Journal articles: 'Signal analysis'

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Gudiškis, Andrius. "HEART BEAT DETECTION IN NOISY ECG SIGNALS USING STATISTICAL ANALYSIS OF THE AUTOMATICALLY DETECTED ANNOTATIONS / ŠIRDIES DŪŽIŲ NUSTATYMAS IŠ IŠKRAIPYTŲ EKG SIGNALŲ ATLIEKANT AUTOMATIŠKAI APTIKTŲ ATSKAITŲ STATISTINĘ ANALIZĘ." Mokslas – Lietuvos ateitis 7, no. 3 (July 13, 2015): 300–303. http://dx.doi.org/10.3846/mla.2015.787.

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This paper proposes an algorithm to reduce the noise distortion influence in heartbeat annotation detection in electrocardiogram (ECG) signals. Boundary estimation module is based on energy detector. Heartbeat detection is usually performed by QRS detectors that are able to find QRS regions in a ECG signal that are a direct representation of a heartbeat. However, QRS performs as intended only in cases where ECG signals have high signal to noise ratio, when there are more noticeable signal distortion detectors accuracy decreases. Proposed algorithm uses additional data, taken from arterial blood pressure signal which was recorded in parallel to ECG signal, and uses it to support the QRS detection process in distorted signal areas. Proposed algorithm performs as well as classical QRS detectors in cases where signal to noise ratio is high, compared to the heartbeat annotations provided by experts. In signals with considerably lower signal to noise ratio proposed algorithm improved the detection accuracy to up to 6%. Širdies ritmas yra vienas svarbiausių ir daugiausia informacijos apie pacientų būklę teikiančių fiziologinių parametrų. Širdies ritmas nustatomas iš elektrokardiogramos (EKG), atliekant QRS regionų, kurie yra interpretuojami kaip širdies dūžio ãtskaitos, paiešką. QRS regionų aptikimas yra klasikinis uždavinys, nagrinėjamas jau keletą dešimtmečių, todėl širdies dūžių nustatymo iš EKG signalų metodų yra labai daug. Deja, šie metodai tikslūs ir patikimi tik esant dideliam signalo ir triukšmo santykiui. Kai EKG signalai labai iškraipomi, QRS aptiktuvai ne visada gali atskirti QRS regioną, o kartais jį randa ten, kur iš tikro jo būti neturėtų. Straipsnyje siūlomas algoritmas, kurį taikant sumažinama triukšmo įtaka nustatant iš EKG signalų QRS regionus. Tam naudojamas QRS aptiktuvas, kartu prognozuojantis širdies dūžio atskaitą. Remiamasi arterinio kraujo spaudimo signalo duomenimis, renkama atskaitų statistika ir atliekama jos analizė.

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Debbal, S. M. "Pathological Electromyogram (EMG) Signal Analysis Parameters." Clinical Cardiology and Cardiovascular Interventions 4, no. 13 (August 9, 2021): 01–14. http://dx.doi.org/10.31579/2641-0419/185.

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Clinical analysis of the electromyogram is a powerful tool for diagnosis of neuromuscular diseases. There fore, the detection and the analysis of electromyogram signals has he attracted much attention over the years. Several methods based on modern signal Processing techniques such as temporal analysis, spectro-temporel analysis ..., have been investigated for electromyogram signal treatment. However, many of these analysis methods are not highly successful due to their complexity and non-stationarity. The aim of this study is to analyse the EMGs signals using nonlinear analysis. This analysis can provide a wide range of information’s related to the type of signal (normal and pathological).

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Linh, Vuong Thuy, Nguyen Van Vu, and Le Ngoc Giang. "Voice Signal Quality Assessment Based on Signal Quality Standards and Analysis." International Journal of Research Publication and Reviews 4, no. 6 (June 2023): 958–63. http://dx.doi.org/10.55248/gengpi.4.623.44854.

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HORIHATA, SATOSHI, ZHONG ZHANG, TAKASHI IMAMURA, TETSUO MIYAKE, HIROSHI TODA, and YOSHIFUMI YASUDA. "BIOLOGICAL SIGNAL ANALYSIS BY INDEPENDENT COMPONENT ANALYSIS USING COMPLEX WAVELET TRANSFORM." International Journal of Wavelets, Multiresolution and Information Processing 08, no. 04 (July 2010): 595–608. http://dx.doi.org/10.1142/s0219691310003663.

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Independent component analysis (ICA) is a useful method for blind source separation of two or more signals. We have previously proposed a new method combining ICA with the complex discrete wavelet transform (CDWT), in which voice and noise signals were separated using a new method. At that time, we used a simulated signal. In this study, we analyze measured biological signals by using a new method, and discuss its effectiveness. As an experiment, we try to separate an electromyogram (EMG) signal from an electrocardiogram (ECG) signal.

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B. Raveendranadh Singh, B. Raveendranadh Singh, Kanchan Sanalkar, and D. Chenna Kesavaiah. "Electrocardiograph (ECG) Signal Analysis by Neural Networks." International Journal of Scientific Research 2, no. 7 (June 1, 2012): 191–95. http://dx.doi.org/10.15373/22778179/july2013/63.

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M, Sankar, Narendra Babu J, Swati S. Halunde, and Maduri B. Mulik. "Brain Signal Processing: Analysis, Technologies and Application." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12 (December 20, 2019): 69–74. http://dx.doi.org/10.5373/jardcs/v11i12/20193213.

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Mitraković, D., I. Grabec, and S. Sedmak. "Simulation of AE signals and signal analysis systems." Ultrasonics 23, no. 5 (September 1985): 227–32. http://dx.doi.org/10.1016/0041-624x(85)90018-6.

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Chandrasekaran, R., R. J. Hemalath, E. Anand Kumar, S. Murali, T. R. Thamizhvani, and Soumya Y.K. "Spectral analysis of polysomnography." International Journal of Engineering & Technology 7, no. 2.25 (May 3, 2018): 86. http://dx.doi.org/10.14419/ijet.v7i2.25.16565.

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The Polysomnography (PSG) is the most commonly used test in the diagnosis of OSAS – Obstructive Sleep Apnea Syndrome. PSG signals consist of simultaneous recording of multiple physiological parameters related to sleep and wakefulness. PSG is used to evaluate abnormalities of sleep and or wakefulness and other physiological disorders that have an impact on or related to sleep and or wakefulness. In this paper, we propped an idea of detection of insomnia based on frequency spectral analysis of PSG signals. The PSG signals consist of EMG of the chin, EEG taken from various lobes, respiratory signal, EOG signals, Temporary rectal signal and ECG signal. From all these physiological parameters, the Spectral analysis of EOG (horizontal), EEG FPZ-CZ and PZ-OZ [EEG 10-20 electrodes paced on midline FPZ,CZ,OZ channels]signals are analyzed and the mean, variance, standard deviation, RMS value and SNR features of the signal are extracted. The proposed methodology is applied to the male as well as female subjects at the age group of 30-40 years. The difference of the frequency range taken at respective intervals of time is noted and compared.

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Zhang, Xin, Feng Tian, Cai Hua Li, Guang Fu Sun, and Gang Ou. "Configurable Modernized Navigation Signal Generation Method and Performance Analysis." Applied Mechanics and Materials 333-335 (July 2013): 711–17. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.711.

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In order to provide higher accuracy performance in navigation and positioning requirements, modernized navigation signals with more complex structure, such as MBOC or AltBOC signal, have been actively researched and used in the modernized GPS system, like emerging Galileo and BD-2 system. For the navigation signal simulator which generates those modernized signals, traditional bipolar BPSK or QPSK signal generation method cannot permit to generate those multi-level signals successfully and different modernized signals flexibly. After analyzing the current public modernized signal structure, a novel configurable signal generation method for generating the modernized navigation signal has been proposed. The amplitude quantization word-length and phase truncation error of the LUT (look-up table) used by the method have been analyzed. Experimental simulation results demonstrate the correctness and efficiency of this new method.

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Evans, David H. "Doppler signal analysis." Ultrasound in Medicine & Biology 26 (May 2000): S13—S15. http://dx.doi.org/10.1016/s0301-5629(00)00153-8.

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Mazinani, Sayyed Majid, and GHASEM SADEGHI BAJESTANI. "PSG DYNAMIC CHANGES IN METHAMPHETAMINE ABUSE USING RECURRENCE QUANTIFICATION ANALYSIS." IIUM Engineering Journal 20, no. 1 (June 1, 2019): 79–89. http://dx.doi.org/10.31436/iiumej.v20i1.956.

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ABSTRACT: Polysomnography (PSG) is a standard approach based on comprehensive monitoring of cardiorespiratory signals during sleep. This study has been conducted on subjects with a record of methamphetamine abuse. The significance of this work is methamphetamine abuse detection and measurement without the use of blood tests. With regard to the nonlinear and chaotic dynamic of vital signals and the richness of PSG, the tool employed to carry out the study is Recurrence Qualification Analysis. The objective behind this is to observe and quantify nonlinear dynamic changes of vital signals caused by methamphetamine abuse. Results reveal that: 1) chaotic signals, in other words, system complexity has decreased; 2) under the influence of methamphetamine, signal entropy has increased, bringing about the irregularity of the signals; 3) methamphetamine consumption prompts signal compression to overtake signal expansion which means signal information has declined. ABSTRAK: Polisomnografi (PSG) adalah pendekatan piawai berdasarkan pengawasan menyeluruh signal kardiorespiratori ketika tidur. Kajian ini telah dijalankan ke atas subjek yang mempunyai rekod salah guna methapitamin. Kepentingan kajian ini adalah bagi mengesan salah guna methapitamin dan mengukurnya tanpa menggunakan ujian darah. Dengan mengambil kira ketidak-linearan dan signal penting dinamik dan PSG yang berharga, kaedah yang digunakan bagi menjalankan kajian ini adalah Analisis Kelayakan Berulang. Objektif di sebalik kajian ini adalah bagi melihat dan mengkuantiti perubahan dinamik tidak linear ke atas signal penting disebabkan salah guna methapitamin. Hasil menunjukkan: 1) Signal huru-hara, atau kata lain, kesulitan sistem telah berkurang; 2) di bawah pengaruh methapitamin, signal entropi telah bertambah, menjadikan signal tidak normal; 3) pengambilan methapitamin menyebabkan signal mampat mengambil alih signal kembang bermaksud informasi signal telah berkurang.

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Merletti, Roberto, and Dario Farina. "Analysis of intramuscular electromyogram signals." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, no. 1887 (November 11, 2008): 357–68. http://dx.doi.org/10.1098/rsta.2008.0235.

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Intramuscular electromyographic (EMG) signals are detected with needles or wires inserted into muscles. With respect to non-invasive techniques, intramuscular electromyography has high selectivity for individual motor unit action potentials and is thus used to measure motor unit activity. Decomposition of intramuscular signals into individual motor unit action potentials consists in detection and classification, usually followed by separation of superimposed action potentials. Although intramuscular EMG signal decomposition is the primary tool for physiological investigations of motor unit properties, it is rarely applied in clinical routine, because of the need for human interaction and the difficulty in interpreting the quantitative data provided by EMG signal decomposition to support clinical decisions. The current clinical use of intramuscular EMG signals relates to the diagnosis of myopathies, of diseases of the α-motor neuron and of the neuromuscular junction through the analysis of the interference signal or of the shape of some motor unit action potentials, usually without a full decomposition of the signal.

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Hore, P. J. "Signal treatment and signal analysis in NMR." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 53, no. 11 (October 1997): 1880. http://dx.doi.org/10.1016/s1386-1425(97)00092-9.

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Ellison, Steve. "Signal treatment and signal analysis in NMR." Chemometrics and Intelligent Laboratory Systems 36, no. 2 (April 1997): 247. http://dx.doi.org/10.1016/s0169-7439(97)00018-x.

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Melinda, Melinda, Oktiana Maulisa, Nissa Hasna Nabila, Yunidar Yunidar, and I. Ketut Agung Enriko. "Classification of EEG Signal using Independent Component Analysis and Discrete Wavelet Transform based on Linear Discriminant Analysis." JOIV : International Journal on Informatics Visualization 7, no. 3 (September 10, 2023): 830. http://dx.doi.org/10.30630/joiv.7.3.1219.

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Autism Spectrum Disorder (ASD) is a neurodevelopment syndrome decreasing sufferers' social interaction, communication skills, and emotional expression. Autism syndrome can be detected using an electroencephalogram (EEG). This study utilized the EEG of autistic people to support the classification study of machine learning schemes to produce the best accuracy. One of the best approaches to classify the EEG signal is The Linear Discriminant Analysis (LDA), a machine learning technique to classify autism and normal EEG signals. LDA was chosen because it can maximize the distance between classes and minimize the number of scatters by utilizing between and within-class functions. This method was combined with other methods: Independent Components Analysis (ICA) and Discrete Wavelet Transform (DWT), to improve the accuracy system. ICA removes artifacts or signals other than brain signals that can cause noise in the EEG signal, so the analyzed signal was a complete EEG signal without other factors. DWT can help increase noise suppression in the EEG signal and provide signal information through frequency and time representation. The EEG dataset was collated from 16 children (eight autistic and eight normal). The signals from the dataset were filtered by artifacts using ICA, decomposed by three levels through DWT, and classified using the Linear Discriminant Analysis (LDA) technique. Using the Confusion Matrix, the results reveal the best accuracy of 99%.

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Kumar, Sandeep, Munish Verma, Vijay K. Lamba, Susheel Kumar, and Avinash Mehta. "IMPLEMENTATION AND ANALYSIS OF FIR FILTER USING TMS 320C6713 DSK." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 2 (October 30, 2012): 266–70. http://dx.doi.org/10.24297/ijct.v3i2b.2873.

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In most of the applications, analog signals are produced in response to some physical phenomenon or activity. But it is quite difficult to process that analog signal; here comes the need to convert an analog signal to a digital signal. For this purpose specific digital signal processors (DSPâ€™s) are developed. TMS 320C6713 is one of such type of processors that can be used to process or handle the signals in a variety of ways. In the current report, basically the architecture of this processor is studied. Along with the processor architecture, the hardware portion DSK (Digital Starter Kit) and the software portion CCS (Code Composer Studio) is also studied. Digital filters are very commonly found in everyday life and include a variety of applications. Mainly they are used for two major purposes: signal separation and signal restoration. Signal separation is needed when a signal has been contaminated with interference, noise, or other signals. Signal restoration is used when a signal has been distorted in some way. So, various programs have been analyzed in this work to implement efficiently those FIR filter structures on TMS 320C6713 DSK. Characteristics of FIR filters are studied in frequency domain.

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Xu, Ze Ning, Hong Yu Liu, and Yong Guo Zhang. "The Wavelet Analysis and Processing of Weak Vibration Signal." Advanced Materials Research 189-193 (February 2011): 1426–31. http://dx.doi.org/10.4028/www.scientific.net/amr.189-193.1426.

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Signal measuring is an important link in machine fault diagnosis. Accurate and reliable fault signals can be achieved by reasonable signal measuring. When the distance between sensor and measuring gear or bearing is comparatively far, the collected signals became weak and disturbed by other vibratory signals in equipments on bearing and gear fault analysis. Useful signals often were submerged in powerful noise, so caused difficult in extracting fault feature. In this paper, according to the feature of vibratory signals in machine test, wavelet analysis basic theory was applied on researching basic feature of wavelet analysis. By selecting suitable wavelet function and applying wavelet elimination noise technology the signal to noise ratio of signal was raised, thus the vibratory impact component can be measured in weak signals. Finally, wavelet analysis was applied on bearing fault diagnosis.

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SEDJELMACI, IBTICEME, and F. BEREKSI-REGUIG. "FRACTAL ANALYSIS OF THE ELECTROCARDIOGRAM SIGNAL." Journal of Mechanics in Medicine and Biology 14, no. 04 (July 3, 2014): 1450055. http://dx.doi.org/10.1142/s0219519414500559.

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In this paper, the analysis of the electrocardiogram (ECG) signal is carried out according a non-linear approach. This concerns the eventual fractal behavior of such signal and the correlation of such behavior with normal and pathological ECG signals. The analysis is carried out on different ECG signals taken from the MIT-BIH arrhythmia database. In fact these signals are those of six subjects with different ages and presenting both normal and abnormal arrhythmias situations. The abnormal situations are atrial premature beat (APB), premature ventricular contraction (PVC), right bundle branch block (RBBB) and left bundle branch block (LBBB). The fractal behavior of these signals is analyzed according to the determination of the multifractal spectrum and the fractal dimension variations and looking for eventually a fractal signature of each heart disease and age of the subject. The obtained results show a fractal signature according to the age and the pathologies for the studied cases. However further investigations are required on larger databases to confirm such results.

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Ngui, Wai Keng, M. Salman Leong, Lim Meng Hee, and Ahmed M. Abdelrhman. "Wavelet Analysis: Mother Wavelet Selection Methods." Applied Mechanics and Materials 393 (September 2013): 953–58. http://dx.doi.org/10.4028/www.scientific.net/amm.393.953.

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Wavelet analysis, being a popular time-frequency analysis method has been applied in various fields to analyze a wide range of signals covering biological signals, vibration signals, acoustic and ultrasonic signals, to name a few. With the capability to provide both time and frequency domains information, wavelet analysis is mainly for time-frequency analysis of signals, signal compression, signal denoising, singularity analysis and features extraction. The main challenge in using wavelet transform is to select the most optimum mother wavelet for the given tasks, as different mother wavelet applied on to the same signal may produces different results. This paper reviews on the mother wavelet selection methods with particular emphasis on the quantitative approaches. A brief description of the proposed new technique to determine the optimum mother wavelet specifically for machinery faults diagnosis is also presented in this paper.

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Mishra, Deepak, Sweta Bhardwaj, Alak Banik, T. V. S. Ram, and Parimal Majithiya. "Navigation Signal Simulator for Performance Analysis of GNSS Signals." Communications on Applied Electronics 1, no. 7 (May 26, 2015): 24–30. http://dx.doi.org/10.5120/cae-1600.

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Tian, Hao, Xiao Yong Kang, Yong Jian Li, and Jun Nuo Zhang. "Fault Diagnosis of Gear Wearing Based on Order Cepstrum Analysis." Applied Mechanics and Materials 543-547 (March 2014): 922–25. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.922.

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The article explored a new signal processing method called order cepstrum analysis, it can analyze the instantaneous signals of the rotary mechanism, and can process the non-stationary vibration signals such as speed up or speed down signals effectively. Firstly, the start-up vibration signals of the gearbox are sampled at constant time increments in time-domain, then the data are resampled with software at constant angle increments in angle-domain. Therefore, the time domain instantaneously signal is changed into angle domain stationary signal. Then, the stationary signal is analyzed by order cepstrum. From the result we can find that it can avoid the frequency fuzzy phenomenon effectively, which cannot be solved with the traditional frequency spectrum analysis.

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Zhang, Chang Hao, and Yun Qi Chen. "Analysis and Exploration for Automotive Engine Vibration Signal." Applied Mechanics and Materials 401-403 (September 2013): 1230–33. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1230.

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In Modern society, most of car engines are multi cylinder four stroke engines, rotate speed is an important parameter of the engine, engine running status is a comprehensive expression of engine operation condition. It is also the result of the interaction by the gas torque, load torque and inertia moment. So the speed measurement is of great significance. Car engine speed measurement method has a lot of kinds, this article is based on the vibration method to measure, different methods used in vibration signal acquisition, analysis, processing and implementation. The vibration of the automobile engine output signals are continuous changes over time, we can say is a continuous signal. The vibration of the automobile engine output signals are continuous changes over time, we can say at this time is a continuous signal, when we use vibration sensor to gather the signals, a certain number of sampling points that are in different time, same time interval the vibration data resulting from the sampling theorem. At this time we deal with discrete time signals [1, 3]. Because of various vibration interference, The useful information we want to extract has been hidden in a lot of vibration under the disturbance signal, therefore, we carried out on the vibration signal analysis and processing, converting vibration wave in the frequency domain analysis, combining the new method of machinery vibration signal feature extraction, using short time Fourier transform, multiple correlation theory and Hilbert Huang transform combined with the application, making us in post-processing can extract the characteristic signal under the strong noise background [4]. The original signal frequency is obtained, based on related formulas to calculate car engine speed.

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Zhou, Jing Lei, and Fan Wang. "Chirp Signal Time-Frequency Analysis Characteristic Comparison." Applied Mechanics and Materials 226-228 (November 2012): 568–71. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.568.

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Chirp signal is a typical non-stationary signal, and have been widely used in communication, sonar, radar and so on. So, this signal is worth to analysis. In order to show the characteristics, this paper first introduces the definition and formula of each algorithm, then with all kinds of time-frequency analysis method to the signals, and the signal to add two sine signal noise are analyzed, the comparison of the characteristics of the method in the paper, and the signal for the analysis, the selection of an appropriate analysis. Through analysis and comparison, when dealing with the signal, Hilbert-Huang transformation not only has a better gathered characteristic, but also has a better resolution to distinguish the sine signal noise. Finally, use the MATLAB software simulation to obtain the result.

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Bukkapatnam, S. T. S., S. R. T. Kumara, and A. Lakhtakia. "Analysis of Acoustic Emission Signals in Machining." Journal of Manufacturing Science and Engineering 121, no. 4 (November 1, 1999): 568–76. http://dx.doi.org/10.1115/1.2833058.

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Acoustic emission (AE) signals are emerging as promising means for monitoring machining processes, but understanding their generation is presently a topic of active research; hence techniques to analyze them are not completely developed. In this paper, we present a novel methodology based on chaos theory, wavelets and neural networks, for analyzing AE signals. Our methodology involves a thorough signal characterization, followed by signal representation using wavelet packets, and state estimation using multilayer neural networks. Our methodology has yielded a compact signal representation, facilitating the extraction of a tight set of features for flank wear estimation.

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Xu, Mengjie, Jianhan Wang, Jiahui Mo, Xingfei Li, Lei Yang, and Feng Ji. "Single-Channel Blind Signal Separation of the MHD Linear Vibration Sensor Based on Singular Spectrum Analysis and Fast Independent Component Analysis." Sensors 22, no. 24 (December 9, 2022): 9657. http://dx.doi.org/10.3390/s22249657.

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An MHD vibration sensor, as a new type of sensor used for vibration measurements, meets the technical requirements for the low-noisy measurement of acceleration, velocity, and micro-vibration in spacecraft during their development, launch, and orbit operations. A linear vibration sensor with a runway type based on MHD was independently developed by a laboratory. In a practical test, its output signal was mixed with a large amount of noise, in which the continuous narrowband interference was particularly prominent, resulting in the inability to efficiently carry out the real-time detection of micro-vibration. Considering the high interference of narrowband noise in linear vibration signals, a single-channel blind signal separation method based on SSA and FastICA is proposed in this study, which provides a new strategy for linear vibration signals. Firstly, the singular spectrum of the linear vibration signal with noise was analyzed to suppress the narrowband interference in the collected signal. Then, a FastICA algorithm was used to separate the independent signal source. The experimental results show that the proposed method can effectively separate the useful linear vibration signals from the collected signals with low SNR, which is suitable for the separation of the MHD linear vibration sensor and other vibration measurement sensors. Compared with EEMD, VMD, and wavelet threshold denoising, the SNR of the separated signal is increased by 10 times on average. Through the verification of the actual acquisition of the linear vibration signal, this method has a good denoising effect.

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Wang, Yantong, and Lingyi Zhou. "Innovative Analysis and Application on Magnetograms Signal." Journal of Physics: Conference Series 2174, no. 1 (January 1, 2022): 012087. http://dx.doi.org/10.1088/1742-6596/2174/1/012087.

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Abstract This study aims to make research on magnetocardiography (magnetograms) signals. Magnetocardiogram is a non-invasive method to record and analyze the magnetic components of the electromagnetic field generated by cardiac electrical activity in the cardiac cycle. We conclude the basic characteristics of the magnetograms signal to summarize the existing magnetograms measurement principles. Then we combine the initial development process and detection means of magnetograms by discussing the signal shielding of magnetograms from two aspects of hardware and software. We compare several methods of shielding electromagnetic signals on hardware and finally obtain an excellent means to shield external signals at this stage. We also conclude the current application of magnetograms in medical treatment.

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V., Haribaabu. "Analysis of Filters in ECG Signal for Emotion Prediction." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 896–902. http://dx.doi.org/10.5373/jardcs/v12sp4/20201559.

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Min, Seung-Ki, Andreas Hense, Heiko Paeth, and Won Tae Kwon. "A Bayesian decision method for climate change signal analysis." Meteorologische Zeitschrift 13, no. 5 (October 20, 2004): 421–36. http://dx.doi.org/10.1127/0941-2948/2004/0013-0421.

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Liu, Baojin, Xiaoyong Lyu, and Wenbing Fan. "Analysis of 5G Signal for Radar Application." Journal of Physics: Conference Series 2356, no. 1 (October 1, 2022): 012027. http://dx.doi.org/10.1088/1742-6596/2356/1/012027.

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In this paper we evaluate the fifth generation (5G) communication signal from the perspective of radar sensing. The passive radars exploit the third-part transmitted signals in the space as the illuminators of opportunity (IoO), and has gained renewed interest. The 5G signal has large bandwidth and advanced modulation technique, offering great potential for radar uses. This paper takes an analysis of the 5G signal from the perspective of radar sensing. We provide a detailed description of the 5G signal protocol, and evaluate the detection capability of the 5G signal based passive radar in terms of bistatic range resolution, range resolution, the signal to noise ratio distribution. Furthermore, we introduce a signal processing method that may be a candidate for target sensing in the 5G signal based passive radar. Simulation verifies the effectiveness of this method.

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Wang, Zucheng, Yanfeng Peng, Yanfei Liu, Yong Guo, Yi Liu, Hongyan Geng, Sai Li, and Chao Fan. "Photovoltaic Power Quality Analysis Based on the Modulation Broadband Mode Decomposition Algorithm." Energies 14, no. 23 (November 27, 2021): 7948. http://dx.doi.org/10.3390/en14237948.

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The Broadband Mode Decomposition (BMD) method was previously proposed to solve the Gibbs phenomenon that occurs during photovoltaic signal decomposition; its main idea is to build a dictionary which contains signal features, and to search in the dictionary to solve the problem. However, BMD has some shortcomings; especially if the relative bandwidth of the decomposed signal is not small enough, it may treat a square wave signal as several narrowband signals, resulting in a deviation in the decomposition effect. In order to solve the problem of relative bandwidth, the original signal is multiplied by a high-frequency, single-frequency signal, and the wideband signal is processed as an approximate wideband signal. This is the modulation broadband mode decomposition algorithm (MBMD) proposed in this article. In order to further identify and classify the disturbances in the photovoltaic direct current (DC) signal, the experiment uses composite multi-scale fuzzy entropy (CMFE) to calculate the components after MBMD decomposition, and then uses the calculated value in combination with the back propagation (BP) neural network algorithm. Simulation and experimental signals verify that the method can effectively extract the characteristics of the square wave component in the DC signal, and can successfully identify various disturbance signals in the photovoltaic DC signal.

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Rügamer, Alexander, Cécile Mongrédien, Santiago Urquijo, and Günter Rohmer. "Theoretical Analysis of Overlay GNSS Receiver Effects." International Journal of Embedded and Real-Time Communication Systems 3, no. 3 (July 2012): 38–53. http://dx.doi.org/10.4018/jertcs.2012070103.

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Having given a short overview of GNSS signals and state-of-the-art multi-band front-end architectures, this paper presents a novel contribution to efficient multi-band GNSS reception. A general overlay based front-end architecture is introduced that enables the joint reception of two signals broadcast in separate frequency bands, sharing just one common baseband stage. The consequences of this overlay are analyzed for both signal and noise components. Signal overlay is shown to have a negligible impact on signal quality. It is shown that the noise floor superposition results in non-negligible degradations. However, it is also demonstrated that these degradations can be minimized by judiciously setting the relative gain between the two signal paths. As an illustration, the analytical optimal path-control expression to combine overlaid signals in an ionospheric-free pseudorange is derived for both Cramér-Rao Lower Bound and practical code tracking parameters. Finally, some practical overlay receiver and path control aspects are discussed.

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Chhavi Saxena, Dr, Dr Avinash Sharma, Dr Rahul Srivastav, and Dr Hemant Kumar Gupta. "Denoising of Ecg Signals Using Fir & Iir Filter: a Performance Analysis." International Journal of Engineering & Technology 7, no. 4.12 (October 4, 2018): 1. http://dx.doi.org/10.14419/ijet.v7i4.12.20982.

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Electrocardiogram (ECG) signal is the electrical recording of coronary heart activity. It is a common routine and vital cardiac diagnostic tool in which in electric signals are measured and recorded to recognize the practical status of heart, but ECG signal can be distorted with noise as, numerous artifacts corrupt the unique ECG signal and decreases it quality. Consequently, there may be a need to eliminate such artifacts from the authentic signal and enhance its quality for better interpretation. ECG signals are very low frequency signals of approximately 0.5Hz-100Hz and digital filters are used as efficient approach for noise removal of such low frequency signals. Noise may be any interference because of movement artifacts or due to power device that are present wherein ECG has been taken. Consequently, ECG signal processing has emerged as a common and effective tool for research and clinical practices. This paper gives the comparative evaluation of FIR and IIR filters and their performances from the ECG signal for proper understanding and display of the ECG signal.

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Balachandran, G., and Praveen Kumar Gupta. "FPGA – Based Electrocardiography Signal Analysis System using (FIR) Filter." International Journal of Advance Research and Innovation 8, no. 1 (2020): 44–48. http://dx.doi.org/10.51976/ijari.812008.

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The cardiovascular attack is a more dangerous than other diseases and it is measured by ECG (Electro cardiograph) signals which is like a noisy signal in real time, especially in the field of telemedicine environment. The noisy ECG signals have more motion artifacts, electrical interference, etc. An adaptive filtering approach based on Discrete Wavelet Transform and an artificial neural network is proposed to reduce the noise in ECG signal. The quality of de-noised signal is improved by SVM algorithm. This suggested approach can successfully take out a broad scope of noise and our method achieve up to almost 82% improvement on the SNR of de-noised signals. The MATLAB simulation results shown clearly about the improvement of ECG signal with SNR value.

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Liu, Shuai, Xiang Chen, Ying Li, and Xiaochun Cheng. "Micro-Distortion Detection of Lidar Scanning Signals Based on Geometric Analysis." Symmetry 11, no. 12 (December 3, 2019): 1471. http://dx.doi.org/10.3390/sym11121471.

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When detecting micro-distortion of lidar scanning signals, current hardwires and algorithms have low compatibility, resulting in slow detection speed, high energy consumption, and poor performance against interference. A geometric statistics-based micro-distortion detection technology for lidar scanning signals was proposed. The proposed method built the overall framework of the technology, used TCD1209DG (made by TOSHIBA, Tokyo, Japan) to implement a linear array CCD (charge-coupled device) module for photoelectric conversion, signal charge storage, and transfer. Chip FPGA was used as the core component of the signal processing module for signal preprocessing of TCD1209DG output. Signal transmission units were designed with chip C8051, FT232, and RS-485 to perform lossless signal transmission between the host and any slave. The signal distortion feature matching algorithm based on geometric statistics was adopted. Micro-distortion detection of lidar scanning signals was achieved by extracting, counting, and matching the distorted signals. The correction of distorted signals was implemented with the proposed method. Experimental results showed that the proposed method had faster detection speed, lower detection energy consumption, and stronger anti-interference ability, which effectively improved micro-distortion correction.

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Niwa, Kazuki, Hidehiro Kubota, Toshiteru Enomoto, Yoshiro Ichino, and Yoshihiro Ohmiya. "Quantitative Analysis of Bioluminescence Optical Signal." Biosensors 13, no. 2 (February 3, 2023): 223. http://dx.doi.org/10.3390/bios13020223.

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Bioluminescence is light emission based on the luciferin–luciferase enzymatic reaction in living organisms. Optical signals from bioluminescence (BL) reactions are available for bioanalysis and bioreporters for gene expression, in vitro, in vivo, and ex vivo bioimaging, immunoassay, and other applications. Although there are numerous bioanalysis methods based on BL signal measurements, the BL signal is measured as a relative value, and not as an absolute value. Recently, some approaches have been established to completely quantify the BL signal, resulting in, for instance, the redetermination of the quantum yield of the BL reaction and counting the photon number of the BL signal at the single-cell level. Reliable and reproducible understanding of biological events in the bioanalysis and bioreporter fields can be achieved by means of standardized absolute optical signal measurements, which is described in an International Organization for Standardization (ISO) document.

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Jiang, Wensheng, Weiwei Ding, Xinke Zhu, and Fei Hou. "A Recognition Algorithm of Seismic Signals Based on Wavelet Analysis." Journal of Marine Science and Engineering 10, no. 8 (August 10, 2022): 1093. http://dx.doi.org/10.3390/jmse10081093.

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In order to meet the requirements of mobile marine seismometers to observe and record seismic signals, a study of fast and accurate seismic signal recognition was carried out. This paper introduces the use of the wavelet analysis method for seismic signal processing and recognition, and compares and analyzes the abilities of different wavelet basis functions to detect the seismic signal. By denoising and reconstructing the signal, the distribution law of the wavelet coefficients of seismic signal at different scales was obtained. On this basis, this paper proposes an identification model of seismic signals based on wavelet analysis and thereby solves the conflict between high speed and high accuracy of seismic signal recognition methods. In this study, the simulation was carried out in the Matlab2020b environment, and the feasibility of wavelet recognition algorithm was proven by applying this algorithm to the seismic signal database for experimental verification.

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Yin, Zhi Hao, Ben Cheng Yu, Zhi Feng Wang, and Yong Yang. "Performance Analysis of Radar Pulse Compression Signals." Advanced Materials Research 734-737 (August 2013): 3248–51. http://dx.doi.org/10.4028/www.scientific.net/amr.734-737.3248.

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In order to enhance the resistance to interference of radar, introduced chirp and chaos BPSK two pulse compression signals principles, has analyzed the respective signal characteristics. Based on MATLAB simulation, from 5 aspect analysis and comparisons the performance of these two radar pulse compression signals. The results show that when these two pulse pressure signals can meet is big request of wide band width, the signal after pulse pressure have the ratio of high.

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Koo, Kil-Mo, Jin-Ho Song, Sang-Baik Kim, Kwang-Il Ahn, Won-Pil Baek, Kil-Nam Oh, and Gyu-Tae Kim. "Response Analysis on Electrical Pulses under Severe Nuclear Accident Temperature Conditions Using an Abnormal Signal Simulation Analysis Module." Science and Technology of Nuclear Installations 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/656590.

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Unlike design basis accidents, some inherent uncertainties of the reliability of instrumentations are expected while subjected to harsh environments (e.g., high temperature and pressure, high humidity, and high radioactivity) occurring in severe nuclear accident conditions. Even under such conditions, an electrical signal should be within its expected range so that some mitigating actions can be taken based on the signal in the control room. For example, an industrial process control standard requires that the normal signal level for pressure, flow, and resistance temperature detector sensors be in the range of 4~20 mA for most instruments. Whereas, in the case that an abnormal signal is expected from an instrument, such a signal should be refined through a signal validation process so that the refined signal could be available in the control room. For some abnormal signals expected under severe accident conditions, to date, diagnostics and response analysis have been evaluated with an equivalent circuit model of real instruments, which is regarded as the best method. The main objective of this paper is to introduce a program designed to implement a diagnostic and response analysis for equivalent circuit modeling. The program links signal analysis tool code to abnormal signal simulation engine code not only as a one body order system, but also as a part of functions of a PC-based ASSA (abnormal signal simulation analysis) module developed to obtain a varying range of theR-Ccircuit elements in high temperature conditions. As a result, a special function for abnormal pulse signal patterns can be obtained through the program, which in turn makes it possible to analyze the abnormal output pulse signals through a response characteristic of a 4~20 mA circuit model and a range of the elements changing with temperature under an accident condition.

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YAO, SUSU, and ZHENYA HE. "ANALYSIS OF MULTICOMPONENT CHIRP SIGNALS USING FREQUENCY-SHEAR REPRESENTATION." Journal of Circuits, Systems and Computers 06, no. 04 (August 1996): 385–401. http://dx.doi.org/10.1142/s0218126696000261.

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This paper is concerned with the problem of multicomponent chirp signal analysis. The traditional analysis tool is time-frequency distribution which has been paid more attention in deterministic nonstationary signal processing. However, for chirp signals, it is difficult to find a best time-frequency representation that has high auto component concentration and cross-term suppression. Generally, any time-frequency representation based on rectangular resolution cell will not result in good performance for signals with time-varying frequency content. In this paper we propose a new representation for analyzing multicomponent chirp signals using resolution cell that shears in the timefrequency plane. In our approach, the kernel function with its shape adapted to the local structure of the chirp signal is used so that the signal components can be clearly mapped onto the frequency-shear plane. It is shown that the frequency-shear representation can reduce spurious values and concentrate on the auto components. Several signals including sinusoidal signals and chirp signals are analyzed and simulated using the proposed approach in comparison with the results by the Wigner distribution.

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Yamada, Shunji, Atsushi Kurotani, Eisuke Chikayama, and Jun Kikuchi. "Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time–Frequency Analysis and Probabilistic Sparse Matrix Factorization." International Journal of Molecular Sciences 21, no. 8 (April 23, 2020): 2978. http://dx.doi.org/10.3390/ijms21082978.

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Nuclear magnetic resonance (NMR) spectroscopy is commonly used to characterize molecular complexity because it produces informative atomic-resolution data on the chemical structure and molecular mobility of samples non-invasively by means of various acquisition parameters and pulse programs. However, analyzing the accumulated NMR data of mixtures is challenging due to noise and signal overlap. Therefore, data-cleansing steps, such as quality checking, noise reduction, and signal deconvolution, are important processes before spectrum analysis. Here, we have developed an NMR measurement informatics tool for data cleansing that combines short-time Fourier transform (STFT; a time–frequency analytical method) and probabilistic sparse matrix factorization (PSMF) for signal deconvolution and noise factor analysis. Our tool can be applied to the original free induction decay (FID) signals of a one-dimensional NMR spectrum. We show that the signal deconvolution method reduces the noise of FID signals, increasing the signal-to-noise ratio (SNR) about tenfold, and its application to diffusion-edited spectra allows signals of macromolecules and unsuppressed small molecules to be separated by the length of the T2* relaxation time. Noise factor analysis of NMR datasets identified correlations between SNR and acquisition parameters, identifying major experimental factors that can lower SNR.

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Carneiro, Mirella, Victor Oliveira, Fernanda Oliveira, Marco Teixeira, and Milena Pinto. "Simulation Analysis of Signal Conditioning Circuits for Plants’ Electrical Signals." Technologies 10, no. 6 (November 25, 2022): 121. http://dx.doi.org/10.3390/technologies10060121.

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Electrical signals are generated and transmitted through plants in response to stimuli caused by external environment factors, such as touching, luminosity, and leaf burning. By analyzing a specific plant’s electrical responses, it is possible to interpret the impact of external aspects in the plasma membrane potential and, thus, determine the cause of the electrical signal. Moreover, these signals permit the whole plant structure to be informed almost instantaneously. This work presents a brief discussion of plants electrophysiology theory and low-cost signal conditioning circuits, which are necessary for the acquisition of plants’ electrical signals. Two signal conditioning circuits, which must be chosen depending on the signal to be measured, are explained in detail and electrical simulation results, performed in OrCAD Capture Software are presented. Furthermore, Monte Carlo simulations were performed to evaluate the impact of components variations on the accuracy and efficiency of the signal conditioning circuits. Those simulations showed that, even after possible component variations, the filters’ cut-off frequencies had at most 4% variation from the mean.

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He, Jiai, Yuxiao Song, Panpan Du, and Lei Xu. "Analysis of Single Channel Blind Source Separation Algorithm for Chaotic Signals." Mathematical Problems in Engineering 2018 (October 8, 2018): 1–9. http://dx.doi.org/10.1155/2018/9571510.

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In a wireless sensor network, the signal received by the terminal processor is usually a complex single channel hybrid chaotic signal. The engineering needs to separate the useful signal from the mixed signal to perform the next transmission analysis. Since chaotic signals are nonlinear and unpredictable, traditional blind separation algorithms cannot effectively separate chaotic signals. Aiming to correct these problems—based on the particle filter estimation algorithm—an extended Kalman particle filter algorithm (EPF) and an unscented Kalman particle filter algorithm (UPF) are proposed to solve the single channel blind separation problem of chaotic signals. Mixing chaotic signals of different intensities performs blind source separation. Using different evaluation indexes carries out the experiment and performance can be analyzed. The results show that the proposed algorithm effectively separates the mixed chaotic signals.

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Brosvic, Gary M., Nancy A. Civale, Patricia Long, Deborah Kieley, Kathryn Kristoff, Nicole Memblatt, Rachel Gordon, Laurell Parris, Carla Giambelluca, and Roberta E. Dihoff. "Signal-Detection Analysis of the Müller-Lyer and the Horizontal-Vertical Illusions." Perceptual and Motor Skills 79, no. 3 (December 1994): 1299–304. http://dx.doi.org/10.2466/pms.1994.79.3.1299.

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Perceptual error in the Müller-Lyer and the Horizontal-Vertical illusions was quantified using nonparametric signal-detection measures of sensitivity and response bias. Sensitivity scores were positively related to signal strength with the greatest values observed for the strongest signals. Sensitivity at each signal strength did not differ between the two illusions. Response-bias scores were inversely related to signal strength, with the most conservative biases observed for the strongest signals. Response biases for each signal strength were significantly more conservative for the Horizontal-Vertical than for the Müller-Lyer illusion.

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Wang, Xinqi, Yikang Yang, Lingyu Deng, Lvyang Ye, Zhanqi Li, Yong Xiao, and Wenliang Dong. "Design and Performance Analysis of Navigation Signal Based on OFDM." Applied Sciences 12, no. 19 (September 21, 2022): 9486. http://dx.doi.org/10.3390/app12199486.

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This paper proposes a new navigation modulation based on orthogonal frequency division multiplexing (OFDM). We derived the autocorrelation function and power spectral density of the OFDM modulation. The influence of the cyclic prefix and zero-padding is discussed. The influence of OFDM modulation parameters on navigation signal performance was deeply analyzed, which can help signal designers choose the OFDM parameters. The main peak of the proposed autocorrelation function is narrow and has good tracking accuracy. The sidelobe is lower, and the delay locking loop is more robust. The power spectrum density is evenly distributed in the main lobe of the signal, and the anti-interference is good. By comparing OFDM navigation signals with other navigation signals, it can be found that OFDM navigation signals have good tracking accuracy and a strong anti-interference ability. Combined with the proposed navigation modulation and communication signal, the OFDM navigation signal has a low bit error rate for the communication signal and has a good communication integration potential, which can meet the business requirements of the future communication and navigation integration market.

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Chen, Yunfei, Shuhan Zhu, Kaimin Yu, Minfeng Wu, Lei Feng, Peibin Zhu, and Wen Chen. "Quantitative Analysis of φ-OTDR Spatial Resolution Influenced by NLM Parameters." Photonics 10, no. 5 (May 4, 2023): 529. http://dx.doi.org/10.3390/photonics10050529.

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Non-local mean (NLM) can significantly improve the signal-to-noise ratio (SNR), but it inevitably reduces the spatial resolution of distributed optical fiber sensors (DFOS), which hinders its practical application and the improvement of DFOS performance. In this paper, the quantitative relationship between the signal broadening of a phase-sensitive optical time-domain reflectometer (φ-OTDR) and the NLM parameters is analyzed to identify the cause and extent of the spatial resolution degradation. The denoising results for the mimic periodic and φ-OTDR vibration signals indicate that the signal broadening is mainly due to the similarity window size of NLM, and the signal amplitude reduction is caused by the Gaussian smoothing parameter. Compared with the reference signals, the signal broadening of the mimic and φ-OTDR signals after denoising are 2.56% and 2.74%, respectively, which is much less than the previous results. The signal amplitude is reduced by 9.25% and 13.62%, respectively. This work promotes the application of NLM and improves the performance of DFOS.

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Pozdniakov, V., and M. Buhaiov. "ACOUSTIC SIGNALS ANALYSIS OF AERIAL ATTACK WEAPON." Проблеми створення, випробування, застосування та експлуатації складних інформаційних систем, no. 25 (I) (December 25, 2023): 58–75. http://dx.doi.org/10.46972/2076-1546.2023.25.06.

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This paper analyzes the acoustic signals of aerodynamic aerial attack weapons used by the Russian Federation during a full-scale invasion. These include strike unmanned aerial vehicles, cruise missiles, jet and turboprop aircraft, and helicopters. It has been established that the time for observing an acoustic signal is limited by the speed of the vehicle. For high-speed targets (cruise missiles, airplanes), it averages 10 s, and for slower targets, 40-50 s. The Welch periodogram method was used to extract the spectral characteristics of acoustic signals. It is shown that the acoustic signal of propeller-driven vehicles is the sum of harmonic and noise-like broadband components, and that of turbojet-powered vehicles has a predominantly noise-like structure with several narrowband components. It was found that at the moment of maximum convergence, the signal spectrum has the greatest width. The characteristic change in the frequency of harmonic components associated with the Doppler effect is investigated. It can be used to estimate the parameters of motion and identify the vehicle. By correlation analysis of acoustic signals, it was found that broadband components have a noise-like structure. An acoustic signal for the case of simultaneous over flight of different types of vehicles was formed by adding records of different acoustic signals. It is shown that the characteristic spectral characteristics of all objects are preserved on the frequency-time plane. The results of the analysis can be used to build mathematical models of acoustic signals and to develop methods for processing signals of aerial attack weapons in an acoustic airspace monitoring system. Keywords: aerial attack weapons; acoustic signal; spectrogram; acoustic monitoring system; harmonic component.

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Chai, Lin, and Jun Ru Sun. "Voltage Flicker Extraction Based on Wavelet Analysis." Applied Mechanics and Materials 385-386 (August 2013): 1389–93. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1389.

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Extracting voltage flicker from the sampling voltage signal is a precondition for management of flicker. Voltage flicker signal is a low frequency time-varying non-stationary signal. The traditional fourier transform has great limitations when analyze the non-stationary signal for not having the time resolution. As wavelet transform has good property of time-frequency localization, it become a powerful tool for analyze this kind of signal. This paper adopts multi-resolution analysis of wavelet to extract voltage flicker signal. Furthermore, according to the characteristics of wavelet function, this paper selects Daubechies wavelet to accomplish the multi-level decomposition and reconstruction of signal, in order to get the frequency and amplitude of voltage flicker signals. Based on the principle of modulus maximum, it can be determined what time the voltage flicker happen and over. The results of MATLAB simulation indicate that voltage flicker signal can be effectively extracted by wavelet multi-resolution analysis. Wavelet multi-resolution analysis is considerably ideal for voltage flicker extraction.

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D’Aloia, Matteo, Annalisa Longo, and Maria Rizzi. "Noisy ECG Signal Analysis for Automatic Peak Detection." Information 10, no. 2 (January 22, 2019): 35. http://dx.doi.org/10.3390/info10020035.

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Cardiac signal processing is usually a computationally demanding task as signals are heavily contaminated by noise and other artifacts. In this paper, an effective approach for peak point detection and localization in noisy electrocardiogram (ECG) signals is presented. Six stages characterize the implemented method, which adopts the Hilbert transform and a thresholding technique for the detection of zones inside the ECG signal which could contain a peak. Subsequently, the identified zones are analyzed using the wavelet transform for R point detection and localization. The conceived signal processing technique has been evaluated, adopting ECG signals belonging to MIT-BIH Noise Stress Test Database, which includes specially selected Holter recordings characterized by baseline wander, muscle artifacts and electrode motion artifacts as noise sources. The experimental results show that the proposed method reaches most satisfactory performance, even when challenging ECG signals are adopted. The results obtained are presented, discussed and compared with some other R wave detection algorithms indicated in literature, which adopt the same database as a test bench. In particular, for a signal to noise ratio (SNR) equal to 6 dB, results with minimal interference from noise and artifacts have been obtained, since Se e +P achieve values of 98.13% and 96.91, respectively.

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Odyniec, Michał. "Uncertainty analysis for signal detection." Journal of Knot Theory and Its Ramifications 27, no. 07 (June 2018): 1841006. http://dx.doi.org/10.1142/s0218216518410067.

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This paper addresses parameter identification of signals that are smooth and have compact support, but are otherwise arbitrary. We propose a definition of the time reference that results in the time uncertainty estimates that are much smaller than what one might expect with a casual analysis. These estimates are easily calculable given the noise level of the recording device and the signal shape.

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Shanmugasundaram, S. "Sub Carrier Analysis for QAM Modulation." Bulletin of Electrical Engineering and Informatics 6, no. 4 (December 1, 2017): 354–57. http://dx.doi.org/10.11591/eei.v6i4.866.

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Orthogonal Frequency Division Multiplexing (OFDM) based wireless data transmission system is a multi-carrier system in which single higher rate data stream can be divided into multiple lower rate data streams. Modulation and De-Modulation technique play a major role in OFDM based data transmission system. Based on Modulation technique only, the frequency transformation method and encoding and decoding methods are enabled. Effective modulation techniques called as “Quadrature Amplitude Modulation (QAM)” modulation are used to design an OFDM System. Carrier signal is one of the important signals used to modulate the original signal. Analyzing the sub-carrier signal for getting the quality of the modulated signal.

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Journal articles on the topic 'Signal analysis'

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