Scientia Iranica
Volume:21 Issue: 3, 2014

Since there is no difference between uplink and downlink subframes in the IEEE 802.16 mesh mode, both downlink and uplink traffics are transmitted within a single time frame. Hence, in most scheduling methods, only one scheduling algorithm is used for both uplink and downlink traffics. However, because of different characteristics of uplink and downlink traffic, different scheduling methods should be used for each of the traffic types. In this paper, we focus on the mesh centralized scheduling of downlink flows in the data subframe. After comprehensive analysis on the characteristics of downlink traffic, we propose a new algorithm called Traffic-aware Scheduling (TAS). In this algorithm, the downlink traffic distribution is tuned for maximum concurrent transmission rate. This goal is achieved by choosing different link selection criteria such as maximum demandant sender, farthest receiver and least interfered path based on the downlink traffic analysis results. The simulation results show that our algorithm outperforms existing methods in terms of scheduling length, link concurrency, and throughput (about 13.7% in average) for the downlink traffic. Moreover, the proposed algorithm is scalable. In particular, in average, 3% improvement is achieved in terms of scheduling length in higher traffic loads and number of nodes.

Recently, the strategy for forecasting option price has become a popular financial topic because options are important tools on risk management in financial investments. The well-known Black-Scholes model (B-S model) is widely used for option pricing. In B-S model, the normal distribution assumption is important. However, the financial data in real life may not follow the normal distribution assumption. For this reason, this paper proposes a novel hybrid model, which is a nonlinear prediction model without normal distribution assumptions to forecast the option prices. The proposed model is composed of a fuzzy time series (FTS) model, a least square support vector regression (LSSVR), and a bootstrap method. In the proposed model, FTS model is firstly used to fuzzify dataset and to build historical database. Subsequently, the proposed method uses the remainder contractual time to search similar historical trends as training samples. Finally, we use the bootstrap method on LSSVR to enhance the prediction accuracy. The experiment results show that the proposed model outperforms traditional time series models and several hybrid models in terms of the root mean square error (RMSE), the mean absolute error (MAE) and the correlation coefficient (r) of actual and forecasted option price.

The ASIC implementation of the high speed multiplier using high accuracy floating point logarithmic number system is reported in this paper. Most popularly used techniques to compute logarithmic calculations for digital signal processors are lookup table based implementation, polynomial approximation, and Taylor series expansion. But, all of these techniques suffer from low accuracy due to choice of only lower order terms of the expanded series. In the present work, logarithmic conversion is implemented by floating point (IEEE-754 single precision) converting methodology thereby eliminate series expansion which eventually resulted in high accuracy. The improvement in speed, by avoidance of carry propagation, was achieved through canonical signed digit code (CSDC) implementation, while the high accuracy was achieved through an error minimization circuitry specially designed for this purpose. The functionality of these circuits was checked and performance parameters like propagation delay and dynamic power consumption were calculated by spice spectre using 90nm CMOS technology. The propagation delay & power consumption of the resulting (128×128) bit multiplier (divider) was only ~93ns & ~80mW respectively for a layout area of ~25mm2. The implementation offered significant improvement in terms of accuracy, delay and power from earlier reported ones.

A newmethod for increasing speed of all-optical Mach-Zehnder Interferometric (MZI) switching with bulk semiconductor optical amplifier (SOA), by use of chirped control signals, is suggested and theoretically analyzed. For 125fs input and chirped control pulses, we show acceleration of gain recovery process using cross phase modulation (XPM) effect. Our method depicts that Tb/s switching speeds using bulk SOA-MZI with proper Q-factor is feasible. For the first time we reach operation capability at 2Tb/s with a Q-factor more than 10. The new scheme also improves the extinction and amplitude ratio of the output power as well as increases the contrast ratio of the switched signal. We use finite difference beam propagation method for MZI analysis, taking into account all nonlinear effects of SOA such as group velocity dispersion (GVD), Kerr effect, two photon absorption (TPA), carrier heating (CH) and spectral hole burning (SHB).

In this paper, a wideband coaxial power combiner with tray configuration employing twenty 1-Watt elements mounted on antipodal finline boards is implemented to achieve a rather high power microwave amplifier in the frequency band of 6-18 GHz. The average gain, Continuous Wave (CW) output power and power-added efficiency (PAE) of the combiner are measured as 17 dB, 15 Watts and %17, respectively with ripples of less than 3 dB. In order to investigate the combining efficiency, the passive structure is fabricated and tested in the first step. Its main components, i.e. the coaxial adapter and the antipodal finline boards, are synthesized by using Klopfenstein’s optimum impedance taper, and simulated by Ansoft HFSS. Next the active devices are added to the system. Stability, thermal management and life time issues are examined and addressed in this step. The system reliability is also demonstrated as graceful degradation due to the failure of devices.

This paper obtains the soliton and other solutions to a few nonlinear wave equations that arise on a daily basis in various engineering disciplines and other elds. The sine-cosine method is adopted to extract these solutions. The ansatz method is also implemented to obtain a singular soliton solution to the Schrodinger-Hirota equation that is studied in electrical engineering in the context of nonlinear ber optics. In this context, both Kerr law and power law nonlinearity are going to be addressed. There are several constraint conditions that will be listed in order for the solutions to exist.

This paper presents a teaching learning based algorithm (TLBO) to solve economic load dispatch (ELD) problems involving different linear, non-linear constraints. The problem formulation also consideredthe non-convex objective functions including the effect of valve-point loading, multi-fuel option of large-scale thermal plants.Many difficulties such as multimodality, dimensionality and differentiability are associated with the optimization of large scale non-linear constraints basednon-convex economic load dispatchproblems.TLBO is a population based technique which implements a group of solutions to proceed for the optimum solution. TLBO uses two different phases ‘Teacher Phase’ and ‘Learner Phase’. TLBO uses the mean value of the population to update the solution. Unlike other optimization techniques TLBO does not require any parameters to be tuned, thus making the implementation of TLBO simpler. TLBO uses the best solution of the iteration to change the existing solution in the population thereby increasing the convergence rate. Therefore, in the present paper Teaching–Learning-Based Optimization (TLBO) is applied to solve such type of complicated problems efficiently and effectively in order to achieve superior quality solution in computationally efficient way.Simulation results show that the proposed approach outperforms several existing optimization techniques. Results also proved the robustness of the proposed methodology.

Compared to multistage grid-connected photovoltaic system (GCPVS), complexity in monitoring and controlling scheme in a single-stage GCPVS is much increased as maximum power point tracking and power sharing with the grid need to be considered simultaneously in the same stage. An efficient technique, for performing aforementioned functions simultaneously, in a single stage system by adjustment of voltage phasor of a voltage source inverter is presented. Active and reactive power flow from inverter is regulated by adjustment of amplitude and phase angle of inverter output voltage with respect to grid voltage. An algorithm is also developed for automatic active and reactive powers sharing for the intended applications in the single stage GCPVSs. A typical 2.8 kWp, 368 V (DC) nominal photovoltaic array based GCPVS is modeled and simulated to analyze proper load matching for different ambient and grid voltage conditions. Performances like efficiency of the inverter, total harmonic distortion of inverter output voltage and current are studied with the variation of ambient conditions and modulation index of sinusoidal pulse width modulation of the IGBT based inverter. Simulation results have been included to show the feasibility of the proposed technique.

According to dynamic core hypothesis, integration and di erentiation are main properties of consciousness. Hence, we expected that the consciousness neuronal correlate covers these properties in structural level. These properties could be captured in smallworldness properties, i.e. high clustering coecient and low path-length. Thalamocortical (TC) loop and cortex are two main candidates for Neural Correlates of Consciousness (NCC). We studied small-worldness in these systems. For this purpose, we calculated clustering coecients, characteristic path lengths and their robustness against lesions. We simulated lesions in two ways: eliminating connections, and deleting nodes. We used anatomical connections of TC and cortex of macaque from the CoCoMac neuroinformatic database. Our results show that: 1) Lesions causes an increase in path length and decrease in clustering coecient which cause the destruction of the integration and segregation capabilities of brain network; 2) Deleting the connections is more destructive than deletingthe nodes; 3) During high levels of lesions, the thalamo-cortical connections are more important than cortico-cortical connections in the sense of clustering coecient. In terms ofpath-length, during high levels of nodes'' lesions, the thalamo-cortical connections are more important than cortico-cortical connections, while during edges'' lesions cortico-cortical connections are more important than thalamo-cortical connections.

One of transient phenomenon that leads to the false trip of the power transformer differential protection during the energization of a loaded power transformer is the ultra-saturation phenomenon. In this paper, at first a new algorithm for three-phase power transformer differential protection considering effect of the ultra-saturation phenomenon is presented.To model the ultra-saturation phenomenon, the nonlinear characteristic of the transformer core and the effect of the saturation of the current transformers are taken into account. It is assumed that the load of the transformer is a resistive and inductive load. In this algorithm, the ultra-saturation phenomenon, external and internal faults of power transformer and magnetic inrush current are simulated and appropriate criteria using signal harmonic components of differential currentwill be presentedfor distinguish between these phenomenon by DiscreteFourier Transform (DFT) algorithm. In this paper, simulation is done by PSCAD and MATLAB programs.

Recent advancements in photovoltaic (PV) system technologies have decreased their investment cost and enabled the construction of large PV farms for bulk power generations.The output power of PV farms is affected by both failure of composed components and solar radiation variability. These two factors cause the output power of PV farms be random and different from that of conventional units. Therefore, suitable models and methods should be developed to assess different aspects of PV farms integrationinto power systems, particularly from the system reliability viewpoint. In this context a reliability model has been developed for PV farms with considering both the uncertainties associated with solar radiation and components outages. The proposed model represents a PV farm by a multi-state generating unit which is suitable for the generation system assessment. Utilizing the developed reliability model, an analytical method is proposed for adequacy assessment of power generation systems including large PV farms. Real solar radiation data is used from Jask region in Iran which are utilized in the studies performed on the RBTS and the IEEE-RTS. Several different analyses are conducted to analyze the reliability impacts of PV farms integration and to estimate the capacity value of large PV farms in power generation systems.

Experimental and analytical explorations of an R-2L-2D circuit were carried out. The responses from the ordinary RL-Diode and R-2L-2D circuits were characterized and compared for wide parameter region. As a new circuit, R-2L-2D has an additional inductor and a diode. The circuit had different attractors compared with the ordinary RL-Diode circuit. It was proven that new circuit exhibited wider chaotic regions on the parameter space (i.e. input voltage Vf and frequency f). Both even and odd subharmonic responses were observed following the multiple periodic doublings. The bifurcation analysis revealed to the dominance of feeding frequency by means of the center manifold theory. However periodic and chaotic attractors differed for each circuit, in fact the new circuit generated symmetric trajectories. A detailed investigation proved that the chaotic responses in the proposed circuit could start at the peak-to-peak voltage of Vf= 1.35V at frequency 40 KHz, which was nearly half of the frequency value found for the ordinary circuit. Besides a wide range of chaotic behavior was observed beyond Vf = 0.675 V and f =200 KHz. Chaotic trajectories dominated the dynamics upto f=500 KHz.

The analysis and design of a tunable low noise active inductor is presented. The noise performance of the proposed gyrator-based active inductor is improved without either degrading its quality factor or consuming more power using a linear feed forward path (FFP). The proposed low noise active inductor has been designed and fabricated in a standard 0.18-m CMOS technology. The measurements show a 3 folds improvement in the input noise current compared to that of the conventional active inductors. The active inductor was tuned and measured at the resonance frequency of 2.5 GHz which could be extended to as high as 5.5 GHz with a quality factor of 30. The circuit draws 4.8 mA from a 1.8 V supply.

This paper presents the analysis of static noise margin (SNM), power dissipation, access time and dynamic noise margin of a novel low power proposed 8T static random access memory (SRAM) cell for read operation. In the proposed structure two voltage sources are used, one is connected with the Bit line and the other is connected with Bitbar line in order to reduce the voltage swing at the output nodes of the bit and the bit bar lines. Simulation results for read static noise margin, read power dissipation, read access time and dynamic noise margin have been compared to those of other SRAM cells, reported in different literatures. It is shown that the proposed SRAM cell has better static noise margin and dissipates lesser power in comparison to other SRAM cells. Analog and schematic simulations have been done in 45nm environment with the help of Microwind 3.1 by using BSimM4 model.