Search Results (4,446)

Recent developments in unmanned aerial vehicle (UAV) activity highlight the need for advanced electromagnetic spectrum monitoring systems that can detect drones operating near sensitive or restricted areas. Such systems can identify emissions from drones even under frequency-hopping conditions, providing an early warning system and enabling a timely response to protect critical infrastructure and ensure secure operations. In this context, the present work proposes the development of a high-performance multichannel broadband monitoring system with real-time analysis capabilities, designed on an SDR architecture based on USRP with three acquisition channels: two broadband (160 MHz and 80 MHz) and one narrowband (1 MHz) channel, for simultaneous, of extended spectrum segments, aligned with current requirements for analyzing emissions from drones in the 2.4 GHz and 5.8 GHz ISM bands. The processing system was configured to support cumulative bandwidths of over 200 MHz through a high-performance hardware platform (powerful CPU, fast storage, GPU acceleration) and fiber optic interconnection, ensuring stable and lossless transfer of large volumes of data. The proposed spectrum monitoring system proved to be extremely sensitive, flexible, and extensible, achieving a reception sensitivity of −130 dBm, thus exceeding the values commonly reported in the literature. Additionally, the parallel multichannel architecture facilitates real-time detection of signals from different frequency ranges and provides a foundation for advanced signal classification. Its reconfigurable design enables rapid adaptation to various signal types beyond unmanned aerial systems. Full article

Background: Full field electroretinography (ERG) is an essential tool for assessing retinal function and diagnosing retinal diseases. In recent years, mydriasis-free handheld ERG devices have emerged as portable, non-invasive alternatives to traditional ERG systems. Their main application has been in the screening and monitoring of diabetic retinopathy (DR), particularly in settings with limited access to standard ERG equipment and in pediatric populations where conventional testing may be difficult to perform. This review aims to evaluate the current evidence on handheld ERG devices in ocular diseases, with a focus on their reliability, diagnostic accuracy, and inherent limitations. Methods: A review was conducted to identify studies evaluating handheld ERG devices in diverse clinical settings, including retinal diseases, DR, pediatric populations, and conditions such as glaucoma. A comprehensive search of the Pubmed and Embase databases was performed for studies published up to December 2024. Search terms included “mydriasis free ERG”, “handheld ERG”, “portable ERG”, “RETeval”, “healthy subjects”, “retinal diseases”, “diabetic retinopathy”, “glaucoma”, and “pediatric diseases”, as well as relevant MeSH terms and synonyms. Case reports, conference abstracts, non-human studies, and letters were excluded. After screening titles and abstracts, additional studies not meeting the inclusion criteria were excluded. Of 279 records that were initially identified, 55 met the eligibility criteria and were included in the final review. Results were synthesized narratively due to heterogeneity in the study design, populations, and outcomes. Findings were organized thematically according to clinical context. Results: A total of 57 studies were included in the review: 19 conducted in healthy subjects, 13 in diabetic retinopathy, eight in selected retinopathies, eight in glaucoma, and 14 in pediatric cohorts. Five studies overlapped between groups due to shared populations or study designs. No meta-analysis was performed due to heterogeneity in study design and outcome measures; therefore, findings were summarized narratively across disease categories. Handheld ERG devices have been evaluated in healthy subjects, patients with DR, other retinal pathologies, glaucoma and pediatric cohorts. Evidence indicates that these devices provide a rapid, non-invasive assessment of retinal function and are particularly valuable where conventional ERG is difficult to implement and potentially well-suited for screening purposes. They show good sensitivity and reasonable specificity for detecting functional changes, making them suitable for screening purposes. However, limitations exist: reduced performance in detecting early-stage disease and cone dysfunction, risk of false positives, and variability in waveform morphology and amplitude compared with traditional ERG systems. Reproducibility challenges are noted among pediatric patients and individuals with poor fixation or unstable eye movements. These discrepancies highlight the need for establishing robust normative datasets for both healthy subjects and specific disease states. Conclusions: Handheld ERG devices provide a rapid, accessible and user-friendly option for retinal assessment. While not a replacement for conventional ERG, they serve as complementary tools, particularly in early disease and in contexts where standard testing is less feasible. Further research is required to refine testing protocols, improve diagnostic accuracy, and validate their application across a broader spectrum of ocular diseases. Full article

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), equipped with the Advanced Topographic Laser Altimeter System (ATLAS), is capable of acquiring large-scale terrain and forest structural information through photon-counting LiDAR. However, photon point clouds exhibit significant noise variability due to diurnal changes and variations in beam intensity, which undermines the accuracy and stability of terrain and canopy height retrievals in forested regions. To address the limited adaptability of existing methods under daytime/nighttime and strong/weak beam conditions, this study proposes a multi-stage processing framework integrating photon denoising, classification, and quasi-full-waveform reconstruction. First, local statistical features combined with adaptive parameter optimization were employed, applying Gaussian and exponential fitting to denoise daytime strong and weak beams and enhance the signal-to-noise ratio (SNR). Subsequently, an improved random sample consensus (RANSAC) algorithm was introduced to remove residual noise and classify photons under both diurnal and beam-intensity variations. Finally, a radial basis function (RBF) interpolation was used to reconstruct quasi-full-waveform curves for terrain and canopy heights. Compared with the ATL08 product (terrain root mean square error (RMSE): 2.65 m for daytime strong beams and 5.77 m for daytime weak beams), the proposed method reduced RMSE by 0.53 m and 1.30 m, respectively, demonstrating enhanced stability and robustness under low-SNR conditions. For canopy height estimation, all beam types showed high consistency with airborne LiDAR measurements, with the highest correlation achieved for nighttime strong beams (R = 0.90), accompanied by the lowest RMSE (4.82 m) and mean absolute error (MAE = 2.97 m). In comparison, ATL08 canopy height errors for nighttime strong beams were higher (RMSE = 5.67 m; MAE = 4.16 m). Notably, significant improvements were observed for weak beams relative to ATL08. These results indicate that the proposed framework effectively denoises and classifies photon point clouds under diverse daytime/nighttime and strong/weak beam conditions, providing a robust methodological reference for high-precision terrain and forest canopy height estimation in forested regions. Full article

This study evaluates the feasibility of estimating stiffness-related parameters and pulse wave velocity (PWV) in a controlled in vitro circulatory setup using artificial silicone vessels with systematically varied Shore A hardness and wall thickness. From synchronized pressure and capacitive waveforms, fiducial points and engineered features are extracted, together with pump settings (stroke volume and heart rate). A Sugeno-type adaptive neuro-fuzzy inference system (ANFIS) is used for hardness-level prediction and benchmarked against linear regression and contemporary machine-learning/deep-learning baselines using stratified cross-validation. PWV estimates derived via hardness-to-elasticity conversion models and the Moens–Korteweg formulation are evaluated against a reference PWV obtained within the same experimental configuration. Under these controlled conditions, the proposed pipeline shows strong agreement with reference labels and measurements. The results should be interpreted as an in vitro validation step; translation to biological tissues or in vivo data will require external validation, calibration of material-property mapping, and robustness testing under physiological variability and measurement noise. Full article

Background: Parkinson’s disease (PD) is a neurodegenerative disorder that affects millions of people worldwide. Speech analysis has emerged as a non-invasive tool for automatic PD detection; however, the scarcity and homogeneity of available datasets often limit the generalization capability of machine learning models, motivating the use of data augmentation strategies to improve robustness. Methods: This study presents a data augmentation-based methodology for speech-based classification between PD patients and healthy control subjects. A deep learning model trained from scratch on Mel spectrograms is evaluated using augmentation techniques applied at both the waveform and time–frequency levels. Multiple training and model selection strategies are analyzed and model performance is assessed through internal validation as well as using an independent dataset Results: Experimental results show that carefully selected data augmentation techniques improve classification performance with respect to the non-augmented counterpart, achieving gains of up to 3% in accuracy. However, when evaluated on an independent dataset, these improvements do not consistently translate into better generalization. Conclusions: These findings demonstrate that, while data augmentation can effectively enhance model performance within a single dataset, this apparent robustness is not sufficient to guarantee generalization on independent speech corpora for PD detection. Full article

Acoustic emission testing is a non-destructive inspection method in which ultrasonic waves emitted by defects in an object are detected and assessed based on their time of arrival and waveform, which strongly depends on the geometry of the object. Those waves appear in different modes with their own velocity and dispersion and different degrees of attenuation can occur for different wave modes. In previous work, a new method for (semi-)automatic recognition of the arrival time of wave modes was presented and validated on a dataset obtained in laboratory conditions on a flat plate. This paper builds upon the previous research and presents a modified method that can be applied to data obtained from an industrial gas storage sphere. The following two wave modes were commonly detected for this sphere: one similar to the zero-order anti-symmetrical mode (A₀) and the other similar to the zero-order symmetrical Lamb mode (S₀) in a plate. The method was adapted to solve the new challenges that were encountered for the sphere. The performance of the adapted automatic mode recognition method was assessed using a dataset with the following four different source types: Hsu–Nielsen sources, sensor pulses, impact by a metallic object and natural sources. The resulting wave mode recognition was compared to manual recognition to determine the rates of successful recognition. The resulting successful recognition rates range from 97% for A₀ and S₀ for Hsu–Nielsen sources down to 73% for A₀ in signals due to natural sources and 74% for A₀ in signals due to impact by a metallic object. Full article

Background/Objectives: Knee osteoarthritis (KOA) is associated with pain, functional decline, and altered biomechanics. The Step-Up and Down-Forwards (StUD-F) task provides an ecologically relevant assessment of challenging movements. This study investigated neuromuscular activation and lower-limb kinematics of leading and trailing-limbs during the StUD-F in individuals with KOA. Methods: Forty participants with KOA (65.3 ± 7.68 years; 21M/19F; BMI 28.9 ± 4.52 kg/m²) completed a 25 cm box StUD-F task. Surface electromyograph recorded bilateral activation of the vastus medialis (VM), vastus lateralis (VL), bicep femoris (BF), and semitendinosus (ST). Triplanar hip, knee, and ankle joint angles were estimated using inertial measurement units. StUD-F events (initial stance; step contact; ascent completion; descent preparation; step-down touchdown; and descent completion) were identified using custom algorithms. Pain was assessed using visual analogue scales and the Knee Injury and Osteoarthritis Outcome Score (KOOS). Limb differences were analysed for leading or trailing roles using paired samples t-tests or non-parametric equivalents; waveforms were visually inspected. Results: Distinct neuromuscular and kinematic asymmetries were observed when affected and contralateral limbs were compared within each role (leading/trailing). During step-up, the affected leading limb demonstrated higher quadriceps activation at initial stance (VM: p = 0.035; VL: p = 0.027) and reduced trailing-limb activation at step contact (VM: p = 0.015; VL: p = 0.018), with sagittal-plane ankle differences (p = 0.004). During step-down, when the affected limb initiated ascent, trailing limb activation was higher at descent completion (VL: p < 0.001; VM: p = 0.003; BF: p = 0.009), with coronal-plane hip deviations (p < 0.001). When the contralateral limb-initiated ascent, trailing-limb muscles activation differences (VM: p < 0.001; VL: p = 0.015; BF: p = 0.007) and ankle/coronal-plane asymmetries (p ≤ 0.049) persisted. Conclusions: The StUD-F task elicits altered strategies in KOA, including elevated quadriceps–hamstring co-activation and altered sagittal/coronal alignment, and habitual limb choice across ascent and descent. These adaptations may enhance stability and joint protection but could increase medial compartment loading. The findings support rehabilitation focused on dynamic control, alignment, and shock absorption. Full article

This study investigates the selection and scaling of recorded strong ground motions in the time-domain spectral matching framework to realistically represent the seismic demands on the superstructure and secondary systems in the seismic design of complex facilities such as marine ports. The time-domain spectral matching method iteratively adjusts the original record in the time domain by adding wavelets with limited durations and specific period ranges to achieve compatibility with the specified target acceleration response spectrum. A site-specific probabilistic seismic hazard analysis (PSHA) was performed for a port facility in İskenderun Bay, an area affected by the 6 February 2023 earthquakes. Horizontal Ground-Motion Response Spectra (GMRS) were derived for different return periods. Based on the hazard deaggregation, recorded ground motions compatible with the seismotectonic context of the region and the site conditions were selected. These records were then processed using time-domain spectral matching (TDSM) to match their elastic response spectra with the target GMRS over specific period ranges. The method utilizes spectral matching in the time domain to improve the match with the target spectrum while preserving the phase information and non-stationary nature of the records. The results show that the mean spectral acceleration curves of the scaled records are highly consistent with the target GMRS over a wide range of periods and that near-fault pulse-like characteristics, when present, are reasonably preserved. These results confirm that time-domain spectral matching provides a reliable framework for the performance-based assessments of complex port infrastructures by achieving high compatibility with the target spectra while preserving the physical characteristics of the waveforms Full article

This paper presents a compact mixed-mode first-order universal filter based on a single voltage differencing current conveyor (VDCC), which can function in all four possible operation modes, i.e., voltage mode (VM), trans-admittance mode (TAM), current mode (CM), and trans-impedance mode (TIM). The proposed configuration requires only two grounded resistors and one floating capacitor, which contributes to a low component count, facilitates integration, and allows for the electronic tunability of the pole frequency through the transconductance gain of the VDCC. This work also demonstrates two practical biomedical applications: an electrocardiogram (ECG) acquisition system utilizing the VM low-pass filter for noise suppression and a bioimpedance (BioZ) measurement system employing the proposed configuration-based CM oscillator circuit as a sinusoidal excitation source. The performance validation confirms the accuracy of impedance extraction and the preservation of waveforms using tissue-equivalent models. The results demonstrate that the proposed VDCC-based filter offers a compact, power-efficient, and versatile analog signal-processing solution suitable for modern biomedical instrumentation. Full article

High-power chirped amplitude-modulated (CAM) lasers serve as essential sources for the promising high-precision single-photon differential ranging technique. However, the development of high-power CAM lasers is fundamentally constrained by the stimulated Brillouin scattering (SBS) effect and the degradation of the CAM waveform during amplification. In this work, we propose a high-power CAM fiber laser system based on a dual linear frequency modulation (dual-LFM) architecture, wherein LFM signals are applied simultaneously to both the phase modulator and the intensity modulator. The experimental results demonstrate effective suppression of SBS, which enables an approximately eightfold enhancement in average output power—from 32.1 W to 256.5 W—while maintaining well-preserved CAM waveforms and a near-diffraction-limited beam quality ($M^2$ = 1.073). To the best of our knowledge, this represents the highest output power reported to date for CAM lasers. Significantly, after amplification, the system exhibits a mere ~2% reduction in average modulation depth, attaining a final modulation depth of over 82%, a total harmonic distortion below 7%, and excellent CAM linearity across the 100 MHz to 1 GHz modulation frequency range. Furthermore, the proposed laser system enables single-photon differential ranging with millimeter-level precision over distances exceeding 100 km. This work represents a significant advancement in CAM laser power scaling, with potential applications in advanced precision ranging, quantum technology, and related emerging fields. Full article

Ventricular–arterial coupling (VAC) describes the dynamic interaction between left ventricular (LV) systolic elastance and the time-varying elastance/impedance of the arterial tree, a relationship that governs the instantaneous generation of aortic flow and ultimately cardiac output. VAC, typically expressed as the ratio of effective arterial elastance (Ea) to LV end-systolic elastance (Ees), has provided valuable mechanistic and prognostic insights, but is limited by its lumped, largely steady-state nature and by the need for pressure–volume modeling or complex surrogate formulas. Contemporary time-domain and wave-intensity approaches have underscored that the shape of proximal aortic pressure–flow waveforms encodes rich beat-by-beat information about ventricular–arterial interaction and energy transfer. Doppler echocardiography of the ascending aorta provides a readily available, high-temporal resolution measure of proximal aortic flow that is already used to quantify stroke volume, cardiac output and valvular lesions. We propose that proximal aortic flow, as recorded by Doppler echocardiography, may serve as a clinically practical proxy for beat-by-beat VAC, reflecting the instantaneous matching of ventricular and aortic elastances, which regulates the ejected flow towards peripheral tissues according to metabolic needs. Full article

LED lamps have not been demonstrating the durability claimed by their manufacturers. One hypothesis is that switching transients may contribute to this. This study investigated switching-induced transients in LED lamps operated through dry contacts: manual switches and contactors. Using an oscilloscope, automated acquisition of waveform records was performed while several lamps were switched on in a 220 V_RMS/60 Hz electrical network. LED lamps of different models and manufacturers, one incandescent lamp, and a group of 48 LED lamps, subdivided into six sets of eight lamps, were all switched simultaneously. A total of 56 waveform-record files were obtained from the oscilloscope, comprising 2920 captured screens and 170 measurements. Transient voltage peaks of 380 and 391 V at the supply side, and 357 and 370 V at the lamp side, as well as voltage slew rates of up to 12 and 13 V/µs at the supply side and up to 16 and 19.5 V/µs at the lamp side, were measured, without considering statistical variations, which may indicate values exceeding the ordinary sinusoidal voltage peak (≅311 V) and its typical worst-case slew rate (≅0.12 V/µs). Future studies are suggested, such as tests in real installations, investigations of transient amplification or attenuation within electrical networks, assessment of the effects of wiring and impedance discontinuities, switch bounce, and semiconductor degradation, among others, to continue these studies. Full article

Background/Objectives: Parkinson’s disease (PD) is associated with alterations in resting-state Electroencephalogram (EEG) biomarkers. Identifying stimulation protocols that reliably shift these biomarkers toward healthy-like patterns is essential for developing personalized neuromodulation strategies. This study introduces a rapid, biomarker-driven framework for screening the EEG effects of diverse Galvanic Vestibular Stimulation (GVS) waveforms in PD. Methods: More than 300 subthreshold GVS stimuli were delivered during resting-state EEG to PD (n = 5) subjects and Healthy Controls (n = 5). A composite biomarker score that included spectral, cross-frequency, aperiodic, and complexity measures quantified stimulation-related changes. A linear classifier and multi-criteria decision analysis were used to evaluate and rank stimuli. Results: Stimulation produced consistent improvements in the composite biomarker score, with the strongest effects observed for beta-range sinusoids, multisine waveforms, frequency-modulated stimuli with a 75 Hz carrier, and PAC-modulated signals. No significant post-stimulation carryover effects were detected. Conclusions: While preliminary, this exploratory framework enables rapid, interpretable profiling of EEG responses to non-invasive stimulation in PD. By prioritizing candidate GVS protocols based on biomarker shifts rather than behavioural endpoints, the approach provides a practical foundation for future personalized neuromodulation strategies. Full article

Full waveform inversion (FWI) iteratively improves the accuracy of the model by minimizing the discrepancies between the predicted and the observed data. However, FWI commonly suffers from cycle skipping when the initial model is poor, leading to an erroneous result. To mitigate this problem, we propose deep-learning-backed adaptive waveform inversion (DL-AWI), which introduces a deep twin neural network to precondition the waveforms and compare the ratio of two signals with a zero-lag spike, thereby enhancing the stability of the inversion process. DL-AWI can project the synthetic and observed signals into an extended latent space via several convolutional neural networks (CNNs) with shared weights, which can accelerate the data matching. Compared with classic FWI methods, the proposed DL-AWI provides a wider space for model updates, significantly decreasing the risk of being trapped in local minima. We use synthetic and field examples to validate its efficiency in subsurface model inversion, and the results show that DL-AWI is robust even when a poor initial model is provided. Full article

The vertical actuation of multi-axis seismic simulators usually requires a redundant parallel scheme for high load capacity. Due to geometric over-constraints, the internal force coupling and the nonlinear hysteresis are high; thus, waveform reproduction quality and structural fatigue may result. A displacement–force dual closed loop cooperative control mechanism can address these problems. First, a real-time kinematic model is developed to overcome the platform pose via actuator extension, and second, a dynamic force balance loop is introduced to actively redistribute the load components. In addition, a fuzzy PID controller is incorporated to optimize gain scheduling online, compensating for hydraulic nonlinearities and time-varying structural parameters. In the experiment on a 3 × 3 m 6-DOF shaking table, the presented method performs very favorably compared to traditional methods. Under broadband random excitation, the THD of acceleration waveform drops from 15.2% (single-loop control) to 3.2%, and the internal momentum oscillation amplitude is suppressed by over 70%. The results show that our proposed method eliminates internal force dependence while maintaining high precision trajectory tracking for seismic simulation. Full article