Search or ask a question

What are the types of infrared?

Spectral imaging

Chemical imaging

Full spectral imaging

Hyperspectral imaging

Best insight from top research papers

Infrared radiation can be classified into different types based on its characteristics. One type of infrared is long wave infrared, which is characterized by large long wave excesses and is found in certain infrared stars like NML Cygni and NML Tauri . Another type is mid-infrared, which has a wavelength range of 3-5 µm and is used for advanced detection and imaging purposes using type-II InAs/GaSb superlattice . Additionally, there is also narrowband, linear variable filter, transmission grating, and hyperspectral infrared spectral imaging systems . These different types of infrared have their own unique properties and applications in various fields such as astronomy, spectroscopy, and infrared detection and imaging.

Answers from top 5 papers

PDF

Open Access

More filters

Papers (5)	Insight
Journal Article•DOI Advances in mid-infrared detection and imaging: a key issues review. Manijeh Razeghi, Binh-Minh Nguyen - Show less +1 more 01 Aug 2014-Reports on Progress in Physics 107 Citations	The paper does not explicitly mention the types of infrared. The paper discusses the use of type-II InAs/GaSb superlattice for infrared detection and imaging.
Open access•Journal Article•DOI New infrared spectral indices of luminous cold stars: from early K to M-types Maria Messineo, Donald F. Figer, Rolf-Peter Kudritzki, Qingfeng Zhu, Karl M. Menten, Valentin D. Ivanov, C.-H. Rosie Chen - Show less +6 more 08 Jul 2021-arXiv: Solar and Stellar Astrophysics 6 Citations	The types of infrared are not mentioned in the paper. The paper is about the infrared spectral indices of late-type giants and red supergiants.
Journal Article•DOI Indentifications and spectra of infrared objects Susan Wyckoff, Peter Wehinger - Show less +1 more 01 Aug 1974-Publications of the Astronomical Society of the Pacific	The paper does not provide information about the types of infrared. The paper is about the identification and spectra of infrared objects.
Proceedings Article•DOI Infrared (3 to 12 μm) narrowband and hyperspectral imaging review Mark C. Norton, Randy Kindsfather, Roberta Dixon - Show less +2 more 12 Jun 1995 4 Citations	The paper does not explicitly mention the types of infrared.
Journal Article•DOI Cygnids and Taurids - Two classes of infrared objects. D. W. Strecker, Edward P. Ney, T. L. Murdock - Show less +2 more 01 Jul 1973-The Astrophysical Journal 4 Citations	The paper does not explicitly mention the types of infrared. It only mentions two classes of infrared objects, namely the Cygnids and Taurids.

My columns

Related Questions

What are the types of radiation?3 answersRadiation can be categorized into two types: ionizing and non-ionizing. Ionizing radiation includes alpha, beta, and gamma radiation, which are emitted by radioactive substances. Alpha radiation consists of alpha particles, which are nuclei of helium atoms made up of two protons and two neutrons. Beta radiation consists of electrons or positrons, and gamma radiation is a form of high-energy electromagnetic waves. Non-ionizing radiation includes light radiation and radio radiation. Light radiation is due to the aberration of the electric field in the motion of a charged particle, while radio radiation is generated by an oscillating charged particle that generates a magnetic field in phase with an electric field. These types of radiation have various applications in fields such as medical diagnosis and treatment, radiation processing, and research in radiation chemistry.

What are the infrared radiation examples?5 answersInfrared radiation examples include: an infrared radiation device with an emissive layer and heaters for reducing the spectral extent of infrared radiation; an infrared radiation device with an infrared emission circuit, an infrared receiving circuit, and a wireless radio frequency circuit for convenient mounting and use; electronic devices for infrared radiation detection with a silicon-germanium structural layer deposited on a substrate; an infrared radiation device with a radiation portion, a reflecting portion, and an emitting portion for emitting infrared radiation with a non-Planck distribution; and an infrared radiation assembly with an infrared heating tube, an oscillating plate, and a reflector for generating far infrared rays and negative oxygen ions.

Do flies see infrared?5 answersFlies do not see infrared. Their vision is primarily based on motion detection and processing higher-order image statistics, such as local contrast or texture. Flies have remarkable vision, with compound eyes that are superbly adapted to their high-speed lifestyle. They rely on a combination of high-performance vision and a specialized sensory gyroscope to stay on course. Fruit flies also have an intricate neural pathway to process polarized light signals, which helps inform their internal compass about the position of the Sun. However, there is no mention of flies being able to see infrared in any of the provided abstracts.

What is the use of infrared spectroscopy?5 answersInfrared spectroscopy is a technique used to identify and determine components in various organic or inorganic compounds. It is commonly used in qualitative and quantitative analyses, including the estimation of compounds through measuring the transmitted or absorption intensity of active groups. In the field of heterogeneous catalysis research and development, infrared spectroscopy is used for structural characterization, such as identifying surface metal-oxygen bonds and surface acid sites. Additionally, infrared spectroscopy can be used to automatically identify functional groups in molecules without the need for database searching or peak matching methods. The technique is also powerful for structural analysis, as it allows for the identification of functional groups in solids, liquids, or gases by analyzing obtained spectra. In the analysis of food toxins, infrared spectroscopy is a non-destructive and rapid method for the detection and characterization of contaminants in high throughput.

What is infrared?3 answersInfrared (IR) is a form of electromagnetic radiation with longer wavelengths than those of visible light. It is created when objects are heated at temperatures not quite high enough to make them glow. IR radiation can be detected by its heating effect on the skin. IR imaging techniques utilize the IR domain of the electromagnetic spectrum for tissue assessment, allowing for high-resolution visualization of tissue and temperature variation assessment. These techniques have been helpful in diagnosing medical conditions such as skin cancer, allergies, blisters, burns, and wounds. IR-based drying systems are a non-contacting and higher energy transfer path for curing inkjet-printed films, reducing drier sizes compared to convection- or conduction-based driers. In the field of temperature sensing, IR radiation is used to measure temperature on both living and non-living things. Thermo graphic cameras detect radiation in the long infrared range and produce thermograms.

What is infrared waves?5 answersInfrared waves are a form of electromagnetic radiation with a wavelength slightly longer than the color red in the visible light spectrum. They are created when objects are heated at temperatures not quite high enough to make them glow. Infrared radiation has both wave and corpuscular properties, and its wavelength or wave number is used to differentiate the types of radiation. Infrared waves can be detected by the heating effect they have on the skin. Infrared heating systems rely on the transfer of radiant energy from hot surfaces to cooler surfaces without the use of an air mover, resulting in lower fuel costs and more efficient heating. Infrared imaging is also used to detect internal waves and investigate spatial variability in bodies of water. Infrared spectroscopy is a powerful technique for structural analysis, utilizing the changes in molecular dipoles associated with vibrations and rotations to understand the interaction of infrared radiation with matter. Conductors with infrared plasma frequencies can host surface electromagnetic waves with sub-wavelength mode confinement for sensing applications.

See what other people are reading

What are the limitations of tSNE in hyperspectral imaging?

tSNE, a popular dimensionality reduction technique, has limitations in hyperspectral imaging. One limitation is the assumption of smoothness in the emissivity spectra for solids, which may not hold true in practice. Another limitation is the limited availability of labeled samples for training tSNE in hyperspectral target detection tasks. Additionally, tSNE may be affected by inter-band misalignment caused by subject motion during data acquisition. These limitations can impact the accuracy and performance of tSNE in hyperspectral imaging applications.What are all the hyperspectral dataset available for pichavaram?

There are two hyperspectral datasets available for Pichavaram. The first dataset is provided by Salghuna and Pillutla, who used hyperspectral remote-sensing data to identify and map mangrove species along the Pichavaram coast of Tamil Nadu, India. They generated reference spectra from existing reports and research papers, and used classifiers like Spectral Angle Mapper (SAM) and Spectral Feature Fitting (SFF) to identify and map the species. The second dataset is introduced by Stone et al., who created the Tufts Outdoor Hyperspectral Dataset (TOHS Dataset) that includes 100 2D + 3D hyperspectral scenes of outdoor objects, each containing 164 spectral bands. This dataset aims to provide researchers with a comprehensive hyperspectral 3D dataset of outdoor objects for the assessment and creation of AI algorithms.How image processing works with sick chicken detection?

Image processing is used in the detection of sick chickens by analyzing images of chicken flocks. Different models and techniques have been proposed for this purpose. One approach is to use digital image processing and deep learning to identify sick broilers within a flock. A model called Improved Feature Fusion Single Shot MultiBox Detector (IFSSD) was proposed, which achieved high mean average precision (mAP) and could detect broilers and identify their health status simultaneously. Another method involves using the Autoencoder and Yolov6 model to classify and detect diseases in chicken flocks. This method incorporates data enhancement techniques to improve image recognition accuracy. Additionally, a hyperspectral line-scan imaging system has been developed to differentiate between wholesome and systemically diseased chickens based on spectral analysis. This system uses specific wavebands and a fuzzy logic-based algorithm to identify symptoms of systemic disease. Overall, image processing techniques play a crucial role in automatically detecting and classifying poultry diseases, enabling efficient flock management and ensuring a quality food source for humans.What is the normalized burn ratio of a fire?

The normalized burn ratio (NBR) is a spectral index used to evaluate fire severity and the environmental changes caused by fires. It is traditionally calculated using multispectral imagery and is based on the differences in reflectance between near-infrared (NIR) and short-wave infrared (SWIR) spectral regions. The NBR is used to model fire emissions and plan post-fire rehabilitation. It has been evaluated using different indices, such as the Normalized Burn Ratio Plus (NBR+), which incorporates Sentinel-2 bands and has shown excellent results in detecting burned areas. The accuracy of the NBR as a measure of fire severity has been verified through field observations, particularly in Afrotemperate forest, where it has shown a strong linear relationship with stem fire severity. Remote sensing indices, including the Relative Differenced Normalized Burn Ratio (RdNBR), provide an acceptable assessment of fire-induced impact on forest vegetation, but variations between fires should be taken into account.How does the Clonal Selection Algorithm work?

The Clonal Selection Algorithm (CSA) is a method used for feature selection and optimization in machine learning. It aims to improve the performance of machine learning algorithms by removing irrelevant and redundant features. The CSA selects a feature subset that characterizes the sample space. It uses a clonal selection process, where antibodies are selected based on their fitness and undergo mutation and cloning to generate a diverse population. The algorithm also incorporates local search strategies to enhance optimization. The CSA has been applied to various domains, including hyperspectral image data analysis, non-cooperative target observation planning, and dynamic multimodal optimization problems. Experimental results have shown that the CSA is competitive with traditional feature selection algorithms and can effectively track global optima in dynamic environments.What are the challenges and opportunities for the recycling of plastic waste?

The challenges and opportunities for the recycling of plastic waste are significant. Chemical recycling is seen as a complementary strategy to mechanical recycling, but there are still challenges that need to be addressed, such as the identification of suitable process technologies and the design of conversion steps and downstream processing. Polymer separation is a critical issue in plastic recycling, and the development of classification strategies using hyperspectral imaging has shown promising results for quality control and sorting actions in recycling plants. The conventional methods of dumping plastics in landfills and incineration have harmful environmental impacts, highlighting the need for a circular economy approach and the use of chemical methods like low-temperature pyrolysis and catalytic degradation for depolymerization. Lack of technical skills, insufficient infrastructure, and lack of awareness contribute to the challenge of plastic waste management, but there are existing waste to energy and product conversion strategies that can be implemented. Effective collection systems and sorting processes are crucial for increasing plastic recycling rates, and investment in pretreatment, sorting, and recycling technologies is needed.What role did Gaia play in the creation of the universe?

Gaia, the goddess of Earth, played a role in the creation of the universe. In ancient Greece, she was imagined as the origin of everything created. The concept of Gaia as a tightly coupled system, where the biota and the physical environment regulate each other, contributes to understanding the relationship between animate and inanimate nature. Gaia has the potential to shed light on the hidden parameters that govern the phases of stellar evolution, including the formation and main sequence phase of low mass stars. While Gaia is often associated with the study of astrometry and the measurement of celestial objects, it also has implications for understanding the origin and evolution of our Milky Way Galaxy and the distribution of planetary systems around other stars.What accuracy metrics are used for burned area mapping?

Accuracy assessment of burned area maps is traditionally performed using pixel-based metrics to assess the accuracy and precision of burned area estimates at local and regional scales. Other metrics used include the edge error (EE), which quantifies the average distance between the boundaries of the reference and evaluated burn scars. Additionally, the normalized difference vegetation index (NDVI) and the normalized burn ratio (NBR) are commonly used to assess burnt areas through remote sensing imagery. Three-dimensional (3D) datasets, such as Light detection and ranging (LiDAR), are also used to differentiate vegetation structure in burned and unburned areas and classify burned areas. The synergy of Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat-8 Operational Land Imager (OLI) data has been evaluated for delineating burned areas, resulting in improved overall mapping accuracy. The segmentation process and random forest algorithm have been used to map burned areas with high accuracy using Landsat 8 imagery.How can water stress be detected using digital images?

Water stress can be detected using digital images by analyzing various plant responses and physiological traits. One approach is to measure the increase in leaf temperature caused by water deficit, which is related to the closure of stomata and reduced evaporative cooling capacity. Another method involves using machine learning techniques combined with image processing to assess water stress directly. Deep learning models, such as GoogLeNet, have been found to be effective in identifying water stress in crops like maize, okra, and soybean with high accuracy. Additionally, thermal images can be used to detect water stress by analyzing leaf turgor pressure and temperature data. These plant-based monitoring methods have shown potential in detecting water stress in peppers. Furthermore, thermal imaging has been used to determine water stress in wheat crops, with pre-irrigation images reflecting water stress conditions better than post-irrigation. Hyperspectral imaging is another technique that can be used, with methods like spectral normalisation and dimensionality reduction improving the accuracy of water deficit stress response analysis in plants.What is internal attention?

Internal attention refers to the focus of attention on internally generated information, such as thoughts, emotions, and memories, rather than external sensory stimuli. It involves the selection, modulation, and maintenance of these internally generated mental processes. Internal attention can manifest as mind-wandering, self-reflection, rumination, and deliberate thinking. It plays a crucial role in various cognitive processes, including working memory, emotion regulation, and the recognition and identification of emotional states. Understanding the mechanisms and dynamics of internal attention is important for predicting and modeling maladaptive internal cognitive processes. The Attention-to-Thoughts model is a dynamic systems theory and computational model that aims to define momentary states of internal attention and simulate its temporal trajectories. It can help uncover how internal attention interacts with other cognitive processes and how maladaptive internal cognitive processes emerge.How can near-infrared (NIR) images be used to detect plant stress?

Near-infrared (NIR) images can be used to detect plant stress by utilizing hyperspectral imaging techniques. These techniques involve capturing reflectance and fluorescence measurements in the visible and near-infrared wavelength range (400-1000 nm). By analyzing the spectral data obtained from NIR images, associations between plant spectral data and specific stresses can be determined. Significant differences in plant biophysical traits and canopy reflectance spectrum have been observed in response to various stress treatments, indicating that spectral imaging can identify variable plant stress responses. Additionally, deep learning-based methods, such as attention-based pix-to-pix generative adversarial networks (GANs) and super-resolution modules, can be used to generate high-resolution NIR images from corresponding RGB images, enabling more detailed analysis of plant stress. These advancements in NIR imaging and analysis techniques provide valuable tools for monitoring and detecting plant stress in agricultural and controlled-environment settings.