Distance education has become an alternative in teaching derived from the Covid-19 pandemic. However, distance education has led to bad practices for some students. For example, it was detected that some students spoofed the teacher in a class or exam. Therefore, facial biometrics can be used to solve, in real-time, the spoofing problem. However, the solution is not exempt from presentation attacks that undermine the reliability of the systems. Other challenges that must be considered are lighting, resolution, and variable size of the faces, among others. In this paper, we present a methodology to address the problem of facial spoofing attacks. We combine the Extended Local Binary Patterns (ELBP) descriptor and YCbCr, HSV color models to highlight the saturation and illumination of an image. For the experiments, we present a comparison of our proposal against other state-of-the-art methods. We obtain an error of 2.45% with the Half Total Error Rate (HTER) metric in the MSU image bank. The results revealed that for environments where the camera resolutions are not controlled, our proposal provides a feasible solution reducing the costs of acquiring specific hardware

Currently, the use of Unmanned Aerial Vehicles (UAVs) in natural and complex environments has been increasing, because they are appropriate and affordable solutions to support different tasks such as rescue, forestry, and agriculture by collecting and analyzing high-resolution monocular images. Autonomous navigation at low altitudes is an important area of research, as it would allow monitoring parts of the crop that are occluded by their foliage or by other plants. This task is difficult due to the large number of obstacles that might be encountered in the drone’s path. The generation of high-quality depth maps is an alternative for providing real-time obstacle detection and collision avoidance for autonomous UAVs. In this paper, we present a comparative analysis of four supervised learning deep neural networks and a combination of two for monocular depth map estimation considering images captured at low altitudes in simulated natural environments. Our results show that the Boosting Monocular network is the best performing in terms of depth map accuracy because of its capability to process the same image at different scales to avoid loss of fine details.

Background: Automatic adventitious lung sound classification using deep learning is a promising strategy for objective respiratory disease screening. Evaluating model performance is challenging, particularly with imbalanced clinical datasets. This study compares CNN architectures and proposes a dual-stream classification approach. Methods: Using the public ICBHI 2017 dataset, we compared five pre-trained architectures: VGG16, VGG19, InceptionV3, MobileNetV2, and ResNet152V2. To mitigate class imbalance, we implemented pitch shifting, random shifting, and mixup data augmentation. We also developed and evaluated a novel VGGish-dual-stream network. The primary endpoint was the Average Score (AS), the arithmetic mean of Sensitivity and Specificity. Results: Among benchmarked models, ResNet152V2 achieved the highest AS ($0.541$), approaching the state-of-the-art range (0.56–0.58). This performance was characterised by a high Specificity ($0.67$) but low Sensitivity ($0.41$). Our proposed dual-stream network yielded a more balanced, albeit slightly lower, performance with an AS of $0.508$. Conclusions: Standard CNN architectures like ResNet152V2 can achieve competitive classification performance but may exhibit a clinically significant bias towards high specificity at the expense of sensitivity. This trade-off poses a risk of missing pathological events (false negatives). To ensure clinical safety and utility, future work must prioritise strategies that explicitly improve model sensitivity.

Continuous passive motion (CPM) machines are used in the rehabilitation of members that have been injured to recover their range of motion and prevent stiffness. Nowadays, some CPM machines for the knee, ankle, arm, and elbow are available commercially. In this paper, ankle and shoulder rehabilitation robots, based on an X-Y table, are presented. The novelty of these rehabilitation robots is that they have a computerized numerical control system, resulting in low-cost machines. Some G-codes for basic and combined movement routines for ankle and shoulder rehabilitation are presented. In addition, the use of a robust generalized PI controller is also proposed to guarantee safe rehabilitation movements and compensate for passive stiffness in the ankle joint of stroke survivors. Some numerical simulations are included to illustrate the dynamic performance of the robust Generalized Proportional Integral (GPI) controller using the virtual prototype.

In the realm of digital libraries, efficiently managing and accessing scientific publications necessitates automated bibliographic reference segmentation. This study addresses the challenge of accurately segmenting bibliographic references, a task complicated by the varied formats and styles of references. Focusing on the empirical evaluation of Conditional Random Fields (CRF), Bidirectional Long Short-Term Memory with CRF (BiLSTM + CRF), and Transformer Encoder with CRF (Transformer + CRF) architectures, this research employs Byte Pair Encoding and Character Embeddings for vector representation. The models underwent training on the extensive Giant corpus and subsequent evaluation on the Cora Corpus to ensure a balanced and rigorous comparison, maintaining uniformity across embedding layers, normalization techniques, and Dropout strategies. Results indicate that the BiLSTM + CRF architecture outperforms its counterparts by adeptly handling the syntactic structures prevalent in bibliographic data, achieving an F1-Score of 0.96. This outcome highlights the necessity of aligning model architecture with the specific syntactic demands of bibliographic reference segmentation tasks. Consequently, the study establishes the BiLSTM + CRF model as a superior approach within the current state-of-the-art, offering a robust solution for the challenges faced in digital library management and scholarly communication.