Search results

This paper aims to present a novel localization scheme using infrared light reflecting artificial landmarks.

By putting the infrared light reflecting landmarks on the ceiling, localization is achieved. The landmark is designed for effective recognition and identification. From the difference of two successive images, one with the infrared light illumination and the other without, the landmarks are clearly identified.

From the mathematical analysis, a greater of landmarks are required if the robot's tilt angles are not known. With the camera's pan/tilt angles information, the distortion of the image can be corrected and fewer landmarks are required. Movement of the camera while getting two successive images is modeled and is compensated.

Thorough analysis of practical issues such as capturing the image from a non‐flat floor, pan/tilt motion of the camera and movement of the camera is presented.

This study aims to evaluate the thermal performance of sodium alginate (SA) aerogel attached to nano SiO₂ and its radiative cooling effect on firefighting clothing without environmental pollution.

SA/SiO₂ aerogel with refractory heat insulation and enhanced radiative cooling performance was fabricated by freeze-drying method, which can be used in firefighting clothing. The microstructure, chemical composition, thermal stability, and thermal emissivity were analyzed using Fourier transform infrared spectroscopy, scanning electron microscopy, thermogravimetric analyzer and infrared emissivity measurement instrument. The radiative cooling effect of aerogel was studied using thermal infrared imager and thermocouple.

When the addition of SiO₂ is 25% of SA, the prepared aerogel has excellent heat insulation and a high radiative cooling effect. Under a clear sky, the temperature of SA/SiO₂ aerogel is 9.4°C lower than that of pure SA aerogel and 22.1°C lower than that of the simulated environment. In addition, aerogel has more exceptional heat insulation effect than other common fabrics in the heat insulation performance test.

SA/SiO₂ aerogel has passive radiative cooling function, which can efficaciously economize global energy, and it is paramount to environment-friendly cooling.

This method could pave the way for high-performance cooling materials designed for firefighting clothing to keep maintain the wearing comfort of firefighters.

SA/SiO₂ aerogel used in firefighting clothing can release heat to the low-temperature outer space in the form of thermal radiation to achieve its own cooling purpose, without additional energy supply.

Purpose – This paper proposes a new procedure for measuring affective responses during social interaction using facial thermographic imaging.

Methodology – We first describe the results of several small pilot experiments designed to develop and refine this new measure that reveal some of the methodological advantages and challenges offered by this measurement approach. We then demonstrate the potential utility of this measure using data from a laboratory experiment (N=114) in which we used performance feedback to manipulate identity deflection and measured several types of affective responses – including self-impressions and emotions.

Findings – We find warming of the brow (near the corrugator muscle) and cheek (near the zygomatic major muscle) related most strongly to emotion valence and self-potency, with those whose brows and cheeks warmed the most feeling less positive emotion and less potent self-impressions. Warming in the eye area (near the orbicularis oculi) related most closely to undirected identity deflection and to positive self-sentiments. Positive self-views and strong identity disruptions both contributed to warming of the eyes.

Implications – The rigor of contemporary sociological theories of emotion exceeds our current ability to empirically test these theories. Facial thermographic imaging may offer sociologists new assessments of affect and emotion that are ecologically valid, socially unreactive, temporally sensitive, and accurate. This could dramatically improve our ability to test and develop affect based theories of social interaction.

A ‘no‐wire’ infrared thermometer from Land Infrared has been designed for use in ‘unsafe’ environments.

Describes the basic optical concepts of infrared gas detection in relation to industrial applications and considers the electro optic components at the heart of the analyzers, the infrared detectors themselves. Discusses the choice of detectors for a given application and looks at the pros and cons of thermal detectors and quantum detectors. Notes the possible need to cool quantum detectors to improve their signal to noise and responsivity characteristics. Concludes with the possibility for custom configurations and lists the various application of infrared gas detection including uses in the transport industry, petrochemical industry and hospitals.

This paper aims to characterize the mineral composition of Martian surfaces based on Thermal Emission Spectrometer (TES; Mars Global Surveyor) as measured in the infrared thermal range. It presents modeling and interpreting of TES spectral data from selected Martian regions from which the atmospheric influences had been removed using radiative transfer algorithm and deconvolution algorithm. The spectra from the dark area of Cimmeria Terra and the bright Isidis Planitia were developed in Philip Christensen’s and Joshua Bandfield’s publications, where these spectra were subjected to spectral deconvolution to estimate the mineral composition of the Martian surface. The results of the analyses of these spectra were used for the modeling of dusty and non-dusty surface of Mars. As an additional source, the mineral compositions of Polish basalts and mafic rocks were used for these surfaces as well as for modeling Martian meteorites Shergottites, Nakhlites and Chassignites. Finally, the spectra for the modeling of the Hellas region were obtained from the Planetary Fourier Spectrometer (PFS) – (Mars Express) and the mineralogical compositions of basalts from the southern part of Poland were used for this purpose. The Hellas region was modeled also using simulated Martian soil samples Phyllosilicatic Mars Regolith Simulant and Sulfatic Mars Regolith Simulant, showing as a result that the composition of this selected area has a high content of sulfates. Linear spectral combination was chosen as the best modeling method. The modeling was performed using PFSLook software written in the Space Research Centre of the Polish Academy of Sciences. Additional measurements were made with an infrared spectrometer in thermal infrared spectroscopy, for comparison with the measurements of PFS and TES. The research uses a kind of modeling that successfully matches mineralogical composition to the measured spectrum from the surface of Mars, which is the main goal of the publication. This method is used for areas where sample collection is not yet possible. The areas have been chosen based on public availability of the data.

The infrared spectra of the Martian surface were modeled by applying the linear combination of the spectra of selected minerals, which then are normalized against the measured surface area with previously separated atmosphere. The minerals for modeling are selected based on the expected composition of the Martian rocks, such as basalt. The software used for this purpose was PFSLook, a program written in C++ at the Space Research Centre of the Polish Academy of Sciences, which is based on adding the spectra of minerals in the relevant percentage, resulting in a final spectrum containing 100 per cent of the minerals.

The results of this work confirmed that there is a relationship between the modeled, altered and unaltered, basaltic surface and the measured spectrum from Martian instruments. Spectral deconvolution makes it possible to interpret the measured spectra from areas that are potentially difficult to explore or to choose interesting areas to explore on site. The method is described for mid-infrared because of software availability, but it can be successfully applied to shortwave spectra in near-infrared (NIR) band for data from the currently functioning Martian spectroscopes.

This work is the only one attempting modeling the spectra of the surface of Mars with a separated atmosphere and to determine the mineralogical composition.

The purpose of this paper is to develop a system to analyse the characteristics of infrared objects.

According to the gray scale of image pixel by the image entropy, gray scale estimating is carries on to construct the neural networks. And then the grey relational analysis and grey clustering methods are applied to filter the possible object. The target is predicted through image segmentation pretreatment based on the forecasting value by grey system and assigned corresponding mark. The forecasting precision is greatly elevated by GM (1, 1) model.

The paper illustrates that, based on the analysis and its experimental results, this system has a good recognition rate for infrared target.

This paper provides a way to grasp the minutial feature of the image. The filtering operation based on pixel level provided auto‐adapted filtering with a new stage.

Applications of grey theory deepened the content of detecting infrared targets and enriched technology of image processing.

This system introduces an effective method for detecting infrared targets.

To present the specifications and the technologies underpinning some new high‐speed cameras, and explore their applications.

Describes the industrial, military and other applications of high‐speed imaging. Traces the recent technical developments in sensors for visible and infrared light. Then describes some specific high‐speed cameras.

CMOS sensors have now taken over from CCD in high‐speed visible cameras. Microbolometers have made low‐cost uncooled cameras available for infrared imaging. Applications range from serious research and development topics, such as air‐bag and seat‐belt performance and combustion studies, to investigations of natural phenomena and analysis of sports equipment.

Monitors the progress of imaging technology and describes its current capabilities to the interested reader.

A low cost two dimensional pyroelectric array technology has been developed, together with advanced signal processing algorithms. This enables the availability of intelligent passive infrared detector systems for surveillance, counting, and related applications. The technology has also made possible for the first time a truly affordable low‐resolution thermal imager for condition monitoring, plant maintenance, and process control.

This paper aims to investigate the self-alignment of 0603 size (1.5 × 0.75 mm) chip resistors, which were soldered by infrared or vapour phase soldering. The results were used for establishing an artificial neural network for predicting the component movement during the soldering.

The components were soldered onto an FR4 testboard, which was designed to facilitate the measuring of the position of the components both prior to and after the soldering. A semi-automatic placement machine misplaced the components intentionally, and the self-alignment ability was determined for soldering techniques of both infrared and vapour phase soldering. An artificial neural network-based prediction method was established, which is able to predict the position of chip resistors after soldering as a function of component misplacement prior to soldering.

The results showed that the component can self-align from farer distances by using vapour phase method, even from relative misplacement of 50 per cent parallel to the shorter side of the component. Components can self-align from a relative misplacement only of 30 per cent by using infrared soldering method. The established artificial neural network can predict the component self-alignment with an approximately 10-20 per cent mean absolute error.

It was proven that the vapour phase soldering method is more stable from the component’s self-alignment point of view. Furthermore, machine learning-based predictors can be applied in the field of reflow soldering technology, and artificial neural networks can predict the component self-alignment with an appropriately low error.