Search Results (877)

Camera rotation estimation is a key component in video stabilization and motion analysis. In many practical scenarios, inertial measurements are unavailable or temporally unreliable, while classical geometric pipelines degrade under blur, low texture, and low illumination. This paper investigates whether substantially downsampled optical flow can retain sufficient structure for accurate frame-to-frame rotation regression. We present VisualRNet, a lightweight rotation-specific visual regression framework trained with cross-modal IMU supervision. Our design uses coordinate-aware feature encoding, depthwise separable convolutions, lightweight attention, and a compact 6D rotation head to model the spatial structure of rotational flow fields. On Deep-FVS, VisualRNet achieves a mean rotation error of 0.3151${}^{\circ }$ on the test set. The VisualRNet regression head contains 7.7 K parameters, 0.002 GFLOPs, and runs at 729 FPS, while the full pipeline with the FastFlowNetv2 frontend contains 1.374 M parameters, 7.194 GFLOPs, and runs at approximately 113 FPS. A cross-camera adaptation experiment on TUM VI further indicates that the learned motion representation can be aligned to a new camera system with limited calibration data. These results support low-resolution optical flow as a practical input for visual rotation estimation and suggest particular value in stabilization-oriented and cost-sensitive applications where approximate rotational trend matters more than full scene geometry. Full article

Rotated object detection is widely adopted in remote sensing to handle arbitrary object orientations and improve localization accuracy. However, existing methods still suffer from limited global context modeling, degraded feature representation under complex backgrounds, and suboptimal optimization caused by task coupling, which jointly restrict detection performance in challenging scenarios. To address these issues, this paper proposes a novel rotated object detection framework, termed LDA-YOLO, which systematically enhances feature modeling and prediction quality. Specifically, a Large Separable Kernel Attention (LSKA) module is introduced to approximate global spatial interactions through a low-rank separable formulation, enabling effective long-range dependency modeling with linear computational complexity. A Dual-Path Feature Refinement (DPFR) module is designed to improve feature representation by decomposing features into complementary subspaces and performing adaptive fusion to suppress redundancy and noise. In addition, an Angle-Aware Decoupled Head (AADH) is developed to explicitly separate classification, localization, and orientation estimation, thereby reducing inter-task interference and improving optimization stability. The proposed method achieves superior performance compared to existing approaches. Specifically, it improves mAP50 by 1.6% over the baseline YOLOv8n-OBB, while maintaining a lightweight design with significantly reduced computational cost. These results indicate that the proposed framework provides an effective solution for rotated object detection in complex remote sensing scenarios. Full article

Scoliosis is a three-dimensional spinal deformity that may affect musculoskeletal alignment, respiratory mechanics, and neuromotor control. Rigid thoraco-lumbo-sacral orthoses (TLSOs) remain the primary conservative treatment during skeletal growth. Most brace systems rely on three-point pressure mechanisms that primarily generate lateral compression forces, while the contribution of shear components to corrective biomechanics has been insufficiently quantified. This study presents the experimental and analytical validation of the Canali Front-Push Orthopedic Brace, a rigid orthotic system designed to generate controlled compressive and shear forces through a frontal thrust mechanism and anterior rib cage engagement. By applying anterior force, the device reduces the frontal-plane lever arm, thereby limiting the mechanical moment that contributes to transverse plane rotation. An instrumented four-segment torso model derived from the internal CAD geometry of the brace was developed to independently measure upper compression, lower compression, and intersegmental shear forces. Controlled misalignment conditions (0 mm, 2 mm, and 4 mm) were introduced to simulate asymmetric engagement of the orthosis. Three load cell configurations (200 N and 500 N capacity) were tested. Mechanical endurance of the rack–latch fastening system was also evaluated. A predictive shear–misalignment relationship was derived and experimentally validated. Peak compressive forces reached approximately 370 N, while shear forces increased from less than 40 N under symmetric alignment (D0) to approximately 170 N under maximal misalignment (D4). Shear activation demonstrated near-linear proportionality to imposed geometric asymmetry (R² > 0.94). Following cyclic loading, the fastening system stabilized mechanically around 300 N. Measurement repeatability showed a coefficient of variation below 5%. These findings demonstrate that the brace produces predictable and controllable shear activation while maintaining high mechanical repeatability. The results provide a quantitative biomechanical framework for understanding shear-induced corrective mechanics in scoliosis bracing and support future studies integrating computational modeling and clinical validation. The proposed mechanical framework may contribute to the development of next-generation orthotic strategies aimed at controlling spinal rotation through vector modulation rather than purely compressive correction. Full article

This work introduces a non-blind watermarking framework for color images to address tamper detection, particularly under geometric transformations. The proposed scheme fuses two watermarks, a personal signature and a biometric fingerprint, into a unified composite watermark embedded into the chrominance component of the cover image using a multi-level transform domain approach, discrete wavelet transforms (DWTs), discrete cosine transforms (DCTs), and singular value decomposition (SVD). By leveraging the rotation-invariant properties of scale-invariant feature transform (SIFT) and oriented FAST and rotated BRIEF (ORB) descriptors, the framework ensures robust tamper detection without requiring alignment, thus mitigating the limitations of conventional detection techniques vulnerable to transformation-induced tamper obfuscation (TITO). Extensive experimentation demonstrates that the method maintains high perceptual fidelity, achieving PSNR values ranging from 50 to 55 dB for embedding strength factor μ (0.01–0.04) and SSIM indices near 1 across multiple benchmark images. Furthermore, the scheme exhibits notable resilience to a range of image processing attacks and geometric distortion. Comparative evaluation reveals its superiority over existing grayscale, color, SIFT-based and DWT-DCT-SVD-based watermarking techniques, affirming its applicability in scenarios demanding secure, imperceptible, and transformation-invariant image watermarking. Full article

This study introduces a novel unsupervised framework, ICR-LLS, for detecting pathological gait patterns using instantaneous center of rotation (ICR) trajectories of the shank in the sagittal plane. ICR trajectories were computed from two-dimensional kinematic data captured at the lateral femoral epicondyle and lateral malleolus for both shanks, producing four-dimensional multivariate time series for each gait trial. Pairwise trajectory dissimilarities were quantified using circularly aligned Dynamic Time Warping (DTW), preserving temporal and spatial structure. The resulting dissimilarity matrix was embedded into a three-dimensional space using a force-directed network layout, enabling intuitive visualization of inter-subject gait relationships. Density-based clustering (DBSCAN), enhanced with a consensus-based ensemble approach, was employed to automatically identify clusters representing typical (healthy) gait patterns and outliers corresponding to pathological deviations. The framework is evaluated on a public dataset comprising individuals with Parkinson’s disease (PD) and healthy controls, achieving a normalized mutual information (NMI) of 0.449 and a Separation-to-Compactness Ratio (SCR) of 6.754, indicating a meaningful cluster structure. In addition, classification-oriented metrics yield an accuracy of 90%, sensitivity of 70%, and specificity of 96.7%, supporting the method’s effectiveness in distinguishing pathological gait. By combining minimal 2D kinematic inputs with unsupervised learning, ICR-LLS provides an interpretable framework for the exploratory analysis of gait variability, and although further validation is required, the findings suggest that ICR trajectories may serve as a meaningful biomechanical descriptor for characterizing pathological locomotion. Full article

The badminton smash is a decisive scoring technique whose effectiveness depends on adequate preparation and a proper proximal-to-distal sequencing of the kinetic chain. This study integrates a You Only Look Once (YOLO)-based real-time vision detector with five wearable inertial measurement units (IMUs) attached to the right shoulder, right elbow, right wrist, right hip, and right knee of right-handed players. A high-speed camera provides video for shuttlecock and joint localization via YOLO, and the IMUs provide instantaneous joint accelerations at impact. The following four coaching-oriented indicators are defined: (1) rapid lowering of the center of mass after the opponent’s shot; (2) immediate forward acceleration after the shuttle is released; (3) alignment at the hitting position with the right shoulder/hip rotated backward and the left shoulder facing the approaching shuttle; and (4) a proximal-to-distal sequence in which the shoulder leads the elbow and then the wrist. Using two athletes with 15 trials each, the system achieved an overall recognition accuracy above 93% against manually annotated video. The method can provide objective feedback for coaches and players and is suitable for instructional use in physical education classes. Full article

In response to the pressing need for miniaturized MEMS microphones in wearable technology and mobile devices, and to surmount the technical limitations inherent in conventional piezoelectric microphones, which typically depend on enlarging chip dimensions or decreasing stiffness to attain low resonance frequencies, this study introduces a novel piezoelectric MEMS microphone (PMM) design predicated on a flexibility-enhanced rotating structure. The proposed design utilizes an aluminum scandium nitride (Al_0.8Sc_0.2N) piezoelectric thin film with 20% scandium doping and incorporates four equivalent sensing units formed by four curved cutting lines centrally located on the chip. This configuration employs a nested arrangement of four cantilever beams to substantially increase vibration compliance, thereby effectively lowering the natural frequency without altering the chip’s external size. Three-dimensional finite element simulations reveal that, relative to traditional triangular cantilever beam architectures, the flexibility-enhanced rotating structure reduces the natural frequency from 15.6 kHz to 13.49 kHz while enhancing sensitivity from −44.6 dB to −40 dB. The device was fabricated via a comprehensive microfabrication process and subsequently characterized within a standardized acoustic testing environment. Experimental results indicate that the microphone attains a sensitivity of −43.84 dB at 1 kHz and exhibits a first resonance frequency of 13.5 kHz, closely aligning with simulation predictions. Furthermore, the signal-to-noise ratio (SNR) reaches 58.3 dB across the full range of human-audible frequencies. By leveraging the flexibility-enhanced rotating structure, this work achieves an optimal compromise between elevated sensitivity and reduced resonance frequency within a compact form factor, thereby offering a viable technical solution for the advancement of high-performance miniature acoustic sensors. Full article

Background/Objectives: To compare the efficacy of two aligner systems (Invisalign and Eon Aligner) across multiple linear and angular movements. Methods: A total sample of 80 patient cases (n = 40 in each group) was recruited retrospectively. Per case, 3 digital models were retrieved in the form of stereolithography (STL) files. Predicted and achieved tooth movements were measured using the 3Shape Clear Aligner Studio. Initial models were aligned on the predicted and achieved models to create superimposition. Differences in measurement between pre-treatment, predicted, and post-treatment scans were measured. Agreement between the two, Invisalign and Eon, was measured using the interclass correlation coefficient (ICC). Results: Both Invisalign (ICC = 0.82; 95% CI 0.66, 0.9) and Eon Aligner (ICC = 0.75; 95% CI 0.53, 0.87) have shown good agreement when calculating the average differences between the achieved and predicted interpremolar width values. Similar results were found for both intercanine width values (Invisalign: ICC = 0.96; 95% CI = 0.93, 0.98 vs. Eon Aligner: ICC = 0.98; 95% CI = 0.97, 0.99). In Eon cases, good to excellent agreement between the achieved and predicted models was observed for lateral (ICC = 0.89; 95% CI = 0.79, 0.94) and central (ICC = 0.93; 95% CI = 0.87, 0.96) mesiodistal rotations. Conversely, Invisalign displayed moderate strength of agreement for the lateral (ICC = 0.68; 95% CI = 0.40, 0.83) and central (ICC = 0.70; 95% CI = 0.44, 0.84) mesiodistal readings. While both aligners demonstrated some level of predictive capacity towards horizontal movements, they were unreliable in predicting vertical movements. Differences in magnitude of change between initial and achieved values between Eon and Invisalign were noted only for certain teeth in the case of horizontal and vertical movements. Conclusions: Both clear aligner therapy systems were able to achieve satisfactory outcomes in terms of inter-premolar and intercanine width changes. Eon Aligner, on the other hand, outperformed Invisalign in terms of rotational accuracy and horizontal movement precision. Notably, both systems demonstrated poor predictability for vertical movements and suffer from significant systemic bias requiring over-correction. Full article

Patellar maltracking is among the most common causes of anterior knee pain after total knee arthroplasty (TKA), underscoring the need for accurate prevention and treatment. Therefore, the purpose of this narrative review is to provide a comprehensive overview of current evidence on post-TKA tracking, focusing on component alignment, preoperative patient assessment, and revision treatment options. A PubMed database search was performed, leveraging the literature from the last 20 years, and the results were qualitatively synthesized. According to current studies, several precautions should be taken to prevent patellofemoral stress and, consequently, patellar maltracking, such as avoiding internal rotation, valgus alignment, and excessive flexion of the femoral component and internal rotation of the tibial component. Regarding alignment strategies, kinematic alignment appears to offer potential benefits over mechanical alignment in certain functional outcomes and patient satisfaction scores. However, these differences should be interpreted cautiously as they may not always exceed the minimal clinically important difference. Furthermore, recent evidence indicates that quadriceps biomechanics influence TKA outcomes, potentially suggesting that conventional surgical approaches may need to be individualized, though these preliminary findings require prospective validation. Currently, robotic-assisted surgery represents a developmental direction for patient-tailored interventions and offers great promise for better prosthesis customization to the individual patient. Integration of imaging data with dynamic soft-tissue assessment enables more predictable reconstruction of joint kinematics. Regarding surgical treatment, the selection of specific methods requires a prior clinical and radiographic assessment. Indications range from patellar maltracking direction and component malrotation to patient preferences and rehabilitation potential. Ultimately, the future of TKA relies on personalized interventions to prevent complications and improve patient outcomes. This evolution is driven by the shift from mechanical alignment to kinematic alignment, alongside quadriceps tendon assessment and intraoperative robotic-assisted measurement, all aimed at optimizing the accuracy of implant positioning. Full article

We present a systematic evaluation of the positional and rotational tracking accuracy of the Meta Quest 3 mixed-reality headset using a TECHMAN TM5-900 collaborative robot (±0.05 mm repeatability) as a highly repeatable robot-driven reference. The headset was rigidly attached to the robot’s tool flange and subjected to single-axis translational motions (200 mm along X, Y, and Z) and rotational motions (Roll ± 65°, Pitch ± 85°, and Yaw ± 85°). Each test was repeated three times, and the resulting trajectories were averaged to improve statistical robustness. Both data sources were integrated into a single Python-based application running on the same computer. The headset streamed its data via UDP, while the robot, implemented as an ROS2 node, published its data to the same host. This configuration enabled simultaneous acquisition of both streams, ensuring temporal consistency without the need for offline interpolation. All comparisons were performed in a relative reference frame, thereby avoiding the need for absolute hand–eye calibration. Coordinate-frame alignment was achieved using Singular Value Decomposition (SVD)-based rigid-body Procrustes analysis. Over 2848 synchronized samples spanning 151.46 s, the Meta Quest 3 achieved a mean translational RMSE of 0.346 mm (3D RMSE = 0.621 mm) and a mean rotational RMSE of 0.143°, with Pearson correlation coefficients greater than 0.9999 on all axes. These results show sub-millimeter positional tracking and sub-degree rotational tracking under controlled conditions, supporting the potential of the Meta Quest 3 for precision-oriented mixed-reality applications in industrial and research settings. Full article

Accurate and stable extrinsic calibration is the foundation of high-quality fusion sensing and positioning of camera and Light Detection and Ranging (LiDAR). However, traditional targetless calibration methods suffer from limitations such as poor scene adaptability and unstable convergence, which significantly restrict calibration accuracy and robustness in complex environments. Aiming at solving those problems, we propose an online cascade-optimization-based extrinsic calibration method of combining motion trajectory alignment and edge feature alignment. In the initial calibration stage, a hand–eye calibration algorithm is designed by minimizing the residual discrepancies between camera odometry and LiDAR odometry sequences. It establishes a robust initialization for subsequent optimization. Then, in order to extract robust edge line features from sparse point clouds, we employ depth difference and planar edges of point clouds in the optimization process. Subsequently, principal component analysis (PCA) is applied to compute the principal direction of the extracted line features, enabling a decoupled optimization scheme that accounts for directional observability. This approach effectively mitigates the adverse effects of uneven environmental feature distributions. Experimental validation on typical urban datasets demonstrates the method’s generalizability and competitive accuracy: rotational parameter errors are constrained within 0.25°, and translational errors are maintained below 0.05 m. This affirms the method’s suitability for high-accuracy engineering applications. Full article

We examine a simple duration-rotation strategy applied to six U.S. Treasury ETFs spanning the full maturity spectrum, using data from 2007 to 2025. At each semi-annual rebalancing date, ETFs are ranked by prior-period return and divided into three equal groups—Winners, Median, and Losers. Contrary to conventional momentum logic, the middle group consistently outperforms. The Median strategy grows USD 100 to USD 199.90 by end-2025, a CAGR of 3.79% against 2.17% for the passive benchmark, with a higher Sharpe ratio (0.606 vs. 0.494) and a shallower maximum drawdown ($-11.6\%$ vs. $-14.4\%$). Newey–West HAC and Lo (2002) tests confirm statistical significance ($p=0.031$ and $p=0.014$), and an expanding-window walk-forward procedure yields $p=0.0005$ across 27 out-of-sample evaluations from 2012 to 2025. The result is robust to calendar alignment, evaluation endpoint, lookback window, and execution timing, and survives transaction costs by a wide margin. The strategy requires no interest rate forecasts, no proprietary data, and is implementable with standard ETF brokerage access. Full article

Real-time coal–rock identification is essential for intelligent mining, enabling hazard prevention and geological modeling. However, existing methods often suffer from unclear bit–rock interaction mechanisms, signal distortion, sensor remoteness, or delayed data acquisition, limiting their effectiveness in continuous operations. This study proposes a novel approach for real-time coal–rock identification based on multi-source near-bit drilling data. A near-bit data acquisition system was developed and positioned directly behind the drill bit, integrating sensors to capture high-fidelity parameters—including weight on bit (WOB), torque, rotational speed, rate of penetration (ROP), natural gamma ray, and borehole trajectory—thereby eliminating frictional interference from the drill string. A data-driven theoretical model was established to derive a near-bit drillability index (NDI) for rock strength and to correlate gamma ray responses with lithology. Field trials were conducted in a coal mine in northern Shaanxi, involving over 30 boreholes and systematic core validation. The results demonstrate that the method enables continuous, high-resolution identification of coal–rock interfaces and strength variations along the borehole trajectory, with interpreted results aligning well with core logs and achieving approximately 85% accuracy in strength estimation. By ensuring compatibility with conventional drilling rigs and supporting real-time data transmission and 3D geological updating, this study offers a practical and robust technical pathway for achieving geological transparency and real-time steering in underground coal mining. Full article

Two spectrin-sensitive relaxations have been reported in the RBC plasma membrane: β_s (1.4 MHz, related to the interface β-relaxation) and γ1_s (9 MHz, rotation alignment of spectrin-bound dipoles by penetrating electric field). Here, a third (α_s) relaxation type is reported within the frequency region of surface (α) relaxation. With low-ion-strength outside media, the adsorption of blood plasma immunoglobulins on RBCs was found to inhibit β_s and γ1_s relaxations, while α_s relaxation was enforced with strong inflammation. The three relaxations are represented by three consecutive segments on the Cole′s plots: Δε_rd″.ω against Δε_r′ and Δε_rd″/ω against Δε_r′. Here, ω is the frequency of the field and Δε_r* = Δε_r′ + j.Δε_rd″ is the change in the relative complex dielectric permittivity of RBC suspension at the denaturation temperature of spectrin. The β_s segment in Δε_rd″.ω against the Δε_r′ plot could be regarded as a vector (complex number) whose projection on the vertical axis (the irreversible loss in energy) could express the ability of the plasma membrane to deform (under the impact of shear stress). Full article

As the “visual perception hub” of unmanned aerial vehicles (UAVs), electro-optical (EO) pods play an increasingly critical role in tasks such as intelligence gathering, situational awareness, target tracking, and localization. With the expanding scope and depth of UAV applications, higher demands are placed on the precision and adaptability of installation error calibration techniques for EO pods. Current mainstream calibration methods typically require specialized procedures under constrained conditions, while few approaches integrate existing UAV system capabilities and mission requirements, which leads to cumbersome, time-consuming processes and suboptimal alignment between calibration outcomes and task objectives. This paper proposes an online calibration method for UAV EO pod installation errors based on target tracking, which can rapidly compute the optimal closed-form solution for installation errors by leveraging UAV tracking missions. First, an observation equation for pod installation errors is established using tracking results. Second, multi-temporal observations are combined to model the calibration problem as an optimal rotation matrix estimation task, and then the optimal closed-form solution for installation errors is derived. Concurrently, a statistics-based approximate calibration method is introduced specifically for tracking missions. Furthermore, an online calibration system compatible with diverse UAV platforms is designed, along with different rapid calibration schemes for emergency response scenarios, fully incorporating existing system capabilities and mission needs. Finally, a fixed-wing UAV experimental platform is developed, with calibration tests conducted under various flight regimes. Experimental results validate the feasibility and robustness of the proposed methodology. Full article