International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada / The demand for additional computing power and more sophisticated graphics displays to strengthen real-time flight testing prompted the Real-time Systems Team to turn to graphics workstations. In order to drive graphics displays with real-time data, the questions became, "What interface to use?" and "How to integrate workstations into our existing telemetry processing system?". This paper discusses the interface and integration of graphics workstations to the Real-time Telemetry Processing System III (RTPS III).

Dysfunction of cholinergic and dopaminergic systems has been implicated in memory function de cits that are core pathology and associated features of several neurological disorders. However, in order to develop more effective treatments, it is crucial to better understand how different aspects of learning and memory are modulated by these neuromodulatory systems. Using optogenetic stimulation or silencing, this thesis aims to investigate the effects of cholinergic and dopaminergic modulation on various hippocamal learning and memory processes. To understand how these neuromodulatory systems modulate hippocampal network activity, I first examined their effects on hippocampal local field potentials in urethane-anaesthetised mice. I demonstrated that optogenetic cholinergic activation suppressed slow oscillations, shifting brain activity to a state dominated by theta and gamma oscillations. In contrast, dopaminergic activation suppressed gamma oscillations. Second, to directly probe the effects of neuromodulation on different stages of spatial learning, I acutely activated or inactivated cholinergic or dopaminergic neurons during various behavioural tasks. My findings suggested that cholinergic activation, solely during the reward phase of a long-term spatial memory task, slowed place learning, highlighting the importance of temporally-precise neuromodulation. Moreover, dopaminergic stimulation may enhance place learning of a food rewarded task, supporting a role for dopamine in spatial learning. In addition, I tested the effects of cholinergic and dopaminergic modulation on reversal learning and found that cholinergic inactivation and dopaminergic activation appear to impair this process. Together, these findings emphasise the importance of cholinergic and dopaminergic modulation in learning and memory. They suggest that precise timing of neuromodulator action is critical for optimal learning and memory performance, and that acetylcholine and dopamine support complementary processes that allow for effective learning and adaptation to changing environments.

Numerous studies from various research groups have already shown the usefulness of alcohol expectancies as predictors of long-term future alcohol consumption. The present study extends this line of research by directly testing whether alcohol expectancies measured in the moment using free association are useful as predictors of alcohol consumption in the next few hours. An ecological momentary assessment (EMA) procedure was used to examine how alcohol expectancies might fluctuate during days in which many people expect to drink (e.g. Fridays, Saturdays) and how these fluctuations in alcohol expectancies might predict future drinking and/or co-vary with important contextual variables during that same day. The results supported our main hypothesis that increases in positively-valenced alcohol expectancies would be observed a few hours to minutes before engaging in alcohol consumption. These findings provide further evidence that anticipatory information processing is a key part of the motivational system that directs future behavior, and that probing expectancies in real-time can be useful for predicting alcohol consumption in the near future.

The shared memory systems should support parallelization at the computation (multi-core), communication (Network-on-Chip, NoC) and memory architecture levels to exploit the potential performance benefits. These parallel systems supporting shared memory abstraction both in the general purpose and application specific domains are confronting the critical issue of memory consistency. The memory consistency issue arises due to the unconstrained memory operations which leads to the unexpected behavior of shared memory systems. The memory consistency models enforce ordering constraints on the memory operations for the expected behavior of the shared memory systems. The intuitive Sequential Consistency (SC) model enforces strict ordering constraints on the memory operations and does not take advantage of the system optimizations both in the hardware and software. Alternatively, the relaxed memory consistency models relax the ordering constraints on the memory operations and exploit these optimizations to enhance the system performance at the reasonable cost. The purpose of this thesis is twofold. First, the novel architecture supports are provided for the different memory consistency models like: SC, Total Store Ordering (TSO), Partial Store Ordering (PSO), Weak Consistency (WC), Release Consistency (RC) and Protected Release Consistency (PRC) in the NoC-based multi-core (McNoC) systems. The PRC model is proposed as an extension of the RC model which provides additional reordering and relaxation in the memory operations. Second, the scalability analysis of these memory consistency models is performed in the McNoC systems. The architecture supports for these different memory consistency models are provided in the McNoC platforms. Each configurable McNoC platform uses a packet-switched 2-D mesh NoC with deflection routing policy, distributed shared memory (DSM), distributed locks and customized processor interface. The memory consistency models/protocols are implemented in the customized processor interfaces which are developed to integrate the processors with the rest of the system. The realization schemes for the memory consistency models are based on a transaction counter and an an an address ddress ddress ddress ddress ddress ddress stack tacktack-based based based based based based novel approaches.approaches.approaches.approaches. approaches.approaches.approaches.approaches.approaches.approaches. The transaction counter is used in each node of the network to keep track of the outstanding memory operations issued by a processor in the system. The address stack is used in each node of the network to keep track of the addresses of the outstanding memory operations issued by a processor in the system. These hardware structures are used in the processor interface to enforce the required global orders under these different memory consistency models. The realization scheme of the PRC model in addition also uses acquire counter for further classification of the data operations as unprotected and protected operations. The scalability analysis of these different memory consistency models is performed on the basis of different workloads which are developed and mapped on the various sized networks. The scalability study is conducted in the McNoC systems with 1 to 64-cores with various applications using different problem sizes and traffic patterns. The performance metrics like execution time, performance, speedup, overhead and efficiency are evaluated as a function of the network size. The experiments are conducted both with the synthetic and application workloads. The experimental results under different application workloads show that the average execution time under the relaxed memory consistency models decreases relative to the SC model. The specific numbers are highly sensitive to the application and depend on how well it matches to the architectures. This study shows the performance improvement under the relaxed memory consistency models over the SC model that is dependent on the computation-to-communication ratio, traffic patterns, data-to-synchronization ratio and the problem size. The performance improvement of the PRC and RC models over the SC model tends to be higher than 50% as observed in the experiments, when the system is further scaled up. / <p>QC 20130204</p>

With the rapid growth of load demand, the distribution system is becoming more and more complicated, and the operational efficiency and service quality deteriorated. Power system protection is important for service reliability and quality assurance. Various faults may occur due to natural and artificial calamity. To reduce the outage duration and promptly restore power services, fault section estimate has to be done effectively with appeared fault alarms. The distribution system containing numerous protective facilities and switch equipment ranges over wide boundary. It becomes very complicated for dispatchers to obtain restoration plan for out-of-service areas. To cope with the problem, an effective tool is helpful for the restoration. This thesis proposes the use of Bi-directional associative memory networks (BAMN) to develop alarm processing. And use of Probabilistic Neural Network (PNN) to develop fault section detection, fault isolation, and restoration system. A distribution system is selected for computer simulation to demonstrate the effectiveness of the proposed system. The thesis proposes to use Bi-directional Associative Memory Network¡]BAMN¡^ to pre-process the signal gained from SCADA Interface, and transmit correct signal to Probabilistic Neural Network (PNN) for restoration plan . Computer simulation shows a simplified model to shorten the processing time in this study.

The automotive industry is inevitably experiencing a paradigm shift from fossil fuels to electric powertrain with significant technological breakthroughs in vehicle electrification. Emerging hybrid electric vehicles were one of the first steps towards cleaner and greener vehicles with a higher fuel economy and lower emission levels. The energy management strategy in hybrid electric vehicles determines the power flow pattern and significantly affects vehicle performance. Therefore, in this thesis, a learning-based strategy is proposed to address the energy management problem of a hybrid electric vehicle in various driving conditions. The idea of a deep recurrent neural network-based energy management strategy is proposed, developed, and evaluated. Initially, a hybrid electric vehicle model with a rule-based supervisory controller is constructed for this case study to obtain training data for the deep recurrent neural network and to evaluate the performance of the proposed energy management strategy. Secondly, due to its capabilities to remember historical data, a long short-term memory recurrent neural network is designed and trained to estimate the powertrain control variables from vehicle parameters. Extensive simulations are conducted to improve the model accuracy and ensure its generalization capability. Also, several hyper-parameters and structures are specifically tuned and debugged for this purpose. The novel proposed energy management strategy takes sequential data as input to capture the characteristics of both driver and controller behaviors and improve the estimation/prediction accuracy. The energy management controller is defined as a time-series problem, and a network predictor module is implemented in the system-level controller of the hybrid electric vehicle model. According to the simulation results, the proposed strategy and prediction model demonstrated lower fuel consumption and higher accuracy compared to other learning-based energy management strategies. / Thesis / Master of Applied Science (MASc)

Various forms of fraud have substantially impacted our lives and caused considerable losses to some people. To reduce these losses, many researchers have devoted themselves to the study of fraud detection. After the development of fraud detection from expert-driven to data-driven systems, the scalability and accuracy of fraud detection have been improved considerably. However, most existing fraud detection methods focus on the feature extraction and classification of a certain transaction, ignoring the temporal and spatial long-term information from accounts. In this work, we propose to address these limitations by employing a lightweight memory network (LiMNet), which is a deep neural network that captures causal relations between temporal interactions. We evaluate our approach on two data sets, the Ether-Fraud dataset, and the Elliptic dataset. The former is a brand new dataset collected from Etherscan with data mining, and the latter is published by the homonymous company. As a set of raw collected data never used before, the Ether-Fraud dataset had some issues, such as huge variation among values and incomplete information. Therefore we have processed Ether-Fraud with data supplementation and normalization, which has solved these problems. A series of experiments were designed based on our analysis of the model and helped us to find the best hyper-parameter setting. Then, we compared the performance of the model with other baselines, and the results showed that Lightweight Memory Network (LiMNet) outperformed traditional algorithms on the Ether-Fraud dataset but was not good as the graph-based method on the Elliptic dataset. Finally, we summarized the experience of applying the model to fraud detection, the strengths and weaknesses of the model, and future directions for improvement. / Olika former av bedrägerier har haft en betydande inverkan på våra liv och har orsakat stora förluster för vissa människor. För att minska dessa förluster har många forskare ägnat sig åt att studera upptäckt av bedrägerier. Efter utvecklingen av bedrägeriutredningen från expertdrivna till datadrivna system har skalbarheten och noggrannheten förbättrats avsevärt. De flesta av de befintliga metoderna för upptäckt av bedrägerier fokuserar dock på utvinning av funktioner och klassificering av en viss transaktion och ignorerar den temporala och spatiala långsiktiga informationen från konton. I det här arbetet föreslår vi att vi tar itu med dessa begränsningar genom att använda ett lättviktigt minnesnätverk (LiMNet), som är ett djupt neuralt nätverk som fångar kausala relationer mellan temporala interaktioner. Vi utvärderar vårt tillvägagångssätt på två datamängder, datamängden Ether-Fraud och Elliptic-datamängden. Det förstnämnda är ett helt nytt dataset som samlats in från Etherscan med hjälp av datautvinning, och det sistnämnda är publicerat av det homonyma företaget. Eftersom det rörde sig om råa insamlade data som aldrig använts tidigare hade Ether-Fraud-datasetet vissa problem, t.ex. en stor variation mellan värdena och ofullständig information. Därför har vi bearbetat Ether-Fraud med datatillägg och normalisering, vilket har löst dessa problem. En serie experiment utformades utifrån vår analys av modellen och hjälpte oss att hitta den bästa inställningen av hyperparametrar. Sedan jämförde vi modellens prestanda med andra baslinjer, resultaten visade att LiMNet överträffade traditionella algoritmer på datasetet Ether-Fraud men var inte lika bra som den grafbaserade metoden på datasetet Elliptic. Slutligen sammanfattade vi erfarenheterna av att tillämpa modellen på bedrägeridetektion, modellens styrkor och svagheter samt framtida riktningar för förbättringar.

Photoplethysmography (PPG) is a method to detect blood volume changes in every heartbeat. The peaks in the PPG signal corresponds to the electrical impulses sent by the heart. The duration between each heartbeat varies, and these variances are better known as heart rate variability (HRV). Thus, finding peaks correctly from PPG signals provides the opportunity to measure an accurate HRV. Additional research indicates that deep learning approaches can extract HRV from a PPG signal with significantly greater accuracy compared to other traditional methods. In this study, deep learning classifiers were built to detect peaks in a noise-contaminated PPG signal and to recognize the performed activity during the data recording. The dataset used in this study is provided by the PhysioBank database consisting of synchronized PPG-, acceleration- and gyro data. The models investigated in this study were limited toa one-layer LSTM network with six varying numbers of neurons and four different window sizes. The most accurate model for the peak classification was the model consisting of 256 neurons and a window size of 15 time steps, with a Matthews correlation coefficient (MCC) of 0.74. The model consisted of64 neurons and a window duration of 1.25 seconds resulted in the most accurate activity classification, with an MCC score of 0.63. Concludingly, more optimization of a deep learning approach could lead to promising accuracy on peak detection and thus an accurate measurement of HRV. The probable cause for the low accuracy of the activity classification problem is the limited data used in this study.

Software prediction is a complex area as there are no accurate models to represent reliability throughout the use of software, unlike hardware reliability. In the context of the software reliability of on-board train systems, ensuring good software reliability over time is all the more critical given the current density of rail traffic and the risk of accidents resulting from a software malfunction. This thesis proposes to use soft computing methods and historical failure data to predict the software reliability of on-board train systems. For this purpose, four machine learning models (Multi-Layer Perceptron, Imperialist Competitive Algorithm Multi-Layer Perceptron, Long Short-Term Memory Network and Convolutional Neural Network) are compared to determine which has the best prediction performance. We also study the impact of having one or more features represented in the dataset used to train the models. The performance of the different models is evaluated using the Mean Absolute Error, Mean Squared Error, Root Mean Squared Error and the R Squared. The report shows that the Long Short-Term Memory Network is the best performing model on the data used for this project. It also shows that datasets with a single feature achieve better prediction. However, the small amount of data available to conduct the experiments in this project may have impacted the results obtained, which makes further investigations necessary. / Att förutsäga programvara är ett komplext område eftersom det inte finns några exakta modeller för att representera tillförlitligheten under hela programvaruanvändningen, till skillnad från hårdvarutillförlitlighet. När det gäller programvarans tillförlitlighet i fordonsbaserade tågsystem är det ännu viktigare att säkerställa en god tillförlitlighet över tiden med tanke på den nuvarande tätheten i järnvägstrafiken och risken för olyckor till följd av ett programvarufel. I den här avhandlingen föreslås att man använder mjuka beräkningsmetoder och historiska data om fel för att förutsäga programvarans tillförlitlighet i fordonsbaserade tågsystem. För detta ändamål jämförs fyra modeller för maskininlärning (Multi-Layer Perceptron, Imperialist Competitive Algorithm Mult-iLayer Perceptron, Long Short-Term Memory Network och Convolutional Neural Network) för att fastställa vilken som har den bästa förutsägelseprestandan. Vi undersöker också effekten av att ha en eller flera funktioner representerade i den datamängd som används för att träna modellerna. De olika modellernas prestanda utvärderas med hjälp av medelabsolut fel, medelkvadratfel, rotmedelkvadratfel och R-kvadrat. Rapporten visar att Long Short-Term Memory Network är den modell som ger bäst resultat på de data som använts för detta projekt. Den visar också att dataset med en enda funktion ger bättre förutsägelser. Den lilla mängd data som fanns tillgänglig för att genomföra experimenten i detta projekt kan dock ha påverkat de erhållna resultaten, vilket gör att ytterligare undersökningar är nödvändiga.

With the accessibility to generous spectrum and development of high gain antenna arrays, wireless communication in higher frequency bands providing multi-gigabit short range wireless access has become a reality. The directional antennas have proven to reduce losses due to interfering signals but are still exposed to blockage events. These events impede the overall user connectivity and throughput. A mobile blocker such as a moving vehicle amplifies the blockage effect. Modelling the blockage effects helps in understanding these events in depth and in maintaining the user connectivity. This thesis proposes the use of a four state channel model to describe blockage events in high-frequency communication. Two deep learning architectures are then designed and evaluated for two possible tasks, the prediction of the signal strength and the classification of the channel state. The evaluations based on simulated traces show high accuracy, and suggest that the proposed models have the potential to be extended for deployment in real systems. / Med tillgängligheten till generöst spektrum och utveckling av antennmatriser med hög förstärkning har trådlös kommunikation i högre frekvensband som ger multi-gigabit kortdistans trådlös åtkomst blivit verklighet. Riktningsantennerna har visat sig minska förluster på grund av störande signaler men är fortfarande utsatta för blockeringshändelser. Dessa händelser hindrar den övergripande användaranslutningen och genomströmningen. En mobil blockerare såsom ett fordon i rörelse förstärker blockeringseffekten. Modellering av blockeringseffekter hjälper till att förstå dessa händelser på djupet och bibehålla användaranslutningen. Denna avhandling föreslår användning av en fyrstatskanalmodell för att beskriva blockeringshändelser i högfrekvent kommunikation. Två djupinlärningsarkitekturer designas och utvärderas för två möjliga uppgifter, förutsägelsen av signalstyrkan och klassificeringen av kanalstatusen. Utvärderingarna baserade på simulerade spår visar hög noggrannhet och föreslår att de föreslagna modellerna har potential att utökas för distribution i verkliga system.