Pigmenttien suodatus

Työn tavoitteena oli tutkia neljän eri koesuotimen soveltuvuutta pigmenttilietteen kiintoainepitoisuuden nostoon. Työssä käytetyt suotimet olivat Larox PF 0,1, Certus-CU-0047, Crossflow-koesuodin ja Steuerung ET06-linko. Suodinväliaineena Larox PF 0,1-suotimessa ja Crossflow-koesuotimessa käytettiin Tamfelt Oyj:n suodinkankaita. Certus-CU-0047-suotimessa suodinväliaineena toimi keraaminen membraani. Työn kirjallisuusosassa tarkasteltiin partikkelien karakterisointia, suodatuksen teoriaa, kakkusuodatusta, suodinväliaineista suodinkankaita ja membraaneja. Lisäksi tarkasteltiin paperin päällystämiseen käytettyjen pigmenttien ominaisuuksia ja niiden suodatusta. Kokeet suoritettiin Larox PF 0,1, Certus-CU-0047 ja Crossflow-koesuotimella vakiopainesuodatuksena yhdellä paineella. Steuerung ET06-lingon kokeet suoritettiin vakiovirtaussuodatuksena käyttäen kolmea eri virtausnopeutta ja kolmea eri lingon ja ruuvin kierrossuhdetta. Työssä suodatettiin neljää erilaista pigmenttilietettä. Suodoksista otettiin näytteitä niiden sameuden määrittämiseksi. Larox Pf 0,1-suotimella ja Steuerung ET06-dekantterilingolla saatiin nostettua pigmenttilietteen kiintoainepitoisuutta erittäin hyvin. Loput kaksi suodinta eivät sovellu tähän tarkoitukseen ollenkaan. Vakiopainesuodatuksissa suodatusajat muodostuivat kuitenkin liian pitkiksi. Suodoksien kiintoainepitoisuudet olivat suurimmaksi osaltaan pieniä lukuun ottamatta tiettyjä suodinkankaita. Lietteen 2 kiintoainepitoisuuden nostaminen oli kaikilla suotimilla erittäin vaikeaa, muut lietteet suodattuivat kohtuullisen hyvin. Näiden kokeiden perusteella voidaan sanoa, että varsinkin vakiovirtaussuodatus soveltuu hyvin ainakin tiettyjen pigmenttilietteiden kiintoainepitoisuuden nostoon.

Kohinan poisto spektrikuvista epälineaaristen suodattimien avulla

Tietokoneiden vuosi vuodelta kasvanut prosessointikyky mahdollistaa spektrikuvien hyö- dyntämisen harmaasävy- ja RGB-värikuvien sijaan yhä useampien ongelmien ratkaisemi- sessa. Valitettavasti häiriöiden suodatuksen tutkimus on jäänyt jälkeen tästä kehityksestä. Useimmat menetelmät on testattu vain harmaasävy- tai RGB-värikuvien yhteydessä, mut- ta niiden toimivuutta ei ole testattu spektrikuvien suhteen. Tässä diplomityössä tutkitaan erilaisia menetelmiä bittivirheiden poistamisessa spektrikuvista. Uutena menetelmänä työssä käytetään kuutiomediaanisuodatinta ja monivaiheista kuutio- mediaanisuodatinta. Muita tutkittuja menetelmiä olivat vektorimediaanisuodatus, moni- vaiheinen vektorimediaanisuodatus, sekä rajattu keskiarvosuodatus. Kuutiosuodattimilla pyrittiin hyödyntämään spektrikuvien kaistojen välillä olevaa korrelaatiota ja niillä pääs- tiinkin kokonaisuuden kannalta parhaisiin tuloksiin. Kaikkien suodattimien toimintaa tutkittiin kahdella eri 224 komponenttisella spektriku- valla lisäämällä kuviin satunnaisia bittivirheitä.

Sellupesurin rakenteellinen kehittäminen

Tämä diplomityö on tehty Andritz Oy:lle Washers & Filters tuoteryhmään. Työ on osa pienten sellupesureiden tuotekehitysprojektia. Tavoitteena on vertailla olemassa olevaa tuotekehitysaineistoa ja tuoda esiin suunnitteluprosessi, jolla DD – sellupesurin osien rakenteita voidaan järjestelmällisesti kehittää. Diplomityössä tutkittuja osia ovat tiiviste–elementti, päätypalkki ja rumpu. Tiiviste–elementtejä vertailtiin olemassa olevan tuotekehitysaineiston osalta, sekä tutkittiin geneettisiin algoritmeihin pohjautuvan topologian optimoinnin soveltuvuutta tiiviste-elementin suunnitteluun. Päätypalkin ja rummun optimaaliset geometriat selvitettiin geneettisiä algoritmejä hyödyntävällä topologisella optimoinnilla. Optimaalisten topologioiden perusteella suunniteltiin valmistettavissa olevat rakenteet joiden ainevahvuudet määrättiin alustavasti vakion variointiin perustuvalla optimoinnilla. Tällä menettelyllä saatiin päätypalkista ja rummusta aikaiseksi aikaisempaa kevyemmät rakenteet. Topologian optimointi huomattiin soveltuvan rakenteisiin, joiden kuormitus- ja kiinnitystiedot ovat yksiselitteisesti määrätyt.

Source separation techniques applied to linear prediction

The prediction filters are well known models for signal estimation, in communications, control and many others areas. The classical method for deriving linear prediction coding (LPC) filters is often based on the minimization of a mean square error (MSE). Consequently, second order statistics are only required, but the estimation is only optimal if the residue is independent and identically distributed (iid) Gaussian. In this paper, we derive the ML estimate of the prediction filter. Relationships with robust estimation of auto-regressive (AR) processes, with blind deconvolution and with source separation based on mutual information minimization are then detailed. The algorithm, based on the minimization of a high-order statistics criterion, uses on-line estimation of the residue statistics. Experimental results emphasize on the interest of this approach.

Improving Pitch Tracking Performance in Hard Noise Conditions by a Preprocessing Based on Mathematical Morphology

In this paper we show how a nonlinear preprocessing of speech signal -with high noise- based on morphological filters improves the performance of robust algorithms for pitch tracking (RAPT). This result happens for a very simple morphological filter. More sophisticated ones could even improve such results. Mathematical morphology is widely used in image processing and has a great amount of applications. Almost all its formulations derived in the two-dimensional framework are easily reformulated to be adapted to one-dimensional context

Security in multicast communication

Multicast is one method to transfer information in IPv4 based communication. Other methods are unicast and broadcast. Multicast is based on the group concept where data is sent from one point to a group of receivers and this remarkably saves bandwidth. Group members express an interest to receive data by using Internet Group Management Protocol and traffic is received by only those receivers who want it. The most common multicast applications are media streaming applications, surveillance applications and data collection applications. There are many data security methods to protect unicast communication that is the most common transfer method in Internet. Popular data security methods are encryption, authentication, access control and firewalls. The characteristics of multicast such as dynamic membership cause that all these data security mechanisms can not be used to protect multicast traffic. Nowadays the protection of multicast traffic is possible via traffic restrictions where traffic is allowed to propagate only to certain areas. One way to implement this is packet filters. Methods tested in this thesis are MVR, IGMP Filtering and access control lists which worked as supposed. These methods restrict the propagation of multicast but are laborious to configure in a large scale. There are also a few manufacturerspecific products that make possible to encrypt multicast traffic. These separate products are expensive and mainly intended to protect video transmissions via satellite. Investigation of multicast security has taken place for several years and the security methods that will be the results of the investigation are getting ready. An IETF working group called MSEC is standardizing these security methods. The target of this working group is to standardize data security protocols for multicast during 2004.

Trust region versus line search for computing the optical flow

We consider the numerical treatment of the optical flow problem by evaluating the performance of the trust region method versus the line search method. To the best of our knowledge, the trust region method is studied here for the first time for variational optical flow computation. Four different optical flow models are used to test the performance of the proposed algorithm combining linear and nonlinear data terms with quadratic and TV regularization. We show that trust region often performs better than line search; especially in the presence of non-linearity and non-convexity in the model.

The induced generalized OWA operator

We present the induced generalized ordered weighted averaging (IGOWA) operator. It is a new aggregation operator that generalizes the OWA operator by using the main characteristics of two well known aggregation operators: the generalized OWA and the induced OWA operator. Then, this operator uses generalized means and order inducing variables in the reordering process. With this formulation, we get a wide range of aggregation operators that include all the particular cases of the IOWA and the GOWA operator, and a lot of other cases such as the induced ordered weighted geometric (IOWG) operator and the induced ordered weighted quadratic averaging (IOWQA) operator. We further generalize the IGOWA operator by using quasi-arithmetic means. The result is the Quasi-IOWA operator. Finally, we also develop a numerical example of the new approach in a financial decision making problem.

Antireflection coatings from analogy between electron scattering and spin precession

We use the analogy between scattering of a wave from a potential, and the precession of a spin-half particle in a magnetic field, to gain insight into the design of an antireflection coating for electrons in a semiconductor superlattice. It is shown that the classic recipes derived for optics are generally not applicable due to the different dispersion law for electrons. Using the stability conditions we show that a Poisson distribution of impedance steps is a better approximation than is a Gaussian distribution. Examples are given of filters with average transmissivity exceeding 95% over an allowed band.

Filtration of effluents for microirrigation systems

Clogging, measured through head loss across filters, and the filtration quality of different filters using different effluents were studied. The filters used were: 115, 130, and 200 m disc filters; 98, 115, 130, and 178 m screen filters; and a sand filter filled with a single layer of sand with an effective diameter of 0.65 mm. The filters were used with a meat industry effluent and secondary and tertiary effluents of two wastewater treatment plants. It was observed that clogging depended on the type of effluent. With the meat industry effluent, the poorest quality effluent, disc filters clogged more than the other filter types. When the wastewater treatment plant effluents were used, the disc filters showed less frequent clogging. Several physical and chemical parameters, such as total suspended solids, chemical oxygen demand, turbidity, electrical conductivity, pH, and number of particles, were analyzed in the effluents at the entry and exit points of the filters. In general, filters did not reduce the values of the main clogging parameters to a great degree. It was found that the parameter that explained the clogging, expressed as Boucher’s filterability index, was different depending on the type of effluent and filter. The best quality of filtration was achieved with a sand filter when the meat industry effluent was used. No significant differences were observed between the quality of filtration of disc and screen filters when operating with the secondary and tertiary effluents

What can ecosystems learn? Expanding evolutionary ecology with learning theory.

BACKGROUND: The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? RESULTS: Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, 'unsupervised learning', well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community's response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. CONCLUSIONS: This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions. REVIEWERS: This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder.

Oxidative potential of particles in different occupational environments: a pilot study

The oxidative potential (OP) of particulate matter has been proposed as a toxicologically relevant metric. This concept is already frequently used for hazard characterization of ambient particles but it is still seldom applied in the occupational field. The objective of this study was to assess the OP in two different types of workplaces and to investigate the relationship between the OP and the physicochemical characteristics of the collected particles. At a toll station, at the entrance of a tunnel ('Tunnel' site), and at three different mechanical yards ('Depot' sites), we assessed particle mass (PM4 and PM2.5 and size distribution), number and surface area, organic and elemental carbon, polycyclic aromatic hydrocarbon (PAH), and four quinones as well as iron and copper concentration. The OP was determined directly on filters without extraction by using the dithiothreitol assay (DTT assay-OP(DTT)). The averaged mass concentration of respirable particles (PM4) at the Tunnel site was about twice the one at the Depot sites (173±103 and 90±36 µg m(-3), respectively), whereas the OP(DTT) was practically identical for all the sites (10.6±7.2 pmol DTT min(-1) μg(-1) at the Tunnel site; 10.4±4.6 pmol DTT min(-1) μg(-1) at the Depot sites). The OP(DTT) of PM4 was mostly present on the smallest PM2.5 fraction (OP(DTT) PM2.5: 10.2±8.1 pmol DTT min(-1) μg(-1); OP(DTT) PM4: 10.5±5.8 pmol DTT min(-1) μg(-1) for all sites), suggesting the presence of redox inactive components in the PM2.5-4 fraction. Although the reactivity was similar at the Tunnel and Depot sites irrespective of the metric chosen (OP(DTT) µg(-1) or OP(DTT) m(-3)), the chemicals associated with OP(DTT) were different between the two types of workplaces. The organic carbon, quinones, and/or metal content (Fe, Cu) were strongly associated with the DTT reactivity at the Tunnel site whereas only Fe and PAH were associated (positively and negatively, respectively) with this reactivity at the Depot sites. These results demonstrate the feasibility of measuring of the OP(DTT) in occupational environments and suggest that the particulate OP(DTT) is integrative of different physicochemical properties. This parameter could be a potentially useful exposure proxy for investigating particle exposure-related oxidative stress and its consequences. Further research is needed mostly to demonstrate the association of OP(DTT) with relevant oxidative endpoints in humans exposed to particles.

Does Rigidity of Prices Hide Collusion?

Cartel detection is one of the most basic and most complicated tasks of competition authorities. In recent years, however, variance filters have provided a fairly simple tool for rejecting the existence of price-fixing, with the added advantage that the methodology requires only a low volume of data. In this paper we analyze two aspects of variance filters: 1- the relationship established between market structure and price rigidity, and 2- the use of different benchmarks for implementing the filters. This paper addresses these two issues by applying a variance filter to a gasoline retail market characterized by a set of unique features. Our results confirm the positive relationship between monopolies and price rigidity, and the variance filter's ability to detect non-competitive behavior when an appropriate benchmark is used. Our findings should serve to promote the implementation of this methodology among competition authorities, albeit in the awareness that a more exhaustive complementary analysis is required.

Predicting richness and composition in mountain insect communities at high resolution: a new test of the SESAM framework

Aim The aim of this study was to test different modelling approaches, including a new framework, for predicting the spatial distribution of richness and composition of two insect groups. Location The western Swiss Alps. Methods We compared two community modelling approaches: the classical method of stacking binary prediction obtained fromindividual species distribution models (binary stacked species distribution models, bS-SDMs), and various implementations of a recent framework (spatially explicit species assemblage modelling, SESAM) based on four steps that integrate the different drivers of the assembly process in a unique modelling procedure. We used: (1) five methods to create bS-SDM predictions; (2) two approaches for predicting species richness, by summing individual SDM probabilities or by modelling the number of species (i.e. richness) directly; and (3) five different biotic rules based either on ranking probabilities from SDMs or on community co-occurrence patterns. Combining these various options resulted in 47 implementations for each taxon. Results Species richness of the two taxonomic groups was predicted with good accuracy overall, and in most cases bS-SDM did not produce a biased prediction exceeding the actual number of species in each unit. In the prediction of community composition bS-SDM often also yielded the best evaluation score. In the case of poor performance of bS-SDM (i.e. when bS-SDM overestimated the prediction of richness) the SESAM framework improved predictions of species composition. Main conclusions Our results differed from previous findings using community-level models. First, we show that overprediction of richness by bS-SDM is not a general rule, thus highlighting the relevance of producing good individual SDMs to capture the ecological filters that are important for the assembly process. Second, we confirm the potential of SESAM when richness is overpredicted by bS-SDM; limiting the number of species for each unit and applying biotic rules (here using the ranking of SDM probabilities) can improve predictions of species composition

Neuroinflammatory TNFα Impairs Memory via Astrocyte Signaling.

The occurrence of cognitive disturbances upon CNS inflammation or infection has been correlated with increased levels of the cytokine tumor necrosis factor-α (TNFα). To date, however, no specific mechanism via which this cytokine could alter cognitive circuits has been demonstrated. Here, we show that local increase of TNFα in the hippocampal dentate gyrus activates astrocyte TNF receptor type 1 (TNFR1), which in turn triggers an astrocyte-neuron signaling cascade that results in persistent functional modification of hippocampal excitatory synapses. Astrocytic TNFR1 signaling is necessary for the hippocampal synaptic alteration and contextual learning-memory impairment observed in experimental autoimmune encephalitis (EAE), an animal model of multiple sclerosis (MS). This process may contribute to the pathogenesis of cognitive disturbances in MS, as well as in other CNS conditions accompanied by inflammatory states or infections.