The availability of digital media has grown tremendously with the fast-paced ever-growing storage and communication technologies. As a result, today, we are facing a problem in indexing and browsing the huge amounts of multimedia data. This amount of data is impossible to be indexed or browsed by hand so automatic indexing and browsing systems are proposed. Audio Event Detection is a research area which tries to analyse the audio data in a semantic and perceptual manner, to bring a conceptual solution to this problem. In this thesis, a method for detecting several audio events in TV broadcast is proposed. The proposed method includes an audio segmentation stage to detect event boundaries. Broadcast audio is classified into 17 classes. The feature set for each event is obtained by using a feature selection algorithm to select suitable features among a large set of popular descriptors. Support Vector Machines and Gaussian Mixture Models are used as classifiers and the proposed system achieved an average recall rate of 88% for 17 different audio events. Comparing with the results in the literature, the proposed method is promising.

Esta dissertação trata do projeto e avaliação de um novo circuito sensor de corrente com célula de memória dinâmica para a detecção de correntes transientes em circuitos integrados CMOS, provocadas pela incidência de partículas ionizantes. As propostas previamente existentes na literatura são avaliadas e suas deficiências são apontadas. É apresentada a topologia e o modo de funcionamento do novo circuito, juntamente com o detalhamento do projeto das versões destinadas à monitoração dos transistores PMOS e NMOS. É apresentado o layout do circuito final em tecnologia 130 nm, destinado à prototipação pelo programa MOSIS, contendo os sensores, os transistores-alvo, os estágios de saída e os circuitos de proteção contra os efeitos da eletricidade estática necessários. Os resultados obtidos através de simulação mostram que o novo circuito proporciona uma redução na área de silício necessária para a implementação, bem como um menor consumo de corrente quiescente em relação às propostas anteriores. / This dissertation deals with the design and evaluation of a new current sensor circuit with dynamic memory cell intended to detect transient currents caused by incidence of ionizing particles in CMOS integrated circuits. Circuits previously proposed are analyzed and their drawbacks are pointed out. The new circuit topology and working principle is presented, along with the detailed design of the versions intended to monitoring PMOS and NMOS transistors. The final circuit is laid out in a 130 nm technology, intended to be prototyped through the MOSIS program. The complete design contains the sensor circuits, target transistors, output stages and electrostatic discharge protection circuitry. Results obtained by post layout simulation shown that the new circuit provides a reduction on silicon area and a smaller quiescent current consumption compared to previous circuits.

Esta dissertação trata do projeto e avaliação de um novo circuito sensor de corrente com célula de memória dinâmica para a detecção de correntes transientes em circuitos integrados CMOS, provocadas pela incidência de partículas ionizantes. As propostas previamente existentes na literatura são avaliadas e suas deficiências são apontadas. É apresentada a topologia e o modo de funcionamento do novo circuito, juntamente com o detalhamento do projeto das versões destinadas à monitoração dos transistores PMOS e NMOS. É apresentado o layout do circuito final em tecnologia 130 nm, destinado à prototipação pelo programa MOSIS, contendo os sensores, os transistores-alvo, os estágios de saída e os circuitos de proteção contra os efeitos da eletricidade estática necessários. Os resultados obtidos através de simulação mostram que o novo circuito proporciona uma redução na área de silício necessária para a implementação, bem como um menor consumo de corrente quiescente em relação às propostas anteriores. / This dissertation deals with the design and evaluation of a new current sensor circuit with dynamic memory cell intended to detect transient currents caused by incidence of ionizing particles in CMOS integrated circuits. Circuits previously proposed are analyzed and their drawbacks are pointed out. The new circuit topology and working principle is presented, along with the detailed design of the versions intended to monitoring PMOS and NMOS transistors. The final circuit is laid out in a 130 nm technology, intended to be prototyped through the MOSIS program. The complete design contains the sensor circuits, target transistors, output stages and electrostatic discharge protection circuitry. Results obtained by post layout simulation shown that the new circuit provides a reduction on silicon area and a smaller quiescent current consumption compared to previous circuits.

Esta dissertação trata do projeto e avaliação de um novo circuito sensor de corrente com célula de memória dinâmica para a detecção de correntes transientes em circuitos integrados CMOS, provocadas pela incidência de partículas ionizantes. As propostas previamente existentes na literatura são avaliadas e suas deficiências são apontadas. É apresentada a topologia e o modo de funcionamento do novo circuito, juntamente com o detalhamento do projeto das versões destinadas à monitoração dos transistores PMOS e NMOS. É apresentado o layout do circuito final em tecnologia 130 nm, destinado à prototipação pelo programa MOSIS, contendo os sensores, os transistores-alvo, os estágios de saída e os circuitos de proteção contra os efeitos da eletricidade estática necessários. Os resultados obtidos através de simulação mostram que o novo circuito proporciona uma redução na área de silício necessária para a implementação, bem como um menor consumo de corrente quiescente em relação às propostas anteriores. / This dissertation deals with the design and evaluation of a new current sensor circuit with dynamic memory cell intended to detect transient currents caused by incidence of ionizing particles in CMOS integrated circuits. Circuits previously proposed are analyzed and their drawbacks are pointed out. The new circuit topology and working principle is presented, along with the detailed design of the versions intended to monitoring PMOS and NMOS transistors. The final circuit is laid out in a 130 nm technology, intended to be prototyped through the MOSIS program. The complete design contains the sensor circuits, target transistors, output stages and electrostatic discharge protection circuitry. Results obtained by post layout simulation shown that the new circuit provides a reduction on silicon area and a smaller quiescent current consumption compared to previous circuits.

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This report presents the design and manufacturing process of a bionic signal messagebroker (BSMB), intended to allow communication between implanted electrodes andprosthetic legs designed by Ossur. The BSMB processes and analyses the data intorelevant information to control the bionic device. The intention is to carry out eventdetection in the BSMB, where events in the muscle signal are matched to the events ofthe gait cycle (toe-o, stance, swing).The whole system is designed to detect muscle contraction via sensors implantedin residual muscles and transmit the signals wireless to a control unit that activatesassociated functions of a prosthetic leg. Two users, one transtibial and one transfemoral,underwent surgery in order to get electrodes implantable into their residual leg muscles.They are among the rst users in the world to get this kind of implanted sensors.A prototype of the BSMB was manufactured. The process took more time thanexpected, mainly due to the fact that it was decided to use a ball grid array (BGA)microprocessor in order to save space. That meant more complicated routing and higherstandards for the manufacturing of the board. The results of the event detection indicatethat the data from the implanted electrodes can be used in order to get sucient controlover prosthetic legs. These are positive ndings for users of prosthetic legs and shouldincrease their security and quality of life.It is important to keep in mind when the results of this report are evaluated that allthe testing carried out were only done on one user each.

Automatically detecting events for people with diabetes mellitus using continuousglucose monitors is an important step in allowing insulin pumps to automaticallycorrect the blood glucose levels and for a more hands-off approach to thedisease. The automatic detection of events could also aid physicians whenassisting their patients when referring to their continuous glucose monitordata. A range of different deep learning algorithms has been applied forpredictions of different events for continuous glucose monitor data, such asthe onset for hyperglycemia, hypoglycemia or mealtime events. This thesisfocused on constructing sequences labelled from an unbalanced and assumedmisslabelled dataset to classify them as such using four different deep learningnetworks using convoluted neural networks and recurrent neural networks.Manual correction of the dataset allowed for only clear events starting witha high positive gradient to be labelled as positive. The classification wasperformed on exact timepoints and in time windows to allow the classificationto to be done around the beginning of an event instead of the exact timepoint.The results from using the unbalanced and assumed misslabelled datasetshowed the networks performing similarly, with high Recall and Precisionbelow 0.5, thus not found to be of use in a for automatic event detection.Further testing by using another dataset or further configurations is neededto clarify the capabilities of automatically detecting events. DDAnalytics willnot use any of the developed networks in any of their products. / Automatisk detection av event för personer med diabetes från deras kontinuerligaglukosmätare är ett viktigt steg för att låta insulinpumpar automatiskt korrigeraglukosnivåer och möjliggöra en mindre självreglering av personens diabetes.Denna automatiska detektion skulle även kunna hjälpa läkare vid samtalmed patienter och deras data från kontinuerliga glukosmätarna. En mängd avolika djupinlärningsalgoritmer har använts för förutsägelser av olika event förkontinuerlig glukosmätardata, som början av hyperglykemier, hypoglykemiereller måltider. Detta examensarbete fokuserar på skapandet av sekvenserfrån ett obalanserat och antaget inte helt korrekt markerade event i dataset,för att kunna klassificera dessa event med fyra olika djupinlärningsnätverk.Dessa nätverk bygger på konvolution och rekursiva neurala nätverk. Manuellkorrektion av datasetet möjliggjorde så att endast tydliga event som börjar meden kraftig positiv ökning av gradienten var markerade som positiva event.Klassificeringen genomfördes på både exakta tidssteg och i tidsfönster såatt början av event kunde detekteras snarare än bara det exakta tidssteget.Resultaten genom användandet av detta tidigare nämnda dataset visade liknanderesultat för samtliga nätverk, med hög Återkallelse och Precision under 0.5.Dessa resultat ledde till att nätverken inte kan antas kunna utföra automatiskevent detektion, och skulle behöva ytterligare testning på ett annat dataset medmer korrekta markerade event eller ytterligare konfigureringar på nätverken föratt verifiera dessas möjligheter att automatiskt klassificera event i kontinuerligglukosdata. DDanalytics kommer inte använda något av dessa framtagnanätverk i några av deras produkter.

The world that we live in is a complex network of agents and their interactions which are termed as events. An instance of an event is composed of directly measurable low-level actions (which I term sub-events) having a temporal order. Also, the agents can act independently (e.g. voting) as well as collectively (e.g. scoring a touch-down in a football game) to perform an event. With the dawn of the new millennium, the low-level vision tasks such as segmentation, object classification, and tracking have become fairly robust. But a representational gap still exists between low-level measurements and high-level understanding of video sequences. This dissertation is an effort to bridge that gap where I propose novel learning, detection, representation, indexing and retrieval approaches for multi-agent events in videos. In order to achieve the goal of high-level understanding of videos, firstly, I apply statistical learning techniques to model the multiple agent events. For that purpose, I use the training videos to model the events by estimating the conditional dependencies between sub-events. Thus, given a video sequence, I track the people (heads and hand regions) and objects using a Meanshift tracker. An underlying rule-based system detects the sub-events using the tracked trajectories of the people and objects, based on their relative motion. Next, an event model is constructed by estimating the sub-event dependencies, that is, how frequently sub-event B occurs given that sub-event A has occurred. The advantages of such an event model are two-fold. First, I do not require prior knowledge of the number of agents involved in an event. Second, no assumptions are made about the length of an event. Secondly, after learning the event models, I detect events in a novel video by using graph clustering techniques. To that end, I construct a graph of temporally ordered sub-events occurring in the novel video. Next, using the learnt event model, I estimate a weight matrix of conditional dependencies between sub-events in the novel video. Further application of Normalized Cut (graph clustering technique) on the estimated weight matrix facilitate in detecting events in the novel video. The principal assumption made in this work is that the events are composed of highly correlated chains of sub-events that have high conditional dependency (association) within the cluster and relatively low conditional dependency (disassociation) between clusters. Thirdly, in order to represent the detected events, I propose an extension of CASE representation of natural languages. I extend CASE to allow the representation of temporal structure between sub-events. Also, in order to capture both multi-agent and multi-threaded events, I introduce a hierarchical CASE representation of events in terms of sub-events and case-lists. The essence of the proposition is that, based on the temporal relationships of the agent motions and a description of its state, it is possible to build a formal description of an event. Furthermore, I recognize the importance of representing the variations in the temporal order of sub-events, that may occur in an event, and encode the temporal probabilities directly into my event representation. The proposed extended representation with probabilistic temporal encoding is termed P-CASE that allows a plausible means of interface between users and the computer. Using the P-CASE representation I automatically encode the event ontology from training videos. This offers a significant advantage, since the domain experts do not have to go through the tedious task of determining the structure of events by browsing all the videos. Finally, I utilize the event representation for indexing and retrieval of events. Given the different instances of a particular event, I index the events using the P-CASE representation. Next, given a query in the P-CASE representation, event retrieval is performed using a two-level search. At the first level, a maximum likelihood estimate of the query event with the different indexed event models is computed. This provides the maximum matching event. At the second level, a matching score is obtained for all the event instances belonging to the maximum matched event model, using a weighted Jaccard similarity measure. Extensive experimentation was conducted for the detection, representation, indexing and retrieval of multiple agent events in videos of the meeting, surveillance, and railroad monitoring domains. To that end, the Semoran system was developed that takes in user inputs in any of the three forms for event retrieval: using predefined queries in P-CASE representation, using custom queries in P-CASE representation, or query by example video. The system then searches the entire database and returns the matched videos to the user. I used seven standard video datasets from the computer vision community as well as my own videos for testing the robustness of the proposed methods.

Event extraction is a type of information extraction(IE) that works on extracting the specific knowledge of certain incidents from texts. Nowadays the amount of available information (such as news, blogs, and social media) grows in exponential order. Therefore, it becomes imperative to develop algorithms that automatically extract the machine-readable information from large volumes of text data. In this dissertation, we focus on three problems in obtaining event-related information from news articles. (1) The first effort is to comprehensively analyze the performance and challenges in current large-scale event encoding systems. (2) The second problem involves event detection and critical information extractions from news articles. (3) Third, the efforts concentrate on event-encoding which aims to extract event extent and arguments from texts. We start by investigating the two large-scale event extraction systems (ICEWS and GDELT) in the political science domain. We design a set of experiments to evaluate the quality of the extracted events from the two target systems, in terms of reliability and correctness. The results show that there exist significant discrepancies between the outputs of automated systems and hand-coded system and the accuracy of both systems are far away from satisfying. These findings provide preliminary background and set the foundation for using advanced machine learning algorithms for event related information extraction. Inspired by the successful application of deep learning in Natural Language Processing (NLP), we propose a Multi-Instance Convolutional Neural Network (MI-CNN) model for event detection and critical sentences extraction without sentence level labels. To evaluate the model, we run a set of experiments on a real-world protest event dataset. The result shows that our model could be able to outperform the strong baseline models and extract the meaningful key sentences without domain knowledge and manually designed features. We also extend the MI-CNN model and propose an MIMTRNN model for event extraction with distant supervision to overcome the problem of lacking fine level labels and small size training data. The proposed MIMTRNN model systematically integrates the RNN, Multi-Instance Learning, and Multi-Task Learning into a unified framework. The RNN module aims to encode into the representation of entity mentions the sequential information as well as the dependencies between event arguments, which are very useful in the event extraction task. The Multi-Instance Learning paradigm makes the system does not require the precise labels in entity mention level and make it perfect to work together with distant supervision for event extraction. And the Multi-Task Learning module in our approach is designed to alleviate the potential overfitting problem caused by the relatively small size of training data. The results of the experiments on two real-world datasets(Cyber-Attack and Civil Unrest) show that our model could be able to benefit from the advantage of each component and outperform other baseline methods significantly. / Ph. D.

Este trabalho apresenta o desenvolvimento de um sensor de corrente transiente destinado a detectar a ocorrência de um evento transiente causado pela incidência de radiação ionizante em um circuito integrado. Iniciando com uma descrição dos efeitos da radiação sobre os circuitos integrados e dos tipos de radiação de interesse, os fundamentos da técnica Bulk- BICS são apresentados e as propostas existentes na literatura são expostas e avaliadas, com ênfase no sensor que utiliza a célula de memória dinâmica DynBICS, resultado de um trabalho prévio e do qual se dispõe de amostras fabricadas. Sobre essas amostras são efetuados testes elétricos, um ensaio de dose total irradiada TID e um ensaio de estimulação laser, cujos resultados são apresentados e confirmam a funcionalidade da topologia da célula de memória dinâmica aplicada a circuitos Bulk-BICS. Em seguida, é apresentada a topologia da célula de memória integrativa como uma evolução da célula de memória dinâmica e propõe-se o circuito de um novo sensor Bulk-BICS baseado na nova célula. O funcionamento elétrico do circuito desse novo sensor TRIBICS é avaliado através de simulação de circuitos determinando-se a sensibilidade e o tempo de resposta do sensor utilizando-se pulsos de corrente em dupla exponencial. É feita uma análise do funcionamento da célula de memória estática e, através de uma comparação de desempenho entre as células de memória estáticas utilizadas em três circuitos propostos e a célula de memória integrativa, utilizando um modelo simplificado, mostra-se que a célula de memória integrativa é mais rápida e sensível do que as contrapartes estáticas O sensor TRIBICS é então simulado em conexão com um modelo de dispositivo, sendo antes apresentados os modelos TCAD do inversor utilizado como alvo da incidência da radiação nas simulações. São apresentados resultados obtidos individualmente para o transistor NMOS e para o transistor PMOS, nos quais se mostra a formação de um canal condutivo entre dreno e fonte durante o SET. Mostra-se, também, que os resultados obtidos com a simulação de dispositivos não concorda com aqueles proporcionados pela simulação de circuitos no tocante à divisão das correntes transitórias entre dreno, fonte e substrato. O resultado das simulações de dispositivo efetuadas com os modelos TCAD em modo misto com o circuito TRIBICS descrito em SPICE mostram a relação entre a transferência de energia da irradiação LET e a efetiva deteção do SET provocado, em função da distância entre os contatos de bulk ou substrato, permitindo determinar a máxima distância entre contatos para 100% de certeza na deteção do SET. Com isso, obtém-se uma estimativa do número de transistores que pode ser monitorado pelos Bulk-BICS. É proposta a estratégia de implementação dos Bulk-BICS na forma de uma standard cell a ser posicionada entre os grupos de transistores sob monitoração, e uma estimativa da relação entre as áreas dos transistores monitorados e do Bulk-BICS é apresentada. Por fim, é estudada a questão da fabricação dos Bulk-BICS no mesmo substrato dos transistores monitorados e uma maneira de fazê-la é proposta. Os resultados encontrados permitem definir a viabilidade e a eficácia da técnica Bulk-BICS como forma de deteção de eventos transientes em sistemas digitais. / A current sensor to detect the occurrence of a single-event transient that is caused by the incidence of ionizing radiation in an integrated circuit is presented. Radiation of interest and their effects on the integrated circuits are discussed. Fundamentals of the Bulk-BICS technique and the circuits proposed in the literature to implement this technique are discussed and evaluated, with emphasis on the dynamic memory cell-based circuit DynBICS, which was developed as a previous work and with fabricated samples available. Experimental results obtained from a series of electrical tests, a TID test, and a laser-stimulated test that were conducted on a number of fabricated and packaged samples are presented. The results confirm that the dynamic memory cell is suitable and robust enough to be used in Bulk-BICS circuits. Next, evolution of the dynamic memory cell into an integrative memory cell is discussed and the circuit of a Bulk-BICS using this new memory cell topology is presented. The electrical operation of this new sensor TRIBICS is evaluated using circuit simulations. By using double-exponential current pulses, both the sensitivity and the response time are determined. The static memory cell operation is analyzed and a comparison of performance between static and integrative cells is performed using a simplified model. The results show that the integrative memory cell is faster and more sensitive than the static cells used in three state-ofthe- art sensors published in literature Then the TRIBICS sensor is simulated connected to a TCAD-modeled device, comprising an inverter, which is used as a target for radiation impact. TCAD models are previously presented and the results obtained when the PMOS and NMOS transistors are separately excited by radiation show the formation of a conductive link between drain and source regions during the occurrence of SET. The simulations also show that the results obtained by using TCAD simulations do not agree with the ones obtained by using circuit simulation regarding the current share among drain, source and bulk during the SET. Mixed-mode simulations using the TCAD models in conjunction of TRIBICS circuits described in SPICE show the relationship between LET and the effective SET-detection with the inter-tap distance as a parameter, and allows to determine the inter-tap distance for 100% of SET detection efficiency. Based on these results, an estimate of how many transistors can be monitored by the Bulk-BICS is obtained. It is proposed to implement the Bulk-BICS as a standard cell, to be positioned in between the standard cell that compose a digital circuit and the area overhead necessary to implant the sensors in a real circuit is estimated. The problem on how to manufacture the Bulk-BICS circuit in the same substrate of the monitored transistors is studied and a solution is proposed. The results show the viability and effectiveness of the Bulk-BICS technique, as a means to detect single-event transients in digital systems.