Signals

Unmanned Aerial Vehicles (UAVs) have progressively gained interest in recent years due to the wide range of related applications, from aerial communications and autonomous flight to agriculture and logistics. However, accurate 3D localization is crucial for enabling these kinds of applications, and commonly used tracking algorithms are often performing unsatisfactorily in critical scenarios like urban canyons and environments, characterized by dense multipath and line of sight obstruction. In this work we derive a novel 3D tracking algorithm which, despite its mathematical simplicity, can efficiently track moving targets handling asynchronous arrival of the anchor measurements or obstructions of line-of-sight links and outperforming commonly used algorithms like the Extended Kalman Filter (EKF) and the Particle Filter (PF). The proposed algorithm tracks the 3D position, velocity, and acceleration of a moving target through the combination of range measurements, between the target and different anchors, which become available in numbers and time instants not necessarily ordered as usually assumed in these applications. We denote this condition as asynchronous measurements, meaning that the ranging measurements are not available from all the anchors and they refer to different positions of the UAV during the tracking. We also show that our estimator is optimal among the linear ones, meaning that within this class, it minimizes the estimation error variance. Finally, we explore the accuracy that can be achieved in simulated scenarios defined by realistic UAV altitudes, velocities, and trajectories, as well as typical ranging errors of wideband localization systems. Full article

Fault diagnosis is essential in industrial production. With the advancement of IoT technology, real-time data acquisition and storage have become feasible, enabling deep learning-based fault diagnosis methods to achieve remarkable results. However, existing approaches often overlook the temporal characteristics of fault occurrences and struggle with data imbalance between normal and faulty conditions, impacting diagnostic performance. To address these challenges, this paper proposes an integrated fault diagnosis method that incorporates data balancing, feature extraction, and temporal information analysis. The approach consists of two key components: (1) dataset construction using digital twin technology and (2) an integrated fault diagnosis model (CNN-BLSTM-attention). Digital twin technology generates virtual data under various operating conditions, mitigating the small-sample issue. The proposed model leverages a sliding window mechanism to capture both feature and temporal information, enhancing fault pattern recognition. Experimental results demonstrate that, compared to traditional methods, this approach effectively reduces noise interference and achieves a high diagnostic accuracy of 96.46%, validating its robustness in complex industrial settings. This research provides valuable theoretical and practical insights for improving fault diagnosis in industrial equipment such as screw presses. Full article

Medical imaging is crucial for disease diagnosis, but noise in CT and MRI scans can obscure critical details, making accurate diagnosis challenging. Traditional denoising methods and deep learning techniques often produce overly smooth images that lack vital diagnostic information. GAN-based approaches also struggle to balance noise removal and content preservation. Existing research has not explored tumor detection after image denoising; instead, it has concentrated on content and noise learning. To address these challenges, this study proposes the Adversarial Content–Noise Complementary Learning (ACNCL) model, which enhances image denoising and tumor detection. Unlike conventional methods focusing solely on content or noise learning, ACNCL simultaneously learns both through dual predictors, ensuring the complementary reconstruction of high-quality images. The model integrates multiple denoising techniques (DnCNN, U-Net, DenseNet, CA-AGF, and DWT) within a GAN framework, using PatchGAN as a local discriminator to preserve fine image textures. The ACNCL separates anatomical details and noise into distinct pathways, ensuring stable noise reduction while maintaining structural integrity. Evaluated on CT and MRI datasets, ACNCL demonstrated exceptional performance compared to traditional models both qualitatively and quantitatively. It exhibited strong generalization across datasets, improving medical image clarity and enabling earlier tumor detection. These findings highlight ACNCL’s potential to enhance diagnostic accuracy and support improved clinical decision-making. Full article

In the field of music education, the incorporation of technology originally designed for professionals presents both significant opportunities and challenges. These technologies, although advanced and powerful, are often not adapted to meet the specific needs of the educational environment. Therefore, this study details the design and implementation process of a system consisting of a hardware device called “Play Box” and associated software “Imaginary Play Box”. The design sciences research methodology (DSRM) specifically adapted to software development was used to structure the project. The three phases shown in this study ranged from the conception of an initial prototype to the realisation of working software. During the design phase, a questionnaire was developed to evaluate various aspects of the software, such as the visual interface, the programming of components, and the sound interactivity provided by the Play Box. The technique of panels of experts in music pedagogy and programming in MAX-MSP was used to obtain critical feedback. This expert evaluation was crucial to iterate and polish the process of iteration and refining the software, culminating in a beta version optimised for the creation of electroacoustic music for music education. Full article

Cardiovascular diseases represent one of the major problems faced by modern society. In addition to reducing people’s quality of life, bringing high costs to the health system, and causing losses in economic productivity, they are the leading cause of death in the world. Early diagnosis and treatment are the best actions to minimize the damage and costs caused by these diseases. For this, developing techniques and technologies that have higher accuracy in the analysis of electrocardiogram (ECG) signals is necessary. Early diagnosis benefits from relevant ECG interpretation. Then, it can contribute to reducing healthcare costs by replacing interventionist responses with preventive actions. This work presents a method and system for heart rate estimation using Linear Prediction Coefficients (LPCs) centered on an ESP32 microprocessor module and an AD8232 ECG signal conditioning module. The proposal was validated with a Tektronix AFG1022 function generator that produces ECG signals and obtained measurements with accuracy above 98.87%, showing performance similar to studies presented in the literature. Also, the LPC algorithm showed good performance in rejecting low-frequency noise caused by some common artifacts, such as body movement and electrode displacement. Full article

This paper presents a microcontroller-controlled closed-loop fluxgate current sensor utilizing digital proportional–integral–derivative (PID) control with a single-neuron-based self-pre-optimization algorithm. The digital PID controller within the microcontroller (MCU) regulates the drive circuit to generate a feedback current in the feedback winding based on the zero-flux principle in a closed-loop system. This feedback current is proportional to the measured external current, thereby achieving magnetic compensation. Although PID parameters can be determined using heuristic approaches, empirical formulas, or model-based methods, these techniques are often labor-intensive and time-consuming. To address this challenge, this study implements a single-neuron-based self-pre-optimization algorithm for PID parameters, which autonomously identifies the optimal values for the closed-loop system. Once the PID parameters are optimized, a conventional positional PID algorithm is employed for the closed-loop control of the fluxgate current sensor. The experimental results show that the developed digital closed-loop fluxgate sensor has a non-linearity within 0.1% at the full scale in the measuring ranges of 0–1 A and 0–10 A DC current, with an effective response time of approximately 120 ms. The limitation of the sensors’ response time is found to be ascribed to its open-loop measuring circuit. Full article

Accurate damping estimation is crucial for structural health monitoring and machinery diagnostics. This article introduces a novel wavelet-based framework for extracting the damping ratio from multiple impulse responses of vibrating systems. Extracting damping ratios is a numerically sensitive task, further complicated by the common assumption in the literature that impacts are perfectly aligned—a condition rarely met in practice. To address the challenge of non-synchronized recordings, we propose two wavelet-based algorithms that leverage wavelet energy for improved alignment and averaging in the wavelet domain to reduce noise, enhancing the robustness of damping estimation. Our approach provides a fresh perspective on the application of wavelets in damping estimation. We conduct a comprehensive evaluation, comparing the proposed methods with four traditional algorithms. The assessment is strengthened by incorporating both numerical simulations and experimental analysis. Additionally, we apply the analysis of variance (ANOVA) test to assess the significance of algorithm performance across varying numbers of recordings. The results highlight the sensitivity of damping estimation to time shifts, noise levels, and the number of recordings. The proposed wavelet-based approaches demonstrate outstanding adaptability and reliability, offering a promising solution for real-world applications. Full article

Accurate identification of geological formations is essential for understanding tectonic structures, planning mining activities, and sustainably managing natural resources. It goes beyond the scientific framework to play a key role in economic development, environmental preservation, and population security. This article proposes a study using machine learning to analyze different parameters from various sources of satellite imagery: multispectral optics (Landsat-8), radar (ALOS PALSAR), and soil and morphometric parameters (soil, altitude, slope, curvature, and shady). The data were preprocessed to remove atmospheric biases and harmonize spatial resolutions. Techniques such as principal component analysis, band ratios, and image fusion have made it possible to enrich imagery by highlighting spectral and textural characteristics. Finally, classifiers such as Random Forest, Gradient Boosting, and XGBoost (version 1.6.2) were used to evaluate the impact of each parameter on the classification. The results show that geographic parameters combined with PCA provide the best overall performance with Random Forest, achieving an accuracy of 55.29% and an MCC of 45.12% while ensuring a rapid training speed (3.6 s). The geographic parameters associated with the OLI spectrometric data show a good balance, with XGBoost achieving a slightly higher MCC (40.3%) with a moderate training time (7.9 s). On the other hand, the OLI spectrometric parameters coupled with PCA display significantly lower performance, with an accuracy of 45.05% and an MCC of 31.81% for Random Forest. These observations highlight the potential of geographic and geological parameters associated with suitable models to improve classification. The multi-source approach thus proves optimal for more robust and precise results. Full article

Inertial measurement units (IMUs) are an example of practical technology for measuring countermovement jump (CMJ) performance, but there is a need to enhance their validity. One potential strategy to achieve this is advancing the literature on IMU placement. Many studies opt to position a single IMU on anatomical landmarks rather than determining placement based on anthropometric principles, despite the knowledge that linear mechanics act through the segmental centers of mass of the human body. The purpose of this study was to evaluate the impact of positioning IMU sensors to approximate the trunk and lower-extremity segmental centers of mass on the validity of vertical acceleration measurements and jump height (JH) estimation during CMJs. Thirty young adults (female n = 10, 21.3 (3.8) years, 166.1 (4.1) cm, 67.6 (11.3) kg; male n = 20, 22.0 (2.6) years, 179.2 (6.4) cm, 83.5 (17.1) kg) from a university setting participated in the study. Seven IMUs were positioned at the approximate centers of mass of the trunk, thighs, shanks, and feet. Using data from these sensors, 15 whole-body center of mass models were developed, including 1-, 2-, 3-, and 4-segment configurations derived from the trunk and three lower-body segments. The root mean square error (RMSE) of vertical acceleration was calculated for each IMU model by comparing its data against vertical acceleration measurements obtained from a force platform. JH estimates were calculated using the take-off velocity method and compared across IMU models and the force platform to evaluate for systematic bias. RMSE and JH values from the best-performing 1-, 2-, 3-, and 4-segment IMU models were analyzed for main effects using one-way analyses of variance. The best performing 2-segment (trunk and shanks; RMSE = 2.1 ± 1.3 m × s⁻²) and 3-segment (trunk, thighs, and feet; RMSE = 2.0 ± 1.2 m × s⁻²) IMU models returned significantly lower RMSE values compared to the 1- segment (trunk; RMSE = 3.0 ± 1.4 m × s⁻²) model (p = 0.021–0.041). No systematic bias was detected between the JH estimates derived from the best-performing IMU models and those obtained from the force platform (p = 0.91–0.99). Positioning multiple IMU sensors to approximate segmental centers of mass significantly improved the validity of vertical acceleration time-series data from CMJs. The findings highlight the importance of anthropometric-based IMU placement for enhancing measurement accuracy without introducing systematic bias. Full article

Whilst balance disturbances are common in people with advanced Parkinson’s disease, it has not previously been possible to record vestibular evoked myogenic potentials (VEMPs), and thus otolithic function, during monopolar deep brain stimulation (DBS) due to an overwhelming number of signal artifacts. A µVEMP device has been developed with parameters to allow VEMP recording during monopolar DBS. The aim of this proof-of-concept study was to ascertain whether, during DBS, VEMP responses could be accurately identified after signal filtering recordings from the µVEMP device. Both cervical and ocular VEMP responses to taps and clicks were recorded with the µVEMP device in five Parkinson’s disease patients with monopolar deep brain stimulation. Additionally, VEMP responses were recorded in one patient whose deep brain stimulation was switched ON and OFF to allow a direct comparison of the signals. Customised post-filtering analysis allowed successful VEMP response extraction from signal noise in all five patients with deep brain stimulation ON. VEMP responses with deep brain stimulation ON after filtering were similar to VEMP responses with deep brain stimulation OFF, validating the filtering analysis. We present the first study to record VEMP signals with monopolar deep brain stimulation using a µVEMP device coupled with customised post-filtering. This finding will allow patients to be assessed without requiring adjustment of their therapeutic deep brain stimulation. Full article

Behind human voice production, a complex biological mechanism generates and modulates sound. Recent research has explored machine-learning (ML) techniques to analyze singing-voice characteristics. However, the classification efficiency reported in such research works suggests the possibility of improvement. In addition, there is also scope for further improvement through the application of still under-utilized optimization techniques. Thus, the present article proposes a novel approach that leverages the Differential Evolution (DE) algorithm to optimize hyperparameters within three selected ML models, with the aim of classifying singing-voice registers i.e., chest, mixed, and head registers). To develop the present study, a dataset of 350 audio files encompassing the three aforementioned registers was constructed. Then, the TSFEL Python library was employed to extract 14 pieces of temporal information from the audio signals for subsequent classification by the employed ML models. The obtained findings demonstrated that the Extreme Gradient Boosting model, optimized with DE, achieved an average classification accuracy of 97.60%, thus indicating the efficacy of the proposed approach for singing-voice register classification. Full article

This study is part of a broader project, the Open Source Bionic Hand, which aims to develop and control, in real time, a low-cost 3D-printed bionic hand prototype using signals from the muscles of the forearm. This work is intended to implement a bimodal signal acquisition system, which uses EMG signals and Force Myography (FMG) in order to optimize the recognition of gesture intention and, consequently, the control of the bionic hand. The implementation of this bimodal EMG-FMG system will be described. It uses two different signals from BITalino EMG modules and Flexiforce™ sensors from Tekscan™. The dataset was built from thirty-six features extracted from each acquisition using two of each EMG and FMG sensors in extensor and flexor muscle groups simultaneously. The extraction of features is also depicted, as well as the subsequent use of these features to train and compare Machine Learning models in gesture recognition through MATLAB’s Classification Learner tool (v2.2.5 software). Preliminary results obtained from a dataset of three healthy volunteers show the effectiveness of this bimodal EMG/FMG system in the improvement of the efficacy on gesture recognition as it is shown, for example, for the Quadratic SVM classifier that raises from 75.00% with EMG signals to 87.96% using both signals. Full article

In biomedical engineering, Information Theory Quantifiers (ITQs) are used to analyze diseases by evaluating bioelectrical signals. This review article presents a meta-analysis to highlight the knowledge gap regarding the various perspectives and existing theories in this field. It intends to serve as an international reference, highlighting new opportunities for analysis in this field. Methodologically, it has gone through several stages: (i) the heuristic stage, which defined the characteristics of the documentary sample; (ii) the systematic classification and review of 70 texts using the Latent Dirichlet Allocation (LDA) model to identify topics; (iii) the hermeneutic analysis of seven thematic focuses; and (iv) the presentation of the final results. Among the findings are that continuous signals are analyzed discretely through sampling, probability distributions, and quantization, allowing entropy to be calculated. The complexity–entropy plane illustrates the relationship between disorder, organization, and structure in a system. It is concluded that the latter is useful to analyze bioelectrical signals in various diseases. However, its limited application in digestive disorders is evident, which highlights the need to integrate these concepts to improve their understanding and clinical diagnosis. Full article

Examining the dynamic interplay of muscle contributions to postural stability enhances our understanding of the neuromuscular mechanisms underlying balance control. This study examined the similarity in shape (using cross-correlation analysis) between seven individual lower limb electromyographic (EMG) signals and center-of-pressure (COP) displacements (i.e., EMG–COP correlation) in 20 young adults (25.2 ± 4.0 years) performing bipedal balance tasks on both stable and multi-axially unstable surfaces, testing the effects of four factors—leg dominance, surface stability, sway direction, and foot position—on individual EMG–COP correlations. The results revealed significant effects of leg dominance (p = 0.004), surface stability (p ≤ 0.001), and sway direction (p ≤ 0.001) on specific muscles. Notably, balancing on the non-dominant leg resulted in a stronger correlation between tibialis anterior activity and postural sway compared to the dominant leg. On a stable surface, postural sway showed stronger correlations with the rectus femoris, semitendinosus, biceps femoris, gastrocnemius medialis, and soleus muscles than on an unstable surface. Additionally, anteroposterior postural sway exhibited a greater correlation with semitendinosus and tibialis anterior activity compared to mediolateral sway. These findings underscore the importance of specific muscles in maintaining bipedal balance, with implications for improving balance performance across various populations. Full article

Rockets generate complex acoustic signatures that can be detected over a thousand kilometers from their source. While many far-field acoustic rocket signatures have been collected and released to the public, very few signatures collected at distances less than 100 km are available. This work presents a curated and annotated dataset of acoustic signatures of 243 rocket launches collected by a network of smartphones stationed at distances between 10 and 70 km from the launch sites, resulting in 1089 individual recordings. Due to the frequency dependence of atmospheric attenuation and the relatively short propagation distances, higher-frequency features not preserved in most publicly available data are observed. The signals are time-aligned to allow for different segments of the signal (ignition, launch, trajectory, chronology) to be more easily examined and compared. Initial analysis of the features of these rocket launch stages is performed, observed features are compared to those found in the existing literature, and comparisons between signals from launches of different rocket types are made. The dataset is annotated and made available to the public to aid future analysis of the characteristics and source mechanisms of rocket acoustics as well as applications such as rocket detection and classification models. Full article

The effective detection of repeated patterns from inputs of unknown fronto-parallel images is an important computer vision task that supports many real-world applications, such as image retrieval, synthesis, and texture analysis. A repeated pattern is defined as the smallest unit capable of tiling the entire image, representing its primary structural and visual information. In this paper, a hybrid method is proposed, overcoming the drawbacks of both traditional and existing deep learning-based approaches. The new method leverages deep features from a pre-trained Convolutional Neural Network (CNN) to estimate initial repeated pattern sizes and refines them using a dynamic autocorrelation algorithm. Comprehensive experiments are conducted on a new dataset of fronto-parallel textile images as well as another set of real-world non-textile images to demonstrate the superiority of the proposed method. The accuracy of the proposed method is 67.3%, which represents 20% higher than the baseline method, and the time cost is only 11% of the baseline. The proposed method has been applied and contributed to textile design, and it can be adapted to other applications. Full article

It may be helpful to integrate multiple aircraft communication and navigation functions into a single software-defined radio (SDR) platform. To transmit these multiple signals, the SDR would first sum the baseband version of the signals. This outgoing composite signal would be passed through a digital-to-analog converter (DAC) before being up-converted and passed through a radio frequency (RF) amplifier. To prevent non-linear distortion in the RF amplifier, it is important to know the peak voltage of the composite. While this is reasonably straightforward when a single modulation is used, it is more challenging when working with composite signals. This paper describes a machine learning solution to this problem. We demonstrate that a generalized gamma distribution (GGD) is a good fit for the distribution of the instantaneous voltage of the composite waveform. A deep neural network was trained to estimate the GGD parameters based on the parameters of the modulators. This allows the SDR to accurately estimate the peak of the composite voltage and set the gain of the DAC and RF amplifier, without having to generate or directly observe the composite signal. Full article

Treadmill running simulates various conditions, including flat, uphill, and downhill gradients, making it useful for training and rehabilitation. This study aimed to examine how incline and decline treadmill running affect local dynamic stability of individual running movement components that cooperatively contribute to achieving the running tasks. Principal component analysis (PCA) was used to decompose movement components, termed principal movements (PMs), from kinematic marker data collected from 19 healthy recreational runners (9 females and 10 males, 23.6 ± 3.7 years) during treadmill running at 10 km/h across different gradients (−6, −3, 0, +3, +6 degrees). The largest Lyapunov exponent (LyE) of individual PM positions (higher LyE = greater instability) was analyzed using repeated-measures ANOVA to assess treadmill gradient effects across PMs. The results showed that the effects of treadmill gradient appear in PM₃, which corresponds to the mid-stance phase of the gait cycle. Specifically, decline treadmill running significantly decreased local dynamic stability (greater LyE) compared to equivalent incline conditions (p ≤ 0.005). These findings suggest that decline treadmill running should be used cautiously in rehabilitation settings due to its potential to reduce an ability to control and respond to small perturbations, thereby increasing the risk of instability during the weight-bearing support phase of gait. Full article

We discuss determining a finite sample linear time-invariant (FS-LTI) system’s impulse response function, $h\left[n\right],$ in the frequency domain when the input testing function is a uniform window function with a width of L and the output is limited to a finite number of effective samples, M. Assuming that the samples beyond M are all zeros, the corresponding infinite sample LTI (IS-LTI) system is a marginally stable system. The ratio of the discrete Fourier transforms (DFT) of the output to input of such an FS-LTI system, $H_0\left[k\right]$, cannot be directly used to find $h\left[n\right]$ via inverse DFT (IDFT). Nevertheless, $H_0\left[k\right]$ contains sufficient information to determine the system’s impulse response function (IRF). In the frequency-domain approach, we zero-pad the output array to a length of N. We present methods to recover $h\left[n\right]$ from $H_0\left[k\right]$ for two scenarios: (1) $N\ge \max (L,M+1)$ and N is a coprime of L, and (2) $N\ge L+M+1$. The marginal stable system discussed here is an artifact due to the zero-value assumption on unavailable samples. The IRF obtained applies to any LTI system up to the number of effective data samples, M. In demonstrating the equivalence of $H_0\left[k\right]$ and $h\left[n\right]$, we derive two interesting DFT pairs. These DFT pairs can be used to find trigonometric sums that are otherwise difficult to prove. The frequency-domain approach makes mitigating the effects of interferences and random noise easier. In an example application in radar remote sensing, we show that the frequency-domain processing method can be used to obtain finer details than the range resolution provided by the radar system’s transmitter. Full article