An Effective Software Pipelining Algorithm for Clustered Embedded VLIW Processors

This paper proposes a software pipelining framework, CALiBeR (ClusterAware Load Balancing Retiming Algorithm), suitable for compilers targetingclustered embedded VLIW processors. CALiBeR can be used by embedded systemdesigners to explore different code optimization alternatives, that is, high-qualitycustomized retiming solutions for desired throughput and program memory sizerequirements, while minimizing register pressure. An extensive set of experimentalresults is presented, demonstrating that our algorithm compares favorablywith one of the best state-of-the-art algorithms, achieving up to 50% improvementin performance and up to 47% improvement in register requirements. In orderto empirically assess the effectiveness of clustering for high ILP applications,additional experiments are presented contrasting the performance achievedby software pipelined kernels executing on clustered and on centralized machines.     

A new time invariant fuzzy time series forecasting method based on particle swarm optimization

In the analysis of time invariant fuzzy time series, fuzzy logic group relationships tables have been generally preferred for determination of fuzzy logic relationships. The reason of this is that it is not need to perform complex matrix operations when these tables are used. On the other hand, when fuzzy logic group relationships tables are exploited, membership values of fuzzy sets are ignored. Thus, in defiance of fuzzy set theory, fuzzy sets’ elements with the highest membership value are only considered. This situation causes information loss and decrease in the explanation power of the model. To deal with these problems, a novel time invariant fuzzy time series forecasting approach is proposed in this study. In the proposed method, membership values in the fuzzy relationship matrix are computed by using particle swarm optimization technique. The method suggested in this study is the first method proposed in the literature in which particle swarm optimization algorithm is used to determine fuzzy relations. In addition, in order to increase forecasting accuracy and make the proposed approach more systematic, the fuzzy c-means clustering method is used for fuzzification of time series in the proposed method. The proposed method is applied to well-known time series to show the forecasting performance of the method. These time series are also analyzed by using some other forecasting methods available in the literature. Then, the results obtained from the proposed method are compared to those produced by the other methods. It is observed that the proposed method gives the most accurate forecasts.     

Robust multilayer neural network based on median neuron model

Multilayer perceptron has been widely used in time series forecasting for last two decades. However, it is a well-known fact that the forecasting performance of multilayer perceptron is negatively affected when data have outliers and this is an important problem. In recent years, some alternative neuron models such as generalized-mean neuron, geometric mean neuron, and single multiplicative neuron have been also proposed in the literature. However, it is expected that forecasting performance of artificial neural network approaches based on these neuron models can be also negatively affected by outliers since the aggregation function employed in these models is based on mean value. In this study, a new multilayer feed forward neural network, which is called median neuron model multilayer feed forward (MNM-MFF) model, is proposed in order to deal with this problem caused by outliers and to reach high accuracy level. In the proposed model, unlike other models suggested in the literature, MNM which has median-based aggregation function is employed. MNM is also firstly defined in this study. MNM-MFF is a robust neural network method since aggregation functions in MNM-MFF are based on median, which is not affected much by outliers. In addition, to train MNM-MFF model, particle swarm optimization method was utilized. MNM-MFF was applied to two well-known time series in order to evaluate the performance of the proposed approach. As a result of the implementation, it was observed that the proposed MNM-MFF model has high forecasting accuracy and it is not affected by outlier as much as multilayer perceptron model. Proposed method brings improvement in 7 % for data without outlier, in 90 % for data with outlier, in 95 % for data with bigger outlier.     

Optimization of clamped columns under distributed axial load and subject to stress constraints

The optimum designs are given for clamped-clamped columns under concentrated and distributed axial loads. The design objective is the maximization of the buckling load subject to volume and maximum stress constraints. The results for a minimum area constraint are also obtained for comparison. In the case of a stress constraint, the minimum thickness of an optimal column is not known a priori, since it depends on the maximum buckling load, which in turn depends on the minimum thickness necessitating an iterative solution. An iterative solution method is developed based on finite elements, and the results are obtained for n=1, 2, 3 defined as I=α _n A ⁿ , with I being the moment of inertia, and A the cross-sectional area. The iterations start using the unimodal optimality condition and continue with the bimodal optimality condition if the second buckling load becomes less than or equal to the first one. Numerical results show that the optimal columns become larger in the direction of the distributed load due to the increase in the stress in this direction. Even though the optimal columns are symmetrical with respect to their mid-points when the compressive load is concentrated at the end-points, in the case of the columns subject to distributed axial loads the optimal shapes are unsymmetrical.     

Using multiplicative neuron model to establish fuzzy logic relationships

Determination of fuzzy logic relationships between observations is quite effective on the forecasting performance of fuzzy time series approaches. In various studies available in the literature, it has been seen that utilizing artificial neural networks for establishing fuzzy relations increase the forecasting accuracy. In this study, a novel high order fuzzy time series forecasting approach in which multiplicative neuron model is used to define fuzzy relations is proposed in order to reach high forecasting level. Also, particle swarm optimization method is utilized to train multiplicative neuron model. In order to show forecasting performance of the proposed method, it is applied to a well-known data Taiwan future exchange and the results produced by the proposed approach is compared to those obtained from other fuzzy time series forecasting models. As a result of the implementation, it is observed that the proposed approach gives the best forecasts for Taiwan future exchange time series.     

Interferometric investigation and simulation of refractive index in glass matrixes containing nanoparticles of varying sizes

The relationship between refractive index and nanoparticle radii of cadmium selenide (CdSe) nanoparticles embedded within glass matrixes was investigated experimentally and by simulations. A homemade automated Michelson interferometer arrangement employing a rotating table and a He-Ne laser source at a wavelength of 632.8 nm determined the refractive index versus nanoparticle radii of embedded cadmium selenide (CdSe) nanoparticles. The refractive index was found to decrease linearly with nanoparticle radius increase. However, one sample showed a step increase in refractive index; on spectroscopic analysis, it was found that its resonant wavelength matched that of the He-Ne source wavelength. The simulations showed that two conditions caused the step increase in refractive index: low plasma frequency and matched sample and source resonances. This simple interferometer setup defines a new method of determining the radii of nanoparticles embedded in substrates and enables refractive index tailoring by modification of exact annealing conditions.     

Production,characterization and optimization of thermoelectric module and investigation of doping effects on thermoelectric performances

The objective of this study is to produce the thermoelectric (TE) module called as a Peltier module or element using new and promising materials that work at high temperature for generation of electricity with thermoelectric energy conversion from waste heat at high temperatures. Peltier modules used commercially nowadays can work at relatively low temperatures and their efficiency increase in proportion to the temperature difference between the surfaces of the modules. They consist of a pair of p- and n-type semiconductor. In this study, calcium cobalt oxide was chosen as a p-type semiconductor whilst zinc oxide was chosen as n-type semiconductor. Pure and aluminum-doped zinc oxide and silver-doped calcium cobalt oxide powders were synthesized via sol–gel processing successfully. The obtained powders were characterized by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), fourier transform infrared (FTIR), differential thermal analysis-thermogravimetry (DTA-TG), and scanning electron microscopy (SEM). In addition, the particle size distribution of the powders obtained via sol–gel processing was determined using a particle size analyzer. One and two leg oxide thermo-electric modules consisting of one pair of p-type 0.03 percent silver doped calcium cobalt oxide and n-type 0.02 percent aluminum doped zinc oxide bulks of 25 square millimeter cross-section and 3 millimeter heights were constructed. The thermoelectric module constructed was tested at high temperatures, and compared to other similar oxide modules reported in literature. Ultimately, the thermal stress and alteration of thermal stress depending on the leg length and side length of semiconductors were calculated using the finite element analysis (FEA) model in ANSYS 15.0 software. According to the results of the analysis, TE module was optimized in terms of mechanical behavior.     

Constituents,Oxidant-Antioxidant Profile,and Antimicrobial Capacity of the Essential Oil Obtained from Ferulago Sandrasica Peşmen and Quézel

Chemical constituents, total phenolic content, total oxidant status, total antioxidant status, lipid hydroperoxides, total free –SH levels, and antimicrobial activity of essential oil obtained from the Ferulago sandrasica (Umbelliferae) were investigated. The essential oil was obtained by hydrodistillation using a Clevenger-type apparatus. The chemical constituents were analyzed by gas chromatography‐mass spectrometry. The main components of the essential oil were ocimene (30.5%), carene-δ-3 (27.4%), and α-pinene (17.8). The antimicrobial activity was tested by a disc diffusion method against E. coli MC 400, E. coli ATCC 25922, E. coli 0157 H7, E. colaecea ATCC 23355, E. feacalis ATCC 19433, P. aeruginosa NRRL B-2679, S. aureus ATCC 25923, B. nischenoformis NRRL B-1001, S. aureus ATCC 33862, B. cereus NRRL B-3711, B. subtilis NRRL B-209, M. luteus NRRL B-1013, L. monocytogenes ATCC 7644, B. subtulis ATCC 6633.     

Multiplicative neuron model artificial neural network based on Gaussian activation function

Multiplicative neuron model-based artificial neural networks are one of the artificial neural network types which have been proposed recently and have produced successful forecasting results. Sigmoid activation function was used in multiplicative neuron model-based artificial neural networks in the previous studies. Although artificial neural networks which involve the use of radial basis activation function produce more successful forecasting results, Gaussian activation function has not been used for multiplicative neuron model yet. In this study, rather than using a sigmoid activation function, Gaussian activation function was used in multiplicative neuron model artificial neural network. The weights of artificial neural network and parameters of activation functions were optimized by guaranteed convergence particle swarm optimization. Two major contributions of this study are as follows: the use of Gaussian activation function in multiplicative neuron model for the first time and the optimizing of central and propagation parameters of activation function with the weights of artificial neural network in a single optimization process. The superior forecasting performance of the proposed Gaussian activation function-based multiplicative neuron model artificial neural network was proved by applying it to real-life time series.