No abstract

No abstract

An experimental study on the influence of the computation of basic nodal nonlinear functions on the performance of (NFSs) is described in this paper. Systems' architecture size, their approximation capability, and the smoothness of provided mappings are used as performance indexes for this comparative paper. Two widely used kernel functions, the sigmoid-logistic function and the Gaussian function, are analyzed by their computation through an accuracy-controllable approximation algorithm designed for hardware implementation. Two artificial neural network (ANN) paradigms are selected for the analysis: backpropagation neural networks (BPNNs) with one hidden layer and radial basis function (RBF) networks. Extensive simulation of simple benchmark approximation problems is used in order to achieve generalizable conclusions. For the performance analysis of fuzzy systems, a functional equivalence theorem is used to extend obtained results to fuzzy inference systems (FISs). Finally, the adaptive neurofuzzy inference system (ANFIS) paradigm is used to observe the behavior of neurofuzzy systems with learning capabilities.

No abstract

The availability of cheap wearable motion and biometric sensors has favoured the research on wearable human activity recognition (HAR) systems. However, a HAR system comprehends many complex signal processing stages that usually require some computationally demanding operations which can hardly be directly performed in an embedded system. Modern FPGA technologies and the system-on-chip (SoC) approach open the door to the implementation of complex single-chip signal processing systems to produce tiny, wearable and autonomous embedded HAR systems. However, compared to a pure embedded software approach, the potentially higher performance-to-power ratio of FPGAs can only be exploited in very demanding applications and by a careful design of the implemented system. In this work we describe a first step in the consecution of an FPGA-based completely autonomous singlechip HAR system which can be adapted and optimized to the user with no need of external computing means neither of human intervention. The system includes all stages in a HAR process, i.e., signal segmentation, signal processing for feature extraction, input space dimensionality reduction (feature selection), and activity estimation by means of a neural classifier. A physical activity recognition example is used as a reference design to evaluate the performance of the system and to draw conclusions on the potential benefits of using FPGAs in future wearable HAR applications.