High-dynamic GPS tracking

The results of comparing four different frequency estimation schemes in the presence of high dynamics and low carrier-to-noise ratios are given. The comparison is based on measured data from a hardware demonstration. The tested algorithms include a digital phase-locked loop, a cross-product automatic frequency tracking loop, and extended Kalman filter, and finally, a fast Fourier transformation-aided cross-product frequency tracking loop. The tracking algorithms are compared on their frequency error performance and their ability to maintain lock during severe maneuvers at various carrier-to-noise ratios. The measured results are shown to agree with simulation results carried out and reported previously

Probabilistic Forecasting in Day-Ahead Electricity Markets: Simulating Peak and Off-Peak Prices

In this paper we include dependency structures for electricity price forecasting and forecasting evaluation. We work with off-peak and peak time series from the German-Austrian day-ahead price, hence we analyze bivariate data. We first estimate the mean of the two time series, and then in a second step we estimate the residuals. The mean equation is estimated by OLS and elastic net and the residuals are estimated by maximum likelihood. Our contribution is to include a bivariate jump component on a mean reverting jump diffusion model in the residuals. The models' forecasts are evaluated using four different criteria, including the energy score to measure whether the correlation structure between the time series is properly included or not. In the results it is observed that the models with bivariate jumps provide better results with the energy score, which means that it is important to consider this structure in order to properly forecast correlated time series.Comment: 30 pages, 11 figures, 3 tables and Accepted in International Journal of Forecastin

Can semi-parametric additive models outperform linear models, when forecasting indoor temperatures in free-running buildings?

A novel application combining semi-parametric Generalized Additive Models (GAMs) with logistic GAMs was developed to forecast indoor temperatures and window opening states during prolonged heatwaves. GAM models were compared to AutoRegressive models with eXogenous inputs (ARX) and validated against monitored data from two case study dwellings, located near to Loughborough in the UK, during the 2013 heatwave. Input variables were selected using backward stepwise regressions based on minimisation of the Akaike Information Criterion (AIC) and Mean Absolute Error (MAE), for the ARX and GAM models respectively. Comparison of the models showed that whilst GAMs are capable of improving the forecasting accuracy, the improvements are significant only up to 3-6 hours ahead. During heatwaves and over longer forecasting horizons, GAMs were found to be less reliable and accurate than ARX models. The marginal improvement in forecasting accuracy at shorter horizons did not justify the additional computational time and risk of instability associated with more complex GAMs, at longer forecasting horizons. Whilst, logistic GAMs were shown to adequately predict the window opening state, incorporating knowledge of the window state did not significantly improve the accuracy of the indoor temperature predictions

Combining domain knowledge and statistical models in time series analysis

This paper describes a new approach to time series modeling that combines subject-matter knowledge of the system dynamics with statistical techniques in time series analysis and regression. Applications to American option pricing and the Canadian lynx data are given to illustrate this approach.Comment: Published at http://dx.doi.org/10.1214/074921706000001049 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Do NOT Misuse the Markov Cipher Assumption - Automatic Search for Differential and Impossible Differential Characteristics in ARX Ciphers

Firstly, we improve the evaluation theory of differential propagation for modular additions and XORs, respectively. By introducing the concept of $additive$ $sums$ and using signed differences, we can add more information of value propagation to XOR differential propagation to calculate the probabilities of differential characteristics more precisely. Based on our theory, we propose the first modeling method to describe the general ARX differential propagation, which is not based on the Markov cipher assumption. Secondly, we propose an automatic search tool for differential characteristics with more precise probabilities in ARX ciphers. We find that some differential characteristics that used to be valid become impossible, and some probabilities that used to be underestimated increase. In applications, for CHAM-64/128 (one of the underlying block ciphers in COMET, one of 32 second-round candidates in NIST’s lightweight cryptography standardization process), we find that there is no valid $39$-round differential characteristic with a probability of $2^{-63}$ computed using previous methods, and we correct the probabilities to $2^{-64}$ and $2^{-64}$ instead of $2^{-65}$ and $2^{-65}$ computed using previous methods for two 39-round differential characteristics starting from the $1$-st round, respectively; however, if we search for differential characteristics starting from the $5$-th round, the two differential characteristics are invalid, which means that the round constants can affect the security of ARX ciphers against differential cryptanalysis; for Alzette with $c = \tt{0xb7e15162}$ (one of the S-boxes in SPARKLE, one of 10 finalists in NIST’s lightweight cryptography standardization process), we correct the probabilities to $0$ and $2^{-22}$ instead of $2^{-23}$ and $2^{-23}$ computed using previous methods for two 4-round differential characteristics, respectively; for XTEA, we correct the probabilities to $0$ and $2^{-49}$ instead of $2^{-58}$ and $2^{-56}$ computed using previous methods for two 10-round differential characteristics, respectively. Moreover, for Alzette with $c = \tt{0xb7e15162}$, XTEA, the $\tt{quarterround}$ function of Salsa20, and the round function of Chaskey, we find some invalid DCs that Leurent’s ARX Toolkit cannot detect. Thirdly, we propose a SAT-based automatic search tool for impossible differential characteristics in ARX ciphers. We find some distinguishers ignored by previous methods. In applications, for CHAM-64/128, we find five $20$-round and nineteen $19$-round impossible differential characteristics starting from the $3$-rd round for the first time. However, if we search for impossible differential characteristics starting from the $1$-st round, we cannot find any $20$-round impossible differential characteristic, which means that the round constants can affect the security of ARX ciphers against impossible differential cryptanalysis. Moreover, we find more impossible differential characteristics for 18-round, 16-round, 14-round, and 12-round CHAM-64/128, respectively. According to our results, the differential (resp. impossible differential) attack constructed by the previous methods of placing a DC (resp. an ID) anywhere in a block cipher may be invalid