A New Classification Technique in Mobile Robot Navigation

This paper presents a novel pattern recognition algorithm that use weightless neural network (WNNs) technique.This technique plays a role of situation classifier to judge the situation around the mobile robot environment and makes control decision in mobile robot navigation. The WNNs technique is choosen due to significant advantages over conventional neural network, such as they can be easily implemented in hardware using standard RAM, faster in training phase and work with small resources. Using a simple classification algorithm, the similar data will be grouped with each other and it will be possible to attach similar data classes to specific local areas in the mobile robot environment. This strategy is demonstrated in simple mobile robot powered by low cost microcontrollers with 512 bytes of RAM and low cost sensors. Experimental result shows, when number of neuron increases the average environmental recognition ratehas risen from 87.6% to 98.5%.The WNNs technique allows the mobile robot to recognize many and different environmental patterns and avoid obstacles in real time. Moreover, by using proposed WNNstechnique mobile robot has successfully reached the goal in dynamic environment compare to fuzzy logic technique and logic function, capable of dealing with uncertainty in sensor reading, achieving good performance in performing control actions with 0.56% error rate in mobile robot speed

Design for novel enhanced weightless neural network and multi-classifier.

Weightless neural systems have often struggles in terms of speed, performances, and memory issues. There is also lack of sufficient interfacing of weightless neural systems to others systems. Addressing these issues motivates and forms the aims and objectives of this thesis. In addressing these issues, algorithms are formulated, classifiers, and multi-classifiers are designed, and hardware design of classifier are also reported. Specifically, the purpose of this thesis is to report on the algorithms and designs of weightless neural systems. A background material for the research is a weightless neural network known as Probabilistic Convergent Network (PCN). By introducing two new and different interfacing method, the word "Enhanced" is added to PCN thereby giving it the name Enhanced Probabilistic Convergent Network (EPCN). To solve the problem of speed and performances when large-class databases are employed in data analysis, multi-classifiers are designed whose composition vary depending on problem complexity. It also leads to the introduction of a novel gating function with application of EPCN as an intelligent combiner. For databases which are not very large, single classifiers suffices. Speed and ease of application in adverse condition were considered as improvement which has led to the design of EPCN in hardware. A novel hashing function is implemented and tested on hardware-based EPCN. Results obtained have indicated the utility of employing weightless neural systems. The results obtained also indicate significant new possible areas of application of weightless neural systems

Theoretical results on a weightless neural classifier and application to computational linguistics

WiSARD é um classificador n-upla, historicamente usado em tarefas de reconhecimento de padrões em imagens em preto e branco. Infelizmente, não era comum que este fosse usado em outras tarefas, devido á sua incapacidade de arcar com grandes volumes de dados por ser sensível ao conteúdo aprendido. Recentemente, a técnica de bleaching foi concebida como uma melhoria à arquitetura do classificador n-upla, como um meio de coibir a sensibilidade da WiSARD. Desde então, houve um aumento na gama de aplicações construídas com este sistema de aprendizado. Pelo uso frequente de corpora bastante grandes, a etiquetação gramatical multilíngue encaixa-se neste grupo de aplicações. Esta tese aprimora o mWANN-Tagger, um etiquetador gramatical sem peso proposto em 2012. Este texto mostra que a pesquisa em etiquetação multilíngue com WiSARD foi intensificada através do uso de linguística quantitativa e que uma configuração de parâmetros universal foi encontrada para o mWANN-Tagger. Análises e experimentos com as bases da Universal Dependencies (UD) mostram que o mWANN-Tagger tem potencial para superar os etiquetadores do estado da arte dada uma melhor representação de palavra. Esta tese também almeja avaliar as vantagens do bleaching em relação ao modelo tradicional através do arcabouço teórico da teoria VC. As dimensões VC destes foram calculadas, atestando-se que um classificador n-upla, seja WiSARD ou com bleaching, que possua N memórias endereçadas por n-uplas binárias tem uma dimensão VC de exatamente N (2n − 1) + 1. Um paralelo foi então estabelecido entre ambos os modelos, onde deduziu-se que a técnica de bleaching é uma melhoria ao método n-upla que não causa prejuízos à sua capacidade de aprendizado.WiSARD é um classificador n-upla, historicamente usado em tarefas de reconhecimento de padrões em imagens em preto e branco. Infelizmente, não era comum que este fosse usado em outras tarefas, devido á sua incapacidade de arcar com grandes volumes de dados por ser sensível ao conteúdo aprendido. Recentemente, a técnica de bleaching foi concebida como uma melhoria à arquitetura do classificador n-upla, como um meio de coibir a sensibilidade da WiSARD. Desde então, houve um aumento na gama de aplicações construídas com este sistema de aprendizado. Pelo uso frequente de corpora bastante grandes, a etiquetação gramatical multilíngue encaixa-se neste grupo de aplicações. Esta tese aprimora o mWANN-Tagger, um etiquetador gramatical sem peso proposto em 2012. Este texto mostra que a pesquisa em etiquetação multilíngue com WiSARD foi intensificada através do uso de linguística quantitativa e que uma configuração de parâmetros universal foi encontrada para o mWANN-Tagger. Análises e experimentos com as bases da Universal Dependencies (UD) mostram que o mWANN-Tagger tem potencial para superar os etiquetadores do estado da arte dada uma melhor representação de palavra. Esta tese também almeja avaliar as vantagens do bleaching em relação ao modelo tradicional através do arcabouço teórico da teoria VC. As dimensões VC destes foram calculadas, atestando-se que um classificador n-upla, seja WiSARD ou com bleaching, que possua N memórias endereçadas por n-uplas binárias tem uma dimensão VC de exatamente N (2n − 1) + 1. Um paralelo foi então estabelecido entre ambos os modelos, onde deduziu-se que a técnica de bleaching é uma melhoria ao método n-upla que não causa prejuízos à sua capacidade de aprendizado

Optimisation of a weightless neural network using particle swarms

Among numerous pattern recognition methods the neural network approach has been the subject of much research due to its ability to learn from a given collection of representative examples. This thesis is concerned with the design of weightless neural networks, which decompose a given pattern into several sets of n points, termed n-tuples. Considerable research has shown that by optimising the input connection mapping of such n-tuple networks classification performance can be improved significantly. In this thesis the application of a population-based stochastic optimisation technique, known as Particle Swarm Optimisation (PSO), to the optimisation of the connectivity pattern of such “n-tuple” classifiers is explored. The research was aimed at improving the discriminating power of the classifier in recognising handwritten characters by exploiting more efficient learning strategies. The proposed "learning" scheme searches for ‘good’ input connections of the n-tuples in the solution space and shrinks the search area step by step. It refines its search by attracting the particles to positions with good solutions in an iterative manner. Every iteration the performance or fitness of each input connection is evaluated, so a reward and punishment based fitness function was modelled for the task. The original PSO was refined by combining it with other bio-inspired approaches like Self-Organized Criticality and Nearest Neighbour Interactions. The hybrid algorithms were adapted for the n-tuple system and the performance was measured in selecting better connectivity patterns. The Genetic Algorithm (GA) has been shown to be accomplishing the same goals as the PSO, so the performances and convergence properties of the GA were compared against the PSO to optimise input connections. Experiments were conducted to evaluate the proposed methods by applying the trained classifiers to recognise handprinted digits from a widely used database. Results revealed the superiority of the particle swarm optimised training for the n-tuples over other algorithms including the GA. Low particle velocity in PSO was favourable for exploring more areas in the solution space and resulted in better recognition rates. Use of hybridisation was helpful and one of the versions of the hybrid PSO was found to be the best performing algorithm in finding the optimum set of input maps for the n-tuple network

An analysis of learning in weightless neural systems

This thesis brings together two strands of neural networks research - weightless systems and statistical learning theory - in an attempt to understand better the learning and generalisation abilities of a class of pattern classifying machines. The machines under consideration are n-tuple classifiers. While their analysis falls outside the domain of more widespread neural networks methods the method has found considerable application since its first publication in 1959. The larger class of learning systems to which the n-tuple classifier belongs is known as the set of weightless or RAM-based systems, because of the fact that they store all their modifiable information in the nodes rather than as weights on the connections. The analytical tools used are those of statistical learning theory. Learning methods and machines are considered in terms of a formal learning problem which allows the precise definition of terms such as learning and generalisation (in this context). Results relating the empirical error of the machine on the training set, the number of training examples and the complexity of the machine (as measured by the Vapnik- Chervonenkis dimension) to the generalisation error are derived. In the thesis this theoretical framework is applied for the first time to weightless systems in general and to n-tuple classifiers in particular. Novel theoretical results are used to inspire the design of related learning machines and empirical tests are used to assess the power of these new machines. Also data-independent theoretical results are compared with data-dependent results to explain the apparent anomalies in the n-tuple classifier's behaviour. The thesis takes an original approach to the study of weightless networks, and one which gives new insights into their strengths as learning machines. It also allows a new family of learning machines to be introduced and a method for improving generalisation to be applied.Open Acces

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

[Abstract] Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recent developments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed. Furthermore, this article enumerates the most relevant open challenges for current DL-UAV solutions, thus allowing future researchers to define a roadmap for devising the new generation affordable autonomous DL-UAV IoT solutions.Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED431C 2016-047Xunta de Galicia; , ED431G/01Centro Singular de Investigación de Galicia; PC18/01Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-

New Waves of IoT Technologies Research – Transcending Intelligence and Senses at the Edge to Create Multi Experience Environments

The next wave of Internet of Things (IoT) and Industrial Internet of Things (IIoT) brings new technological developments that incorporate radical advances in Artificial Intelligence (AI), edge computing processing, new sensing capabilities, more security protection and autonomous functions accelerating progress towards the ability for IoT systems to self-develop, self-maintain and self-optimise. The emergence of hyper autonomous IoT applications with enhanced sensing, distributed intelligence, edge processing and connectivity, combined with human augmentation, has the potential to power the transformation and optimisation of industrial sectors and to change the innovation landscape. This chapter is reviewing the most recent advances in the next wave of the IoT by looking not only at the technology enabling the IoT but also at the platforms and smart data aspects that will bring intelligence, sustainability, dependability, autonomy, and will support human-centric solutions.acceptedVersio

LambdaNet: A Novel Architecture for Unstructured Change Detection

The goal of this thesis is the development of LambdaNet, a new type of network architecture for the performance of unstructured change detection. LambdaNet combines concepts from Siamese and semantic segmentation architectures, and is capable of identifying and localizing the significant differences between image pairs while simultaneously disregarding background noise. Changes are marked at the pixel level, by interpreting change detection as a binary (change/no change) classification problem. Development of this architecture began with an evaluation of several candidate models, inspired by other successful network architectures and layers, including VGG, ResNet, and the Res2Net layer. Once the best performing LambdaNet architecture was determined, it was extended to incorporate a multi-class version of change detection. Referred to as directional change, this technique allows segmentation-based output of change information in four different classes: No change, additive change, subtractive change, and exchange. Lastly, change detection is not the only unstructured operation of interest. One of the most successful unstructured techniques is that of artistic style transfer. This method allows information from a style image to be merged into a supplied content image. In order to implement this technique, a new variant of LambdaNet was developed, called LambdaStyler. This network is capable of learning multiple artistic styles, which can then be selected for application to the desired content image