Relevant bibliographies by topics / Mixed signal processing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Mixed signal processing'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Mixed signal processing"

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Qi, Yang, Taichu Shi, and Ben Wu. "Wideband Mixed Signal Separation Based on Photonic Signal Processing." Telecom 2, no. 4 (2021): 413–29. http://dx.doi.org/10.3390/telecom2040024.

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The growing needs for high-speed and secure communications create an increasing challenge to the contemporary framework of signal processing. The coexistence of multiple high-speed wireless communication systems generates wideband interference. To protect the security and especially the privacy of users’ communications requires stealth communication that hides and recovers private information against eavesdropping attacks. The major problem in interference management and stealth information recovery is to separate the signal of interest from wideband interference/noise. However, the increasing
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Zou, JinFeng, Yi-an Cui, and Jing Xie. "Self-potential signal processing based on NMF." Journal of Physics: Conference Series 2895, no. 1 (2024): 012023. https://doi.org/10.1088/1742-6596/2895/1/012023.

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Abstract In recent years, new algorithms have been continuously applied in the field of geophysical data processing, all of which have achieved good results. However, there is currently no dedicated signal separation method for self-potential field signal processing. In this paper, we propose a self-potential signal separation algorithm based on non-negative matrix factorization (NMF) to perform blind source signal separation. We aim to separate different self-potential signals from the collected mixed signals, laying the foundation for subsequent work such as feature recognition. We utilized
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Vogel, Ch, St Mendel, P. Singerl, and F. Dielacher. "Digital signal processing for data converters in mixed-signal systems." e & i Elektrotechnik und Informationstechnik 126, no. 11 (2009): 390–95. http://dx.doi.org/10.1007/s00502-009-0689-2.

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Celik, Husamettin, and Nurhan Karaboga. "Blind Source Separation with Multi-Objective Optimization for Denoising." Elektronika ir Elektrotechnika 28, no. 5 (2022): 62–67. http://dx.doi.org/10.5755/j02.eie.31232.

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Blind Source Separation is an optimization method frequently used in statistical signal processing applications. There are many application areas such as ambient listening, denoising, signal detection, and so on. In this study, a new Strength Pareto Evolutionary Algorithm 2-based signal separation method is proposed, which combines Multi-Objective Optimization and Blind Source Separation algorithms. The proposed method has been tested for denoising, which is widely used in biomedical signal processing. That is, the Electrocardiogram (ECG) and White Gaussian Noise are mixed together with normal
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Zhao, Xiaoming, Qiang Tuo, Ruosi Guo, and Tengteng Kong. "Research on Music Signal Processing Based on a Blind Source Separation Algorithm." Annals of Emerging Technologies in Computing 6, no. 4 (2022): 24–30. http://dx.doi.org/10.33166/aetic.2022.04.003.

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The isolation of mixed music signals is beneficial to the extraction and identification of music signal features and to enhance music signal quality. This paper briefly introduced the mathematical model for separating blind source from mixed music signals and the traditional Independent Component Analysis (ICA) algorithm. The separation algorithm was optimized by the complex neural network. The traditional and optimized ICA algorithms were simulated in MATLAB software. It was found that the time-domain waveform of the signal isolated by the improved ICA-based separation algorithm was closer to
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Shumway, R. H., E. Smart, and D. A. Clauter. "Mixed Signal Processing for Regional and Teleseismic Arrays." Bulletin of the Seismological Society of America 98, no. 1 (2008): 36–51. http://dx.doi.org/10.1785/0120060140.

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Li, Yanping, Linyan Wu, Tao Wang, Nuo Gao, and Qi Wang. "EEG Signal Processing Based on Genetic Algorithm for Extracting Mixed Features." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 06 (2019): 1958008. http://dx.doi.org/10.1142/s0218001419580084.

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In order to improve the classification of motor imagery EEG accuracy, this paper proposes a method based on Genetic Algorithm (GA) EEG signal classification method to extract mixed characteristics. This method uses wavelet analysis and Hilbert–Huang Transform (HHT) to analyze EEG signals and optimizes the characteristics through Common Spatial Patterns (CSP). Finally, the 14 sub features are optimized by GA, and the weights and data credibility of different sub features are obtained. The experiment was tested with 2003BCI competition data and the EEG signal collected by the laboratory. The acc
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Deng, Jun, Hua Yong Tan, Lun Cai Liu, and Lin Tao Liu. "Research of a Mixed-Signal Programmable SoC Based on FPAA." Applied Mechanics and Materials 556-562 (May 2014): 1741–44. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.1741.

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This paper presents a novel architecture for mixed-signal SoC, which integrates a Field Programmable Analog Array (FPAA) into a SoC based on 32-bit RISC CPU. The FPAA unit can be configured as Filter, Comparator, Gain Amplifier, and so on. The proposed mixed-signal SoC can transform the intermediate frequency (IF) analog signal to baseband digital signal and realize the real-time baseband signal processing, besides this, which can transmit the modulated IF signals which are converted from baseband signals by digital up-conversion (DUC). The proposed mixed-signal SoC is a transceiver on chip ac
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Song, Ning, Lian Ying Ji, and Yong Peng Xu. "Denoising of the Respiratory Signal of Electrical Bio-Impedance." Advanced Materials Research 718-720 (July 2013): 1024–28. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1024.

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Human respiratory signal provides important information in modern medical care. In daily life, respiratory signal is usually captured under different motion states with the help of Electrical impedance pneumography (EIP). Consequently, the captured signal is easily corrupted by electronic/electromagnetic noise, internal mechanical vibration of the lung and motion artifacts. Because respiratory signal and interferences co-exist in an overlapping spectra manner, classical filtering method cannot work here. In this paper, we present a new signal processing method for eliminating the noise and int
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Jin, Xiang Yang, and Shi Sheng Zhong. "Research on Identification Method of Multi-Mixed Vibration Signal of Aeroengine." Key Engineering Materials 419-420 (October 2009): 801–4. http://dx.doi.org/10.4028/www.scientific.net/kem.419-420.801.

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The effectiveness of separation and identification of mechanical signals vibrations is crucial to successful fault diagnosis in the condition monitoring and diagnosis of complex machines.Aeroengine vibration signals always include many complicated components, blind source separation (BSS) provides a efficient way to separate the independent component.In order to get the most effective algorithm of vibration signal separation,experiment has been done to acquire plenty of multi-mixed rotor vibration signals,three sets of vibration data generated from aeroengine rotating shafts were separated fro
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Dissertations / Theses on the topic "Mixed signal processing"

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Pai, Hung-Chuan. "Analytical methods for mixed signal processing systems /." The Ohio State University, 1998. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487949508368344.

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Figueroa, Toro Miguel E. "Adaptive signal processing and correlational learning in mixed-signal VLSI /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/6856.

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Papathanasiou, Konstandinos. "Palmo : a novel pulsed based signal processing technique for programmable mixed-signal VLSI." Thesis, University of Edinburgh, 1998. http://hdl.handle.net/1842/12759.

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Sengupta, Arindam. "Multidimensional Signal Processing Using Mixed-Microwave-Digital Circuits and Systems." University of Akron / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=akron1407977367.

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Chen, Jin. "Fault modeling and test techniques for analog and mixed-signal circuits /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

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Chiang, Da-Ming. "Parametric signal processing techniques for model mismatch and mixed parameter estimation /." The Ohio State University, 1996. http://rave.ohiolink.edu/etdc/view?acc_num=osu14879355737735.

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Nease, Stephen H. "Neural and analog computation on reconfigurable mixed-signal platforms." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53999.

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This work addresses neural and analog computation on reconfigurable mixed-signal platforms. Many engineered systems could gain tremendous benefits by emulating neural systems. For example, neural systems are incredibly power efficient and fault-tolerant. They are also capable of types of computation that we cannot yet match with conventional computers. Neuromorphic engineers typically implement neural computation using analog circuits because they are low-power and naturally model some aspects of neurobiology. One problem with analog circuits is that they are typically inflexible. To address t
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Unno, Takahiro. "An improved mixed excitation linear predicitive (MELP) coder." Thesis, Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/13270.

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Saalfeld, Tobias [Verfasser]. "Mixed-Signal Complex Baseband Processing in Multi-Standard Radio Receivers / Tobias Saalfeld." München : Verlag Dr. Hut, 2021. http://d-nb.info/1233525611/34.

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Wunderlich, Richard Bryan. "Floating-gate-programmable and reconfigurable, digital and mixed-signal systems." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51815.

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This body of work as whole has the theme of using floating-gates and reconfigurable systems to explore and implement non-traditional computing solutions to difficult problems. Various computational methodologies are used simultaneously to solve problems by mapping pieces of them to the appropriate type of computer. There exists no systematic approach to simultaneously apply analog, digital, and neuromorphic techniques to solving general problems. Typically, this is a very difficult task, and one that few attempt to undertake. However, when done right, solutions can be found with orders-of-magn
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Books on the topic "Mixed signal processing"

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Yuan, Fei. Injection-Locking in Mixed-Mode Signal Processing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-17364-7.

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Baker, R. Jacob. CMOS: Mixed signal circuit design. 2nd ed. John Wiley & Sons, 2009.

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Baker, R. Jacob. CMOS: Mixed signal circuit design. 2nd ed. IEEE Press, 2009.

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Kester, Walt. Mixed-signal and DSP design techniques. Analog Devices, 2000.

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Roberts, Gordon W. Analog signal generation for built-in-self-test of mixed-signal integrated circuits. Kluwer Academic Publishers, 1995.

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Alain, Vachoux, ed. Analog and mixed-signal hardware description languages. Kluwer Academic Publishers, 1997.

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Verghese, Nishath K. Simulation techniques and solutions for mixed-signal coupling in integrated circuits. Kluwer Academic Publishers, 1995.

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Fakhfakh, Mourad. Analog/RF and Mixed-Signal Circuit Systematic Design. Springer Berlin Heidelberg, 2013.

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Southwest Symposium on Mixed-Signal Design (1999 Tucson, Arizona). SSMDS 99: 1999 Southwest Symposium on Mixed-Signal Design : April 11-13, 1999, Tucson, Arizona, U.S.A. IEEE, 1999.

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Brian, Antao, ed. Modeling and simulation of mixed analog-digital systems. Kluwer Academic Publishers, 1996.

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Book chapters on the topic "Mixed signal processing"

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Currie, Edward H. "Analog Signal Processing." In Mixed-Signal Embedded Systems Design. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70312-7_11.

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Currie, Edward H. "Digital Signal Processing." In Mixed-Signal Embedded Systems Design. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70312-7_12.

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Vainio, Olli. "Mixed Signal Techniques." In Handbook of Signal Processing Systems. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6859-2_25.

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Vainio, Olli. "Mixed Signal Techniques." In Handbook of Signal Processing Systems. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6345-1_20.

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Moschytz, George S. "Mixed-Mode Signal Processing." In Analog Circuit Theory and Filter Design in the Digital World. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00096-7_1.

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Currie, Edward H. "Embedded System Processing and I/O Protocols." In Mixed-Signal Embedded Systems Design. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70312-7_4.

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Abdinia, Sahel, Arthur H. M. van Roermund, and Eugenio Cantatore. "Analogue and Mixed-Signal Circuit Design." In Analog Circuits and Signal Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21188-6_5.

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Gustard, N. C., and R. E. Massara. "On the Optimal Design of Switched-Capacitor Filter Circuits for Analog and Mixed-Signal Integrated Circuit Realization." In Analog Signal Processing. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4757-4503-0_4.

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Bizzarri, Federico, Angelo Brambilla, Giambattista Gruosso, and Giancarlo Storti Gajani. "Steady State Simulation of Mixed Analog/Digital Circuits." In Integrated Circuits for Analog Signal Processing. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1383-7_11.

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Yuan, Fei. "Injection-Locking of Oscillators: An Overview." In Injection-Locking in Mixed-Mode Signal Processing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17364-7_1.

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Conference papers on the topic "Mixed signal processing"

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Guo, Xin, Yuxia Bie, Xiaoyu Wang, and Xiping Wu. "AOS mixed channel scheduling strategy." In 4th International Conference on Signal Image Processing and Communication, edited by Xianye Ben and Lei Chen. SPIE, 2024. http://dx.doi.org/10.1117/12.3041909.

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Suresh, Spoorthi Kalkunte, and Uma Damotharan. "Kannada-English Code-Mixed Speech Synthesis." In 2024 International Conference on Signal Processing and Communications (SPCOM). IEEE, 2024. http://dx.doi.org/10.1109/spcom60851.2024.10631613.

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Wang, Shoubin, Yitian Song, Zhu Ming, and Lei Shen. "Single Channel Mixed Signal Demodulation Algorithm Based on Oversampling." In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2024. http://dx.doi.org/10.1109/icsp62122.2024.10743731.

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Tsividis, Yannis. "Mixed-Domain Signal Processing." In 2006 IEEE Dallas/CAS Workshop on Design, Applications, Integration and Software. IEEE, 2006. http://dx.doi.org/10.1109/dcas.2006.321024.

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"Signal processing." In 2015 MIXDES - 22nd International Conference "Mixed Design of Integrated Circuits & Systems". IEEE, 2015. http://dx.doi.org/10.1109/mixdes.2015.7208589.

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"Signal Processing." In 2018 25th International Conference "Mixed Design of Integrated Circuits and System" (MIXDES). IEEE, 2018. http://dx.doi.org/10.23919/mixdes.2018.8436682.

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"Signal processing." In 2017 MIXDES - 24th International Conference "Mixed Design of Integrated Circuits and Systems". IEEE, 2017. http://dx.doi.org/10.23919/mixdes.2017.8005262.

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"Signal Processing." In 2019 MIXDES - 26th International Conference "Mixed Design of Integrated Circuits and Systems". IEEE, 2019. http://dx.doi.org/10.23919/mixdes.2019.8787076.

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"Signal processing." In 2016 MIXDES - 23rd International Conference "Mixed Design of Integrated Circuits and Systems". IEEE, 2016. http://dx.doi.org/10.1109/mixdes.2016.7529771.

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"Signal Processing." In 2020 27th International Conference on Mixed Design of Integrated Circuits and System (MIXDES). IEEE, 2020. http://dx.doi.org/10.23919/mixdes49814.2020.9155793.

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Reports on the topic "Mixed signal processing"

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Shumway, Robert H. Advances in Mixed Signal Processing for Regional and Teleseismic Arrays. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada458899.

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