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Abstract: Pattern matching is a fundamental feature in many applications such as functional programming, logic programming, theorem proving, term rewriting and rule-based expert systems. Usually, patterns are pre-processed into a deterministic finite automaton. Using such an automaton allows one to determine the matched pattern(s) by a single scan of the input term. The matching automaton is typically based on left-to-right traversal of patterns. In this paper, we propose a method to build such an automaton. Then, we propose an incremental method to build a deterministic concise automaton for non-necessarily sequential rewriting systems. With ambiguous patterns a subject term may be an instance of more than one pattern. To select the pattern to use, a priority rule is usually engaged. The pre-processing of the patterns adds new patterns, which are instances of the original ones. When the original patterns are ambiguous, some of the instances supplied may be irrelevant for the matching process. They may cause an unnecessary increase in the space requirements of the automaton and may also reduce the time efficiency of the matching process. Here, we devise a new pre-processing operation that recognises and avoids such irrelevant instances. Hence improves space and time requirements for the matching automaton. [Copyright &y& Elsevier]

The automatic speech recognition based on detection of phonemes provides advantages for online recognition of a speech represented by a sound signal. The development of a system for automatic speech recognition is multidisciplinary. It covers several areas of research, such as linguistics, signal processing and computational intelligence. In this work, the process starts with a speech signal pre-processing to extract the main features of the speech signal at a given instant of time. Inspired by the "divide and conquer" principle, we bridge the complexity gap of automatic speech recognition by devising models based on an ensemble of neural network experts, allowing to divide the huge decision space regarding speech recognition so that each expert takes care only of a delimited area of this decision space. This novel application of this strategy improves the precision, sensitivity and accuracy of the recognition process. Each included expert decides regarding each one of the pre-processed input samples. The decision set thus obtained is weighted. So, the expert with the highest weight for the output will determine the sample final classification. After that, a dynamic post-processing step, implemented as a recurrent neural network, is executed. It aims at mitigating the oscillatory effect that occurs during the recognition of classes with similar characteristics. In this work, two ensembles are investigated. The first is based on the clustering of similar phonetics classes while the second takes care of the imbalanced distribution of samples in the training set. The proposed model achieves 7.63% improvement in terms of accuracy with respect to the best so far related model for automatic speech recognition. • It provides an online tool for Automatic Speech Recognition for Portuguese language. • An ensemble of expert neural networks is explored, dividing the decision. • Expert only takes care of a well-delimited area of this decision space. • Two models are proposed: (1) grouping similar phonetics; (2) unbalanced dataset. • A dynamic post-processing using an RNN is performed, mitigating of output oscillatory effects. [ABSTRACT FROM AUTHOR]

The localization problem arises from the need of robots of a swarm robotic system (SRS), or even nodes of a wireless sensors network (WSN), to determine their positions without the use of external references, such as the Global Positioning System (GPS). In this problem, the node location may be established thanks to distance measurements to existing reference nodes, using a range-based method. Reference nodes know their respective positions in the network. In the search for efficient yet accurate methods to determine node locations, some bio-inspired algorithms have been explored. In this sense, targeting a more accurate solution of the localization problem, we propose a new multi-hop method. Two algorithms were developed considering the proposed method: one based on the Particle Swarm Optimization (PSO), and the other based on the Backtracking Search Algorithm (BSA). The method includes a new technique to assess the confidence that should be granted to a contribution received from a neighboring node, and hence incorporating it into the localization computation accordingly. The achieved performance results prove the effectiveness of the proposed method in which the BSA-based algorithm has a performance better than the PSO-based algorithm. Also, the results evince the efficiency entailed by the confidence factor assessment technique, for some network configurations. The impact of the latter is more evident when the number of reference nodes in the network is reduced. This constitutes a very big advantage with respect to state-of-the-art localization methods. [ABSTRACT FROM AUTHOR]

Dynamic task allocation in a robotic swarm is a necessary process for proper management of the swarm. It allows the distribution of the identified tasks to be performed, among the swarm of robots, in such a way that a pre-defined proportion of execution of those tasks is achieved. In this context, there is no central figure to take care of the task allocation. So any algorithm proposal must be distributed, allowing every and each robot in the swarm to identify the task it must perform. This paper proposes a simple yet efficient distributed control algorithm to implement dynamic task allocation in a robotic swarm. In this algorithm, each robot that integrates the swarm runs the algorithm whenever it senses a change in the environment. The algorithm was implemented and extensively tested in different size swarms of robots. The corresponding performance and effectiveness are promising, as shown in the presented results and comparison. The swarm robot used is Elisa-III. [ABSTRACT FROM AUTHOR]

Nowadays, final products often encompass a certain intelligence therein to deal with variation or lack of precision in the sensing input data. This intelligence is usually acquired via the utilization of existing soft techniques, such as artificial neural networks, genetic algorithms and fuzzy control, among others. Thus, it is profitable to have on-the-shelf shell scalable and adaptive hardware designs that implement these soft techniques. This availability allows for an immediate embedding of any of those designs onto final products. This usually entails a reduced time-to-market of the product. Process control is one of the many applications that took advantage of the fuzzy paradigm. In general, controllers are embedded into the controlled device. This paper presents a novel design of a reconfigurable efficient parallel architecture to implement fuzzy controllers on hardware with almost no design effort for final users. The proposed architecture is herein proven suitable for embedding. It is customizable, so it allows the setup and configuration of the controller parameters, and hence its use for any problem application. Two fuzzy controllers that model autonomous car driving are implemented and their cost and performance evaluated. [ABSTRACT FROM AUTHOR]

Highlights: [•] Two novel congestion-aware algorithms based on ACO adapted to the routing problem. [•] An adaptive and static routing. [•] The proposed algorithms overcome the congested routes. [•] The found solutions improve the execution time of applications mapped onto Network-on-Chip. [ABSTRACT FROM AUTHOR]

Abstract: There are several neural network implementations using either software, hardware-based or a hardware/software co-design. This work proposes a hardware architecture to implement an artificial neural network (ANN), whose topology is the multilayer perceptron (MLP). In this paper, we explore the parallelism of neural networks and allow on-the-fly changes of the number of inputs, number of layers and number of neurons per layer of the net. This reconfigurability characteristic permits that any application of ANNs may be implemented using the proposed hardware. In order to reduce the processing time that is spent in arithmetic computation, a real number is represented using a fraction of integers. In this way, the arithmetic is limited to integer operations, performed by fast combinational circuits. A simple state machine is required to control sums and products of fractions. Sigmoid is used as the activation function in the proposed implementation. It is approximated by polynomials, whose underlying computation requires only sums and products. A theorem is introduced and proven so as to cover the arithmetic strategy of the computation of the activation function. Thus, the arithmetic circuitry used to implement the neuron weighted sum is reused for computing the sigmoid. This resource sharing decreased drastically the total area of the system. After modeling and simulation for functionality validation, the proposed architecture synthesized using reconfigurable hardware. The results are promising. [Copyright &y& Elsevier]

Abstract: Network-on-chip (NoC) are considered the next generation of communication infrastructure in embedded systems. In the platform-based design methodology, an application is implemented by a set of collaborative intellectual property (IP) blocks. The selection of the most suited set of IPs as well as their physical mapping onto the NoC infrastructure to implement efficiently the application at hand are two hard combinatorial problems that occur during the synthesis process of Noc-based embedded system implementation. In this paper, we propose an innovative preference-based multi-objective evolutionary methodology to perform the assignment and mapping stages. We use one of the well-known and efficient multi-objective evolutionary algorithms NSGA-II and microGA as a kernel. The optimization processes of assignment and mapping are both driven by the minimization of the required silicon area and imposed execution time of the application, considering that the decision maker’s preference is a pre-specified value of the overall power consumption of the implementation. [Copyright &y& Elsevier]