Cryptography during the French and American Wars in Vietnam

After Vietnam’s Declaration of Independence on 2 September 1945, the country had to suffer through two long, brutal wars, first against the French and then against the Americans, before finally becoming a unified country free of colonial domination in 1975. The authors’ purpose is to examine the role of cryptography in those two wars. Despite the far greater technological resources of their opponents, the communications intelligence specialists of the Vi?t Minh, the National Liberation Front, and the Democratic Republic of Vietnam had considerable success in both protecting Vietnamese communications and acquiring tactical and strategic secrets from the enemy. Perhaps surprisingly, in both wars there was a balance between the sides. Generally speaking, cryptographic knowledge and protocol design were at a high level at the central commands, but deployment for tactical communications in the field was difficult, and there were many failures on all sides.     

On the problem of optimal estimation of balanced and symmetric three-phase signals

This paper revisits the fundamental problem of optimal estimation of the magnitude and phase of balanced and symmetric three-phase voltage or current signals. We analyze and compare various setups for the corresponding optimal Kalman filter, including the direct use of three-phase measurements, as well as measurements subjected to the Clarke transform in real or complex form. One contribution is to show that the standard practice of disregarding the transformed zero-component of the Clarke transformed three-phase signal almost always leads to a sub-optimal performance of the Kalman estimator. Our analysis extends to show that the closely related complex Kalman estimator is also sub-optimal and that optimal performance can be recovered if the zero-component is made available to the filter provided that the noises are properly characterized. These results are illustrated by means of simple numerical examples, which also highlight the importance of correctly modeling the noise characteristics if a real or complex form of the Clarke transformation is to be used. We conclude the paper with a unified set of guidelines or best practices regarding the use of optimal Kalman estimators for balanced and symmetric three-phase signals.     

An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism

Alcoholism affects the structure and functioning of brain. Electroencephalogram (EEG) signals can depict the state of brain. The EEG signals are ensemble of various neuronal activity recorded from different scalp regions having different characteristics and very low magnitude in microvolts. These factors make human interpretation difficult and time consuming to analyze these signals. Moreover, these highly varying EEG signals are susceptible to inter/intra variability errors. So, a Computer-Aided Diagnosis (CAD) can be used to identify the alcoholic and normal subjects accurately. However, these EEG signals exhibit nonlinear and non-stationary properties. Therefore, it needs much effort in deciphering the diagnostic evidence from them using linear time and frequency-domain methods. The nonlinear parameters together with time-frequency/scale domain methods can help to detect tiny changes in these signals. The correntropy is nonlinear indicator which characterizes the dynamic behavior of EEG signals in time-scale domain. In this paper, we present a new way for diagnosis of alcoholism using Tunable-Q Wavelet Transform (TQWT) based features derived from EEG signals. The feature extraction is performed using TQWT based decomposition and extracted Centered Correntropy (CC) from the forth decomposed detail sub-band. The Principal Component Analysis (PCA) is used for feature reduction followed by Least Squares-Support Vector Machine (LS-SVM) for classifying normal and alcoholic EEG signals. In order to make sure reliable classification performance, 10-fold cross-validation scheme is adopted. Our proposed system is able to diagnose the alcoholic and normal EEG signals, with an average accuracy of 97.02%, sensitivity of 96.53%, specificity of 97.50% and Matthews correlation coefficient of 0.9494 for Q-factor (Q) varying between 3 and 8 using Radial Basis Function (RBF) kernel function. Also, we have established a novel Alcoholism Risk Index (ARI) using three clinically significant features to discriminate the given classes by means of a single number. This system can be used for automated diagnosis and monitoring of alcoholic subjects to evaluate the effect of treatment.     

Spurious caustics of dispersion-relation-preserving schemes

A linear dispersive mechanism leading to a burst in the L _∞ norm of the error in numerical simulation of polychromatic solutions is identified. This local error pile-up corresponds to the existence of spurious caustics, which are allowed by the dispersive nature of the numerical error. From the mathematical point of view, spurious caustics are related to extrema of the numerical group velocity and are physically associated with interactions between rays defined by the characteristic lines of the discrete system. This paper extends our previous work about classical schemes to dispersion-relation preserving schemes.     

Discriminant analysis of multivariate time series: Application to diagnosis based on ECG signals

In analysing ECG data, the main aim is to differentiate between the signal patterns of healthy subjects and those of individuals with specific heart conditions. We propose an approach for classifying multivariate ECG signals based on discriminant and wavelet analyses. For this purpose we use multiple-scale wavelet variances and wavelet correlations to distinguish between the patterns of multivariate ECG signals based on the variability of the individual components of each ECG signal and on the relationships between every pair of these components. Using the results of other ECG classification studies in the literature as references, we demonstrate that our approach applied to 12-lead ECG signals from a particular database compares favourably. We also demonstrate with real and synthetic ECG data that our approach to classifying multivariate time series out-performs other well-known approaches for classifying multivariate time series.     

The influence of pedestrian countdown signals on children’s crossing behavior at school intersections

Previous studies have shown that pedestrian countdown signals had different influences on pedestrian crossing behavior. The purpose of this study was to examine the effects of the installation of countdown signals at school intersections on children’s crossing behavior. A comparison analysis was carried out on the basis of observations at two different school intersections with or without pedestrian countdown signals in the city of Jinan, China. Four types of children’s crossing behavior and child pedestrian-vehicle conflicts were analyzed in detail. The analysis results showed that using pedestrian countdown timers during the Red Man phase led to more children’s violation and running behavior. Theses violators created more conflicts with vehicles. However, pedestrian countdown signals were effective at helping child pedestrian to complete crossing before the red light onset, avoid getting caught in the middle of crosswalk. No significant difference was found in children who started crossing during Flashing Green Man phase between the two types of pedestrian signals. Moreover, analysis results indicated that children who crossed the road alone had more violation and adventure crossing behavior than those had companions. Boys were found more likely to run crossing than girls, but there was no significant gender difference in other crossing behavior. Finally, it’s recommended to remove countdown at the end of the Red Man phase to improve children’s crossing behavior and reduce the conflicts with vehicles. Meanwhile other measures are proposed to improve children safety at school intersections.     

Generalized neural network and wavelet transform based approach for fault location estimation of a transmission line

To maintain the efficient and reliable operation of power systems, it is extremely important that the transmission line faults need to be detected and located in a reliable and accurate manner. A number of mathematical and intelligent techniques are available in the literature for estimating the fault location. However, the results are not satisfactory due to the wide variation in operating conditions such as system loading level, fault inception instance, fault resistance and dc offset and harmonics contents in the transient signal of the faulted transmission line. Keeping in view of aforesaid, a new approach based on generalized neural network (GNN) with wavelet transform is presented for fault location estimation. Wavelet transform is used to extract the features of faulty current signals in terms of standard deviation. Obtained features are used as an input to the GNN model for estimating the location of fault in a given transmission systems. Results obtained from GNN model are compared with ANN and well established mathematical models and found more accurate.     

Reduction of quantization noise via periodic code for oversampled input signals and the corresponding optimal code design

This paper proposes to reduce the quantization noise using a periodic code, derives a condition for achieving an improvement on the signal to noise ratio (SNR) performance, and proposes an optimal design for the periodic code. To reduce the quantization noise, oversampled input signals are first multiplied by the periodic code and then quantized via a quantizer. The signals are reconstructed via multiplying the quantized signals by the same periodic code and then passing through an ideal lowpass filter. To derive the condition for achieving an improvement on the SNR performance, first the quantization operator is modeled by a deterministic polynomial function. The coefficients in the polynomial function are defined in such a way that the total energy difference between the quantization function and the polynomial function is minimized subject to a specification on the upper bound of the absolute difference. This problem is actually a semi-infinite programming problem and our recently proposed dual parameterization method is employed for finding the globally optimal solution. Second, the condition for improving the SNR performance is derived via a frequency domain formulation. To optimally design the periodic code such that the SNR performance is maximized, a modified gradient descent method that can avoid the obtained solution to be trapped in a locally optimal point and guarantee its convergence is proposed. Computer numerical simulation results show that the proposed system could achieve a significant improvement compared to existing systems such as the conventional system without multiplying to the periodic code, the system with an additive dithering and a first order sigma delta modulator.