Search Results (166)

The Lomb–Scargle method (LSM) constitutes a robust method for frequency and amplitude estimation in cases where data exhibit irregular or sparse sampling. Conventional spectral analysis techniques, such as the discrete Fourier transform (FT) and wavelet transform, rely on orthogonal mode decomposition and are inherently constrained when applied to non-equidistant or fragmented datasets, leading to significant estimation biases. The classical LSM, originally formulated for univariate time series, provides a statistical estimator that does not assume a Fourier series representation. In this work, we extend the LSM to multivariate datasets by redefining the shifting parameter $\tau$ to preserve the orthogonality of trigonometric basis functions in ${\mathbb{R}}^n$. This generalization enables simultaneous estimation of the frequency, phase, and amplitude vectors while maintaining the statistical advantages of the LSM, including consistency and noise robustness. We demonstrate its application to solar activity data, where sunspots serve as intrinsic markers of the solar dynamo process. These observations constitute a randomly sampled two-dimensional binary dataset, whose characteristic frequencies are identified and compared with the results of solar research. Additionally, the proposed method is applied to an ultrasound velocity profile measurement setup, yielding a three-dimensional velocity dataset with correlated missing values and significant temporal jitter. We derive confidence intervals for parameter estimation and conduct a comparative analysis with FT-based approaches. Full article

This paper proposed an adaptive bandwidth Phase-Locked Loop (PLL) that integrates integer-N and fractional-N switching for energy-efficient RF synthesis in IoT and mobile applications. The architecture exploits wide-bandwidth integer-N mode for rapid lock acquisition, then seamlessly transitions to narrow-bandwidth fractional-N mode for high-resolution synthesis and noise optimization. The architecture features a bandwidth-reconfigurable loop filter with intelligent switching control that monitors phase error dynamics. A novel adaptive digital noise filter mitigates $\Delta \Sigma$ quantization noise, replacing conventional synchronous delay lines. The multi-loop structure incorporates a high-resolution digital phase detector to enhance frequency accuracy and minimize jitter across both operating modes. With 180 nm CMOS technology, the PLL consumes 13.2 mW, while achieving $-119$ dBc/Hz in-band phase noise and 1 $p{s}_{rms}$ integrated jitter. With an operating frequency range at 2.9–3.2 GHz from a 1.8 V supply, the circuit achieves a worst case fractional spur of −62.7 dBc, which corresponds to a figure of merit (FOM) of −228.8 dB. Lock time improvements of 70% are demonstrated compared to single-mode implementations, making it suitable for high-precision, low-power wireless communication systems requiring agile frequency synthesis. Full article

Objectives: The aim of the study was to gauge the clinical usefulness of the Derkay scale in assessing the severity of voice dysfunction in patients with recurrent respiratory papillomatosis (RRP). Material and Methods: The study included 29 patients (8 women and 21 men) with a mean age of 40.2 years. To subjectively assess each patient’s voice, the Polish version of the Voice Handicap Index questionnaire was used. Acoustic parameters were calculated using the Multidimensional Voice Program, which included mean fundamental frequency (F0), frequency changes (% Jitter), amplitude changes (% Shimmer), noise-to-harmonic ratios (NHRs), and the soft phonation Index (SPI). The stage of RRP was assessed using the Derkay scale, together with the anatomical location of the lesion (from laryngeal endoscopy) and the impact RRP had on the general condition of the patient. Results: In women, Derkay clinical and total scores showed significant, positive, and strong correlations with almost all VHI-30 subscales (rho = 0.73–0.76). In men, the correlations were weaker (rho = 0.38–0.55) but were strong between the Derkay total score and F0 and total score and Jitter (rho = 0.63–0.65). Patients with human papilloma virus HPV-6 had significantly higher soft phonation index values (M = 11.97) compared to patients with HPV-11 (M = 6.91, U = 34.0; p = 0.019). Conclusions: The Derkay classification system correlates well with objective acoustic frequency measures and patient-reported voice outcomes. The system may be helpful in identifying patients at increased risk of voice dysfunction. It could be used to guide decisions about voice assessment and rehabilitation. Full article

In automated orchard operations, the straight-line locomotion stability of ground-based weeding robots is critical for ensuring path coverage efficiency and operational reliability. To address the response lag and high-frequency oscillations often observed in conventional PID and fixed-lookahead Pure Pursuit controllers, this study proposes an adaptive lookahead Pure Pursuit method incorporating angular velocity feedback. By dynamically adjusting the lookahead distance according to real-time attitude changes, the method enhances coordination between path curvature and robot stability. To enable systematic evaluation, three time-series-based metrics are introduced: mean absolute yaw error (MAYE), peak-to-peak fluctuation amplitude, and the standard deviation of angular velocity, with overshoot occurrences included as an additional indicator. Field experiments demonstrate that the proposed method outperforms baseline algorithms, achieving lower yaw errors (0.61–0.66°), reduced maximum deviation (≤3.7°), and smaller steady-state variance (<0.44°²), thereby suppressing high-frequency jitter and improving turning convergence. Under typical working conditions, the method achieved a mean yaw deviation of 0.6602°, a fluctuation of 5.59°, an angular velocity standard deviation of 10.79°/s, and 155 overshoot instances. The yaw angle remained concentrated around the target orientation, while angular velocity responses stayed stable without loss-of-control events, indicating a favorable balance between responsiveness and smoothness. Overall, the study validates the robustness and adaptability of the proposed strategy in complex orchard scenarios and establishes a reusable evaluation framework, offering theoretical insights and practical guidance for intelligent agricultural machinery optimization. Full article

Background/Objectives: The post-COVID-19 condition frequently includes dysphonia. We aimed to assess objective and subjective voice disorders and short-term responses to multimodal therapy in patients with isolated post-COVID-19 dysphonia. Methods: This retrospective, single-center pre–post study screened 244 post-COVID-19 patients; a subset of 14 with isolated dysphonia underwent standardized assessment at baseline and at 1-month follow-up. Patient-reported outcomes (Voice Handicap Index, VHI; Voice-Related Quality of Life, V-RQOL) and endoscopic evaluation were performed using videolaryngostroboscopy (LVS) and high-speed videoendoscopy (HSV) with kymographic analysis to quantify parameters describing vocal fold oscillations. The treatment included short-term systemic corticosteroids, inhaled corticosteroids, hyaluronic-acid inhalations, and structured voice therapy. Results: At baseline, HSV revealed signs of glottal insufficiency—irregular and asymmetric vocal fold motion, reduced amplitude and pliability, a disrupted mucosal wave, and an increased open quotient. At follow-up, HSV showed increased oscillation, amplitude, and cycle regularity with reduced left–right asymmetry and phase differences; phonovibrograms displayed clearer and more structured patterns. Perturbation indices decreased across jitter and shimmer measures, and the mean fundamental frequency was lower. Improvements in instrumental measures aligned with better VHI and V-RQOL scores. Conclusions: In patients with persistent dysphonia after acute SARS-CoV-2 infection, comprehensive ENT evaluation with instrumental laryngeal assessment is warranted. Short-term multimodal management was associated with improvements in both HSV-derived measures and patient-reported outcomes; confirmation in controlled studies is needed. Full article

To address the problems of insufficient trajectory tracking accuracy, pronounced jitter over undulating terrain, and limited disturbance rejection in orchard mobile robots, this paper proposes a trajectory tracking control strategy based on a double-loop adaptive sliding mode. Firstly, a kinematic model of the orchard robot is constructed and a time-varying integral terminal sliding surface is designed to achieve global fast finite-time convergence. Secondly, a sinusoidal saturation switching function with a variable boundary is employed to suppress the high-frequency chattering inherent in sliding mode control. Thirdly, an improved double-power reaching law (Improved DPRL) is introduced to enhance disturbance rejection in the inner loop while ensuring continuity of the outer-loop output. Finally, Lyapunov stability theory is used to prove the asymptotic stability of the double-loop system. The experimental results show that attitude angle error settles within 0.01 rad after 0.144 s, while the position errors in both the x-axis and y-axis directions settle within 0.01 m after 0.966 s and 0.753 s, respectively. Regarding position error convergence, the Integral of Absolute Error (IAE)/Integral of Squared Error (ISE)/Integral of Time-Weighted Absolute Error (ITAE) are 0.7629 m, 0.7698 m, and 0.2754 m, respectively; for the attitude angle error, the IAE/ISE/ITAE are 0.0484 rad, 0.0229 rad, and 0.1545 rad, respectively. These results indicate faster convergence of both position and attitude errors, smoother control inputs, and markedly reduced chattering. Overall, the findings satisfy the real-time and accuracy requirements of fast trajectory tracking for orchard mobile robots. Full article

Speech Emotion Recognition (SER) plays a vital role in supporting applications such as healthcare, human–computer interaction, and security. However, many existing approaches still face challenges in achieving robust generalization and maintaining high recall, particularly for emotions related to stress and anxiety. This study proposes a dual-stream hybrid model that combines prosodic features with spatio-temporal representations derived from the Multitaper Mel-Frequency Spectrogram (MTMFS) and the Constant-Q Transform Spectrogram (CQTS). Prosodic cues, including pitch, intensity, jitter, shimmer, HNR, pause rate, and speech rate, were processed using dense layers, while MTMFS and CQTS features were encoded with CNN and BiGRU. A Multi-Head Attention mechanism was then applied to adaptively fuse the two feature streams, allowing the model to focus on the most relevant emotional cues. Evaluations conducted on the RAVDESS dataset with subject-independent 5-fold cross-validation demonstrated an accuracy of 97.64% and a macro F1-score of 0.9745. These results confirm that combining prosodic and advanced spectrogram features with attention-based fusion improves precision, recall, and overall robustness, offering a promising framework for more reliable SER systems. Full article

Femtosecond lasers have evolved continuously over the past three decades, enabling the transition of research from fundamental studies in atomic and molecular physics to the realm of practical applications. In femtosecond laser amplifiers, to ensure strict synchronization between the seed laser pulse and the pump laser, enabling their precise overlap during the amplification process and avoiding a decline in pulse amplification efficiency and the generation of undesired phase noise, this study designed a synchronous timing signal generation system based on the combination of FPGA and analog delay. This system was investigated from three aspects: delay pulse width adjustment within a certain range, precise delay resolution, and external trigger jitter compensation. By using a FPGA digital counter to achieve coarse-delay control over a wide range and combining it with the method of passive precise fine delay, the system can generate synchronous delay signals with a large delay range, high precision, and multiple channels. Regarding the problem of asynchronous phase between the external trigger and the internal clock, a jitter compensation circuit was proposed, consisting of an active gated integrator and an output comparator, which compensates for the uncertainty of trigger timing through analog delay. The verification of this study shows that the system operates stably under an external trigger with a repetition frequency of 80 MHz. The output delay range is from 10 ns to 100 $\mu$s, the coarse-delay resolution is 10 ns, the fine-delay adjustment step is 1.25 ns, and the pulse jitter is reduced from a maximum of 10 ns to the hundred-picosecond level. This meets the requirements of femtosecond laser amplifiers for synchronous trigger signals and offers essential technical support and fundamental assurance for the high-power and high-efficiency amplification of Ti:sapphire ultrashort laser pulses. Full article

The ultrasonic power supply constitutes the core component of an ultrasonic welding system, and its main function is to convert the industrial frequency electricity into resonant high-frequency electricity in order to achieve mechanical energy conversion. However, factors such as changes in ambient temperature or component aging may cause the resonant frequency of the transducer to drift, thus detuning the resonant system and seriously affecting system performance. Therefore, an ultrasonic welding system requires high-frequency tracking in real time. Traditional frequency tracking methods (such as acoustic tracking, PID control, etc.) have defects such as poor stability, narrow bandwidth, or cumbersome parameter setting, making it difficult to meet the demand for fast tracking. To address these problems, this study adopts a T-matching network and utilizes sliding mode control for frequency tracking. In order to solve the problems of slow convergence and obvious jitter in sliding mode control (SMC), a Hippopotamus Optimization (HO) algorithm is introduced to simulate hippopotamuses’ group behavior and predation mechanisms, thereby optimizing the control parameters. It is verified through simulation that the SMC algorithm optimized by the HO algorithm (HO-SMC) is able to suppress frequency drift more effectively and demonstrates the advantages of fast response, high accuracy, and strong robustness in the scenario of sudden load changes. Full article

Forest fires not only destroy vegetation and directly decrease forested areas, but they also significantly impair forest stand structures and habitat conditions, ultimately leading to imbalances within the entire forest ecosystem. Therefore, accurate forest fire detection is critical for ecological safety and for protecting lives and property. However, existing algorithms often struggle with detecting flames and smoke in complex scenarios like sparse smoke, weak flames, or vegetation occlusion, and their high computational costs hinder practical deployment. To cope with it, this paper introduces F3-YOLO, a robust and fast forest fire detection model based on YOLOv12. F3-YOLO introduces conditionally parameterized convolution (CondConv) to enhance representational capacity without incurring a substantial increase in computational cost, improving fire detection in complex backgrounds. Additionally, a frequency domain-based self-attention solver (FSAS) is integrated to combine high-frequency and high-contrast information, thus better handling real-world detection scenarios involving both small distant targets in aerial imagery and large nearby targets on the ground. To provide more stable structural cues, we propose the Focaler Minimum Point Distance Intersection over Union Loss (FMPDIoU), which helps the model capture irregular and blurred boundaries caused by vegetation occlusion or flame jitter and smoke dispersion. To enable efficient deployment on edge devices, we also apply structured pruning to reduce computational overhead. Compared to YOLOv12 and other mainstream methods, F3-YOLO achieves superior accuracy and robustness, attaining the highest mAP@50 of 68.5% among all compared methods on the dataset while requiring only 5.4 GFLOPs of computational cost and maintaining a compact parameter count of 2.6 M, demonstrating exceptional efficiency and effectiveness. These attributes make it a reliable, low-latency solution well-suited for real-time forest fire early warning systems. Full article

Whispering-gallery-mode (WGM) microresonators are amongst the most promising optical sensors for detecting bio-chemical targets. A number of laser interrogation methods have been proposed and demonstrated over the last decade, based on scattering and absorption losses or resonance splitting and shift, harnessing the high-quality factor and ultra-small volume of WGMs. Actually, regardless of the sensitivity enhancement, their practical sensing operation may be hampered by the complexity of coupling devices as well as the signalprocessing required to extract the WGM response. Here, we use a silica microsphere immersed in an aqueous environment and efficiently excite optical WGMs with a free-space visible laser, thus collecting the relevant information from the transmitted and back-scattered light without any optical coupler, fiber, or waveguide. We show that a 640-nm diode laser, actively frequency-locked on resonance, provides real-time, fast sensing of dielectric nanoparticles approaching the surface with direct analog readout. Thanks to our illumination scheme, the sensor can be kept in water and operate for days without degradation or loss of sensitivity. Diverse noise contributions are carefully considered and quantified in our system, showing a minimum detectable particle size below 1 nm essentially limited by the residual laser microcavity jitter. Further analysis reveals that the inherent laserfrequency instability in the short, -mid-term operation regime sets an ultimate bound of 0.3 nm. Based on this work, we envisage the possibility to extend our method in view of developing new viable approaches for detection of nanoplastics in natural water without resorting to complex chemical laboratory methods. Full article

Precise time interval generation is a cornerstone of modern measurement, automation, and distributed control systems, particularly within Internet of Things (IoT) architectures. This paper presents the design, implementation, and evaluation of a low-cost and high-precision time interval generator based on Complementary Metal-Oxide Semiconductor (CMOS) logic counters (Integrated Circuit (IC) IC 7493 and IC 4017) and inverter-based crystal oscillators (IC 74LS04). The proposed system enables frequency division from 1 MHz down to 1 Hz through a cascade of binary and Johnson counters, enhanced with digitally controlled multiplexers for output signal selection. Unlike conventional timing systems relying on expensive Field-Programmable Gate Array (FPGA) or Global Navigation Satellite System (GNSS)-based synchronization, this approach offers a robust, locally controlled reference clock suitable for IoT nodes without network access. The hardware is integrated with Arduino and ESP32 microcontrollers via General-Purpose Input/Output (GPIO) level interfacing, supporting real-time timestamping, deterministic task execution, and microsecond-level synchronization. The system was validated through Python-based simulations incorporating Gaussian jitter models, as well as real-time experimental measurements using Arduino’s micros() function. Results demonstrated stable pulse generation with timing deviations consistently below ±3 µs across various frequency modes. A comparative analysis confirms the advantages of this CMOS-based timing solution over Real-Time Clock (RTC), Network Time Protocol (NTP), and Global Positioning System (GPS)-based methods in terms of local autonomy, cost, and integration simplicity. This work provides a practical and scalable time reference architecture for educational, industrial, and distributed applications, establishing a new bridge between classical digital circuit design and modern Internet of Things (IoT) timing requirements. Full article

A stable microwave signal transmission system for a distributed system that is capable of simultaneous downloads at multiple arbitrary nodes within the optical path is proposed. The download module, which is based on optical circulators and optical couplers, can be inserted at any node position within the transmission optical path to complete the downloading of frequency-synchronization signals. Experimentally, a distributed frequency-synchronization system with multiple download nodes is demonstrated over 40 km of optical fiber. Experimental results show that the signal has been downlink-transferred from different download modules with the standard deviation of phase jitter being 1°@10 GHz at 1 h through 40-km optical fiber. Moreover, the standard deviation of phase jitter between downloaded signals from any two download modules is also better than 1°@10 GHz at 1 h. In addition, the Allan Deviation is better than ${10}^{-12}$@1 h for the download module. Full article

Background: Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s disease, affecting countless individuals worldwide. PD is characterized by the onset of a marked motor symptomatology in association with several non-motor manifestations. The clinical phase of the disease is usually preceded by a long prodromal phase, devoid of overt motor symptomatology but often showing some conditions such as sleep disturbance, constipation, anosmia, and phonatory changes. To date, speech analysis appears to be a promising digital biomarker to anticipate even 10 years before the onset of clinical PD, as well serving as a useful prognostic tool for patient follow-up. That is why, the voice can be nominated as the non-invasive method to detect PD from healthy subjects (HS). Methods: Our study was based on cross-sectional study to analysis voice impairment. A dataset comprising 81 voice samples (41 from healthy individuals and 40 from PD patients) was utilized to train and evaluate common machine learning (ML) models using various types of features, including long-term (jitter, shimmer, and cepstral peak prominence (CPP)), short-term features (Mel-frequency cepstral coefficient (MFCC)), and non-standard measurements (pitch period entropy (PPE) and recurrence period density entropy (RPDE)). The study adopted multiple machine learning (ML) algorithms, including random forest (RF), K-nearest neighbors (KNN), decision tree (DT), naïve Bayes (NB), support vector machines (SVM), and logistic regression (LR). Cross-validation technique was applied to ensure the reliability of performance metrics on train and test subsets. These metrics (accuracy, recall, and precision), help determine the most effective models for distinguishing PD from healthy subjects. Result: Among all the algorithms used in this research, random forest (RF) was the best-performing model, achieving an accuracy of 82.72% with a ROC-AUC score of 89.65%. Although other models, such as support vector machine (SVM), could be considered with an accuracy of 75.29% and a ROC-AUC score of 82.63%, RF was by far the best one when evaluated across all metrics. The K-nearest neighbor (KNN) and decision tree (DT) performed the worst. Notably, by combining a comprehensive set of long-term, short-term, and non-standard acoustic features, unlike previous studies that typically focused on only a subset, our study achieved higher predictive performance, offering a more robust model for early PD detection. Conclusions: This study highlights the potential of combining advanced acoustic analysis with ML algorithms to develop non-invasive and reliable tools for early PD detection, offering substantial benefits for the healthcare sector. Full article

Contemporary phonetics and speech therapy continuously seek new techniques and methods that could contribute to improving verbal communication for individuals with speech disorders. One such phenomenon, Delayed Auditory Feedback (DAF), involves the speaker hearing their own voice with a specific delay relative to real-time speech. Although the research presented in this study was conducted on healthy individuals, it offers valuable insights into the mechanisms controlling speech, which may also apply to individuals with speech disorders. This article introduces a novel method and measurement setup, focusing on selected key speech signal parameters. To characterize the impact of Delayed Auditory Feedback (DAF) on fluent speakers, speech signal parameters were measured in 5 women and 5 men during spontaneous speech and reading. Parameters such as speech rate, fundamental frequency, formants, speech duration, jitter, and shimmer were analyzed both during and prior to the application of DAF. The results of this study may find practical applications in the field of telecommunications, especially in improving the efficiency and quality of human communication. Full article