Nuria López-Ruiz

A smartphone-controlled paper microfluidic platform integrates capacitive flow sensing, thermally triggered reagent release and colour-corrected imaging for automated nitrite determination in water.

This paper presents a novel technique for thermally compensating the power output of a DC-DC converter that supplies automotive lighting/signaling systems with multiple LED branches. The method ensures stable bias voltage for the current drivers controlling each branch, maintaining consistent power consumption across a wide temperature range. This issue has been minimally addressed in existing literature, providing few solutions which are too complex for industrial production. The approach proposed is simple and involves incorporating a temperature-sensitive thermistor into the DC-DC converter’s control loop, enabling the output voltage to adjust with ambient temperature. Different control loop configurations are explored, demonstrating that a simple resistor-thermistor network can approximate the desired voltage response under diverse thermal conditions. The power dissipated in the current drivers is kept within a controlled range, improving system efficiency and reducing heat loss. Additionally, it minimizes the need for additional current drivers, lowering the cost of these systems, improving battery life of the DC-DC converter, and decreasing CO2 emissions. For the case studies analyzed, an optimized configuration with appropriate resistor values and thermistor models achieves a 75% relative reduction in power dissipation by the current driver and a 50% improvement in the relative efficiency of the LED branch system.

The aim of this current work is to identify three different gears of cross-country skiing utilizing embedded inertial measurement units and a suitable deep learning model. The cross-country style studied was the skating style during the uphill, which involved three different gears: symmetric gear pushing with poles on both sides (G3) and two asymmetric gears pushing with poles on the right side (G2R) or to the left side (G2L). To monitor the technique, inertial measurement units (IMUs) were affixed to the skis, recording acceleration and Euler angle data during the uphill tests performed by two experienced skiers using the gears under study. The initiation and termination points of the tests were controlled via Bluetooth by a smartphone using a custom application developed with Android Studio. Data were collected on the smartphone and stored on the SD memory cards included in each IMU. Convolutional neural networks combined with long short-term memory were utilized to classify and extract spatio-temporal features. The performance of the model in cross-user evaluations demonstrated an overall accuracy of 90%, and it achieved an accuracy of 98% in the cross-scene evaluations for individual users. These results indicate a promising performance of the developed system in distinguishing between different ski gears within skating styles, providing a valuable tool to enhance ski training and analysis.

This paper addresses the issue of LED short-circuit fault detection in signaling and lighting systems in the automotive industry. The conventional diagnostic method commonly implemented in newer vehicles relies on measuring the voltage drop across different LED branches and comparing it with threshold values indicating faults caused by open circuits or LED short circuits. With this algorithm, detecting cases of a few LEDs short-circuited within a branch, particularly a single malfunctioning LED, is particularly challenging. In this work, two easily implementable algorithms are proposed to address this issue within the vehicle’s control unit. One is based on a mathematical prediction model, while the other utilizes a neural network. The results obtained offer a 100% LED short-circuit fault detection rate in the majority of analyzed cases, representing a significant improvement over the conventional method, even in scenarios involving a single malfunctioning LED within a branch. Additionally, the neural network-based model can accurately predict the number of failed LEDs.

This work presents an innovative approach employing passive Radiofrequency Identification (RFID) technology to develop a comprehensive ballistocardiography (BCG) system for contactless and non-invasive vital sign monitoring of individuals on a mattress. The method relies on wireless energy transfer through inductive coupling at 13.56 MHz between an active powering board and a passive, flexible sensing board with dimensions of 12.6 × 4.7 cm2. Both boards face each other at a separation distance of 3 cm. The system demonstrates remarkable sensitivity to mattress vibrations resulting from bodily movements, rendering it proficient in generating complete BCG signals capable of discerning heart rate (HR), breathing rate (BR), and activity. Practical trials with six healthy subjects validated the feasibility and efficacy of the system, showing an average absolute error of 1.8 beats/min for HR and 0.6 breaths/min for BR. Unlike conventional BCG systems, the sensing module seamlessly integrates into the mattress side. This is leveraged to enable the simultaneous monitoring of two subjects on a queen-size mattress, taking advantage of the local sensitivity of the sensing mechanism. Extracted BCG values can be wirelessly transmitted to the user’s smartphone through Near Field Communication (NFC) and to a cloud service used for storage, distribution, and categorization of subject data. This work establishes a foundation for future extensive studies and applications, as the obtained BCG signals could provide health-related insights, such as detecting heart diseases, respiratory problems, or evaluating sleep quality.

The world of elite sports has always been characterized by intense competition, where victories are often determined by minimal differences. This means that every little detail in the preparation of top-level athletes is crucial to their performance at the highest level. One of the most significant aspects to monitor is the jumping capacity, as it enables the measurement of performance, progression, and helps prevent injuries. Herein, we present the development of a system capable of measuring the flight time and height reached by the user, reporting the results through a smartphone using an Android ad-hoc application, which handles all the data processing. The system consists of an affordable and portable circuit based on an accelerometer. It communicates with the smartphone via UART using a Bluetooth module, and its battery provides approximately 9 h of autonomy, making it suitable for outdoor operations. To evaluate the system’s precision, we conducted performance tests (counter-movement jumps) with seven subjects. The results confirmed the system’s potential for monitoring high-level sports training sessions, as the average deviation obtained was only 2.1% (~0.01 s) in the analysis of flight time and 4.6% (~0.01 m) in jump height.

Information about the paper titled "SMARTPHONE APPLICATION FOR PHOTOGRAPH-BASED AUTOMATIC SOLVING OF ELECTRONIC CIRCUITS FOR DISTANCE LEARNING" at IATED Digital Library