Near-Threshold Computing

Valmistustekniikoiden kehittyessä IC-piireille saadaan mahtumaan yhä enemmän transistoreja. Monimutkaisemmat piirit mahdollistavat suurempien laskutoimitusmäärien suorittamisen aikayksikössä. Piirien aktiivisuuden lisääntyessä myös niiden energiankulutus lisääntyy, ja tämä puolestaan lisää piirin lämmöntuotantoa. Liiallinen lämpö rajoittaa piirien toimintaa. Tämän takia tarvitaan tekniikoita, joilla piirien energiankulutusta saadaan pienennettyä. Uudeksi tutkimuskohteeksi ovat tulleet pienet laitteet, jotka seuraavat esimerkiksi ihmiskehon toimintaa, rakennuksia tai siltoja. Tällaisten laitteiden on oltava energiankulutukseltaan pieniä, jotta ne voivat toimia pitkiä aikoja ilman akkujen lataamista. Near-Threshold Computing on tekniikka, jolla pyritään pienentämään integroitujen piirien energiankulutusta. Periaatteena on käyttää piireillä pienempää käyttöjännitettä kuin piirivalmistaja on niille alunperin suunnitellut. Tämä hidastaa ja haittaa piirin toimintaa. Jos kuitenkin laitteen toiminnassa pystyään hyväksymään huonompi laskentateho ja pienentynyt toimintavarmuus, voidaan saavuttaa säästöä energiankulutuksessa. Tässä diplomityössä tarkastellaan Near-Threshold Computing -tekniikkaa eri näkökulmista: aluksi perustuen kirjallisuudesta löytyviin aikaisempiin tutkimuksiin, ja myöhemmin tutkimalla Near-Threshold Computing -tekniikan soveltamista kahden tapaustutkimuksen kautta. Tapaustutkimuksissa tarkastellaan FO4-invertteriä sekä 6T SRAM -solua piirisimulaatioiden avulla. Näiden komponenttien käyttäytymisen Near-Threshold Computing –jännitteillä voidaan tulkita antavan kattavan kuvan suuresta osasta tavanomaisen IC-piirin pinta-alaa ja energiankulusta. Tapaustutkimuksissa käytetään 130 nm teknologiaa, ja niissä mallinnetaan todellisia piirivalmistusprosessin tuotteita ajamalla useita Monte Carlo -simulaatioita. Tämä valmistuskustannuksiltaan huokea teknologia yhdistettynä Near-Threshold Computing -tekniikkaan mahdollistaa matalan energiankulutuksen piirien valmistaminen järkevään hintaan. Tämän diplomityön tulokset näyttävät, että Near-Threshold Computing pienentää piirien energiankulutusta merkittävästi. Toisaalta, piirien nopeus heikkenee, ja yleisesti käytetty 6T SRAM -muistisolu muuttuu epäluotettavaksi. Pidemmät polut logiikkapiireissä sekä transistorien kasvattaminen muistisoluissa osoitetaan tehokkaiksi vastatoimiksi Near- Threshold Computing -tekniikan huonoja puolia vastaan. Tulokset antavat perusteita matalan energiankulutuksen IC-piirien suunnittelussa sille, kannattaako käyttää normaalia käyttöjännitettä, vai laskea sitä, jolloin piirin hidastuminen ja epävarmempi käyttäytyminen pitää ottaa huomioon.

Evaluation of follicular lymphoid depletion in the Bursa of Fabricius: an alternative methodology using digital image analysis and artificial neural networks

Fifty Bursa of Fabricius (BF) were examined by conventional optical microscopy and digital images were acquired and processed using Matlab® 6.5 software. The Artificial Neuronal Network (ANN) was generated using Neuroshell® Classifier software and the optical and digital data were compared. The ANN was able to make a comparable classification of digital and optical scores. The use of ANN was able to classify correctly the majority of the follicles, reaching sensibility and specificity of 89% and 96%, respectively. When the follicles were scored and grouped in a binary fashion the sensibility increased to 90% and obtained the maximum value for the specificity of 92%. These results demonstrate that the use of digital image analysis and ANN is a useful tool for the pathological classification of the BF lymphoid depletion. In addition it provides objective results that allow measuring the dimension of the error in the diagnosis and classification therefore making comparison between databases feasible.

Potencial de transdiferenciação neural das células-tronco mesenquimais da medula óssea de equino

Os primeiros estudos demonstrando o potencial de trandiferenciação neural das células-tronco mesenquimais (CTMs) provenientes da medula óssea (MO) foram conduzidos em camundogos e humanos no início da década de 2000. Após esse período, o número de pesquisas e publicações com o mesmo propósito tem aumentado, mas com raros ou escassos estudos na espécie equina. Nesse sentindo, o objetivo desse trabalho foi avaliar o potencial in vitro da transdiferenciação neural das CTMs provenientes da MO de equinos utilizando-se dois protocolos: P1 (forksolin e ácido retinóico) e P2 (2-βmecarptoetanol). Após a confirmação das linhagens mesenquimais, pela positividade para o marcador CD90 (X=97,94%), negatividade para o marcador CD34 e resposta positiva a diferenciação osteogênica, as CTMs foram submetidas a transdiferenciação neural (P1 e P2) para avaliação morfológica e expressão dos marcadores neurais GFAP e β3 tubulina por citometria de fluxo. Os resultados revelaram mudanças morfológicas em graus variados entre os protocolos testados. No protocolo 1, vinte quatro horas após a incubação com o meio de diferenciação neural, grande proporção de células (>80%) apresentaram morfologia semelhante a células neurais, caracterizadas por retração do corpo celular e grande número de projeções protoplasmáticas (filopodia). Por outro lado, de forma comparativa, já nos primeiros 30 minutos após a exposição ao antioxidante β-mercaptoetanol (P2) as CTMs apresentaram rápida mudança morfológica caracterizada principalmente por retração do corpo celular e menor número de projeções protoplasmáticas. Também ficou evidenciado com o uso deste protocolo, menor aderência das células após tempo de exposição ao meio de diferenciação, quando comparado ao P1. Com relação a análise imunofenotípica foi observado uma maior (P<0,001) expressão dos marcadores GFAP e β3 tubulina ao término do P2 quando comparado ao P1. A habilidade das CTMs em gerar tipos celulares relacionados a linhagem neural é complexa e multifatorial, dependendo não só dos agentes indutores, mas também do ambiente no qual estas células são cultivadas. Desta forma um maior número de estudos é necessário para o melhor entendimento do processo de transdiferenciação neural a partir de CTMs de equinos.

Neural mechanisms underlying conscious and unconscious vision. Evidence from event-related potentials and transcranial magnetic stimulation

Vision affords us with the ability to consciously see, and use this information in our behavior. While research has produced a detailed account of the function of the visual system, the neural processes that underlie conscious vision are still debated. One of the aims of the present thesis was to examine the time-course of the neuroelectrical processes that correlate with conscious vision. The second aim was to study the neural basis of unconscious vision, that is, situations where a stimulus that is not consciously perceived nevertheless influences behavior. According to current prevalent models of conscious vision, the activation of visual cortical areas is not, as such, sufficient for consciousness to emerge, although it might be sufficient for unconscious vision. Conscious vision is assumed to require reciprocal communication between cortical areas, but views differ substantially on the extent of this recurrent communication. Visual consciousness has been proposed to emerge from recurrent neural interactions within the visual system, while other models claim that more widespread cortical activation is needed for consciousness. Studies I-III compared models of conscious vision by studying event-related potentials (ERP). ERPs represent the brain’s average electrical response to stimulation. The results support the model that associates conscious vision with activity localized in the ventral visual cortex. The timing of this activity corresponds to an intermediate stage in visual processing. Earlier stages of visual processing may influence what becomes conscious, although these processes do not directly enable visual consciousness. Late processing stages, when more widespread cortical areas are activated, reflect the access to and manipulation of contents of consciousness. Studies IV and V concentrated on unconscious vision. By using transcranial magnetic stimulation (TMS) we show that when early visual cortical processing is disturbed so that subjects fail to consciously perceive visual stimuli, they may nevertheless guess (above chance-level) the location where the visual stimuli were presented. However, the results also suggest that in a similar situation, early visual cortex is necessary for both conscious and unconscious perception of chromatic information (i.e. color). Chromatic information that remains unconscious may influence behavioral responses when activity in visual cortex is not disturbed by TMS. Our results support the view that early stimulus-driven (feedforward) activation may be sufficient for unconscious processing. In conclusion, the results of this thesis support the view that conscious vision is enabled by a series of processing stages. The processes that most closely correlate with conscious vision take place in the ventral visual cortex ~200 ms after stimulus presentation, although preceding time-periods and contributions from other cortical areas such as the parietal cortex are also indispensable. Unconscious vision relies on intact early visual activation, although the location of visual stimulus may be unconsciously resolved even when activity in the early visual cortex is interfered with.

On Energy Efficient Computing Platforms

In accordance with the Moore's law, the increasing number of on-chip integrated transistors has enabled modern computing platforms with not only higher processing power but also more affordable prices. As a result, these platforms, including portable devices, work stations and data centres, are becoming an inevitable part of the human society. However, with the demand for portability and raising cost of power, energy efficiency has emerged to be a major concern for modern computing platforms. As the complexity of on-chip systems increases, Network-on-Chip (NoC) has been proved as an efficient communication architecture which can further improve system performances and scalability while reducing the design cost. Therefore, in this thesis, we study and propose energy optimization approaches based on NoC architecture, with special focuses on the following aspects. As the architectural trend of future computing platforms, 3D systems have many bene ts including higher integration density, smaller footprint, heterogeneous integration, etc. Moreover, 3D technology can signi cantly improve the network communication and effectively avoid long wirings, and therefore, provide higher system performance and energy efficiency. With the dynamic nature of on-chip communication in large scale NoC based systems, run-time system optimization is of crucial importance in order to achieve higher system reliability and essentially energy efficiency. In this thesis, we propose an agent based system design approach where agents are on-chip components which monitor and control system parameters such as supply voltage, operating frequency, etc. With this approach, we have analysed the implementation alternatives for dynamic voltage and frequency scaling and power gating techniques at different granularity, which reduce both dynamic and leakage energy consumption. Topologies, being one of the key factors for NoCs, are also explored for energy saving purpose. A Honeycomb NoC architecture is proposed in this thesis with turn-model based deadlock-free routing algorithms. Our analysis and simulation based evaluation show that Honeycomb NoCs outperform their Mesh based counterparts in terms of network cost, system performance as well as energy efficiency.

Classification of antimicrobial resistance using artificial neural networks and the relationship of 38 genes associated with the virulence of Escherichia coli isolates from broilers

Avian pathogenic Escherichia coli (APEC) is responsible for various pathological processes in birds and is considered as one of the principal causes of morbidity and mortality, associated with economic losses to the poultry industry. The objective of this study was to demonstrate that it is possible to predict antimicrobial resistance of 256 samples (APEC) using 38 different genes responsible for virulence factors, through a computer program of artificial neural networks (ANNs). A second target was to find the relationship between (PI) pathogenicity index and resistance to 14 antibiotics by statistical analysis. The results showed that the RNAs were able to make the correct classification of the behavior of APEC samples with a range from 74.22 to 98.44%, and make it possible to predict antimicrobial resistance. The statistical analysis to assess the relationship between the pathogenic index (PI) and resistance against 14 antibiotics showed that these variables are independent, i.e. peaks in PI can happen without changing the antimicrobial resistance, or the opposite, changing the antimicrobial resistance without a change in PI.

Detection of Horizontal Two-Phase Flow Patterns Through a Neural Network Model

One of the main problems related to the transport and manipulation of multiphase fluids concerns the existence of characteristic flow patterns and its strong influence on important operation parameters. A good example of this occurs in gas-liquid chemical reactors in which maximum efficiencies can be achieved by maintaining a finely dispersed bubbly flow to maximize the total interfacial area. Thus, the ability to automatically detect flow patterns is of crucial importance, especially for the adequate operation of multiphase systems. This work describes the application of a neural model to process the signals delivered by a direct imaging probe to produce a diagnostic of the corresponding flow pattern. The neural model is constituted of six independent neural modules, each of which trained to detect one of the main horizontal flow patterns, and a last winner-take-all layer responsible for resolving when two or more patterns are simultaneously detected. Experimental signals representing different bubbly, intermittent, annular and stratified flow patterns were used to validate the neural model.

What are the opportunities and challenges of cloud computing technology in the healthcare information systems

This thesis discusses the opportunities and challenges of the cloud computing technology in healthcare information systems by reviewing the existing literature on cloud computing and healthcare information system and the impact of cloud computing technology to healthcare industry. The review shows that if problems related to security of data are solved then cloud computing will positively transform the healthcare institutions by giving advantage to the healthcare IT infrastructure as well as improving and giving benefit to healthcare services. Therefore, this thesis will explore the opportunities and challenges that are associated with cloud computing in the context of Finland in order to help the healthcare organizations and stakeholders to determine its direction when it decides to adopt cloud technology on their information systems.

Homogeneous assay technologies in drug screening: Quenching Resonance Energy Transfer (QRET) technique

Information gained from the human genome project and improvements in compound synthesizing have increased the number of both therapeutic targets and potential lead compounds. This has evolved a need for better screening techniques to have a capacity to screen number of compound libraries against increasing amount of targets. Radioactivity based assays have been traditionally used in drug screening but the fluorescence based assays have become more popular in high throughput screening (HTS) as they avoid safety and waste problems confronted with radioactivity. In comparison to conventional fluorescence more sensitive detection is obtained with time-resolved luminescence which has increased the popularity of time-resolved fluorescence resonance energy transfer (TR-FRET) based assays. To simplify the current TR-FRET based assay concept the luminometric homogeneous single-label utilizing assay technique, Quenching Resonance Energy Transfer (QRET), was developed. The technique utilizes soluble quencher to quench non-specifically the signal of unbound fraction of lanthanide labeled ligand. One labeling procedure and fewer manipulation steps in the assay concept are saving resources. The QRET technique is suitable for both biochemical and cell-based assays as indicated in four studies:1) ligand screening study of β2 -adrenergic receptor (cell-based), 2) activation study of Gs-/Gi-protein coupled receptors by measuring intracellular concentration of cyclic adenosine monophosphate (cell-based), 3) activation study of G-protein coupled receptors by observing the binding of guanosine-5’-triphosphate (cell membranes), and 4) activation study of small GTP binding protein Ras (biochemical). Signal-to-background ratios were between 2.4 to 10 and coefficient of variation varied from 0.5 to 17% indicating their suitability to HTS use.

Comparison of Biomass Gasification Technologies for Natural Gas Substitution in Iron Ore Pelletizing Plants

The iron ore pelletizing process consumes high amounts of energy, including nonrenewable sources, such as natural gas. Due to fossil fuels scarcity and increasing concerns regarding sustainability and global warming, at least partial substitution by renewable energy seems inevitable. Gasification projects are being successfully developed in Northern Europe, and large-scale circulating fluidized bed biomass gasifiers have been commissioned in e.g. Finland. As Brazil has abundant biomass resources, biomass gasification is a promising technology in the near future. Biomasses can be converted into product gas through gasification. This work compares different technologies, e.g. air, oxygen and steam gasification, focusing on the use of the product gas in the indurating machine. The use of biosynthetic natural gas is also evaluated. Main parameters utilized to assess the suitability of product gas were adiabatic flame temperature and volumetric flow rate. It was found that low energy content product gas could be utilized in the traveling grate, but it would require burner’s to be changed. On the other hand, bio-SGN could be utilized without any adaptions. Economical assessment showed that all gasification plants are feasible for sizes greater than 60 MW. Bio-SNG production is still more expensive than natural gas in any case.

Video transcoding in a distributed cloud computing environment

Video transcoding refers to the process of converting a digital video from one format into another format. It is a compute-intensive operation. Therefore, transcoding of a large number of simultaneous video streams requires a large amount of computing resources. Moreover, to handle di erent load conditions in a cost-e cient manner, the video transcoding service should be dynamically scalable. Infrastructure as a Service Clouds currently offer computing resources, such as virtual machines, under the pay-per-use business model. Thus the IaaS Clouds can be leveraged to provide a coste cient, dynamically scalable video transcoding service. To use computing resources e ciently in a cloud computing environment, cost-e cient virtual machine provisioning is required to avoid overutilization and under-utilization of virtual machines. This thesis presents proactive virtual machine resource allocation and de-allocation algorithms for video transcoding in cloud computing. Since users' requests for videos may change at di erent times, a check is required to see if the current computing resources are adequate for the video requests. Therefore, the work on admission control is also provided. In addition to admission control, temporal resolution reduction is used to avoid jitters in a video. Furthermore, in a cloud computing environment such as Amazon EC2, the computing resources are more expensive as compared with the storage resources. Therefore, to avoid repetition of transcoding operations, a transcoded video needs to be stored for a certain time. To store all videos for the same amount of time is also not cost-e cient because popular transcoded videos have high access rate while unpopular transcoded videos are rarely accessed. This thesis provides a cost-e cient computation and storage trade-o strategy, which stores videos in the video repository as long as it is cost-e cient to store them. This thesis also proposes video segmentation strategies for bit rate reduction and spatial resolution reduction video transcoding. The evaluation of proposed strategies is performed using a message passing interface based video transcoder, which uses a coarse-grain parallel processing approach where video is segmented at group of pictures level.

Comparison of sustainability of technologies for water desalination

The aim of this work is to perform an in-depth overview on the sustainability of several major commercialized technologies for water desalination and to identify the challenges and propose suggestions for the development of water desalination technologies. The overview of those technologies mainly focuses on the sustainability from the viewpoint of total capital investment, total product cost, energy consumption and global warming index. Additionally, a systematic sustainability assessment methodology has been introduced to validate the assessment process. Conclusions are:1) Reverse osmosis desalination (RO) plants are better than multi-stage flash distillation (MSF) desalination plants and multiple-effect distillation (MED) desalination plants from the viewpoint of energy consumption, global warming index and total production cost; 2)Though energy intensive, MSF plants and MED plants secure their advantages over RO plants by lower total capital investment, wider applicability and purer water desalted and they are still likely to flourish in energy-rich area;3) Water production stage and wastewater disposal stage are the two stages during which most pollutant gases are emitted. The water production stage alone contributes approximately 80~90% of the total pollutant gases emission during its life cycle; 4)The total capital cost per m3 desalted water decreases remarkably with the increasing of plant capacity. The differences between the capital cost per m3 desalted water of RO and other desalination plants will decrease as the capacity increases; 5) It is found that utilities costs serve as the major part of the total product cost, and they account for 91.16%, 85.55% and 71.26% of the total product cost for MSF, MED and RO plants, respectively; 6) The absolute superiority of given technology depends on the actual social-economic situation (energy prices, social policies, technology advancements).

Forecasting of wind speeds and directions with artificial neural networks

In this master’s thesis, wind speeds and directions were modeled with the aim of developing suitable models for hourly, daily, weekly and monthly forecasting. Artificial Neural Networks implemented in MATLAB software were used to perform the forecasts. Three main types of artificial neural network were built, namely: Feed forward neural networks, Jordan Elman neural networks and Cascade forward neural networks. Four sub models of each of these neural networks were also built, corresponding to the four forecast horizons, for both wind speeds and directions. A single neural network topology was used for each of the forecast horizons, regardless of the model type. All the models were then trained with real data of wind speeds and directions collected over a period of two years in the municipal region of Puumala in Finland. Only 70% of the data was used for training, validation and testing of the models, while the second last 15% of the data was presented to the trained models for verification. The model outputs were then compared to the last 15% of the original data, by measuring the mean square errors and sum square errors between them. Based on the results, the feed forward networks returned the lowest generalization errors for hourly, weekly and monthly forecasts of wind speeds; Jordan Elman networks returned the lowest errors when used for forecasting of daily wind speeds. Cascade forward networks gave the lowest errors when used for forecasting daily, weekly and monthly wind directions; Jordan Elman networks returned the lowest errors when used for hourly forecasting. The errors were relatively low during training of the models, but shot up upon simulation with new inputs. In addition, a combination of hyperbolic tangent transfer functions for both hidden and output layers returned better results compared to other combinations of transfer functions. In general, wind speeds were more predictable as compared to wind directions, opening up opportunities for further research into building better models for wind direction forecasting.

International Advanced Water Technologies Centre, IAWTC/LADEC and LUT 2013 : Questionnaire Responses from Russian Drinking Water and Wastewater Treatment Plants

In this report, information is published concerning Russian water and wastewater treatment plants. The information is based on a questionnaire sent to 70 water and wastewater treatment plants in 2012-2013. The questionnaire was prepared by the International Advanced Water Technologies Centre (IAWTC) and Lahti Development Company (LADEC). The questions dealt with an assessment of the present state, the need for changes, renovation, investments, and how to improve the efficiency of the operation by training and investments. A significant need to renew the old pipelines, constructions, and processes was clearly evident. The aggregated answers can be utilized in Russia as internal benchmarking in order to arrange training and plant visits, which were requested in many of the answers. Sharing this open report with the respondents can aid networking and awareness of HELCOM requirements which relate to waste water treatment plants discharging their waste water directly or indirectly into the Baltic Sea. The aim of this report is to provide information for Finnish small and medium size companies (SMEs) as regards possible water related exportation to different parts of Russia.