Simulating Nonlinear Dynamics of a 3D Crystal Lattice of Metals

Oscillations of crystal lattices determine important material properties such as thermal conductivity, heat capacity, thermal expansion, and many others; therefore, their study is an urgent and important problem. Along with experimental studies of the nonlinear dynamics of a crystal lattice, effective computer simulation techniques such as ab initio simulation and the molecular dynamics method are widely used. Mathematical simulation is less commonly used since the calculation error there can reach 10 %. Herewith, it is the least computationally intensive. This paper describes the process and results of mathematical simulation of the nonlinear dynamics of a 3D crystal lattice of metals using the Lennard-Jones potential in the MatLab software package, which is well-proven for solving technical computing problems. The following main results have been obtained: 3D distribution of atoms over the computational cell has been plotted, proving the possibility of displacement to up to five interatomic distances; the frequency response has been evaluated using the Welch method with a relative RMS error not exceeding 30 %; a graphical dependence between the model and the reference cohesive energy data for a metal HCP cell has been obtained with an error of slightly more than 3 %; an optimal model for piecewise-linear approximation has been calculated, and its 3D interpolation built. All studies performed show good applicability of mathematical simulation to the problems of studying dynamic processes in crystal physics.

Enhancing the quality of communication of cellular networks using big data applications

Both software-defined networking and big data have gained approval and preferences from both industry and academia. These two important realms have conventionally been addressed independently in wireless cellular networks. The discussion taken into consideration in this study was to analyze the wireless cellular technologies with the contrast of efficient and enhanced spectral densities at a reduced cost. To accomplish the goal of this study, Welch's method has been used as the core subject. With the aid of previous research and classical techniques, this study has identified that the spectral densities can be enhanced at reduced costs with the help of the power spectral estimation methods. The Welch method gives the result on power spectrum estimation. By reducing the effect of noise, the Welch method is used to calculate the power spectral density of a signal. When data length is increased, Welch's method is considered the best as a conclusion to this paper because excellent results are yielded by it in the area of power spectral density estimation.

Stress Detection using EEG Signal Based on Fast Walsh Hadamard transform and Voting Classifier

Mental stress is currently a significant concern, especially among the young. Stress adversely affects the overall performance of people’s work, and in certain cases, can even cause serious health issues. Everyone experiences stress in life. A unique way to identify and classify stress levels based on Electroencephalogram (EEG) is proposed in this manuscript. In this work, fast Walsh Hadamard transform is used to generate all frequencies which exist in the EEG signals. The range of alpha, beta, gamma, and delta from index value is calculated in subsequent stage. Principal component analysis (PCA) is applied for the feature dimensional reduction which is followed by the standard scaler. The PSD vector has been calculated for healthy and unhealthy EEG signal groups using the Welch method. The PSD vector is used an input to the voting classifier which is the combination of the k-NN and logistic regression classifier. The experimental results found that the proposed method provides better results when compared to the existing methods in terms of Accuracy (Acc) and Mean Square Error (MSE). The proposed method achieves a highest classification accuracy of 94.22%

An Automatic Classification Method of Sleep Apnea Events Based on EEG Frequency Sub-band Division

Sleep apnea is a kind of sleep disorder with a high prevalence rate. It is manifested as the abnormal stop of breathing during sleep and is highly dangerous to human health. The purpose of this research is to find a simple, and effective feature extraction method that can able to distinguish obstructive apnea events, central apnea events, and normal breathing events. Unlike conventional methods, the method illustrated in this study used the Infinite Impulse Response Butterworth Band pass filter to divide the Electroencephalogram (EEG) signal into 5, 7, 9 or 11 frequency sub-bands and then used the Welch method to extract the power features of these frequency sub-band signals, which were subsequently used as classifier input. Random forest, K-nearest neighbors and bagging classifiers were investigated. The results showed that in several different frequency sub-band division methods of EEG signals, the features extracted from the EEG signal that was divided into 11 frequency sub-bands were more conducive to the classification of sleep apnea events. The random forest classifier achieved the highest average accuracy, macro F1 and kappa coefficient in three types of events, which were 90.43%, 90.38% and 0.88, respectively. Compared with existing methods, the method used in the present study has higher classification performance.

Multiple Narrowband Interferences Characterization, Detection and Mitigation Using Simplified Welch Algorithm and Notch Filtering

By increasing the demand for radio frequency (RF) and access of hackers and spoofers to low price hardware and software defined radios (SDR), radio frequency interference (RFI) became a more frequent and serious problem. In order to increase the security of satellite communication (Satcom) and guarantee the quality of service (QoS) of end users, it is crucial to detect the RFI in the desired bandwidth and protect the receiver with a proper mitigation mechanism. Digital narrowband signals are so sensitive into the interference and because of their special power spectrum shape, it is hard to detect and eliminate the RFI from their bandwidth. Thus, a proper detector requires a high precision and smooth estimation of input signal power spectral density (PSD). By utilizing the presented power spectrum by the simplified Welch method, this article proposes a solid and effective algorithm that can find all necessary interference parameters in the frequency domain while targeting practical implantation for the embedded system with minimum complexity. The proposed detector can detect several multi narrowband interferences and estimate their center frequency, bandwidth, power, start, and end of each interference individually. To remove multiple interferences, a chain of several infinite impulse response (IIR) notch filters with multiplexers is proposed. To minimize damage to the original signal, the bandwidth of each notch is adjusted in a way that maximizes the received signal to noise ratio (SNR) by the receiver. Multiple carrier wave interferences (MCWI) is utilized as a jamming attack to the Digital Video Broadcasting-Satellite-Second Generation (DVB-S2) receiver and performance of a new detector and mitigation system is investigated and validated in both simulation and practical tests. Based on the obtained results, the proposed detector can detect a weak power interference down to −25 dB and track a hopping frequency interference with center frequency variation speed up to 3 kHz. Bit error ratio (BER) performance shows 3 dB improvement by utilizing new adaptive mitigation scenario compared to non-adaptive one. Finally, the protected DVB-S2 can receive the data with SNR close to the normal situation while it is under the attack of the MCWI jammer.

Power Spectral Density Computation and Dominant Frequencies Identification from the Vibration Sensor Output under Random Vibration Environment

The objective of the modal and spectral analysis is to determine the vibration characteristics of structures such as natural frequencies, dominant frequencies and mode shapes. Such modal and spectral analyses have major relevance to the study of the dynamic properties of the structures undergoing dynamic vibration. Methods for the estimation of the power spectral density and identification of the dominant frequencies from the sensor responses under random vibrating environment are presented in this paper. Periodogram using FFT, Welch Method and MUSIC algorithm are used to analyse the known frequency sinusoids with additive white noise and output of the vibration sensor mounted on the test object. The resultant spectra obtained using the methods and their corresponding errors with the reference spectrum are analysed. The Welch method is further studied with three different windows, namely, Hann, Hamming and Blackman-Harris and with three different overlapping criteria viz. 0%, 25% and 50%. The same algorithm and methodology were adopted and compared in two different platforms: Mathematical Model Simulation and Hardware-In-Loop-Simulation. It is observed from the results that Welch Method with 25% overlap used in combination either with Hann or Blackman-Harris window yields more accurate results, compared to other combinations. Also, 25% overlap provides better execution time trade-off compared to 50% overlap.