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Table Of Integrals Series And Products

Time-Frequency Analysis.

Power line communication (PLC) is the use of power lines for the purpose of electronic data transmission. The presence of additive noise, namely, background noise and impulsive noise, significantly affects the performance of a PLC system. While the background noise is modeled by Nakagami- $m$ distribution, the impulsive noise is modeled using Middleton class A distribution. In this letter, we study the performance of a PLC system under the combined effect of Nakagami- $m$ background noise and Middleton class A impulsive noise assuming binary phase shift keying signaling. The probability density function of decision variable under the influence of additive noise (sum of background noise and impulsive noise) is derived. We also derive an analytical expression for the average bit error rate of the considered PLC system. The analytical expressions are validated by close matching to the simulation results. The analysis presented in this letter closely predicts the behavior of the PLC system under the combined effect of background and impulsive noises.

Performance Evaluation of PLC Under the Combined Effect of Background and Impulsive Noises

With the development of new energy technology, more and more plug-in electric vehicles (PEVs) have appeared in our lives. In vehicle to grid (V2G) information exchange, the method that PEVs use to access the grid for information and vehicular data collection is the key factor that affects the reliability and real-time nature of V2G communication. Vehicular power line communication (VPLC) reuses vehicular power cables as a communication medium, which avoids the need for an additional vehicular communication line and optimizes the in-vehicle space. VPLC is being considered as a more competitive solution to reduce the complexity of design, weight, and cost associated with the rapid growth of electronic devices installed in vehicles, especially for PEVs. Nevertheless, there are factors that impact VPLC in such a way that disrupts reliability and introduces problems. One of these challenges is VPLC channel noise, which can reduce communication reliability. Hence, in this paper, we introduce a V2G information interaction system structure, based on power line communication, to establish a VPLC channel model for the vehicular data collection system. The model is developed in Simulink and leverages various vehicular noise models and binary frequency shift keying (BFSK) modulation; it also analyzes the channel characteristics by examining different types of noise based on BFSK. Our simulation results demonstrate the feasibility of PLC under BFSK modulation and demodulation, often used in PEVs, which could provide guidance to the implementation of VPLC in support of practical engineering.

/pdf/reliability-oriented-modeling-and-analysis-of-vehicular-4fsg4dtic0.pdf

Reliability Oriented Modeling and Analysis of Vehicular Power Line Communication for Vehicle to Grid (V2G) Information Exchange System

We study the latitudinal distribution of geomagnetic activity in 1966–2009 with local geomagnetic activity indices at 26 magnetic observatories. Using the principal component analysis method we find that more than 97% of the variance in annually averaged geomagnetic activity can be described by the two first principal components. The first component describes the evolution of the global geomagnetic activity, and has excellent correlation with, e.g., the Kp/Ap index. The second component describes the leading pattern by which the latitudinal distribution of geomagnetic activity deviates from the global average. We show that the second component is highly correlated with the relative (annual) fraction of high-speed streams (HSS) in solar wind. The latitudinal distribution of the second mode has a high maximum at auroral latitudes, a local minimum at subauroral latitudes and a low maximum at midlatitudes. We show that this distribution is related to the difference in the average location and intensity between substorms related to coronal mass ejections (CMEs) and HSSs. This paper demonstrates a new way to extract useful, quantitative information about the solar wind from local indices of geomagnetic activity over a latitudinally extensive network.

Annual fractions of high speed streams from principal component analysis of local geomagnetic activity

Power line communication (PLC) systems are being standardized over the globe and some of these standards recommended frequency shift keying (FSK) as their modulation choice. Broadband transmission over a PLC channel is mainly affected by the ever-present background noise and the occasional high-amplitude impulses. It has been recently found that the background noise in PLC can be suitably modelled with Nakagami-m distribution while a standard model for characterizing impulses is to assume Gaussian distributed amplitude and Poisson distributed arrivals. Considering such a model, at first, simple analytical bit error rate (BER) expressions of uncoded binary FSK (BFSK) signals are derived in the paper. Next, a unified analytical framework is presented for evaluating BER when a Reed Solomon (RS) code is used to mitigate the noise effects. The results reveal that when the signal to background noise ratio (SNR) is low, the noise parameter m and the demodulation scheme affects the performance of the coded system. On the contrary, at higher SNR, impulsive noise dominates over background noise, and these effects vanish as the BER curve becomes flat. Numerical evaluations dictated that by allowing a lower code rate (0.7) this error floor may be reduced significantly (up to 10−15). The code gain of the system was found to be an inverse function of the code rate and codeword length.

http://ieeexplore.ieee.org/iel7/6808269/6812309/06812369.pdf

Error performance of RS coded binary FSK in PLC channels with Nakagami and impulsive noise

In the recent past, coronal mass ejection (CME) has received much research attention for its geo-effectiveness. In this paper, an investigation has been made to identify the scaling pattern of the CME linear speed time series data (February 1999 to December 2007 of solar cycle 23) collected from the Solar and Heliospheric Observatory (SOHO) using Multi-Fractal Detrended Fluctuation Analysis (MFDFA) and Multi-Fractal Detrended Moving Average (MFDMA) method. The scaling exponent, generalized Hurst exponent, singularity strength and also the singularity spectrum have been computed to quantify the multifractality and to identify the singularities of the time series data. An effort has also been made to find out the possible sources which are responsible for the multifractality in the signal by studying the scaling patterns of the shuffled and surrogate version of the original data. It has been revealed in this paper that CME linear speed signal exhibit multifractal behaviour with long-term persistence. Both the long-range temporal correlation and the broad probability density function (pdf) are found to be the primary source of multifractality in the signal. The singularities or abruptness present in the signal are found to vary with time, and this fluctuation follows an AR (2) model.

Fractality and singularity in CME linear speed signal: Cycle 23

Bit error rate of RS coded BFSK in broadband powerline channels with background Nakagami and impulsive noise

In this paper initiative has been taken to search the periodicities of linear speed of Coronal Mass Ejection in solar cycle 23. Double exponential smoothing and Discrete Wavelet Transform are being used for detrending and filtering of the CME linear speed time series. To choose the appropriate statistical methodology for the said purpose, Smoothed Pseudo Wigner-Ville distribution (SPWVD) has been used beforehand to confirm the non-stationarity of the time series. The Time-Frequency representation tool like Hilbert Huang Transform and Empirical Mode decomposition has been implemented to unearth the underneath periodicities in the non-stationary time series of the linear speed of CME. Of all the periodicities having more than 95% Confidence Level, the relevant periodicities have been segregated out using Integral peak detection algorithm. The periodicities observed are of low scale ranging from 2–159 days with some relevant periods like 4 days, 10 days, 11 days, 12 days, 13.7 days, 14.5 and 21.6 days. These short range periodicities indicate the probable origin of the CME is the active longitude and the magnetic flux network of the sun. The results also insinuate about the probable mutual influence and causality with other solar activities (like solar radio emission, $A_{p}$
 index, solar wind speed, etc.) owing to the similitude between their periods and CME linear speed periods. The periodicities of 4 days and 10 days indicate the possible existence of the Rossby-type waves or planetary waves in Sun.

Stationarity and periodicities of linear speed of coronal mass ejection: a statistical signal processing approach

Life on earth is protected from the high energy particles and radiation from the sun by the Earth’s magnetic field. Nearly after 44 hours of any mass ejection from the solar corona, the Earth’s magnetic field gets momentarily disturbed causing hazardous geomagnetic storm. This observed association has been verified in this work by unearthing the possible existence of any statistical relationship between the geomagnetic storm and Coronal Mass Ejection (CME) using signal processing tools. This may help to assess the post-CME intensity of destructive geomagnetic storm. Multivariate Singular Spectrum Analysis (MSSA) based on Singular Value Decomposition (SVD) has been used on the time series signals of CME linear speed and geomagnetic storm index (Kp-index). Processing of these two signals reveal that CME is not only a causal factor to induce the Geomagnetic storm but its speed also has a supportive effect to anticipate the intensity of the geomagnetic storm.

Anirban Chattopadhyay

Papers

Error performance of RS coded binary FSK in PLC channels with Nakagami and impulsive noise

Fractality and singularity in CME linear speed signal: Cycle 23

Bit error rate of RS coded BFSK in broadband powerline channels with background Nakagami and impulsive noise

Stationarity and periodicities of linear speed of coronal mass ejection: a statistical signal processing approach

Multivariate Singular Spectral Analysis (MSSA) to explore geomagnetic storm and CME bond