Improved optical character recognition by matched filtering

The effectiveness of linear matched filters for improved character discrimination in presence of random noise and poorly defined characters has been investigated. We have found that although the performance of the filter in presence of random noise is reasonably good (16 dB gain in signal-to-noise-ratio) its performance is poor when the unknown character is distorted (linear shift and rotation).

Improved optical character recognition by matched filtering

The effectiveness of linear matched filters for improved character discrimination in presence of random noise and poorly defined characters has been investigated. We have found that although the performance of the filter in presence of random noise is reasonably good (16 dB gain in signal-to-noise-ratio) its performance is poor when the unknown character is distorted (linear shift and rotation).

Stochastic Filtering

The stochastic filtering has been in general an estimation of indirectly observed states given observed data. This means that one is discussing conditional expected values as being one of the most accurate estimation, given the observations in the context of probability space. In my thesis, I have presented the theory of filtering using two different kind of observation process: the first one is a diffusion process which is discussed in the first chapter, while the third chapter introduces the latter which is a counting process. The majority of the fundamental results of the stochastic filtering is stated in form of interesting equations, such the unnormalized Zakai equation that leads to the Kushner-Stratonovich equation. The latter one which is known also by the normalized Zakai equation or equally by Fujisaki-Kallianpur-Kunita (FKK) equation, shows the divergence between the estimate using a diffusion process and a counting process. I have also introduced an example for the linear gaussian case, which is mainly the concept to build the so-called Kalman-Bucy filter. As the unnormalized and the normalized Zakai equations are in terms of the conditional distribution, a density of these distributions will be developed through these equations and stated by Kushner Theorem. However, Kushner Theorem has a form of a stochastic partial differential equation that needs to be verify in the sense of the existence and uniqueness of its solution, which is covered in the second chapter.

Cointegration and causal relationships between energy consumption and output: Assessing the evidence from Australia

In this paper, we describe our investigation of the cointegration and causal relationships between energy consumption and economic output in Australia over a period of five decades. The framework used in this paper is the single-sector aggregate production function, which is the first comprehensive approach used in an Australian study of this type to include energy, capital and labour as separate inputs of production. The empirical evidence points to a cointegration relationship between energy and output and implies that energy is an important variable in the cointegration space, as are conventional inputs capital and labour. We also find some evidence of bidirectional causality between GDP and energy use. Although the evidence of causality from energy use to GDP was relatively weak when using the thermal aggregate of energy use, once energy consumption was adjusted for energy quality, we found strong evidence of Granger causality from energy use to GDP in Australia over the investigated period. The results are robust, irrespective of the assumptions of linear trends in the cointegration models, and are applicable for different econometric approaches.

The long-run impact of Information and Communication Technology on economic output: The case of Australia

This paper investigates the cointegration and causal relationships between Information and Communication Technology (ICT) and economic output in Australia using data for about five decades. The framework used in this paper is the single-sector aggregate production function, which is the first comprehensive approach of this kind to include ICT and non-ICT capital and other factors to examine long-run Granger causality. The empirical evidence points to a cointegration relationship between ICT capital and output, and implies that ICT capital Granger causes economic output and multifactor productivity, as does non-ICT capital.

Energy-output linkages in Australia: Implications for emissions reduction policies

The study investigates the long-run and dynamic relationships between energy consumption and output in Australia using a multivariate cointegration and causality framework. Using both Engle-Granger and Johansen cointegration approaches, the study finds that energy consumption and real Gross Domestic Product are cointegrated. The Granger causality tests suggest bidirectional Granger causality between energy consumption and real GDP, and Granger endogeineity in the system. Since the energy sector largely contributes to carbon emissions in Australia, we suggest that direct measures to reduce carbon by putting constraints on the energy consumption would pose significant economic costs for the Australian economy.

Effect of fourier domain quantization on matched filtering

It is shown that the use of a coarsely quantized binary digital hologram as a matched filter on an optical computer does not degrade signal-to-noise ratio (SNR) appreciably.

Do Measurement Errors in GDP Announcements Cause Output Fluctuations?

This thesis studies empirically whether measurement errors in aggregate production statistics affect sentiment and future output. Initial announcements of aggregate production are subject to measurement error, because many of the data required to compile the statistics are produced with a lag. This measurement error can be gauged as the difference between the latest revised statistic and its initial announcement. Assuming aggregate production statistics help forecast future aggregate production, these measurement errors are expected to affect macroeconomic forecasts. Assuming agents’ macroeconomic forecasts affect their production choices, these measurement errors should affect future output through sentiment. This thesis is primarily empirical, so the theoretical basis, strategic complementarity, is discussed quite briefly. However, it is a model in which higher aggregate production increases each agent’s incentive to produce. In this circumstance a statistical announcement which suggests aggregate production is high would increase each agent’s incentive to produce, thus resulting in higher aggregate production. In this way the existence of strategic complementarity provides the theoretical basis for output fluctuations caused by measurement mistakes in aggregate production statistics. Previous empirical studies suggest that measurement errors in gross national product affect future aggregate production in the United States. Additionally it has been demonstrated that measurement errors in the Index of Leading Indicators affect forecasts by professional economists as well as future industrial production in the United States. This thesis aims to verify the applicability of these findings to other countries, as well as study the link between measurement errors in gross domestic product and sentiment. This thesis explores the relationship between measurement errors in gross domestic production and sentiment and future output. Professional forecasts and consumer sentiment in the United States and Finland, as well as producer sentiment in Finland, are used as the measures of sentiment. Using statistical techniques it is found that measurement errors in gross domestic product affect forecasts and producer sentiment. The effect on consumer sentiment is ambiguous. The relationship between measurement errors and future output is explored using data from Finland, United States, United Kingdom, New Zealand and Sweden. It is found that measurement errors have affected aggregate production or investment in Finland, United States, United Kingdom and Sweden. Specifically, it was found that overly optimistic statistics announcements are associated with higher output and vice versa.

The Structure and Short-Term Development of Finnish Industries in the 1920s and 1930s : An Input-output Approach

Tutkimuksen tavoitteena on tuottaa uutta tietoa Suomen kansantalouden rakenteesta ja lyhyen aikavälin kehityksestä 1920- ja 1930-luvulla. Tutkimus toteutettiin laatimalla kansantaloutta kuvaava panos-tuotostaulu vuodelle 1928 sekä sen laajennus, panos-tuotosmalli. Aineiston avulla kuvataan kansantalouden rakenteellisia riippuvuuksia, tuotannon avaintoimialoja sekä näiden vaikutusta kansantalouteen. Lisäksi tutkimuksessa tarkastellaan kansantalouden tuontiriippuvuutta sekä tuontitullien vaikutusta hintoihin 1930-luvun laman aikana. Tutkimuksen perusteella voitiin identifioida Suomen kansantalouden avaintoimialat vuonna 1928: maatalous, metsätalous, elintarviketeollisuus, puuteollisuus, paperiteollisuus ja rakennustoiminta. Erityisesti elintarviketeollisuuden vahva rooli kansantaloudessa oli kenties yllättävää, erityisesti kun huomioidaan kuinka vähän toimiala on saanut huomiota osakseen taloushistorian tutkimuksessa. Tutkimus osoitti, että Suomen vienti oli pääomavaltaisempaa kuin tuonti. Vaikka tämän tuloksen tulkinta on varauksellinen, tutkimus pystyi osoittamaan ja kvantifioimaan toimialojen työ- ja pääomapanoksen osuuden tuotoksesta yksityiskohtaisesti. Panos-tuotosmallilla arvioitiin puuteollisuuden, paperiteollisuuden ja rakennustoiminnan ajanjaksona 1928-32 tapahtuneen loppukäytön muutoksen vaikutusta kansantalouteen. Merkittävä havainto on, että rakennustoiminnan loppukäytön muutoksella oli erittäin suuri kasvua vähentävä vaikutus koko kansantaloudessa. Talonrakennusinvestointien romahtaminen aiheutti lähes 13 prosentin tuotannon laskun kansantaloudessa. Vaikutus oli jopa suurempi kuin puuteollisuuden viennin romahtamisen. Tulokset osoittavat toisaalta, että yksityisen kulutuksen merkitys kansantaloudelle oli erittäin vahva. Esimerkiksi puuteollisuuden viennin romahtaminen aiheutti yli 4 % tuotannon vähenemisen mutta huomioitaessa mallissa myös yksityisen kulutuksen väheneminen, oli kokonaisvaikutus yli 10 %. Yksityisen kulutuksen huomioiminen mallissa siis yli kaksinkertaisti toimialojen vaikutukset kansantalouteen. Tulokset vahvistivat aiemmissa tutkimuksissa esitettyjä johtopäätöksiä tullipolitiikasta ja osoittivat maatalouteen läheisesti liittyvän elintarviketeollisuuden olleen eniten suojeltu toimiala kansantaloudessa. Muut kotimarkkinoiden toimialat eivät kuitenkaan hyötyneet tullipolitiikasta lamakauden aikana. Panos-tuotoshintamallilla osoitettiin, ettei tullipolitiikka ollut niin onnistunutta kuin aikalaistutkimuksissa väitettiin, vaan tullit korkeintaan pystyivät hidastamaan hintojen alenemista. Tutkimuksen liitteenä esitetään kaikki keskeiset Suomen kansantaloutta vuonna 1928 kuvaavat tilastolliset taulukot, mukaan lukien käyttö- ja tarjontataulukot, panos-tuotostaulukot, panoskertoimet, Leontiefin käänteismatriisi sekä työ- ja pääomapanoskertoimet.

Output feedback control of networked systems with a stochastic communication protocol

This paper addresses an output feedback control problem for a class of networked control systems (NCSs) with a stochastic communication protocol. Under the scenario that only one sensor is allowed to obtain the communication access at each transmission instant, a stochastic communication protocol is first defined, where the communication access is modelled by a discrete-time Markov chain with partly unknown transition probabilities. Secondly, by use of a network-based output feedback control strategy and a time-delay division method, the closed-loop system is modeled as a stochastic system with multi time-varying delays, where the inherent characteristic of the network delay is well considered to improve the control performance. Then, based on the above constructed stochastic model, two sufficient conditions are derived for ensuring the mean-square stability and stabilization of the system under consideration. Finally, two examples are given to show the effectiveness of the proposed method.

Assimilation of remote sensing and hydrological data using adaptive filtering techniques for watershed modelling

The knowledge of hydrological variables (e. g. soil moisture, evapotranspiration) are of pronounced importance in various applications including flood control, agricultural production and effective water resources management. These applications require the accurate prediction of hydrological variables spatially and temporally in watershed/basin. Though hydrological models can simulate these variables at desired resolution (spatial and temporal), often they are validated against the variables, which are either sparse in resolution (e. g. soil moisture) or averaged over large regions (e. g. runoff). A combination of the distributed hydrological model (DHM) and remote sensing (RS) has the potential to improve resolution. Data assimilation schemes can optimally combine DHM and RS. Retrieval of hydrological variables (e. g. soil moisture) from remote sensing and assimilating it in hydrological model requires validation of algorithms using field studies. Here we present a review of methodologies developed to assimilate RS in DHM and demonstrate the application for soil moisture in a small experimental watershed in south India.

Suppression of the zero-order term in off-axis digital holography through nonlinear filtering

We present experimental validation of a new reconstruction method for off-axis digital holographic microscopy (DHM). This method effectively suppresses the object autocorrelation,namely, the zero-order term,from holographic data,thereby improving the reconstruction bandwidth of complex wavefronts. The algorithm is based on nonlinear filtering and can be applied to standard DHM setups with realistic recording conditions.We study the robustness of the technique under different experimental configurations,and quantitatively demonstrate its enhancement capabilities on phase signals.

Early and late electrophysiological effects of distractor frequency in picture naming: Reconciling input and output accounts

The “distractor-frequency effect” refers to the finding that high-frequency (HF) distractor words slow picture naming less than low-frequency distractors in the picture–word interference paradigm. Rival input and output accounts of this effect have been proposed. The former attributes the effect to attentional selection mechanisms operating during distractor recognition, whereas the latter attributes it to monitoring/decision mechanisms operating on distractor and target responses in an articulatory buffer. Using high-density (128-channel) EEG, we tested hypotheses from these rival accounts. In addition to conducting stimulus- and response-locked whole-brain corrected analyses, we investigated the correct-related negativity, an ERP observed on correct trials at fronto-central electrodes proposed to reflect the involvement of domain general monitoring. The wholebrain ERP analysis revealed a significant effect of distractor frequency at inferior right frontal and temporal sites between 100 and 300-msec post-stimulus onset, during which lexical access is thought to occur. Response-locked, region of interest (ROI) analyses of fronto-central electrodes revealed a correct-related negativity starting 121 msec before and peaking 125 msec after vocal onset on the grand averages. Slope analysis of this component revealed a significant difference between HF and lowfrequency distractor words, with the former associated with a steeper slope on the time windowspanning from100 msec before to 100 msec after vocal onset. The finding of ERP effects in time windows and components corresponding to both lexical processing and monitoring suggests the distractor frequency effect is most likely associated with more than one physiological mechanism.

Virus-Specific Cytolytic Antibodies to Nonstructural Protein 1 of Japanese Encephalitis Virus Effect Reduction of Virus Output from Infected Cells

We demonstrate the presence of nonstructural protein 1 (NS1)-specific antibodies in a significant proportion of convalescent-phase human serum samples obtained from a cohort in an area where Japanese encephalitis virus (JEV) is endemic. Sera containing antibodies to NS1 but not those with antibodies to other JEV proteins, such as envelope, brought about complement-mediated lysis of JEV-infected BHK-21 cells. Target cells infected with a recombinant poxvirus expressing JEV NS1 on the cell surface confirmed the NS1 specificity of cytolytic antibodies. Mouse anti-NS1 cytolytic sera caused a complement-dependent reduction in virus output from infected human cells, demonstrating their important role in viral control. Antibodies elicited by JEV NS1 did not cross lyse West Nile virus- or dengue virus-infected cells despite immunoprecipitating the NS1 proteins of these related flaviviruses. Additionally, JEV NS1 failed to bind complement factor H, in contrast to NS1 of West Nile virus, suggesting that the NS1 proteins of different flaviviruses have distinctly different mechanisms for interacting with the host. Our results also point to an important role for JEV NS1-specific human immune responses in protection against JE and provide a strong case for inclusion of the NS1 protein in next generation of JEV vaccines.

Scouts, Promoters, and Connectors: The Roles of Ratings in Nearest-Neighbor Collaborative Filtering

Recommender systems aggregate individual user ratings into predictions of products or services that might interest visitors. The quality of this aggregation process crucially affects the user experience and hence the effectiveness of recommenders in e-commerce. We present a characterization of nearest-neighbor collaborative filtering that allows us to disaggregate global recommender performance measures into contributions made by each individual rating. In particular, we formulate three roles-scouts, promoters, and connectors-that capture how users receive recommendations, how items get recommended, and how ratings of these two types are themselves connected, respectively. These roles find direct uses in improving recommendations for users, in better targeting of items and, most importantly, in helping monitor the health of the system as a whole. For instance, they can be used to track the evolution of neighborhoods, to identify rating subspaces that do not contribute ( or contribute negatively) to system performance, to enumerate users who are in danger of leaving, and to assess the susceptibility of the system to attacks such as shilling. We argue that the three rating roles presented here provide broad primitives to manage a recommender system and its community.