Neighborhood image processing operations on Field Programmable Gate Array (FPGA) are considered as memory intensive operations. A large memory bandwidth is required to transfer the required pixel data from external memory to the processing unit. On-chip image buffers are employed to reduce this data transfer rate. Conventional image buffers, implemented either by using FPGA logic resources or embedded memories are resource inefficient. They exhaust the limited FPGA resources quickly. Consequently, hardware implementation of neighborhood operations becomes expensive, and integrating them in resource constrained devices becomes unfeasible. This paper presents a resource efficient FPGA based on-chip buffer architecture. The proposed architecture utilizes full capacity of a single Xilinx BlockRAM (BRAM36 primitive) for storing multiple rows of input image. To get multiple pixels/clock in a user defined scan order, an efficient duty-cycle based memory accessing technique is coupled with a customized addressing circuitry. This accessing technique exploits switching capabilities of BRAM to read 4 pixels in a single clock cycle without degrading system frequency. The addressing circuitry provides multiple pixels/clock in any user defined scan order to implement a wide range of neighborhood operations. With the saving of 83% BRAM resources, the buffer architecture operates at 278 MHz on Xilinx Artix-7 FPGA with an efficiency of 1.3 clock/pixel. It is thus capable to fulfill real time image processing requirements for HD image resolution (1080 × 1920) @103 fcps.
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Background: The emergence of smartphones and wearable sensor technologies enables easy and unobtrusive monitoring of physiological and psychological data related to an individual’s resilience. Heart rate variability (HRV) is a promising biomarker for resilience based on between-subject population studies, but observational studies that apply a within-subject design and use wearable sensors in order to observe HRV in a naturalistic real-life context are needed. Objective: This study aims to explore whether resting HRV and total sleep time (TST) are indicative and predictive of the within-day accumulation of the negative consequences of stress and mental exhaustion. The tested hypotheses are that demands are positively associated with stress and resting HRV buffers against this association, stress is positively associated with mental exhaustion and resting HRV buffers against this association, stress negatively impacts subsequent-night TST, and previous-evening mental exhaustion negatively impacts resting HRV, while previous-night TST buffers against this association. Methods: In total, 26 interns used consumer-available wearables (Fitbit Charge 2 and Polar H7), a consumer-available smartphone app (Elite HRV), and an ecological momentary assessment smartphone app to collect resilience-related data on resting HRV, TST, and perceived demands, stress, and mental exhaustion on a daily basis for 15 weeks. Results: Multiple linear regression analysis of within-subject standardized data collected on 2379 unique person-days showed that having a high resting HRV buffered against the positive association between demands and stress (hypothesis 1) and between stress and mental exhaustion (hypothesis 2). Stress did not affect TST (hypothesis 3). Finally, mental exhaustion negatively predicted resting HRV in the subsequent morning but TST did not buffer against this (hypothesis 4). Conclusions: To our knowledge, this study provides first evidence that having a low within-subject resting HRV may be both indicative and predictive of the short-term accumulation of the negative effects of stress and mental exhaustion, potentially forming a negative feedback loop. If these findings can be replicated and expanded upon in future studies, they may contribute to the development of automated resilience interventions that monitor daily resting HRV and aim to provide users with an early warning signal when a negative feedback loop forms, to prevent the negative impact of stress on long-term health outcomes.
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The purpose of this study is to investigate the impact that unevenly allocating buffer capacity has on throughput and average buffer level regarding unreliable lines to better understand the relevant factors in supply chain design. Results show that the best patterns for unreliable merging lines in terms of generating higher throughput rates (TR), as compared to a balanced merging line counterpart, are those where total available buffer capacity is allocated between workstations in either an inverted bowl pattern (i.e. concentrating buffer capacity towards the centre of the line), or a balanced line pattern. In contrast, when considering the trade-off between generating revenue resulting from TR and reducing cost created by average buffer levels (ABL), we found that the balanced pattern was not the best pattern. The best pattern was dependent on the length of the line and on the total buffer capacity as shorter lines with very constrained buffers were best served with an inverted bowl pattern while longer lines had the best results when applying an ascending buffer allocation pattern. Longer lines, in contrast, had the best results regarding the trade-off between TR and ABL, on average, by allocating buffer capacity evenly in one of the parallel lines while applying any other pattern in the remaining parallel line.
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In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
Hanze Entrance ontwikkelt in een consortium een onderwijs infrastructuur voor de toekomstige ‘Heat Heroes’. Het bedrijfsleven wil samen met het MBO en HBO onderwijsveld een opleidingsomgeving creeren die de warmtetransitie in de gebouwde omgeving middels warmtenetten kan versnellen. Dit doet zij door een bi-directionele warmteverbinding tussen Hanze Entrance en Warmtestad aan te leggen en de infrastructuur op Entrance vergaand onderling te integreren en digitaliseren. Entrance fungeert hier met meerdere bronnen (electrolizers, H2-WKK, warmtebuffers) als een ‘prosumer’ die warmte kan afnemen en terugleveren aan warmtestad. Het project wil met een interdisciplanaire aanpak alle aspecten van warmtetransistie op Entrance kunnen testen, emuleren en/of onderzoeken. Hiermee wordt deze infrastructuur een uniek ontwikkelplatform en het fundament waarmee het consortium onderzoek doen en onderwijs-ontwikkelingen worden gestoeld.Beoogd projectresultaat:Een functionerende ‘eerste opstelling’ van een prosumer heathub op entrance. Dit Warmte Ontvangst Station kent meerdere facetten van lokale hergebruik her distributie na opslag van warmte. Daarvoor wordt de ringleiding,WKO en andere facetten op Entrance volledig geïntegreerd. Deze integratie geeft Warmtestad de mogelijkheden om Entrance als een wijkstation te ontwikkelen. Het eindresultaat is een beslisboom en business case voor meerdere wijkstations op basis van de use-case Entrance.
Of het nu om de gezondheid van de stedelingen, de biodiversiteit of ons drinkwater gaat, de kwaliteit van het oppervlaktewater in onze steden is enorm belangrijk. Maar die kwaliteit staat onder druk als gevolg van bijvoorbeeld klimaatverandering en verstedelijking. Nieuwe, creatieve samenwerkingsrelaties tussen kennisinstellingen zijn nodig om de (oorzaken van dalende) waterkwaliteit te onderzoeken, monitoringssystemen en interventies te ontwikkelen en modellen te valideren. Het lectoraat “Meten is weten; gezond oppervlaktewater door innovaties in monitoring, modellering en maatregelen’’ zal hier invulling aan geven door een samenwerking op te zetten tussen Aeres Hogeschool Almere en kennisinstituut Deltares. Ontwikkelde en nog te ontwikkelen innovaties bij Deltares zullen door Aeres met studenten in de praktijk worden ingezet om waterkwaliteit te meten. Het lectoraat zal met overheden zoals gemeentes, waterschappen en provincies en innovatieve bedrijven een inspirerend en lerend netwerk ontwikkelen op het gebied van (stedelijke) waterkwaliteit. Door onderzoek in de praktijk, zoals het inzetten van de drijfvuilverwijderaar op de Floriade of het meten van de waterkwaliteit in de directe omgeving door burgers met te ontwikkelen apps, wordt er niet alleen een beter inzicht in de status van de huidige en toekomstige waterkwaliteit verkregen maar worden burgers ook meer betrokken en kunnen deze meehelpen in het streven naar een optimale waterkwaliteit. Resultaten zullen op diverse manieren en diverse kanalen worden uitgedragen, waarmee het lectoraat ook bijdraagt aan het versterken van de internationale positie van Nederland op het gebied van waterkwaliteitsbeheer
