ARCHAEOLOGICAL LAND USE CHARACTERIZATION USING MULTISPECTRAL REMOTE SENSING DATA
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This document summarizes a new approach for classifying remote sensing signatures extracted from multispectral imagery. It combines spectral signatures to perform accurate classification. Simulation results are provided to verify the efficiency of the proposed weighted pixel statistics approach, which uses information from multiple spectral bands. It is shown to provide more accurate and less smoothed identification of classes compared to traditional weighted order statistics methods.
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![Archaeological Land Use Characterization using Multispectral Remote Sensing Data ,[object Object],[object Object],Monitoring Hidrological Variations using Multispectral SPOT-5 Data: Regional Case of Jalisco in Mexico Dr. Iván Esteban Villalón Turrubiates, Member, IEEE UNIVERSIDAD DE GUADALAJARA CENTRO UNIVERSITARIO DE LOS VALLES](https://image.slidesharecdn.com/igarss-2011-arch1-110725155849-phpapp01/85/ARCHAEOLOGICAL-LAND-USE-CHARACTERIZATION-USING-MULTISPECTRAL-REMOTE-SENSING-DATA-1-320.jpg)
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Civil v-hydrology and irrigation engineering [10 cv55]-notes
WRE: UNIT–I:
Surface Water Hydrology: Water Resources in India, Hydrology in water Resources Planning –Precipitation – Types,
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mass curve; Infiltration – Factors affecting and its determination, Evaporation and Evapo-Transpiration. Runoff – factors
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UNIT-II:
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hydrograph, S-hydrograph, Flood Forecasting Techniques.
Ground Water Hydrology: Mechanics of interstitial flow, definitions, sub surface distribution of water, types of aquifers,
ground water movement, Darcy’s law, Well hydraulics – steady flow into wells in Un-confined and confined aquifers,
determination of hydraulic properties of aquifer, pumping test and recuperation test methods for determination of yield of
open well.
UNIT-III:
Reservoir Planning: Types of reservoir investigations for reservoir planning, selection of site for a reservoir, zones of
storage in a reservoir, reservoir yield, mass curve and demand curve, determination of reservoir capacity, yield from a
reservoir of given capacity, operating schedules, guide curve for reservoir operation, apportionment of total cost of a multi
purpose project, reservoir sedimentation, control of reservoir sedimentation, useful life of a reservoir.
UNIT-IV:
Irrigation: Definition of irrigation, types of irrigation systems – direct and indirect, lift and inundation irrigation system,
methods of irrigation – surface and sprinkler methods, drip irrigation, Soil moisture constants, depth of water held by soil in
root zone. Water requirements of crops, Duty, Delta, Base period and their relationship, crop seasons, factors affecting duty
and methods of improving duty, consumptive use of water, determination of canal capacities for cropping patterns.
UNIT-V:
Canal Systems: Classification of irrigation canals – canal alignment, design of unlined canals, regime theories – Kennedy’s
and lacey’s theories, tractive - force method, design problems – balancing depth of canal.
Remote sensing based drought tolerant maize targeting in SSA
Remote sensing –Beyond images
Mexico 14-15 December 2013
The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)
Managing drought short term strategies in semi arid regions a case study
Abstract
Drought management needs multidisciplinary action. Interdisciplinary efforts among the experts in various fields of the droughts
prone areas are helpful to achieve tangible and permanent solution for this recurring problem. The Gulbarga district having the total
area around 16, 240 sq.km, and accounts 8.45 per cent of the Karnataka state area. The district has been situated with latitude 17º 19'
60" North and longitude of 76 º 49' 60" east. The district is situated entirely on the Deccan plateau positioned at a height of 300 to
750 m above MSL. Sub-tropical, semi-arid type is one among the drought prone districts of Karnataka State. The drought
management is very important for a district like Gulbarga. In this paper various short term strategies are discussed to mitigate the
drought condition in the district.
Keywords: Drought, South-West monsoon, Semi-Arid, Rainfall, Strategies etc.
Flood frequency analyses
This document provides an introduction to flood frequency analysis, which uses historical flood data to estimate the probability and recurrence intervals of future floods of given magnitudes. It discusses how flood frequency analysis is necessary for cost-effective design of bridges, dams, and other structures, as well as flood insurance and zoning. Two common methods for collecting flood data are described: annual peaks and partial duration series. Statistical approaches like the Weibull formula are commonly used to analyze the data and construct flood frequency curves showing the relationship between discharge magnitude and probability or recurrence interval.
Effect of climatic factors on crop
- Pakistan's agriculture is dependent on climate factors like temperature, rainfall, and solar radiation. Climate change is expected to negatively impact crop yields through rising temperatures and changing rainfall patterns.
- Studies project decreases in wheat and rice production in Pakistan's semi-arid and arid regions due to increased temperatures reducing the length of the growing season.
- Changes in precipitation could decrease surface water supplies and shift cropping patterns, while also increasing the risk of flooding or waterlogging in some areas.
- Climate change poses challenges but also opportunities for Pakistan agriculture, which will require adapting cropping practices and developing climate-resilient varieties.
Rainfall and land use/cover analysis in four agricultural watersheds of Ethiopia
Rainfall and land use/cover analysis in four agricultural watersheds of EthiopiaInternational Water Management Institute (IWMI)
Rainfall and land use trends were analyzed in four agricultural watersheds in Ethiopia over several decades. The analysis showed:
1) Significant decreasing trends in annual rainfall and rainfall during the main rainy seasons in some watersheds. This undermines agricultural livelihoods.
2) Increases in dry spells over 10 days, exacerbating risks for farmers.
3) Despite declining rainfall, afforestation and "regreening" occurred in most watersheds as farmers adopted soil and water conservation practices. However, it is unclear if these measures maintained agricultural productivity levels.
4) Continued investment in sustainable land management is needed to adapt agricultural systems to changing rainfall patterns and ensure resilient livelihoodsApplication of Remote Sensing in Agriculture
Remote sensing has been found to be a valuable tool in evaluation, monitoring and management of land, water and crop resources. The launching of the Indian remote sensing satellite (IRS) has enhanced the capabilities for better utilization of this technology and significant progress has been made in soil and land cover mapping, land degradation studies, monitoring of waste land, assessment of crop conditions crop acreage and production estimates
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DIVA-GIS: A tool to link climate date and crop suitability – PGR management
DIVA-GIS: A tool to link climate date and crop suitability – PGR management
Analysis of crop yield prediction using data mining techniques
Analysis of crop yield prediction using data mining techniques
Civil v-hydrology and irrigation engineering [10 cv55]-notes
Civil v-hydrology and irrigation engineering [10 cv55]-notes
Remote sensing based drought tolerant maize targeting in SSA
Remote sensing based drought tolerant maize targeting in SSA
Managing drought short term strategies in semi arid regions a case study
Managing drought short term strategies in semi arid regions a case study
Rainfall and land use/cover analysis in four agricultural watersheds of Ethiopia
Rainfall and land use/cover analysis in four agricultural watersheds of Ethiopia
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Super-Resolution of Multispectral Images
This document summarizes a proposed method for super-resolution of multispectral images using principal component analysis. It begins with background on multispectral imaging and issues with resolution. The proposed method first uses PCA to reduce the dimensionality of the multispectral data. It then learns edge details from a high-resolution database by matching blocks of the principal components. After learning, the modified principal components are inverse transformed to generate a higher resolution multispectral image. The method is tested on real multispectral data sets and shown to reconstruct higher resolution images.
Digital image classification22oct
Digital image classification is the process of sorting pixels into categories based on their spectral values. There are supervised and unsupervised classification methods. Supervised classification involves using training sites of known categories to define statistical signatures for each class. Unsupervised classification groups pixels into clusters without prior class definitions. Validation is needed to assess classification accuracy by comparing results to ground truth data. Factors like training site selection and signature separability impact classification performance.
Object Classification of Satellite Images Using Cluster Repulsion Based Kerne...
Abstract: We investigated the Classification of satellite images and multispectral remote sensing data .we
focused on uncertainty analysis in the produced land-cover maps .we proposed an efficient technique for
classifying the multispectral satellite images using Support Vector Machine (SVM) into road area, building area
and green area. We carried out classification in three modules namely (a) Preprocessing using Gaussian
filtering and conversion from conversion of RGB to Lab color space image (b) object segmentation using
proposed Cluster repulsion based kernel Fuzzy C- Means (FCM) and (c) classification using one-to-many SVM
classifier. The goal of this research is to provide the efficiency in classification of satellite images using the
object-based image analysis. The proposed work is evaluated using the satellite images and the accuracy of the
proposed work is compared to FCM based classification. The results showed that the proposed technique has
achieved better results reaching an accuracy of 79%, 84%, 81% and 97.9% for road, tree, building and vehicle
classification respectively.
Keywords:-Satellite image, FCM Clustering, Classification, SVM classifier.
Fd36957962
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Digital_Image_Classification.pptx
This document discusses digital image classification and accuracy assessment. It covers topics such as spectral signatures, supervised vs. unsupervised classification, object-based image analysis, and accuracy assessment methods. The key points are:
- Digital image classification uses spectral information from pixels to categorize land cover types based on spectral patterns. Both supervised and unsupervised methods are described.
- Supervised classification uses training samples of known identity to classify pixels, while unsupervised classification uses computer clustering to group spectrally similar pixels without training data.
- Object-based image analysis first segments an image into meaningful image objects before classification, allowing use of texture, shape, and context versus just spectral values.
- Accuracy assessment requires reference data
Digital image processing
Digital image processing ,Techniques of Digital Image Processing
Image Enhancement ,Image Histogram,Spatial Filtering,Smoothing and Sharpening Examples,Image Classification,Supervised Classification,Unsupervised Classification
remote sensing applications.
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...Pinaki Ranjan Sarkar
Recent advancement in sensor technology allows very high spatial resolution along with multiple spectral bands. There are many studies, which highlight that Object Based Image Analysis(OBIA) is more accurate than pixel-based classification for high resolution(< 2m) imagery. Image segmentation is a crucial step for OBIA and it is a very formidable task to estimate optimal parameters for segmentation as it does not have any unique solution. In this paper, we have studied different segmentation algorithms (both mono-scale and multi-scale) for different terrain categories and showed how the segmented output depends on upon various parameters. Later, we have introduced a novel method to estimate optimal segmentation parameters. The main objectives of this study are to highlight the effectiveness of presently available segmentation techniques on very high-resolution satellite data and to automate segmentation process. Pre-estimation of segmentation parameter is more practical and efficient in OBIA. Assessment of segmentation algorithms and estimation of segmentation parameters are examined based on the very high-resolution multi-spectral WorldView-3(0.3m, PAN sharpened) data.Digital Image Classification.pptx
Digital image classification involves sorting pixels into discrete classes based on their spectral values. It can be performed using supervised or unsupervised approaches. Supervised classification involves using training data to define classes, while unsupervised classification uses algorithms to automatically group similar pixels. Accuracy assessment involves comparing the classification to reference data to determine accuracy through an error matrix.
COLOUR IMAGE REPRESENTION OF MULTISPECTRAL IMAGE FUSION
This document summarizes a research paper on using the Cholesky decomposition technique to fuse multispectral images and represent them as a color image. It discusses how multispectral image fusion works by combining images from different spectral bands. It then describes the VTVA (Vector valued Total Variation Algorithm) technique in detail, which uses the covariance matrix and Cholesky decomposition to control the correlation between color components in the fused image. This technique is compared to principal component analysis. The document provides background on RGB color space, color perception, and Cholesky decomposition before outlining the specific steps of the VTVA algorithm.
COLOUR IMAGE REPRESENTION OF MULTISPECTRAL IMAGE FUSION
The availability of imaging sensors operating in multiple spectral bands has led to the requirement of
image fusion algorithms that would combine the image from these sensors in an efficient way to give an
image that is more perceptible to human eye. Multispectral Image fusion is the process of combining
images optically acquired in more than one spectral band. In this paper, we present a pixel-level image
fusion that combines four images from four different spectral bands namely near infrared(0.76-0.90um),
mid infrared(1.55-1.75um),thermal- infrared(10.4-12.5um) and mid infrared(2.08-2.35um) to give a
composite colour image. The work coalesces a fusion technique that involves linear transformation based
on Cholesky decomposition of the covariance matrix of source data that converts multispectral source
images which are in grayscale into colour image. This work is composed of different segments that
includes estimation of covariance matrix of images, cholesky decomposition and transformation ones.
Finally, the fused colour image is compared with the fused image obtained by PCA transformation.
Satellite Image Classification using Decision Tree, SVM and k-Nearest Neighbor
Satellite Image Classification using Decision Tree, SVM and k-Nearest NeighborNational Cheng Kung University
This document summarizes research comparing three machine learning classification methods - Decision Tree, Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN) - for classifying land use from high and low resolution satellite imagery. The researchers applied each method to classify Pleiades satellite images of Taiwan and Colorado. SVM achieved the highest overall accuracy of 78.6% for high resolution imagery and 83.3% for low resolution imagery. Decision Trees and k-NN were less accurate. The document outlines the methodology, including image preprocessing, parameter selection, accuracy assessment, and findings.P.maria sheeba 15 mco010
This document discusses image segmentation techniques for hyperspectral images. It first defines image segmentation as extracting particular regions from images, and lists common segmentation methods like edge detection, contour extraction, and clustering. The objective is then stated as improving noise-free estimation for noisy hyperspectral images by fully utilizing correlated spectral information and providing rich spectral-spatial information. Finally, the document surveys several papers on hyperspectral image denoising and restoration, discussing their purposes, methodologies, and limitations.
Lw3620362041
International Journal of Engineering Research and Applications (IJERA) aims to cover the latest outstanding developments in the field of all Engineering Technologies & science.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Detection of urban tree canopy from very high resolution imagery using an ob...
Tree that grows within a town, city and suburban areas, collection of these trees makes the urban forest. These urban forest and urban trees have impact on urban water, pollution and heat. Nowadays we are experiencing drastic climatic changes because of cutting of trees for our growth and increasing population which leads to expansion of roads, towers, and airports. Individual tree crown detection is necessary to map the forest along with feasible planning for urban areas. In this study, using WorldView-2imagery, trees in specific area are detected with object-based image analysis (OBAI) approach. Therefore with improvement in spatial and spectral resolution of an image, extracted from WorldView-2 carried out urban features with better accuracy. The aim of this research is to illustrate how object-based method can be applied to the available data to accurately find out vegetation, which can be further sub-classified to obtain area under tree canopy. The result thus obtained gives area under tree canopy with an accuracy of 92.43 % and a Kappa coefficient of 0.80.
IMAGE QUALITY OPTIMIZATION USING RSATV
Abstract: Primarily due to the progresses in super resolution imagery, the methods of segment-based image analysis for generating and updating geographical information are becoming more and more important. This work presents a image segmentation based on colour features with K-means clustering. The entire work is divided into two stages. First enhancement of color separation of satellite image using de correlation stretching is carried out and then the regions are grouped into a set of five classes using K-means clustering algorithm. At first, the spatial data is concentrated focused around every pixel, and at that point two separating procedures are added to smother the impact of pseudoedges. What's more, the spatial data weight is built and grouped with k-means bunching, and the regularization quality in every district is controlled by the bunching focus esteem. The exploratory results, on both reenacted and genuine datasets, demonstrate that the proposed methodology can adequately lessen the pseudoedges of the aggregate variety regularization in the level.
Basics of remote sensing and GIS.pptx
This document provides an overview of remote sensing and describes its key principles and applications. It defines remote sensing as acquiring information about planetary surfaces from a distance without direct contact. The main components of a remote sensing system are described as the energy source, atmosphere, target interaction, sensor recording, transmission and processing, interpretation and analysis, and applications. Common data types like raster and vector data are also explained. Remote sensing techniques like digital image processing, classification, and analysis are outlined. Examples of satellite imagery and classifications are provided.
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International Journal of Engineering Research and Applications (IJERA) aims to cover the latest outstanding developments in the field of all Engineering Technologies & science.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
CLASSIFICATION AND COMPARISION OF REMOTE SENSING IMAGE USING SUPPORT VECTOR M...
Remote sensing is collecting information about an object without any direct physical contact with the particular object. It is widely used in many fields such as oceanography, geology, ecology. Remote sensing uses the Satellite to detect and classify the particular object or area. They also classify the object on the earth surfaces which includes Vegetation, Building, Soil, Forest and Water. The approach uses the classifiers of previous images to decrease the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is predicted first based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate manner with current training samples. This approach can be further applied as sequential image data, with only a small number of training samples, which are being required from each image. This method uses LANSAT 8 images for Training and Testing processes. First, using the Classifier Prediction technique the Signatures are being generated for the input images. The generated Signatures are used for the Training purposes. SVM Classification is used for classifying the images. The final results describes that the leverage of a priori information from previous images will provide advantageous improvement for future images in multi temporal image classification.
RADIOMETRIC RESOLUTION.pptx
Radiometric resolution refers to a sensor's ability to distinguish slight differences in energy levels and is measured in bits. More bits allows an image to store more grayscale values, enabling better discrimination between classes. For example, Landsat-8 has 12-bit resolution, allowing it to distinguish over 4,000 grayscale levels and better characterize land cover compared to Landsat-7's 8-bit resolution of 256 levels. Higher radiometric resolution improves a sensor's sensitivity to capture subtle differences in surface reflectance.
Shadow Detection and Removal Techniques A Perspective View
This document discusses techniques for shadow detection and removal in images. It provides an overview of various methods used, including those based on texture analysis, color information, Gaussian mixture models, and deterministic non-model based approaches. The document then reviews several published papers on different shadow detection and removal algorithms. These algorithms are compared based on advantages and disadvantages in terms of accuracy, computational efficiency, and applicability to different image types and conditions. The conclusion is that shadows remain a challenging problem for computer vision tasks and that the most suitable detection and removal technique depends on the specific image type and application.
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What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Mind map of terminologies used in context of Generative AI
Mind map of common terms used in context of Generative AI.
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
“I’m still / I’m still / Chaining from the Block”
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
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Mind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AI
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
ARCHAEOLOGICAL LAND USE CHARACTERIZATION USING MULTISPECTRAL REMOTE SENSING DATA
- 10. A) Illumination Source B) Radiation C) Interaction with the object D) Radiation sensing E) Transmission, reception and data processing F) Analysis and interpretation G) Application Process
- 15. Temporal Resolution Time July 1 July 12 July 23 August 3 11 days 16 days July 2 July 8 August 3
- 16. Radiometric Resolution 6-bits Range 0 63 8-bits Range 0 255 0 10-bits Range 1023
- 17. INTRODUCTION TO IMAGE CLASSIFICATION
- 24. Basic Strategy: How to do it? But in reality, that is not the case. Looking at several pixels with vegetation, you’d see variety in spectral signatures. The same would happen for other types of pixels, as well.
- 26. Think of a pixel’s brightness in a 2-Dimensional space. The pixel occupies a point in that space. The vegetation pixel and the soil pixel occupy different points in a 2-D space.
- 27. With variability, the vegetation pixels now occupy a region, not a point, of n-Dimensional space. Soil pixels occupy a different region of n-Dimensional space.
- 30. Supervised Classification The computer then creates... Supervised classification requires the analyst to select training areas where he knows what is on the ground and then digitize a polygon within that area… Mean Spectral Signatures Known Conifer Area Known Water Area Known Deciduous Area Digital Image Conifer Deciduous Water
- 31. Supervised Classification Multispectral Image Information (Classified Image) Mean Spectral Signatures Spectral Signature of Next Pixel to be Classified Conifer Deciduous Water Unknown
- 32. The Result: Image Signatures Water Conifer Deciduous Legend: Land Cover Map
- 34. Unsupervised Classification Digital Image The analyst requests the computer to examine the image and extract a number of spectrally distinct clusters… Spectrally Distinct Clusters Cluster 3 Cluster 5 Cluster 1 Cluster 6 Cluster 2 Cluster 4
- 35. Unsupervised Classification Output Classified Image Saved Clusters Cluster 3 Cluster 5 Cluster 1 Cluster 6 Cluster 2 Cluster 4 Unknown Next Pixel to be Classified
- 36. Unsupervised Classification It is a simple process to regroup (recode) the clusters into meaningful information classes (the legend). The result is essentially the same as that of the supervised classification: Conif. Hardw. Water Land Cover Map Legend Water Water Conifer Conifer Hardwood Hardwood Labels
- 37. MODEL FORMALISM
- 40. Blue Green Red Near-IR Mid-IR Mean Signature 1 Candidate Pixel Mean Signature 2 It appears that the candidate pixel is closest to Signature 1. However, when we consider the variance around the signatures… Relative Reflectance Weighted Pixel Statistics Method
- 41. Blue Green Red Near-IR Mid-IR Mean Signature 1 Candidate Pixel Mean Signature 2 The candidate pixel clearly belongs to the signature 2 group. Relative Reflectance Weighted Pixel Statistics Method
- 42. Weighted Pixel Statistics Method
- 43. Weighted Pixel Statistics Method
- 46. Results: 1 st Synthesized Scene Synthesized Scene WOS Classification WPS Classification
- 47. Quantitative Comparison 1 st Synthesized Scene
- 48. Results: 2 nd Synthesized Scene Synthesized Scene WOS Classification WPS Classification
- 49. Qualitative Comparison 2 nd Synthesized Scene Synthesized Scene WOS Classification WPS Classification
- 50. Quantitative Comparison 2 nd Synthesized Scene
- 51. Results: 3 rd Synthesized Scene Synthesized Scene WOS Classification WPS Classification
- 52. Qualitative Comparison 3 rd Synthesized Scene Synthesized Scene WOS Classification WPS Classification
- 53. Quantitative Comparison 3 rd Synthesized Scene
- 57. Archaeological Site "Guachimontones", Jalisco Mexico
- 58. Simulation Results Scene from "Guachimontones" Original Scene WPS Classification
- 60. Simulation Results Scene from "La Vega" dam, Jalisco Mexico Original Scene WPS Classification
Editor's Notes
- Information usually gathered from spacecraft or an airplane, but can be a handheld or boom-mounted device. Originally defined in 1960’s according to Jensen, to encompass photogrammertry and information gathered from nonphotometric sources.