Piecewise-linear Curve Fitting Research Articles

Novel Linear Companding Transform Design Based on Linear Curve Fitting for PAPR Reduction in OFDM Systems

Peak-to-average power ratio (PAPR) reduction is an important issue for orthogonal frequency division multiplexing (OFDM) systems. In this letter, a novel method of continuous piecewise linear curve fitting (CPLCF) is first proposed to design linear companding transforms (LCTs) for PAPR reduction in OFDM systems. From given nonlinear companding transforms (NCTs), this method can derive corresponding LCTs with similar performance and much lower complexity. The constrained optimization problem is formulated, and parameter selecting method is provided. By employing the CPLCF method, a novel LCT scheme, named ANC-LCF, is derived from newly proposed adjusted nonlinear companding (ANC) prototype. Simulation results verify the validity of the novel design method and the performance superiority of the proposed ANC-LCF scheme.

Optimize globally, control locally: Coordinated optimal local voltage control in hybrid AC/DC microgrid

The increasing penetration of photovoltaic (PV) generation leads to voltage violations in hybrid AC/DC microgrids. Due to the uncertainty and volatility of PV outputs, fast-responding voltage control devices are obliged to coordinate with each other and regulate nodal voltages in real time. This paper follows the concept of optimizing globally and controlling locally to propose a combined central and local voltage control strategy based on a two-stage voltage control framework. In the first stage, minutely optimal power flow based on predicted PV outputs within one hour is performed in microgrid central controller (MGCC) to calculate voltage and optimal power settings for each controllable device. The uncertainty of PV is considered using chance constraint optimal power flow (CCOPF) method. In the second stage, according to minutely voltage and power settings, local controller (LC) generates improved voltage control curves based on curve fitting method. With minutely measurements of nodal voltages, power outputs of PVs, smart loads (SLs) energy storage systems (ESSs) and VSC are cooperated based on improved local control curves to minimize power losses without communication requirements. In order to coordinate the active and reactive power of SLs ESSs, and VSC, a combined P(V) and Q(V) curve is proposed. The piecewise linear curve fitting method is used to generate optimal voltage control curves due to its higher fitting accuracy compared with other fitting methods. Simulations are performed to verify the effectiveness of the proposed voltage control strategy in mitigating voltage rise problems and reducing power losses.

Approximation of Achievable Rates in Additive Gaussian Mixture Noise Channels

In this paper, we detail effective methods to approximate the achievable rates of channels with additive Gaussian mixture (GM) noise for both real and complex channels to achieve any desired level of accuracy. Attention is paid to a Gaussian input, a discrete real input, and a complex input with discrete amplitude and independent uniform phase. Such discrete inputs represent a wide range of input distributions and they include the capacity-achieving inputs as special cases. At first, we propose a simple technique to accurately calculate the noise entropy. Specifically, when the noise level is high, a lower bound on the integrand of the entropy is established and the noise entropy can be estimated using a closed-form solution. In the low noise region, the piecewise-linear curve fitting (PWLCF) method is applied. We then extend this result to calculate the achievable rate when the input is Gaussian distributed, which is shown to be asymptotically optimal. Next, we propose a simple PWLCF-based method to approximate the output entropy for a real GM channel when the input is discrete, and for a complex GM channel when the input is discrete in amplitude with independent uniform phase. In particular, for the real channel, the output entropy is evaluated by examining the output in high and low regions of amplitude using a lower bound on the integrand of the output entropy and PWLCF, respectively. For the complex channel, the output entropy is approximated a similar manner but using polar coordinates and the Kernel function. It is demonstrated that the output entropy, and consequently, the achievable rates, can be computed to achieve any given accuracy level.

An efficient temperature dependent hot carrier injection reliability simulation flow

This paper presents an efficient temperature dependent hot carrier injection reliability simulation flow which is scalable. The flow makes use of some efficient techniques at different design hierarchical levels to enable full chip simulation with a fast run time and high enough accuracy. While the transistor-level HCI effect is modeled based on the conventional reaction–diffusion (R–D) framework, the gate-level characterization method combines HSpice simulation and piecewise linear curve fitting to model the impact of HCI effect over the time. Also, as one of the ways to improve the speed of the simulation, only the NMOS transistors, which suffer much more from the HCI effect, are considered in the modeling. In addition, among these devices, only those which are more significantly affected are included. For each cell, only the transitions which induce the HCI impact are included. Finally, to improve the efficiency of the circuit simulation, logic cells in the circuit are classified into two groups of critical and non-critical where the critical (non-critical) ones are simulated using fine (coarse) granularity simulation time steps. The proposed method reduces the simulation time without losing much of accuracy. Also, due to the considerable impact of the temperature on the reliability, at all levels of the proposed simulation flow, the impact of the temperature on the impact of the HCI phenomena is modeled. The simulations performed on some benchmarks reveal that the proposed circuit-level HCI modeling is able to reduce the runtime of calculating the threshold voltage and mobility drifts of the gates significantly without sacrificing accuracy unacceptably. Also, the circuit-level simulations indicate an about 19% increase in the average of the HCI-induced delay degradation of the benchmarks when the temperature rises from 20°C to 100°C.

Capacity of Bernoulli-Gaussian Interference Channels in Rayleigh Fading with Full CSI

In this paper, we investigate the channel capacity of a Bernoulli-Gaussian (BG) interference channel in Rayleigh fading when the channel state information (CSI) is known at both the transmitter and receiver via tight lower and upper bounds. Specifically, we first derive an upper bound on the channel capacity assuming a Gaussian-distributed output. Under this assumption, an optimal power adaptation scheme is established and the upper-bound is obtained in closed-form. By assuming a Gaussian-distributed input, we then adopt the derived power adaptation scheme to establish a lower bound on channel capacity. A simple approximation of the instantaneous output entropy using a piecewise-linear curve fitting(PWLCF)-based scheme is then developed, which provides a closed-form estimation of the lower bound with a predetermined error level. Finally, a comparison between the derived upper and lower bounds are made. Both analytical and numerical results show that these two bounds are tight in a wide range of input power levels and they can be used effectively to estimate the channel capacity.

Power Loss Research on IGCT-applied NPC Three-level Converter

IGCT has a broad application prospects in high power conversion such as flexible DC transmission due to its characteristics of high voltage and large capacity. Compared with IGBT it has a voltage and current level of 4.5kV/4kA and its slope resistance is less than IGBT’s. In addition, due to the di/dt snubber circuit, IGCT’s opening loss will be reduced. Because of improvement of IGCT performance, the loss of IGCT-applied three-level converter will have some new characteristics, so it is necessary to model the IGCT-type three-level converter. Based on piecewise linear curve fitting method, the IGCT switching loss mathematical model is established firstly. With its use in IGCT-type three-level converter loss model, a IGCT-type three-level converter mathematical model is established. If the model is applied in the ±200kV flexible DC transmission’s calculation example, it can be concluded that the loss rate is 0.31%-0.78% when the power factor angle changes between 0 and π, while the loss rate of IGBT-type converter under same voltage level is 2%-3.5% ,so it can be concluded that IGCT is more suitable than IGBT for use in high voltage and large capacity situation such as frequency control of motor speed and flexible DC transmission.

Estimating Shannon and Constrained Capacities of Bernoulli-Gaussian Impulsive Noise Channels in Rayleigh Fading

This paper presents a novel approach to tightly estimate the ergodic Shannon and constrained capacities of an additive Bernoulli-Gaussian (BG) impulsive noise channel in Rayleigh fading environments where channel gains are known at the receiver, but not at the transmitter. We first show that the differential entropy of the BG impulsive noise can be established in closed-form using Gaussian hypergeometric function <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> (1, 1; ·; ·). The Shannon capacity is then calculated via upper and lower bounds. Specifically, we derive in closed-form two upper bounds on the Shannon capacity using the assumption of a Gaussian output and using full knowledge of noise state, respectively. Under the assumption of a Gaussian input, we propose a novel approach to calculate a lower bound by examining the instantaneous output entropy in two regions of channel gains. In the high-gain region, the lower bound is evaluated via the upper bound obtained under the Gaussian output assumption. In the other region, we apply the piecewise-linear curve fitting (PWLCF) method to estimate the lower bound. It is then demonstrated that the lower bound can be calculated with a predetermined accuracy. By establishing the difference between the lower bound and the two upper bounds, we show that the lower bound can be used to effectively estimate the Shannon capacity. Finally, we detail a PWLCF-based method to estimate the constrained capacity for a finite-alphabet constellation. To this end, we first propose a numerical technique to calculate the instantaneous entropy of the output using 2-dimensional (2-D) Gauss-Hermite quadrature formulas. The average output entropy is then obtained using the PWLCF method. Combined with the closed-form expression of the entropy of the BG impulsive noise, the constrained capacity can be effectively estimated.

Data mining and knowledge discovery in proton nuclear magnetic resonance (1H-NMR) spectra using frequency to information transformation (FIT)

Recent rapid development of research in the fields of structural genomics and bioinformatics has stressed the need for the development of effective methods of data mining and knowledge extraction from complex and convoluted signals. In this paper we introduce frequency to information transformation (FIT) as a novel method of extracting information content of complex signals. Because FIT uses a priori knowledge and is a comparative technique, it is well suited for data mining and knowledge discovery from complex data. In this paper, we introduce FIT and compare it to established methods used in automated conditioning and knowledge discovery in proton-nuclear magnetic resonance (1H-NMR) spectra. FIT transformation was applied to a collection of 80 one-dimensional (1D) 1H-NMR spectra of 23 N-linked oligosaccharides. Three classification methods, namely, cluster analysis, Bayesian analysis and artificial neural networks (ANN) were used to demonstrate the advantages of FIT in information and knowledge extraction in comparison with classical methods such as frequency-based filtering, nonlinear and piecewise linear curve fitting, and correlation coefficient analysis.