Novel proposal for prediction of CO2 course and occupancy recognition in Intelligent Buildings within IoT

Many direct and indirect methods, processes, and sensors available on the market today are used to monitor the occupancy of selected Intelligent Building (IB) premises and the living activities of IB residents. By recognizing the occupancy of individual spaces in IB, IB can be optimally automated in conjunction with energy savings. This article proposes a novel method of indirect occupancy monitoring using CO2, temperature, and relative humidity measured by means of standard operating measurements using the KNX (Konnex (standard EN 50090, ISO/IEC 14543)) technology to monitor laboratory room occupancy in an intelligent building within the Internet of Things (IoT). The article further describes the design and creation of a Software (SW) tool for ensuring connectivity of the KNX technology and the IoT IBM Watson platform in real-time for storing and visualization of the values measured using a Message Queuing Telemetry Transport (MQTT) protocol and data storage into a CouchDB type database. As part of the proposed occupancy determination method, the prediction of the course of CO2 concentration from the measured temperature and relative humidity values were performed using mathematical methods of Linear Regression, Neural Networks, and Random Tree (using IBM SPSS Modeler) with an accuracy higher than 90%. To increase the accuracy of the prediction, the application of suppression of additive noise from the CO2 signal predicted by CO2 using the Least mean squares (LMS) algorithm in adaptive filtering (AF) method was used within the newly designed method. In selected experiments, the prediction accuracy with LMS adaptive filtration was better than 95%.Web of Science1223art. no. 454

Resource management for multimedia traffic over ATM broadband satellite networks

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Sleep Stage Classification: A Deep Learning Approach

Sleep occupies significant part of human life. The diagnoses of sleep related disorders are of great importance. To record specific physical and electrical activities of the brain and body, a multi-parameter test, called polysomnography (PSG), is normally used. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, automatic classification algorithms were developed. In this research work, we focused on pre-processing (filtering boundaries and de-noising algorithms) and classification steps of automatic sleep stage classification. The main motivation for this work was to develop a pre-processing and classification framework to clean the input EEG signal without manipulating the original data thus enhancing the learning stage of deep learning classifiers. For pre-processing EEG signals, a lossless adaptive artefact removal method was proposed. Rather than other works that used artificial noise, we used real EEG data contaminated with EOG and EMG for evaluating the proposed method. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy. In the classification area, we evaluated the performance of the most common sleep stage classifiers using a comprehensive set of features extracted from PSG signals. Considering the challenges and limitations of conventional methods, we proposed two deep learning-based methods for classification of sleep stages based on Stacked Sparse AutoEncoder (SSAE) and Convolutional Neural Network (CNN). The proposed methods performed more efficiently by eliminating the need for conventional feature selection and feature extraction steps respectively. Moreover, although our systems were trained with lower number of samples compared to the similar studies, they were able to achieve state of art accuracy and higher overall sensitivity

Modelagem estocástica de algoritmos adaptativos para equalização ativa de ruído e identificação de sistemas

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Elétrica, Florianópolis, 2016.Este trabalho de pesquisa trata da modelagem estocástica de dois algoritmos adaptativos bem conhecidos na literatura, a saber: o algoritmo FxLMS (filtered x least mean square) e o algoritmo NLMS (normalized least mean square). Particularmente, para o algoritmo FxLMS são desenvolvidos dois modelos estocásticos, ambos considerando aplicações de controle e/ou equalização ativa de ruído periódico, porém em diferentes estruturas (monocanal e multicanal). Baseado nas expressões de modelo obtidas, diversos aspectos do comportamento do algoritmo FxLMS são discutidos, evidenciando o impacto dos parâmetros do algoritmo sobre seu desempenho. Para o algoritmo NLMS, são propostos dois modelos estocásticos, ambos considerando a aplicação de identificação de sistemas tanto com sinal de entrada branco gaussiano quanto correlacionado gaussiano. Especificamente, o primeiro modelo do algoritmo NLMS é derivado assumindo que o filtro adaptativo e a planta a ser estimada podem possuir ordens diferentes (tal suposição, que é condizente com cenários práticos, não é usualmente tratada na literatura devido às dificuldades matemáticas surgidas no desenvolvimento da modelagem estocástica). O segundo modelo do algoritmo NLMS considera uma formulação matemática mais geral (quando comparada a outros trabalhos da literatura) para representar a planta a ser identificada, possibilitando a representação de diversos tipos de sistemas variantes no tempo; originando, assim, um modelo estocástico capaz de predizer o comportamento do algoritmo NLMS em uma ampla gama de cenários de operação. Resultados de simulação são apresentados, ratificando a precisão dos modelos estocásticos propostos, tanto na fase transitória quanto em regime permanente.Abstract : This research work focuses on the stochastic modeling of two well-known adaptive algorithms from the literature, namely: the filtered x least mean square (FxLMS) algorithm and the normalized least mean square (NLMS) algorithm. In particular, for the FxLMS algorithm two stochastic models are developed, both considering applications of active noise control and equalization of periodic noise, but in different structures (single channel and multichannel). Based on the obtained expressions, several aspects of the FxLMS algorithm behavior are discussed, highligting the impact of some parameters on the algorithm performance. For the NLMS algorithm, two stochastic models are proposed, both considering the application of system identification with white Gaussian and correlated Gaussian input signals. Specifically, the first model of the NLMS algorithm is developed assuming that the adaptive filter and the system to be identified can have different orders (such a supposition, which is consistent with practical scenarios, is not usually considered in the literature due to the mathematical difficulties ariasing in the development of the stochastic model). The second model of the NLMS algorithm considers a more general mathematical formulation (compared with other works from the open literature) to represent the system to be identified, allowing to represent several types of time varying systems; resulting in a stochastic model able to predict the NLMS algorithm behavior in several scenarios. Simulation results are presented, confirming the accuracy of the proposed stochastic models for both transient and steady state phases

Discrete Time Systems

Discrete-Time Systems comprehend an important and broad research field. The consolidation of digital-based computational means in the present, pushes a technological tool into the field with a tremendous impact in areas like Control, Signal Processing, Communications, System Modelling and related Applications. This book attempts to give a scope in the wide area of Discrete-Time Systems. Their contents are grouped conveniently in sections according to significant areas, namely Filtering, Fixed and Adaptive Control Systems, Stability Problems and Miscellaneous Applications. We think that the contribution of the book enlarges the field of the Discrete-Time Systems with signification in the present state-of-the-art. Despite the vertiginous advance in the field, we also believe that the topics described here allow us also to look through some main tendencies in the next years in the research area

COSTS AND BENEFITS OF INTEGRATING INFORMATION SEQUENCES

Information from the world unfolds over time, and to navigate the everyday world and make future predictions, our brain needs to integrate information over time. For instance, when having a conversation with someone, our brain needs to accumulate information about words and sentences to comprehend the ongoing discussion and respond appropriately. However, ubiquitous accumulation of information can cause interference, especially if we end up combining unrelated information. For instance, the topic of conversation may change from one sentence to the next, in which case combining information from consecutive sentences could cause interference and confusion. These examples demonstrate that integrating information over time is sometimes necessary for successful comprehension and prediction, but it should not be performed indiscriminately. How then should temporal integration mechanisms be implemented, especially in constrained brain-like learning architectures? What kinds of temporal integration and separation mechanisms are employed by contemporary machine learning models? And how do these integration and separation processes compare against what we observe in human behavior? In this thesis, we examined the costs and benefits of integrating and separating information sequences in humans and machines. In the first two projects we focused on learning and tested the performance of biologically-plausible temporal integration mechanisms in neural networks; we characterized the efficacy of these systems in learning categories from a sequence of examples, and investigated how their internal representations are altered by how they integrate information over time. In two further projects we focused on online comprehension and prediction, in the setting of humans reading natural language sequences, and we contrasted our findings with neural network models that predict and generate natural language sequences. We tested how online comprehension and subsequent memory are affected by interruptions in the text that humans are reading. Finally, we tested how neural language models respond to the insertion of incongruent information into a broader coherent text, and we compared these findings against our observations of how humans handle interruptions while reading. Altogether, these studies identify mechanisms by which humans and machines can exploit temporal continuity in the environment, in the service of learning about, understanding and predicting our dynamic world

Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models.

The realistic dynamics mathematical model of a system is very important for analyzing a system. The mathematical system model can be derived by applying physical, thermodynamic, and chemistry laws. But this method has some drawbacks, among which is difficult for complex systems, sometimes is untraceable for nonlinear behavior that almost all systems have in the real world, and requires much knowledge. Another method is system identification which is also called experimental modeling. System identification can be made offline, but this method has a disadvantage because the features of a dynamic system may change over time. The parameters may vary as environmental conditions change. It requires big data and consumes a long time. This research introduces a developed method for online system identification based on the Hammerstein and Wiener nonlinear block-oriented structure with the artificial neural networks (NN) advantages and recursive weighted least squares algorithm for optimizing neural network learning in real-time. The proposed method aimed to obtain a maximally informative mathematical model that can describe the actual dynamic behaviors of a system, using the DC motor as a case study. The goodness of fit validation based on the normalized root-mean-square error (NRMSE) and normalized mean square error, and Theil’s inequality coefficient are used to evaluate the performance of models. Based on experimental results, for best Wiener parallel NN model and series-parallel NN model are 93.7% and 89.48%, respectively. Best Hammerstein parallel NN polynomial based model and series-parallel NN polynomial model are 88.75% and 93.9% respectively, for best Hammerstein parallel NN sigmoid based model and series-parallel NN sigmoid based model 78.26% and 95.95% respectively, and for best Hammerstein parallel NN hyperbolic tangent based model and series-parallel NN hyperbolic tangent based model 70.7% and 96.4% respectively. The best model of the developed method outperformed the conventional NARX and NARMAX methods best model by 3.26% in terms of NRMSE goodness of fit

Design and implementation of adaptive baseband predistorter for OFDM nonlinear transmitter. Simulation and measurement of OFDM transmitter in presence of RF high power amplifier nonlinear distortion and the development of adaptive digital predistorters based on Hammerstein approach.

The objective of this research work is to investigate, design and measurement of a digital predistortion linearizer that is able to compensate the dynamic nonlinear distortion of a High Power Amplifier (PA). The effectiveness of the proposed baseband predistorter (PD) on the performance of a WLAN OFDM transmitter utilizing a nonlinear PA with memory effect is observed and discussed. For this purpose, a 10W Class-A/B power amplifier with a gain of 22 dB, operated over the 3.5 GHz frequency band was designed and implemented. The proposed baseband PD is independent of the operating RF frequency and can be used in multiband applications. Its operation is based on the Hammerstein system, taking into account PA memory effect compensation, and demonstrates a noticeable improvement compared to memoryless predistorters. Different types of modelling procedures and linearizers were introduced and investigated, in which accurate behavioural models of Radio Frequency (RF) PAs exhibiting linear and nonlinear memory effects were presented and considered, based on the Wiener approach employing a linear parametric estimation technique. Three new linear methods of parameter estimation were investigated, with the aim of reducing the complexity of the required filtering process in linear memory compensation. Moreover, an improved wiener model is represented to include the nonlinear memory effect in the system. The validity of the PA modelling approaches and predistortion techniques for compensation of nonlinearities of a PA were verified by several tests and measurements. The approaches presented, based on the Wiener system, have the capacity to deal with the existing trade-off between accuracy and convergence speed compared to more computationally complex behavioural modelling algorithms considering memory effects, such as those based on Volterra series and Neural Networks. In addition, nonlinear and linear crosstalks introduced by the power amplifier nonlinear behaviour and antennas mutual coupling due to the compact size of a MIMO OFDM transmitter have been investigated