Search Results (60)

Monocular dynamic scene reconstruction is a challenging task due to the inherent limitation of observing the scene from a single viewpoint at each timestamp, particularly in the presence of object motion and illumination changes. Recent methods combine Gaussian Splatting with deformation modeling to enable fast training and rendering; however, their performance in real-world scenarios strongly depends on accurate point cloud initialization. When such initialization is unavailable and random point clouds are used instead, reconstruction quality degrades significantly. To address this limitation, we propose an optimization strategy that relaxes the requirement for accurate initialization in Gaussian-Splatting-based monocular dynamic scene reconstruction. The scene is first reconstructed under a static assumption using all monocular frames, allowing stable convergence of background regions. Based on reconstruction errors, a subset of Gaussians is then activated as dynamic to model motion and deformation. In addition, an annealing jitter regularization term is introduced to improve robustness to camera pose inaccuracies commonly observed in real-world datasets. Extensive experiments on established benchmarks demonstrate that the proposed method enables stable training from randomly initialized point clouds and achieves reconstruction performance comparable to approaches relying on accurate point cloud initialization. Full article

Background/Objectives: Speech and motor control share overlapping neural mechanisms, yet their quantitative relationships in Parkinson’s disease (PD) remain underexplored. This study investigated bidirectional associations between acoustic voice features and objective motor metrics to better understand how vocal and motor systems relate in PD. Methods: Cross-sectional baseline data from participants in a randomized neuromodulation trial were analyzed (n = 13). Motor performance was captured using an Integrated Motion Analysis Suite (IMAS), which enabled quantitative, objective characterization of motor performance during balance, gait, and upper- and lower-limb tasks. Acoustic analyses included harmonic-to-noise ratio (HNR), smoothed cepstral peak prominence (CPPS), jitter, shimmer, median fundamental frequency (F0), F0 standard deviation (SD F0), and voice intensity. Univariate linear regressions were conducted in both directions (voice ↔ motor), as well as partial correlations controlling for PD motor symptom severity. Results: When modeling voice outcomes, faster motor performance and shorter movement durations were associated with acoustically clearer voice features (e.g., higher elbow flexion-extension peak speed with higher voice HNR, β = 8.5, R² = 0.56, p = 0.01). Similarly, when modeling motor outcomes, clearer voice measures were linked with faster movement speed and shorter movement durations (e.g., higher voice HNR with higher peak movement speed in elbow flexion/extension, β = 0.07, R² = 0.56, p = 0.01). Conclusions: Voice and motor measures in PD showed significant bidirectional associations, suggesting shared sensorimotor control. These exploratory findings, while limited by sample size, support the feasibility of integrated multimodal assessment for future longitudinal studies. Full article

Aperiodic pseudo-random impulse (APPI) trains represent deterministic yet reproducible sequences that mimic the irregularity of natural processes. They allow complete control over inter-spike intervals (ISIs) and pulse widths (PWs). Such signals are increasingly relevant for low-probability-of-intercept (LPI) communications, radar testing, and biomedical applications, where controlled variability mitigates adaptation and enhances stimulation efficiency. This paper presents a modular APPI generator implemented on an Arduino Mega platform, featuring programmable statistical models for ISI (exponential distribution) and PW (uniform distribution), dual-timing mechanisms (baseline loop and Timer/ISR, clear-timer on compare (CTC)), a real-time telemetry and software interface, and a safe output chain with opto-isolation and current limitation. The generator provides both reproducibility and tunable stochastic dynamics. Experimental validation includes jitter analysis, Kolmogorov–Smirnov tests, Q–Q plots, spectral and autocorrelation analysis, and load integration using a constant-current source with compliance margins. The results demonstrate that the Timer/ISR (CTC) implementation achieves significantly reduced jitter compared to the baseline loop, while maintaining the statistical fidelity of ISI and PW distributions, broad spectral characteristics, and fast decorrelation. Experimental verification was extended across a wider parameter space (λ = 0.1–100 Hz, PW = 10 µs–100 ms, 10 repetitions per condition), confirming robustness and repeatability. Experimental validation confirmed accurate Poisson/Uniform ISI generation, sub-millisecond jitter stability in the timer-controlled mode, robustness across λ = 0.1–100 Hz and PW = 10 µs–100 ms, and preliminary compliance with isolation and leakage limits. The accompanying Python GUI provides real-time control, telemetry, and data-logging capabilities. This work establishes a reproducible, low-cost, and open-source framework for APPI generation, with direct applicability in laboratory and field environments. Full article

The need for unpredictable sequences of bits is common in many important security applications. These sequences can only be generated by true random number generators (TRNGs). Apart from the natural analog domain for TRNGs, this type of generator is also required as a digital-based solution, particularly leveraging field-programmable gate array (FPGA) platforms. Despite the number of existing FPGA-based implementations, new solutions that use different types of entropy sources, utilize fewer FPGA resources, or ensure higher throughput are still being sought. This paper presents an architecture of a simple TRNG targeted for implementation in FPGAs. As a source of entropy, the TRNG exploits jitter in capacitive oscillators and metastability in flip-flops. The capacitive oscillators, in turn, use the input–output cells of an FPGA chip and unconnected external pins and cyclically charge and discharge the parasitic capacitance associated with these pins. The TRNG needs a small number of FPGA resources, namely 13 look-up tables (LUTs), 12 flip-flops, and 3 unused pins. Its throughput is approximately 12.5 Mbit/s for AMD/Xilinx Artix-7 FPGA family chips. The presented TRNG passes all the NIST statistical tests for a wide range of operating conditions. Full article

This paper presents the design of a Delay-Locked Loop (DLL) with a simple architecture and a wide input clock duty cycle range. The design is tailored to meet the increasing data rate and stringent clock requirements of modern semiconductor chips, with particular applicability to dynamic random-access memory (DRAM) systems. The structure features two Bang-Bang Phase Detectors (BBPDs) to adjust the rising and falling edges of the divided clock. Implemented using a 65 nm CMOS process, the design was verified through simulation. At a working frequency of 3.2 GHz, the input clock duty cycle range spans from 18% to 72%, with a maximum output clock duty cycle error of just 0.6%, a peak-to-peak jitter of 15.73 ps, and a power consumption of 12.7 mW. Full article

Atmospheric turbulence severely degrades high-resolution satellite videos through spatiotemporally coupled distortions, including temporal jitter, spatial-variant blur, deformation, and scintillation, thereby constraining downstream analytical capabilities. Restoring turbulence-corrupted videos poses a challenging ill-posed inverse problem due to the inherent randomness of turbulent fluctuations. While existing turbulence mitigation methods for long-range imaging demonstrate partial success, they exhibit limited generalizability and interpretability in large-scale satellite scenarios. Inspired by refractive-index structure constant ($C_n^2$) estimation from degraded sequences, we propose a physics-informed turbulence signature (TS) prior that explicitly captures spatiotemporal distortion patterns to enhance model transparency. Integrating this prior into a lucky imaging framework, we develop a Physics-Based Turbulence Mitigation Network guided by Turbulence Signature (TMTS) to disentangle atmospheric disturbances from satellite videos. The framework employs deformable attention modules guided by turbulence signatures to correct geometric distortions, iterative gated mechanisms for temporal alignment stability, and adaptive multi-frame aggregation to address spatially varying blur. Comprehensive experiments on synthetic and real-world turbulence-degraded satellite videos demonstrate TMTS’s superiority, achieving 0.27 dB PSNR and 0.0015 SSIM improvements over the DATUM baseline while maintaining practical computational efficiency. By bridging turbulence physics with deep learning, our approach provides both performance enhancements and interpretable restoration mechanisms, offering a viable solution for operational satellite video processing under atmospheric disturbances. 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

This study aims to identify personal, clinical, and acoustic predictors of therapy outcomes based on changes in Korean voice-related quality of life (K-VRQOL) scores, as well as to compare the predictive performance of traditional regression and machine learning models. A total of 102 participants undergoing voice therapy are retrospectively analyzed. Multiple regression analysis and four machine learning algorithms—random forest (RF), gradient boosting (GB), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost)—are applied to predict changes in K-VRQOL scores across the total, physical, and emotional domains. The Shapley additive explanations (SHAP) approach is used to evaluate the relative contribution of each variable to the prediction outcomes. Female gender and comorbidity status emerge as significant predictors in both the total and physical domains. Among the acoustic features, jitter, SFF, and MPT are closely associated with improvements in physical voice function. LightGBM demonstrates the best overall performance, particularly in the total domain (R² = 32.54%), while GB excels in the physical domain. The emotional domain shows relatively low predictive power across the models. SHAP analysis reveals interpretable patterns, highlighting jitter and speaking fundamental frequency (SFF) as key contributors in high-performing models. Integrating statistical and machine learning approaches provides a robust framework for predicting and interpreting voice therapy outcomes. These findings support the use of explainable artificial intelligence (AI) to enhance clinical decision-making and pave the way for personalized voice rehabilitation strategies. Full article

With the development of intelligent connected vehicles, higher demands are being placed on the capabilities of in-vehicle bus networks. Compared to traditional in-vehicle bus networks like Local Interconnect Network (LIN) and Controller Area Network (CAN), the FlexRay bus offers advantages such as high real-time performance and high transmission rates, making it the core technology of the new generation of in-vehicle bus networks. This study focuses on the phenomenon of bandwidth resource waste in the FlexRay bus and innovatively proposes the FlexRay Static Segment Heterogeneous Scheduling Algorithm (SHSA). The SHSA algorithm optimizes the message transmission performance of the FlexRay bus through heterogeneous allocation of communication channels and message scheduling methods. This study established a simulation experimental platform using the CANoe.FlexRay bus network simulation tool and conducted simulation experiments on the proposed algorithm. Experimental results show that the average bandwidth utilization of the SHSA algorithm is 72.5%, which is 20.91%, 51.14%, and 54% higher than that of the existing Heterogeneous Makespan-minimizing DAG Scheduler (HMDS), Message Packing Scheme, and Jitter-aware Message Scheduling-Simulated Annealing and Greedy Randomized Adaptive Search Procedure (JAMS-SG), respectively. This study provides technical support for message transmission in intelligent connected vehicles and enhances the communication efficiency of the in-vehicle FlexRay bus network. Full article

Photoacoustic tomography (PAT), also known as optoacoustic tomography, has been emerging as a biomedical imaging modality that can provide cross-sectional or three-dimensional (3D) visualization of biological tissues such as blood vessels and lymphatic vessels in vivo at high resolution. The principle behind the visualization involves the light being absorbed by the tissues which results in the generation of ultrasound. Depending on the strength of ultrasound and its decay rate, it could be used to visualize the absorber location. In general, pulsed lasers such as the Q-switched Nd-YAG and OPO lasers that provide high-energy widths in the range of a few nanoseconds operating at low repetition rates are commonly used as a light source in photoacoustic imaging. However, such lasers are expensive and occupy ample space. Therefore, PAT systems that use LED as the source instead of lasers, which have the advantage of being obtainable at low cost and portable, are gaining attention. However, LED light sources have significantly low energy, and the photoacoustic signals generated have a low signal-to-noise ratio (SNR). Therefore, in LED-based systems, one way to strengthen the signal and improve the SNR is to significantly increase the repetition rate of LED pulses and use signal processing, which can be achieved using a high-power LED along M-sequence signal decoding. M-sequence signal decoding is effective, especially under high repetition rates, thus improving the SNR. However, power supplies for high-power LEDs have a circuit jitter, resulting in random temporal fluctuations in the emitted light. Such jitters, in turn, would affect the M-sequence-based signal decoding. Therefore, we propose a new decoding algorithm which compensates for LED jitter in the M-sequence signal processing. We show that the proposed new signal processing method can significantly improve the SNR of the photoacoustic signals. Full article

Texture regularity, for example, the repeating pattern of a carpet, brickwork, or tree bark, is a ubiquitous feature of the visual world. The perception of regularity has generally been studied using multi-element textures whose regularity is manipulated by the addition of random jitter to the elements’ nominal positions. Here, we investigate the selectivity of regularity perception for the luminance contrast polarities of the elements. Our psychophysical tool was simultaneous regularity contrast, or SRC, the phenomenon in which the perceived regularity of a central test texture is shifted away from that of the surrounding regularity. Stimuli were composed of arrays of dark and/or white Gaussian elements. Surround and center test textures consisted of either the same (“congruent”) or opposite (“incongruent”) polarities. In addition, we tested a “mixed” condition consisting of a random mixture of polarities in both the surround and test. The perceived regularity of the test was measured using a match stimulus with the same polarity dimension as the test. The regularity of the match stimulus was adjusted on each trial using a forced-choice staircase procedure and the point-of-subjective equality between the match and test regularities was estimated from the resulting psychometric functions. SRC was observed in both congruent and incongruent conditions, but with the mixed condition, the perceived regularity of the test was shifted toward rather than away from the surround regularity, an example of assimilation, not contrast. The analysis revealed no significant difference in the magnitude of SRC between the congruent and incongruent conditions, suggesting that SRC could be mediated solely by polarity agnostic mechanisms, although there are other possible explanations for the “null” result. However, trend analysis using a non-linear (sigmoidal-shaped) function indicated a significant difference between the congruent and incongruent conditions, which, together with the mixed polarity results, suggests the presence of at least some polarity selective mechanisms. Previous reports have suggested that regularity perception is encoded by the “peakedness” in the distribution of spatial-frequency-tuned linear filter responses. We modelled SRC quantitatively by incorporating peakedness with spatial-frequency-selective surround inhibition and found that the model gave a good account of the SRC data. Possible reasons for the assimilation effect—with the mixed polarity condition are discussed. Full article

The timing jitter of a single-photon avalanche diode (SPAD) plays a critical role in the design and optimization of front-end circuits. This paper proposes a simplified timing jitter model based on Verilog-A. This model uses random numbers to determine the locations of photon absorptions and carrier avalanches based on absorption and avalanche probabilities, thereby achieving a calculation of the response time. By introducing photon detection probability, the model has corrected the response time obtained under ideal assumptions and achieved compatibility with excess bias voltage effects, which can describe the Gaussian peak of the timing jitter concisely and effectively. The simulation results are in good agreement with the measurement results, demonstrating the advantages of this model in terms of accuracy, flexibility, and adaptability. The model provides support for the collaborative optimization of the design of SPAD devices and circuits. Full article

The marine environment is complex and variable, with the absorption and scattering effects of seawater and turbulence causing significant attenuation of received optical signals and introducing random jitter, which limits the communication range and stability of underwater wireless optical communication systems. This paper presents the Superposition UEP-Spinal Code structure, which utilizes unequal error protection (UEP) to adjust the transmission performance of different types of information in underwater composite data communication by adjusting the superposition weighting factors in the encoding algorithm. This encoding method enhances the noise immunity of important data, and with the same bandwidth utilization, the overall decoding complexity is reduced by 13.3% compared to the previously improved Spinal code encoding algorithm. The results show that the Superposition UEP-Spinal Code provides a more stable, reliable, and efficient communication solution for underwater wireless optical communication systems with randomly varying channel conditions. Full article

Mobile Ad Hoc Networks (MANETs) are decentralized wireless networks characterized by dynamic topologies and the absence of fixed infrastructure. These unique features make MANETs critical for applications such as disaster recovery, military operations, and IoT systems. However, they also pose significant challenges for efficient and effective routing. This study evaluates the performance of eight MANET routing protocols: Optimized Link State Routing (OLSR), Destination-Sequenced Distance Vector (DSDV), Ad Hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), Ad Hoc On-Demand Multipath Distance Vector (AOMDV), Temporally Ordered Routing Algorithm (TORA), Zone Routing Protocol (ZRP), and Geographic Routing Protocol (GRP). Using a custom simulation environment in OMNeT++ 6.0.1 with INET-4.5.0, the protocols were tested under four scenarios with varying node densities (20, 80, 200, and 500 nodes). The simulations utilized the Random Waypoint Mobility model to mimic dynamic node movement and evaluated key performance metrics, including network load, throughput, delay, energy consumption, jitter, packet loss rate, and packet delivery ratio. The results reveal that proactive protocols like OLSR are ideal for stable, low-density environments, while reactive protocols such as AOMDV and TORA excel in dynamic, high-mobility scenarios. Hybrid protocols, particularly GRP, demonstrate a balanced approach; achieving superior overall performance with up to 30% lower energy consumption and higher packet delivery ratios compared to reactive protocols. These findings provide practical insights into the optimal selection and deployment of MANET routing protocols for diverse applications, emphasizing the potential of hybrid protocols for modern networks like IoT and emergency response systems. Full article

The evolution from 5G to 6G networks is driven by the need to meet the stringent requirements, i.e., ultra-reliable, low-latency, and high-throughput communication. The new services are called Further-Enhanced Mobile Broadband (feMBB), Extremely Reliable and Low-Latency Communications (ERLLCs), Ultra-Massive Machine-Type Communications (umMTCs), Massive Ultra-Reliable Low-Latency Communications (mURLLCs), and Mobile Broadband Reliable Low-Latency Communications (MBRLLCs). Network slicing emerges as a critical enabler in 6G, providing virtualized, end-to-end network segments tailored to diverse application needs. Despite its significance, existing datasets for slice selection are limited to 5G or LTE-A contexts, lacking relevance to the enhanced requirements. In this work, we present a novel synthetic dataset tailored to 6G network slicing. By analyzing the emerging service requirements, we generated traffic parameters, including latency, packet loss, jitter, and transfer rates. Machine Learning (ML) models like Random Forest (RF), Decision Tree (DT), XGBoost, Support Vector Machine (SVM), and Feedforward Neural Network (FNN) were trained on this dataset, achieving over 99% accuracy in both slice classification and handover prediction. Our results highlight the potential of this dataset as a valuable tool for developing AI-assisted 6G network slicing mechanisms. While still in its early stages, the dataset lays a foundation for future research. As the 6G standardization advances, we aim to refine the dataset and models, ultimately enabling real-time, intelligent slicing solutions for next-generation networks. Full article