Idealised operation of zero-voltage-switching series-L/parallel-tuned Class-E power amplifier

An analysis of a modified series-L/parallel-tuned Class-E power amplifier is presented, which includes the effects that a shunt capacitance placed across the switching device will have on Class-E behaviour. In the original series L/parallel-tuned topology in which the output transistor capacitance is not inherently included in the circuit, zero-current switching (ZCS) and zero-current derivative switching (ZCDS) conditions should be applied to obtain optimum Class-E operation. On the other hand, when the output transistor capacitance is incorporated in the circuit, i.e. in the modified series-L/parallel-tuned topology, the ZCS and ZCDS would not give optimum operation and therefore zero-voltage-switching (ZVS) and zero-voltage-derivative switching (ZVDS) conditions should be applied instead. In the modified series-L/parallel-tuned Class-E configuration, the output-device inductance and the output-device output capacitance, both of which can significantly affect the amplifier's performance at microwave frequencies, furnish part, if not all, of the series inductance L and the shunt capacitance COUT, respectively. Further, when compared with the classic shunt-C/series-tuned topology, the proposed Class-E configuration offers some advantages in terms of 44% higher maximum operating frequency (fMAX) and 4% higher power-output capability (PMAX). As in the classic topology, the fMAX of the proposed amplifier circuit is reached when the output-device output capacitance furnishes all of the capacitance COUT, for a given combination of frequency, output power and DC supply voltage. It is also shown that numerical simulations agree well with theoretical predictions.

Series-L/parallel-tuned comparison with shunt-C/series-tuned class-E power amplifier

An analysis of the operation of a series-L/parallel-tuned class-E amplifier and its equivalence to the classic shunt-C/series-tuned class-E amplifier are presented. The first reported closed form design equations for the series-L/parallel-tuned topology operating under ideal switching conditions are given. Furthermore, a design procedure is introduced that allows the effect that nonzero switch resistance has on amplifier performance efficiency to be accounted for. The technique developed allows optimal circuit components to be found for a given device series resistance. For a relatively high value of switching device ON series resistance of 4O, drain efficiency of around 66% for the series-L/parallel-tuned topology, and 73% for the shunt-C/series-tuned topology appear to be the theoretical limits. At lower switching device series resistance levels, the efficiency performance of each type are similar, but the series-L/parallel-tuned topology offers some advantages in terms of its potential for MMIC realisation. Theoretical analysis is confirmed by numerical simulation for a 500mW (27dBm), 10% bandwidth, 5 V series-L/parallel-tuned, then, shunt-C/series-tuned class E power amplifier, operating at 2.5 GHz, and excellent agreement between theory and simulation results is achieved. The theoretical work presented in the paper should facilitate the design of high-efficiency switched amplifiers at frequencies commensurate with the needs of modern mobile wireless applications in the microwave frequency range, where intrinsically low-output-capacitance MMIC switching devices such as pHEMTs are to be used.

Nonlinear time series prediction based on a power-law noise model

In this paper we investigate the influence of a power-law noise model, also called noise, on the performance of a feed-forward neural network used to predict time series. We introduce an optimization procedure that optimizes the parameters the neural networks by maximizing the likelihood function based on the power-law model. We show that our optimization procedure minimizes the mean squared leading to an optimal prediction. Further, we present numerical results applying method to time series from the logistic map and the annual number of sunspots demonstrate that a power-law noise model gives better results than a Gaussian model.

An ultra-short-term wind power prediction method using “offline classification and optimization, online model matching” based on time series features

The applicability of ultra-short-term wind power prediction (USTWPP) models is reviewed. The USTWPP method proposed extracts featrues from historical data of wind power time series (WPTS), and classifies every short WPTS into one of several different subsets well defined by stationary patterns. All the WPTS that cannot match any one of the stationary patterns are sorted into the subset of nonstationary pattern. Every above WPTS subset needs a USTWPP model specially optimized for it offline. For on-line application, the pattern of the last short WPTS is recognized, then the corresponding prediction model is called for USTWPP. The validity of the proposed method is verified by simulations.

Series-L/parallel-tuned Class-E power amplifier analysis

An analysis of the operation of a new series-L/parallel-tuned Class-E amplifier and its equivalence to the classic shunt-C/series-tuned Class-E amplifier are presented. The first reported closed form design equations for the series-L/parallel-tuned topology operating under ideal switching conditions are given, including the switch current and voltage in steady state, the circuit component values, the peak values of switch current and voltage and the power-output capability. Theoretical analysis is confirmed by numerical simulation for a 500 mW (27 dBm), 10% bandwidth, 5 V series-L/parallel-tuned, then, shunt-C/series-tuned Class-E power amplifier, operating at 2.5 GHz. Excellent agreement between theory and simulation results is achieved.

Time series wind power forecasting based on variant Gaussian process and TLBO

Due to the variability and stochastic nature of wind power system, accurate wind power forecasting has an important role in developing reliable and economic power system operation and control strategies. As wind variability is stochastic, Gaussian Process regression has recently been introduced to capture the randomness of wind energy. However, the disadvantages of Gaussian Process regression include its computation complexity and incapability to adapt to time varying time-series systems. A variant Gaussian Process for time series forecasting is introduced in this study to address these issues. This new method is shown to be capable of reducing computational complexity and increasing prediction accuracy. It is further proved that the forecasting result converges as the number of available data approaches innite. Further, a teaching learning based optimization (TLBO) method is used to train the model and to accelerate
the learning rate. The proposed modelling and optimization method is applied to forecast both the wind power generation of Ireland and that from a single wind farm to show the eectiveness of the proposed method.

Universal power quality manager with a new control scheme

A new universal power quality manager is proposed. The proposal treats a number of power quality problems simultaneously. The universal manager comprises a combined series and shunt three-phase PWM controlled converters sharing a common DC link. A control scheme based on fuzzy logic is introduced and the general features of the design and operation processes are outlined. The performance of two configurations of the proposed power quality manager are compared in terms of a recently formulated unified power quality index. The validity and integrity of the proposed system is proved through computer simulated experiments

Exploring figures of merit associated with the suboptimum operation of class-E power amplifiers

Closed-form design equations for the operation of a class-E amplifier for zero switch voltage slope and arbitrary duty cycle are derived. This approach allows an additional degree of freedom in the design of class-E amplifiers which are normally designed for 50 duty ratio. The analysis developed permits the selection of non-unique solutions where amplifier efficiency is theoretically 100 but power output capability is less than that the 50 duty ratio case would permit. To facilitate comparison between 50 (optimal) and non-50 (suboptimal) duty ratio cases, each important amplifier parameter is normalised to its corresponding optimum operation value. It is shown that by choosing a non-50 suboptimal solution, the operating frequency of a class-E amplifier can be extended. In addition, it is shown that by operating the amplifier in the suboptimal regime, other amplifier parameters, for example, transistor output capacitance or peak switch voltage, can be included along with the standard specification criteria of output power, DC supply voltage and operating frequency as additional input design specifications. Suboptimum class-E operation may have potential advantages for monolithic microwave integrated circuit realisation as lower inductance values (lower series resistance, higher self-resonance frequency, less area) may be required when compared with the results obtained for optimal class-E amplifier synthesis. The theoretical analysis conducted here was verified by harmonic balance simulation, with excellent agreement between both methods. © The Institution of Engineering and Technology 2007.

Performance projections and design optimization of planar double gate SOI MOSFETs for logic technology applications

In this paper, by investigating the influence of source/drain extension region engineering (also known as gate-source/drain underlap) in nanoscale planar double gate (DG) SOI MOSFETs, we offer new insights into the design of future nanoscale gate-underlap DG devices to achieve ITRS projections for high performance (HP), low standby power (LSTP) and low operating power (LOP) logic technologies. The impact of high-kappa gate dielectric, silicon film thickness, together with parameters associated with the lateral source/drain doping profile, is investigated in detail. The results show that spacer width along with lateral straggle can not only effectively control short-channel effects, thus presenting low off-current in a gate underlap device, but can also be optimized to achieve lower intrinsic delay and higher on-off current ratio (I-on/I-off). Based on the investigation of on-current (I-on), off-current (I-off), I-on/I-off, intrinsic delay (tau), energy delay product and static power dissipation, we present design guidelines to select key device parameters to achieve ITRS projections. Using nominal gate lengths for different technologies, as recommended from ITRS specification, optimally designed gate-underlap DG MOSFETs with a spacer-to-straggle (s/sigma) ratio of 2.3 for HP/LOP and 3.2 for LSTP logic technologies will meet ITRS projection. However, a relatively narrow range of lateral straggle lying between 7 to 8 nm is recommended. A sensitivity analysis of intrinsic delay, on-current and off-current to important parameters allows a comparative analysis of the various design options and shows that gate workfunction appears to be the most crucial parameter in the design of DG devices for all three technologies. The impact of back gate misalignment on I-on, I-off and tau is also investigated for optimized underlap devices.

Periodic FDTD analysis of a 2-D leaky-wave planar antenna based on dipole frequency selective surfaces

A periodic finite-difference time-domain (FDTD) analysis is presented and applied for the first time in the study of a two-dimensional (2-D) leaky-wave planar antenna based on dipole frequency selective surfaces (FSSs). First, the effect of certain aspects of the FDTD modeling in the modal analysis of complex waves is studied in detail. Then, the FDTD model is used for the dispersion analysis of the antenna of interest. The calculated values of the leaky-wave attenuation constants suggest that, for an antenna of this type and moderate length, a significant amount of power reaches the edges of the antenna, and thus diffraction can play an important role. To test the validity of our dispersion analysis, measured radiation patterns of a fabricated prototype are presented and compared with those predicted by a leaky-wave approach based on the periodic FDTD results.

The power of shell rapping influences rates of eviction in hermit crabs

Hermit crabs fight for ownership of shells, and shell exchange may occur after a period of shell rapping, involving the initiating or attacking crab bringing its shell rapidly and repeatedly into contact with the shell of the noninitiator or defender, in a series of bouts. The temporal pattern of rapping contains information about the motivation and/or relative resource holding potential (RHP) of the initiator and acts as a repeated signal of stamina. Here we investigated the role of the force with which the rapping is performed and how this is related to the temporal pattern of rapping by rubberizing the external surface of shells. Initiators that are prevented from rapping with their usual level of force persist with the activity for longer over the whole encounter but use fewer raps per bout and are less likely to effect an exchange than those supplied with control shells. The fact that the force of rapping affects the likelihood of a crab being victorious suggests that either the force of rapping contains information about motivation or RHP or that force directly affects noninitiators, reducing their ability to maintain an adequate grip on their shells. The data suggest that shell rapping is an agonistic signal rather than one that provides information useful to the noninitiator, as has been suggested by the negotiation model of shell exchange.

Genetic Algorithm Search for Predictive Patterns in Multidimensional Time Series

Based on an algorithm for pattern matching in character strings, we implement a pattern matching machine that searches for occurrences of patterns in multidimensional time series. Before the search process takes place, time series are encoded in user-designed alphabets. The patterns, on the other hand, are formulated as regular expressions that are composed of letters from these alphabets and operators. Furthermore, we develop a genetic algorithm to breed patterns that maximize a user-defined fitness function. In an application to financial data, we show that patterns bred to predict high exchange rates volatility in training samples retain statistically significant predictive power in validation samples.