LEADER 06451nam 2200613 450 001 9910136912303321 005 20221206100346.0 010 $a1-119-08287-0 010 $a1-119-08290-0 024 7 $a10.1002/9781119082934 035 $a(CKB)3710000000635789 035 $a(EBL)4498644 035 $a(MiAaPQ)EBC4498644 035 $a(CaBNVSL)mat07470984 035 $a(IDAMS)0b00006485171469 035 $a(IEEE)7470984 035 $a(PPN)257915958 035 $a(EXLCZ)993710000000635789 100 $a20160607d2008 uy 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 00$aSurface electromyography $ephysiology, engineering, and applications /$fedited by Roberto Merletti and Dario Farina 210 1$aPiscataway, New Jersey :$cIEEE Press,$d2016. 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2016] 215 $a1 online resource (731 p.) 225 1 $aIEEE Press Series on Biomedical Engineering 300 $aDescription based upon print version of record. 311 $a1-118-98702-0 311 $a1-119-08293-5 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aSeries Page; Title Page; Copyright; Introduction; Acknowledgments; Contributors; Chapter 1: Physiology of Muscle Activation and Force Generation; 1.1 Introduction; 1.2 Anatomy of a Motor Unit; 1.3 Motor Neuron; 1.4 Muscle Unit; 1.5 Recruitment and Rate Coding; 1.6 Summary; References; Chapter 2: Biophysics of the Generation of EMG Signals; 2.1 Introduction; 2.2 EMG Signal Generation; 2.3 Anatomical, Physical, and Detection System Parameters Influencing EMG Features; 2.4 Crosstalk; 2.5 EMG Amplitude and Force; 2.6 Conclusion/Summary; References 327 $aChapter 3: Detection and Conditioning of Surface EMG Signals3.1 Introduction; 3.2 The Electrode-Skin Interface and the Front-End Amplifier Stage; 3.3 State of the Art on EMG Signal Conditioning and Interfacing Solutions; 3.4 ASIC Solutions on the Market; 3.5 Perspectives for the Future; References; Chapter 4: Single-Channel Techniques for Information Extraction from the Surface EMG Signal; 4.1 Introduction; 4.2 Spectral Estimation of Deterministic Signals and Stochastic Processes; 4.3 Basic Surface EMG Signal Models; 4.4 Surface EMG Amplitude Estimation 327 $a4.5 Extraction of Information in the Frequency Domain from Surface EMG Signals4.6 Conclusions; References; Chapter 5: Techniques for Information Extraction from the Surface EMG Signal: High-Density Surface EMG; 5.1 Introduction; 5.2 Spatial Distribution of EMG Potential and EMG Features in Muscles with Fibers Parallel to the Skin; 5.3 Spatial Distribution of EMG Potential and Features in Pinnate Muscles; 5.4 Current Applications and Future Perspectives of HDsEMG; References; Chapter 6: Muscle Coordination, Motor Synergies, and Primitives from Surface EMG; 6.1 Introduction 327 $a6.2 Muscle Synergies and Spinal Maps6.3 Muscle Synergies in Posture Control; 6.4 Modular Control of Arm Reaching Movements; 6.5 Motor Primitives in Human Locomotion; 6.6 Conclusions; References; Chapter 7: Surface EMG Decomposition; 7.1 Introduction; 7.2 EMG Mixing Process; 7.3 EMG Decomposition Techniques; 7.4 Validation of Decomposition; References; Chapter 8: EMG Modeling and Simulation; 8.1 Introduction; 8.2 Principles of Modeling and Simulation; 8.3 Phenomenological Surface EMG Models; 8.4 Structure-Based Surface EMG Models; 8.5 Modeling the Action Potential Source 327 $a8.6 Models of Volume Conduction and Detection Systems8.7 Models of the Surface EMG Signal; 8.8 Model Validation; 8.9 Applications of Modeling; 8.10 Conclusions; References; Chapter 9: Electromyography-Driven Modeling for Simulating Subject-Specific Movement at the Neuromusculoskeletal Level; 9.1 Introduction; 9.2 Motion Capturing and Biomechanical Modeling of the Human Body; 9.3 Musculoskeletal Modeling; 9.4 EMG-Driven Musculoskeletal Modeling and Simulation; 9.5 Experimental Results and Applications; 9.6 Conclusions; Acknowledgment; References 330 $aReflects on developments in noninvasive electromyography, and includes advances and applications in signal detection, processing, and interpretation The book presents a quantitative approach to the study and use of noninvasively detected electromyographic (EMG) signals, as well as their numerous applications in various aspects of the life sciences. Surface Electromyography: Physiology, Engineering, and Applications is an update of Electromyography: Physiology, Engineering, and Noninvasive Applications (Wiley-IEEE Press, 2004) and focuses on the developments that have taken place over the last decade. The first nine chapters deal with the generation, detection, understanding, interpretation, and modeling of EMG signals. Detection technology, with particular focus on EMG imaging techniques that are based on two-dimensional electrode arrays are also included in the first half of the book. The latter 11 chapters deal with applications, which range from monitoring muscle fatigue, electrically elicited contractions, posture analysis, prevention of work-related and child-delivery-related neuromuscular disorders, ergonomics, movement analysis, physical therapy, exercise physiology, and prosthesis control. . Addresses EMG imaging technology together with the issue of decomposition of surface EMG. Includes advanced single and multi-channel techniques for information extraction from surface EMG signals. Presents the analysis and information extraction of surface EMG at various scales, from motor units to the concept of muscle synergies. The book is aimed primarily to biomedical engineers, rehabilitation physicians, and movement scientists. However, it may be appreciated by neurophysiologists, and physical and occupational therapists with a background in physics, engineering, and signal processing. 410 0$aIEEE Press series in biomedical engineering. 606 $aElectromyography 606 $aMuscles$xRegeneration 615 0$aElectromyography. 615 0$aMuscles$xRegeneration. 676 $a616.7407547 702 $aMerletti$b Roberto 702 $aFarina$b Dario 801 0$bCaBNVSL 801 1$bCaBNVSL 801 2$bCaBNVSL 906 $aBOOK 912 $a9910136912303321 996 $aSurface electromyography$91899609 997 $aUNINA LEADER 01322nam a2200337 i 4500 001 991000681559707536 005 20020507172211.0 008 951204s1992 ne ||| | eng 020 $a9067640948 035 $ab10742451-39ule_inst 035 $aLE01300639$9ExL 040 $aDip.to Matematica$beng 082 0 $a512.7 084 $aAMS 11-06 084 $aAMS 11K 084 $aAMS 60-06 100 1 $aKubilius, J.$058059 245 10$aAnalytic and probabilistic methods in number theory :$bproceedings of the International conference in honour of J. Kubilius, Palanga, Lithuania, 24-28 September 1991 /$ceditors F. Schweiger and E. Manstavicius 260 $aVilnius, Lithuania :$bTEV ; Utrecht : VSP,$c1992 300 $avii, 386 p. ;$c24 cm. 490 0 $aNew trends in probability and statistics ;$v2 650 4$aNumber theory$xCongresses 650 4$aProbability theory$xCongresses 700 1 $aManstavicius, E. 700 1 $aSchweiger, Fritz 907 $a.b10742451$b23-02-17$c28-06-02 912 $a991000681559707536 945 $aLE013 11-XX KUB11 (1992)$g1$i2013000041049$lle013$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i10833869$z28-06-02 996 $aAnalytic and probabilistic methods in number theory$9910782 997 $aUNISALENTO 998 $ale013$b01-01-95$cm$da $e-$feng$gne $h0$i1 LEADER 06198nam 2201501z- 450 001 9910367735703321 005 20210211 010 $a3-03928-009-0 035 $a(CKB)4100000010106360 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/51126 035 $a(oapen)doab51126 035 $a(EXLCZ)994100000010106360 100 $a20202102d2019 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aKnowledge Manageation and Big Data: Implications for Sustainability, Policy Making and Competitiveness 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2019 215 $a1 online resource (416 p.) 311 08$a3-03928-008-2 330 $aThe evolution of knowledge management theory and the special emphasis on human and social capital sets new challenges for knowledge-driven and technology-enabled innovation. Emerging technologies including big data and analytics have significant implications for sustainability, policy making, and competitiveness. This edited volume promotes scientific research into the potential contributions knowledge management can make to the new era of innovation and social inclusive economic growth. We are grateful to all the contributors of this edition for their intellectual work. The organization of the relevant debate is aligned around three pillars: SECTION A. DATA, KNOWLEDGE, HUMAN AND SOCIAL CAPITAL FOR INNOVATION: We elaborate on the new era of knowledge types and the emerging forms of social capital and their impact on technology-driven innovation. Topics include: Social Networks; Smart Education; Social Capital; Corporate Innovation; Disruptive Innovation; Knowledge integration; Enhanced Decision-Making. SECTION B. KNOWLEDGE MANAGEMENT & BIG DATA ENABLED INNOVATION: In this section, knowledge management and big data applications and systems are presented. Selective topic include: Crowdsourcing Analysis; Natural Language Processing; Data Governance; Knowledge Extraction; Ontology Design Semantic Modeling SECTION C. SUSTAINABLE DEVELOPMENT: In the section, the debate on the impact of knowledge management and big data research to sustainability is promoted with integrative discussion of complementary social and technological factors including: Big Social Networks on Sustainable Economic Development; Business Intelligence 517 $aKnowledge Manageation and Big Data 606 $aEducation$2bicssc 610 $aabsorptive capacity 610 $aadministrative file 610 $aadministrative procedure 610 $abibliometric 610 $abig data 610 $aBig Data 610 $abig data analysis 610 $abig data environment 610 $abusiness intelligence 610 $acitizen-scientist 610 $aclimate change 610 $acloud computing 610 $acloud data governance 610 $aco-citation network 610 $acollaboration network 610 $acommunication 610 $acompetitive advantage 610 $aconceptual maturity model 610 $acorporate sustainability 610 $acrowdsourcing 610 $adata analysis 610 $adata governance 610 $adisruptive innovation 610 $aeducation 610 $aemerging trends 610 $aemployee loyalty-EL 610 $aheterogeneous architectures 610 $aholistic 610 $ahuman capital 610 $ahuman capital investment 610 $ahybrid neural networks 610 $aIC manufacturing 610 $ainnovation 610 $ainnovation capability 610 $ainnovation performance 610 $aintellectual structure 610 $ainternal social networks 610 $ainternational technological collaboration 610 $aJordan 610 $akey performance indicators 610 $akeywords analysis 610 $aknowledge assets 610 $aknowledge creation 610 $aknowledge creation process 610 $aknowledge embeddedness 610 $aknowledge management 610 $aknowledge mapping 610 $aknowledge sharing 610 $aknowledge specificity 610 $aleadership 610 $alinked data 610 $amechanical patent classification 610 $aMTurk 610 $amulti-dimensional data model 610 $anew ventures 610 $aNLP 610 $aontology design 610 $aopen data 610 $aP-PLAN 610 $apatent analysis 610 $apatent association analysis 610 $apersonalized business mode 610 $aprocess innovation capability 610 $aproduct innovation capability 610 $aproject-based organization 610 $aPROV-O 610 $aprovenance 610 $aquality orientation of employees-QOE 610 $aRDF 610 $arisk perception 610 $asix sigma 610 $asmart education 610 $asocial capital 610 $asocial media 610 $asocial network analysis 610 $asocial networks 610 $astrategic decision-making 610 $astructural equation model 610 $asustainability 610 $asustainable competitive advantage 610 $asustainable development 610 $asystematic review 610 $ataxonomy 610 $atechnological information 610 $atechnology acceptance model 610 $atechnology-enhanced learning process 610 $atext feature extraction 610 $atext mining 610 $atext structure 610 $atop management team 610 $atraining 610 $atransformational training programs-TTP 610 $aTwitter 610 $auniversities 610 $auser acceptance 610 $avisualizing 615 7$aEducation 700 $aLytras$b Miltiadis$4auth$01149833 702 $aOrdóñez de Pablos$b Patricia$4auth 906 $aBOOK 912 $a9910367735703321 996 $aKnowledge Manageation and Big Data: Implications for Sustainability, Policy Making and Competitiveness$93030533 997 $aUNINA