Search Results (122)

Critical details in both bright and dark regions are frequently lost in high dynamic range (HDR) images when they are displayed on low dynamic range (LDR) devices. To mitigate this issue, tone mapping operators (TMOs) have been developed to convert HDR images into LDR representations while maintaining perceptual quality. However, it is challenging to effectively balance various key visual attributes, such as naturalness and structural fidelity. To overcome this limitation, a two-stage Bayesian optimization approach was proposed in this work to enhance the perceptual quality of tone-mapped images across multiple evaluation metrics. The first stage adaptively optimizes TMQI parameters to capture image-specific perceptual characteristics, while the second stage refines the tone mapping function to further improve detail preservation and visual realism. Extensive experiments using three distinct HDR benchmark datasets were conducted, indicating that the proposed method generally performs better than the existing tone mapping techniques across most evaluated metrics, including TMQI, Naturalness, and Structural Fidelity. Our adaptive approach offers a robust and effective solution for optimizing HDR image conversion, resulting in a significantly improved perceptual quality compared to traditional methods. Full article

Vocal production requires the coordinated control of respiratory, laryngeal, and autonomic systems. In individuals with high vocal demand, this physiological load may influence autonomic regulation, even in the absence of voice disorders. This scoping review systematically mapped current evidence on the relationship between voice production and autonomic nervous system (ANS) activity in adults, focusing exclusively on studies that assessed both systems simultaneously. A systematic search was conducted in PubMed, Scopus, Web of Science, Embase, and CINAHL, following PRISMA-ScR guidelines. Eligible studies included adults performing structured vocal tasks with concurrent autonomic measurements. Data were extracted and synthesized descriptively. Fifteen studies met the inclusion criteria. Most involved healthy adults with high vocal demand, while some included participants with subclinical or functional voice traits. Vocal tasks ranged from singing and sustained phonation to speech under cognitive or emotional load. Autonomic measures included heart rate (HR), heart rate variability (HRV), and electrodermal activity (EDA), among others. Four thematic trends emerged: autonomic synchronization during group vocalization; modulation of autonomic tone by vocal rhythm and structure; voice–ANS interplay under stress; and physiological coupling in hyperfunctional vocal behaviours. This review’s findings suggest that vocal activity can modulate autonomic function, supporting the potential integration of autonomic markers into experimental and clinical voice research. Full article

The lung is increasingly recognized as an organ with dual endocrine and respiratory roles, participating in a complex bidirectional crosstalk with systemic hormones and local/paracrine activity. Endocrine and paracrine pathways regulate lung development, ventilation, immunity, and repair, while pulmonary cells express hormone receptors and secrete mediators with both local and systemic effects, defining the concept of the “endocrine lung”. This narrative review summarizes current evidence on the endocrine–pulmonary axis. Thyroid hormones, glucocorticoids, sex steroids, and metabolic hormones (e.g., insulin, leptin, adiponectin) critically influence alveologenesis, surfactant production, ventilatory drive, airway mechanics, and immune responses. Conversely, the lung produces mediators such as serotonin, calcitonin gene-related peptide, endothelin-1, leptin, and keratinocyte growth factor, which regulate vascular tone, alveolar homeostasis, and immune modulation. We also describe the respiratory manifestations of major endocrine diseases, including obstructive sleep apnea and lung volume alterations in acromegaly, immunosuppression and myopathy in Cushing’s syndrome, hypoventilation in hypothyroidism, restrictive “diabetic lung”, and obesity-related phenotypes. In parallel, chronic pulmonary diseases such as chronic obstructive pulmonary disease, interstitial lung disease, and sleep apnea profoundly affect endocrine axes, promoting insulin resistance, hypogonadism, GH/IGF-1 suppression, and bone metabolism alterations. Pulmonary neuroendocrine tumors further highlight the interface, frequently presenting with paraneoplastic endocrine syndromes. Finally, therapeutic interactions are discussed, including the risks of hypothalamic–pituitary–adrenal axis suppression with inhaled corticosteroids, immunotherapy-induced endocrinopathies, and inhaled insulin. Future perspectives emphasize mapping pulmonary hormone networks, endocrine phenotyping of chronic respiratory diseases, and developing hormone-based interventions. Full article

This study examines how psycholinguistic features of online reviews relate to guest satisfaction in Bali’s spa hotel market. Using LIWC-22 category rates from Google Maps reviews, a corpus of 15,560 quality-filtered reviews from ten leading spa hotels was analyzed. Exploratory factor analysis yielded four interpretable dimensions—Social, Health and Wellness, Emotional Tone, and Lifestyle. In regressions predicting review star ratings (satisfaction), Social (β = 0.028) and Health and Wellness (β = 0.023) showed small but statistically detectable positive associations, whereas Emotional Tone (β = 0.006, t = 0.727) and Lifestyle (β = 0.004, t = 0.476) were not significant. The model’s explained variance is negligible (R² = 0.001; F = 5.283, p < 0.05), reflecting the many influences on ratings beyond review language; findings are interpreted as directional associations rather than predictive effects. Practically, the results point to prioritizing interpersonal service cues and wellness/treatment assurances, with tone monitoring being used for service-recovery signals. The design favors interpretability (validated, word-based categories; full-history snapshot) over black-box complexity, and transferability is Bali-specific and conditional on comparable market features. Future work should add contextual covariates (e.g., price and location), apply explicit temporal segmentation, extend to multilingual corpora, and triangulate text analytics with brief questionnaires and qualitative inquiry to strengthen validity and explanatory power. Full article

Image-to-image translation inputs an image and transforms it into a new image. Deep learning-based image translation requires numerous training data to prevent overfitting; therefore, this study proposes a method to secure training data efficiently by generating and selecting fake water-droplet images using a cycle-consistent generative adversarial network (CycleGAN) and a convolutional neural network (CNN) for image enhancement under inclement weather conditions. A CNN-based classification model was employed to select 1200 well-formed virtual paired sets, which were then added to the existing dataset to construct an augmented training set. Using this augmented dataset, a CycleGAN-based removal module was trained with a modified L1 loss incorporating a difference map, enabling the model to focus on water-droplet regions while preserving the background color configuration. Additionally, we introduce a second training step with tone-mapped target images based on Retinex theory and CLAHE to enhance image contrast and detail preservation under low-light rainy conditions. Experimental results demonstrate that the proposed framework improves water-droplet removal performance compared to the baseline, achieving higher scores in image quality metrics such as BRISQUE and SSEQ and yielding clearer images with reduced color distortion. These findings indicate that the proposed approach contributes to improving image clarity and the safety of autonomous driving under inclement weather conditions. Full article

Background/Objectives: Parents report unmet information needs relating to childhood brain tumors. Existing research shows that providing information to families supports self-efficacy and well-being. The project therefore aimed to co-design resources tailored to the informational needs of families navigating childhood brain tumors in Australia. Methods: Mixed methods were used across multiple phases. A landscape analysis in Phase 1 confirmed the gap in Australian resources as well as the identification of international resources suitable to inform local solutions. Following the Double Diamond Design Framework, subsequent phases of the project aimed to discover and define the problems faced by families before developing and delivering the solution. Parents of children with brain tumors participated in a journey mapping workshop, content adaptation through feedback, and an online survey to determine the preferred delivery mode of information. Clinicians provided iterative feedback as the resource was developed and refined. Results: Nine mothers participated in journey mapping and iterative adaptation of the resource along with twelve clinicians. There were 46 respondents to the survey, which identified a preference for multi-modal delivery of information, and 23 clinical and consumer reviewers in the final revision phase. The process of adaptation is presented, providing transparency on the development of this national resource. Conclusions: The use of self-efficacy theory and co-design was pivotal in this project. Integration of concepts from self-efficacy moves beyond simply presenting information to empowering the audience to feel capable of the task ahead of them. Co-design ensured the content and tone of the resulting resource are fit-for-purpose from the perspective of both clinicians and consumers. The resource is available as a physical book, digital resource, and audiobook and disseminated through children’s hospitals, professional networks, and brain tumor support groups. Full article

High dynamic range (HDR) tone mapping techniques have been widely studied to effectively represent the broad dynamic range of real-world scenes. However, generating an HDR image from multiple low dynamic range (LDR) images captured at different exposure levels can introduce ghosting artifacts in dynamic scenes. Moreover, methods that estimate HDR information from a single LDR image often suffer from inherent accuracy limitations. To overcome these limitations, this study proposes a novel image processing technique that extends the dynamic range of a single LDR image. This technique achieves the goal through leveraging a Convolutional Neural Network (CNN) to generate a synthetic Near-Infrared (NIR) image—one that emulates the characteristic of real NIR imagery being less susceptible to diffraction, thus preserving sharper outlines and clearer details. This synthetic NIR image is then fused with the original LDR image, which contains color information, to create a tone-distributed HDR-like image. The synthetic NIR image is produced using a lightweight U-Net-based autoencoder, where the encoder extracts features from the LDR image, and the decoder synthesizes a synthetic NIR image that replicates the characteristics of a real NIR image. To enhance feature fusion, a cardinality structure inspired by Extended-Efficient Layer Aggregation Networks (E-ELAN) in You Only Look Once Version 7 (YOLOv7) and a modified convolutional block attention module (CBAM) incorporating a difference map are applied. The loss function integrates a discriminator to enforce adversarial loss, while VGG, structural similarity index, and mean squared error losses contribute to overall image fidelity. Additionally, non-reference image quality assessment losses based on BRISQUE and NIQE are incorporated to further refine image quality. Experimental results demonstrate that the proposed method outperforms conventional HDR techniques in both qualitative and quantitative evaluations. Full article

This paper presents a novel cochlear implant (CI) sound coding strategy called Spectral Feature Extraction (SFE). The SFE is a novel Fast Fourier Transform (FFT)-based Continuous Interleaved Sampling (CIS) strategy that provides less-smeared spectral cues to CI patients compared to Crystalis, a predecessor strategy used in Oticon Medical devices. The study also explores how the SFE can be enhanced into a Temporal Fine Structure (TFS)-based strategy named Spectral Event Extraction (SEE), combining spectral sharpness with temporal cues. Background/Objectives: Many CI recipients understand speech in quiet settings but struggle with music and complex environments, increasing cognitive effort. De-smearing the power spectrum and extracting spectral peak features can reduce this load. The SFE targets feature extraction from spectral peaks, while the SEE enhances TFS-based coding by tracking these features across frames. Methods: The SFE strategy extracts spectral peaks and models them with synthetic pure tone spectra characterized by instantaneous frequency, phase, energy, and peak resemblance. This deblurs input peaks by estimating their center frequency. In SEE, synthetic peaks are tracked across frames to yield reliable temporal cues (e.g., zero-crossings) aligned with stimulation pulses. Strategy characteristics are analyzed using electrodograms. Results: A flexible Frequency Allocation Map (FAM) can be applied to both SFE and SEE strategies without being limited by FFT bandwidth constraints. Electrodograms of Crystalis and SFE strategies showed that SFE reduces spectral blurring and provides detailed temporal information of harmonics in speech and music. Conclusions: SFE and SEE are expected to enhance speech understanding, lower listening effort, and improve temporal feature coding. These strategies could benefit CI users, especially in challenging acoustic environments. Full article

Terahertz (THz) communications and simultaneous wireless information and power transfer (SWIPT) hold the potential to energize battery-less Internet-of-Things (IoT) devices while enabling multi-gigabit data transmission. However, severe path loss, blockages, and rectifier nonlinearity significantly hinder both throughput and harvested energy. Additionally, high-power THz beams pose safety concerns by potentially exceeding specific absorption rate (SAR) limits. We propose a sensing-adaptive power-focusing (APF) framework in which a reconfigurable intelligent surface (RIS) embeds low-rate THz sensors. Real-time backscatter measurements construct a spatial map used for the joint optimisation of (i) RIS phase configurations, (ii) multi-tone SWIPT waveforms, and (iii) nonlinear power-splitting ratios. A weighted MMSE inner loop maximizes the data rate, while an outer alternating optimisation applies semidefinite relaxation to enforce passive-element constraints and SAR compliance. Full-stack simulations at 0.3 THz with 20 GHz bandwidth and up to 256 RIS elements show that APF (i) improves the rate–energy Pareto frontier by 30–75% over recent adaptive baselines; (ii) achieves a 150% gain in harvested energy and a 440 Mbps peak per-user rate; (iii) reduces energy-efficiency variance by half while maintaining a Jain fairness index of 0.999;; and (iv) caps SAR at 1.6 W/kg, which is 20% below the IEEE C95.1 safety threshold. The algorithm converges in seven iterations and executes within <3 ms on a Cortex-A78 processor, ensuring compliance with real-time 6G control budgets. The proposed architecture supports sustainable THz-powered networks for smart factories, digital-twin logistics, wire-free extended reality (XR), and low-maintenance structural health monitors, combining high-capacity communication, safe wireless power transfer, and carbon-aware operation for future 6G cyber–physical systems. Full article

The objective of this study was to analyze the impact of solar and hybrid dryers on the physicochemical characteristics, fingerprints, and cognitive-sensory perceptions of Mexican consumers of traditional tostadas made with corn of different races. Corn tostadas from different native races were evaluated with solar and hybrid (solar-photovoltaic solar panels) dehydration methods. Proximal chemical quantification, instrumental analysis (color, texture), fingerprint by Fourier transform infrared spectroscopy (FTIR), and sensory-cognitive profile (emotions and memories) and its relationship with the level of pleasure were carried out. The data were evaluated using analysis of variance models, Cochran Q, and an external preference map (PREFMAP). The results showed that the drying method and corn race significantly (p < 0.05) affected only moisture content, lipids, carbohydrates, and water activity. Instrumental color was influenced by the corn race effect, and the dehydration type influenced the fracturability effect. FTIR fingerprinting results revealed that hybrid samples exhibited higher intensities, particularly associated with higher lime concentrations, indicating a greater exposure of glycosidic or protein structures. Race and dehydration type effects impacted the intensity of sensory attributes, emotions, and memories. PREFMAP vector model results revealed that consumers preferred tostadas from the Solar-Chiquito, Hybrid-Pepitilla, Hybrid-Cónico, and Hybrid-Chiquito races for their higher protein content, moisture, high fracturability, crunchiness, porousness, sweetness, doughy flavor, corn flavor, and burnt flavor, while images of these tostadas evoked positive emotions (tame, adventurous, free). In contrast, the Solar-Pepitilla tostada had a lower preference because it was perceived as sour and lime-flavored, and its tostada images evoked more negative emotions and memories (worried, accident, hurt, pain, wild) and fewer positive cognitive aspects (joyful, warm, rainy weather, summer, and interested). However, the tostadas of the Solar-Cónico race were the ones that were most rejected due to their high hardness and yellow to blue tones and for evoking negative emotions (nostalgic and bored). Full article

Gear whine noise is governed not only by intentional microgeometry modifications but also by unavoidable pitch (indexing) deviation. This study presents a workflow that couples a tooth-resolved surface scan with a calibrated pitch-deviation table, both imported into a multibody dynamics (MBD) model built in MSC Adams View. Three operating scenarios were evaluated—ideal geometry, measured microgeometry without pitch error, and measured microgeometry with pitch error—at a nominal speed of 1000 r min⁻¹. Time domain analysis shows that integrating the pitch table increases the mean transmission error (TE) by almost an order of magnitude and introduces a distinct 16.66 Hz shaft order tone. When the measured tooth topologies are added, peak-to-peak TE nearly doubles, revealing a non-linear interaction between spacing deviation and local flank shape. Frequency domain results reproduce the expected mesh-frequency side bands, validating the mapping of the pitch table into the solver. The combined method therefore provides a more faithful digital twin for predicting tonal noise and demonstrates why indexing tolerances must be considered alongside profile relief during gear design optimization. Full article

High Dynamic Range (HDR) imaging is capable of capturing vivid and lifelike visual effects, which are crucial for fields such as computer vision, photography, and medical imaging. However, real-time processing of HDR content remains challenging due to the computational complexity of tone mapping algorithms and the inherent limitations of Low Dynamic Range (LDR) capture systems. This paper presents an adaptive HDR tone mapping processor that achieves high computational efficiency and robust image quality under varying exposure conditions. By integrating an exposure-adaptive factor into a bilateral filtering framework, we dynamically optimize parameters to achieve consistent performance across fluctuating illumination conditions. Further, we introduce a high-radix Taylor expansion technique to accelerate floating-point logarithmic and exponential operations, significantly reducing resource overhead while maintaining precision. The proposed architecture, implemented on a Xilinx XCVU9P FPGA, operates at 250 MHz and processes 4K video at 30 frames per second (FPS), outperforming state-of-the-art designs in both throughput and hardware efficiency. Experimental results demonstrate superior image fidelity with an average Tone Mapping Quality Index (TMQI): 0.9314 and 43% fewer logic resources compared to existing solutions, enabling real-time HDR processing for high-resolution applications. Full article

The challenge of accurately representing diverse visual experiences from the real world through image rendering, especially in High Dynamic Range (HDR) imaging, persists due to limitations in conveying luminosity and colour depth on standard displays. In this study, we explore luminosity and Wide Colour Gamut (WCG) in HDR and investigate prevalent HDR/WCG frameworks like hybrid log-gamma (HLG). The focus lies in overcoming the hurdle of displaying transformed HDR images on Standard Dynamic Range (SDR) screens through HDR tone mapping (TM). Despite numerous TM operators available, the need for a detailed comparative analysis remains the same. This study aims to convert HDR images into HLG-transformed images using ISO 22028-5 and transform these to SDR using various TM methods, followed by encoding them into standard displays. Another objective of the study is to also identify the optimal TM method for preserving image quality and artistic integrity on SDR screens, complemented by evaluating content dependencies and optimizing visualization using gain maps. This paper’s comprehensive evaluation involves subjective experiments to discern the most effective TM methodology, providing insights into the transformative potential of HDR images for broader display compatibility. The results indicate that content-aware TM methods combined with gain map optimization provide superior visual fidelity and are recommended for high-quality HDR-to-SDR rendering. Full article

Imaging applications involving outdoor scenes and fast motion require sensing and processing of high-dynamic-range images at video rates. In turn, image signal processing pipelines that serve low-dynamic-range displays require tone mapping operators (TMOs). For high-speed and low-power applications with low-cost field-programmable gate arrays (FPGAs), global TMOs that employ contrast-limited histogram equalization prove ideal. To develop such TMOs, this work proposes a MATLAB–Simulink–Vivado design flow. A realized design capable of megapixel video rates using milliwatts of power requires only a fraction of the resources available in the lowest-cost Artix-7 device from Xilinx (now Advanced Micro Devices). Unlike histogram-based TMO approaches for nonlinear sensors in the literature, this work exploits Simulink modeling to reduce the total required FPGA memory by orders of magnitude with minimal impact on video output. After refactoring an approach from the literature that incorporates two subsystems (Base Histograms and Tone Mapping) to one incorporating four subsystems (Scene Histogram, Perceived Histogram, Tone Function, and Global Mapping), memory is exponentially reduced by introducing a fifth subsystem (Interpolation). As a crucial stepping stone between MATLAB algorithm abstraction and Vivado circuit realization, the Simulink modeling facilitated a bit-true design flow. Full article

This review explores various advanced photonic-assisted techniques for microwave frequency measurement, highlighting their distinct advantages and challenges in detecting multi-tone and broadband microwave frequency signals. Different optical processing techniques for instantaneous frequency measurement, including frequency-to-time mapping techniques, are discussed in detail. The application of multicore and few-mode fibers, artificial intelligence-enhanced, and complex modulation techniques are also discussed. These recent advances collectively push the boundaries of microwave frequency measurement, offering robust and scalable solutions for various applications. Full article