Academic literature on the topic 'Sensor decoding'
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Journal articles on the topic "Sensor decoding"
Flamary, R., N. Jrad, R. Phlypo, M. Congedo, and A. Rakotomamonjy. "Mixed-Norm Regularization for Brain Decoding." Computational and Mathematical Methods in Medicine 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/317056.
Full textSnippe, Herman P. "Parameter Extraction from Population Codes: A Critical Assessment." Neural Computation 8, no. 3 (April 1996): 511–29. http://dx.doi.org/10.1162/neco.1996.8.3.511.
Full textDash, Debadatta, Alan Wisler, Paul Ferrari, Elizabeth Moody Davenport, Joseph Maldjian, and Jun Wang. "MEG Sensor Selection for Neural Speech Decoding." IEEE Access 8 (2020): 182320–37. http://dx.doi.org/10.1109/access.2020.3028831.
Full textOkunsky, M. V., and N. V. Nesterova. "Velodyne LIDAR method for sensor data decoding." IOP Conference Series: Materials Science and Engineering 516 (April 26, 2019): 012018. http://dx.doi.org/10.1088/1757-899x/516/1/012018.
Full textAineto, Diego, Sergio Jimenez, and Eva Onaindia. "Observation Decoding with Sensor Models: Recognition Tasks via Classical Planning." Proceedings of the International Conference on Automated Planning and Scheduling 30 (June 1, 2020): 11–19. http://dx.doi.org/10.1609/icaps.v30i1.6640.
Full textParfenov, V. I., and V. D. Le. "DISTRIBUTED DETECTION BASED ON USING SOFT DECISION DECODING IN A FUSION CENTER." Telecommunications, no. 1 (2022): 2–9. http://dx.doi.org/10.31044/1684-2588-2022-0-1-2-9.
Full textGanin, Dmitriy V., Mokhammed A. Y. Damdam, and Aleksandr L. Savkin. "PERMUTATION DECODING IN LOW-POWER WIRELESS SENSOR NETWORKS." Автоматизация процессов управления 2, no. 68 (2022): 37–44. http://dx.doi.org/10.35752/1991-2927-2022-2-68-37-44.
Full textYufei, Wu, Wang Dandan, and Zhu Yanwei. "Research on the Advantages of Digital Sensor Equipment in Language Audio-Visual and Oral Teaching." Journal of Sensors 2021 (November 30, 2021): 1–13. http://dx.doi.org/10.1155/2021/3006397.
Full textCordeiro, Paulo J., and Pedro Assunção. "Distributed Coding/Decoding Complexity in Video Sensor Networks." Sensors 12, no. 3 (February 29, 2012): 2693–709. http://dx.doi.org/10.3390/s120302693.
Full textDwivedi, Anany, Helen Groll, and Philipp Beckerle. "A Systematic Review of Sensor Fusion Methods Using Peripheral Bio-Signals for Human Intention Decoding." Sensors 22, no. 17 (August 23, 2022): 6319. http://dx.doi.org/10.3390/s22176319.
Full textDissertations / Theses on the topic "Sensor decoding"
Illangakoon, Chathura. "On joint source-channel decoding and interference cancellation in CDMA-based large-scale wireless sensor networks." IEEE, 2013. http://hdl.handle.net/1993/22019.
Full textKOBAYASHI, Kentaro, Takaya YAMAZATO, Hiraku OKADA, and Masaaki KATAYAMA. "Joint Channel Decoding of Spatially and Temporally Correlated Data in Wireless Sensor Networks." IEEE, 2008. http://hdl.handle.net/2237/12086.
Full textPishro-Nik, Hossein. "Applications of Random Graphs to Design and Analysis of LDPC Codes and Sensor Networks." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7722.
Full textKobayashi, Kentaro, Takaya Yamazato, Hiraku Okada, and Masaaki Katayama. "Iterative Joint Channel-Decoding Scheme Using the Correlation of Transmitted Information Sequences in Sensor Networks." IEEE, 2006. http://hdl.handle.net/2237/7755.
Full textBekjarova, Milka. "Packet erasure correcting codes for wireless sensor networks: implementation and field trial measurements." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/3558/.
Full textKaardal, Joel Thomas. "Decoding the Computations of Sensory Neurons." Thesis, University of California, San Diego, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10633582.
Full textThe nervous system encodes information about external stimuli through sophisticated computations performed by vast networks of sensory neurons. Since the space of all possible stimuli is much larger than the space of those that are ultimately meaningful, dimensionality reduction techniques were developed to identify the subspace of stimulus space relevant to neural activity. However, dimensionality reduction methods provide limited insight into the nonlinear functions that build the nervous system’s internal model of the world. In Chapter 2, the functional basis is introduced that transforms the relevant subspace to a basis that describes the computational function of the subunits that make up the neural circuitry. This functional basis is used to uncover novel insights about the computations performed by neurons in low-level vision and, later on, high-level auditory circuitry. For the latter, significant barriers are found in the capability of current dimensionality reduction methods to recover the relevant subspaces of high-level sensory neurons. This barrier is caused by the relative difficulty of stimulating high-level sensory neurons, which are often unresponsive to noise stimuli, while still maintaining a thorough exploration of the stimulus distribution. In response, a new approach to dimensionality reduction is formulated in Chapter 3 called the low-rank maximum noise entropy method that makes it possible to overcome challenges presented by high-level sensory systems. In Chapter 4, functional bases derived from the relevant subspaces recovered by the low-rank maximum noise entropy method are employed to study the neural computations performed by high-level auditory neurons.
Das, Tanmoy. "Exploiting Hidden Resources to Design Collision-Embracing Protocols for Emerging Wireless Networks." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1565807656641553.
Full textSolda', Silvia. "Design of low-power analog circuits for analog decoding and wireless sensors nodes." Doctoral thesis, Università degli studi di Padova, 2009. http://hdl.handle.net/11577/3426488.
Full textLa prima parte di questo lavoro di tesi e' dedicata alla decodifica analogica e presenta la progettazione di un'interfaccia di I/O per un decodificatore iterattivo completamente analogico per un codice convoluzionale concatenato in serie e di un decoder analogico per Trellis Coded Modulation (TCM) per la correzione degli errori in memorie Flash multi-livello. Il decodificatore iterattivo rappresenta un grosso passo avanti nell'evoluzione dei decodificatori analogici in quanto e' possibile riconfigurarne sia la lunghezza di blocco che il rate del codice. Per di piu', con un'efficienza di 2.1nJ/bit, migliora fino a 50 volte le prestazioni in termini di efficienza dei decodificatori digitali con la stessa lunghezza di blocco. Le potenziali prestazioni e le limitazioni dell'approccio analogico per un decodificatore per TCM sono state investigate considerando due diversi decodificatori, uno a 4 stati ed uno ad 8 stati, entrambi sviluppati in un processo CMOS standard con una lunghezza di canale di 0.18um. Nella seconda parte della tesi viene presentato il design di un transciver per una radio ad impulsi a banda larga (UWB-IR), con particolare enfasi sulla progettazione del trasmettitore. Il trasmettitore utilizza una nuova combinazione di mixer e amplificatore di potenza per generare un impulso gaussiano con una larghezza di banda di 1.25GHz ed una frequenza centrale di 7.875GHz. Il nuovo circuito, inoltre, include un trasformatore monolitico in modo tale da generare una tensione di uscita di 3.2Vpp, necessaria per garantire la distanza di connessione richiesta di almeno 10 metri. Il trasformatore e' stato progettato in modo da massimizzare l'efficienza in termini di potenza e, allo stesso tempo, realizzare un filtro ladder del quarto ordine al fine di ridurre le emissioni fuori banda del trasmettitore stesso. Confrontando l'efficienza di questo design con trasmettori per UWB-IR allo stato dell'arte si e' visto come la soluzione da noi proposta porti ad un miglioramento dell'efficienza del trasmettitore di un fattore pari a 10.
Romero, Arandia Iñigo. "Reading out neural populations: shared variability, global fluctuations and information processing." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/404684.
Full textEntender el origen y la función de la actividad de poblaciones neuronales, y cómo esta actividad se relaciona con los estímulos sensoriales, las decisiones o las acciones motoras es un gran desafio en neurociencia. En este trabajo hemos analizado la actividad de decenas de neuronas registradas en la corteza visual primaria de monos mientras rejillas sinusoidales en diferentes orientaciones eran presentadas. Hemos encontrado que las fluctuaciones globales de la red medidas mediante la actividad de la población modulan la selectividad de las neuronas de manera multiplicativa y aditiva. Además, la actividad de la población también afecta a la información presente en grupos pequeños de neuronas, dependiendo de la modulación que ha provocado en la selectividad de estas neuronas. La información en la población completa, sin embargo, no varía con estas fluctuaciones. En la segunda parte hemos desarrollado un método para medir 'correlaciones diferenciales' con datos limitados. Al aplicarlo a los datos experimentales hemos obtenido la primera estimación preliminar del tamaño de estas correlaciones que limitan la información. Nuestros resultados contribuyen al avance del entendimiento sobre la codi ficación de la información en poblaciones neuronales, y al mismo tiempo generan más preguntas sobre cómo éstas procesan y transmiten información.
Understanding the sources and the role of the spiking activity of neural populations, and how this activity is related to sensory stimuli, decisions or motor actions is a crucial challenge in neuroscience. In this work, we analyzed the spiking activity of tens of neurons recorded in the primary visual cortex of macaque monkeys while drifting sinusoidal gratings were presented in di erent orientations. We found that global uctuations of the network measured by the population activity a ect the tuning of individual neurons both multiplicatively and additively. Population activity also has an impact in the information of small ensembles, which depends on the kind of modulation that the tuning of those neurons undergoes. Interestingly, the total information of the network is not altered by these uctuations. In the second part, we developed a method to measure 'di erential correlations' from limited amount of data, and obtained the rst, although preliminary, estimate in experimental data. Our results have important implications for information coding, and they open new questions about the way information is processed and transmitted by the spiking activity of neural populations.
Wåhlin, Peter. "Enhanching the Human-Team Awareness of a Robot." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-16371.
Full textAnvändningen av autonoma robotar i vårt samhälle ökar varje dag och en robot ses inte längre som ett verktyg utan som en gruppmedlem. Robotarna arbetar nu sida vid sida med oss och ger oss stöd under farliga arbeten där människor annars är utsatta för risker. Denna utveckling har i sin tur ökat behovet av robotar med mer människo-medvetenhet. Därför är målet med detta examensarbete att bidra till en stärkt människo-medvetenhet hos robotar. Specifikt undersöker vi möjligheterna att utrusta autonoma robotar med förmågan att bedöma och upptäcka olika beteenden hos mänskliga lag. Denna förmåga skulle till exempel kunna användas i robotens resonemang och planering för att ta beslut och i sin tur förbättra samarbetet mellan människa och robot. Vi föreslår att förbättra befintliga aktivitetsidentifierare genom att tillföra förmågan att tolka immateriella beteenden hos människan, såsom stress, motivation och fokus. Att kunna urskilja lagaktiviteter inom ett mänskligt lag är grundläggande för en robot som ska vara till stöd för laget. Dolda markovmodeller har tidigare visat sig vara mycket effektiva för just aktivitetsidentifiering och har därför använts i detta arbete. För att en robot ska kunna ha möjlighet att ge ett effektivt stöd till ett mänskligtlag måste den inte bara ta hänsyn till rumsliga parametrar hos lagmedlemmarna utan även de psykologiska. För att tyda psykologiska parametrar hos människor förespråkar denna masteravhandling utnyttjandet av mänskliga kroppssignaler. Signaler så som hjärtfrekvens och hudkonduktans. Kombinerat med kroppenssignalerar påvisar vi möjligheten att använda systemdynamiksmodeller för att tolka immateriella beteenden, vilket i sin tur kan stärka människo-medvetenheten hos en robot.
The thesis work was conducted in Stockholm, Kista at the department of Informatics and Aero System at Swedish Defence Research Agency.
Books on the topic "Sensor decoding"
Derval, Diana. Right Sensory Mix: Decoding Customers' Behavior and Preferences. Springer Berlin / Heidelberg, 2021.
Find full textJanczuk, Elena. Stałe motywy metafizyczne w twórczości Mariny Cwietajewej. University of Warsaw Press, 2021. http://dx.doi.org/10.31338/uw.9788323546054.
Full textTroisi, Alfonso. Nonverbal Communication. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199393404.003.0007.
Full textBook chapters on the topic "Sensor decoding"
von Rickenbach, Pascal, and Roger Wattenhofer. "Decoding Code on a Sensor Node." In Distributed Computing in Sensor Systems, 400–414. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69170-9_27.
Full textItuero, Pablo, Gorka Landaburu, Javier Del Ser, Marisa López-Vallejo, Pedro M. Crespo, Vicente Atxa, and Jon Altuna. "Joint Source-Channel Decoding ASIP Architecture for Sensor Networks." In Embedded Software and Systems, 98–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72685-2_10.
Full textTakahashi, Yohsuke, and Takeshi Ito. "Structure and Function of CDPK: A Sensor Responder of Calcium." In Coding and Decoding of Calcium Signals in Plants, 129–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20829-4_9.
Full textHuang, Yu-Hsiung, Wei-Chun Chen, and Su-Chu Hsu. "Creative Design of Gaussian Sensor System with Encoding and Decoding." In Lecture Notes in Computer Science, 385–95. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78361-7_29.
Full textAragon, Nicolas, Jean Pierre Cances, Imad El Qachchach, Philippe Gaborit, and Oussama Habachi. "Extended Low Rank Parity Check Codes and Their Efficient Decoding for Multisource Wireless Sensor Networks." In Ubiquitous Networking, 41–55. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58008-7_4.
Full textKist, Andreas M., Laura D. Knogler, Daniil A. Markov, Tugce Yildizoglu, and Ruben Portugues. "Whole-Brain Imaging Using Genetically Encoded Activity Sensors in Vertebrates." In Decoding Neural Circuit Structure and Function, 321–41. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57363-2_13.
Full textClemens, Jan, and Mala Murthy. "The Use of Computational Modeling to Link Sensory Processing with Behavior in Drosophila." In Decoding Neural Circuit Structure and Function, 241–60. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57363-2_9.
Full textKhomokhoana, Pakiso J., and Liezel Nel. "Decoding Source Code Comprehension: Bottlenecks Experienced by Senior Computer Science Students." In Communications in Computer and Information Science, 17–32. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35629-3_2.
Full textGold, Joshua I. "Multiple Roles of Experience in Decoding the Neural Representation of Sensory Stimuli." In Percept, Decision, Action: Bridging the Gaps, 92–107. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470034989.ch8.
Full textDel Ser, Javier, Mikel Mendicute, Pedro M. Crespo, Sergio Gil-Lopez, and Ignacio Olabarrieta. "Joint Source-Channel-Network Decoding and Blind Estimation of Correlated Sensors Using Concatenated Zigzag Codes." In Ad-Hoc, Mobile and Wireless Networks, 30–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04383-3_3.
Full textConference papers on the topic "Sensor decoding"
Salah, Hamed, Hazem A. Ahmed, Joerg Robert, and Albert Heuberger. "Maximum Likelihood decoding for non-synchronized UHF RFID tags." In 2016 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet). IEEE, 2016. http://dx.doi.org/10.1109/wisnet.2016.7444330.
Full textLiu, Jun, Weitao Xu, and Wen Hu. "Energy Efficient LPWAN Decoding via Joint Sparse Approximation." In SenSys '18: The 16th ACM Conference on Embedded Networked Sensor Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3274783.3275165.
Full textMorey, W. W., G. A. Ball, G. Meltz, J. R. Dunphy, and A. D. Kersey. "Advances in Fiber Grating Sensors." In Photosensitivity and Quadratic Nonlinearity in Glass Waveguides. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/pqn.1995.pmc.1.
Full textLi, Yanpeng, Ziyun Miao, Xiangpeng Xiao, Zhen Li, Zhijun Yan, and Qizhen Sun. "Implantable optical fiber sensor for monitoring the stress evolution in lithium-sulfur battery." In CLEO: Applications and Technology. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/cleo_at.2022.atu5m.4.
Full textShuiyin Liu, Xiaomei Fu, and Cong Ling. "Lattice decoding for cooperative communications using the segment LLL algorithm." In Sensor Signal Processing for Defence (SSPD 2010). IET, 2010. http://dx.doi.org/10.1049/ic.2010.0249.
Full textGraham, Daniel, Arnold Yim, Gang Zhou, and Weizhen Mao. "Real-Time Encoding/Decoding for Pairwise Communication Over an Unreliable Sensor Network." In 8th International Conference on Sensor Networks. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007247000690076.
Full textFazal-E-Asim, Andre L. F. de Almeida, Martin Haardt, Charles C. Cavalcante, and Josef A. Nossek. "Multi-Linear Encoding and Decoding for MIMO Systems." In 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2020. http://dx.doi.org/10.1109/sam48682.2020.9104276.
Full textCosta, Rui A., Daniele Munaretto, Joerg Widmer, and Joao Barros. "Informed network coding for minimum decoding delay." In 2008 5th IEEE International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, 2008. http://dx.doi.org/10.1109/mahss.2008.4660042.
Full textBarros, J., M. Tuchler, and Seong Per Lee. "Scalable source/channel decoding for large-scale sensor networks." In 2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577). IEEE, 2004. http://dx.doi.org/10.1109/icc.2004.1312628.
Full textNguyen, Harrison, Luke Osborn, Mark Iskarous, Christopher Shallal, Christopher Hunt, Joseph Betthauser, and Nitish Thakor. "Dynamic Texture Decoding Using a Neuromorphic Multilayer Tactile Sensor." In 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2018. http://dx.doi.org/10.1109/biocas.2018.8584826.
Full textReports on the topic "Sensor decoding"
Kreller, Cortney. Exploiting Cross-sensitivity by Bayesian Decoding of Mixed Potential Sensor Arrays. Office of Scientific and Technical Information (OSTI), October 2017. http://dx.doi.org/10.2172/1396156.
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