81 research outputs found
Ioan Radu, Viorel Lefter, Cleopatra Sendroiu, Minodora Ursacescu, Mihai Cioc, 2009, Effet du partenariat Public-Privé dans les services publics d’alimentation en eau et d’assainissement. Expérience de la municipalité de Bucarest, Bucarest : ASE, 317 p.
No full textL’ouvrage d’Ioan Radu, Viorel Lefter, Cleopatra Sendroiu, Minodora Ursacescu et Mihai Cioc est consacré à une étude de cas, le Partenariat Public-Privé (PPP) dans le service public de l’eau à Bucarest, en Roumanie. Les 15 chapitres sont répartis en trois parties. La première partie détaille les conditions économiques au moment de la création de ce PPP. La deuxième partie indique le déroulement de ce PPP pour les années 2000 à 2007. Les principaux indicateurs comptables sont présentés et comme..
BRAND LOYALTY OF FEMALE CONSUMERS - STUDY CARRIED IN SFÂNTU GHEORGHE AND THE SURROUNDING AREAS
No full textToday, the role of women in society has significantly changed, they work as men so they have their own income that they can spend as they wish and as necessary. We need to pay particular importance to consumer behaviour of female and as such it should be treated as a particular segment. This paper aims to study the simultaneous effects - based on data from quantitative marketing research – that the independent variables generate on the dependent variable. The variables introduced in the analysis of variance are: How often do you drink coffee /tea every day?, Age, Income, Last graduated school. We applied the hi square test we in order to analyze the links between two variables measured with nominal scale. The variables analyzed are: Do you always buy the some brand and The highest level of education achieved.analysis of hypothesis, brand, loyalty, behaviour, squared hi, ANOVA
Multimodal Surveillance: Behavior analysis for recognizing stress and aggression
No full textNowadays, camera systems are installed in military areas as well as in public spaces like schools, shopping malls, airports, and football stadiums. Human operators are monitoring the screens, looking for any signs of unwanted behavior and negative incidents. The task requires working personnel 24/7. With the ever increasing number of cameras, surveillance operators become overloaded. The nature of the task to constantly watch screens and the sparsity of notable events are bound to decrease the operators' focus. Furthermore, some events are hard to distinguish by video only: severe events such as gunshots and screams are much easier to hear than see. For these reasons, negative events may go by unnoticed and typically the recorded footage is inspected after the fact. A solution to these problems is the development of automatic multimodal (audio-visual) surveillance systems, which was the aim of this research thesis. Such systems should not take over the decisions of the operators, but should assist them in identifying unwanted behaviour. Operators would be notified when and where to focus. This is likely to reduce the number of missed events caused by screen prioritising or external and internal distractions. It is important to note that such a system should not be limited to recognizing violence. It has been shown that negative emotions and stress might precede aggression. Recognizing them in an early stage is very relevant since adopting proper measures at an early time can prevent the situation from escalating. Therefore, in this research thesis, besides a variety of manifestations of aggression, we have focused on automatically recognizing stress. Our aim was to design and implement a surveillance system that is able to emulate human perception. For that reason, we asked people to annotate stress and aggression on audio-visual recordings. We investigated several approaches to compute their annotations automatically. Recordings from real surveillance cameras are in general not available due to privacy reasons. We had to construct our own datasets. In order to ensure a high degree of realism as well as sufficient samples of stress and aggression, we have designed scenarios and hired semi-professional actors to play them. The actors were free to improvise after they received roles and short scenario descriptions. We have recorded stressful scenes at service desk and aggression related scenarios in a train and train station. To automatically recognize the stress and aggression levels, we have extracted acoustic, linguistic and visual features, referred to as low-level features. Using classifiers, we trained models which can be used to make prediction of stress or aggression level on new data samples. One shortcoming of this approach is that there is a semantic gap between the low-level features and the high-level stress and aggression assessment. We have contributed by bridging the semantic gap with semantically-meaningful intermediate representations of the stress concept. The intermediate representation of stress consists of the degrees to which stress is conveyed by speech and gestures with respect to the semantic message and the way in which the semantic message is expressed (e.g. intonation for speech, speed, rhythm, tension for gestures). Adding such a representation as an intermediate level in the stress recognition architecture improves the stress assessment, especially when the level of stress is high. Having both audio and video offers the possibility to construct a more complete representation of the scene. The multimodal fusion approach is expected to be a solution to deal with the shortcomings of each modality (e.g. noise for audio, occlusion for video). Despite the expected benefits, fusing information coming from different modalities is challenging. Typical problems are that some pieces of information are only apparent in one modality (e.g. verbal fight), and that multiple people in the scene can have different behaviors which might lead to different assessments based on where the focus is. These problems can lead to incongruent, or even contradicting information from the different modalities, which makes coming to the correct interpretation hard. To deal with the problem of fusing incongruent information we have proposed and validated five meta-features: audio-focus, video-focus, context, semantics and history. The meta-features and the audio-only and video-only aggression assessments form the intermediate level of the aggression recognition model. This novel approach significantly improved automatic aggression recognition by multimodal fusion.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc
On Leveraging Vertical Proximity in 3D Memory Hierarchies
No full textWithin the past half century, Integrated Circuits (ICs) experienced an aggressive, performance driven, technology feature size scaling. As the technology scaled into the deep nanometer range, physical and quantum mechanical effects that were previously irrelevant become influential, or even dominant, resulting in, e.g., not any longer negligible leakage currents. When attempting to pattern such small-geometry dimensions, the variability of technological parameters considerably gained importance. Furthermore, it became more difficult to reliably handle and integrate such a huge number of tiny transistors into large scale ICs, considering also that a substantial increase in power density needed to be taken into account. Scaling induced performance was no longer sufficient for delivering the expected improvements, which lead to a paradigm switch from uniprocessors to multiprocessor micro-architectures. At the same time, since for certain application domains, such as big data and Internet of things, the to be processed data amount increases substantially, computing system designers become more concerned with ensuring data availability than with reducing functional units latency. As a result, state of the art computing systems employ complex memory hierarchies, consisting of up to four cache levels with multiple shared scenarios, making memory a dominant design element that considerably influences the overall system performance and correct behavior. In this context, 3D Stacked Integrated Circuit (3D SIC) technology emerges as a promising avenue in enabling new design opportunities since it provides the means to interconnect devices with short vertical wires. In this thesis we address the above mentioned memory challenges by investigating the 3D SIC technology utilization in memory designs, as follows. First, we propose a novel banked multi-port polyhedral memory that provides an enriched access mechanism set with a very low bank conflict rate and we evaluate its potential in shared caches. Second, we propose a low power hybrid memory in which 3D technology allows for the smooth co-integration of: (i) short circuit current free Nano-Electro-Mechanical Field Effect Transistor (NEMFET) based inverters for data storage, and, (ii) CMOS-based logic for read/write operations and data preservation. Third, we propose a memory repair framework that exploits the 3D vertical proximity for inter-die redundant resources sharing. Finally, we propose novel schemes for performing user transparent multi-error correction and detection, with the same or even lower redundancy than the one required by state of the art extended Hamming single error correction schemes.Computer Engineerin
Reconfigurable Trigger Logic for Electronic Instrumentation in Space Applications
No full textFuture space missions have to rely on advanced, smart and very light payloads in order to explore the solar system within a reasonable cost envelope. For this reason, efforts are made to obtain higher levels of integration that can reduce costs and allow the presence of more and more instruments on board of small spacecrafts. With the advent of radiation hardened FPGAs, the use of reprogrammable hardware in space is no longer an issue. This opens new perspectives to space electronics. System-on-Chip (SoC) design methodologies for future highly-integrated devices are actively promoted by space agencies. In this thesis we focus on FPGA based SoC architectures for space electronics instrumentation, targeting time related issues. In this line of reasoning we proposed and developed a highly customizable trigger logic block able to reject background events with the highest possible efficiency and to accurately time-stamp the accepted ones. Its features include programmable coincidence window, input ordering, and for every input in particular the possibility to choose different states and to program the delay. The trigger logic block is designed as an AMBA IP core and it can be interfaced with many SoC libraries. For testing purposes we have programmed an AMBA based SoC architecture including a LEON3 on-chip processor and a minimal selection of IP cores from the GRLIB library on a Xilinx XC3S1500 FPGA. The trigger logic IP together with another IP developed for testing reasons were clocked at 100 MHz, while the rest of the system was running at 40 MHz. An average dead time of 1.5 µs was obtained, corresponding to an events frequency of 0.65 MHz. Based on our experimental results we can conclude that the proposed trigger logic approach can potentially successfully function in space applications. In extent to the trigger logic IP design, we have further performed research on the current SpaceWire time-codes, in an attempt to improve the inter-module time distribution accuracy. Several methods were proposed to reach synchronization in the order of nanoseconds, as opposed to the current microseconds synchronization, with little changes over the current SpaceWire standard.Microelectronics & Computer EngineeringElectrical Engineering, Mathematics and Computer Scienc
Multimodal Cross-context Recognition of Negative Interactions
No full textNegative emotions and stress can impact human-human interactions and eventually lead to aggression. From the perspective of surveillance systems, it is of high importance to recognize as soon as an interaction escalates and human intervention is needed. One of the limitations of deploying a system in real life is that in practice it can only be trained on a limited number of situations. In this paper we examined the generalization capabilities of a trained system given context change. For this purpose we developed scenarios and made audio-visual recordings in four different contexts in which negative interactions might occur. To obtain a quantification of cross-context performance we kept the test context fixed and performed training on itself (cross-validation) and on all the other contexts. To explore whether multiple examples in the training set are beneficial, we also trained the classifier on a merged corpus of the three contexts that were not used for testing. These experiments were done with audio features, video features and audio-visual feature level fusion to investigate which modality generalizes best. We found that context change generates a decrease in performance that is varying with within-contexts similarities. Merging multiple contexts for training in most cases results in performance just below the best predictive single context. Audio is the most robust modality and in most cases the performance of audiovisual fusion is very close to the one of audio.System EngineeringInteractive Intelligenc
Engagement in Applied Games
No full textGames have been widely used for purposes other than entertainment due to their engaging nature. However, the concept of game engagement is still not well-defined, which limits its use in analysis and game design. The primary objective of this dissertation is to conceptualize game engagement to guide the analysis and design of applied games.The dissertation first explores the requirements for conceptualizing applied game engagement, identified through an analysis of three applied gaming projects and an empirical study. It then uses these requirements to develop the Applied Games Engagement Model (AGEM). The AGEM posits that engagement is the process of focusing attention on a task and that attention can be purposefully directed through design.The practical use of the AGEM is then explored by analyzing applied games. The theory is extended with relevant game design knowledge and applied to game design practice. This results in the Lens of Engagement for Applied Games, a unique way to view the design of an applied game.Overall, this dissertation provides a comprehensive perspective on applied game engagement, emphasizing the role of attention and its relation to game design. It offers a practical and workable method of considering and discussing game engagement, which can be used by anyone creating or studying applied games
Understanding the Potential of Augmented Reality in Manufacturing Environments
No full textManufacturing companies are confronted with challenges due to increasing flexibility requirements and skill gaps. Augmented Reality applications offer an efficient way to overcome these tensions by enhancing the interaction between people and technology. The positive effects of Augmented Reality solutions are often described in individual models in the scientific literature. This research-in-progress aims to aggregate the empirical findings in the usage of Augmented Reality solutions in manufacturing environments. A meta-analysis is conducted to synthesise several small studies into one large study to achieve this. In particular, the meta-analysis will focus on the impact of Augmented Reality applications on cognitive load levels. Furthermore, the effect on processing time and error rates will be evaluated. Initial results of the meta-analysis will be expected and reported at this year’s NeuroIS Retreat.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Economics of Technology and InnovationSystem Engineerin
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