturization examples are provided.

Machine Learning for Brain Images Classification of Two Language Speakers.

The image analysis of the brain with machine learning continues to be a relevant work for the detection of different characteristics of this complex organ. Recent research has observed that there are differences in the structure of the brain, specifically in white matter, when learning and using a second language. This work focuses on knowing the brain from the classification of Magnetic Resonance Images (MRIs) of bilingual and monolingual people who have English as their common language. Different artificial neural networks of a hidden layer were tested until reaching two neurons in that layer. The number of entries used was nine hundred and the classifier registered a high percentage of effectiveness. The training was supervised which could be improved in a future investigation. This task is usually carried out by an expert human with Tract-Based Spatial Statistics analysis and fractional anisotropy expressed in different colors on a screen. So, this proposal presents another option to quantitatively analyse this type of phenomena which allows to contribute to neuroscience by automatically detecting bilingual people of monolinguals by using machine learning from MRIs. This reinforces what is reported in manual detections and the way that a machine can do it.