The study highlights the crucial role of electromyogram signals (EMG) in recognizing hand and finger movements, and their application in controlling prosthetic limbs. Focusing on the development of human–machine interactions and rehabilitation devices, particularly robotic prostheses. This paper introduces an innovative model utilizing a convolutional neural network (CNN) for classifying fundamental hand grip movements. By converting EMG signals into channel-specific image spectrograms, the model achieved an unprecedented $$100\%$$ 100 % accuracy in classifying 6 different movements, evaluated on the EMG hand gesture dataset from the UCI public repository. The results show superior performance compared to advanced methods, demonstrating the model’s potential as a cost-effective and precise control unit for accurately classifying hand grips from EMG signals.

This work describes a data-level parallelization strategy to accelerate the discrete wavelet transform (DWT) which was implemented and compared in two multi-threaded architectures, both with shared memory. The first considered architecture was a multi-core server and the second one was a graphics processing unit (GPU). The main goal of the research is to improve the computation times for popular DWT algorithms for representative modern GPU architectures. Comparisons were based on performance metrics (i.e., execution time, speedup, efficiency, and cost) for five decomposition levels of the DWT Daubechies db6 over random arrays of lengths ${10}^3$, ${10}^4$, ${10}^5$, ${10}^6$, ${10}^7$, ${10}^8$, and ${10}^9$. The execution times in our proposed GPU strategy were around $1.2\times {10}^{-5}$ s, compared to $3501\times {10}^{-5}$ s for the sequential implementation. On the other hand, the maximum achievable speedup and efficiency were reached by our proposed multi-core strategy for a number of assigned threads equal to 32.

Image recognition and classification have been widely used for research in computer vision systems. This paper aims to implement a new strategy called Wiener-Granger Causality theory for classifying natural scenery images. This strategy is based on self-content images extracted using a Content-Based Image Retrieval (CBIR) methodology (to obtain different texture features); later, a Genetic Algorithm (GA) is implemented to select the most relevant natural elements from the images which share similar causality patterns. The proposed method is comprised of a sequential feature extraction stage, a time series conformation task, a causality estimation phase, causality feature selection throughout the GA implementation (using the classification process into the fitness function). A classification stage was implemented and 700 images of natural scenery were used for validating the results. Tested in the distribution system implementation, the technical efficiency of the developed system is 100% and 96% for resubstitution and cross-validation methodologies, respectively. This proposal could help with recognizing natural scenarios in the navigation of an autonomous car or possibly a drone, being an important element in the safety of autonomous vehicles navigation.

Distance learning has become a necessity and a common practice, using electronic devices and the Internet to facilitate online classes and exams. One course in undergraduate electrical engineering is the Digital Signal Processing (DSP) course, which requires exposure to theoretical concepts as well as simulations. In this paper, we propose a Moodle-based online theoretical-practical course that helps to establish a proper learning and teaching process. One element to highlight is that it has been possible to embed simulators such as Python, Octave, JDSP, amount others, without leaving the Moodle pages. This is the first Moodle based DSP with simulation capability. The DSP course has been evaluated with an important qualitative satisfaction study and has obtained upper \(90\%\).

This paper presents a markerless augmented reality (AR) navigation system for guiding users across a university campus, independent of internet or wireless connectivity, integrating machine learning (ML) and deep learning techniques. The system employs computer vision to detect campus signage “Meeting Point” and “Directory”, and classifies them through a binary classifier (BC) and convolutional neural networks (CNNs). The BC distinguishes between the two types of signs using RGB values with algorithms such as Perceptron, Bayesian classification, and k-Nearest Neighbors (KNN), while the CNN identifies the specific sign ID to link it to a campus location. Navigation routes are generated with the Floyd–Warshall algorithm, which computes the shortest path between nodes on a digital campus map. Directional arrows are then overlaid in AR on the user’s device via ARCore, updated every 200 milliseconds using sensor data and direction vectors. The prototype, developed in Android Studio, achieved over 99.5% accuracy with CNNs and 100% accuracy with the BC, even when signs were worn or partially occluded. A usability study with 27 participants showed that 85.2% successfully reached their destinations, with more than half rating the system as easy or very easy to use. Users also expressed strong interest in extending the application to other environments, such as shopping malls or airports. Overall, the solution is lightweight, scalable, and sustainable, requiring no additional infrastructure beyond existing campus signage.