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Journal articles on the topic 'Signal energy spectrum'
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1
Fang, Yuan, Lixiang Li, Yixiao Li, Haipeng Peng, and Yixian Yang. "Low Energy Consumption Compressed Spectrum Sensing Based on Channel Energy Reconstruction in Cognitive Radio Network." Sensors 20, no. 5 (February 26, 2020): 1264. http://dx.doi.org/10.3390/s20051264.
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For wireless communication networks, cognitive radio (CR) can be used to obtain the available spectrum, and wideband compressed sensing plays a vital role in cognitive radio networks (CRNs). Using compressed sensing (CS), sampling and compression of the spectrum signal can be simultaneously achieved, and the original signal can be accurately recovered from the sampling data under sub-Nyquist rate. Using a set of wideband random filters to measure the channel energy, only the recovery of the channel energy is necessary, rather than that of all the original channel signals. Based on the semi-tensor product, this paper proposes a new model to achieve the energy compression and reconstruction of spectral signals, called semi-tensor product compressed spectrum sensing (STP-CSS), which is a generalization of traditional spectrum sensing. The experimental results show that STP-CSS can flexibly generate a low-dimensional sensing matrix for energy compression and parallel reconstruction of the signal. Compared with the existing methods, STP-CSS is proved to effectively reduce the calculation complexity of sensor nodes. Hence, the proposed model markedly improves the spectrum sensing speed of network nodes and saves storage space and energy consumption.
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Liu, Mingxin, Wei Xue, Peisong Jia, Sergey B. Makarov, and Beiming Li. "Research on Spectrum Optimization Technology for a Wireless Communication System." Symmetry 12, no. 1 (December 23, 2019): 34. http://dx.doi.org/10.3390/sym12010034.
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In this study, the principle of minimum spectral energy leakage is applied, and the mathematical model is also established by the general function through adding different constraints. To allow the target baseband signal to have a high-quality time-domain representation, it is assumed that the baseband signal is an even function. The time-domain waveform has symmetry about the y-axis, and the objective function is obtained by Fourier series approximation. The frequency-domain characteristics of the baseband signals are obtained by adding the energy limitation condition and the boundary restriction condition. Limit a point at the appropriate position of the main lobe of the normalized energy spectral density function, and at the same time, limit the appropriate point at the first side lobe. The changes of the points modified the whole characteristic of the frequency-domain. To more conveniently compare the characteristics of the signal under different constraints, according to the symmetry of the frequency-domain of the signal, the normalized energy spectrum main lobe energy ratio is defined as a parameter, and thereby the spectral performance of the signal is discriminated by the size of this parameter. Through comparative analysis, the signal with the frequency-domain restriction conditions added has a larger normalized energy spectrum main lobe energy ratio. With increasing roll-off factor n, the energy ratio of the main energy spectrum of the normalized spectrum increases accordingly, i.e., the energy leakage is effectively suppressed. The baseband signal can be considered more suitable as a modern wireless communication system and can be obtained by adding a suitable restriction condition and establishing a model with a general function.
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Hu, Wei Bing, Wei Hu, and Yu Zheng. "Wavelet Analysis in Damage Detection for Bridge Structure." Key Engineering Materials 417-418 (October 2009): 813–16. http://dx.doi.org/10.4028/www.scientific.net/kem.417-418.813.
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The damage of structure leads to variation of structural modal parameter，so the wavelet transform for damage detection is introduced in this paper for considering the variation. First, structural dynamic response signal on the basis of the vibration-based structural damage diagnosis methods is calculated by structural analysis in the paper, then, each of sub-signals is calculated according to wavelet analysis, also, the sub-signal energy spectrum of dynamic response signal and energy spectrum variation are known. By observing the difference of the sub-signal and the variation of the sub-signal energy spectrum, we can get the variation of structural modal parameter and the sub-signal energy spectrum due to the performance degradation of the whole structure and local variations of damage level and location ,so that this method can be used in on-line damage detection for bridge structure.
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Wang, Feng Li, and Hui Xing. "An Investigation of Tooth Wear Diagnosis of Gear Based on Process Systems Modeling." Advanced Materials Research 549 (July 2012): 834–38. http://dx.doi.org/10.4028/www.scientific.net/amr.549.834.
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Targeting the advantages of local wave analysis(LWA) and the characteristics of gear fault vibration signals, LWA is introduced into gear fault diagnosis. The concept of the instantaneous energy in time- frequency analysis, based on local wave time-frequency spectrum, was used to measure the energy distribution of the signal in time-frequency domain. Furthermore, when tooth wear occurs in gear, the energy of the gear vibration signal would change correspondingly, whilst local wave time-frequency spectrum can exactly provide the instantaneous energy distribution of the signal with the change of the time and frequency. Thus, the fault information of the gear vibration signal can be extracted effectively from the local wave time-frequency spectrum. The analysis results from the experimental signals show that local wave time-frequency analysis could extract the characteristics information of the gear fault vibration signal effectively.
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Et.al, Tae-Yun Jung. "Spectrum Sensing Based On Deep Learning To Increase Spectrum Utilization." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 10, 2021): 538–43. http://dx.doi.org/10.17762/turcomat.v12i6.1971.
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This paper proposes a new spectrum sensing technique for cognitive radio systems. To determine vacancy of the spectrum, the proposed method employs the recurrent neural network (RNN), one of the popular deep learning techniques. The proposed technique determines the spectrum occupancy of the primary user (PU) by observing the received signal’s energy and any information on the PU signal characteristic is not used. To this end, the received signal’s spectrum is obtained by fast Fourier transform (FFT). This process is performed on consecutive received signals and the resulting spectrums are stacked. Finally, a 2-dimensional spectrum (or spectrogram) is made. This 2-D spectrum is cut into sensing channel bandwidths and inputted to the deep learning model to decide the channel’s occupancy. While the recently published spectrum sensing technique based on convolutional neural network (CNN) relies on an empty channel, the proposed technique does not require any empty channel. Only the channel signal of interest to sense is needed. Since spectrum sensing results is two (busy or idle), binary classification deep learning model is developed. According to the computer simulation results, the proposed method has similar performance with the conventional CNN-based method while the spectral efficiency of the proposed method is much higher than that of the existing scheme. In addition, the overall learnable parameters of the proposed deep learning model is only 2/3 of the existing method
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Strelkovskaya, Irina, Irina Solovskaya, and Anastasiya Makoganiuk. "A Study of the Extremum of the Total Energy of the Selective Signals Constructed by Quadratic Splines." Periodica Polytechnica Electrical Engineering and Computer Science 63, no. 1 (December 20, 2018): 30–36. http://dx.doi.org/10.3311/ppee.12457.
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The development of mobile communication networks in the direction of 5G networks implies the use of the radio interface that is based on new signal-code structures, the choice of which will determine their further development and the ability of operators to provide innovative services. The use of quadratic splines for the synthesis of selective signals with a finite spectrum, free from intersymbol interference, is proposed. An analytical expression for the synthesized signal in the time and frequency domains is obtained. A study was made of the dependence of the total energy of a selective signal on the parameters of a quadratic spline, which is used to interpolate the spectral density in the transition region using the methods of differential calculus of functions of several variables. To study the extremum of the total energy of a selective signal, the parameters of the width of the transition area α was used and the coefficient of rounding the spectrum ρ, the variation of whose spectral density allowed us to establish the limits of the change in the total energy of the signal in question. Conducted studies allow us to synthesize the signal and formulate recommendations on how by changing the parameters of the signal, you can get a signal with the desired properties. This will allow to obtain the optimal waveform in accordance with the selected criteria, providing for the required energy performance of the signal in the radio interface of 5G networks.
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Xu, Hui Bin, and Kui Zhang. "The UWB Signals of Power Spectral Density." Advanced Materials Research 472-475 (February 2012): 2748–51. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.2748.
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System or the waveform is energy, or has the power value. Generally, periodic signal and random signal is power signal,while the determine nonperiodic signal is energy signal. For the energy signal,we can use the energy spectrum density to describe the signal on the energy unit bandwidth,the unit is the joule/Hertz.For the power signal,we can use the power spectral density to describe the signal on the energy unit bandwidth,the unit for w/Hertz.
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Li, Linna, and Yue You. "Time-frequency energy analysis of deepwater explosion shock wave signals based on HHT." MATEC Web of Conferences 336 (2021): 01017. http://dx.doi.org/10.1051/matecconf/202133601017.
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In order to study the time-frequency characteristics of shock wave signals under deep water explosion conditions, experiments are performed using water medium explosion containers to simulate different water depth conditions, and signal analysis is performed on the shock wave data obtained in the experiments. Traditional time-frequency analysis methods such as Fourier transform and wavelet transform have many limitations on deep-water explosion shock wave signal analysis, the HHT method is used to analyse the experimental data from the three-dimensional Hilbert spectrum, marginal spectrum and instantaneous energy spectrum. The results show that the time-frequency method can effectively extract the frequency components of the deep-water explosion load signal in different periods. It provides a reference for people to understand the time frequency characteristics of shock wave signals in deep water.
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Liu, Yao Lin, Feng Han, Zhen Liu, and Min Chen Zhai. "Analysis of Energy Loss-Gain Error in Discrete Fourier Transform." Applied Mechanics and Materials 568-570 (June 2014): 172–75. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.172.
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In asynchronous sampling, discrete Fourier transform (DFT) spectrum involves errors. Scholars have done great investigations on the correction techniques of DFT spectrum, but the errors have not been completely eliminated all along. In this paper, spectrums were examined from the principle of conservation of energy. It is unnoticed before that the energy of the digital signal, which is the analysis object of DFT, isn't equal to that of the finite continuous signal truncated by rectangular window. Thus the energy of their spectrums are different according to the Parseval's theorem. The Energy Loss-Gain (ELG) error was introduced to express the energy difference between these two spectrums. The ELG error is zero if the observed continuous signal is truncated in integral multiple of half cycle and it is related to the cycle number and sampling number in one cycle. Analysis show that the ELG error decreases with the increment of these two parameters, which are helpful to the engineering.
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Zhai, Juan, Jiehao Chen, Hongjian Lin, Jinge Zhou, and Jinchu Huang. "Development of a Nuclear Energy Spectrum Signal Generator." Journal of Physics: Conference Series 1739 (January 2021): 012017. http://dx.doi.org/10.1088/1742-6596/1739/1/012017.
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Liu, Fu Lai, Shou Ming Guo, and Rui Yan Du. "An Effective Spectrum Sensing Method Based on Correlation Coefficient and Energy Detection." Advanced Materials Research 1023 (August 2014): 210–13. http://dx.doi.org/10.4028/www.scientific.net/amr.1023.210.
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Spectrum sensing is the key functionality for dynamic spectrum access in cognitive radio networks. Energy detection is one of the most popular spectrum sensing methods due to its low complexity and easy implementation. However, performance of the energy detector is susceptible to uncertainty in noise power. To overcome this problem, this paper proposes an effective spectrum sensing method based on correlation coefficient. The proposed method utilizes a single receiving antenna with a delay device to acquire the original received signal and the delayed signal. Then the correlation coefficient of the two signals is computed and the result is used as the test statistic. Theoretical analysis shows that the decision threshold is unrelated to noise power, thus the proposed approach can effectively overcome the influence of noise power uncertainty. Simulation results testify the effectiveness of the proposed method even in low signal-to-noise (SNR) conditions.
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Li, Han, Yanzhu Hu, and Song Wang. "Signal Detection Based on Power-Spectrum Sub-Band Energy Ratio." Electronics 10, no. 1 (December 31, 2020): 64. http://dx.doi.org/10.3390/electronics10010064.
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The power-spectrum sub-band energy ratio (PSER) has been applied in a variety of fields, but reports on its statistical properties and application in signal detection have been limited. Therefore, the statistical characteristics of the PSER were investigated and a signal detection method based on the PSER was created in this paper. By analyzing the probability and independence of power spectrum bins, as well as the relationship between F and beta distributions, we developed a probability distribution for the PSER. Our results showed that in a case of pure noise, the PSER follows beta distribution. In addition, the probability density function exhibited no relationship with the noise variance—only with the number of bins in the power spectrum. When Gaussian white noise was mixed with the signal, the resulting PSER followed a doubly non-central beta distribution. In this case, the probability density and cumulative distribution functions were represented by infinite double series. Under the constant false alarm strategy, we established a signal detector based on the PSER and derived the false alarm probability and detection probability of the PSER. The main advantage of this detector is that it did not need to estimate noise variance. Compared with time-domain energy detection and local spectral energy detection, we found that the PSER had better robustness under noise uncertainty. Finally, the results in the simulation and real signal showed that this detection method was valid.
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Lin, Shen Yung, C. K. Chang, C. T. Chung, F. C. Hsu, C. C. Wang, and Yuan Chuan Hsu. "Excited Energy Attenuation Study through Vibration Measurement and Spectrum Analysis." Key Engineering Materials 437 (May 2010): 482–86. http://dx.doi.org/10.4028/www.scientific.net/kem.437.482.
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This paper presents the use of vibration measurement in conjunction with spectrum signal analysis to investigate the vibration phenomena and the dynamic response of the absorber system frame and rigid axle of a vehicle body under real road-driving conditions. Ford 1.6L sedan was selected as the vehicle body to carry out the experiments with different driving speeds. B&K PULSE dynamic signal analyzer was used to detect the vibration signal induced from the absorber system frame and rigid axle of the vehicle body in motion. The acquired on-line signals are then processed through the Fast Fourier Transform using the power spectrum density, the cepstrum method, and the overall analysis. The vibration energy attenuated from the absorber system is analyzed by comparing with that on the rigid axle excited from the road conditions corresponding to different driving speeds. Furthermore, the effects of various road conditions and driving speeds on oscillation of the vehicle body are studied. The corresponding results may be extensively treated as a guiding reference of the absorber system design and manufacturing for those vehicle manufacturing companies.
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Satoh, T., K. Ishii, S. Matsuyama, H. Yamazaki, Ts Amartivan, A. Tanaka, S. Sugihara, et al. "INVESTIGATION ON THE INFLUENCE OF BACK SCATTERED PROTONS IN PIXE SPECTRUM." International Journal of PIXE 11, no. 01n02 (January 2001): 49–59. http://dx.doi.org/10.1142/s0129083501000086.
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The pre-amplifier with an active reset system was examined to carry out PIXE analysis under the condition that X-rays and back scattered protons are detected simultaneously. Rejection of false signals produced by back scattered protons is discussed, and it is confirmed that false signals can be completely rejected by using an inhibit signal of the preamplifier. Energy resolution of an X-ray detector and a live-time of measurement system were measured as a function of counting rate. As a result, energy resolution did not change and the live-time was 70 % at the high counting rate over 3kcps. The pile-up effect on the X-ray signal with the proton signal tail is also discussed.
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Tang, Guiji, and Tian Tian. "Compound Fault Diagnosis of Rolling Bearing Based on Singular Negentropy Difference Spectrum and Integrated Fast Spectral Correlation." Entropy 22, no. 3 (March 23, 2020): 367. http://dx.doi.org/10.3390/e22030367.
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Compound fault diagnosis is challenging due to the complexity, diversity and non-stationary characteristics of mechanical complex faults. In this paper, a novel compound fault separation method based on singular negentropy difference spectrum (SNDS) and integrated fast spectral correlation (IFSC) is proposed. Firstly, the original signal was de-noised by SNDS which improved the noise reduction effect of singular difference spectrum by introducing negative entropy. Secondly, the de-noised signal was analyzed by fast spectral correlation. Finally, IFSC took the fourth-order energy as the index to determine the resonance band and separate the fault features of different single fault. The proposed method is applied to analyze the simulated compound signals and the experimental vibration signals, the results show that the proposed method has excellent performance in the separation of rolling bearing composite faults.
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Nguyen, Tu Thanh, Khoa Le Dang, Thu Thi Hong Nguyen, and Phuong Huu Nguyen. "Spectrum sensing based on energy of unknown deterministic signals over fading channels." Science and Technology Development Journal 17, no. 1 (March 31, 2014): 17–31. http://dx.doi.org/10.32508/stdj.v17i1.1239.
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In cognitive radio network, how to minimize the impact of secondary user on primary user’s signal plays a very important and complex role. Therefore, spectrum sensing is one of the most essential components of cognitive radio. Therefore, the effect of spectrum sensing algorithms plays a key role to the system’s performance. In this paper, we concentrate on spectrum sensing algorithms in order to find out spectrum hole or while hole for reusing it. Specifically, we will highlight the energy detector algorithm of unknown deterministic signals over fading channels. The numerical results match well with theoretical analysis. The system’s performance of energy detection in AWGN channel is acceptable in case of relatively low signal to noise ratio (SNR). However, the performance of system will be degraded remarkable over fading environments.
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Phruksahiran, Narathep. "Amateur radio sensing technique using a combination of energy detection and waveform classification." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (February 1, 2022): 399. http://dx.doi.org/10.11591/ijece.v12i1.pp399-410.
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<p>A critical problem in spectrum sensing is to create a detection algorithm and test statistics. The existing approaches employ the energy level of each channel of interest. However, this feature cannot accurately characterize the actual application of public amateur radio. The transmitted signal is not continuous and may consist only of a carrier frequency without information. This paper proposes a novel energy detection and waveform feature classification (EDWC) algorithm to detect speech signals in public frequency bands based on energy detection and supervised machine learning. The energy level, descriptive statistics, and spectral measurements of radio channels are treated as feature vectors and classifiers to determine whether the signal is speech or noise. The algorithm is validated using actual frequency modulation (FM) broadcasting and public amateur signals. The proposed EDWC algorithm's performance is evaluated in terms of training duration, classification time, and receiver operating characteristic. The simulation and experimental outcomes show that the EDWC can distinguish and classify waveform characteristics for spectrum sensing purposes, particularly for the public amateur use case. The novel technical results can detect and classify public radio frequency signals as voice signals for speech communication or just noise, which is essential and can be applied in security aspects.</p>
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Tilinin, I. S., A. Jablonski, and S. Tougaard. "Emission-depth Dependence of the Signal Photoelectron Energy Spectrum." Surface and Interface Analysis 25, no. 2 (February 1997): 119–31. http://dx.doi.org/10.1002/(sici)1096-9918(199702)25:2<119::aid-sia209>3.0.co;2-y.
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Kedadouche, Mourad, Marc Thomas, and Antoine Tahan. "Monitoring Machines by Using a Hybrid Method Combining MED, EMD, and TKEO." Advances in Acoustics and Vibration 2014 (March 20, 2014): 1–10. http://dx.doi.org/10.1155/2014/592080.
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Amplitude demodulation is a key for diagnosing bearing faults. The quality of the demodulation determines the efficiency of the spectrum analysis in detecting the defect. A signal analysis technique based on minimum entropy deconvolution (MED), empirical mode decomposition (EMD), and Teager Kaiser energy operator (TKEO) is presented. The proposed method consists in enhancing the signal by using MED, decomposing the signal in intrinsic mode functions (IMFs) and selects only the IMF which presents the highest correlation coefficient with the original signal. In this study the first IMF1 was automatically selected, since it represents the contribution of high frequencies which are first excited at the early stages of degradation. After that, TKEO is used to track the modulation energy. The spectrum is applied to the instantaneous amplitude. Therefore, the character of the bearing faults can be recognized according to the envelope spectrum. The simulation and experimental results show that an envelope spectrum analysis based on MED-EMD and TKEO provides a reliable signal analysis tool. The experimental application has been developed on acoustic emission and vibration signals recorded for bearing fault detection.
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Bu, Yong Xia, Jian De Wu, Jun Ma, Yu Gang Fan, and Xiao Dong Wang. "Rolling Bearings Fault Diagnosis Based on Generalized Demodulation Time-Frequency Analysis Method." Advanced Materials Research 971-973 (June 2014): 701–4. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.701.
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In view of the characteristics of the non-stationary and multi-component AM-FM signals of vibration signals in the rolling element bearing, the generalized demodulation time-frequency analysis method is used for its fault diagnosis, overcoming the problem that the maximal overlap discrete wavelet packet transform (MODWPT) has no adaptability. First of all, the original vibration signal is took preprocessing by generalized Fourier; Then, using MODWPT to decompose signals after pretreatment and obtaining weights; Once again, the weights are carried out the inverse generalized Fourier transform to get the weights of the original signal; Finally, reconstructing principal component of the original signal to get the Hilbert instantaneous energy spectrum, roller bearing fault diagnoses based on the Hilbert instantaneous energy spectrum. The experimental results show that the method can effectively diagnose rolling bearing fault.
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Xu, Fu Ze, Xue Jun Li, Guang Bin Wang, and Da Lian Yang. "Research on the Imbalance-Crack Coupling Fault Diagnosis Based on Wavelet Packet and Energy Spectrum Analysis." Applied Mechanics and Materials 143-144 (December 2011): 675–79. http://dx.doi.org/10.4028/www.scientific.net/amm.143-144.675.
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It is common for the imbalance-crack coupling fault in rotating machinery, while the crack information is often overshadowed by unbalanced fault information, which is difficult to extract the crack signal. In order to extract the crack signal of the imbalance-crack coupling fault, and realize the fault diagnosis, the paper mainly analyzes its mechanical properties, and then use wavelet packet to de-nosing, decomposing and reconstructing the acquisition of vibration acceleration signal, and then analyzing the characteristics of frequency domain of the fault signal by using the energy spectrum. So the experiment proved that analyze and dispose the acquisition of the fault signal by using the method of the energy spectrum and the wavelet packet, which can effectively distinguish between the crack signal and unbalanced signals in imbalance-crack coupling faults .It also can provide some reference for the diagnosis and prevention for such fault.
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Turunen, Vesa, Marko Kosunen, Sami Kallioinen, Aarno Pärssinen, and Jussi Ryynänen. "The effects of non-linearity in spectrum sensing receivers." International Journal of Microwave and Wireless Technologies 8, no. 7 (July 15, 2015): 995–1003. http://dx.doi.org/10.1017/s1759078715001130.
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This paper analyses the effects of receiver non-linearity on the performance of the most commonly utilized signal detectors in cognitive radio systems. The analysis covers both self-modulation products of a single orthogonal frequency-division multiplexing (OFDM) signal and intermodulation(IM)products of two OFDM signals, and also their contribution to the probability of false detections. As a result, this work presents the linearity requirements for the spectrum sensor receiver front-end as a function of the sensitivity of the signal detector. Furthermore, we show that the cyclostationary feature detectors are more robust than the energy detectors against IM products of multiple interferers. Theoretical results are verified in measurements with a cyclostationary feature detector using digital video broadcasting – terrestrial (DVB-T) signals as an example.
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Lagutkin, V. N. "ESTIMATION OF SHAPE PARAMETERS OF SPACE OBJECTS WITH USE OF WIDEBAND RADAR SIGNALS." Issues of radio electronics, no. 3 (March 20, 2018): 50–56. http://dx.doi.org/10.21778/2218-5453-2018-3-50-56.
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Theoretical questions of parametric descriptions of received broadband radar signals, in particular, signals with linear frequency modulation, and their processing for evaluation of form parameters of tracked space objects are considered. Analytical expressions for the received broadband radar signal at the output of the antenna system and its spectrum are obtained. Received expression take into account the spectral characteristics of polarization matrix of radio waves backscattering from space objects, as well as movement parameters of monitored objects. Address issues of physical diffraction theory methods for calculating spectral characteristics of radio waves backscattering from space objects. At considering of processing of received broadband signals from objects they are represented as the sum of component from individual «shining spots», then parameters of objects form are number of «shining spots», delays and complex amplitudes of their signals. It is shown that sufficient statistics at processing of received broadband signals to evaluate form parameters of space objects is the Fourier transform of product of complex conjugate of complex spectrum of envelope received signal and spectrum of complex envelope of sounding signal, modified in view of object movement. On the basis of computations of delay response functions for axisymmetric objects with circular kinked surfaces and sounding signals with linear frequency modulation the features of the task of evaluating the form parameters of space objects are shown and general requirements for parameters of sounding signals, for energy and a range of angles of observation are formulated.
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Hassan, Kazi Mahmudul, Md Ekramul Hamid, and Takayoshi Nakai. "An Improvement in Representation of Audio Signal in Time-Frequency Plane using EMD-2TEMD Based Approach." Rajshahi University Journal of Science and Engineering 44 (November 19, 2016): 141–50. http://dx.doi.org/10.3329/rujse.v44i0.30399.
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This study proposed an enhanced time-frequency representation of audio signal using EMD-2TEMD based approach. To analyze non-stationary signal like audio, timefrequency representation is an important aspect. In case of representing or analyzing such kind of signal in time-frequency-energy distribution, hilbert spectrum is a recent approach and popular way which has several advantages over other methods like STFT, WT etc. Hilbert-Huang Transform (HHT) is a prominent method consists of Empirical Mode Decomposition (EMD) and Hilbert Spectral Analysis (HSA). An enhanced method called Turning Tangent empirical mode decomposition (2T-EMD) has recently developed to overcome some limitations of classical EMD like cubic spline problems, sifting stopping condition etc. 2T-EMD based hilbert spectrum of audio signal encountered some issues due to the generation of too many IMFs in the process where EMD produces less. To mitigate those problems, a mutual implementation of 2T-EMD & classical EMD is proposed in this paper which enhances the representation of hilbert spectrum along with significant improvements in source separation result using Independent Subspace Analysis (ISA) based clustering in case of audio signals. This refinement of hilbert spectrum not only contributes to the future work of source separation problem but also many other applications in audio signal processing.
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Zhao, Jiang, Meng Shang, and Jing Tian. "Feature Extraction of the Small Leakage Diagnosis of Oil Pipeline Based on Acoustic Signal." Applied Mechanics and Materials 511-512 (February 2014): 389–92. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.389.
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This paper introduces the related theory of sound waves , we collect the data through the acoustic wave sensors of the pipeline fault diagnosis system platform, decompose the signals to five layers by Mallat algorithm and wavelet function db4, compare the normal waves and leakage acoustic signal spectrum, and then get the power spectrum estimation for the decomposed signal at each level, we can see the signals energy feature in different frequency band. Feature extraction method based on wavelet transform can make the category of signal characteristics fully displayed in the different resolution band, it has a good application prospect in the field of acoustic signal processing.
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Chen, Wantong, Hailong Wu, and Shiyu Ren. "CM-LSTM Based Spectrum Sensing." Sensors 22, no. 6 (March 16, 2022): 2286. http://dx.doi.org/10.3390/s22062286.
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This paper presents spectrum sensing as a classification problem, and uses a spectrum-sensing algorithm based on a signal covariance matrix and long short-term memory network (CM-LSTM). We jointly exploited the spatial cross-correlation of multiple signals received by the antenna array and the temporal autocorrelation of single signals; we used the long short-term memory network (LSTM), which is good at extracting temporal correlation features, as the classification model; we then input the covariance matrix of the signals received by the array into the LSTM classification model to achieve the fusion learning of spatial correlation features and temporal correlation features of the signals, thus significantly improving the performance of spectrum sensing. Simulation analysis shows that the CM-LSTM-based spectrum-sensing algorithm shows better performance compared with support vector machine (SVM), gradient boosting machine (GBM), random forest (RF), and energy detection (ED) algorithm-based spectrum-sensing algorithms for different signal-to-noise ratios (SNRs) and different numbers of secondary users (SUs). Among them, SVM is a classical machine-learning algorithm, GBM and RF are two integrated learning methods with better generalization capability, and ED is a classical, traditional, and spectrum-sensing algorithm.
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Bratkov, V. A., A. A. Sentsov, and V. B. Polyakov. "Using information processing algorithms that factor in the effect of secondary modulation of radar signals." Radio industry (Russia) 30, no. 2 (June 6, 2020): 42–48. http://dx.doi.org/10.21778/2413-9599-2020-30-2-42-48.
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The article provides an assessment of the impact the dynamic structures of power systems of aircraft systems have on the spectrum of the reflected radar signal. In the conditions of autonomous actions of the fighter, the tasks of long-range detection of airborne targets are assigned directly to the onboard radar station. It is necessary to resolve the contradictions associated with increasing the detection range without changing the energy indicators of the onboard radar station. This is possible by optimizing the processing of many spectral components of the signal reflected from the propeller and turboprop engine of airborne targets. The location of propeller reflections in the spectrum of the reflected signal is determined by the technical parameters of the power plant and its mode of operation. The percent of the total energy of the reflected signal spectrum outside the main spectral component is comparable with the energy reflected from the airframe of the aviation complex, most of which is the energy reflected from the rotating elements of power plants. Therefore, the development of an algorithm for detecting a signal with a complex spectral structure that maximizes the probability of detection under time and computational resources restrictions is a very relevant scientific task. The scientific novelty lies in the development of an algorithm for detecting a signal with a complex spectral structure and its detection characteristics taking the effect of secondary modulation into account as well as in the development of practical recommendations for optimizing algorithms for detecting airborne targets. Using the developed algorithm in the fighter’s pulse-Doppler radar station will increase the detection range of an air target against the background of interfering reflections from the water surface.
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Wang, Bao Ping, Zeng Cai Wang, and Yun Xia Li. "Application of Wavelet Packet Energy Spectrum in Coal-Rock Interface Recognition." Key Engineering Materials 474-476 (April 2011): 1103–6. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1103.
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The recognition of coal-rock interface in the top caving was investigated via the vibration signals of the tail beam of the hydraulic support. Wavelet packet transform was used to process the vibration signals. A newly feature based on wavelet packet energy spectrum was proposed to identify the coal-rock interface in top coal caving. The interface was determined by comparing the features in the two cases of coal dropping and rock dropping. The experimental results show that the energy proportion of vibration signal in high frequency bands when rock dropping is greater than that when coal dropping.
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Li, H., H. Q. Zheng, and L. W. Tang. "Hilbert-Huang Transform and Its Application in Gear Faults Diagnosis." Key Engineering Materials 291-292 (August 2005): 655–60. http://dx.doi.org/10.4028/www.scientific.net/kem.291-292.655.
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Time-frequency and transient analysis have been widely used in signal processing and faults diagnosis. These methods represent important characteristics of a signal in both time and frequency domain. In this way, essential features of the signal can be viewed and analyzed in order to understand or model the faults characteristics. Historically, Fourier spectral analyses have provided a general approach for monitoring the global energy/frequency distribution. However, an assumption inherent to this method is the stationary and linear of the signal. As a result, Fourier methods are not generally an appropriate approach in the investigation of faults signals with transient components. This work presents the application of a new signal processing technique, empirical mode decomposition and the Hilbert spectrum, in analysis of vibration signals and gear faults diagnosis for a machine tool. The results show that this method may provide not only an increase in the spectral resolution but also reliability for the gear faults diagnosis.
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Srinivasan, Avinash, Weiding Han, and Anjam Khursheed. "Secondary Electron Energy Contrast of Localized Buried Charge in Metal–Insulator–Silicon Structures." Microscopy and Microanalysis 24, no. 5 (October 2018): 453–60. http://dx.doi.org/10.1017/s1431927618015052.
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AbstractThis paper presents a new method for creating and monitoring controlled localized negatively charged regions inside insulators with a scanning electron microscope (SEM). A localized buried charged region is created and observed close to the point where a high voltage primary beam (10 kV) strikes a metal–insulator–silicon specimen. The amount of buried charge within the insulator at any given moment can be dynamically monitored by detecting the appearance of a second peak in the secondary electron (SE) energy spectrum. SE energy spectral signals were obtained through the use of a compact high signal-to-noise energy analyzer attachment that was fitted on to the SEM specimen stage. An electrostatic model, together with Monte Carlo simulations, is presented to explain how the SE charge contrast effect functions. This model is then experimentally confirmed by using the SE energy spectral signal induced by a gallium ion beam inside a dual focused ion beam-SEM instrument.
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Li, Han, Yanzhu Hu, and Song Wang. "A Novel Blind Signal Detector Based on the Entropy of the Power Spectrum Subband Energy Ratio." Entropy 23, no. 4 (April 11, 2021): 448. http://dx.doi.org/10.3390/e23040448.
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In this paper, we present a novel blind signal detector based on the entropy of the power spectrum subband energy ratio (PSER), the detection performance of which is significantly better than that of the classical energy detector. This detector is a full power spectrum detection method, and does not require the noise variance or prior information about the signal to be detected. According to the analysis of the statistical characteristics of the power spectrum subband energy ratio, this paper proposes concepts such as interval probability, interval entropy, sample entropy, joint interval entropy, PSER entropy, and sample entropy variance. Based on the multinomial distribution, in this paper the formulas for calculating the PSER entropy and the variance of sample entropy in the case of pure noise are derived. Based on the mixture multinomial distribution, the formulas for calculating the PSER entropy and the variance of sample entropy in the case of the signals mixed with noise are also derived. Under the constant false alarm strategy, the detector based on the entropy of the power spectrum subband energy ratio is derived. The experimental results for the primary signal detection are consistent with the theoretical calculation results, which proves that the detection method is correct.
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Tan, Yong, Ian Tay, Liang Loy, Ke Aw, Zhi Ong, and Sergei Manzhos. "A Scheme for Ultrasensitive Detection of Molecules with Vibrational Spectroscopy in Combination with Signal Processing." Molecules 24, no. 4 (February 21, 2019): 776. http://dx.doi.org/10.3390/molecules24040776.
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We show that combining vibrational spectroscopy with signal processing can result in a scheme for ultrasensitive detection of molecules. We consider the vibrational spectrum as a signal on the energy axis and apply a matched filter on that axis. On the example of a nerve agent molecule, we show that this allows detection of a molecule by its vibrational spectrum, even when the recorded spectrum is completely buried in noise when conventional spectroscopic detection is impossible. Detection is predicted to be possible with signal-to-noise ratios in the recorded spectra as low as 0.1. We have studied the importance of the spectral range used for detection as well as of the quality of the computed spectrum used to program the filter, specifically, the role of anharmonicity, of the exchange correlation functional, and of the basis set. The use of the full spectral range rather than of a narrow spectral window with key vibrations is shown to be advantageous, as well as accounting for anharmonicity.
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Venkatesh, Y. V., S. Kumar Raja, and G. Vidya Sagar. "On bandlimited signals with minimal product of effective spatial and spectral widths." International Journal of Mathematics and Mathematical Sciences 2005, no. 10 (2005): 1589–99. http://dx.doi.org/10.1155/ijmms.2005.1589.
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It is known that signals (which could be functions ofspaceortime) belonging to𝕃2-space cannot be localized simultaneously in space/time and frequency domains. Alternatively, signals have a positive lower bound on theproductof theireffective spatial andeffective spectral widths, for simplicity, hereafter called theeffective space-bandwidthproduct(ESBP). This is the classical uncertainty inequality (UI), attributed to many, but, from a signal processing perspective, to Gabor who, in his seminal paper, established the uncertainty relation and proposed a joint time-frequency representation in which the basis functions have minimal ESBP. It is found that the Gaussian function is the only signal that has thelowestESBP. Since the Gaussian function is not bandlimited, no bandlimited signal can have the lowest ESBP. We deal with the problem of determining finite-energy, bandlimited signals which have the lowest ESBP. The main result is as follows. By choosing the convolution product of a Gaussian signal (withσas the variance parameter) and a bandlimited filter with a continuous spectrum, we demonstrate that there exists a finite-energy, bandlimited signal whose ESBP can be made to be arbitrarily close (dependent on the choice ofσ) to the optimal value specified by the UI.
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Ma, Jun, Jiande Wu, and Xiaodong Wang. "A hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing." Journal of Low Frequency Noise, Vibration and Active Control 37, no. 4 (May 22, 2018): 928–54. http://dx.doi.org/10.1177/1461348418765973.
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Rolling bearing is one of the most crucial components in rotating machinery and due to their critical role, it is of great importance to monitor their operation conditions. However, due to the background noise in acquired signals, it is not always possible to identify probable faults. Therefore, signal denoising preprocessing has become an essential part of condition monitoring and fault diagnosis. In the present study, a hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing is proposed. First, as a denoising preprocessing method, singular value difference spectrum denoising is applied to reduce the noise of the bearing vibration signal and improve the signal-to-noise ratio. Then, local mean decomposition method is used to decompose the denoised signals into several product functions. And product functions corresponding to the fault feature are selected according to the correlation coefficient criterion. Finally, Teager energy spectrum is analyzed by applying the Teager energy operator to the constructed amplitude modulation component. The proposed method is successfully applied to analyze the vibration signals collected from an experimental motive rolling bearing and rolling bearing of the self-made rotor experimental platform. The experimental results demonstrate that the proposed singular value difference spectrum denoising and local mean decomposition method can achieve fairly or slightly better performance than the normal local mean decomposition-Teager energy operator method, fast kurtogram, and the wavelet denoising and local mean decomposition method.
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Jia, Wang, Mingjun Diao, Lei Jiang, and Guibing Huang. "Time-Frequency Characteristics of Fluctuating Pressure on the Bottom of the Stilling Basin with Step-Down Floor Based on Hilbert–Huang Transform." Mathematical Problems in Engineering 2021 (September 6, 2021): 1–12. http://dx.doi.org/10.1155/2021/7246488.
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Fluctuating pressure is an important feature of the bottom of a stilling basin with step-down floor. To analyze the frequency domain characteristics and energy distribution of this fluctuating pressure, the Hilbert–Huang transform (HHT) method is used. First, empirical mode decomposition is performed on the pressure fluctuation signal to obtain a number of intrinsic mode functions (IMFs), and then the Hilbert transformation is performed on each IMF to obtain the Hilbert spectrum and marginal spectrum for characterizing the pressure fluctuation signal. The results show that the fluctuating pressure signal of the stilling basin with step-down floor has obvious characteristics of low frequency and large amplitude. The dominant frequencies of the head and tail of the stilling basin are very prominent, and most of the energy is concentrated below 5.0 Hz; with the increase in the relative position of the measuring point, the energy distribution in stilling basin with step-down floor changes from high-frequency component to low-frequency component. The fluctuating pressure signal of the stilling basin with step-down floor has random amplitude modulation and frequency modulation. The marginal spectrum obtained by the HHT method can obtain the local characteristics of the signal more accurately and is more suitable for processing nonlinear and nonstationary signals.
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Xu, Yidong, Wei Xue, and Wenjing Shang. "A Pan-Function Model for the Utilization of Bandwidth Improvement and PAPR Reduction." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/658093.
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Aiming at the digital quadrature modulation system, a mathematical Pan-function model of the optimized baseband symbol signals with a symbol length of4Twas established in accordance with the minimum out-band energy radiation criterion. The intersymbol interference (ISI), symbol-correlated characteristics, and attenuation factor were introduced to establish the mathematical Pan-function model. The Pan-function was added to the constraints of boundary conditions, energy of a single baseband symbol signal, and constant-envelope conditions. Baseband symbol signals with the optimum efficient spectrum were obtained by introducing Fourier series and minimizing the Pan-function. The characteristics of the spectrum and peak-to-average power ratio (PAPR) of the obtained signals were analyzed and compared with the minimum shift keying (MSK) and quadrature phase-shift keying (QPSK) signals. The obtained signals have the characteristics of a higher spectral roll-off rate, less out-band radiation, and quasi-constant envelope. We simulated the performance of the obtained signals, and the simulation results demonstrate that the method is feasible.
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Mantravadi, Nagesh, Md Zia Ur Rahman, Sala Surekha, and Navarun Guptha. "Spectrum Sensing using Energy Measurement in Wireless Telemetry Networks using Logarithmic Adaptive Learning." ACTA IMEKO 11, no. 1 (March 31, 2022): 7. http://dx.doi.org/10.21014/acta_imeko.v11i1.1231.
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<p>To identify primary user signals in cognitive radios spectrum sensing method is used. Due to statistical variances in received signal, noise is present in primary user signals, this noise powers are varied due to random nature of noise signals and leads to noise uncertainty problem in the performance of energy detection. The task of energy measurement and further detecting the unused frequency spectrum is a key task in cognitive radio applications. For avoiding these problems, least logarithmic absolute difference (LLAD) algorithm is proposed in which noise powers are adjusted at sensing point of licensed users. With help of proposed method, estimated noise signals are eliminated. Sign regressor version of LLAD algorithm is considered due to it reduces computational complexity and convergence rate is improved. Further probability of detection (<em>P</em><sub>od</sub>), probability of false alarm (<em>P</em><sub>ofa</sub>) is estimated to know threshold value. From results, it is clear that good performance in terms of <em>P</em><sub>ofa</sub> versus <em>P</em><sub>od</sub> in range of low signal to noise ratio in multiple nodes. Therefore, the proposed energy measurement-based spectrum sensing method is useful in remote health care monitoring, medical telemetry applications by sharing the un-used spectrum.</p>
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Parshin, Alexander, and Yury Parshin. "Optimal signal processing algorithm at the background of non-Gaussian flicker noise." ITM Web of Conferences 30 (2019): 04016. http://dx.doi.org/10.1051/itmconf/20193004016.
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The problem of receiving and processing ultra-low-power signals of information transmission systems is being solved. High requirements for energy efficiency on the one hand and a low information transfer rate allows the use of signals with a small spectrum width, including flicker noise spectral regions. A non-Gaussian flicker noise model is used based on a stochastic differential equation with a nonlinear drift coefficient. An optimal signal processing algorithm is being developed against the background of the sum of flicker noise and thermal noise based on an estimated-correlation-compensation approach. The analysis of the effectiveness of optimal signal processing against a background of non-Gaussian flicker noise and thermal noise is carried out.
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Wang, Yan, Ji Fei Cai, Yang Zhang, Guang Li, and Rui Ming Fang. "Study on Acoustic Emission Detective Signal Restoration under Multifactor Interference." Applied Mechanics and Materials 329 (June 2013): 274–77. http://dx.doi.org/10.4028/www.scientific.net/amm.329.274.
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For solving signal restoration distortion affected by the signal spectrum composition changes and tested baseline drift, this papers methods mainly involve time-frequency energy analysis, wavelet soft threshold de-noising algorithm and baseline drift cancellation method. The result shows that these methods can effectively eliminate the interference of different micro-variable fault signals. The restored high-fidelity signals provide base for subsequent identification of fault reasons.
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Cai, Jianhua. "Feature extraction of rolling bearing fault signal based on local mean decomposition and Teager energy operator." Industrial Lubrication and Tribology 69, no. 6 (November 13, 2017): 872–80. http://dx.doi.org/10.1108/ilt-12-2015-0200.
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Purpose This paper aims to explore a new way to extract the fault feature of a rolling bearing signal on the basis of a combinatorial method. Design/methodology/approach By combining local mean decomposition (LMD) with Teager energy operator, a new feature-extraction method of a rolling bearing fault signal was proposed, called the LMD–Teager transform method. The principles and steps of method are presented, and the physical meaning of the time–frequency power spectrum and marginal spectrum is discussed. On the basis of comparison with the fast Fourier transform method, a simulated non-stationary signal was processed to verify the effect of the new method. Meanwhile, an analysis was conducted by using the recorded vibration signals which include inner race, out race and bearing ball fault signal. Findings The results show that the proposed method is more suitable for the non-stationary fault signal because the LMD–Teager transform method breaks through the difficulty of the Fourier transform method that can process only the stationary signal. The new method can extract more useful information and can provide better analysis accuracy and resolution compared with the traditional Fourier method. Originality/value Combining the advantage of the local mean decomposition and the Teager energy operator, the LMD–Teager method suits the nature of the fault signal. A marginal spectrum obtained from the LMD–Teager method minimizes the estimation bias brought about by the non-stationarity of the fault signal. So, the LMD–Teager transform has better analysis accuracy and resolution than the traditional Fourier method, which provides a good alternative for fault diagnosis of the rolling bearing.
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Liu, Wei. "Application of Hilbert-Huang Transform to Vibration Signal Analysis of Coal and Gangue." Applied Mechanics and Materials 40-41 (November 2010): 995–99. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.995.
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In this paper, a new method of vibration signal analysis of coal and gangue based on Hilbert-Huang transform is presented. Empirical mode decomposition algorithm was used to decompose the original vibration signal of coal and gangue into the intrinsic modes for further extract useful information contained in response signals under complicated environment. By analyzing local Hilbert marginal spectrum and local energy spectrum of the first four intrinsic mode function components, we found the difference of coal and rock in specific frequency interval that the amplitude and energy mainly distributed at frequency interval between 100Hz and 600Hz when coal was drawn, while the amplitude and energy were more concentrated at 1000Hz or so when gangue was drawn. Furthermore, the further analysis result from marginal spectrum of each intrinsic mode function component agreed well with the conclusion above. So the extracted features with the propose approach can be served as coal and gangue interface recognition.
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Lorincz, Josip, Ivana Ramljak, and Dinko Begušić. "Analysis of the Impact of Detection Threshold Adjustments and Noise Uncertainty on Energy Detection Performance in MIMO-OFDM Cognitive Radio Systems." Sensors 22, no. 2 (January 14, 2022): 631. http://dx.doi.org/10.3390/s22020631.
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Due to the capability of the effective usage of the radio frequency spectrum, a concept known as cognitive radio has undergone a broad exploitation in real implementations. Spectrum sensing as a core function of the cognitive radio enables secondary users to monitor the frequency band of primary users and its exploitation in periods of availability. In this work, the efficiency of spectrum sensing performed with the energy detection method realized through the square-law combining of the received signals at secondary users has been analyzed. Performance evaluation of the energy detection method was done for the wireless system in which signal transmission is based on Multiple-Input Multiple-Output—Orthogonal Frequency Division Multiplexing. Although such transmission brings different advantages to wireless communication systems, the impact of noise variations known as noise uncertainty and the inability of selecting an optimal signal level threshold for deciding upon the presence of the primary user signal can compromise the sensing precision of the energy detection method. Since the energy detection may be enhanced by dynamic detection threshold adjustments, this manuscript analyses the influence of detection threshold adjustments and noise uncertainty on the performance of the energy detection spectrum sensing method in single-cell cognitive radio systems. For the evaluation of an energy detection method based on the square-law combining technique, the mathematical expressions of the main performance parameters used for the assessment of spectrum sensing efficiency have been derived. The developed expressions were further assessed by executing the algorithm that enabled the simulation of the energy detection method based on the square-law combining technique in Multiple-Input Multiple-Output—Orthogonal Frequency Division Multiplexing cognitive radio systems. The obtained simulation results provide insights into how different levels of detection threshold adjustments and noise uncertainty affect the probability of detection of primary user signals. It is shown that higher signal-to-noise-ratios, the transmitting powers of primary user, the number of primary user transmitting and the secondary user receiving antennas, the number of sampling points and the false alarm probabilities improve detection probability. The presented analyses establish the basis for understanding the energy detection operation through the possibility of exploiting the different combinations of operating parameters which can contribute to the improvement of spectrum sensing efficiency of the energy detection method.
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Fu, Gang, Yue Feng, Guang Zhi Wu, and Li Ping Wang. "Research on Demodulation Algorithm of PCM/FM Signal." Applied Mechanics and Materials 532 (February 2014): 142–46. http://dx.doi.org/10.4028/www.scientific.net/amm.532.142.
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According to the characteristics of PCM/FM signal demodulation, the research based on short-time Fourier transform STFT software resolving method. First detected using STFT f1 and f0 corresponding spectrum energy, according to the comparison of two energy spectrum of element. Software demodulation system is discussed in detail the synchronous digital detection and software algorithms, analysis of demodulation performance of the algorithm. The simulation and test results verify the effectiveness of the method.
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Huang, Weilin, Runqiu Wang, Yimin Yuan, Shuwei Gan, and Yangkang Chen. "Signal extraction using randomized-order multichannel singular spectrum analysis." GEOPHYSICS 82, no. 2 (March 1, 2017): V69—V84. http://dx.doi.org/10.1190/geo2015-0708.1.
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Multichannel singular spectrum analysis (MSSA) is an effective algorithm for random noise attenuation; however, it cannot be used to suppress coherent noise. This limitation results from the fact that the conventional MSSA method cannot distinguish between useful signals and coherent noise in the singular spectrum. We have developed a randomization operator to disperse the energy of the coherent noise in the time-space domain. Furthermore, we have developed a novel algorithm for the extraction of useful signals, i.e., for simultaneous random and coherent noise attenuation, by introducing a randomization operator into the conventional MSSA algorithm. In this method, which we call randomized-order MSSA, the traces along the trajectory of each signal component are randomly rearranged. Two ways to extract the trajectories of different signal components are investigated. The first is based on picking the extrema of the upper envelopes, a method that is also constrained by local and global gradients. The second is based on dip scanning in local processing windows, also known as the Radon method. The proposed algorithm can be applied in 2D and 3D data sets to extract different coherent signal components or to attenuate ground roll and multiples. Different synthetic and field data examples demonstrate the successful performance of the proposed method.
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Wang, Bing Cheng, and Zhao Hui Ren. "Study on Fault Diagnosis of Rotating Machinery Based on Lyapunov Dimension and Exponent Energy Spectrum." Advanced Materials Research 591-593 (November 2012): 2042–45. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.2042.
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In connection with the nonlinear dynamic characteristics shown from the performance of fault rotating mechanical system, the authors propose the analysis method of Lyapunov dimension and exponent energy Spectrum to the signal feature of mechanical fault. Using theory of phase space reconstruction, simulating fault signal of rotating machine is reconstructed. In order to reconstruct the phase space which can be adequately reflect the movement characteristics of the system, the time delay and embedding dimension are discussed emphatically, on this basis, calculated the Lyapunov dimension and exponential energy spectrum. From the analysis and calculation on simulation of different fault signals, it shows that under different rotating machinery fault conditions, its Lyapunov dimension and exponential energy are significantly different, which verifies that this two nonlinear feature quantities is effective parameters for fault information
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Shehata, Nader, Ahmed H. Hassanin, Eman Elnabawy, Remya Nair, Sameer A. Bhat, and Ishac Kandas. "Acoustic Energy Harvesting and Sensing via Electrospun PVDF Nanofiber Membrane." Sensors 20, no. 11 (May 31, 2020): 3111. http://dx.doi.org/10.3390/s20113111.
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This paper introduces a new usage of piezoelectric poly (vinylidene fluoride) (PVDF) electrospun nanofiber (NF) membrane as a sensing unit for acoustic signals. In this work, an NF mat has been used as a transducer to convert acoustic signals into electric voltage outcomes. The detected voltage has been analyzed as a function of both frequency and amplitude of the excitation acoustic signal. Additionally, the detected AC signal can be retraced as a function of both frequency and amplitude with some wave distortion at relatively higher amplitudes and within a certain acoustic spectrum region. Meanwhile, the NFs have been characterized through piezoelectric responses, beta sheet calculations and surface morphology. This work is promising as a low-cost and innovative solution to harvest acoustic signals coming from wide resources of sound and noise.
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Falih, Muntaser S., and Hikmat N. Abdullah. "DWT Based Energy Detection Spectrum Sensing Method for Cognitive Radio System." Iraqi Journal of Information & Communications Technology 3, no. 3 (December 31, 2020): 1–11. http://dx.doi.org/10.31987/ijict.3.3.99.
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In this paper a new blind energy detection spectrum-sensing method based on Discreet Wavelet Transform (DWT) is proposed. The method utilizes the DWT sub-band to collects the received energy. The proposed method recognizes the Primary User (PU) signal from noise only signal using the differences in the collected energy in first and last sub-bands of one level DWT. The simulation results show that the proposed method achieves improved detection probability especially at low Signal to Noise Ratio (SNR) compared to Conventional Energy Detector (CED). The results also show that the proposed method has shorter sensing time and less Energy Consumption (EC) compared to CED due to using small number of processed sample. Therefore, this method is suitable for Cognitive Radio (CR) applications where only limited energy like device battery is available.
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A. Rahim, A., C. H. Chin, S. Abdullah, S. S. K. Singh, M. Z. Nuawi, and F. H. A. Hassan. "Characterization of Wavelet Decomposition Strain Signal Using the K-Mean Clustering Method." International Journal of Engineering & Technology 7, no. 3.17 (August 1, 2018): 158. http://dx.doi.org/10.14419/ijet.v7i3.17.16642.
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This paper aims to study the characterisation of time-frequency domain to analyse the fatigue strain signal due to weaknesses in time domain and frequency domain approaches. The objectives were to determine the behaviour of strain signal, characterise the fatigue life of strain signal and validate the fatigue life in time-frequency domain. The strain signal was obtained using data acquisition devices and strain gauges on two types of road condition including highway and industrial area. The acquired signals were analysed with time domain, frequency domain and time-frequency domain approaches. In time-frequency domain, the signals were decomposed using 4th Daubechies discrete wavelet transform. To validate the effectiveness of time-frequency approach in characterising vibration fatigue signal, fatigue data was clustered by mapping of the data based on the spectrum energy, root-mean-square and fatigue life obtained. The clustering was performed by comparing the centroid values which both data had five clusters as the optimum data clustering with 0.836 average distance to centroid. From this, the relationship between fatigue life, root-mean-square and spectrum energy can be determined and thus a new fatigue life criterion was developed.
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Lu, Jin Ming, Fan Lin Meng, Hua Shen, Li Bing Ding, and Su Nin Bao. "Gear Fault Diagnoise Based on Ensemble Empirical Mode Decomposition and Instantaneous Energy Density Spectrum." Applied Mechanics and Materials 142 (November 2011): 3–6. http://dx.doi.org/10.4028/www.scientific.net/amm.142.3.
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A very short impulse energy called ‘impulsion energy’ can be produced when the gear meshing with gear pitting fault and excited the resonance of the structure. The common techniques have inconvenience to deal with this vibration signal. A new fault diagnosis method based on EEMD and instantaneous energy density spectrum is proposed here. The IMFs generated by EEMD can alleviate the problem of mode mixing and approach the reality IMFs. The characteristic frequencies were found in the instantaneous energy density of Hilbert spectrum. The effectiveness of this method was demonstrated by analysis the vibration signals of a gear with pitting fault.
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Kolyadenko, Yu Yu, and N. А. Chursanov. "5 G communication network signal propagation models." Radiotekhnika, no. 205 (July 2, 2021): 161–68. http://dx.doi.org/10.30837/rt.2021.2.205.17.
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The next generation 5G / IMT-2020 technology, like any new technology, brings its own specific features to all aspects related to the practice of its application. One of these particularly important aspects is electromagnetic compatibility. At the stage of preparation for the introduction of 5G radio networks, called NewRadio, it is necessary to take early measures to assess effectively the electromagnetic compatibility conditions for these networks based on a thorough analysis of the features of 5G technology. Correct and accurate assessments of these conditions means successful provision of the electromagnetic compatibility of radio equipment of new networks.
The World Radio Communication Conference WRC-15 identified new radio frequency bands for 5G, including centimeter and millimeter wave bands. In general, this RF spectrum is located in three regions: below 1 GHz, 1 GHz to 6 GHz, and above 6 GHz (up to 100 GHz). From the EMC standpoint, the following can be distinguished as the main features of this spectrum: different nature of losses during signal propagation, in particular, a significant influence of additional factors (gases – oxygen, water vapor, etc.) on the level of losses previously unknown in cellular communication.
The mathematical model of signal propagation of 5 G communication networks has been developed which takes into account: the attenuation of signals in free space; attenuation of signals caused by the influence of walls and floor slabs, loss of signal energy, when space is filled with various objects; attenuation of signals caused by loss of energy of radio waves, when propagating through rains; signal attenuation due to loss of radio wave energy due to fog; signal attenuation, when propagating through tree leaves, slow and fast random fading.