Search Results (1,157)

Automotive dashboard cameras are widely used to record driving events and often serve as critical evidence in accident investigations and insurance claims. However, the availability of free and low-cost editing tools has increased the risk of video tampering, underscoring the need for reliable methods to verify video authenticity. Temporal tampering typically involves manipulating frame order through insertion, deletion, or duplication. This paper proposes a computationally efficient framework that transforms high-dimensional video into compact one-dimensional temporal signals and learns tampering patterns using a shallow one-dimensional convolutional neural network (1D-CNN). Five complementary features are extracted between consecutive frames: frame-difference magnitude, structural similarity drift (SSIM drift), optical-flow mean, forward–backward optical-flow consistency error, and compression-aware temporal prediction error. Per-video robust normalization is applied to emphasize intra-video anomalies. Experiments on a custom dataset derived from D²-City demonstrate strong detection performance in single-attack settings: 95.0% accuracy for frame deletion, 100.0% for frame insertion, and 95.0% for frame duplication. In a four-class setting (non-tampered, insertion, deletion, duplication), the model achieves 96.3% accuracy, with AUCs of 0.994, 1.000, 0.997, and 0.988, respectively. Efficiency analysis confirms near real-time CPU inference (≈12.7–12.9 FPS) with minimal memory overhead. Cross-dataset tests on BDDA and VIRAT reveal domain-shift sensitivity, particularly for deletion and duplication, highlighting the need for domain adaptation and augmentation. Overall, the proposed multi-feature 1D-CNN provides a practical, interpretable, and resource-aware solution for temporal tampering detection in dashcam videos, supporting trustworthy video forensics in IoT-enabled transportation systems. Full article

In modern sports, straight-line sprinting alone is insufficient for assessing overall sprint performance, as athletes must also decelerate and change direction efficiently. Existing methods lack a single metric that integrates all abilities, enabling holistic assessment. This study aimed to develop a comprehensive and novel measurement of multidirectional sprinting ability. Fifty-four university athletes (21.0 ± 1.5 years; 69.6 ± 9.1 kg; 172.6 ± 7.8 cm) performed linear sprints, decelerations, and 45°, 90°, and 135° change of direction (COD) tests in both directions over 30 m. Sprint accelerations and decelerations were recorded using a Stalker ATS II radar gun, while COD times were measured with stationary time gates. Sprint velocities were used to generate a multidirectional sprint area (MDSA), which was divided into forward, backward, left, and right sections. The MDSA method is calculated as the area of the octagonal polygon created by plotting eight velocity vectors from different angles of sprints. Paired t-tests compared area differences across directions, and ANOVA tests were used to compare sporting codes and sex. The resulting model reported differences across sporting codes (p < 0.001), sex (p < 0.001), the total area value (p < 0.001), and total area percentage (p < 0.001). The results showed a significant difference between forward and backward accelerations (p < 0.001), but no significant difference between left and right movements (p = 0.244). The MDSA method offers a reliable, quantitative intra-session approach for assessing athletes’ multidirectional sprint abilities by calculating the octagonal area on the basis of velocity data. This holistic analysis identifies asymmetries and performance weaknesses, providing valuable insights for coaches. Full article

Vista landscapes of historic cities embody unique spatial order and cultural memory, and the scientific quantification of their visual quality presents a common challenge for both heritage conservation and urban renewal. Focusing on the Beijing Central Axis, this study integrates VR panoramic technology with the SBE-SD evaluation method to develop a visual quality assessment framework suitable for vista landscapes of historic cities, systematically evaluating sectional differences in scenic beauty and identifying their key influencing factors. Thirteen typical viewing places and 17 assessment points were selected, and panoramic images were captured at each point. The evaluation framework comprising 3 first-level factors, 11 secondary factors, and 24 third-level factors was established, and a corresponding scoring table was designed through which students from related disciplines were recruited to conduct the evaluation. After obtaining valid data, scenic beauty values and landscape factor scores were analyzed, followed by correlation tests and backward stepwise regression. The results show the following: (1) The scenic beauty of the vista landscapes along the Central Axis shows sectional differentiation, with the middle section achieving the highest scenic beauty value, followed by the northern section, with the southern section scoring the lowest; specifically, Wanchunting Pavilion South scored the highest, while Tianqiao Bridge scored the lowest. (2) In terms of landscape factor scores, within spatial form, color scored the highest, followed by texture and scale, with volume scoring the lowest; within marginal profile, integrity scored higher than visual dominance; within visual structure, visual organization scored the highest, followed by visual patches, with visual hierarchy scoring the lowest. (3) Regression analysis identified six key influencing factors, ranked in descending order of significance as follows: color coordination degree of traditional buildings, spatial openness, spatial symmetry, hierarchy sense of buildings, texture regularity of traditional buildings, and visual dominance of historical landmark buildings. This study establishes a quantitative assessment pathway that connects subjective perception and objective environment with a replicable process, providing methodological support for the refined conservation and optimization of vista landscapes in historic cities while demonstrating the application potential of VR panoramic technology in urban landscape evaluation. Full article

Background/Objectives: Chronic total occlusion (CTO) is one of the most complex forms of coronary artery disease, and myocardial perfusion in patients with CTO largely depends on the adequacy of coronary collateral circulation (CCC). Identifying simple and non-invasive electrocardiographic markers associated with impaired collateralization remains clinically important. The R-wave peak time (RWPT), a surface electrocardiography (ECG) marker representing the time to peak R-wave deflection and an electrocardiographic surrogate of early intraventricular conduction, may provide insight into ischemia-related ventricular activation delay. The aim of this study was to evaluate whether RWPT is associated with poor CCC in patients with CTO. Methods: This cross-sectional observational study included 92 consecutive patients with CTO and complete clinical, angiographic, and 12-lead ECG data. Patients were categorized according to CCC adequacy into good (n = 52) and poor (n = 40) CCC groups. Demographic, laboratory, angiographic, and ECG parameters were compared. Variables showing significant differences were subjected to univariate analysis, followed by multivariate logistic regression using a backward stepwise selection method. Statistical significance was set at p < 0.05. Results: Patients with poor CCC were significantly older and exhibited longer QRS duration and prolonged RWPT, whereas triglyceride levels were significantly lower. In multivariate analysis, both age (OR: 1.058; 95% CI: 1.005–1.114; p = 0.033) and RWPT (OR: 1.069; 95% CI: 1.013–1.128; p = 0.015) were significantly associated with poor CCC. Conclusions: RWPT may provide adjunctive, non-invasive information regarding collateral adequacy rather than serving as a definitive predictive marker. As an easily obtainable ECG parameter, RWPT may offer incremental diagnostic information when interpreted alongside clinical and angiographic findings in patients with CTO. Full article

Stick–slip piezoelectric actuators are widely used in high-precision positioning systems, yet their performance is limited by backward motion during the slip stage. Although the effects of preload force, driving voltage, and driving frequency have been extensively examined, the specific influence of mover mass and its coupling with these parameters remains insufficiently understood. This study aims to clarify the mass-dependent stepping behavior of stick–slip actuators and to provide guidance for structural design. A compact stick–slip actuator incorporating a lever-type amplification mechanism is developed. Its deformation amplification capability and structural reliability are verified through motion principle analysis, finite element simulations, and modal analysis. A theoretical model is formulated to describe the inverse dependence of backward displacement on the mover mass. Systematic experiments conducted under different mover masses, preload forces, voltages, and frequencies demonstrate that the mover mass directly affects stepping displacement and interacts with input conditions to determine motion linearity and backward-slip suppression. Light movers exhibit pronounced backward motion, whereas heavier movers improve smoothness and stepping stability, although excessive mass slows the dynamic response. These results provide quantitative insight into mass-related dynamic behavior and offer practical guidelines for optimizing the performance of stick–slip actuators in precision motion control. Full article

Domain adaptation methods have been extensively studied for rolling bearing fault diagnosis under various conditions. However, some existing methods only consider the one-way embedding of original space into a low-dimensional subspace without backward validation, which leads to inaccurate embeddings of data and poor diagnostic performance. In this paper, a rolling bearing fault diagnosis method based on multi-source domain joint structure preservation transfer with autoencoder (MJSPTA) is proposed. Firstly, similar source domains are screened by inter-domain metrics; then, the high-dimensional data of both the source and target domains are projected into a shared subspace with different projection matrices, respectively, during the encoding stage. Finally, the decoding stage reconstructs the low-dimensional data back to the original high-dimensional space to minimize the reconstruction accuracy. In the shared subspace, the difference between source and target domains is reduced through distribution matching and sample weighting. Meanwhile, graph embedding theory is introduced to maximally preserve the local manifold structure of the samples during domain adaptation. Next, label propagation is used to obtain the predicted labels, and a voting mechanism ultimately determines the fault type. The effectiveness and robustness of the method are verified through a series of diagnostic tests. Full article

This article introduces an iterative algorithm that is created by integrating the $S^{*}$-iteration process with the inertial forward-backward algorithm. The algorithm is designed to solve optimization problems formulated as variational inclusions in a real Hilbert space. We establish the weak convergence of the algorithm under conventional assumptions. One of the applications of the algorithm is to solve the extreme learning machine, which can be transformed into the variational inclusion problem. Different algorithms, with all parameters set to be identical, are employed to solve the stroke classification problem in order to evaluate the algorithm’s performance. The results indicate that our algorithm converges faster than others and achieves a precision of 93.90%, a recall of 100%, and an F1-score of 96.58%. Full article

This paper focuses on finite-horizon optimum state feedback control problems for interconnected systems of two players involved with asymmetric one-step delay information. For the finite horizon optimum decentralized control problem, a crucial and adequate condition is derived by using Pontryagin’s maximum principle. Under this framework, player 1 transmits its state and control input data with a one-step delay to the controller of player 2, while player 1’s controller does not have access to the real-time or delayed states and control inputs of player 2, resulting in an asymmetric information structure characterized by a one-step delay Then, the solutions to the forward and backward stochastic difference equations are derived. A target tracking system is given in numerical examples to verify the proposed algorithm. Full article

Recent advances in mobile robotics have emphasized the need for systems capable of operating in unstructured environments, combining obstacle negotiation, stability, and adaptability. This study presents the preliminary design and testing of Brush.Q, an articulated ground robot featuring a novel structure distinct from existing wheel-legged robots, equipped with compliant brush-like wheels composed of multiple spokes. The main contribution is the experimental analysis of suspension capability across different wheel geometric profiles, combined with the assessment of obstacle-climbing performance. A simplified prototype was constructed to evaluate the effects of wheel rotation direction, spoke number, and spoke tapering. Results show that reducing the number of spokes improves obstacle-climbing at the expense of suspension, while higher spoke count and compliant geometry enhance suspension and stability. Spoke tapering improves obstacle climbing in the backward-facing configuration but consistently reduces suspension. Overall, these findings highlight the critical role of wheel geometry and the potential for reconfigurable spoked wheels to enhance adaptability and versatility in unstructured terrains. Full article

Background/Objectives: Primary progressive aphasia (PPA) is characterised by progressive decline in language and communication. However, existing diagnostic frameworks and assessment tools are largely based on Indo-European languages, which limits their applicability to Chinese bilingual speakers whose linguistic profiles differ markedly in tonal phonology, logographic writing, and bilingual organisation. This review aimed to (a) describe how PPA presents in Chinese bilingual speakers, (b) evaluate how well current speech–language and neuropsychological assessments capture these impairments, and (c) identify linguistically and culturally informed strategies to improve clinical practice. Methods: A systematic review was conducted in accordance with the PRISMA-ScR guidelines. Four databases (PubMed, Scopus, Web of Science, PsycINFO) were searched, complemented by backward and forward citation chaining. Eight empirical studies met the inclusion criteria. Data were extracted on participant characteristics, PPA variant, language background, speech–language and writing profiles, and assessment tools used. Thematic analysis was applied to address the research questions. Results: Across variants, Chinese bilingual speakers demonstrated universal PPA features expressed through language-specific pathways. Mandarin speakers exhibited tone-segment integration errors, tonal substitution, and disruptions in logographic writing. Lexical-semantic degradation reflected homophony and compounding characteristics. Bilingual individuals showed parallel or asymmetric decline influenced by dominance and usage. Standard English-based naming, repetition, and writing assessments did not reliably capture tone accuracy, radical-level writing errors, or bilingual patterns. Sociocultural factors, including stigma, delayed help-seeking, and family-centred care expectations, further shaped diagnostic pathways. Conclusions: Chinese PPA cannot be meaningfully assessed using tools designed for Indo-European languages. Findings highlight the need for tone-sensitive repetition tasks, logographic writing assessments, bilingual diagnostic protocols, and culturally responsive communication-partner support. This review provides a comprehensive synthesis to date on Chinese bilingual PPA and establishes a foundation for linguistically inclusive diagnostic and clinical models. Full article

This study presents a systematic comparative analysis of nine stator current discretization methods within the Model Predictive Current Control (MPCC) framework for Permanent Magnet Synchronous Motors (PMSMs). These methods have generally been examined individually or in limited combinations in previous research, and this holistic and comprehensive comparison constitutes the core contribution of this work by addressing a significant gap in the existing literature. The investigated MPCC methods—Forward Euler (FE), Backward Euler (BE), Midpoint Euler (ME), Fourth-Order Runge–Kutta (RK4), Runge–Kutta Ralston (RKR), Taylor Series (TS), Verlet Integration (VI), Crank–Nicolson (CN), and Adams–Bashforth (AB)—are comprehensively evaluated for their dynamic performance, including speed tracking, torque response, settling time, rise time, overshoot, and Total Harmonic Distortion (THD). Additionally, these analysis results are benchmarked against conventional Proportional–Integral–Derivative (PID) and Field-Oriented Control (FOC) methods. In terms of key performance indicators, the MPCC–RKR method proved optimal for speed tracking under no-load conditions, achieving the lowest overshoot, specifically ranging from 0.097% to 1.450%. Conversely, MPCC–ME and MPCC–CN demonstrated superior transient performance under sudden-load conditions (1.7 Nm), yielding the smallest torque deviations, fastest settling times. Specifically, MPCC-ME recorded the lowest overshoot (1.512%) at the 7 s load step, while MPCC-CN performed best at 9 s (1.220%) and 11 s (1.577%). Among the predictive schemes, the MPCC–RKR method achieved the highest current quality with a minimum THD of 3.69% at nominal speed. Finally, it has been confirmed through the applied statistical analysis techniques that the performance differences among the discretization methods are significant. The comparative analysis examines both the dynamic performance of the methods and the fundamental trade-off between accuracy and computational burden in MPCC design. Simple single-step explicit methods (FE, ME, RKR, VI, AB) offer low computational cost and are well suited for high–sampling-frequency real-time applications, especially with sufficiently small sampling times, whereas more complex multi-step or implicit methods (BE, RK4, TS, CN) may increase the processor load despite their potential gains in accuracy and stability. This study provides practical, evidence-based guidelines for selecting an optimal discretization method by balancing accuracy and dynamic performance requirements for PMSM applications. Full article

This study investigates underwater acoustic propagation patterns under mesoscale eddy conditions through numerical modeling and parametric analysis. A mathematical model of mesoscale eddies was developed, and acoustic transmission loss was computed using the BELLHOP ray-tracing model. Systematic simulations were conducted to examine the effects of source depth, eddy polarity (cold/warm), eddy intensity, and seabed topography. The results reveal distinct acoustic behaviors: cold-core eddies shift convergence zones forward, reduce their width, elevate their depth, and enhance convergence gain within certain ranges. In contrast, warm-core eddies displace convergence zones backward, broaden their width, and can induce surface duct formation. Furthermore, seabed topography exerts minimal influence on acoustic propagation under cold-core eddies but significantly modulates propagation under warm-core eddies, with different topographies producing markedly distinct effects. These findings provide valuable insights for marine scientific research and engineering applications leveraging mesoscale eddy phenomena. Full article

Background: Motor competence (MC) is defined as the ability to perform a wide range of motor skills with proficiency and control. The present quasi-experimental study design examines the impact of two structured intervention programs on MC in children who practiced athletics at the same club, aged 6 to 10 years, implemented over 12 weeks. Methods: The sample consisted of 64 children, assigned to two intervention groups: Intervention Group A (IG_A) composed of 15 male and 17 female children (9.57 ± 0.86 years) and Intervention Group B (IG_B), of 14 male and 18 female children (9.08 ± 1.33 years). IG_A received athletics-based training exclusively, three times per week, while IG_B undertook two weekly athletics sessions and one complementary activity session, such as handball, gymnastics, swimming, and motor games. MC was assessed using the modified Körperkoordinationstest für Kinder (KTK3+). The KTK3+ consists of three original KTK tasks, [Backward Balance (BB), Sideways Moving (MS), and Jumping Sideways (JS)] and an additional Eye–Hand Coordination (EHC) task. For statistical analysis, ANOVA repeated measures 2 × 2 was used. Results: In relation to JS, the performance on this test did not change with the intervention programs in either of the two groups. For BB and MS, both groups improved their performances in a similar way through the program implementation. Differently, for EHC, results showed that only IG_B improved its performance significantly (p < 0.001) with the program’s intervention, with a large Cohen’s d effect size (0.84). Finally, as a general analysis, the KTK3+ raw results (RS) and results translated to Global Motor Quotient (GQM), revealed significant differences between IG_A and IG_B post-intervention, with p < 0.001 for both variables’ comparison and with large Cohen’s d effect sizes for both (1.581 for RS and 1.595 for GQM), favoring IG_B. Conclusions: Both programs led to improvements in the various KTK3+ battery tasks. However, only the program that combined athletics training with multiactivity training led to significant improvements in the EHC test and in the overall KTK3+ results of the children involved. Full article

Background/Objective: Postural control differs between individuals with lower limb amputation and the general population. Although previous studies examined the effects of unexpected surface perturbations on postural control in individuals with transtibial amputation (TTA) and individuals with transfemoral amputation (TFA), their impact on lower limb muscle activation remains unclear. This study aimed to assess postural control on a stable surface and to evaluate the effects of unexpected surface perturbations on lower limb muscle activation in unilateral TTAs, TFAs, and in a healthy control group (CG). Methods: The study included 10 TTAs, 9 TFAs, and 10 healthy controls. Postural control was assessed using a force platform, and lower limb muscle activity was recorded with surface electromyography during unexpected surface perturbations. Results: The TFAs showed the highest anteroposterior and lateral postural sway under compliant surface eyes closed and the highest lateral sway under normal surface eyes closed, whereas the CG showed the lowest values (p < 0.05). During forward perturbations, rectus femoris (RF) and tibialis anterior (TA) activations were significantly higher than biceps femoris (BF) and medial head of the gastrocnemius (GM) activations, respectively, across all groups (p < 0.05). During backward perturbations, GM activations exceeded TA activations in all groups, while BF activations were higher than RF only in TTAs (p < 0.05). Significant group effects were found for RF and BF during forward perturbations, and side effects for BF (forward) and RF (backward) activations (p < 0.05). Conclusions: Postural control responses vary with the level of lower limb amputation. TFAs relied more on visual input during quiet standing, whereas TTAs demonstrated greater reliance on thigh muscle activation during surface perturbations. These findings highlight the need to consider amputation level in balance and rehabilitation programs. Full article

Aiming to improve the accuracy of multi-target tracking in multi-sensor scenarios, this paper proposes a centralized multi-sensor (MS) generalized labeled multi-Bernoulli (GLMB) smoother, abbreviated as MS-GLMB-S. The developed smoother is built on the multi-target forward–backward Bayesian smoothing framework, which uses an MS-GLMB filter for forward recursion and is subsequently followed by backward propagation via the multi-sensor backward corrector to obtain the GLMB smoothing density. In the backward smoothing process, expressions for the multi-sensor backward corrector and the multi-target smoothing density are detailed. By deriving the time-decoupled form of the smoothing weight, a suboptimal Gibbs sampling method is introduced to achieve efficient implementation of the proposed smoother, enabling independent sampling across each sensor at different time steps within the lag interval during the backward smoothing process. Additionally, a Gaussian mixture implementation of MS-GLMB-S is formulated. Simulations conducted in both linear and nonlinear scenarios demonstrate the effectiveness and real-time performance of MS-GLMB-S. Full article