Penentuan Lokasi Wireless Device Berbasis 3d Access Point Location Based

Perkembangan penggunaan wireless saat ini telah mengubah cara hidup manusia. Dengan menganalisa gelombang yang diterima dari pemancar menuju sebuah perangkat tersebut. Untuk itu dibutuhkan sebuah model yang mampu memprediksi lokasi dari sebuah perangkat penerima. Pada penelitian ini, dikembangkan suatu metode untuk penentuan lokasi terhadap sebuah perangkat di dalam ruangan. Penelitian ini menerapkan konsep neural network dengan mendeteksi sinyal wireless yang ada di sekitar perangkat penerima. Sinyal-sinyal tersebut kemudian dikirimkan menuju server untuk kemudian diproses lebih lanjut. Proses terbagi menjadi dua, yaitu learning dan production. Pada tahap learning, sistem akan membentuk sebuah model yang akan mampu beradaptasi dengan kombinasi input dan output yang telah diberikan sebelumnya. Dengan memanfaatkan konsep Parallel Resilient Back Propagation, hasil akurasi yang diberikan pada penelitian ini mencapai 89%

Penentuan Lokasi Wireless Device Berbasis 3d Access Point Location Based

Perkembangan penggunaan wireless saat ini telah mengubah cara hidup manusia. Dengan menganalisa gelombang yang diterima dari pemancar menuju sebuah perangkat tersebut. Untuk itu dibutuhkan sebuah model yang mampu memprediksi lokasi dari sebuah perangkat penerima. Pada penelitian ini, dikembangkan suatu metode untuk penentuan lokasi terhadap sebuah perangkat di dalam ruangan. Penelitian ini menerapkan konsep neural network dengan mendeteksi sinyal wireless yang ada di sekitar perangkat penerima. Sinyal-sinyal tersebut kemudian dikirimkan menuju server untuk kemudian diproses lebih lanjut. Proses terbagi menjadi dua, yaitu learning dan production. Pada tahap learning, sistem akan membentuk sebuah model yang akan mampu beradaptasi dengan kombinasi input dan output yang telah diberikan sebelumnya. Dengan memanfaatkan konsep Parallel Resilient Back Propagation, hasil akurasi yang diberikan pada penelitian ini mencapai 89%

Object Shape Classification Utilizing Magnetic Field Disturbance and Supervised Machine Learning

Various narrow artificial intelligence architectures are on the rise due to the development of Graphics Processing Units and, thus, computational capabilities. Massive number multiplication capabilities of GPUs enabled researches to create more complicated and advanced algorithms. Initially, a gaming hardware became a base for modern time Industrial Revolution. Machine learning, once a forgotten branch of computer science, attracts huge investments and interest. In 2014, Google acquired an UK-based start-up Deep Mind for over £400M. In 2016 Volkswagen invested $680M in autonomous vehicle and cyber security start-ups (1). Same year Microsoft announced a newly created AI fund (2) and in May this year it resulted in investment of$7.6M in Bonsai, an AI start-ups that hopes to help companies to integrate machine learning in the infrastructure (3). It seems that almost never-ending pockets of investors are motivated by a promise of automation of difficult tasks, which, until now, have never been performed by humans. This thesis explores various supervised machine learning algorithms, beginning with the simplest k-Nearest Neighbours and Multi-layer Perceptron, to the state of the art architecture created by the industry experts (Deep Residual Network from Microsoft Research), and prominent academic figures (i.e. GG from Oxford). Furthermore, the author of the thesis proposes two additional network structures, named Deep Inception and Stacked Artificial Residual Architecture, inspired by previously mentioned research

Field programmable gate array based sigmoid function implementation using differential lookup table and second order nonlinear function

Artificial neural network (ANN) is an established artificial intelligence technique that is widely used for solving numerous problems such as classification and clustering in various fields. However, the major problem with ANN is a factor of time. ANN takes a longer time to execute a huge number of neurons. In order to overcome this, ANN is implemented into hardware namely field-programmable-gate-array (FPGA). However, implementing the ANN into a field-programmable gate array (FPGA) has led to a new problem related to the sigmoid function implementation. Often used as the activation function for ANN, a sigmoid function cannot be directly implemented in FPGA. Owing to its accuracy, the lookup table (LUT) has always been used to implement the sigmoid function in FPGA. In this case, obtaining the high accuracy of LUT is expensive particularly in terms of its memory requirements in FPGA. Second-order nonlinear function (SONF) is an appealing replacement for LUT due to its small memory requirement. Although there is a trade-off between accuracy and memory size. Taking the advantage of the aforementioned approaches, this thesis proposed a combination of SONF and a modified LUT namely differential lookup table (dLUT). The deviation values between SONF and sigmoid function are used to create the dLUT. SONF is used as the first step to approximate the sigmoid function. Then it is followed by adding or deducting with the value that has been stored in the dLUT as a second step as demonstrated via simulation. This combination has successfully reduced the deviation value. The reduction value is significant as compared to previous implementations such as SONF, and LUT itself. Further simulation has been carried out to evaluate the accuracy of the ANN in detecting the object in an indoor environment by using the proposed method as a sigmoid function. The result has proven that the proposed method has produced the output almost as accurately as software implementation in detecting the target in indoor positioning problems. Therefore, the proposed method can be applied in any field that demands higher processing and high accuracy in sigmoid function outpu

Algoritmos probabilísticos para WiFi Fingerprinting

Dissertação de mestrado integrado em Engenharia de Telecomunicações e InformáticaA técnica Wi-Fi Fingerprinting é uma técnica amplamente utilizada no posicionamento em interiores. Através desta técnica é possível determinar a posição do dispositivo, combinando os valores da intensidade do sinal recebidos com os valores da intensidade do sinal pré-adquiridos, presentes numa base de dados. O grande problema desta técnica é que, ao longo do tempo o cenário vai sofrendo várias alterações, condicionando a estimativa do posicionamento. Já foram propostos vários algoritmos de localização baseados em fingerprinting, sendo o mais popular o algoritmo k Nearest Neighbors (KNN). O propósito desta dissertação centra-se em construir novos algoritmos que permitam estimar o posicionamento, baseados na técnica Wi-Fi fingerprinting. São abordados nesta dissertação dois tipos de algoritmos, algoritmos determinísticos e algoritmos probabilísticos, com o intuito de avaliar o desempenho de cada um deles em ambientes indoor. Entre os algoritmos determinísticos, foi escolhido e implementado um algoritmo hierárquico já existente. Este algoritmo inclui três etapas distintas, nomeadamente a identificação do edifício, depois do respetivo piso e finalmente a estimativa da localização. Tendo em conta o ambiente em estudo, este algoritmo hierárquico apresenta resultados satisfatórios, sendo utilizado como referência na análise de desempenho dos restantes algoritmos aqui apresentados. Ainda nos algoritmos determinísticos, são efetuadas propostas de alteração ao algoritmo hierárquico de forma a melhorar os resultados. Relativamente aos algoritmos probabilísticos, são descritas e implementadas três variantes. Estas três variantes calculam a probabilidade de uma fingerprint pertencer a um determinado local, utilizando diferentes metodologias. A primeira variante, faz uso de uma distribuição baseada em histogramas. É construído um histograma de valores da intensidade do sinal para cada ponto de acesso de uma fingerprint. A segunda variante recorre à probabilidade de um ponto de acesso ter sido observado numa determinada posição. A terceira variante utiliza a função gaussiana de Kernel para cada ponto de acesso. Todos estes algoritmos, tanto os determinísticos como os probabilísticos foram testados recorrendo a datasets de dados reais, que permitiram obter os resultados descritos neste documento.Wi-Fi Fingerprinting is a widely used technique in interior positioning systems. Due to this technique it is possible to determine the position of a device, combining the values of the received signal intensity with the values of the signals intensity pre-acquired from a database. The main problem of this technique is that, over the time the scenario suffer several changes conditioning the estimated position. There have been proposed several localization algorithms based in fingerprinting in which the most popular is the k Nearest Neighbors algorithm. This dissertation focuses on developing new algorithms that permit the estimation of the positioning, based in the Wi-Fi fingerprint technique. In this dissertation we make two approaches, deterministic algorithms and probabilistic algorithms, with the aim to evaluate the performance of each one in indoor environments. Between the deterministic algorithms, an existent hierarchical algorithm was chosen and then implemented. This algorithm includes three different steps, the building identification, the floor identification and finally the estimated localization. Taking into account the study environment, this hierarchical algorithm shows decent results, so it is used as a reference in the performance analyses of the other algorithms presented here. Still in the deterministic algorithms, it is made several proposals to modify the hierarchical algorithm in order to improve the results. Relatively to the probabilistic algorithms it is described and implemented three variants. These three variants calculate the probability of a fingerprint belong to a particular location, using several methodologies. The first uses distribution histograms. It is built an histogram of the signal intensity values for each access point of a fingerprint. The second resorts on the probability of an access point being observed in a certain position. The third uses the Kernel’s gaussian function for each access point. All of these algorithms, both deterministic as probabilistic were tested using datasets of real data, that permitted to obtain the results described in this document

Advances in Intelligent Robotics and Collaborative Automation

This book provides an overview of a series of advanced research lines in robotics as well as of design and development methodologies for intelligent robots and their intelligent components. It represents a selection of extended versions of the best papers presented at the Seventh IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2013 that were related to these topics. Its contents integrate state of the art computational intelligence based techniques for automatic robot control to novel distributed sensing and data integration methodologies that can be applied to intelligent robotics and automation systems. The objective of the text was to provide an overview of some of the problems in the field of robotic systems and intelligent automation and the approaches and techniques that relevant research groups within this area are employing to try to solve them.The contributions of the different authors have been grouped into four main sections:• Robots• Control and Intelligence• Sensing• Collaborative automationThe chapters have been structured to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area

Advances in Intelligent Robotics and Collaborative Automation

This book provides an overview of a series of advanced research lines in robotics as well as of design and development methodologies for intelligent robots and their intelligent components. It represents a selection of extended versions of the best papers presented at the Seventh IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2013 that were related to these topics. Its contents integrate state of the art computational intelligence based techniques for automatic robot control to novel distributed sensing and data integration methodologies that can be applied to intelligent robotics and automation systems. The objective of the text was to provide an overview of some of the problems in the field of robotic systems and intelligent automation and the approaches and techniques that relevant research groups within this area are employing to try to solve them.The contributions of the different authors have been grouped into four main sections:• Robots• Control and Intelligence• Sensing• Collaborative automationThe chapters have been structured to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area