Feature Extraction And Classification Information Technology Essay Example

Feature Extraction And Classification Information Technology Essay Any given remote feeling image can be decomposed into several characteristics. The term characteristic refers to remote feeling scene objects ( e.g. flora types, urban stuffs, etc ) with similar features ( whether they are spectral, spacial or otherwise ) . Therefore, the chief aim of a feature extraction technique is to accurately recover these characteristics. The term Feature Extraction can therefore be taken to embrace a really wide scope of techniques and procedures, runing from simple ordinal / interval measurings derived from single sets ( such as thermic temperature ) to the coevals, update and care of distinct characteristic objects ( such as edifices or roads ) . The definition can besides be taken to embrace manual and semi-automated ( or assisted ) vector characteristic gaining control nevertheless Feature Collection is the subject of a separate White Paper non discussed farther here. Similarly, derivation of height information from stereo or interferometric techniques could be considered feature extraction but is discussed elsewhere. What follows is a treatment of the scope and pertinence of characteristic extraction techniques available within Leica Geosystems Geospatial Imaging s suite of distant feeling package applications. Derived Information Figure 1: Unsupervised Categorization of the Landsat informations on the left and manual killing produced the land screen categorization shown on the We will write a custom essay sample on Feature Extraction And Classification Information Technology specifically for you for only $16.38 $13.9/page Order now We will write a custom essay sample on Feature Extraction And Classification Information Technology specifically for you FOR ONLY $16.38 $13.9/page Hire Writer We will write a custom essay sample on Feature Extraction And Classification Information Technology specifically for you FOR ONLY $16.38 $13.9/page Hire Writer To many analysts, even ordinal or interval measurings derived straight from the DN values of imagination represent characteristic extraction. ERDAS IMAGINEAÂ ® and ERDAS ERM Pro provide legion techniques of this nature, including ( but non limited to ) : The direct standardization of the DN values of the thermic sets of orbiter and airborne detectors to deduce merchandises such as Sea Surface Temperature ( SST ) and Mean Monthly SST. One of the most widely known derived characteristic types is flora wellness through the Normalized Difference Vegetation Index ( NDVI ) , where the ruddy and near-infrared ( NIR ) wavelength sets are ratioed to bring forth a uninterrupted interval measuring taken to stand for the proportion of flora / biomass in each pel or the health/vigor of a peculiar flora type. Other types of characteristics can besides be derived utilizing indices, such as clay and mineral composing. Chief Component Analysis ( PCA Jia and Richards, 1999 ) and Minimum Noise Fraction ( MNF Green et al. , 1988 ) are two widely employed characteristic extraction techniques in distant detection. These techniques aim to de-correlate the spectral sets to retrieve the original characteristics. In other words, these techniques perform additive transmutation of the spectral sets such that the resulting constituents are uncorrelated. With these techniques, the characteristic being extracted is more abstract for illustration, the first chief constituent is by and large held to stand for the high frequence information nowadays in the scene, instead than stand foring a specific land usage or screen type. The Independent Component Analysis ( ICA ) based feature extraction technique performs a additive transmutation to obtain the independent constituents ( ICs ) . A direct deduction of this is that each constituent will incorporate information matching to a specific characteristic. Equally good as being used as stand-alone characteristic extraction techniques, many are besides used as inputs for the techniques discussed below. This can take one of two signifiers for high dimensionality informations ( hyperspectral imagination, etc ) , the techniques can minimise the noise and the dimensionality of the information ( in order to advance more efficient and accurate processing ) , whereas for low dimensionality informations ( grayscale informations, RGB imagination, etc. ) they can be used to deduce extra beds ( NDVI, texture steps, higher-order Principal Components, etc ) . The extra beds are so input with the beginning image in a categorization / characteristic extraction procedure to supply end product that is more accurate. Other techniques aimed at deducing information from raster informations can besides be thought of as characteristic extraction. For illustration, Intervisibility/Line Of Site ( LOS ) computations from Digital Elevation Models ( DEMs ) represent th e extraction of a what can I see characteristic. Similarly, tools like the IMAGINE Modeler Maker enable clients to develop usage techniques for characteristic extraction in the broader context of geospatial analysis, such as where is the best location for my mill or where are the locations of important alteration in land screen. Such derived characteristic information are besides campaigners for input to some of the more advanced characteristic extraction techniques discussed below, such as supplying accessory information beds to object-based characteristic extraction attacks. Supervised Categorization Multispectral categorization is the procedure of screening pels into a finite figure of single categories, or classs of informations, based on their informations file values. If a pel satisfies a certain set of standards, the pel is assigned to the category that corresponds to those standards. Depending on the type of information you want to pull out from the original informations, categories may be associated with known characteristics on the land or may merely stand for countries that look different to the computing machine. An illustration of a classified image is a land screen map, demoing flora, bare land, grazing land, urban, etc. To sort, statistics are derived from the spectral features of all pels in an image. Then, the pels are sorted based on mathematical standards. The categorization procedure interrupt down into two parts: preparation and classifying ( utilizing a determination regulation ) . First, the computing machine system must be trained to acknowledge forms in the information. Training is the procedure of specifying the standards by which these forms are recognized. Training can be performed with either a supervised or an unsupervised method, as explained below. Supervised preparation is closely controlled by the analyst. In this procedure, you select pels that represent forms or set down screen characteristics that you recognize, or that you can place with aid from other beginnings, such as aerial exposures, land truth informations or maps. Knowledge of the information, and of the categories desired, is hence needed before categorization. By placing these forms, you can teach the computing machine system to place pels with similar features. The pels identified by the preparation samples are analyzed statistically to organize what are referred to as signatures. After the signatures are defined, the pels of the image are sorted into categories based on the signatures by usage of a categorization determination regulation. The determination regulation is a mathematical algorithm that, utilizing informations contained in the signature, performs the existent sorting of pels into distinguishable category values. If the categorization is accurate, the ensuing categories represent the classs within the informations that you originally identified with the preparation samples. Supervised Categorization can be used as a term to mention to a broad assortment of feature extraction attacks ; nevertheless, it is traditionally used to place the usage of specific determination regulations such as Maximum Likelihood, Minimum Distance and Mahalonobis Distance. Unsupervised Categorization Unsupervised preparation is more computer-automated. It enables you to stipulate some parametric quantities that the computing machine uses to bring out statistical forms that are built-in in the information. These forms do non needfully correspond to straight meaningful features of the scene, such as immediate, easy recognized countries of a peculiar dirt type or land usage. The forms are merely bunchs of pels with similar spectral features. In some instances, it may be more of import to place groups of pels with similar spectral features than it is to screen pels into recognizable classs. Unsupervised preparation is dependent upon the informations itself for the definition of categories. This method is normally used when less is known about the informations before categorization. It is so the analyst s duty, after categorization, to attach significance to the resulting categories. Unsupervised categorization is utile merely if the categories can be suitably interpreted. ERDAS IMAGI NE provides several tools to help in this procedure, the most advanced being the Grouping Tool. The Unsupervised attack does hold its advantages. Since there is no trust on user-provided preparation samples ( which might non stand for pure illustrations of the category / characteristic desired and which would therefore bias the consequences ) , the algorithmic grouping of pels is frequently more likely to bring forth statistically valid consequences. Consequently, many users of remotely sensed informations have switched to leting package to bring forth homogeneous groupings via unsupervised categorization techniques and so utilize the locations of developing informations to assist label the groups. The authoritative Supervised and Unsupervised Classification techniques ( every bit good as intercrossed attacks using both techniques and fuzzed categorization ) have been used for decennaries with great success on medium to lower declaration imagination ( imagination with pixel sizes of 5m or larger ) , nevertheless one of their important disadvantages is that their statistical premises by and large preclude their application to high declaration imagination. They are besides hampered by the necessity for multiple sets to increase the truth of the categorization. The tendency toward higher declaration detectors means that the figure of available sets to work with is by and large reduced. Hyperspectral Optical detectors can be broken into three basic categories: panchromatic, multispectral and hyperspectral. Multispectral detectors typically collect a few ( 3-25 ) , broad ( 100-200 nanometer ) , and perchance, noncontiguous spectral sets. Conversely, Hyperspectral detectors typically collect 100s of narrow ( 5-20 nanometer ) immediate sets. The name, hyperspectral, implies that the spectral sampling exceeds the spectral item of the mark ( i.e. , the single extremums, troughs and shoulders of the spectrum are resolvable ) . Given finite informations transmittal and/or managing capableness, an operational orbiter system must do a tradeoff between spacial and spectral declaration. This same tradeoff exists for the analyst or information processing installation. Therefore, in general, as the figure of sets additions there must be a corresponding lessening in spacial declaration. This means that most pels are assorted pels and most marks ( characteristics ) are subpixel in size. It is, hence, necessary to hold specialized algorithms which leverage the spectral declaration of the detector to clear up subpixel marks or constituents. Hyperspectral categorization techniques constitute algorithms ( such as Orthogonal Subspace Projection, Constrained Energy Minimization, Spectral Correlation Mapper, Spectral Angle Mapper, etc. ) tailored to expeditiously pull out characteristics from imagination with a big dimensionality ( figure of sets ) and where the characteristic by and large does non stand for the primary component of the detectors instantaneous field of position. This is besides frequently performed by comparing to research lab derived stuff ( characteristic ) spectra as opposed to imagery-derived preparation samples, which besides necessitate a suite of pre-processing and analysis stairss tailored to hyperspectral imagination. Subpixel Classification IMAGINE Subpixel Classifiera„? is a supervised, non-parametric spectral classifier that performs subpixel sensing and quantification of a specified stuff of involvement ( MOI ) . The procedure allows you to develop material signatures and use them to sort image pels. It reports the pixel fraction occupied by the stuff of involvement and may be used for stuffs covering every bit low as 20 % of a pel. Additionally, its alone image standardization procedure allows you to use signatures developed in one scene to other scenes from the same detector. Because it addresses the assorted pel job, IMAGINE Subpixel Classifier successfully identifies a specific stuff when other stuffs are besides present in a pel. It discriminates between spectrally similar stuffs, such as single works species, specific H2O types or typical edifice stuffs. Additionally, it allows you to develop spectral signatures that are scene-to-scene movable. IMAGINE Subpixel Classifier enables you to: aˆ? Classify objects smaller than the spacial declaration of the detector aˆ? Discriminate specific stuffs within assorted pels aˆ? Detect stuffs that occupy from 100 % to every bit small as 20 % of a pel aˆ? Report the fraction of material nowadays in each pel classified aˆ? Develop signatures portable from one scene to another aˆ? Normalize imagination for atmospheric effects aˆ? Search wide-area images rapidly to observe little or big characteristics mixed with other stuffs The primary difference between IMAGINE Subpixel Classifier and traditional classifiers is the manner in which it derives a signature from the preparation set and so applies it during categorization. Traditional classifiers typically form a signature by averaging the spectra of all preparation set pels for a given characteristic. The resulting signature contains the parts of all stuffs present in the preparation set pels. This signature is so matched against whole-pixel spectra found in the image informations. In contrast, IMAGINE Subpixel Classifier derives a signature for the spectral constituent that is common to the preparation set pels following background remotion. This is usually a pure spectrum of the stuff of involvement. Since stuffs can change somewhat in their spectral visual aspect, IMAGINE Subpixel Classifier accommodates this variableness within the signature. The IMAGINE Subpixel Classifier signature is hence purer for a specific stuff and can more accurately observe the MOI. During categorization, the procedure subtracts representative background spectra to happen the best fractional lucifer between the pure signature spectrum and campaigner residuary spectra. IMAGINE Subpixel Classifier and traditional classifiers perform best under different conditions. IMAGINE Subpixel Classifier should work better to know apart different species of flora, typical edifice stuffs or specific types of stone or dirt. You would utilize it to happen a specific stuff even when it covers less than a pel. You may prefer a traditional classifier when the MOI is composed of a spectrally varied scope of stuffs that must be included as a individual categorization unit. For illustration, a wood that contains a big figure of spectrally distinguishable stuffs ( heterogenous canopy ) and spans multiple pels in size may be classified better utilizing a minimal distance classifier. IMAGINE Subpixel Classifier can congratulate a traditional classifier by placing subpixel happenings of specific species of flora within that forest. When make up ones minding to utilize IMAGINE Subpixel Classifier, callback that it identifies a individual stuff, the MOI, whereas a traditional classifier will sort many stuffs or characteristics happening with a scene. The Subpixel Classification procedure can therefore be considered a feature extraction procedure instead than a wall to palisade categorization procedure. Figure 2: Trial utilizing panels highlights the greater truth of sensing provided by a subpixel classifier over a traditional classifier, In rule, IMAGINE Subpixel Classifier can be used to map any stuff that has a distinguishable spectral signature relation to other stuffs in a scene. IMAGINE Subpixel Classifier has been most exhaustively evaluated for flora categorization applications in forestry, agribusiness and wetland stock list, every bit good as for semisynthetic objects, such as building stuffs. IMAGINE Subpixel Classifier has besides been used in specifying roads and waterways. Classification truth depends on many factors. Some of the most of import are: 1 ) Number of spectral sets in the imagination. Discrimination capableness additions with the figure of sets. Smaller pixel fractions can be detected with more sets. The 20 % threshold used by the package is based on 6-band informations. 2 ) Target/background contrast. 3 ) Signature quality. Ground truth information helps in developing and measuring signature quality. 4 ) Image quality, including band-to-band enrollment, standardization and resampling ( nearest neighbor preferred ) . Two undertakings affecting subpixel categorization of wetland tree species ( Cypress and Tupelo ) and of an invasive wood tree species ( Loblolly Pine ) included extended field look intoing for categorization polish and truth appraisal. The categorization truth for these stuffs was 85-95 % . Categorization of pels outside the preparation set country was greatly improved by IMAGINE Subpixel Classifier in comparing to traditional classifiers. In a separate quantitative rating survey designed to measure the truth of IMAGINE Subpixel Classifier, 100s of semisynthetic panels of assorted known sizes were deployed and imaged. The approximative sum of panel in each pel was measured. When compared to the Material Pixel Fraction ( the sum of stuff in each pel ) reported by IMAGINE Subpixel Classifier, a high correlativity was measured. IMAGINE Subpixel Classifier outperformed a maximal likeliness classifier in observing these panels. It detected 190 % more of the pels incorporating panels, with a lower mistake rate, and reported the sum of panel in each pel classified. IMAGINE Subpixel Classifier works on any multispectral informations beginning, including airborne or satellite, with three or more spatially registered sets. The information must be in either 8-bit or 16-bit format. Landsat Thematic Mapper ( TM ) , SPOT XS and IKONOS multispectral imagination have been most widely used because of informations handiness. It will besides work with informations from other high declaration commercial detectors such as Quickbird, FORMOSAT-2, airborne beginnings and OrbView-3. IMAGINE Subpixel Classifier will besides work with most hyperspectral informations beginnings. Expert Knowledge-Based Classification One of the major disadvantages to most of the techniques discussed supra is that they are all per-pixel classifiers. Each pel is treated in isolation when utilizing the technique to find which characteristic or category to delegate it to there is no proviso to utilize extra cues such as context, form and propinquity, cues which the human ocular reading system takes for granted when construing what it sees. One of the first commercially available efforts to get the better of these restrictions was the IMAGINE Expert Classifier. The adept categorization package provides a rules-based attack to multispectral image categorization, post-classification polish and GIS mold. In kernel, an adept categorization system is a hierarchy of regulations, or a determination tree that describes the conditions for when a set of low degree component information gets abstracted into a set of high degree informational categories. The constitutional information consists of user-defined variables and includes raster imagination, vector beds, spacial theoretical accounts, external plans and simple scalars. A regulation is a conditional statement, or list of conditional statements, about the variable s informations values and/or attributes that find an informational constituent or hypotheses. Multiple regulations and hypotheses can be linked together into a hierarchy that finally describes a concluding set of mark informational categories or terminal hypotheses. Assurance values associated with each status are besides combined to supply a assurance image matching to the concluding end product classified image. While the Expert Classification attack does enable accessory informations beds to be taken into consideration, it is still non genuinely an object based agencies of image categorization ( regulations are still evaluated on a pel by pixel footing ) . Additionally, it is highly user-intensive to construct the theoretical accounts an expert is required in the morphology of the characteristics to be extracted, which besides so necessitate to be turned into graphical theoretical accounts and plans that feed complex regulations, all of which need constructing up from the constituents available. Even one time a cognition base has been constructed it may non be easy movable to other images ( different locations, day of the months, etc ) . Image Cleavage Cleavage means the grouping of neighbouring pels into parts ( or sections ) based on similarity standards ( digital figure, texture ) . Image objects in remotely sensed imagination are frequently homogeneous and can be delineated by cleavage. Therefore, the figure of elements, as a footing for a undermentioned image categorization, is tremendously reduced if the image is foremost segmented. The quality of subsequent categorization is straight affected by cleavage quality. Ultimately, Image Segmentation is besides another signifier of unsupervised image categorization, or characteristic extraction. However, it has several advantages over the authoritative multispectral image categorization techniques, the cardinal differentiators being the ability to use it to panchromatic informations and besides to high declaration informations. However, Image Segmentation is besides similar to the unsupervised attack of image categorization in that it is an machine-controlled segregation of the ima ge into groups of pels with like features without any effort to delegate category names or labels to the groups. It suffers from an extra drawback in that there is by and large no effort made at the point of bring forthing the cleavage to utilize the section features to place similar sections. With Unsupervised Classification you may hold widely separated, distinguishable groups of pels, but their statistical similarity means they are assigned to the same category ( even though you do non yet cognize what characteristic type that category is ) , whereas with Image Segmentation, each section is merely uniquely identified. Statistical steps can normally be recorded per section to assist with station processing. Consequently, in order to label the sections with a characteristic type / land screen, the technique must be combined with some other signifier of categorization, such as Expert Knowledge-Based Classification or as portion of the Feature Extraction work flow provided by IMAGINE Objective. OBJECT-BASED FEATURE EXTRACTION AND CLASSIFICATION Globally, GIS sections and mapping establishments invest considerable gross into making and, possibly more significantly, keeping their geospatial databases. As the Earth is invariably altering, even the most precise base function must be updated or replaced on a regular basis. Traditionally, the gaining control and update of geospatial information has been done through labour and cost intensive manual digitisation ( for illustration from aerial exposure ) and post-production surveying. Since so, assorted efforts have been made to assist automatize these work flows by analysing remotely sensed imagination. Remotely perceived imagination, whether airborne or orbiter based, provides a rich beginning of timely information if it can be easilly exploited into functional information. These efforts at mechanization have frequently resulted in limited success, particularly as the declaration of imagination and the intended function graduated table additions. With recent inventions in geospat ial engineering, we are now at a topographic point where work flows can be successfully automated. Figure 4: The basic construction of a characteristic theoretical account demoing the additive mode in which the information is analyzed. Operators are designed as plugins so that more can be easy added as required for specific characteristic extraction scenarios. When Landsat was launched more than 30 old ages ago, it was heralded as a new age for automatizing function of the Earth. However, the imagination, and hence the geospatial informations dervied from it, was of comparatively harsh resoution, and thereby became limited to smaller graduated table function applications. Its analysis was besides restricted to remote feeling experts. Equally, the traditional supervised and unsupervised categorization techniques developed to pull out information from these types of imagination were limited to coarser declarations. Today s beginnings for higher declaration imagination ( primarilly intending 1m or smaller pel sizes, such as that produced by the IKONOS, QuickBird, and WorldView satelittes or by airborne detectors ) do non endure from the assorted pel phenomenon seen with lower declaration imagination, and, hence the statistical premises which must be met for the traditional supervised and unsupervised categorization techniques do non keep. Therefore, more advanced techniques are required to analyse the high declaration imagination required to make and keep big graduated table function and geospatial databases. The best techniques for turn toing this job analyze the imagination on an object, as opposed to pixel, footing. IMAGINE Objective provides object based multi-scale image categorization and characteristic extraction capablenesss to reliably physique and maintain accurate geospatial content. With IMAGINE Objective, imagination and geospatial informations of all sorts can be analyzed to bring forth GIS-ready function. IMAGINE Objective includes an advanced set of tools for characteristic extraction, update and change sensing, enabling geospatial informations beds to be created and maintained through the usage of remotely sensed imagination. This engineering crosses the boundary of traditional image processing with computing machine vision through the usage of pixel degree and true object processing, finally emulating the human ocular system of image reading. Providing to both experts and novitiates likewise, IMAGINE Objective contains a broad assortment of powerful tools. For distant detection and sphere experts, IMAGINE Objective includes a desktop authoring system for edifice and put to deathing characteristic particular ( edifices, roads, etc ) and/or landcover ( e.g. , flora type ) processing methodological analysiss. Other users may set and use bing illustrations of such methodological analysiss to their ain informations. The user interface enables the expert to put up feature theoretical accounts required to pull out specific characteristic types from specific types of imagination. For illustration, route center lines from 60cm Color-Infrared ( CIR ) orbiter imagination require a specific characteristic theoretical account based around different image-based cues. Constructing footmarks from six inch true colour aerial picture taking and LIDAR surface theoretical accounts require a different characteristic theoretical account. For those familiar with bing ERDAS IMAGINEAÂ ® capablenesss, an analogy can be drawn with Model Maker, with its ability to enable experient users to diagrammatically construct their ain spacial theoretical accounts utilizing the crude edifice blocks provided in the interface. The less experient user can merely utilize constitutional illustration Feature Models or those built by experts, using them as-is or modifying through the user interface. While similar to the IMAGINE Expert Classifier attack, the building and usage of characteristic theoretical accounts within IMAGINE Objective is simpler and more powerful. Constructing a characteristic theoretical account is more additive and intuitive to the expert constructing the theoretical account. In add-on, the support for supervised preparation and evidentiary acquisition of the classifier itself means that the characteristic theoretical accounts are more movable to other images one time built.

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Impact of IT on Individuals, Communities and Society Since its inception, IT has had a substantial impact on the world. The ability to access information at the touch of a button has transformed the way we learn. Education and Training have never been the same, before the dawn of the internet. However, all of this is not as amazing as it first seems. Malicious users roam the far reaches of the internet trying to steal peoples bank details, child pornography sites hidden behind proxies and VPNs deep in the dark net, even illegal drug and weapon sales. Online Shopping Online shopping is an amazing invention since its initiation in 1979 by Michael Aldrich. Michael Aldrich connected a 26 coloured consumer television by a telephone line to a real-time transaction processing computer. He called his new invention teleshopping, this is the forefather to our online shopping today. It even allows people who cant leave their homes, such as disabled people, elderly people, single parents and so much more. However, this godsend isnt as brilliant for local shop owners as it is for consumers; it can leave local economies decimated as people that used to be loyal customers move to services like Amazon and ASDA Direct. Not all is how it seems. Although online shopping can be accessible by many people, a lot of people still dont have access to it. 21.6% of UK residents dont have regular access to the internet. This has become a problem for many rural areas of the UK that seem to be neglected by ISPs (Internet Service Providers), low income areas also seem to have a smaller percent of online activity. In 2015 the UK government tried to combat this issue by passing a bill that was intended to provide everyone with at least 15 mbps (megabits per second) internet access for free. As of February 2017 the bill has disappeared. Free time The way we spend our free time has changed drastically over the last couple of decades. From the dawn of social media to the invention of complex graphically intense video games our choices of media consumption during our free time has vastly increased since the very first commercial computers were produced.   Websites like Twitter and YouTube have become the places where most will spend their free time. This has caused content creators commonly referred to as YouTubers, to make a living. Some even become millionaires. Video games have also become one of the most popular forms of peoples pass times, over 33 million out of the UKs 64 million residents play video games on a daily basis. Thats roughly 51% of the total populous, over half! So its no surprise that the British games market is worth a whopping  £4.193 billion as of 2015. Streaming websites are also among the most popular for internet users, they account for roughly 60% 70% of web traffic. They stream videos and other media like music to their users, some of the most frequently visited websites for streaming include: Netflix, Amazon Prime video and music, Spotify and Crunchyroll An anime streaming site. Communication Communication within the IT industry has shaped how we all communicate on a daily basis. From Emails to Short Message Service (SMS), daily communication has vastly changed from the days of letters and telegrams; this is thanks to the wide adoption of computers and mobile devices. This has only improved as technology has advanced to the internet vastly improved what mobile devices could do thus allowing us to communicate in better and faster ways. With the invention of 3G (short for 3rd Generation, in reference to it being the 3rd iteration of wireless mobile technology).   Users could surf the web from their devices. This newly found technology paved the way for smartphones, has the technology improved with H/HSDPA (High-Speed Downlink Packet Access) offering a theoretical 7.2 mbps connection speed and later H+HSPA+ (Evolved High-Speed Packet Access) offering an insanely fast theoretical speed of 168 mbps. Legal Impacts The legal impacts of IT have always been up for debate, whether it was the ability to copy games from cassette to cassette or the sudden unlimited access to bountiful amounts of information that came from the incredible creation that is the internet. In order to protect peoples data and information, many governments around the world implemented improved copyright and plagiarism laws. In the UK this law is called the Copyright, Designs and Patent Act 1998. Law lays down foundations to help copyright and patent holders to take legal action again those who steal their works. Hacking, Fraud and other malicious acts also came along with the dawn of commonly available personal computers. The UK government passed the Computer Misuse Act 1990, this bill outlined the dos and donts when it comes to computer use; accessing a computer without permission is considered a crime under the Computer Misuse Act.   Ãƒâ€šÃ‚   Ethical Impacts The ethical impacts of IT mainly from the constant documentation of our information from services such as Google and Amazon has been a heated topic for several years. Should we allow such services to store our personal information and information about items we like or search most often? Although there are many benefits in allowing such information to be stored which can help to form algorithms to better improve our online experiences with search engines and online shops. This can allow services such as Amazon target specific advertisements to be shown to us based on our interests and to have products recommended to us based on our past purchases. However, if this information were to reach the wrong hands, such as a fraudster, it could be extremely dangerous to the users whom data has been stolen. Another ethical question that comes to play with the use of IT is for those who dont have access to high-speed internet that may of us take for granted. For instance, many people who live in rural areas of the UK dont have access to broadband due to the vast distance between them and the telephone exchange. In some cases, even trying to install cables for rural areas are just impossible due to the high cost and low reward aspect of the area. For a broadband company it is more profitable to place expensive high-speed cables in densely populated urban areas since the vast amount of customers would allow the companies to recover their investments much quicker. There are solutions to this however, many mobile communication networks have started providing 4G internet access to those who cant get access to the internet or who have rather weak connections. Most of the time these solutions are cheap and quick to set up however the 800Mhz (megahertz) frequency band, previously used for analog ue television, used by most telecommunication companies although has far reach due to its small wavelength this has the negative effect of not having the ability to transmit as much data as a fibre optic cabling. Life before computers As much as it is hard for some of us who have grown up with and around this amazing technology, we must not forget that computers have not always been around. Even earlier versions of cameras have been around longer than computers. Video games, Instant messaging, Email, DVDs, Cassettes, Laserdiscs: There was a time when all of these werent even thought of. A time of newspapers and radio, vinyl disc and Classic music and Jazz. Going outside to play with friends, working for a sixpence, 240 pence to a pound, before the days of post decimalisation. I asked my Nan about what it was like growing up, what she would have liked to do when she left school. Getting a job as a typist working for the Ministry of Defence (MOD) was one of the flashiest jobs for women to get, everyone wanted it. Sustainability The sustainability of our modern technological position has been a question for many centuries. What can we do to help preserve our environment and sustain our current lifestyle? Recycling our old hardware and reusing the precious metals inside them can help us since it uses less energy to regain these metals than it does to mine and refine them. Another advantage to this is that metals are also a finite substance and we only have a set amount of it. Moving to a more sustainable energy source to power our homes and electronics is also a vital way to be stainable. Solar, wind and nuclear energy are the current candidates for us to replace our dependent on fossil fuels. All three of these present a positives and negatives but one thing they all have in common is their amount of pollution they produce or lack there of. PAGE 1 OF YOUR BLOG: Understand the impact of IT on individuals, communities and society. Impacts Social Impacts How we spend free time. Effects on local communities PAGE 2 OF YOUR BLOG: ECONOMIC IMPACTS Employment structure and working practices. Sustainability. PAGE 3 OF YOUR BLOG: Legal impacts Ownership, copyright and plagiarism PAGE 4 OF YOUR BLOG Ethical impacts Privacy of information PAGE 5 OF YOUR BLOG Activity from page 23 Life before computers investigate and write up in your own words.

Premature Infants Essay -- Health, NICU

Thousands of infants are born prematurely on an annual basis, and it is a challenge in the neonatal intensive care unit (NICU) to facilitate parent-child attachment while still providing the safest clinical environment for the infant. One significant area of research where premature infants are concerned is the effect of early skin to skin contact (SSC), or kangaroo care, between the parent(s) and child during their stay in the NICU. Although it has been found that early and frequent SSC promotes positive physiological responses in preterm infants, there is mostly speculative data regarding the long-term psychosocial effects where parent-infant bonding is concerned. An important question for the clinician working in this specialty area to find a quantifiable answer for is, â€Å"Do parents who are permitted to touch and/or hold their infant in the NICU bond with their infant better than those who are not able to do this?† This type of question is structured in the PICO mod el, which is one of the most common models used in evidenced-based practice (EBP). The question is structured in a way that the patient population and practice or intervention are clearly identified, making it easier for the researcher to find relevant research data using the internet and databases. More specifically, PICO can be broken down into: P (patient population or condition of interest), I (intervention of interest), C (comparison of interest), and O (outcome of interest). (Schmidt & Brown, 2012) For the question at hand, the P (population) was parents of infants in the NICU, the I (intervention of interest) considered was the ability to touch and/or hold their infant, C (comparison) was parents who were not permitted physical contact with their infant,... ...es have to realize that they are not just caring for a premature infant, but also a new family. It is also important for the nurse to understand that the mothers’ and fathers’ approach to touching and bonding with their infant may differ. Although quantitative data from Chiu and Anderson (2009) did not reveal significant differences between the control group and the SSC group at 18 months, the data from Latva et al. (2008) showed significant behavioral differences at six years old when infants were touched as newborns and formed a secure attachment. Therefore, for the health and well-being of both parents and child, time and opportunity to have SSC and bonding experiences must be priorities in the plan of care for infants in the NICU. As one mother stated, â€Å"I need to be allowed to feel that he is mine.† (Fegran, Helseth, & Fagermoen, 2007, pg. 813)

African American Oral Tradition Essay

Modern African American Literature was formed under a stressful time for Africans, slavery. The only way the stories of the indigenous people of Africa were passed down was through oral recollections, or stories of the events. In America this was especially difficult for the slaves because of laws preventing them from learning English. By not being allowed to learn English, the slaves had to learn English solely on auditory purposes. This essentially made the slaves illiterate. When the slaves transferred the language that they heard to paper, a new style of language was formed which was referred to as dialect. Dialect is what the slaves thought they heard and the correct spelling of those words, not standard English. Dunbar, who wrote fluently in both standard English and dialect was praised by white critics only for his dialect poems, and not praised for his poems in standard English. His literacy works are still alive today, however the dialect works were attached with a stigma. Usually whites despised the African’s dialect. Therefore, the slaves would not try to publish any type of work with dialect because the slaves did not want to be associated with the stigma. In all, by creating a unique dialect gave the slaves a bilingual type of style. By not being able to write, slaves also made Genres such as, spiritual, folk songs and gospels. Songs such as these were ways of passing down stories to the next generation. These songs also contained secret messages. These messages may have contained information about escape routes or even the underground- railroad. However most of the songs were spiritual in nature. The songs also progressed through the years. The originals slave folk songs, spirituals, and gospels are now prevalent in modern day jazz and the blues. Martin even gives the example of Hayden, who mixes his song ideas with the ideas of Bessie Smith. Even though African are allowed to read and write, this is a form how their culture is still expressed today. Martin made the emphasis that the oral tradition is part of the African Americans distinct culture.

Halifax & Bank of Scotland Essay

The UK has one of the most diverse and dynamic banking sectors in the world. Banking is now a highly competitive industry. Financial consumers are now more sophisticated as they are now more aware of available banking options. The assets of the UK banking system were i 3,441bn (August 2001), which were dominated by a dozen or so retail banks, with national networks, mostly serving domestic, personal and corporate customers. Currently, the big four banks – HSBC, the Royal Bank of Scotland, Lloyds TSB and Barclays, dominate retail and business banking, jointly accounting for 68% of all UK current accounts. Both Halifax, founded in 1853, and 306-year-old Bank of Scotland are seen as business icons in their regions. Halifax is based in England, while the Bank of Scotland has very few branches south of the border. A merger between these firms would increase the geographic scope for potential customers. Halifax started as a building society and is now more widely known as a big mortgage lender. In the wider community, the Halifax Bank has a very active community-banking sector catering for charity and non-profit organizations including housing associations, credit unions and community development operations. In comparison, the Bank of Scotland’s strength lies in the corporate market. It would seem very likely that both firms would like to achieve higher profitability and growth opportunity through cross-selling products to each other’s customers. For example, the products developed by Halifax could be marketed effectively to Bank of Scotland’s customers and vice versa. Because both banks operate complementary activities, it is possible the combining of both firms will result in synergies, which may also result in increased efficiency. There may also be opportunities to achieve savings through cutting some unnecessary costs. For example, the amount of staff needed for the combined firm is likely to be reduced. By merging together, the size of the combined firm will certainly increase, thus leveraging the combined spend to negotiate better deals. The market position of the combined firm will be strengthened. Its market share within the industry will increase, maybe even enough to compete with the big-four banks, thus increasing the competition within the banking industry. In reality, there are wide ranges of techniques that can help analyse a firm’s performance – some firms may base their performance on sales, whereas others through the quality of products. Economists usually analyse a firm’s performance based on the amount of profit it is making. For a thorough analysis, this paper will be looking at the firm’s: market value, profitability, stability, value for shareholders, efficiency, and capital adequacy. It must be noted that firms within the banking sector are subject to many economic uncertainties, which can influence how well a firm is doing from year to year. In this case, these uncertainties include: interest rates, employment rates, as well as the condition of the equity markets. For example, the base rate in January 2000 was 5.75%, however, at January 2002, the base rate was at 4. 00%5. To analyse the performance of the banks before and after the merger, the firms’ financial accounts will be examined and ratios will also be calculated. 6 The main performance indicators that will be analysed include: Profit before tax; Total assets; Dividends and Earnings per share. In addition, the return on equity, cost:income ratio and the firm’s capital strength will be examined. These ratios will give a clear assessment of the firm’s performance compared with that of other firms. Before the merger, in 2000, Halifax and Bank of Scotland had market values of $22,105million and $11,762million respectively. Post-merger, in 2002, HBOS then had a market value in excess of $31billion7. This immediately signifies the success of the merger, as the combined company is worth now worth a lot more in the market. Figure 1 – Profit before tax From an economic point of view, it is important that a firm makes a profit otherwise there would be no point of the existence of the firm. The Profit & Loss account of a firm shows the results of trading over the previous 12 months. It shows the net effect of income less expenses. The reason that profit before tax is analysed rather than profit after tax is due to the fact that interest rates and inflation changes could affect the amount of tax that is paid each year. In 2000, Halifax made i 1,715million profit (before tax), compared with Bank of Scotland, which made i 911million. It would be expected that when both companies have merged together, the pre-tax profit should increase. Figure 1 shows that in 2002, HBOS made a pre-tax profit of i 2,909million, which is more than the separate firms’ pre-tax profit added together. This shows that HBOS are actually performing better than the previously separate firms.

Table of Chemicals Used to Grow Crystals

Table of Chemicals Used to Grow Crystals This is a table of common chemicals that produce nice crystals. The color and shape of the crystals are included. Many of these chemicals are available in your home. Other chemicals in this list are readily available online and are safe enough for growing crystals at home or in a school. Recipes and specific instructions are available for hyperlinked chemicals. Table of Common Chemicals for Growing Crystals Chemical Name Color Shape aluminum potassium sulfate(potassium alum) coloreless cubic ammonium chloride colorless cubic sodium borate(borax) colorless monoclinic calcium chloride colorless hexagonal sodium nitrate colorless hexagonal copper acetate(cupric acetate) green monoclinic copper sulfate(cupric sulfate) blue triclinic iron sulfate(ferrous sulfate) pale blue-green monoclinic potassium ferricyanide red monoclinic potassium iodide white cupric potassium dichromate orange-red triclinic potassium chromium sulfate(chrome alum) deep purple cubic potassium permanganate dark purple rhombic sodium carbonate(washing soda) white rhombic sodium sulfate, anhydrous white monoclinic sodium thiosulfate colorless monoclinic cobalt chloride purple-red ferric ammonium sulfate(iron alum) pale violet octohedral magnesium sulfateepsom salt colorless monoclinic (hydrate) nickel sulfate pale green cubic (anhydrous)tetragonal (hexahydrate)rhombohedral (hexahydrate) potassium chromate yellow potassium sodium tartrateRochelle salt colorless to blue-white orthorhombic sodium ferrocyanide light yellow monoclinic sodium chloridetable salt colorless cubic sucrosetable sugarrock candy colorless monoclinic sodium bicarbonatebaking soda silver silver bismuth rainbow over silver tin silver monoammonium phosphate colorless quadratic prisms sodium acetate(hot ice) colorless monoclinic calcium copper acetate blue tetragonal

Evalutating country risk analysis Essay Example | Topics and Well Written Essays - 1000 words

Evalutating country risk analysis - Essay Example This inter mix of factors creates a complexity in understanding and application of CRA. (Meldrum: 2000). The measures used for risk evaluation may differ based on the experience and judgement of analysts. These may employ a number of common points initially and then lead to detailed discussion of specific issues affecting a specific sphere of interest. Thus a combination of actual and potential imbalances are calculated to apply to a broad investment category.These decisions are judgemental and hence may have limited universal application across the board. (Meldrum: 2000). Broadly the measures applied by the Political Risk Services' International Country Risk Guide (ICRG) for CRA include political, economic and financial risk. The ICRG also calculates a composite risk which is generally evolved from these base indices. A final measure which some analysts examine with reference to CRA is Institutional Investor's country credit ratings. Thus it would be seen that information is defined in a number of ways. (Erb.Harvey.Tadas:1996). Another problem in CRA is limited availability of historical data in emerging economies. This increases the uncertainty of future prediction. (Damodaran: 2004). Since risk implies identification of a well defined event from a large number of observations which is amenable to probability analysis, lack of the same results in basing CRA on uncertainy. (Meldrum: 2000). Thus analysts tend to construct the risk based on judgmental factors rather than probabilistic criteria. CRA ratings which are easily accessible are by ratings age ncies which measure default risk and equity risk which is generally derived. (Damodaran: 2003). These differing perspectives necessitate the need to evolve systematic methodologies for CRA. Impact of Differing Geographical and Time Perspectives Risks between countries can vary due to national differences in economy, policy, geography, currency and a host of socio-political factors. For example comparing the period in Romania in the pre and post Cold War era uniformly is likely to result in totally varied results. However many times risk analysts tend to use uniform criteria to assess country risks beyond time as well as situations differential faced in making such an assessment. While inclusive country risk measures are correlated with each other, for higher returns risk analysts recommend value-oriented strategies across the board which may create anomalies. (Erb.Harvey.Tadas:1996). Thus factors which are common for all countries need to be identified. Application of financial risk measures is likely to be done uniformly evolving information of future expected returns and political risk criteria are likely to be ignored. (Erb.Harvey.Tadas:1996). This is supported by evidence from ICRG composite, financial and economic ratings, which appear standardised. (Erb.Harvey.Tadas:1996). While economic factors are also evolving the real challenge is to assess the political risk particularly in emerging economies as Romania. Problems of Quantitative and Qualitative Methods CRA include a mix of qualitative and quantitative analysis. Some as the Bank of America World Information Services is based exclusively on quantitative information while the Institutional Investor is a qualitative survey based on opinions of banking professionals taking a number of non quantitative factors

The ICT Industry in Canada Term Paper Example | Topics and Well Written Essays - 4500 words

The ICT Industry in Canada - Term Paper Example According to the research findings the ICT industry within any societal setting is vital in ensuring progress to the economy to deliver an update on the necessary resources. The resources that are under consideration issue support to the political and economic sustenance. The desire to analyze the ICT provision of any company is attributed to the requirements presented in identifying the main contributions of the entity. The ICT industry has been a leading form of intellectual property in the modern century with a provision issued to mark the features that have contributed to its advancement. Canadian ICT sector has made leading developments in creating stability in the economy, while maintain a noticeable balance in the principles that are applied to create sustainability in the economy. The ICT industry in Canada has presented numerous developments in the generation of GDP, and measures to maintain its effectiveness need to be implemented to realize its contributions. Canada had es tablished its economy as the leader of the information advances, and recent advancements have seen it fail to maintain its status among the developed powers that are capable of offering sustainability within the ICT sector. Policies to balance the ICT industry with the developmental inputs presented within its economy are the factors that might revive the industry. The major composer of the industry has been articulated to comprise minor companies. These are an estimated 33, 000 firms, 80% of the number specializing in software and computer development. The other numbers are concerned with wholesaling and manufacturing. On the other hand, companies with a large work force form the minority in the industry with an estimated 20% of firms that are engaged in ICT sector. The value of the ICT composition with the manufacturing and software development sector has been boosted by the need to provide investment of qualified professionals, who are capable of delivering the developments to ac hieve the economic progress desired. However, the large companies with an employee turnover of 500 professionals support the sector with the need to provide regulation of the needed gadgets. In 2010, leading companies owned the number that constituted the manufacturing segment of the ICT area. These companies held a minimal employee capacity with the number of employees estimated to be 50 per firm, and the record revealed that this number in the total ICT share occupied 3.7%. Contribution to the economy Research conducted revealed that the ICT sector had increased its total revenue between 2009 and