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Journal articles on the topic 'Signal processing – Digital technique'

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Author: Grafiati

Published: 4 June 2021

Last updated: 7 September 2023

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1

Okamoto, Akihiro, and Akio Miyazaki. "A digital watermark technique using morphological signal processing." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 86, no. 6 (February 4, 2003): 67–75. http://dx.doi.org/10.1002/ecjc.10007.

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2

Uchino, Masaharu, and Ken Mochizuki. "Frequency stability measuring technique using digital signal processing." Electronics and Communications in Japan (Part I: Communications) 87, no. 1 (September 9, 2003): 21–33. http://dx.doi.org/10.1002/ecja.10097.

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3

Yan, Zheng Guo, and Juan Su. "Through-Casing Resistivity Logging Signal Acquisition and Processing Techniques." Advanced Materials Research 403-408 (November 2011): 2659–62. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2659.

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Weak signal detection is the key technique in developing through-casing resistivity logging tool. In this paper, ultra-low-noise preamplifier, oversampling method, sampling integration and sampling average method, digital phase-sensitive detection technique are applied in detecting logging signals and 30nV is achieved. The indoor calibration test and field experiment of through-casing resistivity logging model machine with those weak signal detection techniques were carried out. The result showed that the measurement range of formation resistivity is 0~200 Ω.m.

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4

Sabatini, A. M. "A digital-signal-processing technique for ultrasonic signal modeling and classification." IEEE Transactions on Instrumentation and Measurement 50, no. 1 (2001): 15–21. http://dx.doi.org/10.1109/19.903873.

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5

Liu, Chanzi, Qingchun Chen, Hongbin Liang, and Hengchao Li. "Digital Watermarking Processing Technique Based on Overcomplete Dictionary." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 10 (November 23, 2016): 1658002. http://dx.doi.org/10.1142/s0218001416580027.

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A novel sparse domain-based information hiding framework is proposed in this paper to attach the watermarking signal to the most significant sparse components of the host signal over the pre-defined overcomplete dictionary. The adaptive sparse domain can be utilized to embed watermarking logo with better security and robustness. This can be realized owing to the fact that, not only the sparse domain can be customized from the given samples, but also the sparse transform coefficients of the original watermarking signal can be embedded, which provides inherent privacy. This paper provides two kinds of methods that embed watermark directly and embed the sparse representation coefficients of watermarking logo, and analyzes the condition of uniqueness of the sparse solution. Experimental results demonstrate the superiority of the proposed sparse domain digital watermarking technique over the traditional frequency domain or spatial domain schemes.

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6

Li, De Xin. "Random Sampling Approaches for Implementation of FPGA in Signal Processing." Applied Mechanics and Materials 670-671 (October 2014): 1184–87. http://dx.doi.org/10.4028/www.scientific.net/amm.670-671.1184.

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Nowadays, digital front-end devices are widely used in large number of fields such as electronic appliances. Signals must be presented in an appropriate format, for example, the original analog signals must be converted into digital formats that can be recognized and used by digital equipment. In this paper, the random sampling technique is implemented by original design of a Pseudorandom Signal Sampler circuit for controlling ADC to relax constraints of receiver circuits supporting multiband signal processing. This new idea of using non-uniform sampling technique for multiband signals sampling allows the main advantage of suppressing spectral aliases at integer multiples of sampling frequency produced by conventional uniform sampling technique. This approach could reduce the constraints on the anti-aliasing filter, relax the automatic gain control dynamic range, and decrease the ADC dynamic power consumption. Experiments show that this approach achieve good performance and it could be implemented in FPGA in signal processing successfully.

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7

Benton, David M. "Photonic Processing for Wideband Cancellation and Spectral Discrimination of RF Signals." Advances in Optical Technologies 2013 (December 5, 2013): 1–8. http://dx.doi.org/10.1155/2013/738427.

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Photonic signal processing is used to implement common mode signal cancellation across a very wide bandwidth utilising phase modulation of radio frequency (RF) signals onto a narrow linewidth laser carrier. RF spectra were observed using narrow-band, tunable optical filtering using a scanning Fabry Perot etalon. Thus functions conventionally performed using digital signal processing techniques in the electronic domain have been replaced by analog techniques in the photonic domain. This technique was able to observe simultaneous cancellation of signals across a bandwidth of 1400 MHz, limited only by the free spectral range of the etalon.

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Sabatini, A. M. "A digital signal-processing technique for compensating ultrasonic sensors." IEEE Transactions on Instrumentation and Measurement 44, no. 4 (1995): 869–74. http://dx.doi.org/10.1109/19.392873.

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9

Martinez, O., M. Parrilla, M. A. G. Izquierdo, and L. G. Ullate. "Application of digital signal processing techniques to synthetic aperture focusing technique images." Sensors and Actuators A: Physical 76, no. 1-3 (August 1999): 448–56. http://dx.doi.org/10.1016/s0924-4247(99)00028-x.

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10

Jorge, P. A. S., L. A. Ferreira, and J. L. Santos. "Digital signal processing technique for white light based sensing systems." Review of Scientific Instruments 69, no. 7 (July 1998): 2595–602. http://dx.doi.org/10.1063/1.1148986.

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11

Marciniak, T., R. Weychan, A. Stankiewicz, and A. Dąbrowski. "Biometric speech signal processing in a system with digital signal processor." Bulletin of the Polish Academy of Sciences Technical Sciences 62, no. 3 (September 1, 2014): 589–94. http://dx.doi.org/10.2478/bpasts-2014-0064.

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Abstract This paper presents an analysis of issues related to the fixed-point implementation of a speech signal applied to biometric purposes. For preparing the system for automatic speaker identification and for experimental tests we have used the Matlab computing environment and the development software for Texas Instruments digital signal processors, namely the Code Composer Studio (CCS). The tested speech signals have been processed with the TMS320C5515 processor. The paper examines limitations associated with operation of the realized embedded system, demonstrates advantages and disadvantages of the technique of automatic software conversion from Matlab to the CCS and shows the impact of the fixed-point representation on the speech identification effectiveness.

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12

Joshi, Sunil, and Gaurav Sharma. "Non-linear Distortion & its Reduction Techniques for Coherent Optical OFDM System: A Review." Journal of Advance Research in Electrical & Electronics Engineering (ISSN: 2208-2395) 2, no. 8 (August 31, 2015): 13–16. http://dx.doi.org/10.53555/nneee.v2i8.179.

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In this paper the literature review is done for different compensation technique to reduce the nonlinear distortion for optical coherent system. Comparative analysis done so for dispersion compensation fiber, method of Fiber Bragg Grating, digital signal processing (DSP), pre and post symmetric-DCF techniques. The digital signal processing is very practical for all type of nonlinear distortion is minimized while dispersion technique is use to compensate the dispersion loss only and other methods are useful to reduce nonlinear distortion.

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13

Omar, Siti Nashayu. "Application of digital signal processing and machine learning for Electromyography: A review." Asian Journal Of Medical Technology 1, no. 1 (July 30, 2021): 30–45. http://dx.doi.org/10.32896/ajmedtech.v1n1.30-45.

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This paper reviewed the Application of Digital Signal Processing (DPS) and Machine Learning (ML) for Electromyography (EMG) by previous studies. There is a need of the DSP and ML application into the EMG study to classify the signal in order to minimize the EMG noise of signal and the EMG signal characteristic. The common techniques analysis of signal processing is disccussed and compared to identify the best techniques used in order to process from raw data of EMG signal info EMG signal analysis, then some types of machine learning is discussed to identify which types of machine learning have gave the best performance of EMG signal identification and signal characteristic with the highest percentage of the accuracy and efficiency. Digital signal processing and the technique of signal analysis and machine learning for classification method in order to provide the best method and classification for EMG signal.

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14

BOIKO, JULIY, ILYA PYATIN, and IGOR PARKHOMEY. "SIGNAL PROCESSING AND SYNCHRONIZATION TECHNIQUE IN SOFTWARE-DEFINED RADIO SYSTEMS WITH OFDM." Herald of Khmelnytskyi National University. Technical sciences 307, no. 2 (May 2, 2022): 123–32. http://dx.doi.org/10.31891/2307-5732-2022-307-2-123-132.

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The article describes modern concepts for the development of Software-defined Radio (SDR) technology. A functional diagram of a software-defined radio station using the method of transmitting information with modulation based on Orthogonal Frequency Division Multiplexing (OFDM) is proposed. The conditions for ensuring symbol synchronization of OFDM signals are investigated. It is determined that the disruption of the orthogonality of OFDM symbols is accompanied by such phenomena as the occurrence of inter-symbol interference and inter-channel interference. The method of constellation diagrams was used to study the effect of errors caused by interference on the quality of processing of multiposition signals. The effect of interference in a channel from OFDM on the phase of signals during their processing is estimated. Mathematical models of signals with OFDM in the presence of interference effects are obtained. Two types of errors are considered, which are caused by carrier distortion. In particular, the occurrence of phase noise is possible, the cause of which is associated with the instability of the generating equipment of the carrier signals both on the transmitting and on the receiving side. Another factor is carrier offset due to Doppler frequency. The proposed SDR receiver structure is based on the digital IF architecture. This architecture has more flexibility than traditional RF architectures and is not sensitive to DC offset, LO leakage, etc. Moreover, since the I/Q up/down conversion is done digitally in the IF stage, the negative effects caused by I/Q imbalance will be minimized. The SDR consists of a small piece of hardware at the RF input, i.e., an antenna and a high-rate ADC capable of capturing and digitizing broadband radio signals. To increase the communication range in SDR, we use amplifiers in front of two DAC/ADC stages. The receiver has a low-noise amplifier to reduce the quantization noise of the transducer and increase the Signal-to-noise Ratio (SNR). The data is then processed on specialized computational units within the embedded system, enabling important demodulation, synchronization, and decoding techniques to be implemented.

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15

Frey, M., and F. Taylor. "A table reduction technique for logarithmically architected digital filters." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 3 (June 1985): 718–19. http://dx.doi.org/10.1109/tassp.1985.1164597.

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16

YAN, LIUMING, YUEFEI MA, and JORGE M. SEMINARIO. "TERAHERTZ SIGNAL TRANSMISSION IN MOLECULAR SYSTEMS." International Journal of High Speed Electronics and Systems 16, no. 02 (June 2006): 669–75. http://dx.doi.org/10.1142/s0129156406003928.

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Terahertz signal transmission in DNA is simulated and analyzed using molecular dynamics and digital signal processing techniques to demonstrate that signals encoded in vibrational movements of hydrogen bonds can travel along the backbone of DNA and eventually be recovered and analyzed using digital signal processing techniques.

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17

Biganeh, A., M. Azizi, O. Kakuee, H. Rafi-Kheiri, M. Sedaghati Boorkhani, and B. Ghasemi. "Digital signal processing for neutron-gamma discrimination using a liquid scintillator detector." Journal of Instrumentation 18, no. 03 (March 1, 2023): P03039. http://dx.doi.org/10.1088/1748-0221/18/03/p03039.

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Abstract Digital signal processing has shown promise as a reliable approach for nuclear radiation spectroscopy. In this paper, the charge-to-digital conversion technique was applied to neutron-gamma discrimination. The method is based on the direct comparison of the charge at the fast and slow components of the anode signal of the scintillator detector. The performance of the technique was tested using experimental investigation by 252Cf source and D-D neutron generator. The minimum detectable energy for reasonable separation was found to be around 0.2 MeVee. The FOM of the discrimination parameter was maximized by the optimum selection of the charge integration parameters. Excellent discrimination between neutron and gamma signals was confirmed by the presented setup. The results confirmed that the presented technique can replace analog nuclear electronics conventionally uses for pulse shape discrimination.

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18

Yamamoto, Yutaka, Kaoru Yamamoto, Masaaki Nagahara, and Pramod P. Khargonekar. "Signal processing via sampled-data control theory." Impact 2020, no. 2 (April 15, 2020): 6–8. http://dx.doi.org/10.21820/23987073.2020.2.6.

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Digital sounds and images are used everywhere today, and they are all generated originally by analogue signals. On the other hand, in digital signal processing, the storage or transmission of digital data, such as music, videos or image files, necessitates converting such analogue signals into digital signals via sampling. When these data are sampled, the values from the discrete, sampled points are kept while the information between the sampled points is lost. Various techniques have been developed over the years to recover this lost data, but the results remain incomplete. Professor Yutaka Yamamoto's research is focused on improving how we can recover or reconstruct the original analogue data.

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19

Astharini, Dwi, Tsaura Aulia, Putri Wulandari, Rahmat Alamtaha, and Rifqy Afisha. "GUI Design for Comparative Output Signal DFT and DTFT with LabVIEW." INSIST 4, no. 1 (April 1, 2019): 210. http://dx.doi.org/10.23960/ins.v4i1.210.

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An application GUI (Graphical User Interface) is designed as a software simulation to compare the output signal of DFT and DTFT. DFT (Discrete Fourier Transform) and DTFT (Discrete Time Fourier Transform) are part of the digital signal processing. Digital signal processing is an analog signal processing method uses a mathematical technique to perform a transformation or retrieving information in digital form. One of the benefits of digital signal processing is to facilitate the representation of the signal, because the signal in digital form will be more visible, easily processed and has high accuracy. This GUI application designed using LabVIEW from National Instruments. LabVIEW is a software graphical programming or a block diagram. LabVIEW program known as VI or Virtual Instrument. The input signal in this application is a square signal

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20

Kozlov, I. O., E. A. Zherebtsov, K. V. Podmasteryev, and A. V. Dunaev. "Digital Laser Doppler Flowmetry: Device, Signal Processing Technique, and Clinical Testing." Biomedical Engineering 55, no. 1 (May 2021): 12–16. http://dx.doi.org/10.1007/s10527-021-10061-7.

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21

Rong, G. H., and K. Slater. "Analysis of Yarn Unevenness by Using a Digital-signal-processing Technique." Journal of the Textile Institute 86, no. 4 (January 1995): 590–99. http://dx.doi.org/10.1080/00405009508659037.

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22

Simoes, P. C. P. S., J. C. Martins, and C. M. B. A. Correia. "A new digital signal processing technique for applications in nuclear spectroscopy." IEEE Transactions on Nuclear Science 43, no. 3 (June 1996): 1804–9. http://dx.doi.org/10.1109/23.507226.

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23

Nakhostin, M., T. Oishi, and M. Baba. "Time resolution measurement of avalanche counters using digital signal processing technique." Radiation Measurements 43, no. 9-10 (October 2008): 1493–97. http://dx.doi.org/10.1016/j.radmeas.2008.05.016.

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24

Nam Quoc Ngo and Binh. "Synthesis of tunable optical waveguide filters using digital signal processing technique." Journal of Lightwave Technology 24, no. 9 (September 2006): 3520–31. http://dx.doi.org/10.1109/jlt.2006.880160.

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25

Javidi, Giti, and Ehsan Sheybani. "Application of Digital Signal Processing in USRP Satellite Signal Detection." International Journal of Interdisciplinary Telecommunications and Networking 9, no. 2 (April 2017): 16–25. http://dx.doi.org/10.4018/ijitn.2017040102.

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The Universal Software Radio Peripheral development technique is designing and implementing radio frequency based systems. The distinctiveness originates from the interchangeable daughterboard within the USRP. The system is designed around the Xilinx Vertex 3 FPGA chip. This means C++, Python, and VHDL can be used to program this device. The project consists of creating a receiver. The objective of the project is to research and comprehend the hardware functionalities of the USRP. The purpose is to create codes in C++ and Python to implement receiving capabilities of the device. The goal of this project was to design a GPS receiver that is capable of recording the L1 signal from a DirecTV satellite. The USRP is used a lot for research. This project consisted of more than just one method. We used GNU Radio Companion and Matlab/Simulink. GNU Radio is open source for building software defined radios. It is also known as GRC. While using GRC the USRP1 was the device used. This software has rapid development. It runs in Ubuntu, a Linux operating system. Within this software there are logic blocks. Each block consists of information to create a flow graph. The flow graph builds and generates the program. Simulink can be compared to GRC. They both have logic blocks that have to be connected to run. Simulink can be used to create a transmitter or a receiver for software radio development and signal processing. Software-defined radio can only be defined if its baseband operations can be completely defined by software. A SDR converts digital to analog signals. The USRP can also convert digital signals from a computer to Radio Frequency Signals (RF). This software is one way to communicate between hardware and software.

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26

Känsäkoski, M., O. Voutilainen, and T. Seppänen. "The Performance of near Infrared Analysers Can Be Improved by Digital Filtering Techniques." Journal of Near Infrared Spectroscopy 6, no. 1 (January 1998): 97–104. http://dx.doi.org/10.1255/jnirs.126.

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On-line near infrared (NIR) analysers are used widely for quantitative composition measurements in real-time process control systems. The accuracy and repeatability of the measurements are amongst the most important factors when evaluating the total performance of these analysers, but the lower detection limit is often limited by noise in the measurement signal. There are two major alternatives for reducing noise in an optical analyser: prevention of noise contamination and post-processing of the signal by filtering. In the second alternative, the measurement signal can be post-processed by digital filtering techniques, for example, to enhance the desired signal component. Although digital signal processing (DSP) technology offers many advantages for on-line process measurements, the behaviour of the signal must be understood thoroughly before a successful application of this technology can be developed. A digital filtering technique called matched filter was used in an experimental set-up. The performance of this filter was compared to an analog filtering of a pulse shaped signal. Experimental data were collected and filtered with a novel digital spectrometer which consists of a modulated light source, a spectrograph, a linear array detector and the analog and digital signal processing electronics needed to control and filter the signal. In this case the matched filter gave a clear improvement of 2.2–4.6 dB in the signal-to-noise ratio (SNR) relative to an analog lock-in amplifier. Among the other advantages afforded by digital filters are that they are programmable, easy to design, test and implement on a PC and do not suffer from drift. Also digital filters are extremely stable with respect to both time and temperature and versatile in their ability to process signals in a variety of ways.

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27

Smith, Steward G., Ralph W. Morgan, and Julian Payne. "ASIC techniques for high-performance digital signal processing." Annales des Télécommunications 46, no. 1-2 (January 1991): 40–48. http://dx.doi.org/10.1007/bf02995434.

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28

Mikkelsen, H. F. "Using digital signal processing techniques in light controllers." IEEE Transactions on Consumer Electronics 39, no. 2 (May 1993): 122–30. http://dx.doi.org/10.1109/30.214817.

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29

Xiong, Yu Shu, and Sheng Yi Ran. "Research on Broadband Power Amplifier Adaptive Digital Pre-Distortion Based on FPGA Technology." Applied Mechanics and Materials 556-562 (May 2014): 1761–65. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.1761.

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In this paper, we use the adaptive function model to improve FPGA digital pre-distortion technique, design the adaptive pre-distortion function and algorithm, and adopt PC-CFR technique to process digital distortion problem in high order modulation, and obtain very good effect of digital signal processing. In order to verify the validity and reliability of PC-CFR technology, we use Agilent 89600 VSA software to exquisite power output signal before and after the broadband power amplifier pre-distortion. The results shows that before PC-CFR pre-distortion processing, phase difference of AM/PM reaches a maximum of 0.258, signal distortion is more serious; and after the PC-CFR pre-distortion processing, phase difference is only 0.021, the minimum is 0.005, which controls the distortion signal and improves the signal transmission quality. It provides technical reference to study the broadband power amplifier technology.

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30

Laddomada, M., G. J. Dolecek, L. Yong Ching, Fa-Long Luo, M. Renfors, and L. Wanhammar. "Editorial: Advanced techniques on multirate signal processing for digital information processing." IET Signal Processing 5, no. 3 (2011): 313. http://dx.doi.org/10.1049/iet-spr.2011.9058.

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31

R.Prashanth, R. Prashanth. "A Low-Power VLSI Technique for Digital Signal Processing Portable Electronic Devices." IOSR journal of VLSI and Signal Processing 2, no. 2 (2013): 20–24. http://dx.doi.org/10.9790/4200-0222024.

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32

Sidhu, T. S. "Accurate measurement of power system frequency using a digital signal processing technique." IEEE Transactions on Instrumentation and Measurement 48, no. 1 (1999): 75–81. http://dx.doi.org/10.1109/19.755064.

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33

Ning-Ning Feng, Gui-Rong Zhou, and Wei-Ping Huang. "Mode calculation by beam propagation method combined with digital signal processing technique." IEEE Journal of Quantum Electronics 39, no. 9 (September 2003): 1111–17. http://dx.doi.org/10.1109/jqe.2003.816096.

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34

Kelleci, Burak. "Pulse Suppression Technique for Mitigating Digital Clock Noise." Circuits, Systems, and Signal Processing 33, no. 5 (November 6, 2013): 1325–36. http://dx.doi.org/10.1007/s00034-013-9697-x.

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35

Rawski, Mariusz, Bogdan Falkowski, and Tadeusz Łuba. "Digital signal processing designing for FPGA architectures." Facta universitatis - series: Electronics and Energetics 20, no. 3 (2007): 437–59. http://dx.doi.org/10.2298/fuee0703437r.

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This paper presents the discussion on efficiency of different implementation methodologies of DSP algorithms targeted for modern FPGA architectures. Modern programmable structures are equipped with specialized DSP embedded blocks that allow implementing digital signal processing algorithms with use of the methodology known from digital signal processors. On the first place however, programmable architectures give the designer the possibility to increase efficiency of designed system by exploitation of parallelism of implemented algorithms. Moreover, it is possible to apply special techniques such as distributed arithmetic (DA) that will boost the performance of designed processing systems. Additionally, application of the functional decomposition based methods, known to be best suited for FPGA structures allows utilizing possibilities of programmable technology in very high degree. The paper presents results of comparison of different design approaches in this area.

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Campanella, R. G., and W. P. Stewart. "Seismic cone analysis using digital signal processing for dynamic site characterization." Canadian Geotechnical Journal 29, no. 3 (June 1, 1992): 477–86. http://dx.doi.org/10.1139/t92-052.

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In situ measurement of the dynamic characteristics of surficial soils is becoming more common in geotechnical practice for prediction of ground-surface motions from earthquake excitation and to evaluate foundations for vibrating equipment. Techniques for these measurements have been under development at the University of British Columbia (UBC) since 1980. The paper discusses many practical considerations with respect to equipment (sources, receivers, trigger, etc.) and procedures that can affect the interpretation and analysis of seismic cone results. A brief review is given of the cross-over method as used at UBC to determine interval shear velocity travel times from downhole seismic cone testing. A more detailed description is provided for the cross-correlation technique used in the frequency domain, which has recently been incorporated into the analysis procedure. Comparisons of these two methods are presented and discussed. It has been found useful to isolate the main shear wave before further calculations, and the effects of this procedure are provided. A summary of findings concerning the characteristics of the measured signals is also included. Key words : seismic, cone penetrometer, sources, receivers, accelerometers, shear wave, velocity, downhole, digital, signal processing.

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A.O., Bello, and Kabari L.G. "Digital Signal Processing for Predicting Stock Prices." British Journal of Computer, Networking and Information Technology 4, no. 2 (September 5, 2021): 12–21. http://dx.doi.org/10.52589/bjcnit-xnp3ubpl.

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With the exponential growth of big data and data warehousing, the amount of data collected from various stock markets around the world has increased significantly. It is now impossible to process and analyze data using mathematical techniques and basic statistical calculations to forecast trends such as closing and opening prices, as well as daily stock market lows and highs. The development of smart and automated stock market forecasting systems has made significant progress in recent years. Digital signal processing is required for analysis and preprocessing because of the accuracy and speed with which these large amounts of data must be processed and analyzed. In this paper, we evaluate some of these predictive algorithms based on three parameters such as speed, accuracy and complexity, we analyze the data using the dataset from kaggle.com and we implement these algorithms using pythons. The results of our analysis in this paper shows a significant correlation between the yearly prices until the year 2018 where there is a significant increase in stock price.

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Raghavendra, V., N. Vinay kumar, and Manish Kumar. "Latest advancement in image processing techniques." International Journal of Engineering & Technology 7, no. 2.12 (April 3, 2018): 390. http://dx.doi.org/10.14419/ijet.v7i2.12.11357.

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Image processing is method of performing some operations on an image, for enhancing the image or for getting some information from that image, or for some other applications is nothing but Image Processing [1]. Image processing is one sort of signal processing, where input is an image and output may be an image, characteristics of that image or some features that image [1]. Image will be taken as a two dimensional signal and signal processing techniques will be applied to that two dimensional image. Image processing is one of the growing technologies [1]. In many real time applications image processing is widely used. In the field of bio technology, computer science, in medical field, envi-ronmental areas etc., image processing is being used for mankind benefits. The following steps are the basics of image processing:Image is taken as an inputImage will be processed (manipulation, analyzing the image, or as per requirement)Altered image will be the outputImage processing is of two typesAnalog Image Processing:As the name implies, analog image processing is applied on analog signals. Television image is best example of analog signal processing [1].(DIP) Digital Image Processing:DIP techniques are used on images, which are in the format of digital for processing them, and get the required output as per the application. Operations were applied on the digital images for processing [1].In this paper, we will discuss about the technologies or tools for image processing especially by using Open CV. With the help of Open CV image processing will be very easy and efficient. When Open CV is collaborated or integrated with python the results are mind blowing. We will discuss about the process of using python and Open CV.

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Hiam, Sohani Munteha. "Implementation of Optimized Filter Order to the PSD Analysis of QRS Detected ECG Signal." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 4 (November 30, 2022): 33–37. http://dx.doi.org/10.35940/ijrte.d7328.1111422.

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Filtration of ECG signal is an important part in the analysis and signal processing of ECG signal. There are different types of digital and analog filters available for the filtration of an ECG signal. And choosing the right filter order for different filters has always been a tough task in biomedical signal processing. This study aims to solve this problem of filter order optimization by cascading digital filters and evaluating their performance based on SNR using ECG-ID Database from PHYSIONET. Besides, this optimized filter order has been applied to both normal and abnormal QRS detected ECG signals to choose the right filter order based on average power received from PSD analysis. QRS detection has been performed by differentiation technique. In this study, design of the filters has been carried out using MATLAB software-based FDA tool in both monitoring and diagnostic modes. This analysis has strong potential for analyzing low amplitude based biological signals like ECG or EEG.

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Ranjan, Rakesh, and Dr Vinay Avasthi. "Enhanced Edge Detection Technique in Digital Images Using Optimised Fuzzy Operation." Webology 19, no. 1 (January 20, 2022): 5402–16. http://dx.doi.org/10.14704/web/v19i1/web19362.

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In image processing, edge detection is a critical issue. Edge detection is a key approach for evaluating the edge of various objects in a digital image. These edges are found using the gradients, which are present in the image. The intensity and value of pixels determine the gradients. In digital images, edge detection lowers the quantity of data and filters out irrelevant data while maintaining the image's key structural features. In this paper, a new edge detection approach based on a fuzzy rule-based system is proposed. In digital image processing, the proposed method typically depends on fuzzy logic systems. The main goal of this system is to show how fuzzy logic may be used in image processing. This paper provides a fuzzy logic-based edge detection technique that uses a sharpening Gabor filter to regulate edge quality and a Gaussian filter to reduce noise caused by sharpening. This is determined by utilizing applications such as “Peak Signal to Noise Ratio (PSNR) F-Measure, and Hausdorff distance (HoD) to prove that fuzzy logic outperforms the proposed system. The findings for edge detection approaches are included in high quality. The proposed approach outperforms most commonly used traditional edge detection methods. The proposed method also reduces the number of noisy features and may be used for a wide range of images.

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Zhong, Meisu, Yongsheng Yang, Yamin Zhou, M. Octavian Postolache, M. Chandrasekar, G. Venkat Babu, C. Manikandan, V. S. Balaji, S. Saravanan, and V. Elamaran. "Advanced Digital Signal Processing Techniques on the Classification of the Heart Sound Signals." Journal of Medical Imaging and Health Informatics 10, no. 9 (August 1, 2020): 2010–15. http://dx.doi.org/10.1166/jmihi.2020.3127.

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Speech processing subject primarily depends on the digital signal processing (DSP) methods, such as convolution, discrete Fourier transform (DFT), fast Fourier transforms (FFT), finite impulse response (FIR) and infinite impulse response (IIR) filters, FFT recursive and non-recursive digital filters, FFT processing, random signal theory, adaptive filters, upsampling and downsampling, etc. Recursive and non-recursive digital filters are primarily deployed to absorb the signal of interest signals and to block the unwanted signals (noise). Broadly, low-pass, high-pass, band-pass, and band-stop filters are implemented for filtering functions. In frequent, the DSP theories can be used for further biomedical engineering domains like biomedical imaging (MRI, ultrasound, CT, X-ray, PET) and genetic signal analysis-cum-processing too. In this article, the experiments such as voiced/unvoiced detection, formants estimation using FFT and spectrograms, pitch estimation and tracking and yes/no sound classification are used. Also, the analysis of normal/abnormal heart sound signals using simple energy computation and the zero-crossing rate and their results are obtained. For the entire study, the Matlab R2018a tool is used to obtain the simulation results. At last, the criticism, feedbacks, comments, reactions from the student are detailed for the exceptional development of the course.

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Worth, Daniel. "A Method for Implementing Force-Limited Vibration Control." Journal of the IEST 40, no. 4 (July 31, 1997): 34–41. http://dx.doi.org/10.17764/jiet.2.40.4.u00q32736352n282.

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NASA/Goddard Space Flight Center (NASA/GSFC) has implemented force-limited vibration control on a controller which can only accept one profile. The method uses a personal computer-based digital signal processing board to convert force and/or moment signals into what appears to be an acceleration signal to the controller. This technique allows test centers with older controllers to use the latest force-limited control techniques for random vibration testing. The paper describes the method, hardware, and test procedures used. An example from a test performed at NASA/GSFC is used as a guide.

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Lorino, H., C. Mariette, M. Karouia, and A. M. Lorino. "Influence of signal processing on estimation of respiratory impedance." Journal of Applied Physiology 74, no. 1 (January 1, 1993): 215–23. http://dx.doi.org/10.1152/jappl.1993.74.1.215.

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Respiratory impedance was estimated between 4 and 30 Hz by spectral analysis of the mouth flow and pressure signals measured in spontaneously breathing subjects when applying a pseudorandom pressure excitation at the mouth. The signals were submitted to antialiasing low-pass filtering followed by digital preprocessing before the calculation of spectra by a fast Fourier transform algorithm. The effectiveness of signal preprocessing in eliminating the leakage error due to breathing noise was illustrated in both a mechanical analogue and a patient. Five preprocessing techniques that combined high-pass filtering and windowing were then compared in 32 randomly selected patients by examining the influence of these techniques on 1) the values of impedance at 5, 10, and 20 Hz, and 2) the parameters of linear models fitting the real (Zr) and imaginary (Zi) parts of impedance for coherence values higher than a preset threshold. The impedance values and derived parameters were either the mean of the estimates separately obtained in the three data recordings (PA) or the single estimate obtained from average spectra (SP). Small but significant differences between filtering and windowing, as well as between SP and PA, were evidenced for the Zr, whereas Zi was only slightly sensitive to the type of averaging technique. We conclude that the signal preprocessing and data averaging techniques selected in this study have similar effects on spectral estimation of respiratory impedance.

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Darlis, Arsyad Ramadhan. "Fractal Communication System Using Digital Signal Processing Starter Kit (DSK) TMS320c6713." ComTech: Computer, Mathematics and Engineering Applications 6, no. 4 (December 1, 2015): 613. http://dx.doi.org/10.21512/comtech.v6i4.2202.

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In 1992, Wornell and Oppenheim did research on a modulation which is formed by using wavelet theory. In some other studies, proved that this modulation can survive on a few channels and has reliability in some applications. Because of this modulation using the concept of fractal, then it is called as fractalmodulation. Fractal modulation is formed by inserting information signal into fractal signals that are selffractal similary. This modulation technique has the potential to replace the OFDM (Orthogonal Frequency Division Multiplexing), which is currently used on some of the latest telecommunication technologies. The purpose of this research is to implement the fractal communication system using Digital Signal Processing Starter Kit (DSK) TMS320C6713 without using AWGN and Rayleigh channel in order to obtain the ideal performance of the system. From the simulation results using MATLAB7.4. it appears that this communication system has good performance on some channels than any other communication systems. While in terms of implementation by using (DSK) via TMS320C6713 Code Composer Studio (CCS), it can be concluded that thefractal communication system has a better execution time on some tests.

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., Umashanker Sahu. "DIGITAL SIGNAL PROCESSING TECHNIQUES FOR LTI FIBER IMPAIRMENT COMPENSATION." International Journal of Research in Engineering and Technology 02, no. 10 (October 25, 2013): 168–72. http://dx.doi.org/10.15623/ijret.2013.0210024.

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Myers, D. G., Azizul H. Quazi, and Shakila A. Quazi. "Digital Signal Processing—Efficient Convolution and Fourier Transform Techniques." Journal of the Acoustical Society of America 91, no. 1 (January 1992): 536. http://dx.doi.org/10.1121/1.402719.

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Andria, Gregorio, Filippo Attivissimo, and Nicola Giaquinto. "Digital signal processing techniques for accurate ultrasonic sensor measurement." Measurement 30, no. 2 (September 2001): 105–14. http://dx.doi.org/10.1016/s0263-2241(00)00059-2.

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Eriksson, Larry John. "Active sound attenuation using adaptive digital signal processing techniques." Journal of the Acoustical Society of America 79, no. 2 (February 1986): 575. http://dx.doi.org/10.1121/1.393503.

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Conway, G. D., and J. A. Elliott. "Digital signal processing techniques for plasma dispersion curve measurements." Journal of Physics E: Scientific Instruments 20, no. 11 (November 1987): 1341–50. http://dx.doi.org/10.1088/0022-3735/20/11/006.

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Pan, Zhongqi, Junyi Wang, and Yi Weng. "Digital signal processing techniques in Nyquist-WDM transmission systems." Photonic Network Communications 32, no. 2 (January 12, 2016): 236–45. http://dx.doi.org/10.1007/s11107-015-0598-8.

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