Game Theoretic Bidding Strategies for Auctions in Green Electricity Markets

Electricity markets in the United States presently employ an auction mechanism to determine the dispatch of power generation units. In this market design, generators submit bid prices to a regulation agency for review, and the regulator conducts an auction selection in such a way that satisfies electricity demand. Most regulators currently use an auction selection method that minimizes total offer costs ["bid cost minimization" (BCM)] to determine electric dispatch. However, recent literature has shown that this method may not minimize consumer payments, and it has been shown that an alternative selection method that directly minimizes total consumer payments ["payment cost minimization" (PCM)] may benefit social welfare in the long term. The objective of this project is to further investigate the long term benefit of PCM implementation and determine whether it can provide lower costs to consumers. The two auction selection methods are expressed as linear constraint programs and are implemented in an optimization software package. Methodology for game theoretic bidding simulation is developed using EMCAS, a real-time market simulator. Results of a 30-day simulation showed that PCM reduced energy costs for consumers by 12%. However, this result will be cross-checked in the future with two other methods of bid simulation as proposed in this paper.

The information bottleneck method for genome-wide association studies

In population studies, most current methods focus on identifying one outcome-related SNP at a time by testing for differences of genotype frequencies between disease and healthy groups or among different population groups. However, testing a great number of SNPs simultaneously has a problem of multiple testing and will give false-positive results. Although, this problem can be effectively dealt with through several approaches such as Bonferroni correction, permutation testing and false discovery rates, patterns of the joint effects by several genes, each with weak effect, might not be able to be determined. With the availability of high-throughput genotyping technology, searching for multiple scattered SNPs over the whole genome and modeling their joint effect on the target variable has become possible. Exhaustive search of all SNP subsets is computationally infeasible for millions of SNPs in a genome-wide study. Several effective feature selection methods combined with classification functions have been proposed to search for an optimal SNP subset among big data sets where the number of feature SNPs far exceeds the number of observations. ^ In this study, we take two steps to achieve the goal. First we selected 1000 SNPs through an effective filter method and then we performed a feature selection wrapped around a classifier to identify an optimal SNP subset for predicting disease. And also we developed a novel classification method-sequential information bottleneck method wrapped inside different search algorithms to identify an optimal subset of SNPs for classifying the outcome variable. This new method was compared with the classical linear discriminant analysis in terms of classification performance. Finally, we performed chi-square test to look at the relationship between each SNP and disease from another point of view. ^ In general, our results show that filtering features using harmononic mean of sensitivity and specificity(HMSS) through linear discriminant analysis (LDA) is better than using LDA training accuracy or mutual information in our study. Our results also demonstrate that exhaustive search of a small subset with one SNP, two SNPs or 3 SNP subset based on best 100 composite 2-SNPs can find an optimal subset and further inclusion of more SNPs through heuristic algorithm doesn't always increase the performance of SNP subsets. Although sequential forward floating selection can be applied to prevent from the nesting effect of forward selection, it does not always out-perform the latter due to overfitting from observing more complex subset states. ^ Our results also indicate that HMSS as a criterion to evaluate the classification ability of a function can be used in imbalanced data without modifying the original dataset as against classification accuracy. Our four studies suggest that Sequential Information Bottleneck(sIB), a new unsupervised technique, can be adopted to predict the outcome and its ability to detect the target status is superior to the traditional LDA in the study. ^ From our results we can see that the best test probability-HMSS for predicting CVD, stroke,CAD and psoriasis through sIB is 0.59406, 0.641815, 0.645315 and 0.678658, respectively. In terms of group prediction accuracy, the highest test accuracy of sIB for diagnosing a normal status among controls can reach 0.708999, 0.863216, 0.639918 and 0.850275 respectively in the four studies if the test accuracy among cases is required to be not less than 0.4. On the other hand, the highest test accuracy of sIB for diagnosing a disease among cases can reach 0.748644, 0.789916, 0.705701 and 0.749436 respectively in the four studies if the test accuracy among controls is required to be at least 0.4. ^ A further genome-wide association study through Chi square test shows that there are no significant SNPs detected at the cut-off level 9.09451E-08 in the Framingham heart study of CVD. Study results in WTCCC can only detect two significant SNPs that are associated with CAD. In the genome-wide study of psoriasis most of top 20 SNP markers with impressive classification accuracy are also significantly associated with the disease through chi-square test at the cut-off value 1.11E-07. ^ Although our classification methods can achieve high accuracy in the study, complete descriptions of those classification results(95% confidence interval or statistical test of differences) require more cost-effective methods or efficient computing system, both of which can't be accomplished currently in our genome-wide study. We should also note that the purpose of this study is to identify subsets of SNPs with high prediction ability and those SNPs with good discriminant power are not necessary to be causal markers for the disease.^

Adaptive clinical trial design for targeted therapy development

The development of targeted therapy involve many challenges. Our study will address some of the key issues involved in biomarker identification and clinical trial design. In our study, we propose two biomarker selection methods, and then apply them in two different clinical trial designs for targeted therapy development. In particular, we propose a Bayesian two-step lasso procedure for biomarker selection in the proportional hazards model in Chapter 2. In the first step of this strategy, we use the Bayesian group lasso to identify the important marker groups, wherein each group contains the main effect of a single marker and its interactions with treatments. In the second step, we zoom in to select each individual marker and the interactions between markers and treatments in order to identify prognostic or predictive markers using the Bayesian adaptive lasso. In Chapter 3, we propose a Bayesian two-stage adaptive design for targeted therapy development while implementing the variable selection method given in Chapter 2. In Chapter 4, we proposed an alternate frequentist adaptive randomization strategy for situations where a large number of biomarkers need to be incorporated in the study design. We also propose a new adaptive randomization rule, which takes into account the variations associated with the point estimates of survival times. In all of our designs, we seek to identify the key markers that are either prognostic or predictive with respect to treatment. We are going to use extensive simulation to evaluate the operating characteristics of our methods.^

La financiación y la inversión inmobiliaria : repercusiones en el urbanismo y la arquitectura

El presente trabajo de tesis doctoral tiene por finalidad demostrar que las decisiones de financiación y de inversión inmobiliaria en España influyen de manera determinante en la configuración del proceso urbanístico y arquitectónico. Este planteamiento inicial obliga a formular las siguientes cuestiones: - En una primera fase, la situación en España del sector inmobiliario y su relación con el sector financiero en el contexto de la actual crisis financiera, iniciada en el año 2008. - Los métodos de análisis y selección de inversiones y su aplicación a los proyectos de inversión en función de la naturaleza de los activos inmobiliarios. - La valoración inmobiliaria para determinadas entidades financieras. - Características de la gestión financiera empresarial y de las empresas inmobiliarias. - El origen de fondos para financiar el proceso urbanístico y arquitectónico mediante las instituciones de inversión colectiva inmobiliaria y el mercado inmobiliario. - La regulación actual en España, a nivel estatal, del suelo como materia prima del sector inmobiliario. - La edificación residencial y el mercado inmobiliario en el actual contexto económico. - La posibilidad de crear en España un mercado de derivados basado en activos inmobiliarios. - Cómo repercute la actividad económica, a través de la inversión y la financiación, en los activos inmobiliarios, en el urbanismo y la arquitectura. Las cuestiones precedentes se resuelven de forma sistemática y metodológica en esta tesis doctoral estructurada en tres grandes bloques (inversión, financiación y repercusiones en el urbanismo y la arquitectura), obteniendo una serie de respuestas reflejadas en el desarrollo del presente trabajo que se sintetizan en las siguientes líneas: - La actual crisis financiera iniciada en el año 2008 ha provocado en España el colapso del sector inmobiliario y una nueva concepción en la naturaleza de los activos inmobiliarios. El sector inmobiliario trae causa del sector financiero, en especial del crédito bancario. - Dependencia y vinculación del sector inmobiliario español de la política monetaria europea: la incorporación de España a la moneda única transforma por completo el sector inmobiliario español. - Los métodos de análisis y selección de inversiones se conforman como instrumentos relevantes que nos permiten jerarquizar nuestros proyectos. No obstante, presentan una serie de limitaciones y dificultades de aplicación práctica, por lo que no deben considerarse como herramientas que nos aporten una única solución irrefutable. - La valoración de activos inmobiliarios se constituye en un pilar básico que fundamenta la correcta aplicación de los fondos. - La inversión en activos inmobiliarios puede realizarse de forma directa o indirecta. En este último supuesto, con una influencia relevante de las innovaciones financieras surgidas en los últimos años. - Las instituciones de inversión colectiva y el mercado hipotecario constituyen instituciones fundamentales capaces de captar importantes cantidades de fondos que impulsan y financian el proceso urbanístico y arquitectónico. - El complejo y cambiante sistema jurídico español en materia de suelo, dificulta la implementación de los procesos urbanísticos y arquitectónicos. - Tras la crisis financiera de 2008, los activos inmobiliarios tienen un comportamiento similar a otros activos en cuanto a subidas y bajadas de precios. En el actual sistema económico, la especulación forma parte inherente a la naturaleza de los activos inmobiliarios. - Desde una perspectiva teórica, existe la posibilidad de crear un mercado de derivados que tenga como subyacente activos de naturaleza inmobiliaria. - Sin actividad económica, el proceso urbanístico y arquitectónico carecerá finalmente de sentido y tenderá a desaparecer. No obstante, son las innovaciones tecnológicas, a nivel de producto y proceso, las principales causantes del impulso de la actividad económica. - A pesar de lo expresado en los documentos urbanísticos internacionales, la transformación del urbanismo y la arquitectura dependen principalmente de la actividad económica y la tecnología. En un segundo nivel, la inversión y la financiación condicionan y definen el urbanismo y la arquitectura, incluso a nivel de proyecto si se pretende su materialización. En base al desarrollo previo, el objetivo fundamental de esta tesis doctoral ha sido demostrar que las decisiones de financiación y de inversión tienen una importancia capital y determinan la configuración de los activos inmobiliario, del urbanismo y la arquitectura, por lo que deben ser tenidas en cuenta no sólo en su materialización sino incluso en la propia concepción del proceso creativo. ABSTRACT The present dissertation aims to show that real estate financing and investment decisions in Spain play a predominant role in structuring urban development and architectural solutions. The issues addressed to support that contention include: - As a preliminary study, the situation of the real estate industry in Spain and its relationship to the financial sector in the context of the 2008 financial crisis. - The methods used to analyse and select investments and their application to investment projects, by type of real estate asset. - Appraisal of certain financial institutions’ real estate asset holdings. - Characteristics of financial institution and real estate company corporate management. - Sourcing funds for financing urban development and architecture through real estate investment trusts and the real estate market. - Present nation-wide regulations on landed property in Spain as a raw material for the real estate industry. - Residential building construction and the real estate market in the present economic context. - The possibility of creating a real estate asset-based derivatives market in Spain - The impact of economic activity, through investment and financing, on real estate assets, urban development and architecture. The aforementioned issues are addressed systematically and methodically in this dissertation, which is divided into three major units: investment, financing, and impact on urban development and architecture. The conclusions drawn are summarised below. - The financial crisis that began in 2008 has induced the collapse of the Spanish real estate industry and spawned a new perception of the nature of real estate assets. The real estate industry is dependent upon the financial sector, in particular on bank loans. - The Spanish real estate industry also depends on and is related to European monetary policy: Spain’s adherence to the single currency ushered in a thorough overhaul of its real estate industry. - Investment analysis and selection methods constitute highly suitable tools for project evaluation and ranking. Nonetheless, inasmuch as their practical implementation is subject to a series of limitations and difficulties, they should not be thought able to deliver a single irrefutable solution. - Real estate asset appraisal is a mainstay to the rightful application of funds. - Real estate asset investments can be made directly or indirectly. The latter approach is heavily influenced by the financial innovations forthcoming in recent years. - Investment trusts and the mortgage market are key institutions able to raise substantial funding, thereby driving and financing urban development and architecture. - Spain’s complex and changing legal provisions on land management are an obstacle to urban development and architecture. - Since the 2008 crisis, real estate assets have behaved much like other assets in terms of rising and falling prices. In the present economic context, speculation is indivisible from real estate assets. - Theoretically speaking, a derivatives market with real estate holdings as the underlying assets lies within the realm of possibility. - In the absence of economic activity, urban development and architecture are senseless pursuits and tend to disappear. Technological innovation in products and processes are the main drivers of economic activity. - Despite the opinion expressed in international papers on the subject, the transformation of urban development and architecture depend primarily on economic activity and technology. In a second dimension, investment and financing condition and define urban development and architecture, even at the design level for projects aspiring to actual construction. Pursuant to the foregoing, the primary aim of this dissertation is to show that financial and investment decisions are of cardinal importance and determine the structure of real estate assets, urban development and architecture. They must consequently be borne in mind not only in connection with implementation, but also with conceptual design and the creative process itself. I

Marine mammal species detection and classification

Thesis (Ph.D.)--University of Washington, 2016-04

The HIPASS catalogue - II. Completeness reliability and parameter accuracy

The H I Parkes All Sky Survey (HIPASS) is a blind extragalactic H I 21-cm emission-line survey covering the whole southern sky from declination -90degrees to +25degrees. The HIPASS catalogue (HICAT), containing 4315 H I-selected galaxies from the region south of declination +2degrees, is presented in Meyer et al. (Paper I). This paper describes in detail the completeness and reliability of HICAT, which are calculated from the recovery rate of synthetic sources and follow-up observations, respectively. HICAT is found to be 99 per cent complete at a peak flux of 84 mJy and an integrated flux of 9.4 Jy km. s(-1). The overall reliability is 95 per cent, but rises to 99 per cent for sources with peak fluxes >58 mJy or integrated flux >8.2 Jy km s(-1). Expressions are derived for the uncertainties on the most important HICAT parameters: peak flux, integrated flux, velocity width and recessional velocity. The errors on HICAT parameters are dominated by the noise in the HIPASS data, rather than by the parametrization procedure.

A new method for conservation planning for the persistence of multiple species

Although the aim of conservation planning is the persistence of biodiversity, current methods trade-off ecological realism at a species level in favour of including multiple species and landscape features. For conservation planning to be relevant, the impact of landscape configuration on population processes and the viability of species needs to be considered. We present a novel method for selecting reserve systems that maximize persistence across multiple species, subject to a conservation budget. We use a spatially explicit metapopulation model to estimate extinction risk, a function of the ecology of the species and the amount, quality and configuration of habitat. We compare our new method with more traditional, area-based reserve selection methods, using a ten-species case study, and find that the expected loss of species is reduced 20-fold. Unlike previous methods, we avoid designating arbitrary weightings between reserve size and configuration; rather, our method is based on population processes and is grounded in ecological theory.

The 2df SDSS LRG and QSO survey: evolution of the luminosity function of luminous red galaxies to z=0.6

We present new measurements of the luminosity function (LF) of luminous red galaxies (LRGs) from the Sloan Digital Sky Survey (SDSS) and the 2dF SDSS LRG and Quasar (2SLAQ) survey. We have carefully quantified, and corrected for, uncertainties in the K and evolutionary corrections, differences in the colour selection methods, and the effects of photometric errors, thus ensuring we are studying the same galaxy population in both surveys. Using a limited subset of 6326 SDSS LRGs (with 0.17 < z < 0.24) and 1725 2SLAQ LRGs (with 0.5 < z < 0.6), for which the matching colour selection is most reliable, we find no evidence for any additional evolution in the LRG LF, over this redshift range, beyond that expected from a simple passive evolution model. This lack of additional evolution is quantified using the comoving luminosity density of SDSS and 2SLAQ LRGs, brighter than M-0.2r - 5 log h(0.7) = - 22.5, which are 2.51 +/- 0.03 x 10(-7) L circle dot Mpc(-3) and 2.44 +/- 0.15 x 10(-7) L circle dot Mpc(-3), respectively (< 10 per cent uncertainty). We compare our LFs to the COMBO-17 data and find excellent agreement over the same redshift range. Together, these surveys show no evidence for additional evolution (beyond passive) in the LF of LRGs brighter than M-0.2r - 5 log h(0.7) = - 21 ( or brighter than similar to L-*).. We test our SDSS and 2SLAQ LFs against a simple 'dry merger' model for the evolution of massive red galaxies and find that at least half of the LRGs at z similar or equal to 0.2 must already have been well assembled (with more than half their stellar mass) by z similar or equal to 0.6. This limit is barely consistent with recent results from semi-analytical models of galaxy evolution.

Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.

Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.

Self-adaptive probabilistic roadmap generation for intelligent virtual agents

Agents inhabiting large scale environments are faced with the problem of generating maps by which they can navigate. One solution to this problem is to use probabilistic roadmaps which rely on selecting and connecting a set of points that describe the interconnectivity of free space. However, the time required to generate these maps can be prohibitive, and agents do not typically know the environment in advance. In this paper we show that the optimal combination of different point selection methods used to create the map is dependent on the environment, no point selection method dominates. This motivates a novel self-adaptive approach for an agent to combine several point selection methods. The success rate of our approach is comparable to the state of the art and the generation cost is substantially reduced. Self-adaptation therefore enables a more efficient use of the agent's resources. Results are presented for both a set of archetypal scenarios and large scale virtual environments based in Second Life, representing real locations in London.

Fenntarthatósági szempontok beépítése a beszállító értékelésébe a DEA/CI összetett indikátorok módszere alkalmazásával (Integrating sustainability criteria in supplier evaluation with application of the method of of DEA/CI complex indicators)

A környezeti hatások rendszerint túlmutatnak egy vállalat határain, éppen ezért az ellátási lánc kontextusban a környezeti szempontok érvényesítése során fontos szerep jut a beszerzési döntéseknek is. Számos olyan példát lehetne említeni, amikor egy adott szempont szerint egy alternatíva környezetileg előnyös, de az ellátási lánc egészét nézve már környezetterhelő. A környezeti hatások ellátási lánc szinten való mérése azonban komoly kihívásokat jelent. Ezzel jelentős kutatásokat és fejlesztéseket inspirált a téma. Az egyik olyan terület, amelyben komoly kutatási eredmények születtek, az a környezeti szempontok beszállítói értékelésbe való beépítése. A kutatások ezen irányához csatlakozva a szerzők tanulmányunkban azt keresik, hogyan lehet meghatározni az egyik legáltalánosabban használt szállítóértékelési módszerben, a súlyozott pontrendszerben egy adott szemponthoz azt a súlyt, amely mellett az adott szempont már döntésbefolyásoló tényezővé válik. Ehhez a DEA (Data Envelopment Analysis) összetett indikátorok (Composite Indicators, CI) módszerét alkalmazzák. A szempontok közös súlyának fontossága megállapításához a lineáris programozás elméletét használják. _____ Management decisions often have an environmental effect not just within the company, but outside as well, this is why supply chain context is highlighted in literature. Measuring environmental issues of supply decisions raise a lot of problems from methodological and practical point of view. This inspires a rapidly growing literature as a lot of studies were published focusing on how to incorporate environmental issues into supplier evaluation. This paper contributes to this stream of research as it develops a method to help weight selection. In the authors’ paper the method of Data Envelope Analysis (DEA) is used to study the extension of traditional supplier selection methods with environmental factors. The selection of the weight system can control the result of the selection process.

Statistical analysis and meta-analysis of microarray data

The microarray technology provides a high-throughput technique to study gene expression. Microarrays can help us diagnose different types of cancers, understand biological processes, assess host responses to drugs and pathogens, find markers for specific diseases, and much more. Microarray experiments generate large amounts of data. Thus, effective data processing and analysis are critical for making reliable inferences from the data. ^ The first part of dissertation addresses the problem of finding an optimal set of genes (biomarkers) to classify a set of samples as diseased or normal. Three statistical gene selection methods (GS, GS-NR, and GS-PCA) were developed to identify a set of genes that best differentiate between samples. A comparative study on different classification tools was performed and the best combinations of gene selection and classifiers for multi-class cancer classification were identified. For most of the benchmarking cancer data sets, the gene selection method proposed in this dissertation, GS, outperformed other gene selection methods. The classifiers based on Random Forests, neural network ensembles, and K-nearest neighbor (KNN) showed consistently god performance. A striking commonality among these classifiers is that they all use a committee-based approach, suggesting that ensemble classification methods are superior. ^ The same biological problem may be studied at different research labs and/or performed using different lab protocols or samples. In such situations, it is important to combine results from these efforts. The second part of the dissertation addresses the problem of pooling the results from different independent experiments to obtain improved results. Four statistical pooling techniques (Fisher inverse chi-square method, Logit method. Stouffer's Z transform method, and Liptak-Stouffer weighted Z-method) were investigated in this dissertation. These pooling techniques were applied to the problem of identifying cell cycle-regulated genes in two different yeast species. As a result, improved sets of cell cycle-regulated genes were identified. The last part of dissertation explores the effectiveness of wavelet data transforms for the task of clustering. Discrete wavelet transforms, with an appropriate choice of wavelet bases, were shown to be effective in producing clusters that were biologically more meaningful. ^

A comprehensive portfolio construction under stochastic environment

Prior research has established that idiosyncratic volatility of the securities prices exhibits a positive trend. This trend and other factors have made the merits of investment diversification and portfolio construction more compelling. ^ A new optimization technique, a greedy algorithm, is proposed to optimize the weights of assets in a portfolio. The main benefits of using this algorithm are to: (a) increase the efficiency of the portfolio optimization process, (b) implement large-scale optimizations, and (c) improve the resulting optimal weights. In addition, the technique utilizes a novel approach in the construction of a time-varying covariance matrix. This involves the application of a modified integrated dynamic conditional correlation GARCH (IDCC - GARCH) model to account for the dynamics of the conditional covariance matrices that are employed. ^ The stochastic aspects of the expected return of the securities are integrated into the technique through Monte Carlo simulations. Instead of representing the expected returns as deterministic values, they are assigned simulated values based on their historical measures. The time-series of the securities are fitted into a probability distribution that matches the time-series characteristics using the Anderson-Darling goodness-of-fit criterion. Simulated and actual data sets are used to further generalize the results. Employing the S&P500 securities as the base, 2000 simulated data sets are created using Monte Carlo simulation. In addition, the Russell 1000 securities are used to generate 50 sample data sets. ^ The results indicate an increase in risk-return performance. Choosing the Value-at-Risk (VaR) as the criterion and the Crystal Ball portfolio optimizer, a commercial product currently available on the market, as the comparison for benchmarking, the new greedy technique clearly outperforms others using a sample of the S&P500 and the Russell 1000 securities. The resulting improvements in performance are consistent among five securities selection methods (maximum, minimum, random, absolute minimum, and absolute maximum) and three covariance structures (unconditional, orthogonal GARCH, and integrated dynamic conditional GARCH). ^

Off-line and on-line affective recognition of a computer user through a biosignal processing approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. ^ In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment ("relaxation" vs. "stress") are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. ^ For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). ^ In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the "relaxation" vs. "stress" states.^