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Hybrid renewable energy-based generation can improve the reliability in power supply. While avoiding the big cost in energy storage/battery, it is apparent that, such hybrid plants could be interlinked with the conventional power system for ensuring continuous power supply in the local area and better utilization of renewable energy sources. As generation from the renewable energy sources are intermittent, the interaction of such hybrid plants with the traditional power network can abruptly disturb the power/frequency level. Thus, devising a robust power–frequency control (PFC) scheme for the contemporary hybrid renewable interlinked power systems is highly obligatory. In this context, this paper presents a fractional-order-proportional-integral-derivative (FOPID) controller based advanced PFC scheme. Bacterial-foraging optimization (BFO) technique is adopted for computing the design parameters, namely, the controller gains and set-point weights. A typical hybrid wind- photovoltaic (PV) Plant interlinked thermal power system is modelled and simulated in order to investigate the system dynamics. Effectiveness of the proposed PFC scheme corroborated with the renewable power variations as well as load perturbation is confirmed and the results are presented.

Image classification is one of the most popular tasks used to analyze remote sensing signatures (RSS) of a geographical region from remotely sensed hyperspectral images. However, the high dimensionality of such hyperspectral images raises a series of new challenges. In this paper, a new approach for real-time intelligent classification of large-scale hyperspectral imagery which aggregates Fuzzy logic and the fused weighted order statistic (WOS) with the minimum distance to mean (MDM) techniques using commodity graphics processing units (GPUs) is addressed. Within this context, intelligent image processing methods are algorithmically adapted via parallel computing techniques and efficiently implemented in two NVIDIA Tesla C2075 GPUs. Experimental results demonstrate how such unification reduces drastically the computational load of the real-world hyperspectral classification tasks resulting in efficient numerical algorithms suitable for real-time multi-GPU-adapted implementation.

Recent communications standards, such as vehicle-to-vehicle and fifth generation, include applications where the transmitted signal encounters rapid changes of propagation scenarios, resulting in wireless links characterized as non-stationary (NS) channels. Hence, channel models that correctly explain and represent the measured time-varying channel statistics, and their associated simulation methods for testing purposes, are all required. Although the body of works devoted to NS channel modeling is vast, due to the complexity and variety of this problem, the provided NS statistics are defined only within a limited observation time, and therefore, the generated channel realizations do not include the changes between scenarios. In light of this problem, this paper introduces a channel model that mimics the continuous change of the mobile propagation channel via a continual renewal of channel parameters, in which all stationary and NS channels are represented under a unified structure. Theoretical and simulation results provided in this paper confirm that the proposed model reproduces stationary models with high accuracy. In addition, NS channel realizations with predefined time-varying power spectral density and time-varying envelope distributions are also shown in this paper, providing a means for testing modern communications systems.

The pixel’s classification of images obtained from random heterogeneous materials (RHM) is a relevant step for 3D stochastic reconstruction and to compute their physical properties, like Effective Transport Coefficients (ETC). A bad classification will impact on the computed properties. However, the literature on the topic discusses mainly the correlation functions or the properties formulae, giving little or no attention to the classification; authors mention either the use of a threshold or, in few cases, the use of Otsu’s method. This paper presents a classification approach based on Support Vector Machines (SVM) and a comparison with the Otsu-based approach, based on accuracy, precision and recall. The data used for the SVM training are the key for a better classification; these data are the grayscale value, the magnitude and direction of pixels gradient. For the validation cases, the recall of the solid phase is significantly better, whilst improving the accuracy for the SVM method. Finally, a discussion about the impact on the correlation functions is presented in order to show the benefits of the proposal.

Today, embedded systems (ES) tend towards miniaturization and the carrying out of complex tasks in applications such as the Internet of Things, medical systems, telecommunications, among others. Currently, ES structures based on artificial intelligence using hardware neural networks (HNNs) are becoming more common. In the design of HNN, the activation function (AF) requires special attention due to its impact on the HNN performance. Therefore, implementing activation functions (AFs) with good performance, low power consumption, and reduced hardware resources is critical for HNNs. In light of this, this paper presents a hardware-based activation function-core (AFC) to implement an HNN. In addition, this work shows a design framework for the AFC that applies a piecewise polynomial approximation (PPA) technique. The designed AFC has a reconfigurable architecture with a wordlength-efficient decoder, i.e., reduced hardware resources are used to satisfy the desired accuracy. Experimental results show a better performance of the proposed AFC in terms of hardware resources and power consumption when it is compared with state of the art implementations. Finally, two case studies were implemented to corroborate the AFC performance in widely used ANN applications.

The emergence of modern technologies, such as Wireless Sensor Networks (WSNs), the Internet-of-Things (IoT), and Machine-to-Machine (M2M) communications, involves the use of batteries, which pose a serious environmental risk, with billions of batteries disposed of every year. However, the combination of sensors and wireless communication devices is extremely power-hungry. Energy Harvesting (EH) is fundamental in enabling the use of low-power electronic devices that derive their energy from external sources, such as Microbial Fuel Cells (MFC), solar power, thermal and kinetic energy, among others. Plant Microbial Fuel Cell (PMFC) is a prominent clean energy source and a step towards the development of self-powered systems in indoor and outdoor environments. One of the main challenges with PMFCs is the dynamic power supply, dynamic charging rates and low-energy supply. In this paper, a PMFC-based energy harvester system is proposed for the implementation of autonomous self-powered sensor nodes with IoT and cloud-based service communication protocols. The PMFC design is specifically adapted with the proposed EH circuit for the implementation of IoT-WSN based applications. The PMFC-EH system has a maximum power point at 0.71 V, a current density of 5 mA cm &minus, 2 , and a power density of 3.5 mW cm &minus, 2 with a single plant. Considering a sensor node with a current consumption of 0.35 mA, the PMFC-EH green energy system allows a power autonomy for real-time data processing of IoT-based low-power WSN systems.

Image enhancement and edge-preserving denoising are relevant steps before classification or other postprocessing techniques for remote sensing images. However, multisensor array systems are able to simultaneously capture several low-resolution images from the same area on different wavelengths, forming a high spatial/spectral resolution image and raising a series of new challenges. In this paper, an open computing language based parallel implementation approach is presented for near real-time enhancement based on Bayesian maximum entropy (BME), as well as an edge-preserving denoising algorithm for remote sensing imagery, which uses the local linear Stein's unbiased risk estimate (LLSURE). BME was selected for its results on synthetic aperture radar image enhancement, whereas LLSURE has shown better noise removal properties than other commonly used methods. Within this context, image processing methods are algorithmically adapted via parallel computing techniques and efficiently implemented using CPUs and commodity graphics processing units (GPUs). Experimental results demonstrate the reduction of computational load of real-world image processing for near real-time GPU adapted implementation.

In this paper, an embedded design for implementing a real-time photovoltaic (PV) system emulator with user-defined environmental conditions is proposed. The design implemented on a Sitara Cortex 9 ARM processor allows the evaluation of PV cells in a low-cost, portable and affordable system. This embedded PV emulation system uses an LCD screen to establish a human-system interface (HSI), and it contains an extensible database of commercial PV cells with their manufacturer’s datasheet parameters. Based on the accurate two-diode model, the proposed design allows the generation of high precision I – V , P – V characteristic curves with the embedded ARM, which represents an affordable and simple solution. Experimental results demonstrate the accuracy of the real-time embedded system for different solar cells and environmental conditions.

In this paper, we present a channel model that characterizes the non-stationarities of small-scale multipleinput multiple-output (MIMO) vehicle-to-vehicle (V2V) double-Rayleigh fading channels that arise from the temporal variations of the propagation delays. The analysis of the correlation properties of the proposed channel model is the main objetive of our paper. To achieve this goal, we derive an analytical expression for the space-time-frequency cross-correlation function (STF-CCF) following a geometrical plane-wave propagation (PWP) framework. An important feature of this expression is that it does not depend on a particular arrangement of the interfering objects (IOs) in the propagation scenario. Furthermore, a closedform expression of the STF-CCF is derived for the particular case of the geometrical two-rings model. The analytical and simulation results show that the elements of the MIMO channel matrix are non-wide-sense stationary (non-WSS) random processes both in the time and the frequency domains, as well as pairwise jointly non-WSS both in the time and the frequency domains.

Electric arc furnaces (EAFs) contribute to almost one third of the global steel production. Arc furnaces use a large amount of electrical energy to process scrap or reduced iron and are relevant to study because small improvements in their efficiency account for significant energy savings. Optimal controllers need to be designed and proposed to enhance both process performance and energy consumption. Due to the random and chaotic nature of the electric arcs, neural networks and other soft computing techniques have been used for modeling EAFs. This study proposes a methodology for modeling EAFs that considers the time varying arc length as a relevant input parameter to the arc furnace model. Based on actual voltages and current measurements taken from an arc furnace, it was possible to estimate an arc length suitable for modeling the arc furnace using neural networks. The obtained results show that the model reproduces not only the stable arc conditions but also the unstable arc conditions, which are difficult to identify in a real heat process. The presented model can be applied for the development and testing of control systems to improve furnace energy efficiency and productivity.

Authors version of an article published in the journal: Procedia Technology. Also available from the publisher at: http://dx.doi.org/10.1016/j.protcy.2012.03.004 Sum-of-cisoids (SOC) processes provide a physically and numerically appealing framework for the modelling and simulation of a wide class of mobile radio channels. This paper is concerned with the problem of finding a general solution for the level-crossing rate (LCR) and the average duration of fades (ADF) of the envelope of SOC processes. Exact expressions are derived for the LCR and the ADF by taking into account that the inphase component, the quadrature component, and the time derivatives of the inphase and quadrature components of SOC processes are in general mutually correlated. The validity of the theoretical results is confirmed by simulation results. This study reveals new insight into the fading behaviour of SOS-based multipath fading channel models. The results of this study are indispensable for the performance assessment of mobile radio channel simulators employing the SOC principle.

This chapter presents a novel wireless data acquisition system. The system has been designed to take advantage of inexpensive robotics systems like Lego NXT, which establishes a new paradigm in the wireless ad-hoc networks field. The system architecture can record data from different variables in greenhouse environments. These wireless systems employ wireless access points (WAPs) and wireless-fidelity (Wi-Fi) adapter modules for data acquisition that sample the environment. The measurements data are transmitted to a central station or inclusive to another different node. The acquisition system was designed and adapted to be used in greenhouses located in Quintana Roo, Mexico, where the typical relevant variables are temperature, luminosity and humidity. The developed system uses virtual instrumentation to measure and record environmental variables. The proposed implementation uses commercial data acquisition boards and sensors to gather data, which are processed and visualized with the LabVIEW-based software.

In this work, we implemented the robust evolutionary controlled (REC) filter in a FPGA platform. Due unknown a priori statistical information, we replace the Optimal Wiener Filter by its rough approximation referred to as REC filter. The evolution of the filter is controlled by changing/choosing the vector of the controlled parameters gamma. Such a control provides additional degrees of freedom of the filter. We report and discuss some implementation results in the Xilinx Virtex-II Pro XC2VP30 FPGA related to enhancement of the uncertain real-world RS imagery, these are indicative of the significantly increased performance efficiency gained with the developed approach.

This paper presents the design and implementation of a MPS control system using a FPGA. The control of these Modular Production System (MPS) was designed by the implementation of finite states machines for Distributing, Testing and Handling MPS's Station processes. The system contemplates the remote enable or disable of each module by an microcontrollers, who works as web server and it allows to access from any site. The SPI protocol was also realized. The embedded microcontrollers inside the FPGA allow rapid prototyping design and reduce the overall system development time.