Methods and Reagents for Green Chemistry By Pietro Tundo 1
Mesoporous carbon silica encapsulated molybdenum and ruthenium nanocatalysts for green chemistry applications
... MESOPOROUS CARBON/ SILICA ENCAPSULATED MOLYBDENUM AND RUTHENIUM NANOCATALYSTS FOR GREEN CHEMISTRY APPLICATIONS DOU JIAN (M ENG., NUS) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR ... within mesoporous supports for green chemistry applications In this work, molybdenum oxide and ruthenium nanoparticles are selected for assembly within mesoporous carbon and silica supports Three ... selective and stable nanocatalysts for green chemistry applications In this thesis, molybdenum oxide and ruthenium nanoparticles were selected as model metal oxide and metal compounds to be encapsulated...
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technologies and applications for green growth
... NETWORKS: TECHNOLOGIES AND APPLICATIONS FOR GREEN GROWTH TABLE OF CONTENTS SMART SENSOR NETWORKS: TECHNOLOGIES AND APPLICATIONS FOR GREEN GROWTH Summary Sensor technology for green growth ... 36 - SMART SENSOR NETWORKS: TECHNOLOGIES AND APPLICATIONS FOR GREEN GROWTH SMART SENSOR NETWORKS: TECHNOLOGIES AND APPLICATIONS FOR GREEN GROWTH Summary Sensors and sensor networks have an important ... descriptions and examples 14 SMART SENSOR NETWORKS: TECHNOLOGIES AND APPLICATIONS FOR GREEN GROWTH 15 Figure 6: Overview of smart grid communication applications and technologies An expanded view...
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Báo cáo toán học: " Admissible Functions and Asymptotics for Labelled Structures by Number of Components Edward" ppsx
... g(r)1+ ) for all (19) > Proof: Using the asymptotic formula for the coefficients of admissible functions and an argument like that in Hayman’s proof of Theorem H.II, the results for a and B follow ... extend R and Θ to include these variables by forming the Cartesian product of R with copies of (0, ∞) and the Cartesian product of Θ with copies of [−π, π] We extend a and B by adding entries of zeroes; ... coefficients of such functions Section provides theorems for establishing the admissibility of a variety of functions, especially those related to counting structures by number of components of various...
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Advanced Methods and Tools for ECG Data Analysis - Part 1 ppt
... CHAPTER 11 Probabilistic Approaches to ECG Segmentation and Feature Extraction 2 91 11. 1 11 .2 11 .3 11 .4 11 .5 11 .6 11 .7 11 .8 Introduction The Electrocardiogram 11 .2 .1 The ECG Waveform 11 .2.2 ECG Interval ... References 10 6 10 8 10 9 11 0 11 1 11 3 11 4 11 5 11 6 11 7 12 6 12 7 CHAPTER Linear Filtering Methods 13 5 5 .1 Introduction 5.2 Wiener Filtering 5.3 Wavelet Filtering 5.3 .1 The Continuous Wavelet Transform 5.3.2 ... Component Analysis for Source Separation and Filtering 5.5 Summary and Conclusions References 13 5 13 6 14 0 14 1 14 2 14 4 14 8 14 9 15 1 15 7 16 7 16 7 CHAPTER Nonlinear Filtering Techniques 17 1 6 .1 Introduction...
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Advanced Methods and Tools for ECG Data Analysis - Part 2 doc
... Through 22 2 Omitted) Record Nn Vn Fn On Nv Vv Fv Ov No Vo Fo Q Se Q+P 100 101 103 105 106 108 22 3 22 8 23 0 23 1 23 2 23 3 23 4 Sum Gross Average 1901 1 521 1 725 21 17 123 6 1461 1736 122 5 1858 127 8 ... Chest, Vol 124 , No 6, 20 03, pp 20 57 20 63 P1: Shashi August 24 , 20 06 11:36 Chan-Horizon 2. 6 Summary [11] [ 12] [13] [14] [15] [16] [17] [18] [19] [20 ] [21 ] [22 ] [23 ] [24 ] [25 ] [26 ] [27 ] [28 ] Azuaje˙Book ... becoming the standard for ECGs); HL7 [26 , 27 ] (an XML-based format for the exchange of data in hospitals); and WaveForm DataBase (WFDB), a set of libraries developed at MIT [28 , 29 ] HL7 is by...
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Advanced Methods and Tools for ECG Data Analysis - Part 3 docx
... 24, 2006 11 :39 94 Chan-Horizon Azuaje˙Book ECG Statistics, Noise, Artifacts, and Missing Data [20] [21] [22] [ 23] [24] [25] [26] [27] [28] [29] [30 ] [31 ] [32 ] [33 ] [34 ] [35 ] [36 ] [37 ] Malik, M., ... Chan-Horizon Azuaje˙Book 3. 7 Heart Rate Variability 71 Figure 3. 8 Zero-mean unit-variance clean ECG with additive brown, pink, and white noise (also zero-mean and unit-variance, and hence SNR = in ... account for the increased dimensionality of the input feature space See Chapters 12 and 13 for further discussions of training, test, and validation data sets 3. 6 Noise and Artifact in the ECG 3. 6.1...
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Advanced Methods and Tools for ECG Data Analysis - Part 4 ppsx
... [32] [33] [ 34] [35] [36] [37] [38] [39] [40 ] [41 ] [42 ] [43 ] [44 ] [45 ] [46 ] [47 ] [48 ] [49 ] [50] [51] Azuaje˙Book 129 Mateo, J., and P Laguna, “Improved Heart Rate Variability Signal Analysis from ... http://www.physionet.org/physiotools/ecgsyn/ P1: Shashi August 24, 2006 11 :42 Chan-Horizon Azuaje˙Book 4. 3 ECG Models Figure 4. 5 119 Spectral characteristics of (4. 9), the RR interval generator for ECGSYN two Gaussian ... Shashi August 24, 2006 11 :42 Chan-Horizon 4. 3 ECG Models Azuaje˙Book 123 Figure 4. 8 Synthetic ECG signals for different mean heart rates: (a) 30 bpm, (b) 60 bpm, and (c) 120 bpm Table 4. 1 ensure...
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Advanced Methods and Tools for ECG Data Analysis - Part 5 pdf
... Azuaje˙Book 147 Figure 5. 6 Raw ECG with 50 Hz mains noise, IIR 50 -Hz notch filtered ECG, 0. 1- to 4 5- Hz bandpass filtered ECG and bior3.3 wavelet filtered ECG The left-most arrow indicates the low ... where s > α i=1 si and α is some fraction ≈ 0. 95 i=1 i P1: Shashi August 30, 2006 11 :5 Chan-Horizon Azuaje˙Book 150 Linear Filtering Methods Figure 5. 7 SVD of 20 R-peak-aligned P-QRS-T complexes: ... Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis, ” Proc R Soc Lond A, Vol 454 , 1998, pp 903–9 95 Huang, N E., and S S Shen, The Hilbert-Huang Transform and Its...
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Advanced Methods and Tools for ECG Data Analysis - Part 6 docx
... 20 06 11: 46 1 96 Chan-Horizon Azuaje˙Book Nonlinear Filtering Techniques [45] [ 46] [47] [48] [49] [50] [51] [52] [53] [54] [55] [ 56] [57] [58] [59] [60 ] [61 ] [62 ] [63 ] [64 ] [65 ] Lehnertz, K., and ... 20 06 11: 46 Chan-Horizon 6. 5 Model-Based Filtering Azuaje˙Book 187 Figure 6. 6 Demonstration of ICA noise reduction for γ = 10: (a) original noise-free ECG signal, x(t); (b) χ -optimized noise-reduced ... 2003, pp 61 6 62 7 Pincus, S M., and A L Goldberger, “Physiological Time-Series Analysis: What Does Regularity Quantify?” Am J Physio Heart Circ Physiol., Vol 266 , 1994, pp H 164 3–H 165 6 Costa, M.,...
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Advanced Methods and Tools for ECG Data Analysis - Part 7 ppt
... r ( N) [%] [%] [%] 74 77 47 54 27 37 10 14 27 13 21 14 ⇑ 19 14 18 14 16 12 ST Segment Patterns, Human Annotator: r c ( N) r n ( N) r ( N) [%] [%] [%] 74 74 47 48 27 29 14 21 7 15 ⇑ 12 11 10 coefficients ... feature-vector time series in long-term ECGs for the purpose of ST segment analysis may involve following phases: Preprocessing; Derivation of time-domain and OFM transform-based diagnostic and ... which are visually informative and accessible for analysis at any time instant Therefore, a technique which converts such point event series into a form suitable for standard analysis techniques...
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Advanced Methods and Tools for ECG Data Analysis - Part 8 docx
... [ 18] , Neural net, KLT [g] [a] [g] [a] [g] [a] [g] [a] [g] [a] [g] [a] [g] [a] ESC DB S E [%] Se +P 81 76 84 81 – – 84 .7 86 .1 79.2 81 .4 81 .5 82 .5 85 .2 86 .2 87 .1 87 .7 77.2 86 .3 81 .3 89 .2 85 .0 68. 7 ... Chan-Horizon Azuaje˙Book 284 ST Analysis The open-source tool EVAL ST [45] provides first (record-by-record) and secondorder (aggregate gross and average) performance statistics for evaluation and ... Se +P Se +P – – – – – – – – – – – – – – – – – – – – – – – – 77.0 58. 8 48. 5 47 .8 74.0 61.4 54 .8 58. 4 79.6 78. 3 68. 4 67.3 78. 9 80 .7 73.1 74.9 – – – – – – – – – – – – – – – – The system from [7]...
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Advanced Methods and Tools for ECG Data Analysis - Part 9 ppsx
... 0.74E-2 1.46E-2 1.49E-2 1.47E-2 1.64E-2 1.72E-2 0.59E-2 Second-Order Cumulants 0.31E-2 0.60E-2 0 .94 E-2 0.67E-2 0.68E-2 0.52E-2 0.42E-2 Third-Order Cumulants 0.28E-2 1.03E-2 1.06E-2 0.85E-2 0.71E-2 ... 0.85E-2 0.71E-2 0.34E-2 0.40E-2 Fourth-Order Cumulants 0.24E-2 0.51E-2 0.55E-2 0.38E-2 0.54E-2 0.24E-2 0.60E-2 We have chosen the values of the cumulants of the second, third, and fourth orders ... to were used to form a seven-dimensional encoding for each ECG signal Table 11.2 shows the five-fold cross-validation results for HMMs trained on ECG waveforms from leads II and V2 which had...
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Advanced Methods and Tools for ECG Data Analysis - Part 10 pps
... and 13.15 The SOM Toolbox automatically selects the map size for each data set In this example: 23 × 16 neurons for the ECG beat data set, and 28 × neurons for the sleep apnea data set The U-matrices ... Learning for ECG Classification healthcare environment, ECG data are commonly stored and analyzed using different formats and software tools Thus, there is a need to develop cross-platform solutions ... support data analysis tasks and applications [64] A relevant solution consists of applying eXtensible Markup Language (XML) for representing ECG information ecgML [65], a markup language for ECG data...
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