An out-of-plane digital holographic interferometry system is used to detect and measure insect’s wing micro deformations. The in-vivo phenomenon of the flapping is registered using a high power cw laser and a high speed camera. A series of digital holograms with the deformation encoded are obtained. Full field deformation maps are presented for an eastern tiger swallowtail butterfly (Pterourus multicaudata). Results show no uniform or symmetrical deformations between wings. These deformations are in the order of hundreds of nanometers over the entire surface. Out-of-plane deformation maps are presented using the unwrapped phase maps.

Breast cancer is a disease that has emerged as the second leading cause of cancer deaths in women worldwide. The annual mortality rate is estimated to continue growing. Cancer detection at an early stage could significantly reduce breast cancer death rates long-term. Many investigators have studied different breast diagnostic approaches, such as mammography, magnetic resonance imaging, ultrasound, computerized tomography, positron emission tomography and biopsy. However, these techniques have limitations, such as being expensive, time consuming and not suitable for women of all ages. Proposing techniques that support the effective medical diagnosis of this disease has undoubtedly become a priority for the government, for health institutions and for civil society in general. In this paper, an associative pattern classifier (APC) was used for the diagnosis of breast cancer. The rate of efficiency obtained on the Wisconsin breast cancer database was 97.31%. The APC’s performance was compared with the performance of a support vector machine (SVM) model, back-propagation neural networks, C4.5, naive Bayes, k-nearest neighbor (k-NN) and minimum distance classifiers. According to our results, the APC performed best. The algorithm of the APC was written and executed in a JAVA platform, as well as the experimental and comparativeness between algorithms.

Coronavirus disease 2019 (COVID-19) is a highly communicable viral infection caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2), which has spread rapidly throughout the world. From a computer science point of view, research efforts have focused on the use of approaches such as machine learning and curve fitting to predict or simulate disease behavior. However, the mathematical characterization of the spread of COVID-19 is a topic that has not yet been explored by these techniques. In this work, we propose a novel metaheuristic framework called META-COVID19, which merges the Generalized Boltzmann distribution and the family of Jacobi polynomials to automatically characterize the COVID-19 spread without prior knowledge of the data and without involving a human expert. In general terms, the algorithm receives as input a time series of daily reported cases and the output is a polynomial mathematical model. Our framework only needs a single parameter, which is the number of Jacobi polynomials to analyze during the iterative process, and it is capable of proposing polynomials whose adjustment error is close to 1E-3. Finally, we show the applicability of the polynomial models found by META-COVID19, through a theoretical mathematical analysis in order to know attributes of the spread of COVID-19 in different periods of time, allowing to generate better strategies to face it in the future.

Autonomous Vehicles (AV) have been extensively studied in both scientific and social contexts. Over the past two decades, there has been a significant rise in their real-world applications, including neural networks, Blockchain, Internet of Things, autonomous navigation, computer vision, automation processes, and various other areas. Hence, it is imperative to investigate the interplay between software, hardware, and individuals. To guarantee secure and unaffected interactions within autonomous vehicle devices and networks, decentralized Blockchain technology is proposed. This study presents the introduction of a framework we named “DEMU-NAV” for an ecosystem that includes Artificial Intelligence (AI), humans, and robots. The framework makes use of a decentralized Blockchain, Smart-Contract (SC), and Internet of things (IoT) network. Our framework was implemented using Ethereum and Python, enabling us to oversee Blockchain, Smart-Contracts, and the IoT for the facilitation of autonomous vehicle navigation.

The flagship monthly journal of SPIE, Optical Engineering (OE) publishes peer-reviewed papers reporting on research and development in all areas of optics, photonics, and imaging science and engineering.

Recent progress in computer vision and embedded systems has facilitated real-time monitoring of bioprocesses; however, lightweight and scalable solutions for resource-constrained settings remain limited. This work presents a modular framework for monitoring Chlorella vulgaris growth by integrating RGB image processing with multimodal sensor fusion. The system incorporates a Logitech C920 camera and low-cost pH and temperature sensors within a compact photobioreactor. It extracts RGB channel statistics, luminance, and environmental data to generate a 10-dimensional feature vector. A feedforward artificial neural network (ANN) with ReLU activations, dropout layers, and SMOTE-based data balancing was trained to classify growth phases: lag, exponential, and stationary. The optimized model, quantized to 8 bits, was deployed on an ESP32 microcontroller, achieving 98.62% accuracy with 4.8 ms inference time and a 13.48 kB memory footprint. Robustness analysis confirmed tolerance to geometric transformations, though variable lighting reduced performance. Principal component analysis (PCA) retained 95% variance, supporting the discriminative power of the features. The proposed system outperformed previous vision-only methods, demonstrating the advantages of multimodal fusion for early detection. Limitations include sensitivity to lighting and validation limited to a single species. Future directions include incorporating active lighting control and extending the model to multi-species classification for broader applicability.