Novel real-time homodyne coherent receiver using a feed-forward based carrier extraction scheme for phase modulated signals

We report a novel real-time homodyne coherent receiver based on a DPSK optical-electrical-optical (OEO) regenerator used to extract a carrier from carrier-less phase modulated signals based on feed-forward based modulation stripping. The performance of this non-DSP based coherent receiver was evaluated for 10.66Gbit/s BPSK signals. Self-homodyne coherent detection and homodyne detection with an injection-locked local oscillator laser was demonstrated. The performance was evaluated by measuring the electrical signal-to-noise (SNR) and recording the eye diagrams. Using injection-locking for the LO improves the performance and enables homodyne detection with optical injection-locking to operate with carrier-less BPSK signals without the need for polarization multiplexed pilot-tones.

Authorship profiling in a forensic context

There are several unresolved problems in forensic authorship profiling, including a lack of research focusing on the types of texts that are typically analysed in forensic linguistics (e.g. threatening letters, ransom demands) and a general disregard for the effect of register variation when testing linguistic variables for use in profiling. The aim of this dissertation is therefore to make a first step towards filling these gaps by testing whether established patterns of sociolinguistic variation appear in malicious forensic texts that are controlled for register. This dissertation begins with a literature review that highlights a series of correlations between language use and various social factors, including gender, age, level of education and social class. This dissertation then presents the primary data set used in this study, which consists of a corpus of 287 fabricated malicious texts from 3 different registers produced by 96 authors stratified across the 4 social factors listed above. Since this data set is fabricated, its validity was also tested through a comparison with another corpus consisting of 104 naturally occurring malicious texts, which showed that no important differences exist between the language of the fabricated malicious texts and the authentic malicious texts. The dissertation then reports the findings of the analysis of the corpus of fabricated malicious texts, which shows that the major patterns of sociolinguistic variation identified in previous research are valid for forensic malicious texts and that controlling register variation greatly improves the performance of profiling. In addition, it is shown that through regression analysis it is possible to use these patterns of linguistic variation to profile the demographic background of authors across the four social factors with an average accuracy of 70%. Overall, the present study therefore makes a first step towards developing a principled model of forensic authorship profiling.

Állami támogatások és gazdasági teljesítmény. Támogatás-túladagolás a magyar gazdaságfejlesztésben? (An overdose of state aid in the Hungarian economy)

Magyarországon az elmúlt évtizedben a vállalatoknak nyújtott állami támogatás GDP-arányosan számolva az európai uniós átlag 2,7-szerese volt. A cikk megvizsgálja, hogy a támogatások hatása megjelenik-e a magyar gazdaság beruházási, foglalkoztatási, jövedelemtermelési teljesítményében és versenyképességében. Arra a következtetésre jut, hogy egyik területen sem jobb a helyzet, mint azokban az országokban, amelyekben lényegesen alacsonyabb a támogatási arány. A mikroszintű elemzések, értékelések sem támasztják alá azt a vélekedést, hogy az állami támogatások érzékelhetően javítják a gazdasági teljesítményt. A források bősége önmagában is okoz hatékonysági problémákat, mert sok program és szervezet versenyez egymással. A rossz (gyengébb hatékonysági követelményeket támasztó) programok kiszorítják a jó programokat. Ha a versenyképességet érdemben befolyásoló tényezők, például a kedvező jogi szabályozási környezet és az üzleti szolgáltatások jól működő piacai nem adottak, akkor ezek hiányát nem ellensúlyozza a támogatások magas szintje. Magyarország az idén indult hétéves programozási időszakban tovább kívánja növelni a vállalatoknak nyújtott állami támogatások mértékét, miközben nincs egyértelmű válasz arra a kérdésre, hogy milyen módon növelhető a támogatási rendszer jelenleg alacsony hatékonysága. _____ State aid given to enterprises as a proportion of Hungary�s GDP has been 2.7 times the EU average over the past decade. The article examines whether any impact of this high level of state aid can be discerned in investment, employment, income- generation performance, or competitiveness of the Hungarian economy. It seems that in none of these areas is the situation better than in countries that have a markedly lower rate of state aid. Micro-level analyses and evaluations do not support the belief that state aid appreciably improves economic performance. A wealth of resources on its own can cause problems with efficiency, as many programs and organizations compete with each other. Bad (less demanding) programs nudge out the good ones. If the factors significantly determining competitiveness, including a favourable legal and regulatory environment and well-functioning markets of business services, are not in place, a high level of state aid cannot be a proxy for them. In the seven-year programming period beginning this year, Hungary plans to further increase the amount of state aid to enterprises, while there is no clear answer as to how to improve the currently poor efficiency of the state aid system.

A multimedia indexing and retrieval framework for multimedia database systems

The main challenges of multimedia data retrieval lie in the effective mapping between low-level features and high-level concepts, and in the individual users' subjective perceptions of multimedia content. ^ The objectives of this dissertation are to develop an integrated multimedia indexing and retrieval framework with the aim to bridge the gap between semantic concepts and low-level features. To achieve this goal, a set of core techniques have been developed, including image segmentation, content-based image retrieval, object tracking, video indexing, and video event detection. These core techniques are integrated in a systematic way to enable the semantic search for images/videos, and can be tailored to solve the problems in other multimedia related domains. In image retrieval, two new methods of bridging the semantic gap are proposed: (1) for general content-based image retrieval, a stochastic mechanism is utilized to enable the long-term learning of high-level concepts from a set of training data, such as user access frequencies and access patterns of images. (2) In addition to whole-image retrieval, a novel multiple instance learning framework is proposed for object-based image retrieval, by which a user is allowed to more effectively search for images that contain multiple objects of interest. An enhanced image segmentation algorithm is developed to extract the object information from images. This segmentation algorithm is further used in video indexing and retrieval, by which a robust video shot/scene segmentation method is developed based on low-level visual feature comparison, object tracking, and audio analysis. Based on shot boundaries, a novel data mining framework is further proposed to detect events in soccer videos, while fully utilizing the multi-modality features and object information obtained through video shot/scene detection. ^ Another contribution of this dissertation is the potential of the above techniques to be tailored and applied to other multimedia applications. This is demonstrated by their utilization in traffic video surveillance applications. The enhanced image segmentation algorithm, coupled with an adaptive background learning algorithm, improves the performance of vehicle identification. A sophisticated object tracking algorithm is proposed to track individual vehicles, while the spatial and temporal relationships of vehicle objects are modeled by an abstract semantic model. ^

Fuzzy Modeling and Control Based Virtual Machine Resource Management

Virtual machines (VMs) are powerful platforms for building agile datacenters and emerging cloud systems. However, resource management for a VM-based system is still a challenging task. First, the complexity of application workloads as well as the interference among competing workloads makes it difficult to understand their VMs’ resource demands for meeting their Quality of Service (QoS) targets; Second, the dynamics in the applications and system makes it also difficult to maintain the desired QoS target while the environment changes; Third, the transparency of virtualization presents a hurdle for guest-layer application and host-layer VM scheduler to cooperate and improve application QoS and system efficiency. This dissertation proposes to address the above challenges through fuzzy modeling and control theory based VM resource management. First, a fuzzy-logic-based nonlinear modeling approach is proposed to accurately capture a VM’s complex demands of multiple types of resources automatically online based on the observed workload and resource usages. Second, to enable fast adaption for resource management, the fuzzy modeling approach is integrated with a predictive-control-based controller to form a new Fuzzy Modeling Predictive Control (FMPC) approach which can quickly track the applications’ QoS targets and optimize the resource allocations under dynamic changes in the system. Finally, to address the limitations of black-box-based resource management solutions, a cross-layer optimization approach is proposed to enable cooperation between a VM’s host and guest layers and further improve the application QoS and resource usage efficiency. The above proposed approaches are prototyped and evaluated on a Xen-based virtualized system and evaluated with representative benchmarks including TPC-H, RUBiS, and TerraFly. The results demonstrate that the fuzzy-modeling-based approach improves the accuracy in resource prediction by up to 31.4% compared to conventional regression approaches. The FMPC approach substantially outperforms the traditional linear-model-based predictive control approach in meeting application QoS targets for an oversubscribed system. It is able to manage dynamic VM resource allocations and migrations for over 100 concurrent VMs across multiple hosts with good efficiency. Finally, the cross-layer optimization approach further improves the performance of a virtualized application by up to 40% when the resources are contended by dynamic workloads.

Using Image Processing Methods to Improve the Detection of Buried Explosive Threats in GPR Data

Current state of the art techniques for landmine detection in ground penetrating radar (GPR) utilize statistical methods to identify characteristics of a landmine response. This research makes use of 2-D slices of data in which subsurface landmine responses have hyperbolic shapes. Various methods from the field of visual image processing are adapted to the 2-D GPR data, producing superior landmine detection results. This research goes on to develop a physics-based GPR augmentation method motivated by current advances in visual object detection. This GPR specific augmentation is used to mitigate issues caused by insufficient training sets. This work shows that augmentation improves detection performance under training conditions that are normally very difficult. Finally, this work introduces the use of convolutional neural networks as a method to learn feature extraction parameters. These learned convolutional features outperform hand-designed features in GPR detection tasks. This work presents a number of methods, both borrowed from and motivated by the substantial work in visual image processing. The methods developed and presented in this work show an improvement in overall detection performance and introduce a method to improve the robustness of statistical classification.

Using phase-space localized basis functions to obtain vibrational energies of molecules

For decades scientists have attempted to use ideas of classical mechanics to choose basis functions for calculating spectra. The hope is that a classically-motivated basis set will be small because it covers only the dynamically important part of phase space. One popular idea is to use phase space localized (PSL) basis functions. This thesis improves on previous efforts to use PSL functions and examines the usefulness of these improvements. Because the overlap matrix, in the matrix eigenvalue problem obtained by using PSL functions with the variational method, is not an identity, it is costly to use iterative methods to solve the matrix eigenvalue problem. We show that it is possible to circumvent the orthogonality (overlap) problem and use iterative eigensolvers. We also present an altered method of calculating the matrix elements that improves the performance of the PSL basis functions, and also a new method which more efficiently chooses which PSL functions to include. These improvements are applied to a variety of single well molecules. We conclude that for single minimum molecules, the PSL functions are inferior to other basis functions. However, the ideas developed here can be applied to other types of basis functions, and PSL functions may be useful for multi-well systems.

Broadband Dual-Polarized Antenna with High Port Isolation and Polarization Purity

This paper presents a novel high symmetry balun which significantly improves the performance of dipole-based dual-polarized antennas. The new balun structure provides enhanced differential capability leading to high performance in terms of port-to-port isolation and far-field cross polarization. An example antenna using this balun is proposed. The simulated results show 53.5% of fractional bandwidth within the band 1.71−2.96 GHz (VSWR<1.5) and port-to-port isolation >59 dB. The radiation characteristic shows around 9 dBi of gain and far-field cross polarization <−48 dBi over the entire bandwidth. The detailed balun functioning and full antenna measurements will be presented during the conference. Performance comparison with similar structures will be also provided.

Parcerias municipais com o terceiro setor: o caso do conselho municipal dos direitos da criança e do adolescente de Natal

The relationship between the State and the non-governmental organizations (NGOs) needs to be analyzed and debated by the objective to extinguish or to reduce the existent failures in this partnership in order that the whole society may benefit from it. To understand how the partnership between the public and NGOs work is fundamental. The present study searches to contribute to a better understanding of this matter. With this aim, the research focused the partnership formed between Natal Child and Adolescent Council (COMDICA) and NGOs which were selected by public notice in 2007. Theoretical references were based on the Continuum of Collaboration proposed by Austin (2001) that serves to differentiate the degree and the mode of interaction between the two organizations. It was observed that in some points there is a lack in the interaction between COMDICA and the NGOs. The frequent change of the government counselors makes difficult a more intense involvement and partnership awareness with the NGOs. The NGOs members need to be more involved with the activities of COMDICA and search for a larger participation in the assemblies, on the discussions and on the intrinsic council actions. The relationship must also be rethought, since that the partnership must not be limited to financial resources support. The channels of communication must be improved and become more frequent. The evaluation and monitoring of social projects are poor and own methodologies need to be elaborated. Therefore, it is necessary to make some adjustments in this relationship involving not only the partnerships made by the selected ONGs, but also all those who assist the child and the adolescent. A closer relation makes possible a greater effectiveness of the public policies on one side and on the other side improves the performance of the COMDICA and the NGOs

Datasets, features, learning, and models in visual recognition

Visual recognition is a fundamental research topic in computer vision. This dissertation explores datasets, features, learning, and models used for visual recognition. In order to train visual models and evaluate different recognition algorithms, this dissertation develops an approach to collect object image datasets on web pages using an analysis of text around the image and of image appearance. This method exploits established online knowledge resources (Wikipedia pages for text; Flickr and Caltech data sets for images). The resources provide rich text and object appearance information. This dissertation describes results on two datasets. The first is Berg’s collection of 10 animal categories; on this dataset, we significantly outperform previous approaches. On an additional set of 5 categories, experimental results show the effectiveness of the method. Images are represented as features for visual recognition. This dissertation introduces a text-based image feature and demonstrates that it consistently improves performance on hard object classification problems. The feature is built using an auxiliary dataset of images annotated with tags, downloaded from the Internet. Image tags are noisy. The method obtains the text features of an unannotated image from the tags of its k-nearest neighbors in this auxiliary collection. A visual classifier presented with an object viewed under novel circumstances (say, a new viewing direction) must rely on its visual examples. This text feature may not change, because the auxiliary dataset likely contains a similar picture. While the tags associated with images are noisy, they are more stable when appearance changes. The performance of this feature is tested using PASCAL VOC 2006 and 2007 datasets. This feature performs well; it consistently improves the performance of visual object classifiers, and is particularly effective when the training dataset is small. With more and more collected training data, computational cost becomes a bottleneck, especially when training sophisticated classifiers such as kernelized SVM. This dissertation proposes a fast training algorithm called Stochastic Intersection Kernel Machine (SIKMA). This proposed training method will be useful for many vision problems, as it can produce a kernel classifier that is more accurate than a linear classifier, and can be trained on tens of thousands of examples in two minutes. It processes training examples one by one in a sequence, so memory cost is no longer the bottleneck to process large scale datasets. This dissertation applies this approach to train classifiers of Flickr groups with many group training examples. The resulting Flickr group prediction scores can be used to measure image similarity between two images. Experimental results on the Corel dataset and a PASCAL VOC dataset show the learned Flickr features perform better on image matching, retrieval, and classification than conventional visual features. Visual models are usually trained to best separate positive and negative training examples. However, when recognizing a large number of object categories, there may not be enough training examples for most objects, due to the intrinsic long-tailed distribution of objects in the real world. This dissertation proposes an approach to use comparative object similarity. The key insight is that, given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. This dissertation develops a regularized kernel machine algorithm to use this category dependent similarity regularization. Experiments on hundreds of categories show that our method can make significant improvement for categories with few or even no positive examples.

Robust CDM and pole placement PID based thrust controllers for multirotor motor-rotor simplified model

Positioning and orientation precision of a multirotor aerial robot can be increased by using additional control loops for each of the driving units. As a result, one can eliminate lack of balance between true thrust forces. A control performance comparison of two proposed thrust controllers, namely robust controller designed with coefficient diagram method (CDM) and proportional, integral and derivative (PID) controller tuned with pole-placement law, is presented in the paper. The research has been conducted with respect to model/plant matching uncertainty and with the use of antiwindup compensators for a simple motor-rotor model approximated by first-order inertia plus delay. From the obtained simulation results one concludes that appropriate choice of AWC compensator improves tracking performance and increases robustness against parametric uncertainty.

Parcerias municipais com o terceiro setor: o caso do conselho municipal dos direitos da criança e do adolescente de Natal

The relationship between the State and the non-governmental organizations (NGOs) needs to be analyzed and debated by the objective to extinguish or to reduce the existent failures in this partnership in order that the whole society may benefit from it. To understand how the partnership between the public and NGOs work is fundamental. The present study searches to contribute to a better understanding of this matter. With this aim, the research focused the partnership formed between Natal Child and Adolescent Council (COMDICA) and NGOs which were selected by public notice in 2007. Theoretical references were based on the Continuum of Collaboration proposed by Austin (2001) that serves to differentiate the degree and the mode of interaction between the two organizations. It was observed that in some points there is a lack in the interaction between COMDICA and the NGOs. The frequent change of the government counselors makes difficult a more intense involvement and partnership awareness with the NGOs. The NGOs members need to be more involved with the activities of COMDICA and search for a larger participation in the assemblies, on the discussions and on the intrinsic council actions. The relationship must also be rethought, since that the partnership must not be limited to financial resources support. The channels of communication must be improved and become more frequent. The evaluation and monitoring of social projects are poor and own methodologies need to be elaborated. Therefore, it is necessary to make some adjustments in this relationship involving not only the partnerships made by the selected ONGs, but also all those who assist the child and the adolescent. A closer relation makes possible a greater effectiveness of the public policies on one side and on the other side improves the performance of the COMDICA and the NGOs

Inovação em serviços e desempenho organizacional : uma nova abordagem de avaliação ambiental pelo TCU

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Economia, Administração e Contabilidade, Programa de Pós-Graduação em Administração, Mestrado Profissional em Administração, 2015.

Essays in Gender Economics

This thesis consists of three papers on gender economics. Chapter 1 studies whether people dislike collaborating with someone who corrects them and whether the dislike is stronger when that person is a woman. Having a good relationship with colleagues is integral in group work, potentially leading to successful collaborations. However, there are occasions when people have to correct their colleagues. Using a quasi-laboratory experiment, I find that people, including those with high productivity, are less willing to collaborate with a person who has corrected them even if the correction improves group performance. In addition, I find suggestive evidence that men respond more negatively to women’s corrections, which is not driven by their beliefs about the difference in women’s and men’s abilities. These findings suggest that there is a behavioral bias in group work that distorts the optimal selection of talents and penalizes those who correct others’ mistakes, and the distortion may be stronger when women correct men. Chapter 2 studies the role of gender and cognitive skills on other peoples’ generosity. Using a novel experimental design where I exogenously vary gender and cognitive skills and sufficiently powered analysis, I find neither the two attributes nor their interactions affect other people’s generosity; if anything, people are more generous to women with high potential. Chapter 3 studies how increased legal tolerance toward domestic violence affects married women’s welfare using the domestic violence decriminalization bill introduced to the Russian national congress in 2016. Using difference-in-differences and flexibly controlling for macroeconomic shocks, I find that the bill decreased married women’s life satisfaction and increased depression, especially among those with a college degree and a highly qualified white-collar occupation supposed to be more sensitive to gender regressive atmosphere. Consistent with this conjecture, people became more tolerant toward general and domestic violence after the bill.

ECG-triggered skeletal muscle stimulation improves hemodynamics and physical performance of heart failure patients

BACKGROUND: Muscular counterpulsation (MCP) was developed for circulatory assistance by stimulation of peripheral skeletal muscles. We report on a clinical MCP study in patients with and without chronic heart failure (CHF). METHODS AND RESULTS: MCP treatment was applied (30 patients treated, 25 controls, all under optimal therapy) for 30 minutes during eight days by an ECG-triggered, battery-powered, portable pulse generator with skin electrodes inducing light contractions of calf and thigh muscles, sequentially stimulated at early diastole. Hemodynamic parameters (ECG, blood pressure and echocardiography) were measured one day before and one day after the treatment period in two groups: Group 1 (9 MCP, 11 no MCP) with ejection fraction (EF) above 40% and Group 2 (21 MCP, 14 no MCP) below 40%. In Group 2 (all patients suffering from CHF) mean EF increased by 21% (p<0.001) and stroke volume by 13% (p<0.001), while end systolic volume decreased by 23% (p<0.001). In Group 1, the increase in EF (6%) and stroke volume (8%) was also significant (p<0.05) but less pronounced than in Group 2. Physical exercise duration and walking distance increased in Group 2 by 56% and 72%, respectively. CONCLUSIONS: Noninvasive MCP treatment for eight days substantially improves cardiac function and physical performance in patients with CHF.