Search Results (190)

Detecting naval vessels at extreme distances using infrared search and track (IRST) systems presents severe physical challenges, notably the complete loss of geometric texture and the non-linear submersion of weak target signals within high-dynamic-range sea clutter. Traditional pure data-driven convolutional neural networks (CNNs) rely heavily on visual appearances and suffer from critical feature blind spots under such extreme physical degradation. To overcome this, we propose a Physics-Aware Deep Coupling Network that shifts the detection paradigm from appearance-based feature extraction to physics-guided attribute recognition. Our method deconstructs the degraded infrared signal into three complementary physical domains: an adaptive radiation energy mapping, corresponding to the energy domain, to rescue weak targets; a bio-inspired spatial saliency filtering mechanism, corresponding to the frequency domain, to maximize the signal-to-clutter ratio; and a PSF-coherent gradient topology framework, corresponding to the gradient domain, to discriminate genuine point targets from chaotic sun glints and island edges. These processed priors, alongside the raw image, are integrated into a 4-channel tensor and fused via a Cross-Domain Attention Module, ensuring deep network coupling. To evaluate this architecture, we conduct extensive experiments on the real-world Maritime-SIRST dataset. Since the original dataset provides only pixel-level segmentation masks, we generate axis-aligned bounding-box detection labels from these masks and retrain both the proposed method and a suite of state-of-the-art YOLO detectors under a unified detection paradigm. Extensive benchmarking demonstrates that our physics-aware methodology consistently outperforms these detectors, achieving a mAP50 of 0.923 and an F1 score of 89.92%, thus providing a highly interpretable and robust solution for maritime domain awareness under extreme physical constraints. Full article

Shortwave infrared (SWIR) imagery plays an important role in land–water boundary delineation, coastal monitoring, and complex aquatic environment observation. However, the spatial resolution of SWIR bands is usually lower than that of visible bands, which limits their capability to represent fine-scale targets and boundary structures. To address this problem, this study proposes MLE-ResUNet, a SWIR image super-resolution method that integrates along-track oversampling with visible-light-guided deep learning. The proposed method first exploits dual-view SWIR observations with sub-pixel displacement generated by increasing the sampling line rate in the push-broom imaging process. A maximum likelihood estimation (MLE)-based physical prior module is then introduced to transform multi-view degraded observations into a physically consistent latent high-resolution prior. Finally, high-resolution visible images are used to provide edge, texture, and structural guidance, and a ResUNet-based network is employed for multi-source feature fusion and residual reconstruction. Based on multi-region measured data acquired by the LHRSI (Lightweight High-Resolution Spectral Imager) payload onboard the BlueCarbon-1A satellite, a SWIR super-resolution dataset covering typical urban, farmland, and coastal scenarios was constructed. Comparative experiments were conducted against PCA, BDSD, PanNet, GPPNN, and two additional lightweight-guided deep learning baselines, namely LGPConv and a CANConv-style visible-guided baseline. The results show that MLE-ResUNet achieves the best performance across different scenarios and consistently outperforms the comparison methods in terms of SSIM, SAM, ERGAS, and Q-index. The proposed method effectively enhances spatial detail recovery while maintaining favorable spectral consistency. Ablation experiments further demonstrate that both along-track oversampling information and the MLE-based physical prior contribute to improved reconstruction quality and more stable training convergence. These findings indicate that the proposed method can enhance fine-scale SWIR observation capability without substantially increasing hardware complexity, providing an effective technical solution for shoreline identification, land–water boundary extraction, and complex surface target monitoring. Full article

Nanoparticles offer a versatile platform for the selective eradication of pathogenic or diseased cells by integrating therapeutic payload delivery with precision targeting. Precision targeting can be achieved (1) actively through ligand conjugation, (2) passively by exploiting the physiological abnormalities of diseased tissues, or (3) intrinsically through the innate biophysical properties of the nanoparticle. Intrinsically selective nanoplatforms (iNPs) are particularly advantageous when the disease-promoting agent does not possess distinct surface markers, such as in the case of certain “untargetable cancers” or cancers without known targets. Indeed, nanocarriers for chemotherapeutic or gene delivery have achieved selective cancer cell apoptosis without requiring marker presentation, thereby expanding the therapeutic window of the payload. Disease-promoting agents whose physical properties are different from those of healthy cells are also good candidates for intrinsic nanoparticle targeting. For example, antimicrobial nanomaterials have been designed to disrupt bacterial membranes and reduce the risk of antimicrobial resistance by leveraging stiffness differentials between bacterial cell walls and eukaryotic membranes. Nanoparticle systems with intrinsic targeting mechanisms can also enable non-invasive imaging with near-infrared fluorescence, MRI, and photoacoustic imaging for real-time biodistribution tracking and treatment monitoring. This review synthesizes current innovations in nanoplatform design with intrinsic targeting capabilities, spans applications in infectious and non-communicable diseases, and discusses emerging strategies to enhance specificity, overcome resistance, and translate these platforms toward clinical and field deployment. Full article

This paper presents a proposed architecture for an artificial intelligence-driven unmanned aerial vehicle (UAV) system intended for tactical intelligence, surveillance, and reconnaissance (ISR) missions. The architecture brings together electro-optical imaging, long-wave infrared sensing, two-dimensional light detection and ranging (LiDAR), inertial navigation support, onboard edge computing, and resilient communication links within a unified system-level framework. Unlike many existing approaches that treat perception, autonomy, communication, and safety as loosely coupled functions, the proposed architecture combines multi-modal sensing, operator-supervised autonomy, and a safety-oriented decision validation layer intended for future integration with Ansys SCADE. The system is structured around operational and sensor-performance requirements used to justify the selection and interaction of the main onboard subsystems. At the architectural level, the proposed framework is intended to support target detection, tracking, environment awareness, and mission-level decision support under degraded visibility, constrained communication, and contested operating conditions. The paper therefore contributes a requirement-driven and safety-aware ISR UAV architecture that provides a scalable basis for future implementation, validation, and multi-UAV extension. Full article

Thermal hazards in industrial environments often remain undetected until critical failure or injury occurs. Conventional handheld infrared cameras require manual operation and limit continuous situational awareness. This study presents the design and field validation of a wearable helmet-mounted real-time thermal system based on the MLX90640 infrared array sensor and a Raspberry Pi 5 edge computing platform. Experimental validation was performed across multiple scenarios of 400 measurements based on industrial distances of 100 cm and 150 cm. The performance of the system was tested against a pre-calibrated hotspot infrared thermometer using linear regression analysis and standard error metrics to determine proportional agreement. The results indicate a strong proportional relationship between the two systems at both industrial distances, with R² values ranging from 0.9885 to 0.9973 at 100 cm and from 0.9586 to 0.9867 at 150 cm. A moderate increase in mean absolute error (MAE) was observed as the measurement distance increased. Statistically significant increases in error were identified in mechanically dynamic scenarios where statistically significant increases in measurement error were observed (p-value < 0.05), indicating distance-dependent sensitivity under moving mechanical conditions. The higher absolute errors at longer distances mainly result from field-of-view expansion, reduced target occupancy, and mixed-pixel hotspot effects rather than weakened proportional trend stability. An industrial distance-aware linear regression model was developed to describe behavior and support calibrations under different deployment conditions. Despite minor absolute deviations during dynamic operations, the system maintained strong trend-tracking performance, suggesting suitability for daily preliminary hazard monitoring in industrial safety maintenance. Full article

Images obtained from imaging instruments can endure issues such as high degradation, color distortion, and weak brightness. Effective systems for enhancing these images are critically required. To improve the image quality, herein, we propose two filters based on simple functions, including cosine, sine, hyperbolic secant, and the inverse of hyperbolic cosecant. These filters are used for enhancing the images obtained from a fluorescence imaging system, a near-infrared camera, and low-light condition. The contrast is increased while the image quality is improved. They perform better than a matched filter. Moreover, the combination of our filters with the filter based on the watershed algorithm or the matched filter can be used to extract the marginal features from images generated under water environment. Furthermore, their application in image fusion is explored. Our designed filters may be potentially used for future applications on target identification and tracking. Full article

The query propagation paradigm provides a unified theoretical framework for end-to-end multi-object tracking, yet it still faces challenges in complex scenarios involving multi-scale variations, dense interactions, and trajectory fragmentation, including insufficient query initialization quality, imprecise feature alignment, and difficult identity recovery. Building upon MOTRv2, this paper proposes three core improvements. First, we design a geometric prior injection strategy based on sine–cosine encoding, which explicitly encodes target location and scale information into detection queries, providing high-quality initialization for tracking queries. Second, we propose a width–height-modulated deformable attention mechanism that dynamically adjusts the sampling range of deformable convolution according to target size, enabling fine-grained feature matching for multi-scale targets. Third, we construct a motion-direction-consistency-based trajectory re-association module that leverages motion continuity to efficiently recover lost trajectories without introducing additional appearance models. Furthermore, we introduce a progressive joint training strategy that optimizes detection and tracking modules in stages, effectively mitigating gradient competition in multi-task learning. Extensive quantitative and qualitative experiments on the BEE24, UAVSwarm, and VTMOT infrared datasets validate the effectiveness of the proposed method. On the UAVSwarm dataset, our method achieves state-of-the-art performance with 52.4% HOTA, 72.1% MOTA, and only 51 identity switches. Ablation studies further reveal the synergistic enhancement mechanism among the proposed modules. Full article

Wide-swath longwave infrared (LWIR) imaging from Low Earth Orbit (LEO) demands fast optics and rectilinear (F-tan) mapping for thermal mapping and multi-frame registration. Achieving an F/1.2 aperture with a 112° diagonal field of view (FOV) and distortion within ±5% is challenging, as mapping constraints and field-dominant off-axis aberrations become strongly coupled at large chief-ray angles. The low-distortion target is not only a geometric specification, but also a practical requirement that reduces peripheral compression, helps maintain edge-detail consistency, and lowers digital de-warping effort in the processing pipeline. While traditional LWIR secondary imaging is predominantly restricted to narrow-field cooled systems for cold-stop constraints, the proposed architecture utilizes a curved intermediate image to effectively decouple mapping formation in the field-dominant front objective from aperture-dominant correction in the rear group. Using chalcogenide glasses, the lens achieves a 5.7 mm effective focal length within a 186.9 mm total track. Analysis over the 8–12 μm band confirms performance approaching the diffraction limit at the 50 lp/mm Nyquist frequency alongside stable geometric fidelity across the full field. Thermal analysis from −40 °C to 80 °C and Monte Carlo tolerance analysis demonstrate stable imaging performance and manufacturing feasibility, confirming the effectiveness of the proposed design approach. Full article

Background: Cancer is a major health concern that significantly impacts the global population. Selective chemotherapeutic delivery is needed to improve the efficacy of cancer therapy while minimizing side effects in healthy cells. This study investigated the potential of gold nanoclusters (AuNCs) functionalized with poly(amidoamine) dendrimers (PAMAM) and folic acid (FA) to selectively deliver doxorubicin (DOX) to cancer cells that express the folate receptor (FR). Methods: AuNC synthesis was confirmed via UV–visible and Fourier transform infrared spectroscopy, nanoparticle tracking analysis, and transmission electron microscopy. Folic acid (FA) was incorporated for cell surface receptor targeting, while the triphenylphosphonium cation (TPP⁺) was added to improve mitochondrial localization. Cytotoxicity (MTT), apoptosis, caspase 3/7, mitopotential, and oxidative stress assays were assessed using human MCF-7 (breast adenocarcinoma), HeLa (cervical carcinoma), Caco-2 (colon adenocarcinoma), MDA-MB-231 (epithelial breast cancer), and the embryonic kidney (HEK293) cells. Results: Favorable DOX loading (>78%), with more than 90% of the drug released at pH 4.5, was achieved. A dose-dependent increase in cytotoxicity was observed, with IC50 values lower in cancer cells than HEK293 cells, indicating selective toxicity and minimal off-target effects. Targeting nanocomplexes produced the best responses in the mitopotential, caspase, and oxidative stress assays in HeLa and MCF-7 cells. Conclusions: The improved cytotoxicity in cancer cells may be due to folate-receptor-mediated cellular uptake, as well as the mitochondrial uptake of TPP⁺ nanocomplexes. This highlighted the potential of the drug–AuNC nanocomplexes to limit systemic side effects, proposing a potential novel strategy for drug delivery to cancer cells. Full article

Full-penetration laser welding (FPLW) is increasingly adopted in manufacturing pipelines, yet its industrial scalability is constrained by in-process defect formation, particularly incomplete penetration. To address this, we propose a sensor-driven framework for non-destructive monitoring and automated defect detection that uses infrared (IR) thermography as the primary in situ sensing modality and applies deep learning to the acquired thermal signals. High-speed IR camera recordings were processed to track spatter and the weld zone, yielding a time series of physically interpretable spatiotemporal features (mean spatter area, mean spatter temperature, number of spatters, and mean welding zone temperature). Defect recognition is formulated as a multi-label classification problem targeting incomplete penetration, sagging, shrinkage groove, and linear misalignment, and multiple temporal models were evaluated on the same sensor-derived feature sequences. Experimental validation on 09G2S pipeline steel demonstrates that the proposed time series pipeline based on a hybrid CNN–transformer achieves a mean Average Precision (mAP) of 0.85 while preserving near-real-time inference on a CPU. The results indicate that IR thermography-based spatter dynamics provide actionable sensing signatures for automated defect prediction and can serve as a foundation for closed-loop quality control in industrial laser pipeline welding. Full article

Background: Fluorescence-guided surgery based on near-infrared imaging, most often using indocyanine green (ICG), is increasingly used in colorectal cancer (CRC) surgery. This narrative review integrates current evidence across four clinically relevant domains-anastomotic perfusion, lymphatic mapping, tumor localization, and metastasis detection and emphasizes the technical and translational factors that will determine broader implementation. Methods: We performed a structured narrative review of clinical and translational studies identified through PubMed and citation tracking, with emphasis on ICG-based workflows and emerging targeted tracers. Because the literature spans heterogeneous interventions, imaging platforms, and endpoints, no de novo meta-analysis or formal risk-of-bias assessment was undertaken. Results: ICG fluorescence angiography is the most mature application and can refine transection-line selection, although its effect on anastomotic leak appears protocol dependent. In lymphatic mapping, ICG improves visualization of drainage pathways and nodal basins but does not reliably distinguish benign from metastatic nodes. For tumor localization, ICG supports lesion marking and dynamic tissue characterization, while targeted probes and contrast-free adjuncts may improve oncologic specificity. For metastatic disease, ICG is most useful for liver margin guidance and for excluding residual disease, whereas CEA-targeted and multimodal approaches appear particularly promising for peritoneal metastases. Conclusions: The added value of this review lies in linking current clinical maturity to the translational steps still required for routine adoption. In CRC surgery, fluorescence imaging is already useful in selected settings, but broader implementation will depend on standardized protocols, objective real-time quantification, and multicenter validation of targeted tracers against clinically meaningful outcomes. Full article

Spaceborne infrared small-target detection plays a critical role in space-sky early warning, disaster rescue, and reconnaissance tracking, benefiting from all-time, all-weather, and wide-area monitoring capabilities. The deployment of high-spatial-resolution infrared payloads (ground sampling distance, GSD < 10 m) has introduced pronounced scale diversity among targets, leading to size-sensitive performance degradation in existing detectors and heightened risks of missed detections or false alarms in mixed-size scenarios. Furthermore, multi-frame infrared small-target detection methods often face challenges in maintaining consistent temporal coherence during feature propagation across sequences. To overcome these limitations in high-resolution spaceborne infrared videos, we propose DTRFR, an end-to-end unified detection framework built on an enhanced recurrent feature refinement architecture. This approach incorporates a realistic SITP-QLSD dataset derived from QLSAT-2 infrared backgrounds, featuring diverse scenes, multi-size small targets, and a dedicated generalization sub-test set with extremely small targets partially unseen in training; a multi-scale IRFeatureExtractor leveraging parallel convolutions and dilated receptive fields for improved cross-scale discrimination and clutter suppression; and an adaptive gating pyramid deformable alignment module to optimize sequence alignment and enhance temporal consistency, enabling robust performance across various clutter levels and dynamic backgrounds. Extensive evaluations on SITP-QLSD demonstrate that DTRFR attains competitive performance, achieving mIoU of 74.32% and Pd of 94.51% on the main set, with strong robustness on the generalization sub-test set (Pd = 92.37%). Compared to single-frame and multi-frame baselines, the proposed method achieves higher detection accuracy with significantly reduced false alarms, benefiting from multi-scale feature extraction that enables robust detection of small targets of different sizes in infrared videos. Full article

This study addresses key challenges in obtaining reliable infrared data for maritime ship observation and limitations of existing models, such as simplified reflectance assumptions and incomplete multi-band coverage. To improve modeling accuracy and computational efficiency, a high-precision Bidirectional Reflectance and Pseudo-random Vector Enhanced Reverse Monte Carlo Method (BP-ERMCM) is developed. By combining the Bidirectional Reflectance Distribution Function (BRDF), pseudo-random vector approaches, and improved ray-tracking algorithms with precomputed thermal radiation and MODTRAN’s atmospheric transfer model, BP-ERMCM provides multi-view infrared characteristic simulations across 3–5 μm and 8–12 μm bands. Simulations using a 3D ship model with 191 viewpoints reveal seasonal sensitivity, with summer peak intensity at 9.8 μm being 39.3% higher than in winter, and viewpoint dependency showing oblique overhead radiation 5.65 times greater than that from bow angles. Long-wave contours enhance target distinction, while mid-wave regions are dominated by reflection, increasing intensity at 3.8 μm by 56.1–85.7%. These findings highlight BP-ERMCM’s potential to inform infrared signature database construction, detector optimization, and maritime observation strategies. The findings underscore BP-ERMCM’s capability to enhance efficiency and accuracy, providing valuable insights for infrared databases, sensor selection, and maritime observation strategies, thereby advancing infrared signature analysis in maritime applications. Full article

Terahertz waves are located in the “transition zone” between millimeter waves and infrared light. Terahertz video synthetic aperture radar (THz-ViSAR) utilizes the high operating frequency, strong radar cross-section intensity, and high azimuth repetition frequency of terahertz waves to detect and track ground moving targets. The conventional methods for detecting moving targets do not take into account the imaging characteristics of moving targets in THz-ViSAR. The constant false alarm rate (CFAR) detection method is used together with other methods to detect moving targets, resulting in unsatisfactory detection performance. This article proposes a new detection method for single channel slow-moving targets in THz-ViSAR based on shadows and light spots, which extracts the features of the shadow and spot areas of the moving target, and determines the position and direction of the moving target through the identification of the shadow and spot areas. The progressiveness of this method is verified by simulation and experimental tests. Full article

Background and Objectives: Septic shock is marked by profound circulatory and cellular dysfunction, with mortality rates of 25–40% despite guideline-based resuscitation. Normalization of macrohemodynamic variables often fails to restore tissue perfusion, a concept known as hemodynamic incoherence. Persistent microcirculatory dysfunction is associated with organ failure and poor outcomes, underscoring the limitations of systemic monitoring alone. This focused narrative review synthesizes current evidence on microcirculatory monitoring in septic shock, with emphasis on bedside and emerging optical technologies, and evaluates their role as adjuncts to traditional hemodynamic assessment for perfusion-targeted resuscitation. Materials and Methods: A concept-driven search of PubMed/MEDLINE (January 2015 to January 2026) was performed, incorporating MeSH and free-text terms for septic shock, microcirculation, hemodynamic coherence, and monitoring modalities. Foundational pre-2015 studies were included for context. Articles were screened using predefined inclusion/exclusion criteria to minimize bias, with thematic qualitative synthesis. A PRISMA-inspired flow diagram was used to summarize the study selection process. Results: Microcirculatory alterations in septic shock include reduced functional capillary density, perfusion heterogeneity, and impaired oxygen extraction, persisting despite macrohemodynamic correction. Bedside markers, such as capillary refill time (CRT) and mottling, track microvascular recovery more closely than lactate. When used to guide resuscitation, CRT-based strategies show a non-significant mortality trend in randomized evaluation, with later studies reporting benefit in composite clinical outcomes. Optical technologies offer non-invasive insights: photoplethysmography (PPG) and perfusion index (PI) show prognostic value and early detection of incoherence; automated CRT (aCRT) enhances reproducibility; advanced modalities, such as laser speckle contrast imaging (LSCI), near-infrared spectroscopy (NIRS), and sublingual videomicroscopy, provide detailed physiological data but face standardization challenges. Recent interventional evidence, including peripheral perfusion-targeted RCTs, supports improved outcomes, though large-scale trials remain limited. Conclusions: Microcirculatory monitoring provides complementary, physiologically relevant information to macrohemodynamic assessment in septic shock. Emerging bedside tools, such as PI and aCRT, are poised for routine use, while multimodal integration may enable personalized management. Future research should prioritize standardization, AI-driven analysis, and randomized trials to confirm outcome benefits. Full article