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Relevant bibliographies by topics / Image processing Imaging systems Image analysis

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Academic literature on the topic 'Image processing Imaging systems Image analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Image processing Imaging systems Image analysis"

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Su, Su Yi Mon, and Jian Cheng Fang. "Analysis of Synthetic Aperture Radar Imaging and Signal Processing." Advanced Materials Research 433-440 (January 2012): 2004–10. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2004.

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Digital signal processing is used to focus the image and obtain a higher resolution than achieved by conventional radar systems. This paper presents details of Synthetic Aperture Radar (SAR) signal processing and imaging technique with the goal of generating images. A Matlab based program is developed and coded for imaging simulation. It facilitates processing data and producing desired output. Then, we investigate the characteristics of Linear Frequency Modulated (LFM) signal prior to getting image results. The SAR properties in range and azimuth directions are described. The received signal and SAR raw data is theoretically described. In addition, the target reflection signal processing is also well presented. The SAR image formation is described using Range Doppler Algorithm (RDA). Finally, simulation parameters are computed and imaging simulation test is finished.

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M, Reshma, and Priestly B. Shan. "Oretinex-DI: Pre-Processing Algorithms for Melanoma Image Enhancement." Biomedical and Pharmacology Journal 11, no. 3 (2018): 1381–87. http://dx.doi.org/10.13005/bpj/1501.

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In Medical imaging, the dermoscopic images analysis is quite useful for the skin cancer detection. The automatic computer assisted diagnostic systems (CADS) require dermoscopic image enhancement for human perception and analysis. The traditional image enhancements methods lack the synchronization among contrast perception between human and the digital images. This paper proposes an optimized-Retinex (ORetinex) image enhancement algorithm to remove light effects, which is quite suitable for the dermoscopic image for clinical analysis for Melanoma. The value of global contrast factor (GCF) and contrast per pixel (CPP) is computed and compared with the traditional methods of image enhancements including contrast enhancement, CLAHE,Adaptive histogram equalization, Bilinear filtering and the proportion of GCF and CPP is found quite optimal as compare to these traditional methods.

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Mohammed, Bakhtyar Ahmed, and Muzhir Shaban Al-Ani. "Review Research of Medical Image Analysis Using Deep Learning." UHD Journal of Science and Technology 4, no. 2 (2020): 75. http://dx.doi.org/10.21928/uhdjst.v4n2y2020.pp75-90.

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In modern globe, medical image analysis significantly participates in diagnosis process. In general, it involves five processes, such as medical image classification, medical image detection, medical image segmentation, medical image registration, and medical image localization. Medical imaging uses in diagnosis process for most of the human body organs, such as brain tumor, chest, breast, colonoscopy, retinal, and many other cases relate to medical image analysis using various modalities. Multi-modality images include magnetic resonance imaging, single photon emission computed tomography (CT), positron emission tomography, optical coherence tomography, confocal laser endoscopy, magnetic resonance spectroscopy, CT, X-ray, wireless capsule endoscopy, breast cancer, papanicolaou smear, hyper spectral image, and ultrasound use to diagnose different body organs and cases. Medical image analysis is appropriate environment to interact with automate intelligent system technologies. Among the intelligent systems deep learning (DL) is the modern one to manipulate medical image analysis processes and processing an image into fundamental components to extract meaningful information. The best model to establish its systems is deep convolutional neural network. This study relied on reviewing of some of these studies because of these reasons; improvements of medical imaging increase demand on automate systems of medical image analysis using DL, in most tested cases, accuracy of intelligent methods especially DL methods higher than accuracy of hand-crafted works. Furthermore, manually works need a lot of time compare to systematic diagnosis.

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Rosenfeld, Michael E. "Low-cost digital-image processing and analysis system for on-line morphometric measurements of sem images." Proceedings, annual meeting, Electron Microscopy Society of America 46 (1988): 432–33. http://dx.doi.org/10.1017/s0424820100104224.

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The widespread use of digital image processing systems for high resolution morphometric applications has been limited by the cost and difficulty of interfacing systems to electron microscopes. The recent development of image acquisition boards and software for PC-AT based systems and the availability of scanning electron microscopes that are capable of scanning at true TV rates (RS-170 compatible signals), have alleviated these problems. We have assembled a system consisting of a Compaq Portable 286 (Compaq Computer Corp. Houston, TX) equipped with an FG-100 image acquisition board (Imaging Technology Inc. Woburn, MA), interfaced with a Philips 515 SEM containing a motorized stage and Edax stage controller (Philips Electronics Inc. Mahwah, NJ).Utilizing commercially available software, this system has extensive image processing and morphometric analysis capabilities. For example, because TV scan rates generate images with low signal to noise ratios, improvement of the image quality is possible via real time image averaging and background subtraction using the the FG-100 board Feedback/Input lookup table.

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Truong, Thomas, Anh Dinh, and Khan Wahid. "An Ultra-Wideband Frequency System for Non-Destructive Root Imaging." Sensors 18, no. 8 (2018): 2438. http://dx.doi.org/10.3390/s18082438.

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Understanding the root system architecture of plants as they develop is critical for increasing crop yields through plant phenotyping, and ultra-wideband imaging systems have shown potential as a portable, low-cost solution to non-destructive imaging root system architectures. This paper presents the design, implementation, and analysis of an ultra-wideband imaging system for use in imaging potted plant root system architectures. The proposed system is separated into three main subsystems: a Data Acquisition module, a Data Processing module, and an Image Processing and Analysis module. The Data Acquisition module consists of simulated and experimental implementations of a non-contact synthetic aperture radar system to measure ultra-wideband signal reflections from concealed scattering objects in a pot containing soil. The Data Processing module is responsible for interpreting the measured ultra-wideband signals and producing an image using a delay-and-sum beamforming algorithm. The Image Processing and Analysis module is responsible for improving image quality and measuring root depth and average root diameter in an unsupervised manner. The Image Processing and Analysis module uses a modified top-hat transformation alongside quantization methods based on energy distributions in the image to isolate the surface of the imaged root. Altogether, the proposed subsystems are capable of imaging and measuring concealed taproot system architectures with controlled soil conditions; however, the performance of the system is highly dependent on knowledge of the soil conditions. Smaller roots in difficult imaging conditions require future work into understanding and compensating for unwanted noise. Ultimately, this paper sought to provide insight into improving imaging quality of ultra-wideband (UWB) imaging systems for plant root imaging for other works to be followed.

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Castro, Jose M., Erich de Leon, Jennifer K. Barton, and Raymond K. Kostuk. "Analysis of diffracted image patterns from volume holographic imaging systems and applications to image processing." Applied Optics 50, no. 2 (2011): 170. http://dx.doi.org/10.1364/ao.50.000170.

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Jovanovic, Aleksandar, Zoran Djordjevic, Filip Maric, Miroslav Maric, and Dragoljub Perisic. "CCD microscopy-image analysis by Group for Intelligent Systems GIS." Archive of Oncology 11, no. 2 (2003): 109–14. http://dx.doi.org/10.2298/aoo0302109j.

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BACKGROUND: Classic microscopy does not allow dealing with visual and photographic phenomena, observation of the distinctions where they are needed, or carrying out of any image processing other than cutting. Digital imaging provided more detailed and distinctive insights, and formed the basis for the development of the image processing tools and techniques that transcended in potential and power all initial expectations. The purpose of this article is to present our achievements in this area. METHODS: The starting point is that the matrix. Digital image is a discrete approximation of the continuous two-argument function. Analogue original is a fair basis to start building good mathematical representations of present objects structures, features, which are then subjected to calculations transformations and analyses that could precisely match the predefined aims. These analyses are: Photomorphology 3D model with morphometrics and full 3D navigation, mathematical representations of granular forms in images, object contours, mathematical representations of chromosomes with mathematical definition of similarity, automatic procedures, such as pattern normalizations, matching, classifications, which leads to broader application of Artificial Intelligence methodology. RESULTS: We developed a method and a complex software environment for microscopic imaging, with many tools and algorithms that proved to be useful in genetics, pathology, and oncology. The presented method is prepared and available for further generalizations and automatization, easily bridging to intelligent systems. CONCLUSION: Microscopic imaging is powerful new high-tech domain of great assistance in biomedical research and medical practice that is revolutionizing real time diagnostic methods and potential, matching the power of molecular biology techniques. Being the pioneers in the microscopic imaging, we are pleased that it is exponentially expanding to the general benefit.

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Furat, Orkun, Thomas Leißner, Ralf Ditscherlein, et al. "Description of Ore Particles from X-Ray Microtomography (XMT) Images, Supported by Scanning Electron Microscope (SEM)-Based Image Analysis." Microscopy and Microanalysis 24, no. 5 (2018): 461–70. http://dx.doi.org/10.1017/s1431927618015076.

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AbstractIn this paper, three-dimensional (3D) image data of ore particle systems is investigated. By combining X-ray microtomography with scanning electron microscope (SEM)-based image analysis, additional information about the mineralogical composition from certain planar sections can be gained. For the analysis of tomographic images of particle systems the extraction of single particles is essential. This is performed with a marker-based watershed algorithm and a post-processing step utilizing a neural network to reduce oversegmentation. The results are validated by comparing the 3D particle-wise segmentation empirically with 2D SEM images, which have been obtained with a different imaging process and segmentation algorithm. Finally, a stereological application is shown, in which planar SEM images are embedded into the tomographic 3D image. This allows the estimation of local X-ray attenuation coefficients, which are material-specific quantities, in the entire tomographic image.

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BERRY, IAN, JULIE WILSON, JON DIPROSE, DAVE STUART, STEPHEN FULLER, and ROBERT ESNOUF. "IMAGE STORAGE FOR AUTOMATED CRYSTALLIZATION IMAGING SYSTEMS." International Journal of Neural Systems 15, no. 06 (2005): 415–25. http://dx.doi.org/10.1142/s0129065705000384.

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To use crystallography for the determination of the three-dimensional structures of proteins, protein crystals need to be grown. Automated imaging systems are increasingly being used to monitor these crystallization experiments. These present problems of accessibility to the data, repeatability of any image analysis performed and the amount of storage required. Various image formats and techniques can be combined to provide effective solutions to high volume processing problems such as these, however lack of widespread support for the most effective algorithms, such as JPeg2000 which yielded a 64% improvement in file size over the bitmap, currently inhibits the immediate take up of this approach.

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Adedoyin, Ajayi Olayinka, Olamide Timothy Tawose, and Olu Sunday Adetolaju. "Image Segmentation Techniques in Bone Structure Psychiatry." International Journal of Engineering and Computer Science 9, no. 07 (2020): 25102–12. http://dx.doi.org/10.18535/ijecs/v9i07.4502.

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Today, a large number of x-ray images are interpreted in hospitals and computer-aided system that can perform some intelligent task and analysis is needed in order to raise the accuracy and bring down the miss rate in hospitals, particularly when it comes to diagnosis of hairline fractures and fissures in bone joints. This research considered some segmentation techniques that have been used in the processing and analysis of medical images and a system design was proposed to efficiently compare these techniques. The designed system was tested successfully on a hand X-ray image which led to the proposal of simple techniques to eliminate intrinsic properties of x-ray imaging systems such as noise. The performance and accuracy of image segmentation techniques in bone structures were compared and these eliminated time wasting on the choice of image segmentation algorithms. Although there are several practical applications of image segmentation such as content-based image retrieval, machine vision, medical imaging, object detection, recognition tasks, etc., this study focuses on the performance comparison of several image segmentation techniques for medical X-ray images.

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Dissertations / Theses on the topic "Image processing Imaging systems Image analysis"

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Lai, Di. "Independent component analysis (ICA) applied to ultrasound image processing and tissue characterization /." Online version of thesis, 2009. http://hdl.handle.net/1850/11367.

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Eddins, Steven L. "Subband analysis-synthesis and edge modeling methods for image coding." Diss., Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/15697.

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Nezamabadi, Mahdi. "The effect of image size on the color appearance of image reproductions /." Online version of thesis, 2008. http://hdl.handle.net/1850/7053.

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Guo, Hongyu. "Diffeomorphic point matching with applications in medical image analysis." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0011645.

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Chan, Wai-san. "Design and analysis of integrated computational imaging systems." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B38960357.

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Gomez, Gonzalez Carlos Alberto, Olivier Wertz, Olivier Absil, et al. "VIP: Vortex Image Processing Package for High-contrast Direct Imaging." IOP PUBLISHING LTD, 2017. http://hdl.handle.net/10150/624676.

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We present the Vortex Image Processing (VIP) library, a python package dedicated to astronomical high-contrast imaging. Our package relies on the extensive python stack of scientific libraries and aims to provide a flexible framework for high-contrast data and image processing. In this paper, we describe the capabilities of VIP related to processing image sequences acquired using the angular differential imaging (ADI) observing technique. VIP implements functionalities for building high-contrast data processing pipelines, encompassing pre- and post-processing algorithms, potential source. position and flux estimation, and sensitivity curve. generation. Among the reference point-spread. function subtraction techniques for ADI post-processing, VIP includes several flavors of principal component analysis (PCA) based algorithms, such as annular PCA and incremental PCA algorithms capable of processing big datacubes (of several gigabytes) on a computer with limited memory. Also, we present a novel ADI algorithm based on non-negative matrix factorization, which comes from the same family of low-rank matrix approximations as PCA and provides fairly similar results. We showcase the ADI capabilities of the VIP library using a deep sequence on HR 8799 taken with the LBTI/LMIRCam and its recently commissioned L-band vortex coronagraph. Using VIP, we investigated the presence of additional companions around HR 8799 and did not find any significant additional point source beyond the four known planets. VIP is available at http://github. com/vortex-exoplanet/VIP and is accompanied with Jupyter notebook tutorials illustrating the main functionalities of the library.

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Chan, Wai-san, and 陳慧珊. "Design and analysis of integrated computational imaging systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38960357.

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Johnson, David C. "A shift variant filter applied to edge trace analysis /." Online version of thesis, 1989. http://hdl.handle.net/1850/11357.

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Wang, Yalin. "Document analysis : table structure understanding and zone content classification /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/6079.

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Hamid, Muhammed Hamed. "Hyperspectral Image Generation, Processing and Analysis." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-5905.

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Books on the topic "Image processing Imaging systems Image analysis"

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Shevlin, F. Image processing for pattern analysis. Trinity College, Department of Computer Science, 1992.

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G, Driggers Ronald, ed. Analysis of sampled imaging systems. Spie Optical Engineering Press, 2000.

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Russ, John C. Introduction to image processing and analysis. Taylor & Francis, 2008.

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International Conference on Image Analysis and Processing (3rd 1985 Rapallo, Italy). Image analysis and processing. Plenum Press, 1986.

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Humphrey, Keith. Image analysis: A literature review. PiraInternational, 1995.

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Donald, Reago, and Driggers Ronald G, eds. Analysis and evaluation of sampled imaging systems. SPIE Press, 2010.

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Campana, Steven E. Image analysis for microscope-based observations: An inexpensive configuration. Marine Fish Division, Biological Sciences Branch, Bedford Institute of Oceanography, Dept. of Fisheries and Oceans, 1987.

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The image processing handbook. 6th ed. CRC Press, 2011.

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Descombes, Xavier. Stochastic geometry for image analysis. ISTE, 2011.

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1949-, Kasturi Rangachar, ed. Executive briefing: Document image analysis. IEEE Computer Society Press, 1997.

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Book chapters on the topic "Image processing Imaging systems Image analysis"

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Mosaddegh, Saleh, David Fofi, and Pascal Vasseur. "A Generic Method of Line Matching for Central Imaging Systems under Short-Baseline Motion." In Image Analysis and Processing – ICIAP 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04146-4_100.

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Dacal-Nieto, Angel, Arno Formella, Pilar Carrión, Esteban Vazquez-Fernandez, and Manuel Fernández-Delgado. "Common Scab Detection on Potatoes Using an Infrared Hyperspectral Imaging System." In Image Analysis and Processing – ICIAP 2011. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24088-1_32.

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Rosenberg, S., R. Itti, and L. Benjelloun. "Prolog for Symbolic Image Analysis." In Information Processing in Medical Imaging. Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4261-5_8.

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Pieroni, Goffredo G., Gian Luca Foresti, and Vittorio Murino. "Integration of optical and acoustical imaging sensors for underwater applications." In Image Analysis and Processing. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63508-4_192.

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Barrett, A. "Image Segmentation and Analysis." In Knowledge-Based Image Processing Systems. Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0635-7_9.

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Jan, Jiří. "Nuclear Imaging." In Medical Image Processing, Reconstruction and Analysis. CRC Press, 2019. http://dx.doi.org/10.1201/b22391-8.

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Neumann, Anke, and Cristian Lorenz. "Comparison and application of selected statistical shape models in medical imaging." In Image Analysis and Processing. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63508-4_183.

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Chassery, Jean Marc. "Expert Systems and Image Processing." In Image Analysis and Processing. Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2239-9_3.

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Tesei, Alessandra, Andrea Trucco, and Daniele Zambonini. "A fast object orientation estimation and recognition technique for underwater acoustic imaging." In Image Analysis and Processing. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60298-4_337.

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Toescu, Emil C. "Simple Principles of Imaging — Image Files, Image Processing and Image Analysis." In Measuring Calcium and Calmodulin Inside and Outside Cells. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56851-0_2.

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Conference papers on the topic "Image processing Imaging systems Image analysis"

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Barton, Daniel L., Jeremy A. Walraven, Edward R. Dowski, Rainer Danz, Andreas Faulstich, and Bernd Faltermeier. "Wavefront Coded Imaging Systems for MEMS Analysis." In ISTFA 2002. ASM International, 2002. http://dx.doi.org/10.31399/asm.cp.istfa2002p0295.

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Abstract A new imaging technique called Wavefront Coding allows real-time imaging of three-dimensional structures over a very large depth. Wavefront Coding systems combine aspheric optics and signal processing to achieve depth of fields ten or more times greater than that possible with traditional imaging systems. Understanding the relationships between traditional and modern imaging system design through Wavefront Coding is very challenging. In high performance imaging systems nearly all aspects of the system that could reduce image quality are carefully controlled. Modifying the optics and using signal processing can increase the amount of image information that can be recorded by microscopes. For a number of applications this increase in information can allow a single image to be used where a number of images taken at different object planes had been used before. Having very large depth of field and real-time imaging capability means that very deep structures such as surface micromachined MEMS can be clearly imaged with one image, greatly simplifying defect and failure analysis.

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Oosterlinck, A., P. Suetens, O. Wu, and M. Baird. "Pattern Recognition And Expert Image Analysis Systems In Biomedical Image Processing." In Pattern Recognition and Acoustical Imaging, edited by Leonard A. Ferrari. SPIE, 1987. http://dx.doi.org/10.1117/12.940247.

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Dreyer, Keith J., Joseph Simko, and A. C. Held. "Quantitative analysis of cardiac imaging using expert systems." In Medical Imaging V: Image Processing, edited by Murray H. Loew. SPIE, 1991. http://dx.doi.org/10.1117/12.45237.

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Ni, Catherine W. "Tracking 3D display systems by image processing analysis." In Photonics West '98 Electronic Imaging, edited by Ming H. Wu. SPIE, 1998. http://dx.doi.org/10.1117/12.305526.

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"IST/2004 Image Analysis, and Processing." In 2004 IEEE International Workshop on Imaging Systems and Techniques (IST). IEEE, 2004. http://dx.doi.org/10.1109/ist.2004.1397275.

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Higgins, William E., Wolfgang J. T. Spyra, Ronald A. Karwoski, and Erik L. Ritman. "Automatic analysis system for three-dimensional angiograms." In Medical Imaging V: Image Processing, edited by Murray H. Loew. SPIE, 1991. http://dx.doi.org/10.1117/12.45225.

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Lyra, Maria, Stavroula Lyra, Basil Kostakis, Spyros Drosos, Constantine Georgosopoulos, and Katerina Skouroliakou. "Digital mammography texture analysis by computer assisted image processing." In 2008 IEEE International Workshop on Imaging Systems and Techniques (IST). IEEE, 2008. http://dx.doi.org/10.1109/ist.2008.4659944.

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Hild, M., and G. Umeda. "Image registration in stereo-based multi-modal imaging systems." In Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis. IEEE, 2005. http://dx.doi.org/10.1109/ispa.2005.195386.

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Lanba, Asheesh, and Benjamin Hall. "Laser Ablation Tomography for 3D Tissue Imaging and Analysis." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-12282.

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Abstract Laser ablation tomography (LATscan) can produce 3D tissue models at micron-scale resolution within a few minutes, being amenable to high-throughput applications. The RGB images obtained from LATscan allow for enhanced and accurate feature segmentation and quantification. The technology uses an ultrafast, ultraviolet pulsed laser to continually ablate a sample as it is fed into the laser ablation plane. The ultrafast nature of the laser pushes the process into being athermal, minimizing structural damage to the material being imaged. The surfaces are imaged at the ablation plane. Precise motion control allows for sub-micron separation between consecutive images. The ablation results in color images due to the ultraviolet laser inducing multi-spectral fluorescence. The LATscan system can also be programmed allow for co-registration of cross-sections under different lighting conditions. The images are then stacked, further processed and reconstructed into volume renderings with a voxel size that can go down to 0.2 μm3 for further analysis and virtual dissection. Image processing allows for the 3D visualization and quantification of desired anatomy. LATscan has been successfully applied in the fields of plant science, entomology and materials science. It shows great promise for biomedical imaging and tissue analysis, and this paper presents a few results from the LATscan imaging of murine tissue. Various murine organs have been imaged, including the gut, kidney, and brain (inside the skull). The imaging and analysis combined have the potential to provide pathologists, researchers and diagnosticians with insights and solutions not available to them before.

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Wang, Zhonghua, Shaohong Wang, and Carlos Gonzalez. "Vibration analysis using digital image processing for in vitro imaging systems." In SPIE Optical Engineering + Applications, edited by Alson E. Hatheway. SPIE, 2011. http://dx.doi.org/10.1117/12.892430.

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Reports on the topic "Image processing Imaging systems Image analysis"

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Lasko, Kristofer, and Sean Griffin. Monitoring Ecological Restoration with Imagery Tools (MERIT) : Python-based decision support tools integrated into ArcGIS for satellite and UAS image processing, analysis, and classification. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40262.

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Monitoring the impacts of ecosystem restoration strategies requires both short-term and long-term land surface monitoring. The combined use of unmanned aerial systems (UAS) and satellite imagery enable effective landscape and natural resource management. However, processing, analyzing, and creating derivative imagery products can be time consuming, manually intensive, and cost prohibitive. In order to provide fast, accurate, and standardized UAS and satellite imagery processing, we have developed a suite of easy-to-use tools integrated into the graphical user interface (GUI) of ArcMap and ArcGIS Pro as well as open-source solutions using NodeOpenDroneMap. We built the Monitoring Ecological Restoration with Imagery Tools (MERIT) using Python and leveraging third-party libraries and open-source software capabilities typically unavailable within ArcGIS. MERIT will save US Army Corps of Engineers (USACE) districts significant time in data acquisition, processing, and analysis by allowing a user to move from image acquisition and preprocessing to a final output for decision-making with one application. Although we designed MERIT for use in wetlands research, many tools have regional or global relevancy for a variety of environmental monitoring initiatives.

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