Search Results (467)

Vibrotactile interfaces commonly encode information using changes in stimulus amplitude and frequency, yet it remains unclear how reliably these parameters can be distinguished when spatial cues are unavailable. The present study examined discrimination of vibrotactile signals that differed in amplitude, frequency, or both, with sequential stimulation delivered to a single location on the wrist. Vibrotactile stimuli were presented through a wearable actuator, and participants judged whether pairs of signals were the same or different. Discrimination performance was high when stimuli differed in amplitude, whereas signals differing only in frequency were difficult to distinguish and often produced performance near chance. Importantly, adding frequency differences to amplitude differences did not improve discrimination beyond amplitude differences alone. These findings indicate that, under non-spatial and sequential presentation conditions, amplitude provides a robust cue for vibrotactile signal discrimination, whereas frequency modulations on their own offer limited benefits for perceptual discrimination. The results highlight basic constraints on vibrotactile perception that are relevant for the design of wearable tactile interfaces and sensory substitution devices. Full article

In regenerative medicine, three-dimensional (3D) printing provides precise spatial control over the fabrication of complex, biomimetic tissue constructs, enabling the production of architecturally defined and functionally tailored scaffolds. By enabling precise layer-by-layer deposition of cells, biomaterials, and bioactive compounds, 3D printing overcomes many limitations associated with conventional scaffold fabrication methods. This approach facilitates the development of tailored structures that mimic the mechanical, biological, and structural characteristics of native tissues, thereby enhancing cellular organization, proliferation, and differentiation. Extensive research in tissue engineering has led to the development of 3D-printed scaffolds for the regeneration of vascular, skin, bone, cartilage, and soft tissues. Advances in bioink formulations—including growth factor-loaded systems, decellularized extracellular matrix components, and natural and synthetic polymers—have further improved tissue-specific functionality. Moreover, multimaterial and multiscale printing strategies enable the fabrication of heterogeneous constructs with controlled porosity, mechanical gradients, and spatially regulated biological cues. Although vascularized tissue constructs remain a major challenge for clinical translation, recent bioprinting advancements have significantly accelerated progress in this area. Integration of computer-aided design with patient-specific imaging data has further strengthened the potential of 3D printing for personalized regenerative therapies. Despite these advances, challenges related to scalability, regulatory approval, and long-term functionality persist. Nevertheless, continued progress in printing technologies, biomaterials, and regulatory and standards frameworks is expected to drive the clinical adoption of 3D printing. Ultimately, 3D printing represents a transformative approach in tissue engineering, redefining strategies for functional tissue regeneration and translational regenerative medicine. Full article

As population aging accelerates, age-related declines in visual sensitivity and attentional control make older drivers more vulnerable to suboptimal in-vehicle interface designs. Head-up displays (HUDs) are intended to reduce gaze shifts by overlaying information within the forward field of view, yet empirical evidence remains limited on how specific HUD presentation strategies reshape older drivers’ visual attention allocation. Grounded in theories of visual attention and cognitive load, this study systematically investigates three design variables that are increasingly common in contemporary HUDs (including AR-HUDs): (1) dynamic versus static navigation cues, (2) pedestrian warning strategies under different lighting conditions, and (3) the spatial placement of high-priority information. We first conducted a formative user study to define variables and operationalizations, and then carried out three within-subject driving-simulator experiments using controlled HUD stimuli and eye tracking. Objective gaze measures (e.g., fixation count, total fixation duration, and time to first fixation) were combined with subjective preference ratings to characterize attentional capture, search efficiency, and potential attentional costs. Findings reveal a robust trade-off: continuously changing navigation cues enhance attentional capture but can also increase attentional “stickiness,” unnecessarily consuming older drivers’ limited attentional resources. In pedestrian hazard tasks, real-time overlay warnings that were spatially aligned with the hazard significantly improved visual localization under low-light conditions, outperforming early warnings and multi-stage strategies. Across tasks and layout conditions, the central HUD region showed a stable attentional advantage—placing critical information centrally elicited greater visual attention and stronger subjective preference. These results provide mechanistic evidence for how HUD parameters modulate older drivers’ attention and yield actionable implications for prioritization, temporal pacing of dynamic navigation cues, and a “center-first” layout strategy to guide age-friendly HUD design. Full article

Accurate environmental perception is fundamental to safe autonomous driving; however, most existing multimodal systems rely on fixed or heuristic sensor fusion strategies that cannot adapt to scene-dependent variations in sensor reliability. This paper proposes Cross-Modal Adaptive Attention (CMAA), a unified end-to-end Bird’s-Eye-View (BEV) perception framework that dynamically fuses camera, LiDAR, and RADAR information through learnable, context-aware modality gating. Unlike static fusion approaches, CMAA adaptively reweights sensor contributions based on global scene descriptors, enabling the robust integration of semantic, geometric, and motion cues without manual tuning. The proposed architecture jointly performs 3D object detection, multi-object tracking, and motion forecasting within a shared BEV representation, preserving spatial alignment across tasks and supporting efficient real-time deployment. Experiments conducted on the official nuScenes validation split demonstrate that CMAA achieves 0.528 mAP and 0.691 NDS, outperforming fixed-weight fusion baselines while maintaining a compact model size and efficient inference. Additional tracking evaluation using the official nuScenes tracking devkit reports improved tracking performance, while motion forecasting experiments show reduced trajectory displacement errors (minADE and minFDE). Ablation studies further confirm the complementary contributions of adaptive modality gating and bidirectional cross-modal refinement, and a stratified dynamic analysis reveals consistent reductions in velocity estimation error across object classes, motion regimes, and environmental conditions. These results demonstrate that adaptive multimodal fusion improves robustness, motion reasoning, and perception reliability in complex traffic environments while remaining computationally efficient for deployment in safety-critical autonomous driving systems. Full article

Unsupervised visible–infrared person re-identification (USL-VI-ReID) plays a pivotal role in cross-modal computer vision applications for intelligent surveillance and public safety. However, the task remains hampered by large modality gaps and limited granularity in feature representations. In particular, channel augmentation (CA) is typically used only for data augmentation, and its potential as an independent input modality remains unexplored. To address these shortcomings, we present a multimodule collaborative USL-VI-ReID framework that explicitly treats CA as a separate input modality. The framework combines four complementary modules. The Person-ReID Adaptive Convolutional Block Attention Module (PA-CBAM) module extracts discriminative features using a two-level attention mechanism that refines salient spatial and channel cues. The Varied Regional Alignment (VRA) module performs cross-modal regional alignment and leverages the Multimodal Assisted Adversarial Learning (MAAL) to reinforce region-level correspondence. The Varied Regional Neighbor Learning (VRNL) implements reliable neighborhood learning via multi-region association to stabilize pseudo-labels and capture local structure. Finally, the Uniform Merging (UM) module merges split clusters through alternating contrastive learning to improve cluster consistency. We evaluate the proposed method on SYSU-MM01 and RegDB. On RegDB’s visible-to-infrared setting, the approach achieves Rank-1 = 93.34%, mean Average Precision (mAP) = 87.55%, and mean Inverse Negative Penalty (mINP) = 76.08%. These results indicate that our method effectively reduces modal discrepancies and increases feature discriminability. It outperforms most existing unsupervised baselines and several supervised approaches, thereby advancing the practical applicability of USL-VI-ReID. Full article

Physical separation between family members arises not only from life choices such as education and employment, but also from health-related constraints that limit physical co-presence. This paper presents StepsConnect, a real-time step-sensing-based ambient display system that transforms personal walking data into dynamic digital art, providing low-effort and non-intrusive presence cues for family members living apart. The system continuously captures step data via smartphones and renders them as spatial and embodied visual cues embedded in everyday environments. We conducted a 90 min laboratory study with 15 young adult–parent dyads, in which young adults engaged in a simulated work session while viewing real-time visualizations of their parents’ step activity. Young adults’ perceived connectedness was measured using the Inclusion of Other in the Self (IOS) scale and complemented with semi-structured interviews, while parents’ walking data were logged to provide an objective behavioral reference. Quantitative results indicated modest and heterogeneous changes in IOS scores at the group level, with individual variability across participants. Qualitative findings suggested that step-based visualizations primarily functioned as ambient reminders and cues of presence, supporting momentary relational awareness while remaining calm and non-intrusive within the workspace context. Walking data exhibited large variation across dyads, providing objective context for participants’ subjective experience of presence, although connectedness was not simply proportional to activity magnitude. The findings suggest that aesthetic step-based ambient visualization primarily supports momentary relational awareness rather than immediate shifts in stable closeness. By clarifying this distinction, the study advances understanding of how sensing-based digital art may function as a complementary presence layer in intergenerational contexts. Full article

Perceived photorealism in architectural 3D rendering is not determined solely by physical accuracy or rendering complexity but also by a limited set of visual cues that observers rely on when judging realism. This literature review synthesizes findings from 41 peer-reviewed studies spanning perception science, computer graphics, and immersive visualization, with the aim of identifying the cues that most strongly contribute to perceived photorealism in rendered scenes. Convergent evidence from psychophysical experiments, user studies in virtual and augmented reality, and rendering-oriented analyses indicate that six cue categories consistently dominate realism judgments. Across the reviewed literature, realism judgments depend less on scene complexity or the number of visual elements and more on the consistency and plausibility of these cues for supporting inferences about shape, material, and spatial layout. These findings suggest that photorealism emerges from the alignment of the rendered image structure with perceptual expectations learned from real-world visual experience. The implications for architectural visualization workflows and directions for future research on cue interactions and perceptual thresholds are discussed. Full article

High-fidelity static map construction is essential for reliable autonomous navigation, yet dynamic environments introduce severe artifacts caused by moving objects (also referred to as dynamic artifacts) in accumulated maps. While geometry-based methods perform well on dense point clouds, their performance notably degrades on sparse 16-beam LiDAR due to the “Sparsity Trap”: dynamic objects are frequently missed by ray-based geometry, and purely geometric cues fail in radiometrically ambiguous scenarios. To address this, we propose RI-DVP, a physics–geometry dual-driven framework. Unlike conventional approaches, RI-DVP first performs a physics-inspired radiometric normalization that compensates for range attenuation and incidence-angle effects to establish a consistent signal baseline. Subsequently, a Dual-Residual Aggressive Removal (DRAR) module jointly exploits geometric residuals—bounded by a range-dependent spatial uncertainty envelope—and calibrated intensity residuals to detect geometrically indistinguishable objects. To balance recall and precision, a Hierarchical Static Reversion strategy (HSR) employs two-stage recovery to retrieve large-scale structures and correct fine-grained artifacts via topology-based adhesion reasoning. Experiments on SemanticKITTI and custom sparse datasets demonstrate that RI-DVP outperforms state-of-the-art geometric baselines, improving Dynamic Accuracy by over 36 percentage points in sparse scanning scenarios using a VLP-16 LiDAR sensor (Velodyne Acoustics, Inc., Morgan Hill, CA, USA) compared to baselines that fail under the sparsity trap while achieving real-time performance at approximately 15.3 Hz. Full article

Reliable visual characterization of food composition is a fundamental prerequisite for image-based dietary assessment and health-oriented food analysis. In fine-grained food recognition, models often suffer from large intra-class variation and small inter-class differences, where visually similar dishes exhibit subtle yet discriminative differences in ingredient compositions, spatial distribution, and structural organization, which are closely associated with different nutritional characteristics and health relevance. Capturing such composition-related visual structures in a non-invasive manner remains challenging. In this work, we propose a fine-grained food classification framework that enhances spatial relation modeling and key-region awareness to improve discriminative feature representation. The proposed approach strengthens sensitivity to composition-related visual cues while effectively suppressing background interference. A lightweight multi-branch fusion strategy is further introduced for the stable integration of heterogeneous features. Moreover, to support reliable classification under large intra-class variation, a token-aware subcenter-based classification head is designed. The proposed framework is evaluated on the public FoodX-251 and UEC Food-256 datasets, achieving accuracies of 82.28% and 82.64%, respectively. Beyond benchmark performance, the framework is designed to support practical image-based dietary analysis under real-world dining conditions, where variations in appearance, viewpoint, and background are common. By enabling stable recognition of the same food category across diverse acquisition conditions and accurate discrimination among visually similar dishes with different ingredient compositions, the proposed approach provides reliable food characterization for dietary interpretation, thereby supporting practical dietary monitoring and health-oriented food analysis applications. Full article

Infrared small target detection (IRSTD) is a fundamental task in remote sensing-based surveillance and early warning systems. However, extremely small target size, low signal-to-noise ratio, and complex background clutter make reliable detection highly challenging. To address these issues, we propose a Hierarchical Perception Fusion Network (HPFNet) for IRSTD. Specifically, the Patch-Wise Context Feature Extraction module (PCFE) jointly integrates the Patch Nonlocal Block, convolutional blocks and attention mechanism to enable global–local feature extraction and enhancement, thereby strengthening weak target representations. In addition, the Multi-Level Sparse Cross-Fusion module (MSCF) explicitly performs cross-level feature interaction between encoder and decoder representations, enabling effective fusion of low-level spatial details and high-level semantic cues. A dual Top-K sparsification mechanism is adopted to filters’ irrelevant background features, enabling the attention mechanism to focus more on the target region and thereby bolstering the discriminative power of feature representation. Finally, the Efficient Upsampling Module (EUM) combines upsampling with multi-branch dilated convolutions to enhance feature reconstruction and improve localization accuracy. Extensive experiments on publicly available benchmark datasets demonstrate that HPFNet consistently outperforms existing state-of-the-art IRSTD methods. Full article

Proximate visual cues play a crucial role for flies (Order: Diptera) in locating suitable foraging and oviposition sites. This study examined the behavioural responses of two sheep myiasis-causing blowfly species in Australia, Lucilia cuprina (Wiedemann) and Chrysomya rufifacies (Macquart), to six different reflectance levels of green and yellow hues. Both species were influenced primarily by reflectance intensity and proximity to the light source. Lucilia cuprina displayed a nonsignificant preference for moderate yellow (p = 0.25), whereas Ch. rufifacies showed a significant attraction to moderate green (p = 0.004) when presented with a two-choice comparison between moderate green and yellow. When exposed to three reflectance levels under each hue, both species responded most strongly to mid-range intensities in green but not yellow, with no significant differences observed among light and dark shades (p > 0.05). Zonal analyses revealed a significant aggregation of individuals near the light source (Zone C; p < 0.05), indicating that spatial orientation cues may be stronger determinants of attraction. Overall, L. cuprina was more responsive to moderate yellow and Ch. rufifacies to moderate green, but both species were predominantly guided by light-related spatial and intensity cues. These findings provide valuable insight into the visual ecology of blowflies and may be useful in optimising colour and reflectance parameters in the design of future commercial fly traps. Full article

In dynamic and complex real-world environments, artificial intelligence (AI) vision systems continue to face significant challenges in accurately detecting and tracking small objects. The core difficulty lies in the fact that small targets usually exhibit limited spatial and textural features, while dynamic backgrounds often generate numerous misleading motion cues, thereby interfering with reliable discrimination between targets and backgrounds. Inspired by the remarkable capability of the insect brain in detecting small moving objects, this study proposes a visual neural network model enhanced by a feedback mechanism. By adaptively responding to temporal variations, the proposed model is able to more precisely distinguish small targets from background-induced false targets. The network architecture consists of two main pathways: a motion detection pathway that extracts motion-related features from minute targets, and a feedback attention pathway that enhances the focus on true targets by leveraging the feature differences between real and false motion signals. In addition, a global inhibition module is incorporated to reduce the false alarm rate by filtering out background-induced false positives, thereby improving the overall detection performance of the model. Experimental results demonstrate that the proposed model achieves a detection rate of 0.81 in complex visual scenarios, whereas the compared models all achieve detection rates below 0.59, indicating a significant improvement in detection performance. Meanwhile, in terms of Precision and F1-score, the proposed model achieves values of 0.0648 and 0.12, respectively, while the compared models obtain values lower than 0.0077 and 0.015, further validating the superiority of the proposed method in detection accuracy and robustness. Full article

Semantic segmentation of remote sensing imagery is crucial for applications such as land resource management and urban planning, yet it remains challenging due to low intra-class variation, ambiguous boundaries, and the coexistence of multi-scale geospatial features. To tackle these issues, we propose GS-USTNet, a novel architecture that enhances both feature representation and boundary recovery. First, we introduce a Global–Local Adaptive Convolution (GLAConv) module that dynamically fuses global contextual cues with local responses to generate content-aware convolutional weights, thereby improving feature discriminability. Second, we design a Skip-Guided Attention (SGA) mechanism that leverages spatial–channel joint attention to guide the decoder, effectively mitigating attention dispersion in complex scenes or under class imbalance and significantly sharpening object boundaries. Built upon the efficient USTNet framework, our model achieves substantial performance gains without compromising computational efficiency. Extensive experiments on benchmark datasets demonstrate that GS-USTNet achieves consistent improvements over the original USTNet, with gains of approximately 3.5% in overall accuracy and 6.0% in F1-score across datasets. Ablation studies further confirm the effectiveness of the proposed GLAConv and SGA modules. This work provides an efficient and robust approach for fine-grained semantic segmentation of high-resolution remote sensing imagery. Full article

Effective indoor navigation remains a challenge in complex built environments such as hospitals and airports, where disorientation can lead to anxiety, inefficiency, and safety risks. While prior research has focused on outdoor wayfinding or single-metric performance assessments, few studies have examined spatial cognitive efficiency—a multidimensional metric defined as the standardized difference between spatial knowledge acquisition (P) and cognitive resource expenditure (R). In this study, P was derived from expert-rated sketch maps that captured participants’ environmental understanding, while R was indexed by navigation path length, which reflected their exploration effort. This study employed virtual reality to investigate how individual differences and environmental cues shape cognitive efficiency during indoor navigation. Thirty participants explored a high-fidelity virtual environment while behavioral, sketch-based, and questionnaire data were collected. Results revealed a non-significant linear correlation between P and R, consistent with cognitive efficiency as a distinct construct. High-efficiency participants relied more on boundary cues and exhibited “low-speed, short-distance” exploration patterns, whereas landmark-dependent strategies showed lower stability. These findings underscore the theoretical and practical value of cognitive efficiency as a multidimensional metric, offering evidence-based guidance for designing cognitively supportive indoor navigation systems. Full article

Unmanned aerial vehicle (UAV) forest fire detection is vital for forest safety. However, early-stage UAV fire scenarios often involve small targets, weak smoke signals, and strict onboard resource constraints, which pose significant challenges to existing detectors. To improve the speed and accuracy of UAV forest fire detection, this paper proposes a lightweight fire detection algorithm, AHE-YOLO, specifically designed for UAVs. The proposed method adopts a coordinated lightweight design to improve feature preservation and cross-scale representation under limited computational budgets. Specifically, the Adaptive Downsampling (ADown) module preserves shallow fire-related cues during spatial reduction, improving sensitivity to small flame and smoke targets. The high-level screening-feature fusion pyramid network (HS-FPN) introduces cross-scale attention to promote more discriminative multi-level feature interaction while reducing redundant computation. Furthermore, the Efficient Mobile Inverted Bottleneck Convolution (EMBC) module is employed to improve receptive-field efficiency and feature selectivity under lightweight constraints, further enhancing detection accuracy and inference speed. Finally, the performance of AHE-YOLO is comprehensively evaluated through ablation and comparative experiments on the same dataset. The final experimental results show that YOLO-AHE achieves a mean average precision (mAP) of 94.8% while reducing model parameters by 39.7%, decreasing FLOPs by 27.0%, and shrinking the model size by 36.4%. In addition, its inference speed improves by 16.5%. Beyond detection performance, the proposed framework supports sustainable forest monitoring by enabling early fire warning with reduced computational and energy demands, showing strong potential for real-time deployment on resource-constrained UAV and edge platforms. Full article