Book review: Applied cryptography: Protocols, algorithms, and source code in C

We use a mechanistically based ecosystem simulation model to describe and analyze the spatial and temporal patterns of terrestrial net primary productivity (NPP) in South America. The Terrestrial Ecosystem Model (TEM) is designed to predict major carbon and nitrogen fluxes and pool sizes in terrestrial ecosystems at continental to global scales. Information from intensively studies field sites is used in combination with continental-scale information on climate, soils, and vegetation to estimate NPP in each of 5888 non-wetland, 0.5° latitude °0.5° longitude grid cells in South America, at monthly time steps. Preliminary analyses are presented for the scenario of natural vegetation throughout the continent, as a prelude to evaluating human impacts on terrestrial NPP. The potential annual NPP of South America is estimated to be 12.5 Pg/yr of carbon (26.3 Pg/yr of organic matter) in a non-wetland area of 17.0 ° 106 km2 . More than 50% of this production occurs in the tropical and subtropical evergreen forest region. Six independent model runs, each based on an independently derived set of model parameters, generated mean annual NPP estimates for the tropical evergreen forest region ranging from 900 to 1510 g°m-2 °yr-1 of carbon, with an overall mean of 1170 g°m-2 °yr-1 . Coefficients of variation in estimated annual NPP averaged 20% for any specific location in the evergreen forests, which is probably within the confidence limits of extant NPP measurements. Predicted rates of mean annual NPP in other types of vegetation ranged from 95 g°m-2 °yr-1 in arid shrublands to 930 g°m@ ?yr-1 in savannas, and were within the ranges measured in empirical studies. The spatial distribution of predicted NPP was directly compared with estimates made using the Miami mode of Lieth (1975). Overall, TEM predictions were °10% lower than those of the Miami model, but the two models agreed closely on the spatial patterns of NPP in south America. Unlike previous models, however, TEM estimates NPP monthly, allowing for the evaluation of seasonal phenomena. This is an important step toward integration of ecosystem models with remotely sensed information, global climate models, and atmospheric transport models, all of which are evaluated at comparable spatial and temporal scales. Seasonal patterns of NPP in South America are correlated with moisture availability in most vegetation types, but are strongly influenced by seasonal differences in cloudiness in the tropical evergreen forests. On an annual basis, moisture availability was the factor that was correlated most strongly with annual NPP in South America, but differences were again observed among vegetation types. These results allow for the investigation and analysis of climatic controls over NPP at continental scales, within and among vegetation types, and within years. Further model validation is needed. Nevertheless, the ability to investigate NPP-environment interactions with a high spatial and temporal resolution at continental scales should prove useful if not essential for rigorous analysis of the potential effects of global climate changes on terrestrial ecosystems.

/pdf/potential-net-primary-productivity-in-south-america-2xjja5q7y2.pdf

Potential Net Primary Productivity in South America: Application of a Global Model

[1] This paper presents an analysis of the Arctic Ocean and its constituent seas for seafloor area distribution versus depth and ocean volume. The bathymetry from the International Bathymetric Chart of the Arctic Ocean (IBCAO) is used together with limits defining this ocean and its constituent seas from the International Hydrographic Organization (IHO) as well as redefined limits constructed to confine the seas to the shallow shelves. IBCAO is a bathymetric grid model with a resolution of 2.5 × 2.5 km, which significantly improved the portrayal of the Arctic Ocean seafloor through incorporation of newly released bathymetric data including echo soundings from U.S. and British navies, scientific nuclear submarine cruises, and icebreaker cruises. This analysis of seafloor area and ocean volume is the first for the Arctic Ocean based on this new and improved portrayal of the seafloor as represented by IBCAO. The seafloor area and volume are calculated for different depths starting from the present sea level and progressing in increments of 10 m to a depth of 500 m and in increments of 50 m from 550 m down to the deepest depth within each of the analyzed seas. Hypsometric curves expressed as simple histograms of the frequencies in different depth bins and depth plotted against cumulative area for each of the analyzed seas are presented. The area and volume calculations show that the entire IHO-defined Arctic Ocean makes up ∼4.3% of the total ocean area but only ∼1.4% of the volume. Furthermore, the IHO Arctic Ocean is the shallowest (mean depth 1201 m) of all the major oceans and their adjacent seas. The continental shelf area, from the coasts out to the shelf break, make up as much as ∼52.9% of the total area in the Arctic Ocean, defined in this work as consisting of the oceanic deep Arctic Ocean Basin; the broad continental shelves of the Barents, Kara, Laptev, East Siberian, Chukchi, and Beaufort Seas; the White Sea; and the narrow continental shelf off both the Canadian Arctic Archipelago and northern Greenland. This result indicates that the Arctic Ocean has significantly larger continental shelves compared with all the other oceans, where previous studies show that the proportion of shelves, from the coasts out to the foot of the continental slopes, only ranges between about 9.1 and 17.7%. Furthermore, the derived hypsometric curves show that most of the Arctic Ocean shelf seas besides the Barents Sea, Beaufort Sea, and the shelf off northern Greenland have a similar shape, with the largest seafloor area between 0 and 50 m. The East Siberian and Laptev seas, in particular, show area distributions concentrated in this shallow depth range, and together with the Chukchi Sea they form a large flat shallow shelf province composing as much as 22% of the entire Arctic Ocean area but only 1% of the volume. This implies that the circulation in the Arctic Ocean might be very sensitive to eustatic sea level changes. One of the aims with this work is to make up-to-date high-resolution area and volume calculations for the Arctic Ocean at various depths available for download.

/pdf/hypsometry-and-volume-of-the-arctic-ocean-and-its-4xq4sqkp40.pdf

Hypsometry and Volume of the Arctic Ocean and Its Constituent Seas

Coral reef ecosystems, the most varied on earth, continually face destruction from anthropogenic and natural threats. The U.S. Coral Reef Task Force seeks to characterize and map priority coral reef ecosystems in the U.S./Trust Territories by 2009. Building upon NOAA Biogeography shallow-water classifications based on Ikonos imagery, presented here are new methods, based on acoustic data, for classifying benthic terrain below 30 m, around Tutuila, American Samoa. The result is a new classification scheme for American Samoa that extends and improves the NOAA Biogeography scheme, which, although developed for Pacific island nations and territories, is only applicable to a maximum depth of 30 m, due to the limitations of satellite imagery. The scheme may be suitable for developing habitat maps pinpointing high biodiversity around coral reefs throughout the western Pacific.

A Benthic Terrain Classification Scheme for American Samoa

Stereoscopic images are hard to get right, and comfortable images are often only produced after repeated trial and error. The main difficulty is controlling the stereoscopic camera parameters so that the viewer does not experience eye strain or double images from excessive perceived depth. Additionally, for head tracked displays, the perceived objects can distort as the viewer moves to look around the displayed scene. We describe a novel method for calculating stereoscopic camera parameters with the following contributions: (1) Provides the user intuitive controls related to easily measured physical values. (2) For head tracked displays; necessarily ensures that there is no depth distortion as the viewer moves. (3) Clearly separates the image capture camera/scene space from the image viewing viewer/display space. (4) Provides a transformation between these two spaces allowing precise control of the mapping of scene depth to perceived display depth. The new method is implemented as an API extension for use with OpenGL, a plug-in for 3D Studio Max and a control system for a stereoscopic digital camera. The result is stereoscopic images generated correctly at the first attempt, with precisely controlled perceived depth. A new analysis of the distortions introduced by different camera parameters was undertaken.

/pdf/controlling-perceived-depth-in-stereoscopic-images-3indqf3i56.pdf

Controlling perceived depth in stereoscopic images

This paper reports on an experimental approach to adjusting stereo parameters automatically and thereby providing a low eye strain, easily accommodated stereo view for computer graphics applications. To this end, the concept of virtual eye separation is defined. Experiment 1 shows that dynamic changes in virtual eye separation are not noticed if they occur over a period of a few seconds. Experiment 2 shows that when subjects are given control over their virtual eye separation, they change it depending on the amount of depth in the scene. Based partly on these results, an algorithm is presented for enhancing stereo depth cues for moving computer generated 3D images. It has the effect of doubling the stereo depth in flat scenes and limiting the stereo depth for deep scenes. It also reduces the occurrence of double images and the discrepancy between focus and vergence. The algorithm is applied dynamically in real time with an optional damping factor applied so the disparities never change too abruptly. Finally, Experiment 3 provides a qualitative assessment of the algorithm with a dynamic "flight" over a digital elevation map.

Dynamic adjustment of stereo display parameters

Recent years have seen remarkable advances in sonar technology, positioning capabilities, and computer processing power that have revolutionized the way we image the seafloor. The massive amounts of data produced by these systems present many challenges but also offer tremendous opportunities in terms of visualization and analysis. The authors have developed a suite of interactive 3D visualization and exploration tools specifically designed to facilitate the interpretation and analysis of very large (10s to 100s of megabytes), complex, multi-component spatial data sets. If properly georeferenced and treated, these complex data sets can be presented in a natural and intuitive manner that allows the integration of multiple components each at their inherent level of resolution and without compromising the quantitative nature of the data. Artificial sun-illumination, shading, and 3D rendering can be used with digital bathymetric data (DTMs) to form natural looking and easily interpretable, yet quantitative, landscapes. Color can be used to represent depth or other parameters (like backscatter or sediment properties) which can be draped over the DTM, or high resolution imagery can be texture mapped on bathymetric data. When combined with interactive analytical tools, this environment has facilitated the use of multibeam sonar and other data sets in a range of geologic, environmental, fisheries, and engineering applications.

/pdf/interactive-3-d-visualization-a-tool-for-seafloor-navigation-1hvb97vstx.pdf

Interactive 3-D visualization: a tool for seafloor navigation, exploration and engineering

Many hydrographic and oceanographic agencies have moved or are moving towards gridded bathymetric products. However, there is no accepted format to allow these grids to be exchanged while maintaining data and metadata integrity. This paper describes the Open Navigation Surface (ONS) Project, which aims to fill this gap. The ONS Project is an open-source software project designed to provide a freely available, portable source-code library to encapsulate gridded bathymetric surfaces with associated uncertainty values. The data file format is called a Bathymetric Attributed Grid (BAG). The BAG is developed and maintained by the ONS Working Group (ONSWG), and the source code is available via the ONS website.

/pdf/the-open-navigation-surface-project-ctp73or0lh.pdf

The Open Navigation Surface Project

This paper presents an algorithm for enhancing stereo depth cues for moving computer generated 3D images. The algorithm incorporates the results from an experiment in which observers were allowed to set their preferred eye separation with a set of moving screens. The data derived from this experiment were used to design an algorithm for the dynamic adjustment of eye separation (or disparity) depending on the scene characteristics. The algorithm has the following steps: (1)Determine the near and far points in the computer graphics scene to be displayed. This is done by sampling the Z buffer. (2) Scale the scene about a point corresponding to the midpoint between the observer's two eyes. This scaling factor is calculated so that the nearest part of the scene comes to be located just behind the monitor. (3) Adjust an eye separation parameter to create stereo depth according to the empirical function derived from the initial study. This has the effect of doubling the stereo depth in flat scene but limiting the stereo depth for deep scenes. Steps 2 and 3 both have the effect of reducing the discrepancy between focus and vergence for most scenes. The algorithm is applied dynamically in real time with a damping factor applied so the disparities never change too abruptly.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Algorithm for dynamic disparity adjustment

Three dimensional imaging of the Belousov-Zhabotinsky reaction using magnetic resonance.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Mark Paton

Author Tools

Chat about Author