Search Results (471)

To precisely delineate the contours of insulators in complex transmission line images obtained from Unmanned Aerial Vehicle (UAV) inspections and thereby facilitate subsequent defect analysis, this study proposes an instance segmentation framework predicated upon an enhanced SOLOv2 model. The proposed framework integrates a preprocessed edge channel, generated through the Non-Subsampled Contourlet Transform (NSCT), which augments the model’s capability to accurately capture the edges of insulators. Moreover, the input image resolution to the network is heightened to 1200 × 1600, permitting more detailed extraction of edges. Rather than the original ResNet + FPN architecture, the improved HRNet is utilized as the backbone to effectively harness multi-scale feature information, thereby enhancing the model’s overall efficacy. In response to the increased input size, there is a reduction in the network’s channel count, concurrent with an increase in the number of layers, ensuring an adequate receptive field without substantially escalating network parameters. Additionally, a Convolutional Block Attention Module (CBAM) is incorporated to refine mask quality and augment object detection precision. Furthermore, to bolster the model’s robustness and minimize annotation demands, a virtual dataset is crafted utilizing the fourth-generation Unreal Engine (UE4). Empirical results reveal that the proposed framework exhibits superior performance, with AP0.50 (90.21%), AP0.75 (83.34%), and AP[0.50:0.95] (67.26%) on a test set consisting of images supplied by the power grid. This framework surpasses existing methodologies and contributes significantly to the advancement of intelligent transmission line inspection. Full article

(1) Background: Integrating assessment with training helps to enhance precision prosthetic rehabilitation of trans-radial amputees. This study aimed to validate a self-developed closed-loop rehabilitation platform combining accurate measurement in comprehensive assessment and immediate interaction in virtual reality (VR) training in refining patient-centered myoelectric prosthesis rehabilitation. (2) Methods: The platform consisted of two modules, a multimodal assessment module and an sEMG-driven VR game training module. The former included clinical scales (OPUS, DASH), task performance metrics (modified Box and Block Test), kinematics analysis (inertial sensors), and surface electromyography (sEMG) recording, verified on six trans-radial amputees and four healthy subjects. The latter aimed for muscle coordination training driven by four-channel sEMG, tested on three amputees. Post 1-week training, task performance and sEMG metrics (wrist flexion/extension activation) were re-evaluated. (3) Results: The sEMG in the residual limb of the amputees upgraded by 4.8%, either the subjects’ number of gold coins or game scores after 1-week training. Subjects uniformly agreed or strongly agreed with all the items on the user questionnaire. In reassessment after training, the average completion time (CT) of all three amputees in both tasks decreased. CTs of the A1 and A3 in the placing tasks were reduced by 49.52% and 50.61%, respectively, and the CTs for the submitting task were reduced by 19.67% and 55.44%, respectively. Average CT of all three amputees in the ADL task after training was 9.97 s, significantly lower than the pre-training time of 15.17 s. (4) Conclusions: The closed-loop platform promotes patients’ prosthesis motor-control tasks through accurate measurement and immediate interaction according to the sensorimotor recalibration principle, demonstrating a potential tool for precision rehabilitation. Full article

This study proposes an image processing framework based on Bionic principles to optimize 3D visual perception in virtual reality (VR) systems. By simulating the physiological mechanisms of the human visual system, the framework significantly enhances depth perception and visual fidelity in VR content. The research focuses on three core algorithms: Gabor texture feature extraction algorithm based on directional selectivity of neurons in the V1 region of the visual cortex, which enhances edge detection capability through fourth-order Gaussian kernel; improved Retinex model based on adaptive mechanism of retinal illumination, achieving brightness balance under complex illumination through horizontal–vertical dual-channel decomposition; the RGB adaptive adjustment algorithm, based on the three color response characteristics of cone cells, integrates color temperature compensation with depth cue optimization, enhances color naturalness and stereoscopic depth. Build a modular processing system on the Unity platform, integrate the above algorithms to form a collaborative optimization process, and ensure per-frame processing time meets VR real-time constraints. The experiment uses RMSE, AbsRel, and SSIM metrics, combined with subjective evaluation to verify the effectiveness of the algorithm. The results show that compared with traditional methods (SSAO, SSR, SH), our algorithm demonstrates significant advantages in simple scenes and marginal superiority in composite metrics for complex scenes. Collaborative processing of three algorithms can significantly improve depth map noise and enhance the user’s subjective experience. The research results provide a solution that combines biological rationality and engineering practicality for visual optimization in fields such as implantable metaverse, VR healthcare, and education. Full article

Air-to-air (A2A) communication plays a vital role in low-altitude unmanned aerial vehicle (UAV) networks and demands accurate channel modeling to support system analysis and design. A key challenge in A2A channel modeling lies in extracting reliable cluster characteristics, which are often limited due to the scarcity of measurement data. To overcome this limitation, a cluster characteristic analysis method is proposed for UAV A2A channels in built-up environments. First, we reconstruct virtual urban environments, followed by the acquisition of A2A channel data using ray tracing (RT) techniques. Then, a kernel power density (KPD) clustering algorithm is applied to group the multipath components (MPCs). To enhance the modeling accuracy of intra-cluster angular offsets in both elevation and azimuth domains, a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is further introduced for generative modeling. A comprehensive analysis is conducted on key cluster characteristics, including the intra-cluster number of MPCs, intra-cluster delay and angular spreads, number of clusters, and angular distributions. The numerical results demonstrate that the proposed WGAN-GP-based approach achieves superior angular fitting accuracy compared to conventional empirical distribution methods. Full article

Global virtual exchange is a mode of teaching that can reach classrooms beyond national borders and across disciplines. This paper utilizes students’ online conversations and learning projects as primary data to demonstrate experiential learning and critical thinking processes in a global virtual classroom between students in the U.S. and China. Findings reveal that guided weekly online conversations between American and Chinese students provided experiential learning about personal and familial experiences as well as deep insights into healthcare and pension policies affecting individuals and societies. Furthermore, collaborative learning projects on healthcare and pension systems among international students embedded critical thinking in the learning process. These learning projects are comparative and thought-provoking, offering students a chance to apply a critical and global lens to the understanding of social policies and services in different social and cultural contexts. The expansion of global virtual exchange may be a byproduct of COVID-19 distant learning; it may have opened new channels for breaking geographic boundaries of learning sociology in global and critical perspectives. Full article

This study investigates how young Chinese consumers across generational lines engage with wine, addressing three key research questions: What motivates their wine purchases? What sensory preferences do they exhibit? And through which channels do they prefer to buy wine? Based on a qualitative design combining focus groups and semi-structured interviews, the study identifies significant generational differences between millennials and post-millennials. Millennials treat wine as a social tool for networking and status, while post-millennials view wine as a medium of personal identity shaped by digital culture. Similarly, millennials prefer a balance of traditional and digital retail, whereas post-millennials favour online platforms. Experiential consumption follows the same pattern, from formal tourism to virtual tastings. By linking these findings to institutional and cultural theories of consumer behaviour, the study contributes to a nuanced understanding of wine consumption in an emerging market. It provides practical implications for wine marketers aiming to localize their strategies for younger Chinese segments. Full article

The demand for high data rates and large system capacity has posed significant challenges for medium access control (MAC) methods. Successive interference cancellation (SIC) is a classical multi-user detection (MUD) method; however, it suffers from an error propagation problem. To address this deficiency, we propose a method called Virtual Signal Processing-Based Integrated Multi-User Detection (VSP-IMUD). In VSP-IMUD, the received mixed multi-user signals are treated as an equivalent signal. The channel ambiguity corresponding to each user’s signal is then examined. For channels with non-zero ambiguity values, the signal components are detected using zero-forcing (ZF) reception. Next, the detected ambiguous signal components are reconstructed and subtracted from the received mixed signal using SIC. Once all the ambiguous signals are detected, the remaining signal components with zero ambiguity values are equated to a virtual integrated signal, to which a matched filter (MF) is applied. Finally, by selecting the signal with the highest channel gain and adopting its data as the reference symbol, the remaining signals’ dataset can be determined. Our theoretical analysis and simulation results demonstrate that VSP-IMUD effectively reduces the frequency of SIC applications and mitigates its error propagation effects, thereby improving the system’s bit-error rate (BER) performance. Full article

Many speech coding strategies have been developed over the years, but comparing them has been convoluted due to the difficulty in disentangling brand-specific and patient-specific factors from strategy-specific factors that contribute to speech understanding. Here, we present a comparison with a ‘virtual’ patient, by comparing two strategies from two different manufacturers, Advanced Combination Encoder (ACE) versus HiResolution Fidelity 120 (F120), running on two different implant systems in a computational model with the same anatomy and neural properties. We fitted both strategies to an expected T-level and C- or M-level based on the spike rate for each electrode contact’s allocated frequency (center electrode frequency) of the respective array. This paper highlights neural and electrical differences due to brand-specific characteristics such as pulse rate/channel, recruitment of adjacent electrodes, and presence of subthreshold pulses or interphase gaps. These differences lead to considerably different recruitment patterns of nerve fibers, while achieving the same total spike rates, i.e., loudness percepts. Also, loudness growth curves differ significantly between brands. The model is able to demonstrate considerable electrical and neural differences in the way loudness growth is achieved in CIs from different manufacturers. Full article

Phase-sensitive surface plasmon resonance (SPR) detection is widely employed in molecular dynamics studies and SPR imaging owing to its real-time capability, high sensitivity, and compatibility with imaging systems. A key research objective is to achieve higher measurement resolution of refractive index under optimal dynamic range conditions. We present an enhanced SPR phase imaging system combining a quad-polarization filter array for phase differential detection with a novel polarization pair, block matching, and 4D filtering (PPBM4D) algorithm to extend the dynamic range and enhance resolution. By extending the BM3D framework, PPBM4D leverages inter-polarization correlations to generate virtual measurements for each channel in the quad-polarization filter, enabling more effective noise suppression through collaborative filtering. The algorithm demonstrates 57% instrumental noise reduction and achieves 1.51 × 10⁻⁶ RIU resolution (1.333–1.393 RIU range). The system’s algorithm performance is validated through stepwise NaCl solution switching experiments (0.0025–0.08%) and protein interaction assays (0.15625–20 μg/mL). This advancement establishes a robust framework for high-resolution SPR applications across a broad dynamic range, particularly benefiting live-cell imaging and high-throughput screening. Full article

Deep learning-based super-resolution (SR) frameworks are widely used in remote sensing applications. However, existing SR models still face limitations, particularly in recovering contours, fine features, and textures, as well as in effectively integrating channel information. To address these challenges, this study introduces a novel residual model named OARN (Optimized Attention Residual Network) specifically designed to enhance the visual quality of low-resolution images. The network operates on the Y channel of the YCbCr color space and integrates LKA (Large Kernel Attention) and OCM (Optimized Convolutional Module) blocks. These components can restore large-scale spatial relationships and refine textures and contours, improving feature reconstruction without significantly increasing computational complexity. The performance of OARN was evaluated using satellite images from WorldView-2, GaoFen-2, and Microsoft Virtual Earth. Evaluation was conducted using objective quality metrics, such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Edge Preservation Index (EPI), and Perceptual Image Patch Similarity (LPIPS), demonstrating superior results compared to state-of-the-art methods in both objective measurements and subjective visual perception. Moreover, OARN achieves this performance while maintaining computational efficiency, offering a balanced trade-off between processing time and reconstruction quality. Full article

Modern x86 processors incorporate performance-enhancing features such as prefetching mechanisms, cache coherence protocols, and support for large memory pages (e.g., 2 MB huge pages). While these architectural innovations aim to reduce memory access latency, boost throughput, and maintain cache consistency across cores, they can also expose subtle microarchitectural side channels that adversaries may exploit. This study investigates how the combination of prefetching techniques and huge pages can significantly enhance the throughput and accuracy of covert channels in controlled computing environments. Building on prior work that examined the impact of the MESI cache coherence protocol using single-cache-line access without huge pages, our approach expands the attack surface by simultaneously accessing multiple cache lines across all 512 L1 lines under a 2 MB huge page configuration. As a result, our 9-bit covert channel achieves a peak throughput of 4940 KB/s—substantially exceeding previously reported benchmarks. We further validate our channel on AMD SEV-SNP virtual machines, achieving up to an 88% decoding accuracy using write-access encoding with 2 MB huge pages, demonstrating feasibility even under TEE-enforced virtualization environments. These findings highlight the need for careful consideration and evaluation of the security implications of common performance optimizations with respect to their side-channel potential. Full article

Background/Objectives: Time processing is crucial for managing several aspects of our daily experiences: the continuous interaction with a changing environment requires individuals to make precise temporal judgments. Following right hemisphere damage, patients exhibited a significant alteration in perceiving temporal duration. However, this impairment usually emerges with “abstract” computerized tasks, not in everyday contexts. This study investigates estimation and reproduction of time intervals in left (LBD) and right brain damaged (RBD) patients compared to healthy controls. Methods: We adopt computerized tasks (Experiment 1) and novel virtual reality (VR) tasks where participants judged the duration of their own actions framed within a realistic VR context (Experiment 2). Results: RBD but not LBD patients underestimated time intervals, and reproduced time intervals as longer than they are. Crucially, when participants judged the temporal duration of meaningful actions performed in a realistic context through the VR scenarios, the impairment in processing time observed in RBD patients was reduced. The Voxel-lesion-symptom-mapping (VLSM) analysis revealed the neurocognitive basis of time perception. Conclusions: Our results show that meaningful actions within familiar contexts can provide a channel of information that is essential for optimal time processing, suggesting the importance of assessing time processing in an ecologically controlled manner using VR. Full article

This study examines how contract structures influence coordination and innovation incentives in dual-channel telecommunications supply chains. We consider a setting where a mobile network operator (MNO) supplies services both directly to consumers and indirectly through a mobile virtual network operator (MVNO), which competes in the retail market. Using a game-theoretic framework, we evaluate how different contracts—single wholesale pricing, revenue sharing, and quantity discounts—shape strategic decisions, particularly in the presence of investment spillovers between parties. A key coordination problem emerges from the externalized gains of innovation, where one party’s investment generates value for both participants. Our results show that single wholesale and revenue sharing contracts often lead to suboptimal investment and profit outcomes. In contrast, quantity discount contracts, especially when combined with appropriate transfer payments, improve coordination and enhance the total performance of the supply chain. We also find that innovation led by the MVNO, while generally less impactful, can still yield reciprocal benefits for the MNO, reinforcing the value of cooperative arrangements. These findings emphasize the importance of contract design in managing interdependence and improving efficiency in decentralized supply chains. This study offers theoretical and practical implications for telecommunications providers and policymakers aiming to promote innovation and mutually beneficial outcomes through well-aligned contractual mechanisms. Full article

As artificial intelligence agents become integral to immersive virtual reality environments, their inherent opacity presents a significant challenge to transparent human–agent communication. This study aims to determine if a virtual agent can effectively communicate its learning state to a user through facial expressions, and to empirically validate a set of designed expressions for this purpose. We designed three animated facial expression sequences for a stylized three-dimensional avatar, each corresponding to a distinct learning outcome: clear success (Case A), mixed performance (Case B), and moderate success (Case C). An initial online survey ($n=93$) first confirmed the general interpretability of these expressions, followed by a main experiment in virtual reality ($n=30$), where participants identified the agent’s state based solely on these visual cues. The results strongly supported our primary hypothesis (H1), with participants achieving a high overall recognition accuracy of approximately 91%. While user background factors did not yield statistically significant differences, observable trends suggest they may be worthy of future investigation. These findings demonstrate that designed facial expressions serve as an effective and intuitive channel for real-time, affective explainable artificial intelligence (affective XAI), contributing a practical, human-centric method for enhancing agent transparency in collaborative virtual environments. Full article

High aspect ratio (HAR) sample-induced aberrations seriously affect the topography measurement for the bottom of the microstructure by coherence scanning interferometry (CSI). Previous research proposed an aberration compensating method using deformable mirrors at the conjugate position of the pupil. However, it failed to compensate for the shift-variant aberrations introduced by the HAR hybrid trench array composed of multiple trenches with different parameters. Here, we propose a computational aberration correction method for measuring the topography of the HAR structure by the particle swarm optimization (PSO) algorithm without constructing a database and prior knowledge, and a phase filter in the spatial frequency domain is constructed to restore interference signals distorted by shift-variant aberrations. Since the aberrations of each sampling point are basically unchanged in the field of view corresponding to a single trench, each trench under test can be considered as a separate isoplanatic region. Therefore, a multi-channel aberration correction scheme utilizing the virtual phase filter based on isoplanatic region segmentation is established for hybrid trench array samples. The PSO algorithm is adopted to derive the optimal Zernike polynomial coefficients representing the filter, in which the interference fringe contrast is taken as the optimization criterion. Additionally, aberrations introduce phase distortion within the 3D transfer function (3D-TF), and the 3D-TF bandwidth remains unchanged. Accordingly, we set the non-zero part of the 3D-TF as a window function to preprocess the interferogram by filtering out the signals outside the window. Finally, experiments are performed in a single trench sample and two hybrid trench array samples with depths ranging from 100 to 300 μm and widths from 10 to 30 μm to verify the effectiveness and accuracy of the proposed method. Full article