High Speed Under-Sampling Frequency Measurements on FPGA

A Sampling rate is less than Nyquist rate in some applications because of hardware limitations. Consequently, extensive researches have been conducted on frequency detection from sub-sampled signals. Previous studies on under-sampling frequency measurements have mostly discussed under-sampling frequency detection in theory and suggested possible methods for fast under-sampling frequencies detection. This study examined few suggested methods on Field Programmable Gate Array (FPGA) for fast under-sampling frequencies measurement. Implementation of the suggested methods on FPGA has issues that make them improper for fast data processing. This study tastes and discusses different methods for frequency detection including Least Squares (LS), Direct State Space (DSS), Goertzel filter, Sliding DFT, Phase changes of Fast Furrier Transform (FFT), peak amplitude of FFT to conclude which one from these methods are suitable for fast under-sampling frequencies detection on FPGA. Moreover, our proposed approach for sub-sampling detection from real waveform has less complextity than previous approaches from complex waveform

Moth flame-random search optimization of a zero-dimensional model of a proton exchange membrane fuel cell

Modelling of proton exchange membrane fuel cell (PEMFC) is important for better understanding, simulation, and design of high-efficiency fuel cell systems. PEMFC models are often multivariate with several nonlinear elements. Metaheuristic algorithms that are successful in solving nonlinear optimization problems are good candidates for this purpose. This study proposes a new metaheuristic algorithm called MFORS that uses the advantages of the moth-flame optimization algorithm in global search and the non-deterministic properties of the random search algorithm to identify the optimal parameters of the PEMFC model. The sum of squared errors between the estimated and measured voltage is a quality of fit criterion. To show the effectiveness of the proposed algorithm, two case studies of zero-dimensional models of SR-12 Modular PEM Generator and Ballard Mark V fuel cell are considered. The sum of squared errors for the parameter estimated of electrical PEMFCs by the proposed MFORS algorithm is compared with recent works. The results showed that by the MFORS algorithm, the minimum sum of absolute errors of the actual stack voltage and the simulated stack voltage in the two PEMFC are 0.095037 and 0.018019, compared with the second-best algorithm results giving 0.09681 and 0.8092, respectively

Hybrid Obfuscation Using Signals and Encryption

Obfuscation of software and data is one of the subcategories of software security. Hence, the outlines of the obfuscation problem and its various methods have been studied in this article. This paper proposes a hybrid of two signals and encryption obfuscation to hide the behaviour program and prevent reconstruction of the normal code by hackers. The usual signal method is strong enough for obfuscation, but its problem is the high complexity because of a lot of call and return instructions. In this study, a new dispatcher was added to the source code to reconstruct the original control flow graph from the hidden one to solve the problem of the signal method. This dispatcher code is encrypted to preclude access by the hacker. In this paper, the potency that makes the obfuscation strong has been increased and the resilience that makes the obfuscation poor has been decreased. The results of a comparison of the similarity among the ambiguous data with its original code and with available efficient methods present a performance advantage of the proposed hybrid obfuscation algorithm

A Representation of Membrane Computing with a Clustering Algorithm on the Graphical Processing Unit

Long-timescale simulations of biological processes such as photosynthesis or attempts to solve NP-hard problems such as traveling salesman, knapsack, Hamiltonian path, and satisfiability using membrane systems without appropriate parallelization can take hours or days. Graphics processing units (GPU) deliver an immensely parallel mechanism to compute general-purpose computations. Previous studies mapped one membrane to one thread block on GPU. This is disadvantageous given that when the quantity of objects for each membrane is small, the quantity of active thread will also be small, thereby decreasing performance. While each membrane is designated to one thread block, the communication between thread blocks is needed for executing the communication between membranes. Communication between thread blocks is a time-consuming process. Previous approaches have also not addressed the issue of GPU occupancy. This study presents a classification algorithm to manage dependent objects and membranes based on the communication rate associated with the defined weighted network and assign them to sub-matrices. Thus, dependent objects and membranes are allocated to the same threads and thread blocks, thereby decreasing communication between threads and thread blocks and allowing GPUs to maintain the highest occupancy possible. The experimental results indicate that for 48 objects per membrane, the algorithm facilitates a 93-fold increase in processing speed compared to a 1.6-fold increase with previous algorithms

Climatic zonation and land suitability determination for saffron in Khorasan-Razavi province using data mining algorithms

Yield prediction for agricultural crops plays an important role in export-import planning, purchase guarantees, pricing, secure profits and increasing in agricultural productivity. Crop yield is affected by several parameters especially climate. In this study, the saffron yield in the Khorasan-Razavi province was evaluated by different classification algorithms including artificial neural networks, regression models, local linear trees, decision trees, discriminant analysis, random forest, support vector machine and nearest neighbor analysis. These algorithms analyzed data for 20 years (1989-2009) including 11 climatological parameters. The results showed that a few numbers of climatological parameters affect the saffron yield. The minimum, mean and maximum of temperature, had the highest positive correlations and the relative humidity of 6.5h, sunny hours, relative humidity of 18.5h, evaporation, relative humidity of 12.5h and absolute humidity had the highest negative correlations with saffron cultivation areas, respectively. In addition, in classification of saffron cultivation areas, the discriminant analysis and support vector machine had higher accuracies. The correlation between saffron cultivation area and saffron yield values was relatively high (r=0.38). The nearest neighbor analysis had the best prediction accuracy for classification of cultivation areas. For this algorithm the coefficients of determination were 1 and 0.944 for training and testing stages, respectively. However, the algorithms accuracy for prediction of crop yield from climatological parameters was low (the average coefficients of determination equal to 0.48 and 0.05 for training and testing stages). The best algorithm i.e. nearest neighbor analysis had coefficients of determination equal to 1 and 0.177 for saffron yield prediction. Results showed that, using climatological parameters and data mining algorithms can classify cultivation areas. By this way it is possible to identify areas that have similar climate to prone areas and recognize suitable areas for cultivation

An Improved Harmony Search Algorithm for Proactive Routing Protocol in VANET

Vehicular ad-hoc network (VANET) is the direct application of mobile ad-hoc network (MANET) in which the nodes represent vehicles moving in a city or highway scenario. The deployment of VANET relies on routing protocols to transmit the information between the nodes. Different routing protocols that have been designed for MANET were proposed to be applied in VANET. However, the real-time implementation is still facing challenges to fulfill the quality of service (QoS) of VANET. Therefore, this study mainly focuses on the well-known MANET proactive optimized link state routing (OLSR) protocol. The OLSR in VANET gives a moderate performance; this is due to its necessity of maintaining an updated routing table for all possible routes. The performance of OLSR is highly dependent on its parameter. Thus, finding optimal parameter configurations that best fit VANET features and improve its quality of services is essential before its deployment. The harmony search (HS) is an emerging metaheuristic optimization algorithm with features of simplicity and exploration efficiency. Therefore, this paper aims to propose an improved harmony search optimization (EHSO) algorithm that considers the configuration of the OLSR parameters by coupling two stages, a procedure for optimization carried out by the EHSO algorithm based on embedding two popular selection methods in its memory, namely, roulette wheel selection and tournament selection. The experimental analysis shows that the proposed approach has achieved the QoS requirement, compared to the existing algorithms

MWCNT-Fe3O4 as a superior adsorbent for microcystins LR removal: Investigation on the magnetic adsorption separation, artificial neural network modeling, and genetic algorithm optimization

Magnetic multi-wall carbon nanotube (MMWCNT) was prepared by simple protocol and its structural features were characterized using SEM, TEM, and XRD analysis. The association between removal (%) and variables such as pH (3 − 11), adsorbent amounts (0.005, 0.1, 0.25, 0.5, 0.75, and 1 g/L), reaction time (5–180 min), and concentration of microcystins-LR (10, 25, 50, 75, and 125 μg/L) was investigated and optimized. The results of the isotherm study indicated that Langmuir offered high determination coefficients (R2 = 0.993, 0.996, and 0.998, for the three different working temperatures of 20 °C, 35 °C, and 50 °C respectively) and was the optimum isotherm to anticipate adsorption of MC-LR (microcystins-LR) by magnetic MWCNT adsorbent. The kinetic study revealed that the adsorption kinetics of MC-LR could be better defined using the pseudo-second-order model. A three-layer model of an artificial neural network was applied to forecast the MC-LR removal efficiency by magnetic MWCNTs over 66 runs. To forecast the MC-LR removal efficiency, the minimum mean squared error of 0.0011 and determination coefficient (R2) of 0.9813 were obtained. The use of the artificial neural network model achieved a good level of compatibility between the acquired and anticipated data