Gamla Årstabron i Stockholm uppvisade efter 80 år i drift omfattande skador på de bärande betongkonstruktionerna enligt en utredning som genomfördes 2006 av dåvarande Carl Bro AB på uppdrag av dåvarande Banverket Region Öst. Det konstaterades att omfattande reparationer och förstärkningar av brons betongvalv var nödvändiga för framtida rationell drift av bron. Det beslutades efter vidare utredning att de första tre valven på Södermalm i Stockholm skulle renoveras med injekteringsbetong. Renoveringen av de tre valven på fastlandet på Södermalm var ett prov i full skala för att hitta den optimala metodiken för den fortsatta renoveringen av resterande 17 betongvalv. Det här examensarbetet syftar till att utvärdera metoden med injekteringsbetong med hänsyn till injekteringsbetongens egenskaper, material och produktionsteknik.   Bakgrunden till detta examensarbete är att det finns ett stort behov av att följa upp renoveringsmetoden med injekteringsbetong eftersom den inte har använts i någon större utsträckning i Sverige sedan slutet av 1970-talet. Utvärderingen av injekteringsbetongens egenskaper har utförts genom att analysera resultaten av de provningar som utförts på betongen. Provningen har gått till så att utborrade kärnor och tillverkade provkuber har provats för bland annat tryck- och draghållfasthet (vidhäftning). Utvärderingen av material och produktionsteknik har gjorts genom observationer på arbetsplatsen där rapportförfattaren praktiserade hos NCC under juni och augusti 2008. Utöver praktiken har jag närvarat vid och dokumenterat många av de injekteringar som gjorts under projektets första år.   Huvudsyftet med att använda injekteringsbetong var att få fram en betong som uppvisar tillräckligt hög tryckhållfasthet, en viss draghållfasthet och en fri krympning av högst 0,2 ‰. Tyvärr var inte provtagningen tillräckligt omfattande för att dra statistiskt säkerställda slutsatser gällande betongens egenskaper. Det som dock kan sägas är att provtagningen samt information från nyckelpersoner från beställaren (numera Trafikverket) tenderar att bekräfta det som konstaterats i de förstudier som gjordes innan brorenoveringen startade, nämligen att injekteringsbetongen uppvisar en klart lägre krympning än konventionellt gjuten brobetong. Vad gäller tryck- och draghållfasthet så uppfyller den färdiga betongen de krav som ställdes.   Blandning av ingående material i form av ballast och cementbruk samt de enskilda materialens egenskaper visade sig vara en kritisk punkt, vilket bekräftar det som framgår av litteraturen på området. Att kravet på renhet hos ballasten, stenmaterialet, är uppfyllt är av yttersta vikt för slutresultatet. Dessutom är det mycket viktigt att cementbruket som blandas med vatten precis innan det injekteras i den stenfyllda formen håller mycket hög kvalité och är stabilt. I vissa aspekter ställer också metoden högre krav på yrkesarbetare, platsledning och övrig produktionspersonal som pumpförare jämfört med att gjuta med konventionell betong. Förutom de enskilda gruppernas kompetens är också samordningen och logistiken på arbetsplatsen en mycket viktig faktor. Att använda metoden innebär dessutom att beställare och specialister måste ha kunskap och förståelse för att metoden ur vissa synvinklar skiljer från konventionell betong, särskild med tanke på den begränsade användningen av metoden i Sverige i modern tid.   Baserat på slutresultatet av renoveringen, de provningar som utförts samt omdömen från nyckelpersoner hos beställaren var injekteringsbetong rätt metod att använda för att renovera Gamla Årstabron. / According to an investigation conducted in 2006 by the former Carl Bro AB commissioned by the former Swedish railway authority (Banverket), the old Årsta bridge in Stockholm, Sweden, was  after 80 years in operation showing signs of extensive damage on the load-bearing concrete structures. It was pointed out that the concrete vaults of the bridge needed to be repaired and reinforced in order to keep the bridge in an operational state. After further investigation it was decided that the first three vaults on the north side of the bridge were to be renovated by using pre-placed aggregate concrete. The renovation of the three vaults on the north side served as a full-scale test to find the best possible methodology for the continued renovation of the concrete structures that consists of another seventeen concrete vaults. This thesis aims to evaluate the method of using pre-placed aggregate concrete regarding its properties and materials as well as the construction technology.   The reason for this thesis is that there is a great need to follow up the renovation method using pre-placed aggregate concrete since it hasn’t been used in any great extent in Sweden since late 1970’s.The evaluation of the concrete’s properties has been done by analyzing the results of the testing that has been performed on the concrete. Test specimens consisting of concrete cores and fabricated cubes have been tested for compressive and tensile (bond) strength. The evaluation of materials and construction technology has been done by practical observations on the work site where the author worked as an intern for the contractor NCC during June and August 2008. Apart from my internship I also attended and documented many of the grouting occasions during the first year of the project.   The main intention of using pre-placed aggregate concrete was to produce a concrete with high compressive strength, certain tensile strength and a free shrinkage of at most 0,2 ‰. The testing of the concrete was unfortunately not extensive enough to draw any unambiguous conclusions concerning the properties of the concrete. The results of the tests performed as well as information from key persons from the current Swedish traffic administration (Trafikverket) do however tend to confirm what was found during the pilot studies conducted before the renovation of the bridge started, namely that the pre-placed aggregate concrete has a much lower shrinkage than conventional concrete normally used in bridges. As for compressive and tensile strength, the pre-placed aggregate concrete meets the quality requirements.    The mix of included materials, aggregate and cement-based grout, as well as the properties of the materials themselves turned out to be critical for the result, which the literature in the field confirms. The purity of the aggregate is essential for the result. Moreover, it’s very important that the cement-based grout is of high quality and stable. When comparing with traditional concreting, the method imposes higher requirements on the workforce, management and subcontractors, in some aspects. Two other key factors, apart from the competence of each group, are the coordination and the logistics on the worksite. Using pre-placed aggregate concrete also implies that clients and specialists must have knowledge and understanding concerning the differences compared to traditional concreting, especially since the method hasn’t been used in any greater extent in Sweden the last 40 years.   Based on the result of renovation, tests conducted and reviews from key persons at the Swedish transport administration the decision to use pre-placed aggregate concrete was the right one.

Im Genehmigungsverfahren für den Bau hochliegender Schallquellen (z.B. Windenergieanlagen) muss der Nachweis geführt werden, dass von den Anlagen keine schädlichen Umwelteinwirkungen ausgehen. Es ist es daher notwendig, die Schallausbreitung derartiger Quellen grundsätzlich zu untersuchen. Eine Schwierigkeit stellt dabei die gekoppelte Wirkung von Temperatur-, Windgeschwindigkeits- und Windrichtungsprofil in Zusammenhang mit dem Bodeneinfluss auf die Schallausbreitung dar. Dieser zeitlich und räumlich variable Atmosphäreneinfluss wird insbesondere bei Langzeituntersuchungen der Schallimmission bisher nur unzureichend in den operationellen Modellen beschrieben. Das Ziel der Studie besteht deshalb darin, die gekoppelte Wirkung von Atmosphäre- und Boden-Einfluss auf die Schallausbreitung in einem Bereich bis zu 2 km Entfernung von der Schallquelle mit dem Modell SMART (Sound propagation model of the atmosphere using ray-tracing ) zu untersuchen. / The licensing procedure for the construction of high-placed sound sources (e.g. wind power stations) demands to proof that no (significant and) harmful impact on environment is outgoing from these systems. Therefore, it is necessary to analyse the sound propagation of such a kind of sources. In this context one central problem has to be managed: the coupled effect of temperature, wind speed and wind direction profiles combined with the influence of surface on sound propagation. The temporally and spatially variable influence of the atmosphere is only insufficiently described by the operational models, especially in relation to long-time investigations of sound immission. Consequently, the aim of this study was to investigate the coupled effect of atmospheric and surface influence on sound propagation up to distances of 2 km away from the sound source using the model SMART (Sound propagation model of the atmosphere using ray-tracing).

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

This qualitative inquiry explored how art educators might broaden their views of place through critical encounters with art, local visual culture, and working with older artists. I combined place-based (PB) education and intergenerational (IG) learning as the focus of an art education curriculum writing initiative with in-service art educators within a museum setting to produce PBIG art education. This study engaged art educators in cooperative action research using a multi-modal approach, including identifying and interviewing local artists to construct new understandings about local place and art to share with students and community. I used critical reflection in our cooperative action research by troubling paradoxes in local visual culture, which formed views of place including Indigenous cultures. Using Deleuze's Logic of Sense (LOS) theories of sense and event, enabled concept development through embracing the paradoxes of this research as sense producing. LOS theory of duration complements IG learning by clarifying the contributions of place and time to memory and experience. Duration suggests that place locates the virtual past, which is actualized through memories--one of the shared experiences of IG learning. Rethinking IG relationships as a sharing of experience and memory while positioning place as a commonality, dismantles ageist notions by offering alternatives to binary thinking about old and young. By triangulating participant data based on the extended epistemology of cooperative action research and Deleuze's pure event, I assess the credibility of participant learning. Critical reflection in cooperative action research combined with LOS theory is significant because the reflective aspect of action research aligns with Deleuze's pure event. Vital curricula and teacher praxes resulted when participants integrated localized experiences of place through older artists' memories and art.

Ungefär 20 000 barn är familjehemsplacerade i Sverige. Att bli placerad i ett familjehem och ryckas upp samt separeras från sin hemmiljö kan påverka ett barns anknytningsmönster. Teorin om anknytning påvisar vikten av att barn skapar sig trygga anknytningsmönster vilket leder till ökade möjligheter för fortsatt god utveckling. Den här studien har haft som syfte att få en fördjupad förståelse för hur socialsekreterare beskriver anknytning i förhållande till familjehemsplacerade barn mellan 0-3 år. Med stöd av en fenomenologisk studie har semistrukturerade intervjuer genomförts för att få en djupare förståelse av socialsekreterares syn på anknytning och hur kunskapen om den tillämpas vid familjehemsplaceringar. Studien har påvisat att olika definitioner används, olika förhållningssätt tillämpas och att anknytning som begrepp inte tydligt praktiseras vid familjehemsplaceringar. Slutsatsen med studien är att anknytningen är viktig, speciellt i förhållande till familjehemsplaceringar. Det krävs fortfarande mer forskning och kunskap inom anknytningsteorin i förhållande till familjehemsplaceringar då kunskaperna om teorin kan vara bristande. / Approximately 20 000 children are placed in foster homes in Sweden. Being placed in a foster home and separated from their biological family and environment can affect a child's attachment pattern. The attachment theory demonstrates the importance of secure attachment patterns in children leading to increased opportunities for a healthy development. The purpose of this study is to achieve a deeper understanding of how the social secretary views attachment in relation to children between 0-3 years old placed in foster homes. In order to achieve a deeper understanding of how the social secretary views attachment, and how this knowledge is being applied when placing a child in foster care, I have made a phenomenological study using semi-structured interview. The study has shown that the social secretary's view of attachment varies and the term attachment is not being routinely practiced when placing a child in a foster home. The conclusion of the study is that the attachment theory is important especially when placing a child in foster home.

Syftet med den här studien har varit att undersöka familjehemsföräldrars relation till placerade barns biologiska föräldrar, det stöd familjehemsföräldrar upplever att de får eller saknar i relationen till de biologiska föräldrarna samt hur det stödet påverkar de placerade barnen. Vi har använt oss av en kvalitativ metod med semistrukturerade intervjuer med 14 familjehemsföräldrar från 11 familjehem, från olika kommuner i Södra delen av Sverige, för att få svar på vårt syfte och våra frågeställningar. Vi har sedan analyserat empirin och det resultatet visar är att majoriteten av familjehemsföräldrarna i vår studie är positiva till att ha en relation till de biologiska föräldrarna men att de saknar stöd i den, ofta svåra, relationen. Det som också framkommer i vårt resultat är att familjehemsföräldrarna är överens om vilket stöd de skulle önska för att få till en fungerande relation och att relationen till de biologiska föräldrarna fungerar bättre för de familjehemsföräldrar som har stor erfarenhet av att vara familjehem. Som teoretisk utgångspunkt för vår analys av intervjuerna har vi valt ekologisk systemteori och socialkonstruktionism. / The purpose of this study has been to research foster parents' relationship to the biological parents of foster children, the support foster parents receive or lack from the social workers in the relationship to the biological parents and how that support affects the children. We have used a qualitative method with semi-structured interviews with 14 foster parents from 11 foster homes, from different municipalities in the southern part of Sweden, to get answers to our purpose and our questions. We have then analyzed the empirical data and the results show that the majority of the foster parents in our study are positive about having a relationship with the biological parents but that they lack support in the, often difficult, relationship. What also emerges in our result is that the foster parents agree on what support they would like to have in a functioning relationship and that the relationship with the biological parents works better for the foster parents who have extensive experience of being a foster home. As a theoretical starting point for our analysis of the interviews, we have chosen ecological systems theory and social constructionism.

Since a large part of the hydropower structures in Sweden was built in the 1950s and 1960s, many of them are slowly but surely exhibiting deterioration. The hydropower companies are facing big challenges and are consequently investing in effective repairing methods since a hydropower structure failure could pose serious consequences and dangers to people, the environment, and the community. Many structures within hydropower are made of concrete and the demands on the new supplementing concrete are high. Concrete with the potential to meet these high demands is the pre-placed aggregate concrete, which has shown promising results regarding its mechanical properties in previous studies. For this reason, this type of concrete is of interest to investigate. The focus has not been on optimizing the pre-placed aggregate concrete for full-scale productions. Instead, the main objectives of this master thesis were to study and analyze the mechanical properties of this type of concrete, such as shrinkage, compressive strength, splitting tensile strength, freeze-thaw resistance and moreover investigate parameters of importance in the mix design to obtain a homogenous and easy flowing grout that successfully could fill the voids between the coarse aggregates. The investigations were carried out by laboratory experiments in the research and laboratory facilities of Vattenfall in Älvkarleby. The mix design of the grout was developed using the methods and requirements stated in the American Society for Testing and Materials, ASTM standards, and The Swedish Institute for Standards, SiS. A total of 15 grout-mixes were made. However, only the last five were used to cast specimens as the air content was insufficient in the first ten. The results indicated that it is necessary to replace the air-entraining admixture with microspheres in order for the pre-placed aggregate concrete to meet the requirements in exposure class XF3 and XC4. The scaling of the pre-placed aggregate concrete was less than 0.1 kg/m2 at 56 cycles, and thus, the freeze-thaw resistance was classed as very good. Moreover, the use of slag considerably reduced the bleeding of the grout and also improved the casting results. However, on the other hand, it increased the shrinkage of the pre-placed aggregate concrete. An efficiency factor of 0.6 proved to be too low since the compressive strength of the specimen with slag was approximately 50 % higher than the ones without. Furthermore, the shrinkage of the pre-placed aggregate concrete was after 63 days found to be lower than that of the conventional concrete. Also, the compressive strength of the pre-placed aggregate concrete without slag proved to be approximately 15 % lower than that of conventional concrete. Additionally, vibration during casting was found to increase the compressive strength of the pre-placed aggregate concrete and also improved the casting results. Low bleeding, combined with a high discharge time of approximately 45 seconds for 1.7 liters of grout, generated the best casting results. The results from the investigations have shown that this type of concrete has great potential. However, actions and further investigations should be made to see whether changing the fine aggregate size to a smaller one improves the ability of the grout to penetrate the voids between the coarse aggregates. Moreover, pump injection of the grout should be tested instead of pouring it over the coarse aggregates to see whether it improves the casting results and the mechanical properties. / Då en stor del av vattenkraftsdammarna i Sverige byggdes på 1950 och 1960-talet börjar många av dessa sakta men säkert brytas ner. Vattenkraftföretagen står inför stora utmaningar och investerar följaktligen i effektiva reparationsmetoder då dammbrott skulle kunna få allvarliga konsekvenser för människor, den omgivande miljön och för samhället. Flertalet konstruktioner inom vattenkraften är gjorda av betong och kraven på den nya kompletterande betongen är höga. En betong med potentialen att möta och uppfylla dessa höga krav är injekteringsbetongen som i tidigare studier uppvisat lovande resultat beträffande dess mekaniska egenskaper. Med anledning av detta är injekteringsbetongen av intresse att undersöka. Fokus har inte varit på att optimera injekteringsbetongen i syfte att genomföra fullskaliga försök. Istället har huvudsyftet med detta examensarbete varit att studera och analysera injekteringsbetongens mekaniska egenskaper såsom krympning, tryckhållfasthet, spräckhållfasthet, frostbeständighet samt undersöka viktiga parametrar i skapandet av ett homogent och lättflytande cementbruk som med god framgång kunde fylla ut hålrummen mellan grova ballasten. Undersökningarna utfördes genom laboratorieförsök på Vattenfalls betonglaboratorium i Älvkarleby. Vidare har skapandet och utvecklandet av bruket utförts i enlighet med metoder och krav angivna i American Society for Testing and Materials, ASTM standards, samt i Svenska institutet för Standarder, SiS. Totalt gjordes 15 bruksblandningar, dock användes enbart de sista fem till gjutning av provkroppar då lufthalten visade sig vara för låg i dem första tio. Resultaten indikerade på att det är nödvändigt att ersätta luftporbildare med mikrosfärer för att erhålla en lufthalt som uppfyller kraven för betong i exponeringsklass XF3 samt XC4. Injekteringbetongens avflagning efter 56 dygn var mindre än 0.1 kg/m2 och frostbeständigheten kunde därmed klassas som mycket god. Användningen av slagg minskade cementbrukets vattenseparation avsevärt och bidrog även till förbättrade gjutresultat. Dock bidrog det å andra sidan till en ökad krympning hos injekteringsbetongen. En effektivitetsfaktor på 0.6 visade sig vara för låg då injekteringsbetongen med slagg hade en cirka 50 % högre tryckhållfasthet än dem utan. Dessutom visade sig injekteringsbetongens krympning vara mindre än den konventionella betongens efter 63 dagar. Tryckhållfastheten hos injekteringsbetongen utan slagg uppvisade även en cirka 15 % lägre tryckhållfasthet än den konventionella betongens. Vibrering under gjutning visade sig höja tryckhållfastheten hos injekteringsbetongen samt förbättra gjutresultaten. En låg vattenseparation i kombination med en flödestid på cirka 45 sekunder för 1.7 liter bruk visade sig ge bästa gjutresultaten. Resultaten från laboratorieförsöken har visat på att injekteringsbetongen besitter stor potential. Dock bör ytterligare undersökningar genomföras för att bedöma huruvida en mindre ballastfraktion för sanden påverkar brukets förmåga att penetrera den grova ballasten. Vidare bör bruket pumpas in istället för att hällas över den grova ballasten, detta för att se huruvida gjutresultaten samt de mekaniska egenskaperna hos injekteringsbetongen skulle förbättras.

Denna studie avhandlar föräldrars upplevelser gällande sina barns placering i enlighet med lagen med särskilda bestämmelser om vård av unga, (LVU) 2§. Studien har genomförts med en kvalitativ metod, där sju föräldrar intervjuats. Studien undersöker föräldrars upplevelser och därav metodvalet, att kunna ställa respondenternas perspektiv i centrum, i detta avseende deras individuella upplevelser. Studien utgår från teoretiska utgångspunkterna symbolisk interaktionism och intersubjektivitet för att belysa föräldrars upplevelser av att vara förälder till ett placerat barn. Studien belyser även det negativa sociala arvet samt generationstrauma, vilket visar sig i att flera av föräldrarna har erfarenhet av att själva ha utsatts för våld från sina föräldrar och flera har även själva varit familjehemsplacerade som barn. Studiens resultat visade på varierande upplevelser av att få sitt barn placerat enligt 2§ LVU. Flera respondenter beskrev en känsla av att inte uppleva sig själva som förälder förutom under umgänge, andra respondenter ansåg sig själva fortfarande vara föräldrar trots placeringen. De föräldrar som upplevde placeringen som negativ uppgav en känsla av motstånd från familjehemmet samt att bli fråntagen sin roll som förälder. Vissa föräldrar beskrev även en känsla av ambivalens utifrån att de upplevde en viss tacksamhet gentemot familjehemmet trots att föräldern inte samtycker till placeringen. / ABSTRACTThis study deals with parents' experiences regarding the placement of their children in accordance with “lagen med särskilda bestämmelser om vård av unga, (LVU) 2§”. The study was carried out using a qualitative method, where seven parents were interviewed. The study investigates parents' experiences and hence the choice of method, to be able to place the respondents' perspective in the center, in this respect their individual experiences. The study is based on the theoretical starting points of symbolic interactionism and intersubjectivity to shed light on parents' experiences of being the parent of a placed child. The study also highlights the negative social legacy and generational trauma, which is shown in the fact that several of the parents have experience of having been subjected to violence from their parents themselves and several have also themselves been placed in family homes as children. The results of the study showed different experiences of being seated according to § 2 LVU. Several respondents described a feeling of not experiencing themselves as a parent except during socializing, other respondents still considered themselves to be parents despite the placement. The parents who experienced the placement as negative stated a feeling of resistance from the family home and of being deprived of their role as a parent. Some parents also described a feeling of ambivalence based on the fact that they felt a certain gratitude towards the family home despite the parent not consenting to the placement.

Uppsatsens syfte är att genom familjehemsföräldrars berättelser få en ökad kunskap och fördjupad förståelse för hur eftervården tillämpas i praktiken gällande ungdomar i åldern 18 - 23 år som varit placerade i familjehem. För att uppnå studiens syfte har en kvalitativ forskningsansats använts och det empiriska materialet har framtagits genom kvalitativa intervjuer. Inom studien genomfördes sex semistrukturerade intervjuer med totalt åtta familjehemsföräldrar. Under intervjuerna var temaområden vägledande: familjehemsföräldrars bild av dagens "stöd och hjälp efter avslutad  placering”/ eftervård, familjehemsföräldrars upplevelse av ungdomarnas behov av eftervård, samt familjehemsföräldrars syn på sin funktion i eftervården. Utifrån Meads symboliska interaktionism samt Bowlbys anknytingsteori analyserades det empiriska materialet. Resultatet visar att familjehemsföräldrarna ser behovet av ett långvarigt stöd för denna målgrupp, men anser att det som idag finns inte kan kallas eftervård. Vår slutsats är att det finns stora brister i eftervården för ungdomar som har varit placerade i familjehem, samt att insatser som finns inte är funktionellt utformade för denna målgrupp. För att detta ska kunna förändras krävs att ungdomarnas och familjehemsföräldrarnas behov i eftervården blir synliga. Familjehemsföräldrars uppdrag i eftervården behöver även utvecklas och förstärkas. / The purpose of the essay is to gain an increased knowledge and in-depth understanding of the use of aftercare in the practice of adolescents aged 18 - 23, who have been placed in a family home, through foster parents stories. To achieve the aim of the study, a qualitative research approach has been used and the empirical material has been developed through qualitative interviews. The study was conducted by six semistructured interviews of eight foster parents in total. During the interviews, thematic areas were indicative: foster parents view of “support and help after leaving care” a.k.a. aftercare, foster parents perception of adolescent need for aftercare and the foster parents view of their function in the aftercare. The empirin was analyzed based on concepts within Mead´s symbolic interactionism and Bowly´s theory of attachment. The result shows that foster parents see the need for a long term support for this target group, but believes that the existing support after leaving care cannot be called aftercare. Our conclusion is that there are major shortcomings in the aftercare of adolescents who have been placed in foster families, and that efforts that are available are not functionally designed for this target group. In order for this to change, it is necessary that the adolescents and foster parents needs of the aftercare are highlighted. The foster parents assignments in the aftercare need also to be developed and strengthened.

<p>This thesis describes the implementation of a real-time, full search, 16x16 bidirectional motion estimation at 24 frames per second with the record performance of 155 Gop/s (1538 ops/pixel) at a high clock rate of 125 MHz. The core of bidirectional motion estimation uses close to 100% FPGA resources with 7 Gbit/s bandwidth to external memory. The architecture allows extremely controlled, macro level floor-planning with parameterized block size, image size, placement coordinates and data words length. The FPGA chip is part of the board that was developed at the Institute of Computer & Communication Networking Engineering, Technical University Braunschweig Germany, in collaboration with Grass Valley Germany in the FlexFilm research project. The goal of the project was to develop hardware and programming methodologies for real-time digital film image processing. Motion estimation core uses FlexWAFE reconfigurable architecture where FPGAs are configured using macro components that consist of weakly programmable address generation units and data stream processing units. Bidirectional motion estimation uses two cores of motion estimation engine (MeEngine) forming main data processing unit for backward and forward motion vectors. The building block of the core of motion estimation is an RPM-macro which represents one processing element and performs 10-bit difference, a comparison, and 19-bit accumulation on the input pixel streams. In order to maximize the throughput between elements, the processing element is replicated and precisely placed side-by-side by using four hierarchal levels, where each level is a very compact entity with its own local control and placement methodology. The achieved speed was further improved by regularly inserting pipeline stages in the processing chain.</p>