Narrow Results

This paper presents an investigation of the design optimization in microstrip lines to reduce the crosstalk level using Fuzzy Logic. In microstrip lines length and spacing, termination conditions of interconnection and output impedance of gates are the major components that cause crosstalk. In order to design high speed printed circuit board (PCB) with optimum interconnection configuration, it is essential to reduce the crosstalk to its minimum tolerance level. A design methodology is proposed to correlate electrical parameters and physical configuration of lines to the crosstalk phenomena. This design is subsequently optimized using Fuzzy Logic to reduce the level of crosstalk. A set of experiments is carried out to demonstrate the capabilities of the design and optimization methods. The effect of the geometrical configuration of the lines on crosstalk, particularly the spacing, is highlighted.

This paper proposes several improved CMOS analog integrated circuits for fuzzy inference system as the general modules, including voltage-mode implementations of minimization circuit, programmable Gaussian-like membership function circuit, and centroid algorithm normalization circuit without using division. A two-input/one-output fuzzy system composed of these circuits is implemented and testified as a nonlinear function approximator. HSPICE simulation results show that the proposed circuits provide characteristics of high operation capacity, simple inference, low power dissipation, and high precision.

Many attributes contribute to product failures that result in warranty claims. In particular, there are situations where several attributes are used together as criteria for judging the warranty eligibility of a failed product. For example, automobiles warranty coverage has both age and mileage limits. The warranty policy characterized by a region in a two-dimensional plane with one axis representing product age and the other axis representing product usage is known as the "two-attribute" warranty policy. A number of procedures have been developed for analyzing the two-dimensional warranty policy. These procedures use many crisp data obtained from strictly controlled reliability tests. However, in real situations, these requirements might not be fulfilled. In extreme cases, the warranty claims data come from users whose reports are expressed in a vague way. This may be due to subjective and imprecise perception of failures by a user, imprecise warranty data record, or imprecise rate of usage record. This paper suggests fuzziness as an alternative to randomness for describing the two-dimensional warranty uncertainty. A new sets-as-points geometric view of fuzzy warranty sets is developed in this study. This view can reduce many errors of estimation and prediction of the cost associated with a variety of warranty policies including the "two-attribute" warranties and some reliability improvement warranties.

Microgrids (MGs) are small scale energy unit networks that can offer an adequate energy supply to cover local demand by incorporating renewable energy and storage technologies. The system capacity is generally between several kW to several MW. They work in terms of low voltage (LV) level or medium voltage (MV) level. They can also be connected/disconnected from main grid whenever it is necessary. This paper presents a comparison of two soft computing (SC) techniques fuzzy logic (FL)/artificial neural network (ANN) over a conventional proportional integral (PI)-based voltage frequency controllers used for improving the performance of MG under islanding mode. Microgrid is formed by using three 7.5kW, four pole, 50Hz, self-excited induction generators (SEIGs) driven by small hydro turbine feeding three-phase four-wire consumer load. The proposed topology functions excellently in maintaining phase angle, voltage and frequency (VF) regulation of the micro sources (MSs) in islanded mode as well as in resynchronization when one of the MSs is turned off due to fault or unavailability of resources. The conventional PI controller is replaced by a controller based on SC techniques, as it has disadvantages like explicit description of mathematical model, affected by variations in consumer loads and sources, thus the proposed SC techniques enhance the performance of VF controller. A comparative analysis of PI/FL/ANN controller is also carried out to highlight the superiority of AI controller. The performance of controller with proposed configuration is verified for balanced/unbalanced non-linear load. Microgrid and control schemes are simulated in MATLAB Sim Power Systems environment.

Moore’s law has been one of the reason behind the evolution of multicore architectures. Modern multicore architectures offer great amount of parallelism and on-chip resources that remain underutilized. This is partly due to inefficient resource allocation by operating system or application being executed. Consequently the poor resource utilization results in greater energy consumption and less throughput. This paper presents a fuzzy logic-based design space exploration (DSE) approach to reconfigure a multicore architecture according to workload requirements. The target design space is explored for L1 and L2 cache size and associativity, operating frequency, and number of cores, while the impact of various configurations of these parameters is analyzed on throughput, miss ratios for L1 and L2 cache and energy consumption. MARSSx86, a cycle accurate simulator, running various SPALSH-2 benchmark applications has been used to evaluate the architecture. The proposed fuzzy logic-based DSE approach resulted in reduction in energy consumption along with an overall improved throughput of the system.

The energy efficiency in wireless sensor networks (WSNs) is a fundamental challenge. Cluster-based routing is an energy saving method in this type of networks. This paper presents an energy-efficient clustering algorithm based on fuzzy c-means algorithm and genetic fuzzy system (ECAFG). By using FCM algorithm, the clusters are formed, and then cluster heads (CHs) are selected utilizing GFS. The formed clusters will be remaining static but CHs are selected at the beginning of each round. FCM algorithm forms balanced clusters and distributes the consumed energy among them. Using static clusters also reduces the data overhead and consequently the energy consumption. In GFS, nodes energy, the distance from nodes to the base station and the distance from each node to its corresponding cluster center are considered as determining factors in CHs selection. Then, genetic algorithm is also used to obtain fuzzy if–then rules of GFS. Consequently, the system performance is improved and appropriate CHs can be selected, hence energy dissipation is reduced. The simulation results show that ECAFG, compared with the existing methods, significantly reduces the energy consumption of the sensor nodes, and prolongs the network lifetime.

This paper proposes a Solar Photovoltaic (SPV) interfaced Impedance Source Inverter (ZSI) based shunt Active Power Filter (APF) for compensation of power quality events such as current harmonics, voltage interruption and reactive power burden. The instantaneous reactive power theories with Fuzzy Logic Controller (FLC) based DC link voltage regulator is used to estimate the reference current signal and control the operation of the SPV interfaced shunt APF. Maximum Power Point Tracking (MPPT) algorithm is also employed to obtain the optimum maximum power point. The response of the SPV interfaced ZSI-shunt APF for mitigation of current harmonic distortions and reactive power compensation are investigated and compared with SPV interfaced Voltage Source Inverter (VSI) based shunt APF. The proposed SPV interfaced shunt APF employed with the FLC-based instantaneous reactive power theory control algorithm offers long lasting compensation against current-based distortions and reactive power requirements. The performance of the SPV interfaced ZSI based shunt APF has been verified by the simulation and experimental study. These results confirm the practicability of the proposed system in various load conditions with effective harmonic mitigation capability.

The network performance is an imperative factor for the customers to select a mobile network operator (MNO). The customers demand seamless mobility and services with minimal packet loss and ultra-low latency from the subscribed MNO. Device-to-Device (D2D) communication is one of the key enabling solutions of fifth generation (5G), which has the potential to enhance throughput, latency, packet loss rate (PLR) performances of the network. 5G is expected to support high mobility and smaller range heterogeneous cells. This leads to frequent handovers. The unessential handovers may cause wastage of network resources. The improper network selection may prompt extreme quality degradation. In this work, a three-stage fuzzy-logic-based handover necessity estimation and target selection scheme is proposed for general heterogeneous networks. The simulation results prove that PLR, number of handovers executed and throughput performances of the proposed scheme are superior than the conventional and fuzzy-based multi-attribute decision-making (MADM) schemes. Even though this scheme is demonstrated for D2D application, it can be extended for any heterogeneous network scenarios.

Edge is basically the symbol and reflection of partial image discreteness. It is one of the most commonly used operations in image processing and pattern recognition, it contains a wealth of internal information leading to strong interpretation of image. Resisting against noise, illumination and extracting appropriate features from an image is a great challenge in many computer vision applications. Indeed this topic participates to reduce the handled information and focuses on those related to existing objects. Efficient and accurate edge detection will lead to increase in the performance of many computer vision applications, including image segmentation, object-based image coding and image retrieval. Contour detection contributes to locate pixel sets which correspond to sudden intensities variation, these unstable properties of the given image commonly suggest to important events on going in the scene. In this paper, we present in the first time a novel and robust method for edge detection based on joint and conditional entropy when we highlight a Shannon theory, the second part of this paper is dedicated to decision making of edge pixels membership by intelligent method based on fuzzy logic tool.

This study manifests a fuzzy-based trust prediction model for detecting malicious nodes in wireless sensor networks (WSN) by preventing black hole attack. Besides, a new routing protocol based on the shortest path and trust to the path nodes is presented with a fuzzy estimator, which uses the data mining methods to detect the malicious nodes (black holes). In a black hole attack, a malicious node selects the RREP (Route Replay) message as the shortest path from the source node to the destination node. After that, the packet sent to the malicious node is not received by the network. Eventually, the malicious node releases the entire data packet instead of sending it to the destination node. Optimal path selection is performed according to different algorithms on the objective function and its results are observed for route selection. After defining the objective function, different algorithms such as Genetic Algorithm (GA) and teaching–learning-based optimization (TLBO) are utilized for routing. For each algorithm, the objective function for the most secure node is evaluated and simulated based on the parameters defined in fuzzy logic. Based on the simulation results under MATLAB software, TLBO algorithm has obtained the best response for path selection with the least cost for the target performance. Significantly, the proposed method is simple and based on the exchange of control packets between the sensor node and the base station. In accession, the results show that the proposed algorithms are effective in detecting and preventing black hole attacks.

Several scheduling algorithms that have been proposed for Real-Time Operating System (RTOS) are supposed to be optimal. However, optimal scheduling is only theoretical due to the possibility of system overload where it cannot meet the deadlines of tasks. Besides, these algorithms are implemented in the RTOS, which generates additional overheads that can lead to the “nonscheduling” of certain independent tasks. In this paper, we propose an original solution for nonschedulable independent tasks in embedded systems. This solution, named Hybrid Fuzzy Earliest Deadline First Scheduling algorithm (HFEDFS), is based on the Earliest Deadline First algorithm (EDF) and Fuzzy Logic. It is characterized by a rejection policy and a rescheduling mechanism. The experimental results show that our proposed algorithm improves the system’s performance. To reduce extra overheads of RTOS, this algorithm is implemented on a Field-Programmable Gate Array (FPGA) circuit (Xilinx Virtex-5 LX50T-1156 board from DIGILENT).

This paper proposes an efficient fuzzy logic-based fault detection scheme for diagnosing the inter-turn short-circuit (ITSC) faults in induction motors (IMs). The proposed approach utilizes the fast Fourier transforms (FFTs) and wavelet packet transform (WPT) for this detection of fault. To improve the efficiency and secure the operation, the proposed approach is detecting the fault in online manner. The WPT is utilized to extract the stator current signal into time-frequency domain characteristics. The variation in the amplitude of the vibration spectrum at different characteristic frequencies by FFT is utilized to identify the stator ITSC. The vibration signal is dignified by a MEMS accelerometer. The performance of the fuzzy logic fault detector (FLFD) for online condition is monitored with stator current, vibration and input speed. The performance of the proposed approach is performed at MATLAB/Simulink working site, and then the performance is compared to other existing works. The accuracy, precision, recall and specificity of the proposed approach are analyzed. Similarly, the statistical measures like root mean square error (RMSE), mean absolute percentage error (MAPE), mean bias error (MBE) and consumption time are analyzed.