Academic literature on the topic 'Conditioning and processing electrical signal'
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Journal articles on the topic "Conditioning and processing electrical signal"
Rei, Silviu, Dan Chicea, Beriliu Ilie, and Sorin Olaru. "Dynamic Light Scattering Signal Conditioning for Data Processing." ACTA Universitatis Cibiniensis 69, no. 1 (December 20, 2017): 130–35. http://dx.doi.org/10.1515/aucts-2017-0016.
Full textZulkiflli, Nur Amira, Kaviarasu Nandaguru, Omar Fahmi Arm, Feisal Mohamed Khamis, Ahmad Ridhwan Wahap, Fatin Aliah Phang Abdullah, Kian Sek Tee, Nurul Hidayat, and Jaysuman Pusppanathan. "Electrical Impedance Tomography Signal Conditioning for Lung Imaging Applications." Journal of Human Centered Technology 2, no. 2 (August 6, 2023): 78–87. http://dx.doi.org/10.11113/humentech.v2n2.58.
Full textPayo, Ismael, José L. Polo, Blanca López, Diana Serrano, Antonio M. Rodríguez, M. Antonia Herrero, Ana Martín-Pacheco, Inmaculada Sánchez, and Ester Vázquez. "Signal conditioning circuit for gel strain sensors." Smart Materials and Structures 31, no. 1 (November 25, 2021): 015020. http://dx.doi.org/10.1088/1361-665x/ac36e0.
Full textCelka, Patrick, Rolf Vetter, Philippe Renevey, Christophe Verjus, Victor Neuman, Jean Luprano, Jean-Dominique Decotignie, and Christian Piguet. "Wearable biosensing: signal processing and communication architectures issues." Journal of Telecommunications and Information Technology, no. 4 (December 30, 2005): 90–104. http://dx.doi.org/10.26636/jtit.2005.4.340.
Full textAllén, Markus, Jaakko Marttila, and Mikko Valkama. "Modeling and mitigation of nonlinear distortion in wideband A/D converters for cognitive radio receivers." International Journal of Microwave and Wireless Technologies 2, no. 2 (April 2010): 183–92. http://dx.doi.org/10.1017/s1759078710000292.
Full textRiches, S. T., C. Johnston, M. Sousa, and P. Grant. "High Temperature Endurance of Packaged SOI Devices for Signal Conditioning and Processing Applications." Additional Conferences (Device Packaging, HiTEC, HiTEN, and CICMT) 2011, HITEN (January 1, 2011): 000251–54. http://dx.doi.org/10.4071/hiten-paper8-sriches.
Full textWu, Chenning, Martin Hutton, and Manuchehr Soleimani. "Smart Water Meter Using Electrical Resistance Tomography." Sensors 19, no. 14 (July 10, 2019): 3043. http://dx.doi.org/10.3390/s19143043.
Full textde Faria, Gabriella Maria, Eugênia Gonzales Lopes, Eleonora Tobaldini, Nicola Montano, Tatiana Sousa Cunha, Karina Rabello Casali, and Henrique Alves de Amorim. "Advances in Non-Invasive Neuromodulation: Designing Closed-Loop Devices for Respiratory-Controlled Transcutaneous Vagus Nerve Stimulation." Healthcare 12, no. 1 (December 22, 2023): 31. http://dx.doi.org/10.3390/healthcare12010031.
Full textChen, Xiyuan, Loic Maxwell, Franklin Li, Amrita Kumar, Elliot Ransom, Tanay Topac, Sera Lee, Mohammad Faisal Haider, Sameh Dardona, and Fu-Kuo Chang. "Design and Integration of a Wireless Stretchable Multimodal Sensor Network in a Composite Wing." Sensors 20, no. 9 (April 29, 2020): 2528. http://dx.doi.org/10.3390/s20092528.
Full textNathan, Arokia. "Microsensors for physical signals: Principles, device design, and fabrication technologies." Canadian Journal of Physics 74, S1 (December 1, 1996): 115–30. http://dx.doi.org/10.1139/p96-844.
Full textDissertations / Theses on the topic "Conditioning and processing electrical signal"
Valero, Daniel. "Wireless Signal Conditioning." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc862776/.
Full textLee, Li 1975. "Distributed signal processing." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86436.
Full textEldar, Yonina Chana 1973. "Quantum signal processing." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/16805.
Full textIncludes bibliographical references (p. 337-346).
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Quantum signal processing (QSP) as formulated in this thesis, borrows from the formalism and principles of quantum mechanics and some of its interesting axioms and constraints, leading to a novel paradigm for signal processing with applications in areas ranging from frame theory, quantization and sampling methods to detection, parameter estimation, covariance shaping and multiuser wireless communication systems. The QSP framework is aimed at developing new or modifying existing signal processing algorithms by drawing a parallel between quantum mechanical measurements and signal processing algorithms, and by exploiting the rich mathematical structure of quantum mechanics, but not requiring a physical implementation based on quantum mechanics. This framework provides a unifying conceptual structure for a variety of traditional processing techniques, and a precise mathematical setting for developing generalizations and extensions of algorithms. Emulating the probabilistic nature of quantum mechanics in the QSP framework gives rise to probabilistic and randomized algorithms. As an example we introduce a probabilistic quantizer and derive its statistical properties. Exploiting the concept of generalized quantum measurements we develop frame-theoretical analogues of various quantum-mechanical concepts and results, as well as new classes of frames including oblique frame expansions, that are then applied to the development of a general framework for sampling in arbitrary spaces. Building upon the problem of optimal quantum measurement design, we develop and discuss applications of optimal methods that construct a set of vectors.
(cont.) We demonstrate that, even for problems without inherent inner product constraints, imposing such constraints in combination with least-squares inner product shaping leads to interesting processing techniques that often exhibit improved performance over traditional methods. In particular, we formulate a new viewpoint toward matched filter detection that leads to the notion of minimum mean-squared error covariance shaping. Using this concept we develop an effective linear estimator for the unknown parameters in a linear model, referred to as the covariance shaping least-squares estimator. Applying this estimator to a multiuser wireless setting, we derive an efficient covariance shaping multiuser receiver for suppressing interference in multiuser communication systems.
by Yonina Chana Eldar.
Ph.D.
Vasconcellos, Brett W. (Brett William) 1977. "Parallel signal-processing for everyone." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/9097.
Full textIncludes bibliographical references (p. 65-67).
We designed, implemented, and evaluated a signal-processing environment that runs on a general-purpose multiprocessor system, allowing easy prototyping of new algorithms and integration with applications. The environment allows the composition of modules implementing individual signal-processing algorithms into a functional application, automatically optimizing their performance. We decompose the problem into four independent components: signal processing, data management, scheduling, and control. This simplifies the programming interface and facilitates transparent parallel signal processing. For tested applications, our system both runs efficiently on single-processors systems and achieves near-linear speedups on symmetric-multiprocessor (SMP) systems.
by Brett W. Vasconcellos.
M.Eng.
Baran, Thomas A. (Thomas Anthony). "Conservation in signal processing systems." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/74991.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 205-209).
Conservation principles have played a key role in the development and analysis of many existing engineering systems and algorithms. In electrical network theory for example, many of the useful theorems regarding the stability, robustness, and variational properties of circuits can be derived in terms of Tellegen's theorem, which states that a wide range of quantities, including power, are conserved. Conservation principles also lay the groundwork for a number of results related to control theory, algorithms for optimization, and efficient filter implementations, suggesting potential opportunity in developing a cohesive signal processing framework within which to view these principles. This thesis makes progress toward that goal, providing a unified treatment of a class of conservation principles that occur in signal processing systems. The main contributions in the thesis can be broadly categorized as pertaining to a mathematical formulation of a class of conservation principles, the synthesis and identification of these principles in signal processing systems, a variational interpretation of these principles, and the use of these principles in designing and gaining insight into various algorithms. In illustrating the use of the framework, examples related to linear and nonlinear signal-flow graph analysis, robust filter architectures, and algorithms for distributed control are provided.
by Thomas A. Baran.
Ph.D.
South, Colin R. "Signal processing in a loudspeaking telephone." Thesis, Aston University, 1985. http://publications.aston.ac.uk/8053/.
Full textBoufounos, Petros T. 1977. "Signal processing for DNA sequencing." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/17536.
Full textIncludes bibliographical references (p. 83-86).
DNA sequencing is the process of determining the sequence of chemical bases in a particular DNA molecule-nature's blueprint of how life works. The advancement of biological science in has created a vast demand for sequencing methods, which needs to be addressed by automated equipment. This thesis tries to address one part of that process, known as base calling: it is the conversion of the electrical signal-the electropherogram--collected by the sequencing equipment to a sequence of letters drawn from ( A,TC,G ) that corresponds to the sequence in the molecule sequenced. This work formulates the problem as a pattern recognition problem, and observes its striking resemblance to the speech recognition problem. We, therefore, propose combining Hidden Markov Models and Artificial Neural Networks to solve it. In the formulation we derive an algorithm for training both models together. Furthermore, we devise a method to create very accurate training data, requiring minimal hand-labeling. We compare our method with the de facto standard, PHRED, and produce comparable results. Finally, we propose alternative HMM topologies that have the potential to significantly improve the performance of the method.
by Petros T. Boufounos.
M.Eng.and S.B.
Ponnala, Lalit. "Analysis of Genetic Translation using Signal Processing." NCSU, 2007. http://www.lib.ncsu.edu/theses/available/etd-02072007-174200/.
Full textWang, Yingying. "ADVANCED ANALOG SIGNAL PROCESSING FOR WIRELESS COMMUNICATIONS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1585776428631869.
Full textAli-Bakhshian, Mohammad. "Digital processing of analog information adopting time-mode signal processing." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=114237.
Full textLes technologies CMOS progressant vers les procédés 22 nm et au delà, la abrication des circuits analogiques dans ces technologies se heurte a de nombreuses limitations. Entre autres limitations on peut citer la réduction d'amplitude des signaux, la sensibilité aux effets du bruit thermique et la perte de fonctions précises de commutation. Le traitement de signal en mode temps (TMSP pour Time-Mode Signal Processing) est une technique que l'on croit être bien adapté pour résoudre un grand nombre de problèmes relatifs a ces limitations. TMSP peut être défini comme la détection, le stockage et la manipulation de l'information analogique échantillonnée en utilisant des quantités de temps comme variables. L'un des avantages importants de TMSP est la capacité à réaliser des fonctions analogiques en utilisant des structures logiques digitales. Cette technique a une longue histoire en terme d'application en électronique. Cependant, en raison du manque de certaines fonctions fondamentales, l'utilisation de variables en mode temps a été limitée à une utilisation comme étape intermédiaire dans le traitement d'un signal et toujours dans le contexte d'une conversion tension/courant-temps et temps-tension/courant. Ces conversions nécessitent l'inclusion de blocs analogiques qui vont a l'encontre de l'avantage numérique des TMSP. Cette thèse fournit un fondement approprié pour le développement de TMSP comme outil général de traitement de signal. En proposant le concept nouveau d'interruption de retard, une toute nouvelle approche asynchrone pour la manipulation de variables en mode temps est suggéré. Comme conséquence directe de cette approche, des techniques pratiques pour le stockage, l'addition et la soustraction de variables en mode temps sont présentées. Pour étendre l'implémentation digitale de TMSP à une large gamme d'applications, la conception d'un intégrateur (accumulateur) à double voie temps- à -temps est démontrée. cet intégrateur est ensuite utilisé pour implémenter un modulateur delta-sigma de second ordre.Enfin, pour démontrer l'avantage de TMSP, une Interface de très basse puissance, compacte et réglable pour capteurs capacitifs est présenté. Cette interface est composé d'un certain nombre de blocs de retard associés à des portes logiques typiques. Toutes les théories proposées sont soutenues par des résultats expérimentaux et des simulations post-layout. L'implémentation digitale des circuits proposés a été la première priorité de cette thèse. En effet, une implémentation des bloc avec des structures digitales permet des conceptions simples, synthétisable et reconfigurables où des circuits de calibration très abordables peuvent être adoptées pour éliminer les effets des variations de process.
Books on the topic "Conditioning and processing electrical signal"
Larson, William E. Universal signal conditioning amplifier. [Washington, D.C: National Aeronautics and Space Administration, 1994.
Find full textWebster, John G. Electrical measurement, signal processing, and displays. Boca Raton: CRC Press, 2003.
Find full text1932-, Webster John G., ed. Electrical measurement, signal processing, and displays. Boca Raton: CRC Press, 2004.
Find full textSeippel, Robert G. Transducer interfacing: Signal conditioning for process control. Englewood Cliffs, N.J: Prentice-Hall, 1988.
Find full textDas, Apurba. Signal Conditioning: An Introduction to Continuous Wave Communication and Signal Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Find full textVeatch, Donald W. Analog signal conditioning for flight-test instrumentation. Washington, D.C: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1986.
Find full textVeatch, Donald W. Analog signal conditioning for flight-test instrumentation. [Washington, DC]: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1987.
Find full textVeatch, Donald W. Analog signal conditioning for flight-test instrumentation. [Washington, DC]: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1987.
Find full textVeatch, Donald W. Analogue signal conditioning for flight test instrumentation. Neuilly sur Seine, France: North Atlantic Treaty Organization, Advisory Group for Aerospace Research and Development, 1986.
Find full textAl-Bassiouni, Abdel-Aziz Mahmoud. Optimum signal processing in distributed sensor systems. Monterey, Calif: Naval Postgraduate School, 1987.
Find full textBook chapters on the topic "Conditioning and processing electrical signal"
Barboni, Leonardo, and Maunzio Valle. "Signal Conditioning System Analysis for Adaptive Signal Processing in Wireless Sensors." In Lecture Notes in Electrical Engineering, 291–94. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3606-3_56.
Full textDas, Apurba. "Wavelets: Multi-Resolution Signal Processing." In Signal Conditioning, 243–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28818-0_10.
Full textBerns, Karsten, Alexander Köpper, and Bernd Schürmann. "Signal Processing." In Lecture Notes in Electrical Engineering, 197–226. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65157-2_7.
Full textLu, Julia Hsin-Lin, and Byunghoo Jung. "Sensor Conditioning Circuits." In Integrated Circuits for Analog Signal Processing, 223–42. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1383-7_10.
Full textSalvatori, Stefano, Sara Pettinato, and Maria Cristina Rossi. "A Configurable Readout Circuit for Detector Signal Conditioning." In Lecture Notes in Electrical Engineering, 220–29. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48711-8_26.
Full textSozański, Krzysztof. "Analog Signals Conditioning and Discretization." In Digital Signal Processing in Power Electronics Control Circuits, 23–81. London: Springer London, 2017. http://dx.doi.org/10.1007/978-1-4471-7332-8_2.
Full textSozański, Krzysztof. "Analog Signals Conditioning and Discretization." In Digital Signal Processing in Power Electronics Control Circuits, 23–72. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5267-5_2.
Full textYang, Zhengbo, Jiaquan Ye, Jing Liu, Ping Yang, and Fei Liang. "Digital Signal Processing Technology of DVOR Navigation Signal." In Lecture Notes in Electrical Engineering, 290–95. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6504-1_37.
Full textNatarajan, Dhanasekharan. "Overview of Digital Signal Processing." In Lecture Notes in Electrical Engineering, 1–12. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36196-9_1.
Full textAnand, G., T. Thyagarajan, B. Aashique Roshan, L. Rajeshwar, and R. Shyam Balaji. "Signal Conditioning Circuits for GMR Sensor in Biomedical Applications." In Lecture Notes in Electrical Engineering, 93–106. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4943-1_10.
Full textConference papers on the topic "Conditioning and processing electrical signal"
Franco-Piña, J. Alejandro, Luis Contreras, and Juan C. Jauregui. "Real Time Conditioning Monitoring for Failure Prediction." In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-65145.
Full textDimcev, V., D. Taskovski, Z. Kokolanski, D. Denic, D. Zivanovic, and M. Simic. "Signal conditioning for power quality." In 2011 11th International Conference on Electrical Power Quality and Utilisation - (EPQU). IEEE, 2011. http://dx.doi.org/10.1109/epqu.2011.6128809.
Full textStanco, J., G. Sliwinski, J. Konefał, P. Kukielło, G. Rabczuk, Z. Rozkwitalski, and R. Zaremba. "Investigation of a transverse-excited high-power CO2 laser." In International Laser Science Conference. Washington, D.C.: Optica Publishing Group, 1986. http://dx.doi.org/10.1364/ils.1986.thl7.
Full textMazurek, Gustaw. "Signal Conditioning for DAB-Illuminated Passive Radar." In 2021 Signal Processing Symposium (SPSympo). IEEE, 2021. http://dx.doi.org/10.1109/spsympo51155.2020.9593458.
Full textLevonen, M. J., L. Persson, and S. McLaughlin. "Conditioning Lofargrams Using Empirical Mode Decomposition." In 2006 7th Nordic Signal Processing Symposium. IEEE, 2006. http://dx.doi.org/10.1109/norsig.2006.275220.
Full textErtugrul, Omer Faruk, and Mehmet Emin Tagluk. "Learning with classical conditioning." In 2014 22nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2014. http://dx.doi.org/10.1109/siu.2014.6830382.
Full textZhao, Guopeng, Zhiqi Shen, Chunyan Miao, and Zhihong Man. "On improving the conditioning of extreme learning machine: A linear case." In Signal Processing (ICICS). IEEE, 2009. http://dx.doi.org/10.1109/icics.2009.5397617.
Full textSzakacs-Simon, Peter, Sorin-Aurel Moraru, and Florian Neukart. "Signal conditioning techniques for health monitoring devices." In 2012 35th International Conference on Telecommunications and Signal Processing (TSP). IEEE, 2012. http://dx.doi.org/10.1109/tsp.2012.6256369.
Full textAmblard, Pierre-Olivier, and Olivier J. J. Michel. "Causal conditioning and instantaneous coupling in causality graphs." In 2012 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2012. http://dx.doi.org/10.1109/ssp.2012.6319633.
Full textUlaganathan, Abdul Lateef, K. M. Sadyojatha, and Raymond Irudayaraj. "Design and implementation of front end biological signal conditioning." In 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT). IEEE, 2017. http://dx.doi.org/10.1109/iceeccot.2017.8284586.
Full textReports on the topic "Conditioning and processing electrical signal"
Fayette, Daniel F., and Nancy A. Koziarz. Electrical Characterization of Signal Processing Microcircuit. Fort Belvoir, VA: Defense Technical Information Center, April 1989. http://dx.doi.org/10.21236/ada209078.
Full textBruce and Fiore. L51629 Users Manual-Field Validation of the Low-Frequency Eddy Current Instrument-Software Listings. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), October 1990. http://dx.doi.org/10.55274/r0010602.
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