The development of remote blood pressure (BP) measurement algorithms using remote photoplethysmography (rPPG) has significant limitations, including the small size of publicly available datasets, privacy concerns regarding facial videos, and a lack of diverse, realistic datasets associated with actual BP measurements. To address these challenges, this study aimed to provide comprehensive, simultaneous recordings of participants' faces, along with reference physiological measurements, for 300 adult participants aged 18–65 years. For each imaging session, systolic and diastolic blood pressure and reference heart rate (HR) were recorded using clinical electronic BP monitors in addition to recording illuminance (lux) values for indoor and outdoor environments. The collected data, called CLBP-300, is a crucial resource for developing and evaluating remote vital signs from facial rPPG signals. A sample of videos is publicly available to demonstrate data quality, while academic researchers can access the complete dataset under a strict data use agreement. The data and python code presented in this study are available on https://sites.google.com/view/clbp-300?usp=sharing.

Situation awareness is essential for ensuring safety in hazardous environments, where timely and accurate information is critical for decision-making. Unmanned Aerial Vehicles (UAVs) have emerged as valuable tools in enhancing situation awareness by providing real-time data and monitoring capabilities in high-risk areas. This study explores the integration of advanced technologies, focusing on imaging and sensor technologies such as thermal, spectral, and multispectral cameras, deployed in critical zones. By merging these technologies into UAV platforms, responders gain access to essential real-time information while reducing human exposure to hazardous conditions. This study presents case studies and practical applications, highlighting the effectiveness of these technologies in a range of hazardous situations.

Insects enhance aerodynamic flight control using the dynamic movement of their appendages, aiding in balance, stability, and manoeuvrability. Although biologists have observed these behaviours, the phenomena have not been expressed in a unified mathematical flight dynamics framework. For instance, relevant existing models tend to disregard either the aerodynamic or the inertial effects of the appendages of insects, such as the abdomen, based on the assumption that appendage dynamic effects dominate in comparison to aerodynamic effects, or that appendages are stationary. However, appendages in insects exist in various shapes and sizes, which affect the level of both the inertial and aerodynamic contributions to the overall system. Here, the effects of the individual dynamic, inertial and aerodynamic contributions of biologically inspired appendages in fixed wing forward flight demonstrate the utility of the framework on an example system. The analysis demonstrates the effect of these aerodynamic appendages on the steady flight and manoeuvre performance of a small aircraft with an actuated aft appendage capable of movement in the longitudinal and lateral axes, analogous to an insect abdomen. We use the method to consider designs with different appendage areas. The example case showed that ignoring the aerodynamic contribution might yield useful insights depending on the size of the appendage, but including the aerodynamic effects as part of a consistent mathematical framework leads to a more comprehensive understanding of the role of appendage morphology. The method allows improved modelling for modern multivariate control system design using bioinspired appendages. Inertia-dominated appendages provided more advantages in energy-based longitudinal manoeuvres and in trimmed flight, with reduced advantage in initiating lateral manoeuvres.

Spiral fractures are a frequent clinical manifestation of child abuse, particularly in non-ambulatory infants. Approximately 50% of fractures in children under one year of age are non-accidental, yet differentiating between accidental and abusive injuries remains challenging, as no single fracture type is diagnostic in isolation. The objective of this study is to investigate the biomechanics of spiral fractures in immature long bones and the role of the periosteum in modulating fracture behavior under torsional loading. Methods: Paired metatarsal bone specimens from immature sheep were tested using controlled torsional loading at two angular velocities (90°/s and 180°/s). Specimens were prepared through potting, application of a base coat, and painting of a speckle pattern suitable for high-speed digital image correlation (HS-DIC) analysis. Both periosteum-intact and periosteum-removed groups were included. Results: Spiral fractures were successfully induced in over 85% of specimens. Digital image correlation revealed localized diagonal tensile strain at the fracture initiation site, with opposing compressive zones. Notably, bones with intact periosteum exhibited broader tensile stress regions before and after failure, suggesting a biomechanical role in constraining deformation. Conclusion: This study presents a novel integration of high-speed digital image correlation (DIC) with paired biomechanical testing to evaluate the periosteum’s role in spiral fracture formation—an area that remains underexplored. The findings offer new insight into the strain distribution dynamics in immature long bones and highlight the periosteum’s potential protective contribution under torsional stress.

We are exploring the use of the Milky Way as a compass at a system level, emulating the function of direction-finding by nocturnal organisms. Our objective is a future role for the MW as a compass for robotics, planetary exploration and aerospace applications. A functional compass can also be used as a tool to study the ecology of light pollution. Simulation is a required step in developing aerospace navigation and control systems, which requires novel approaches when the Milky Way is the input to the system. The Milky Way is a large streak across the southern sky, and the beetles and moths that have been found to use it have been southern hemisphere species. It is prone to being washed out by light pollution. Despite these limitations, it is the dominant direction signal in the southern sky when the moon is absent, and it has unique properties compared to other celestial cues. We introduce the Milky Way and how it differs from stars as a navigation aid. The distributed form of the Milky Way creates challenges for simulation, as distinct from almanacs of the coordinates of individual stars. We introduce a representative compass solution that combines traditional computer vision measurement techniques to achieve approximately +/- 5. compass heading accuracy as an example. We then demonstrate the system with modelled camera characteristics in an image generator for a software in the loop flight simulator that includes input from the simulated night sky.

Each spring, billions of Bogong moths escape hot conditions across southeast Australia by migrating up to 1,000 km to a place that they have never previously visited—a limited number of cool caves in the Australian Alps, historically used for aestivating over summer 1,2 . At the beginning of autumn, the same individuals make a return migration to their breeding grounds to reproduce and die. Here we show that Bogong moths use the starry night sky as a compass to distinguish between specific geographical directions, thereby navigating in their inherited migratory direction towards their distant goal. By tethering spring and autumn migratory moths in a flight simulator 3–5 , we found that, under naturalistic moonless night skies and in a nulled geomagnetic field (disabling the moth’s known magnetic sense 4 ), moths flew in their seasonally appropriate migratory directions. Visual interneurons in different regions of the moth’s brain responded specifically to rotations of the night sky and were tuned to a common sky orientation, firing maximally when the moth was headed southwards. Our results suggest that Bogong moths use stellar cues and the Earth’s magnetic field to create a robust compass system for long-distance nocturnal navigation towards a specific destination.

There are flapping wing-style systems being developed by various institutions around the world. However, despite there being many systems that superficially appear robust, there is no viable flapping wing flying system at this time. We identified a gap in knowledge and capability, which is that the lack of appropriate instrumentation seems to be a major roadblock in further developing flapping wing flying systems. There is no complete solution in regards to instrumentation and sensing at the appropriate scales. This paper seeks to critically examine and classify the existing instrumentation utilized and reported in the literature and attempts to identify the path forward for flapping wing-style instrumentation.

Early diagnosis of eye diseases is very important to prevent visual impairment and guide appropriate treatment methods. This paper presents a unique approach that can detect numerous eye diseases automatically. Initially, this approach used the pre‐trained ImageNet models that provides various pre‐trained models for training the acquired data. The existing data sets are composed of 645 data images acquired clinically, represented by two groups of subjects as healthy and others holding the proposed eye defect like cataracts, foreign bodies, glaucoma, subconjunctival haemorrhage, and viral conjunctivitis. Followed by comparisons of the pre‐trained model's coefficients and prediction performance. Later, the first‐class execution model is integrated within the Raspberry Pi staging and the real‐time digital camera detection. The evaluation process used the confusion matrix, model accuracy, precision factor, recall coefficient, F1 score, and the Matthews Correlation Coefficient (MCC). Resulting in the performance of these pre‐trained ImageNet models used in this study represented by 93% (InceptionResNetV2), 90% (MobileNet), 86% (Residual Network ResNet50), 85% (InceptionV3), 78% (Visual Geometry Group VGG19), and 72% (Neural Architecture Search Network NASNetMobile). The results show that the InceptionResNetV2 achieved the highest performance. This proposed approach shows its efficiency and strength by early detection of the subject's unhealthy eyes through real‐time monitoring in the field of ophthalmology.

Introduction: Image-based heart rate estimation technology offers a contactless approach to healthcare monitoring that could improve the lives of millions of people. In order to comprehensively test or optimize image-based heart rate extraction methods, the dataset should contain a large number of factors such as body motion, lighting conditions, and physiological states. However, collecting high-quality datasets with complete parameters is a huge challenge.Methods: In this paper, we introduce a bionic human model based on a three-dimensional (3D) representation of the human body. By integrating synthetic cardiac signal and body involuntary motion into the 3D model, five well-known traditional and four deep learning iPPG (imaging photoplethysmography) extraction methods are used to test the rendered videos.Results: To compare with different situations in the real world, four common scenarios (stillness, expression/talking, light source changes, and physical activity) are created on each 3D human. The 3D human can be built with any appearance and different skin tones. A high degree of agreement is achieved between the signals extracted from videos with the synthetic human and videos with a real human-the performance advantages and disadvantages of the selected iPPG methods are consistent for both real and 3D humans.Discussion: This technology has the capability to generate synthetic humans within various scenarios, utilizing precisely controlled parameters and disturbances. Furthermore, it holds considerable potential for testing and optimizing image-based vital signs methods in challenging situations where real people with reliable ground truth measurements are difficult to obtain, such as in drone rescue.

Wide angle star sensors are becoming more prevalent in aeronautics. A wide angle lens provides a greater field of view for star detection, but consequently incurs significant lens distortion. The effects of distortion complicate star identification, causing algorithms to fail or report false identifications. We address the issue of calibrating a wide angle star sensor without any specialized equipment, by analyzing two time-separated images captured from a static camera. An initial estimate of the focal length is obtained by observing the displacement of stars between the images. The focal length is subsequently used to build an initial estimate of camera intrinsics, and to identify stars in the image. A RANSAC-augmented Kabsch algorithm is implemented to determine camera orientation, while simultaneously removing false identifications. The identified stars are used to provide a precise estimate of camera focal length, before applying non-linear optimization in a radial search algorithm. The methodology was tested on two cameras, demonstrating the effectiveness of this algorithm in achieving a precise geometric calibration using real hardware, without any specialized calibration equipment.

Respiration rate (RR) holds significance as a human health indicator. Presently, the conventional RR monitoring system requires direct physical contact, which may cause discomfort and pain. Therefore, this paper proposes a non-contact RR monitoring system integrating RGB and thermal imaging through RGB-thermal image alignment. The proposed method employs an advanced image processing algorithm for automatic region of interest (ROI) selection. The experimental results demonstrated a close correlation and a lower error rate between measured thermal, measured RGB, and reference data. In summary, the proposed non-contact system emerges as a promising alternative to conventional contact-based approaches without the associated discomfort and pain.

Many species rely on celestial cues as a reliable guide for maintaining heading while navigating. In this paper, we propose a method that extracts the Milky Way (MW) shape as an orientation cue in low-light scenarios. We also tested the method on both real and synthetic images and demonstrate that the performance of the method appears to be accurate and reliable to motion blur that might be caused by rotational vibration and stabilisation artefacts. The technique presented achieves an angular accuracy between a minimum of 0.00° and a maximum 0.08° for real night sky images, and between a minimum of 0.22° and a maximum 1.61° for synthetic images. The imaging of the MW is largely unaffected by blur. We speculate that the use of the MW as an orientation cue has evolved because, unlike individual stars, it is resilient to motion blur caused by locomotion.

Moving in straight lines is a behaviour that enables organisms to search for food, move away from threats, and ultimately seek suitable environments in which to survive and reproduce. This study explores a vision-based technique for detecting a change in heading direction using the Milky Way (MW), one of the navigational cues that are known to be used by night-active insects. An algorithm is proposed that combines the YOLOv8m-seg model and normalised second central moments to calculate the MW orientation angle. This method addresses many likely scenarios where segmentation of the MW from the background by image thresholding or edge detection is not applicable, such as when the moon is substantial or when anthropogenic light is present. The proposed YOLOv8m-seg model achieves a segment mAP@0.5 of 84.7% on the validation dataset using our own training dataset of MW images. To explore its potential role in autonomous system applications, we compare night sky imagery and GPS heading data from a field trial in rural South Australia. The comparison results show that for short-term navigation, the segmented MW image can be used as a reliable orientation cue. There is a difference of roughly 5–10° between the proposed method and GT as the path involves left or right 90° turns at certain locations.

The development of resilience in manufacturing systems has drawn more attention than ever. Using redundant components is one of the key strategies for building and enhancing the resilience of a manufacturing system. However, current redundancy strategies require duplicated machinery employed either in active or in standby status. This in turn causes extra costs in designing and achieving resilience. Achieving an efficient deployment of the redundant component in the face of failures is also challenging. In this paper, we introduce a novel redundancy strategy, called adaptive standby redundancy (ASR), to achieve resilient performance for discrete manufacturing systems while reducing the cost of employing the duplicated components that are typically used in traditional systems. This novel strategy permits achievement of high levels of utilisation of the system and graceful degradation in case of failure, keeping the system functional. The strategy is then validated in a developed robotic flexible assembly cell (RFAC), which is tested and results on its efficacy and performance enhancement are discussed.

The diagnosis of tongue disease is based on the observation of various tongue characteristics, including color, shape, texture, and moisture, which indicate the patient’s health status. Tongue color is one such characteristic that plays a vital function in identifying diseases and the levels of progression of the ailment. With the development of computer vision systems, especially in the field of artificial intelligence, there has been important progress in acquiring, processing, and classifying tongue images. This study proposes a new imaging system to analyze and extract tongue color features at different color saturations and under different light conditions from five color space models (RGB, YcbCr, HSV, LAB, and YIQ). The proposed imaging system trained 5260 images classified with seven classes (red, yellow, green, blue, gray, white, and pink) using six machine learning algorithms, namely, the naïve Bayes (NB), support vector machine (SVM), k-nearest neighbors (KNN), decision trees (DTs), random forest (RF), and Extreme Gradient Boost (XGBoost) methods, to predict tongue color under any lighting conditions. The obtained results from the machine learning algorithms illustrated that XGBoost had the highest accuracy at 98.71%, while the NB algorithm had the lowest accuracy, with 91.43%. Based on these obtained results, the XGBoost algorithm was chosen as the classifier of the proposed imaging system and linked with a graphical user interface to predict tongue color and its related diseases in real time. Thus, this proposed imaging system opens the door for expanded tongue diagnosis within future point-of-care health systems.

In recent years, the use of renewable energy sources by many power grid companies worldwide has increased significantly. The trend towards the use of renewable energy sources is mainly due to environmental issues and rising fuel prices associated with conventional electricity generation. Distributed generation units are power generation plants that are very important for the grid architecture of today’s power system. The benefit of adding these Distributed Generation (DG) units is to increase the power supply to the grid. However, the installation of DG units can cause a negative impact if not properly allocated and/or sized. Therefore, there is a need for their optimal sizing and allocation to avoid situations such as voltage instability and high investment cost. In this paper, four heuristic based algorithms, namely Particle Swarm Optimization (PSO) algorithm, Whale Optimization Algorithm (WOA), Dolphin Echolocation Optimization (DEO), and Slime Mould Algorithm (SMA) are applied to solve the optimal placement and sizing of DG units in distribution network planning. Three cases were used to address the network problems, which are represented by adding photovoltaic cells and wind turbines individually. In the last case, both were used, and the feasibility of algorithms was confirmed for two systems, IEEE 33-bus and 69-bus test systems. The comparison results showed that the SMA algorithm produces good solutions. In general, the SMA algorithm was able to reduce the two system losses. The reduction of real power losses in SMA, taking into account the technical and economic constraints in the IEEE 33 system, is reduced to a minimum of 66.31%, 67.3%, and 81.1%, While in 69 bus, reduced to a minimum of 90.7%,91%,and 97.30% for three cases respectively, as well as improving the voltage profile, thus obtaining a more efficient system.

The growing trend of using large image datasets to support the training of computer vision algorithms in applications such as unmanned systems has seen a push towards the use of synthetic image data as an alternative source of data. Synthetic data has the potential enable the use of computer vision in applications that would previously have insufficient data to train a good model. However, the use of synthetic data to train machine learning models is not without caveats. The effectiveness of synthetic data as a training source, as compared with real data, is difficult to evaluate. Different papers that have explored the use of synthetic data have noted varying levels of effectiveness depending on the type of synthetic data tested and the type of model being trained. Outside of a general consensus that synthetic data is likely not detrimental, there is limited information available on what effect different synthesis parameters can have on the effectiveness of synthesised data. This paper evaluates the performance of composite synthetic data by training a human pose and action recognition model, investigating the effect different synthesis parameters have on a model trained using real and synthetic data.