AvDR-Based Wireless Secure Key Generation with Colored Noise for IoT

2019 ◽  
Vol 19 (01) ◽  
pp. 2050013
Author(s):  
Ankit Soni ◽  
Raksha Upadhyay ◽  
Abhay Kumar
Keyword(s):  
Dimensionality Reduction ◽  
System Performance ◽  
Colored Noise ◽  
Additive White Gaussian Noise ◽  
Principal Component ◽  
Research Area ◽  
Wireless Channel ◽  
Resource Constraint ◽  
Key Generation ◽  
Correlated Components

Physical layer key generation exploiting inherent channel randomness is an open research area in securing the networks with resource constraint nodes; therefore reduction of numerical computation is desirable to save battery power. However, the correlated components due to colored noise also affect the system performance. In this work, we consider the correlated colored noise components along with the additive white Gaussian noise (AWGN) in the wireless channel and analyze the effect of these correlated components on the system performance. We further propose a hybrid averaging and dimensionality reduction (AvDR), based received signal strength (RSS) preprocessing which is the combination of moving window averaging (Av) and principal component analysis (PCA) as dimensionality reduction technique (DR) to improve the system performance. Further, the system performance was evaluated by numerical simulations, and it is observed that the same improvement in system performance is achieved by generating keys from a fewer number of points selected after PCA as compared to processing all the points. Picking a few of the points in the data sequence instead of all reduces the total number of numerical calculations and saves system power, which is the primary requirement of resource constraint networks like the IoT.

Principal component analysis in the linear theory of vibrations: Continuous mechanical systems driven by different kinds of external noise

2020 ◽  
pp. 095440622093294
Author(s):  
J Awrejcewicz ◽  
VA Krysko ◽  
SA Mitskievich ◽  
IE Kutepov ◽  
IV Papkova ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Colored Noise ◽  
Wavelet Packet ◽  
Additive White Gaussian Noise ◽  
Mechanical Systems ◽  
Principal Component ◽  
Component Analysis ◽  
External Noise ◽  
Spherical Shells ◽  
First Time

In this study, an analysis of mechanical vibrations influenced by external additive white Gaussian noise and colored noise is conducted using the principal component analysis. The principal component analysis is widely employed for encoding images in image processing, biology, economics, sociology, and political science. However, it is hereby applied to analyze nonlinear dynamics of continuous mechanical systems for the first time. A rich class of objects, including straight beams, beams on Winkler foundations and spherical shells, is investigated in the present paper. The basic differential equations are obtained based on the Bernoulli–Euler hypothesis, and solutions of the linear PDEs are analyzed by means of the principal component analysis. Results obtained with the principal component analysis are compared with those for the method of empirical modal decomposition and the wavelet-packet decomposition.

scholarly journals Intelligent Non-Contact Sensing for Connected Health Using Software Defined Radio Technology

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1558
Author(s):  
Muhammad Bilal Khan ◽  
Mubashir Rehman ◽  
Ali Mustafa ◽  
Raza Ali Shah ◽  
Xiaodong Yang
Keyword(s):  
Psychological Health ◽  
Software Defined Radio ◽  
Orthogonal Frequency Division Multiplexing ◽  
Additive White Gaussian Noise ◽  
Vital Signs ◽  
Wireless Channel ◽  
Machine Learning Algorithms ◽  
Breathing Patterns ◽  
Connected Health ◽  
Innovative Solution

The unpredictable situation from the Coronavirus (COVID-19) globally and the severity of the third wave has resulted in the entire world being quarantined from one another again. Self-quarantine is the only existing solution to stop the spread of the virus when vaccination is under trials. Due to COVID-19, individuals may have difficulties in breathing and may experience cognitive impairment, which results in physical and psychological health issues. Healthcare professionals are doing their best to treat the patients at risk to their health. It is important to develop innovative solutions to provide non-contact and remote assistance to reduce the spread of the virus and to provide better care to patients. In addition, such assistance is important for elderly and those that are already sick in order to provide timely medical assistance and to reduce false alarm/visits to the hospitals. This research aims to provide an innovative solution by remotely monitoring vital signs such as breathing and other connected health during the quarantine. We develop an innovative solution for connected health using software-defined radio (SDR) technology and artificial intelligence (AI). The channel frequency response (CFR) is used to extract the fine-grained wireless channel state information (WCSI) by using the multi-carrier orthogonal frequency division multiplexing (OFDM) technique. The design was validated by simulated channels by analyzing CFR for ideal, additive white gaussian noise (AWGN), fading, and dispersive channels. Finally, various breathing experiments are conducted and the results are illustrated as having classification accuracy of 99.3% for four different breathing patterns using machine learning algorithms. This platform allows medical professionals and caretakers to remotely monitor individuals in a non-contact manner. The developed platform is suitable for both COVID-19 and non-COVID-19 scenarios.

scholarly journals A model for spectroscopic food sample analysis using data sonification

2021 ◽  
Author(s):  
Hsein Kew
Keyword(s):  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Reduction Method ◽  
Real Life ◽  
Principal Component ◽  
Relevant Information ◽  
Analysis Model ◽  
Linear Discriminant ◽  
Audio Output ◽  
Dimensionality Reduction Method

AbstractIn this paper, we propose a method to generate an audio output based on spectroscopy data in order to discriminate two classes of data, based on the features of our spectral dataset. To do this, we first perform spectral pre-processing, and then extract features, followed by machine learning, for dimensionality reduction. The features are then mapped to the parameters of a sound synthesiser, as part of the audio processing, so as to generate audio samples in order to compute statistical results and identify important descriptors for the classification of the dataset. To optimise the process, we compare Amplitude Modulation (AM) and Frequency Modulation (FM) synthesis, as applied to two real-life datasets to evaluate the performance of sonification as a method for discriminating data. FM synthesis provides a higher subjective classification accuracy as compared with to AM synthesis. We then further compare the dimensionality reduction method of Principal Component Analysis (PCA) and Linear Discriminant Analysis in order to optimise our sonification algorithm. The results of classification accuracy using FM synthesis as the sound synthesiser and PCA as the dimensionality reduction method yields a mean classification accuracies of 93.81% and 88.57% for the coffee dataset and the fruit puree dataset respectively, and indicate that this spectroscopic analysis model is able to provide relevant information on the spectral data, and most importantly, is able to discriminate accurately between the two spectra and thus provides a complementary tool to supplement current methods.

Simulation modeling of free space optical communication system

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ajay Sharma ◽  
Rajinder Singh Kaler
Keyword(s):  
Optical Communication ◽  
Free Space ◽  
Simulation Modeling ◽  
Additive White Gaussian Noise ◽  
Wireless Channel ◽  
Free Space Optical Communication ◽  
Optical Wireless ◽  
Free Space Optical ◽  
Wireless Signal ◽  
Space Optical Communication

Abstract The optical wireless communication has been designed by developing a model with the support of MATLAB simulator using Simulink where channel considered to be a free space. In this model, Additive White Gaussian Noise (AWGN) channel has used to analyze bit error rate (BER) and power loss of optical wireless signal at receiver. The consequence due to turbulence in atmosphere of free space on transmitted signal has examined. The BER and signal power have extremely ruined on rigorous atmospheric unstable condition even for a short distance in an optical wireless channel. The BER of less than 10−3 has been achieved for free space optical communication considered to be an excellent BER for FSO.

scholarly journals A Strategy for Dimensionality Reduction and Data Analysis Applied to Microstructure–Property Relationships of Nanoporous Metals

Materials ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 1822
Author(s):  
Norbert Huber
Keyword(s):  
Machine Learning ◽  
Dimensionality Reduction ◽  
Work Hardening ◽  
Large Range ◽  
Scaling Law ◽  
Principal Component ◽  
Underlying Structure ◽  
Structure Property ◽  
Work Hardening Rate ◽  
Nanoporous Metals

Nanoporous metals, with their complex microstructure, represent an ideal candidate for the development of methods that combine physics, data, and machine learning. The preparation of nanporous metals via dealloying allows for tuning of the microstructure and macroscopic mechanical properties within a large design space, dependent on the chosen dealloying conditions. Specifically, it is possible to define the solid fraction, ligament size, and connectivity density within a large range. These microstructural parameters have a large impact on the macroscopic mechanical behavior. This makes this class of materials an ideal science case for the development of strategies for dimensionality reduction, supporting the analysis and visualization of the underlying structure–property relationships. Efficient finite element beam modeling techniques were used to generate ~200 data sets for macroscopic compression and nanoindentation of open pore nanofoams. A strategy consisting of dimensional analysis, principal component analysis, and machine learning allowed for data mining of the microstructure–property relationships. It turned out that the scaling law of the work hardening rate has the same exponent as the Young’s modulus. Simple linear relationships are derived for the normalized work hardening rate and hardness. The hardness to yield stress ratio is not limited to 1, as commonly assumed for foams, but spreads over a large range of values from 0.5 to 3.

Measurement and Simulation of System Performance of Digital Broadcasting System

2013 ◽  
Vol 284-287 ◽  
pp. 2908-2912
Author(s):  
Hsien Wei Tseng ◽  
Wei Chien ◽  
Shih Nan Lu ◽  
Wei Chen Lee ◽  
Yih Guang Jan ◽  
...  
Keyword(s):  
System Performance ◽  
Simulation Analysis ◽  
Convolutional Code ◽  
Additive White Gaussian Noise ◽  
Viterbi Decoding ◽  
Modulation Formats ◽  
Reed Solomon Code ◽  
Broadcasting System ◽  
Digital Broadcasting ◽  
Transmitted Signal

In this paper, we use MATLAB software to build the physical layer transceiver of the Digital Video Broadcasting Terrestrial System (DVB-T) and Additive White Gaussian Noise (AWGN) is added into the transmitted signal during its transmission. The transmitted signal passes through modulation, demodulation, encoding and decoding processes the resulting demodulated signal is compared with the transmitted signal to calculate its Bit Error Rate (BER). Three modulation formats, QPAK, 16-QAM and 64-QAM are simulated and through various Signal to Noise (SNR) ratio to evaluate the system performance. Various encoding techniques such as Reed Solomon Code, Convolutional Code and Viterbi Decoding [1-6] have been implemented and through simulation to make detailed system performance analysis and comparison. detailed system performance simulation, analysis and comparisons.

Sign in / Sign up

Export Citation Format

Share Document