Addressing environmental and atmospheric challenges for capturing high-precision thermal infrared data in the field of astro-ecology

Using thermal infrared detectors mounted on drones, and applying techniques from astrophysics, we hope to support the field of conservation ecology by creating an automated pipeline for the detection and identification of certain endangered species and poachers from thermal infrared data. We test part of our system by attempting to detect simulated poachers in the field. Whilst we find that we can detect humans hiding in the field in some types of terrain, we also find several environmental factors that prevent accurate detection, such as ambient heat from the ground, absorption of infrared emission by the atmosphere, obscuring vegetation and spurious sources from the terrain. We discuss the effect of these issues, and potential solutions which will be required for our future vision for a fully automated drone-based global conservation monitoring system.Comment: Published in Proceedings of SPIE Astronomical Telescopes and Instrumentation 2018. 8 pages, 3 figure

Near-infrared synchrotron emission from the compact jet of GX339-4

We have compiled contemporaneous broadband observations of the black hole candidate X-ray binary GX 339-4 when in the low/hard X-ray state in 1981 and 1997. The data clearly reveal the presence of two spectral components, with thermal and non-thermal spectra, overlapping in the optical -- near-infrared bands. The non-thermal component lies on an extrapolation of the radio spectrum of the source, and we interpret it as optically thin synchrotron emission from the powerful, compact jet in the system. Detection of this break from self-absorbed to optically thin synchrotron emission from the jet allows us to place a firm lower limit on the ratio of jet (synchrotron) to X-ray luminosities of $\geq 5$%. We further note that extrapolation of the optically thin synchrotron component from the near-infrared to higher frequencies coincides with the observed X-ray spectrum, supporting models in which the X-rays could originate via optically thin synchrotron emission from the jet (possibly instead of Comptonisation).Comment: Accepted for publication in ApJ Lette

Addressing environmental and atmospheric challenges for capturing high-precision thermal infrared data in the field of astro-ecology

Using thermal infrared detectors mounted on drones, and applying techniques from astrophysics, we hope to support the field of conservation ecology by creating an automated pipeline for the detection and identification of certain endangered species and poachers from thermal infrared data. We test part of our system by attempting to detect simulated poachers in the field. Whilst we find that we can detect humans hiding in the field in some types of terrain, we also find several environmental factors that prevent accurate detection, such as ambient heat from the ground, absorption of infrared emission by the atmosphere, obscuring vegetation and spurious sources from the terrain. We discuss the effect of these issues, and potential solutions which will be required for our future vision for a fully automated drone-based global conservation monitoring system

Toward DMD Illuminated Spatial-Temporal Modulated Thermography

This paper reports on a system using a Digital Micromirror Device (DMD) to modulate a near-infrared laser source spatially and temporally. The DMD can produce an arbitrary heat source varying both spatially and temporally over the target. When the thermal response of the target surface is recorded using a thermal imager, this provides new possibilities in subsurface defect detection, partially with regard to features whose orientation does not allow them to be resolved using conventional thermographic inspection techniques. In this respect it is similar to conventional focused spot detection approaches; however, the DMD allows the signal to be frequency/phase multiplexed which provides for simultaneous interrogation over a large area. The parallel nature of the process permits a longer inspection time at each point which has signal-to-noise benefits. Preliminary experiments demonstrating the multiplexing approach are presented using a low-cost thermal imager. A NIR laser is spatially and temporary modulated to generated multiple thermal line sources on the surface of a composite circuit board. The infrared response is demodulated point-by-point at each drive frequency. This permits the thermal response from each line source to be resolved individually. Beyond damage detection the approach also has applications to system identification. Initial limitations due to the test setup are discussed along with future system improvements

An examination of thermal features' relevance in the task of battery-fault detection

Uninterruptible power supplies (UPS), represented by lead-acid batteries, play an important role in various kinds of industries. They protect industrial technologies from being damaged by dangerous interruptions of an electric power supply. Advanced UPS monitoring performed by a complex battery management system (BMS) prevents the UPS from sustaining more serious damage due to its timely and accurate battery-fault detection based on voltage metering. This technique is very advanced and precise but also very expensive on a long-term basis. This article describes an experiment applying infrared thermographic measurements during a long term monitoring and fault detection in UPS. The assumption that the battery overheat implies its damaged state is the leading factor of our experiments. They are based on real measured data on various UPS battery sets and several statistical examinations confirming the high relevancy of the thermal features with mostly over 90% detection accuracy. Such a model can be used as a supplement for lead-acid battery based UPS monitoring to ensure their higher reliability under significantly lower maintenance costs.Web of Science82art. no. 18

Volcanic Hot-Spot Detection Using SENTINEL-2: A Comparison with MODIS−MIROVA Thermal Data Series

In the satellite thermal remote sensing, the new generation of sensors with high-spatial resolution SWIR data open the door to an improved constraining of thermal phenomena related to volcanic processes, with strong implications for monitoring applications. In this paper, we describe a new hot-spot detection algorithm developed for SENTINEL-2/MSI data that combines spectral indices on the SWIR bands 8a-11-12 (with a 20-meter resolution) with a spatial and statistical analysis on clusters of alerted pixels. The algorithm is able to detect hot-spot-contaminated pixels (S2Pix) in a wide range of environments and for several types of volcanic activities, showing high accuracy performances of about 1% and 94% in averaged omission and commission rates, respectively, underlining a strong reliability on a global scale. The S2-derived thermal trends, retrieved at eight key-case volcanoes, are then compared with the Volcanic Radiative Power (VRP) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) and processed by the MIROVA (Middle InfraRed Observation of Volcanic Activity) system during an almost four-year-long period, January 2016 to October 2019. The presented data indicate an overall excellent correlation between the two thermal signals, enhancing the higher sensitivity of SENTINEL-2 to detect subtle, low-temperature thermal signals. Moreover, for each case we explore the specific relationship between S2Pix and VRP showing how different volcanic processes (i.e., lava flows, domes, lakes and open-vent activity) produce a distinct pattern in terms of size and intensity of the thermal anomaly. These promising results indicate how the algorithm here presented could be applicable for volcanic monitoring purposes and integrated into operational systems. Moreover, the combination of high-resolution (S2/MSI) and moderate-resolution (MODIS) thermal timeseries constitutes a breakthrough for future multi-sensor hot-spot detection systems, with increased monitoring capabilities that are useful for communities which interact with active volcanoes

COMPUTER-AIDED QUANTITATIVE EARLY DIAGNOSIS OF DIABETIC FOOT

Diabetes is an incurable metabolic disease characterized by high blood sugar levels. The feet of people with diabetes are at the risk of a variety of pathological consequences including peripheral vascular disease, deformity, ulceration, and ultimately amputation. The key to managing the diabetic foot is prevention and early detection. Unfortunately, current hospital centered reactive diabetes care and the availability of inadequate qualitative diagnostic screening procedures causes physicians to miss the diagnosis in 61% of the patients. We have developed a computer aided diagnostic system for early detection of diabetic foot. The key idea is that diabetic foot exhibits significant neuropathic and vascular damages. When a diabetic foot is placed under cold stress, the thermal recovery will be much slower. This thermal recovery speed can be a quantitative measure for the diagnosis of diabetic foot condition. In our research, thermal recovery of the feet following cold stress is captured using an infrared camera. The captured infrared video is then filtered, segmented, and registered. The temperature recovery at each point on the foot is extracted and analyzed using a thermal regulation model, and the problematic regions are identified. In this thesis, we present our research on the following aspects of the developed computer aided diagnostic systems: subject measurement protocols, a trustful numerical model of the camera noise and noise parameter estimations, infrared video segmentation, new models of thermal regulations, thermal patterns classifications, and our preliminary findings based on small scale clinical study of about 40 subjects, which demonstrated the potential the new diagnostic system

Emerging thermal imaging techniques for seed quality evaluation: Principles and applications

Due to the massive progress occurred in the past few decades in imaging, electronics and computer science, infrared thermal imaging technique has witnessed numerous technological advancement and smart applications in non-destructive testing and quality monitoring of different agro-food produces. Thermal imaging offers a potential non-contact imaging modality for the determination of various quality traits based on the infrared radiation emitted from target foods. The technique has been moved from just an exploration method in engineering and astronomy into an effective tool in many fields for forming unambiguous images called thermograms eventuated from the temperature and thermal properties of the target objects. It depends principally on converting the invisible infrared radiation emitted by the objects into visible two-dimensional temperature data without making a direct contact with the examined objects. This method has been widely used for different applications in agriculture and food science and technology with special applications in seed quality assessment. This article provides an overview of thermal imaging theory, briefly describes the fundamentals of the system and explores the recent advances and research works conducted in quality evaluation of different sorts of seeds. The article comprehensively reviewed research efforts of using thermal imaging systems in seed applications including estimation of seed viability, detection of fungal growth and insect infections, detection of seed damage and impurities, seed classification and variety identification.info:eu-repo/semantics/acceptedVersio