Exploiting dynamic timing margins in microprocessors for frequency-over-scaling with instruction-based clock adjustment

Static timing analysis provides the basis for setting the clock period of a microprocessor core, based on its worst-case critical path. However, depending on the design, this critical path is not always excited and therefore dynamic timing margins exist that can theoretically be exploited for the benefit of better speed or lower power consumption (through voltage scaling). This paper introduces predictive instruction-based dynamic clock adjustment as a technique to trim dynamic timing margins in pipelined microprocessors. To this end, we exploit the different timing requirements for individual instructions during the dynamically varying program execution flow without the need for complex circuit-level measures to detect and correct timing violations. We provide a design flow to extract the dynamic timing information for the design using post-layout dynamic timing analysis and we integrate the results into a custom cycle-accurate simulator. This simulator allows annotation of individual instructions with their impact on timing (in each pipeline stage) and rapidly derives the overall code execution time for complex benchmarks. The design methodology is illustrated at the microarchitecture level, demonstrating the performance and power gains possible on a 6-stage OpenRISC in-order general purpose processor core in a 28nm CMOS technology. We show that employing instruction-dependent dynamic clock adjustment leads on average to an increase in operating speed by 38% or to a reduction in power consumption by 24%, compared to traditional synchronous clocking, which at all times has to respect the worst-case timing identified through static timing analysis.

Spatio-Temporal Rich Model For Motion Vector Steganalysis

We propose a spatio-temporal rich model of motion vector planes as a part of a full steganalytic system against motion vector based steganography. Superior detection accuracy of the rich model over the previous methods has been lately demonstrated for digital images in both spatial and DCT domain. It has not been heretofore used for detection of motion vector steganography. We also introduced a transformation so as to extend the feature set with temporal residuals. We carried out the tests along with most recent motion vector steganalysis and steganography methods. Test results show that the proposed model delivers an outstanding performance compared to the previous methods.

Assessment Of various sensors for structural health monitoring for bridge weigh-in-motion (B-WIM).

In recent years, Structural Health Monitoring (SHM) systems have been developed to monitor bridge deterioration, assess real load levels and hence extend bridge life and safety. A road bridge is only safe if the stresses caused by the passing vehicles are less than the capacity of the bridge to resist them. Conventional SHM systems can be used to improve knowledge of the bridges capacity to resist stresses but generally give no information on the causes of any increase in stresses (based on measuring strain). The concept of in Bridge Weigh-in-Motion (B-WIM) is to establish axle loads, without interruption to traffic flow, by using strain sensors at a bridge soffit and subsequently converting the data to real time axle loads or stresses. Recent studies have shown it would be most beneficial to develop a portable system which can be easily attached to existing and new bridge structures for a specified monitoring period. The sensors could then be left in place while the data acquisition can be moved for various other sites. Therefore it is necessary to find accurate sensors capable of capturing peak strains under dynamic load and suitable methods for attaching these strain sensors to existing and new bridge structures. Additionally, it is important to ensure accurate strain transfer between concrete and steel, the adhesives layer and the strain sensor. This paper describes research investigating the suitably of using various sensors for the monitoring of concrete structures under dynamic vehicle load. Electrical resistance strain (ERS) gauges, vibrating wire (VW) gauges and fibre optic sensors (FOS) are commonly used for SHM. A comparative study will be carried out to select a suitable sensor for a bridge Weigh in Motion System. This study will look at fixing methods, durability, scanning rate and accuracy range. Finite element modeling is used to predict the strains which are then validated in laboratory trials.

Recurrent Convolutional Network for Video-based Person Re-Identification

In this paper we propose a novel recurrent neural networkarchitecture for video-based person re-identification.Given the video sequence of a person, features are extracted from each frame using a convolutional neural network that incorporates a recurrent final layer, which allows information to flow between time-steps. The features from all time steps are then combined using temporal pooling to give an overall appearance feature for the complete sequence. The convolutional network, recurrent layer, and temporal pooling layer, are jointly trained to act as a feature extractor for video-based re-identification using a Siamese network architecture.Our approach makes use of colour and optical flow information in order to capture appearance and motion information which is useful for video re-identification. Experiments are conduced on the iLIDS-VID and PRID-2011 datasets to show that this approach outperforms existing methods of video-based re-identification.

https://github.com/niallmcl/Recurrent-Convolutional-Video-ReID
Project Source Code

Development of a novel immersive interactive virtual reality cricket simulator for cricket batting

Research in the field of sports performance is constantly developing new technology to help extract meaningful data to aid in understanding in a multitude of areas such as improving technical or motor performance. Video playback has previously been extensively used for exploring anticipatory behaviour. However, when using such systems, perception is not active. This loses key information that only emerges from the dynamics of the action unfolding over time and the active perception of the observer. Virtual reality (VR) may be used to overcome such issues. This paper presents the architecture and initial implementation of a novel VR cricket simulator, utilising state of the art motion capture technology (21 Vicon cameras capturing kinematic profile of elite bowlers) and emerging VR technology (Intersense IS-900 tracking combined with Qualisys Motion capture cameras with visual display via Sony Head Mounted Display HMZ-T1), applied in a cricket scenario to examine varying components of decision and action for cricket batters. This provided an experience with a high level of presence allowing for a real-time egocentric view-point to be presented to participants. Cyclical user-testing was carried out, utilisng both qualitative and quantitative approaches, with users reporting a positive experience in use of the system.

Stakeholders based branding : uma abordagem integradora dos actores locais na gestão de marcas territoriais

O presente trabalho situa a investigação em torno do marketing, particularmente do branding, territorial numa perspectiva holística e consubstanciadora de comportamentos, identidade e desenvolvimento territorial. Nesse âmbito, focaliza-se na problemática da amplitude e heterogeneidade de actores com capacidade de impacte na construção e transmissão da marca territorial e na necessidade da sua contemplação nos pressupostos de branding para a sustentação efectiva das marcas territoriais. A tese defendida advoga a relevância de empreender marcas territoriais assentes na colaboração e integração dos stakeholders no processo construtivo, de forma a potenciar a relação directa entre o branding, a identidade e comportamento territorial e aumentar o output da marca. Nesse sentido, essa orientação é consubstanciada sob a edificação conceptual de Stakeholders Based Branding e procede-se à exploração e aferição de contributos para o seu desenvolvimento e modelização. Empiricamente e tendo por base uma abordagem descritiva e exploratória, a investigação orienta-se a um trabalho de natureza qualitativa e interpretativa que estuda, neste âmbito e através da metodologia de Grounded Theory, 6 casos de estudo de municípios portugueses, através de 48 entrevistas em profundidade realizadas a líderes políticos e stakeholders territoriais e dados secundários. Os resultados obtidos em campo demonstram a relação entre a integração de stakeholders e o sentimento de branding e imagem territorial, reiterando que quanto mais envolvidos os stakeholders se sentem no processo construtivo da marca territorial, mais tendem a assumir a sua auto-imputação e que os territórios com posturas mais colaborativas na construção de branding são os que tendem a possuir auto-imagens e imagens públicas mais positivas. Paralelamente permitem aferir um conjunto de factores impulsores, implementados e/ou idealizados, tidos como relevantes para promover uma orientação de Stakeholders Based Branding, nos respectivos territórios. Do percurso investigativo emana um constructo de Stakeholders Based Branding, com carácter indutivo, respeitando os pressupostos da Grounded Theory e assente na modelização e constituição de proposições teóricas que visam contribuir para orientar a construção de marcas territoriais alicerçadas na integração e colaboração de stakeholders.

Automatic information and safety systems for driving assistance

O objeto principal desta tese é o estudo de algoritmos de processamento e representação automáticos de dados, em particular de informação obtida por sensores montados a bordo de veículos (2D e 3D), com aplicação em contexto de sistemas de apoio à condução. O trabalho foca alguns dos problemas que, quer os sistemas de condução automática (AD), quer os sistemas avançados de apoio à condução (ADAS), enfrentam hoje em dia. O documento é composto por duas partes. A primeira descreve o projeto, construção e desenvolvimento de três protótipos robóticos, incluindo pormenores associados aos sensores montados a bordo dos robôs, algoritmos e arquitecturas de software. Estes robôs foram utilizados como plataformas de ensaios para testar e validar as técnicas propostas. Para além disso, participaram em várias competições de condução autónoma tendo obtido muito bons resultados. A segunda parte deste documento apresenta vários algoritmos empregues na geração de representações intermédias de dados sensoriais. Estes podem ser utilizados para melhorar técnicas já existentes de reconhecimento de padrões, deteção ou navegação, e por este meio contribuir para futuras aplicações no âmbito dos AD ou ADAS. Dado que os veículos autónomos contêm uma grande quantidade de sensores de diferentes naturezas, representações intermédias são particularmente adequadas, pois podem lidar com problemas relacionados com as diversas naturezas dos dados (2D, 3D, fotométrica, etc.), com o carácter assíncrono dos dados (multiplos sensores a enviar dados a diferentes frequências), ou com o alinhamento dos dados (problemas de calibração, diferentes sensores a disponibilizar diferentes medições para um mesmo objeto). Neste âmbito, são propostas novas técnicas para a computação de uma representação multi-câmara multi-modal de transformação de perspectiva inversa, para a execução de correcção de côr entre imagens de forma a obter mosaicos de qualidade, ou para a geração de uma representação de cena baseada em primitivas poligonais, capaz de lidar com grandes quantidades de dados 3D e 2D, tendo inclusivamente a capacidade de refinar a representação à medida que novos dados sensoriais são recebidos.

Electromechanical properties of engineered lead free potassium sodium niobate based materials

K0.5Na0.5NbO3 (KNN), is the most promising lead free material for substituting lead zirconate titanate (PZT) which is still the market leader used for sensors and actuators. To make KNN a real competitor, it is necessary to understand and to improve its properties. This goal is pursued in the present work via different approaches aiming to study KNN intrinsic properties and then to identify appropriate strategies like doping and texturing for designing better KNN materials for an intended application. Hence, polycrystalline KNN ceramics (undoped, non-stoichiometric; NST and doped), high-quality KNN single crystals and textured KNN based ceramics were successfully synthesized and characterized in this work. Polycrystalline undoped, non-stoichiometric (NST) and Mn doped KNN ceramics were prepared by conventional ceramic processing. Structure, microstructure and electrical properties were measured. It was observed that the window for mono-phasic compositions was very narrow for both NST ceramics and Mn doped ceramics. For NST ceramics the variation of A/B ratio influenced the polarization (P-E) hysteresis loop and better piezoelectric and dielectric responses could be found for small stoichiometry deviations (A/B = 0.97). Regarding Mn doping, as compared to undoped KNN which showed leaky polarization (P-E) hysteresis loops, B-site Mn doped ceramics showed a well saturated, less-leaky hysteresis loop and a significant properties improvement. Impedance spectroscopy was used to assess the role of Mn and a relation between charge transport – defects and ferroelectric response in K0.5Na0.5NbO3 (KNN) and Mn doped KNN ceramics could be established. At room temperature the conduction in KNN which is associated with holes transport is suppressed by Mn doping. Hence Mn addition increases the resistivity of the ceramic, which proved to be very helpful for improving the saturation of the P-E loop. At high temperatures the conduction is dominated by the motion of ionized oxygen vacancies whose concentration increases with Mn doping. Single crystals of potassium sodium niobate (KNN) were grown by a modified high temperature flux method. A boron-modified flux was used to obtain the crystals at a relatively low temperature. XRD, EDS and ICP analysis proved the chemical and crystallographic quality of the crystals. The grown KNN crystals exhibit higher dielectric permittivity (29,100) at the tetragonal-to-cubic phase transition temperature, higher remnant polarization (19.4 μC/cm2) and piezoelectric coefficient (160 pC/N) when compared with the standard KNN ceramics. KNN single crystals domain structure was characterized for the first time by piezoforce response microscopy. It could be observed that <001> - oriented potassium sodium niobate (KNN) single crystals reveal a long range ordered domain pattern of parallel 180° domains with zig-zag 90° domains. From the comparison of KNN Single crystals to ceramics, It is argued that the presence in KNN single crystal (and absence in KNN ceramics) of such a long range order specific domain pattern that is its fingerprint accounts for the improved properties of single crystals. These results have broad implications for the expanded use of KNN materials, by establishing a relation between the domain patterns and the dielectric and ferroelectric response of single crystals and ceramics and by indicating ways of achieving maximised properties in KNN materials. Polarized Raman analysis of ferroelectric potassium sodium niobate (K0.5Na0.5)NbO3 (KNN) single crystals was performed. For the first time, an evidence is provided that supports the assignment of KNN single crystals structure to the monoclinic symmetry at room temperature. Intensities of A′, A″ and mixed A′+A″ phonons have been theoretically calculated and compared with the experimental data in dependence of crystal rotation, which allowed the precise determination of the Raman tensor coefficients for (non-leaking) modes in monoclinic KNN. In relation to the previous literature, this study clarifies that assigning monoclinic phase is more suitable than the orthorhombic one. In addition, this study is the basis for non-destructive assessments of domain distribution by Raman spectroscopy in KNN-based lead-free ferroelectrics with complex structures. Searching a deeper understanding of the electrical behaviour of both KNN single crystal and polycrystalline materials for the sake of designing optimized KNN materials, a comparative study at the level of charge transport and point defects was carried out by impedance spectroscopy. KNN single crystals showed lower conductivity than polycrystals from room temperature up to 200 ºC, but above this temperature polycrystalline KNN displays lower conductivity. The low temperature (T < 200 ºC) behaviour reflects the different processing conditions of both ceramics and single crystals, which account for less defects prone to charge transport in the case of single crystals. As temperature increases (T > 200 ºC) single crystals become more conductive than polycrystalline samples, in which grain boundaries act as barriers to charge transport. For even higher temperatures the conductivity difference between both is increased due to the contribution of ionic conduction in single crystals. Indeed the values of activation energy calculated to the high temperature range (T > 300 ºC) were 1.60 and 0.97 eV, confirming the charge transport due to ionic conduction and ionized oxygen vacancies in single crystals and polycrystalline KNN, respectively. It is suggested that single crystals with low defects content and improved electromechanical properties could be a better choice for room temperature applications, though at high temperatures less conductive ceramics may be the choice, depending on the targeted use. Aiming at engineering the properties of KNN polycrystals towards the performance of single crystals, the preparation and properties study of (001) – oriented (K0.5Na0.5)0.98Li0.02NbO3 (KNNL) ceramics obtained by templated grain growth (TGG) using KNN single crystals as templates was undertaken. The choice of KNN single crystals templates is related with their better properties and to their unique domain structure which were envisaged as a tool for templating better properties in KNN ceramics too. X-ray diffraction analysis revealed for the templated ceramics a monoclinic structure at room temperature and a Lotgering factor (f) of 40% which confirmed texture development. These textured ceramics exhibit a long range ordered domain pattern consisting of 90º and 180º domains, similar to the one observed in the single crystals. Enhanced dielectric (13017 at TC), ferroelectric (2Pr = 42.8 μC/cm2) and piezoelectric (d33 = 280 pC/N) properties are observed for textured KNNL ceramics as compared to the randomly oriented ones. This behaviour is suggested to be due to the long range ordered domain patterns observed in the textured ceramics. The obtained results as compared with the data previously reported on texture KNN based ceramics confirm that superior properties were found due to ordered repeated domain pattern. This study provides an useful approach towards properties improvement of KNN-based piezoelectric ceramics. Overall, the present results bring a significant contribution to the pool of knowledge on the properties of sodium potassium niobate materials: a relation between the domain patterns and di-, ferro-, and piezo-electric response of single crystals and ceramics was demonstrated and ways of engineering maximised properties in KNN materials, for example by texturing were established. This contribution is envisaged to have broad implications for the expanded use of KNN over the alternative lead-based materials.

A congestion management and transmission price simulator for competitive electricity markets

In a liberalized electricity market, the Transmission System Operator (TSO) plays a crucial role in power system operation. Among many other tasks, TSO detects congestion situations and allocates the payments of electricity transmission. This paper presents a software tool for congestion management and transmission price determination in electricity markets. The congestion management is based on a reformulated Optimal Power Flow (OPF), whose main goal is to obtain a feasible solution for the re-dispatch minimizing the changes in the dispatch proposed by the market operator. The transmission price computation considers the physical impact caused by the market agents in the transmission network. The final tariff includes existing system costs and also costs due to the initial congestion situation and losses costs. The paper includes a case study for the IEEE 30 bus power system.

MASCEM improvement for agent based coalitions

This paper presents MASCEM - Multi-Agent Simulator for Electricity Markets improvement towards an enlarged model for Seller Agents coalitions. The simulator has been improved, both regarding its user interface and internal structure. The OOA, used as development platform, version was updated and the multi-agent model was adjusted for implementing and testing several negotiations regarding Seller agents’ coalitions. Seller coalitions are a very important subject regarding the increased relevance of Distributed Generation under liberalised electricity markets.

Agent-based simulation of electronic marketplaces with decision support

This paper presents a Multi-Agent Market simulator designed for analyzing agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, considering user risk preferences and game theory for scenario analysis. The system includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions, and capable of considering other agents reactions.

LMP based bid formation for virtual power players operating in smart grids

Power system organization has gone through huge changes in the recent years. Significant increase in distributed generation (DG) and operation in the scope of liberalized markets are two relevant driving forces for these changes. More recently, the smart grid (SG) concept gained increased importance, and is being seen as a paradigm able to support power system requirements for the future. This paper proposes a computational architecture to support day-ahead Virtual Power Player (VPP) bid formation in the smart grid context. This architecture includes a forecasting module, a resource optimization and Locational Marginal Price (LMP) computation module, and a bid formation module. Due to the involved problems characteristics, the implementation of this architecture requires the use of Artificial Intelligence (AI) techniques. Artificial Neural Networks (ANN) are used for resource and load forecasting and Evolutionary Particle Swarm Optimization (EPSO) is used for energy resource scheduling. The paper presents a case study that considers a 33 bus distribution network that includes 67 distributed generators, 32 loads and 9 storage units.

Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.

MASCEM - an electricity market simulator providing coalition support for virtual power players

This paper presents MASCEM - a multi-agent based electricity market simulator. MASCEM uses game theory, machine learning techniques, scenario analysis and optimization techniques to model market agents and to provide them with decision-support. This paper mainly focus on the MASCEM ability to provide the means to model and simulate Virtual Power Players (VPP). VPPs are represented as a coalition of agents, with specific characteristics and goals. The paper details some of the most important aspects considered in VPP formation and in the aggregation of new producers and includes a case study based on real data.

ANN based day-ahead spinning reserve forecast for electricity market simulation

Electricity market players operating in a liberalized environment require adequate decision support tools, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. This paper deals with short-term predication of day-ahead spinning reserve (SR) requirement that helps the ISO to make effective and timely decisions. Based on these forecasted information, market participants can use strategic bidding for day-ahead SR market. The proposed concepts and methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.