Bootstrap geometric ground calibration method for wide angle star sensors.

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.

Estimating the cardiac signals of chimpanzees using a digital camera: validation and application of a novel non-invasive method for primate research.

Cardiac measures such as heart rate measurements are important indicators of both physiological and psychological states. However, despite their extraordinary potential, their use is restricted in comparative psychology because traditionally cardiac measures involved the attachment of sensors to the participant's body, which, in the case of undomesticated animals such as nonhuman primates, is usually only possible during anesthesia or after extensive training. Here, we validate and apply a camera-based system that enables contact-free detection of animals' heart rates. The system automatically detects and estimates the cardiac signals from cyclic change in the hue of the facial area of a chimpanzee. In Study 1, we recorded the heart rate of chimpanzees using the new technology, while simultaneously measuring heart rate using classic PPG (photoplethysmography) finger sensors. We found that both methods were in good agreement. In Study 2, we applied our new method to measure chimpanzees' heart rate in response to seeing different types of video scenes (groupmates in an agonistic interaction, conspecific strangers feeding, nature videos, etc.). Heart rates changed during video presentation, depending on the video content: Agonistic interactions and conspecific strangers feeding lead to accelerated heart rate relative to baseline, indicating increased emotional arousal. Nature videos lead to decelerated heart rate relative to baseline, indicating a relaxing effect or heightened attention caused by these stimuli. Our results show that the new contact-free technology can reliably assess the heart rate of unrestrained chimpanzees, and most likely other primates. Furthermore, our technique opens up new avenues of research within comparative psychology and facilitates the health management of captive individuals.

Simulating cardiac signals on 3D human models for photoplethysmography development.

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.

A Review of Synthetic Image Data and Its Use in Computer Vision.

Development of computer vision algorithms using convolutional neural networks and deep learning has necessitated ever greater amounts of annotated and labelled data to produce high performance models. Large, public data sets have been instrumental in pushing forward computer vision by providing the data necessary for training. However, many computer vision applications cannot rely on general image data provided in the available public datasets to train models, instead requiring labelled image data that is not readily available in the public domain on a large scale. At the same time, acquiring such data from the real world can be difficult, costly to obtain, and manual labour intensive to label in large quantities. Because of this, synthetic image data has been pushed to the forefront as a potentially faster and cheaper alternative to collecting and annotating real data. This review provides general overview of types of synthetic image data, as categorised by synthesised output, common methods of synthesising different types of image data, existing applications and logical extensions, performance of synthetic image data in different applications and the associated difficulties in assessing data performance, and areas for further research.

Sparse Optical Flow Implementation Using a Neural Network for Low-Resolution Thermal Aerial Imaging.

This study is inspired by the widely used algorithm for real-time optical flow, the sparse Lucas-Kanade, by applying a feature extractor to decrease the computational requirement of optical flow based neural networks from real-world thermal aerial imagery. Although deep-learning-based algorithms have achieved state-of-the-art accuracy and have outperformed most traditional techniques, most of them cannot be implemented on a small multi-rotor UAV due to size and weight constraints on the platform. This challenge comes from the high computational cost of these techniques, with implementations requiring an integrated graphics processing unit with a powerful on-board computer to run in real time, resulting in a larger payload and consequently shorter flight time. For navigation applications that only require a 2D optical flow vector, a dense flow field computed from a deep learning neural network contains redundant information. A feature extractor based on the Shi-Tomasi technique was used to extract only appropriate features from thermal images to compute optical flow. The state-of-the-art RAFT-s model was trained with a full image and with our proposed alternative input, showing a substantial increase in speed while maintain its accuracy in the presence of high thermal contrast where features could be detected.

Optimal Maneuvering for Autonomous Vehicle Self-Localization.

We consider the problem of optimal maneuvering, where an autonomous vehicle, an unmanned aerial vehicle (UAV) for example, must maneuver to maximize or minimize an objective function. We consider a vehicle navigating in a Global Navigation Satellite System (GNSS)-denied environment that self-localizes in two dimensions using angle-of-arrival (AOA) measurements from stationary beacons at known locations. The objective of the vehicle is to travel along the path that minimizes its position and heading estimation error. This article presents an informative path planning (IPP) algorithm that (i) uses the determinant of the self-localization estimation error covariance matrix of an unscented Kalman filter as the objective function; (ii) applies an l-step look-ahead (LSLA) algorithm to determine the optimal heading for a constant-speed vehicle. The novel algorithm takes into account the kinematic constraints of the vehicle and the AOA means of measurement. We evaluate the performance of the algorithm in five scenarios involving stationary and mobile beacons and we find the estimation error approaches the lower bound for the estimator. The simulations show the vehicle maneuvers to locations that allow for minimum estimation uncertainty, even when beacon placement is not conducive to accurate estimation.

A Comparison of Dense and Sparse Optical Flow Techniques for Low-Resolution Aerial Thermal Imagery.

It is necessary to establish the relative performance of established optical flow approaches in airborne scenarios with thermal cameras. This study investigated the performance of a dense optical flow algorithm on 14 bit radiometric images of the ground. While sparse techniques that rely on feature matching techniques perform very well with airborne thermal data in high-contrast thermal conditions, these techniques suffer in low-contrast scenes, where there are fewer detectable and distinct features in the image. On the other hand, some dense optical flow algorithms are highly amenable to parallel processing approaches compared to those that rely on tracking and feature detection. A Long-Wave Infrared (LWIR) micro-sensor and a PX4Flow optical sensor were mounted looking downwards on a drone. We compared the optical flow signals of a representative dense optical flow technique, the Image Interpolation Algorithm (I2A), to the Lucas-Kanade (LK) algorithm in OpenCV and the visible light optical flow results from the PX4Flow in both X and Y displacements. The I2A to LK was found to be generally comparable in performance and better in cold-soaked environments while suffering from the aperture problem in some scenes.

Robust Instant Angle Speed Measurement for Internal Combustion Engines-A Novel Sensing Suite and Methodology.

This paper presents the development and implementation of a novel robust sensing and measurement system that achieves fine granularity and permits new insights into operation of rotational machinery. Instant angle speed measurements offer a wealth of useful information for complex machines in which the motion is the result of multidimensional, internal, and external interactions. The implementation of the proposed system was conducted on an internal combustion engine. The internal combustion engine crankshaft's angular velocity is the result of the integration of all variables of motor and resisting forces. The crankshaft angular velocity variation also reflects the interaction between the internal thermodynamic cycle of the engine and the plant it powers. To minimise the number of variables, we used for our experiments an aero piston engine for small air-vehicles-a well-made and reliable powerplant-connected to a propeller. This paper presents the need for a better sensing and measurement system. Then, we show the development of the system, the measurement protocol and process, recording and analysis of the data, and results of some experiments. We then demonstrate the possibilities this sensing suite can achieve-a deeper insight into the operation of the machine-by performing high-quality analyses of engine cycles, well beyond capabilities in the state of the art. This system can be generalised for other rotational machines and equipment.

A Review of Modern Thermal Imaging Sensor Technology and Applications for Autonomous Aerial Navigation.

Limited navigation capabilities of many current robots and UAVs restricts their applications in GPS denied areas. Large aircraft with complex navigation systems rely on a variety of sensors including radio frequency aids and high performance inertial systems rendering them somewhat resistant to GPS denial. The rapid development of computer vision has seen cameras incorporated into small drones. Vision-based systems, consisting of one or more cameras, could arguably satisfy both size and weight constraints faced by UAVs. A new generation of thermal sensors is available that are lighter, smaller and widely available. Thermal sensors are a solution to enable navigation in difficult environments, including in low-light, dust or smoke. The purpose of this paper is to present a comprehensive literature review of thermal sensors integrated into navigation systems. Furthermore, the physics and characteristics of thermal sensors will also be presented to provide insight into challenges when integrating thermal sensors in place of conventional visual spectrum sensors.

A Low Redundancy Wavelet Entropy Edge Detection Algorithm.

Fast edge detection of images can be useful for many real-world applications. Edge detection is not an end application but often the first step of a computer vision application. Therefore, fast and simple edge detection techniques are important for efficient image processing. In this work, we propose a new edge detection algorithm using a combination of the wavelet transform, Shannon entropy and thresholding. The new algorithm is based on the concept that each Wavelet decomposition level has an assumed level of structure that enables the use of Shannon entropy as a measure of global image structure. The proposed algorithm is developed mathematically and compared to five popular edge detection algorithms. The results show that our solution is low redundancy, noise resilient, and well suited to real-time image processing applications.

Noncontact Sensing of Contagion.

The World Health Organization (WHO) has declared COVID-19 a pandemic. We review and reduce the clinical literature on diagnosis of COVID-19 through symptoms that might be remotely detected as of early May 2020. Vital signs associated with respiratory distress and fever, coughing, and visible infections have been reported. Fever screening by temperature monitoring is currently popular. However, improved noncontact detection is sought. Vital signs including heart rate and respiratory rate are affected by the condition. Cough, fatigue, and visible infections are also reported as common symptoms. There are non-contact methods for measuring vital signs remotely that have been shown to have acceptable accuracy, reliability, and practicality in some settings. Each has its pros and cons and may perform well in some challenges but be inadequate in others. Our review shows that visible spectrum and thermal spectrum cameras offer the best options for truly noncontact sensing of those studied to date, thermal cameras due to their potential to measure all likely symptoms on a single camera, especially temperature, and video cameras due to their availability, cost, adaptability, and compatibility. Substantial supply chain disruptions during the pandemic and the widespread nature of the problem means that cost-effectiveness and availability are important considerations.

Non-Contact Automatic Vital Signs Monitoring of Infants in a Neonatal Intensive Care Unit Based on Neural Networks.

Infants with fragile skin are patients who would benefit from non-contact vital sign monitoring due to the avoidance of potentially harmful adhesive electrodes and cables. Non-contact vital signs monitoring has been studied in clinical settings in recent decades. However, studies on infants in the Neonatal Intensive Care Unit (NICU) are still limited. Therefore, we conducted a single-center study to remotely monitor the heart rate (HR) and respiratory rate (RR) of seven infants in NICU using a digital camera. The region of interest (ROI) was automatically selected using a convolutional neural network and signal decomposition was used to minimize the noise artefacts. The experimental results have been validated with the reference data obtained from an ECG monitor. They showed a strong correlation using the Pearson correlation coefficients (PCC) of 0.9864 and 0.9453 for HR and RR, respectively, and a lower error rate with RMSE 2.23 beats/min and 2.69 breaths/min between measured data and reference data. A Bland-Altman analysis of the data also presented a close correlation between measured data and reference data for both HR and RR. Therefore, this technique may be applicable in clinical environments as an economical, non-contact, and easily deployable monitoring system, and it also represents a potential application in home health monitoring.

Soil color analysis based on a RGB camera and an artificial neural network towards smart irrigation: A pilot study.

Irrigation operations in agriculture are one of the largest water consumers in the world, and it has been increasing due to rising population and consequent increased demand for food. The development of advanced irrigation technologies based on modern techniques is of utmost necessity to ensure efficient use of water. Smart irrigation based on computer vision could help in achieving optimum water-utilization in agriculture using a highly available digital technology. This paper presents a non-contact vision system based on a standard video camera to predict the irrigation requirements for loam soils using a feed-forward back propagation neural network. The study relies on analyzing the differences in soil color captured by a video camera at different distances, times and illumination levels obtained from loam soil over four weeks of data acquisition. The proposed system used this color information as input to an artificial neural network (ANN) system to make a decision as to whether to irrigate the soil or not. The proposed system was very accurate, achieving a mean square error (MSE) of 1.616 × 10-6 (training), 1.004 × 10-5 (testing) and 1.809 × 10-5 (validation). The proposed system is simple, robust and affordable making it promising technology to support precision agriculture.

Efficient flapping wing drone arrests high-speed flight using post-stall soaring.

The aerobatic maneuvers of swifts could be very useful for micro aerial vehicle missions. Rapid arrests and turns would allow flight in cluttered and unstructured spaces. However, these decelerating aerobatic maneuvers have been difficult to demonstrate in flapping wing craft to date because of limited thrust and control authority. Here, we report a 26-gram X-wing ornithopter of 200-millimeter fuselage length capable of multimodal flight. Using tail elevation and high thrust, the ornithopter was piloted to hover, fly fast forward (dart), turn aerobatically, and dive with smooth transitions. The aerobatic turn was achieved within a 32-millimeter radius by stopping a dart with a maximum deceleration of 31.4 meters per second squared. In this soaring maneuver, braking was possible by rapid body pitch and dynamic stall of wings at relatively high air speed. This ornithopter can recover to glide stability without tumbling after a 90-degree body flip. We showed that the tail presented a strong stabilizing moment under high thrust, whereas the wing membrane flexibility alleviated the destabilizing effect of the forewings. To achieve these demands for high thrust, we developed a low-loss anti-whirl transmission that maximized thrust output by the flapping wings to 40 grams in excess of body weight. By reducing the reactive load and whirl, this indirect drive consumed 40% less maximum electrical power for the same thrust generation than direct drive of a propeller. The triple roles of flapping wings for propulsion, lift, and drag enable the performance of aggressive flight by simple tail control.

Hybrid Coils-Based Wireless Power Transfer for Intelligent Sensors.

Most wearable intelligent biomedical sensors are battery-powered. The batteries are large and relatively heavy, adding to the volume of wearable sensors, especially when implanted. In addition, the batteries have limited capacity, requiring periodic charging, as well as a limited life, requiring potentially invasive replacement. This paper aims to design and implement a prototype energy harvesting technique based on wireless power transfer/magnetic resonator coupling (WPT/MRC) to overcome the battery power problem by supplying adequate power for a heart rate sensor. We optimized transfer power and efficiency at different distances between transmitter and receiver coils. The proposed MRC consists of three units: power, measurement, and monitoring. The power unit included transmitter and receiver coils. The measurement unit consisted of an Arduino Nano microcontroller, a heart rate sensor, and used the nRF24L01 wireless protocol. The experimental monitoring unit was supported by a laptop to monitor the heart rate measurement in real-time. Three coil topologies: spiral-spiral, spider-spider, and spiral-spider were implemented for testing. These topologies were examined to explore which would be the best for the application by providing the highest transfer power and efficiency. The spiral-spider topology achieved the highest transfer power and efficiency with 10 W at 87%, respectively over a 5 cm air gap between transmitter and receiver coils when a 200 Ω resistive load was considered. Whereas, the spider-spider topology accomplished 7 W and 93% transfer power and efficiency at the same airgap and resistive load. The proposed topologies were superior to previous studies in terms of transfer power, efficiency and distance.

Experimental method for 3D reconstruction of Odonata wings (methodology and dataset).

Insect wings are highly evolved structures with aerodynamic and structural properties that are not fully understood or systematically modeled. Most species in the insect order Odonata have permanently deployed high aspect ratio wings. Odonata have been documented to exhibit extraordinary flight performance and a wide range of interesting flight behaviors that rely on agility and efficiency. The characteristic three-dimensional corrugated structures of these wings have been observed and modeled for a small number of species, with studies showing that corrugations can provide significant aerodynamic and structural advantages. Comprehensive museum collections are the most practical source of Odonata wing, despite the risk of adverse effects caused by dehydration and preservation of specimens. Museum specimens are not to be handled or damaged and are best left undisturbed in their display enclosures. We have undertaken a systematic process of scanning, modeling, and post-processing the wings of over 80 Odonata species using a novel and accurate method and apparatus we developed for this purpose. The method allows the samples to stay inside their glass cases if necessary and is non-destructive. The measurements taken have been validated against micro-computed tomography scanning and against similar-sized objects with measured dimensions. The resulting publicly available dataset will allow aeronautical analysis of Odonata aerodynamics and structures, the study of the evolution of functional structures, and research into insect ecology. The technique is useable for other orders of insects and other fragile samples.

Hand Gesture Recognition Based on Computer Vision: A Review of Techniques.

Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf-mute people, robot control, human-computer interaction (HCI), home automation and medical applications. Research papers based on hand gestures have adopted many different techniques, including those based on instrumented sensor technology and computer vision. In other words, the hand sign can be classified under many headings, such as posture and gesture, as well as dynamic and static, or a hybrid of the two. This paper focuses on a review of the literature on hand gesture techniques and introduces their merits and limitations under different circumstances. In addition, it tabulates the performance of these methods, focusing on computer vision techniques that deal with the similarity and difference points, technique of hand segmentation used, classification algorithms and drawbacks, number and types of gestures, dataset used, detection range (distance) and type of camera used. This paper is a thorough general overview of hand gesture methods with a brief discussion of some possible applications.

A Pilot Study for Estimating the Cardiopulmonary Signals of Diverse Exotic Animals Using a Digital Camera.

Monitoring the cardiopulmonary signal of animals is a challenge for veterinarians in conditions when contact with a conscious animal is inconvenient, difficult, damaging, distressing or dangerous to personnel or the animal subject. In this pilot study, we demonstrate a computer vision-based system and use examples of exotic, untamed species to demonstrate this means to extract the cardiopulmonary signal. Subject animals included the following species: Giant panda (Ailuropoda melanoleuca), African lions (Panthera leo), Sumatran tiger (Panthera tigris sumatrae), koala (Phascolarctos cinereus), red kangaroo (Macropus rufus), alpaca (Vicugna pacos), little blue penguin (Eudyptula minor), Sumatran orangutan (Pongo abelii) and Hamadryas baboon (Papio hamadryas). The study was done without need for restriction, fixation, contact or disruption of the daily routine of the subjects. The pilot system extracts the signal from the abdominal-thoracic region, where cardiopulmonary activity is most likely to be visible using image sequences captured by a digital camera. The results show motion on the body surface of the subjects that is characteristic of cardiopulmonary activity and is likely to be useful to estimate physiological parameters (pulse rate and breathing rate) of animals without any physical contact. The results of the study suggest that a fully controlled study against conventional physiological monitoring equipment is ethically warranted, which may lead to a novel approach to non-contact physiological monitoring and remotely sensed health assessment of animals. The method shows promise for applications in veterinary practice, conservation and game management, animal welfare and zoological and behavioral studies.

Energy-Efficient Elderly Fall Detection System Based on Power Reduction and Wireless Power Transfer.

Elderly fall detection systems based on wireless body area sensor networks (WBSNs) have increased significantly in medical contexts. The power consumption of such systems is a critical issue influencing the overall practicality of the WBSN. Reducing the power consumption of these networks while maintaining acceptable performance poses a challenge. Several power reduction techniques can be employed to tackle this issue. A human vital signs monitoring system (HVSMS) has been proposed here to measure vital parameters of the elderly, including heart rate and fall detection based on heartbeat and accelerometer sensors, respectively. In addition, the location of elderly people can be determined based on Global Positioning System (GPS) and transmitted with their vital parameters to emergency medical centers (EMCs) via the Global System for Mobile Communications (GSM) network. In this paper, the power consumption of the proposed HVSMS was minimized by merging a data-event (DE) algorithm and an energy-harvesting-technique-based wireless power transfer (WPT). The DE algorithm improved HVSMS power consumption, utilizing the duty cycle of the sleep/wake mode. The WPT successfully charged the HVSMS battery. The results demonstrated that the proposed DE algorithm reduced the current consumption of the HVSMS to 9.35 mA compared to traditional operation at 85.85 mA. Thus, an 89% power saving was achieved based on the DE algorithm and the battery life was extended to 30 days instead of 3 days (traditional operation). In addition, the WPT was able to charge the HVSMS batteries once every 30 days for 10 h, thus eliminating existing restrictions involving the use of wire charging methods. The results indicate that the HVSMS current consumption outperformed existing solutions from previous studies.

Noncontact Heart and Respiratory Rate Monitoring of Preterm Infants Based on a Computer Vision System: Protocol for a Method Comparison Study.

BACKGROUND: Biomedical research in the application of noncontact methods to measure heart rate (HR) and respiratory rate (RR) in the neonatal population has produced mixed results. This paper describes and discusses a protocol for conducting a method comparison study, which aims to determine the accuracy of a proposed noncontact computer vision system to detect HR and RR relative to the HR and RR obtained by 3-lead electrocardiogram (ECG) in preterm infants in the neonatal unit. OBJECTIVE: The aim of this preliminary study is to determine the accuracy of a proposed noncontact computer vision system to detect HR and RR relative to the HR and RR obtained by 3-lead ECG in preterm infants in the neonatal unit. METHODS: A single-center cross-sectional study was planned to be conducted in the neonatal unit at Flinders Medical Centre, South Australia, in May 2018. A total of 10 neonates and their ECG monitors will be filmed concurrently for 10 min using digital cameras. Advanced image processing techniques are to be applied later to determine their physiological data at 3 intervals. These data will then be compared with the ECG readings at the same points in time. RESULTS: Study enrolment began in May 2018. Results of this study were published in July 2019. CONCLUSIONS: The study will analyze the data obtained by the noncontact system in comparison to data obtained by ECG, identify factors that may influence data extraction and accuracy when filming infants, and provide recommendations for how this noncontact system may be implemented into clinical applications. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/13400.