Bruno A. Angelico

A dynamic fuzzy cognitive map applied to chemical process supervision

This work develops an intelligent tool based on fuzzy cognitive maps to supervisory process control. Fuzzy cognitive maps are a neuro-fuzzy methodology that can accurate model complexly system using a causal-effect fuzzy reasoning. In the proposed approach, new types of concept and relation, not restricted to cause-effect ones, are added to the model resulting in a dynamic fuzzy cognitive map (D-FCM). In this sense, a supervisory system is developed in order to control a fermentation process. This process has a non-linear behavior and presents several problems, such as non-minimum phase and large accommodation time. The supervisor goal is to operate the process in normal and critical conditions. The expert knowledge about the process behavior in both conditions is used to build the D-FCM supervisor. Simulation results are presented in order to validate the proposed intelligent supervisor.

S/MIMO MC-CDMA Heuristic Multiuser Detectors Based on Single-Objective Optimization

This paper analyzes the complexity-performance trade-off of several heuristic near-optimum multiuser detection (MuD) approaches applied to the uplink of synchronous single/multiple-input multiple-output multicarrier code division multiple access (S/MIMO MC-CDMA) systems. Genetic algorithm (GA), short term tabu search (STTS) and reactive tabu search (RTS), simulated annealing (SA), particle swarm optimization (PSO), and 1-opt local search (1-LS) heuristic multiuser detection algorithms (Heur-MuDs) are analyzed in details, using a single-objective antenna-diversity-aided optimization approach. Monte- Carlo simulations show that, after convergence, the performances reached by all near-optimum Heur-MuDs are similar. However, the computational complexities may differ substantially, depending on the system operation conditions. Their complexities are carefully analyzed in order to obtain a general complexity-performance framework comparison and to show that unitary Hamming distance search MuD (uH-ds) approaches (1-LS, SA, RTS and STTS) reach the best convergence rates, and among them, the 1-LS-MuD provides the best trade-off between implementation complexity and bit error rate (BER) performance.

Hybrid heuristic-waterfilling game theory approach in MC-CDMA resource allocation

Abstract: This paper discusses the power allocation with fixed rate constraint problem in multi-carrier code division multiple access (MC-CDMA) networks, that has been solved through game theoretic perspective by the use of an iterative water-filling algorithm (IWFA). The problem is analyzed under various interference density configurations, and its reliability is studied in terms of solution existence and uniqueness. Moreover, numerical results reveal the approach shortcoming, thus a new method combining swarm intelligence and IWFA is proposed to make practicable the use of game theoretic approaches in realistic MC-CDMA systems scenarios. The contribution of this paper is twofold: (i) provide a complete analysis for the existence and uniqueness of the game solution, from simple to more realist and complex interference scenarios; (ii) propose a hybrid power allocation optimization method combining swarm intelligence, game theory and IWFA. To corroborate the effectiveness of the proposed method, an outage probability analysis in realistic interference scenarios, and a complexity comparison with the classical IWFA are presented.

Multiple Access Network Optimization Aspects via Swarm Search Algorithms

This chapter explores the application of particle swarm optimization (PSO) search algorithm in two different optimization aspects of code division multiple access (DS/CDMA) systems, namely multiuser detection and resource allocation problem. In the first problem, a discrete PSO version is considered while in the latter, a continuous PSO algorithm is adopted. In both cases, the input parameters, specially the weight factors (acceleration coefficients), are optimized for each considered scenario. The motivation to use heuristic search algorithms is due to the nature of the NP complexity posed by the wireless network optimization problems. Hence, from the practical engineering point-of-view, the challenge is to obtain suitable performances in solving those hard complexity problem in a polynomial time, allowing the algorithm implementation using modern digital signal processing (DSP) platforms. Previous results indicated that the application of heuristic search algorithm in several wireless optimization problems have been achieved excellent performance-complexity tradeoffs, particularly the use of genetic algorithm (GA), evolutionary program (EP), particle swarm optimization (PSO), and local search (LS). For the heuristic multiuser detection (Heur-MUD) problem, the performance is extensively evaluated and characterized under single input single/multiple output (SISO/SIMO) flat Rayleigh channels, as well as frequency selective (multipath) Rayleigh channels. For this class of problem, single-objective optimization criterion (SOO) and a discrete PSO algorithm are adopted. Extensive simulations are carried out in order to demonstrate that, after convergence, the performance reached by all the analyzed Heur-MUD is much better than the conventional detector (CD), and somewhat close to the single-user bound (SuB), with the advantage of the computational complexities substantially lower than optimum multiuser detection (OMUD) and not excessively higher than CD complexity. For the heuristic resource allocation (Heur-RA) problem, there are several challenging optimization problems associated. The total network power minimization subject to multiclass information rate constraints, as well as the problem of throughput maximization subject to the transmitted power limitation are considered in this chapter. Multirate users associated with different types of traffic are aggregated to distinct classes of users, with the assurance of minimum target rate allocation per user and quality of service (QoS). For this class of problem, the SOO methodology is also carried out using a continuous PSO search algorithm, aiming to achieve promising performance-complexity tradeoffs. The numerical results are promising in terms of sum rate maximization while simultaneously minimizing the total power allocated to the multirate mobile terminals.

Improvement of MISO Single-User Time Reversal Ultra-Wideband Using a DFE Channel Equalizer

In this paper a baseband multiple-input single-output (MISO) time reversal ultra-wideband system (TR-UWB) incorporating a decision feedback equalizer (DFE) is evaluated over the scenarios CM1 and CM3 of the IEEE 802.15.3a channel model. A semi-analytical performance based on a Gaussian approximation is derived and compared with simulation results. The results show that such approach represents a good approximation for the bit error rate (BER) analysis, and that the DFE, as well as an increase in the number of transmit antennas, improve the system performance.

Reduced Cluster Search ML Decoding for QO-STBC Systems

Since the maximum likelihood (ML) decoding results too complex when the modulation order and the number of receive antennas increase, an efficient reduced complexity ML-based decoding scheme applied to a multiple-input-multiple-output (MIMO) antenna systems with quasi-orthogonal space-time block code (QO-STBC) is proposed, and named reduced cluster search ML decoding (RCS-ML). Its performance and complexity aspects are compared to the conventional ML decoding approach. High-order modulation indexes and short low density parity check codes (LDPC) are considered. Numerical results have indicated no degradation in the performance and an increasing reduction in the complexity of RCS-ML decoding with respect to the conventional ML when the modulation order increases.Coarse-Grained Reconfigurable Architecture (CGRAs) are a promising parallel architecture with both high performance and high power-efficiency. Inner loop pipelining and outer loop merging techniques are usually used to improve the execution performance when mapping loops ontoCGRA. However, the number of concurrently executable operators (CEOs) from the kernel still can not make the best of PEs in a PEA as the kernel width is limited by loop body size. In this paper, we evaluate the number of CEOs from kernel and proposed a novel outer loop parallelization scheme to increase the number of CEOs, and further reduce the execution time of loops. Our experimental results using loops from polybench demonstrated that our approach can increase the performance of nested loop by up to 1.37 times compared to the epilog-prolog merging method. Moreover, modest compilation time is needed to generates the final solution.There is an increasing interest for cloud services to be provided in a more energy efficient way. The growing deployment of large-scale, complex workflow applications onto cloud computing hosts is being faced with crucial challenges in reducing the power consumption without violating the service level agreement (SLA). In this paper, we consider cloud hosts which can operate in different power states with different capacities respectively, and propose a novel scheduling heuristic for workflows to reduce energy consumption while still meeting deadline constraint. The proposed heuristic is evaluated using simulation with four different real-world applications. The observed results indicates that our heuristic does significantly outperform the existing approaches.As enterprises shift from using direct-attached storage to network-based storage for housing primary data, flash-based, host-side caching has gained momentum as the primary latency reduction technique. In this paper, we make the case for integration of flash caching algorithms at the file level, as opposed to the conventional block-level integration. In doing so, we will show how our extensions to Loris, a reliable, file-oriented storage stack, transform it into a framework for designing layout-independent, file-level caching systems. Using our Loris prototype, we demonstrate the effectiveness of Loris-based, file-level flash caching systems over their block-level counterparts, and investigate the effect of various write and allocation policies on the overall performance.

Weighting particle swarm, simulation annealing and local search optimization for S/MIMO MC-CDMA systems

This paper analyzes the complexity-performance trade-off of three heuristic approaches applied to synchronous multicarrier multiuser detection (MUD) of single/multiple transmit antennas and multiple receive antennas code division multiple access (S/MIMO MC-CDMA) systems. Weighting particle swarm optimization (WOPSO) and unitary Hamming distance search-based strategies, specifically 1-opt local search (1-LS) and simulation annealing (SA) multiuser detection algorithms, were analyzed in details using a single-objective antenna-diversity-aided optimization approach. Monte-Carlo simulations show that, after convergence, the performances reached by the three heuristic MUD (HEUR-MUD) S/MIMO MC-CDMA algorithms are identical, with computational complexities remarkably smaller than the optimum multiuser detector (OMUD). However, the computational complexities could differ substantially depending on the operation system conditions. The complexities of the HEUR-MUDs were carefully analyzed in order to demonstrate that 1-LS scheme provides the best trade-off between implementation complexity aspects and bit error rate (BER) performance when applied to multiuser detection of S/MIMO MC-CDMA systems with low order modulation.

Delay and estimation uncertainty in distributed power control algorithm for optical CDMA networks

In this work, the performance of a distributed power control algorithm (DPCA) considering time-delay and estimation uncertainty has been investigated. Control theory concepts have been deployed aiming to derive the Foschini-Miljanic DPCA as well as to analyse their stability properties under time-varying delays and estimation uncertainty applicable to optical networks. Although delays introduce transient response for initial iterations, our numerical results have shown a suitable convergence of the transmitted power obtained with the DPCA under strong delay conditions. Furthermore, the DPCA was able to converge also under high level of uncertainty on the estimated SNIR, despite the solution quality decreasing.

Heuristic Search Applied to Fuzzy Cognitive Maps Learning

Fuzzy Cognitive Maps were initially proposed by Kosko [1–3], as an extension of cognitive maps proposed by Axelrod [4]. FCM is a graph used for representing causal relationships among concepts that stand for the states and variables of the system, emulating the cognitive knowledge of experts on a specific area. FCM can be interpreted as a combination of Fuzzy Logic and Neural Networks, because it combines the sense rules of Fuzzy Logic with the learning of the Neural Networks. A FCM describes the behavior of a knowledge based system in terms of concepts, where each concept represents an entity, a state, a variable, or a characteristic of the system. The human knowledge and experience about the system determines the type and the number of the nodes as well as the initial conditions of the FCM.

Increasing energy efficiency in OCDMA network via distributed power control

In this work, we investigate the utilization of transmission power control as mechanism to increase the energy efficiency in optical code division multiplexing access (OCDMA) networks. We have modeled the energy efficiency considering the optical fiber transmission and network infrastructure as encoders, decoders and star coupler. In the analyzed scenario, this model confirms that the energy consumption of the network infrastructure is larger than the energy consumption of the transmission infrastructure; On the other hand, we have proposed a scheme to define the energy efficiency according to the BER level accomplished with the quality of service (QoS) requirements. The main results showed that it is possible to save 70% of the transmitted energy per bit with the penalty of one order of magnitude of BER.