mean median and mode for discrete data
Dùng mảng tính Mean, Median và Mode
... ******** Mean ******** The mean is the average value of the data items The mean is equal to the total of all the data items divided by the number of data items (99) The mean value for this run ... value of the data\ n" "items The mean is equal to the total of\n" "all the data items divided by the number\n" "of data items ("
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Chapter 10 Functional Dependencies and Normalization for Relational Databases
... Practical Use of Normal Forms Definitions of Keys and Attributes Participating in Keys First Normal Form Second Normal Form Third Normal Form General Normal Form Definitions (For Multiple Keys) BCNF ... the criteria for "good" base relations? Informal Design Guidelines for Relational Databases (2) We first discuss informal guidelines for good relational design Then we discuss formal concepts ... dependencies and normal forms - 1NF (First Normal Form) - 2NF (Second Normal Form) - 3NF (Third Normal Form) - BCNF (Boyce-Codd Normal Form) Additional types of dependencies, further normal forms,...
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Báo cáo hóa học: " Research Article Cooperative Coding and Caching for Streaming Data in Multihop Wireless Networks" ppt
... > j , and c j is a more recent data segment than that c j if j > j For simplicity, we will use only the subscript or superscript for a data segment if the context is clear Thus, for a data segment ... independency is over 99.6% for q = 28 , and this is almost independent of N As such, for the network-coding-based data storage and collection scheme, the success ratio with M = N and B = is close to ... original data segments, and we refer to it as Cooperative Coding and Caching (C ); compre for example Network Coding (NC) and no coding at all (Non-NC ) 3.1 Overview of Cooperative Coding and Caching...
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Advanced Methods and Tools for ECG Data Analysis - Part 1 ppt
... wavelet and hidden Markov model–based procedure for robust QT-analysis Chapter 12 describes techniques for supervised classification and hybrid techniques for classifying ECG metrics, where the data ... does not mean the same as a 20-bpm change, for example) and their associated distributions differ, the task at hand is far more difficult In fact, normalizing for these differences, and building ... candidates for such a problem The development of closed-loop systems It is always problematic for a clinician to relinquish the task of classification and intervention for personal, legal, and...
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Advanced Methods and Tools for ECG Data Analysis - Part 2 doc
... sufficient for the signal processing tasks Data formats: When storing data, it is important to use an accurate and verifiable data format (at each step) If data are to be converted to another format, ... Transmission, and Representation questions to ask when choosing a data format and access libraries include: • • • • • • • • • • • • • What are the data going to be used for? Are the data format and libraries ... electroencephalograms (and more increasingly is 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...
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Advanced Methods and Tools for ECG Data Analysis - Part 3 docx
... Azuaje˙Book CHAPTER Models for ECG and RR Interval Processes Patrick E McSharry and Gari D Clifford 4.1 Introduction The availability of open-source computational models and simulators can greatly ... classification performance statistics (sensitivity, specificity) on the MIT-BIH database [2] of 93.2%, 94.4% for sinus rhythm, 100%, 96.2% for superventricular tachycardia, 97.7%, 98.6% for VT, and 98.6%, ... examples to 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...
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Advanced Methods and Tools for ECG Data Analysis - Part 4 ppsx
... downsampling, for instance), information is often lost and the transformation is not invertible In general, lossy transformations involve a noninvertible transformation of data using a transformation ... (4.9) with means f1 , f2 and standard deviations c1 , c2 Power in the LF and HF bands is 2 given by σ1 and σ2 , respectively, whereas the variance equals the total area σ = 2 2 σ1 + σ2 and the ... of these models have included model fitting [29, 108], compression and filtering [119, 123], and classification [124] However, the required complexity for realistic models (particularly for ECG...
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Advanced Methods and Tools for ECG Data Analysis - Part 5 pdf
... by [51] ρ= [xn − µx ][zn − µz ] σ x σz (6.13) where µx and σx are the mean and standard deviation of xn , and µz and σz are the mean and standard deviation of zn A value of ρ ∼ reflects a strong ... different signals can have the same mean and variance For instance, all the signals in Figure 5.10 have a mean of zero and unit variance The mean (central tendency) of a random variable x is defined ... empirical modes of the HHT are also determined by the data and are therefore a special case where a JTFA technique (the Hilbert transform) is combined with a data- determined empirical mode decomposition...
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Advanced Methods and Tools for ECG Data Analysis - Part 6 docx
... frequency and it therefore has a bandpass filtering effect leading to a lossy transformation of the data into a set of integrable Gaussians distributed over time This approach could therefore be ... usual respiratory frequency band For example, the discrete wavelet transform has been applied to the single-lead ECG and the scale corresponding to the frequency band 0.2 to 0.4 Hz can be selected ... approach for filtering ECG signals is now described The model parameters that are fit basically constitute a nondynamic version of the model described in [6, 59] and add an extra parameter for each...
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Advanced Methods and Tools for ECG Data Analysis - Part 7 ppt
... conclude that five KLT coefficients for ST segment and five for the QRS complex are sufficient and necessary for separating between noisy and clean patterns and for accurately representing the features ... events 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 ... the transformed mean of the feature ylm [i.e., the mth component of the transformed mean µl = E(xl − µ0 ) of the pattern vectors xl from the lth class relative to the overall mean µ0 ], and v0m...
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Advanced Methods and Tools for ECG Data Analysis - Part 8 docx
... the original database, and performance statistics are derived for each new database The mean, standard deviation and median of expected performance, as well as the minimum expected performance (5% ... average value over 72 seconds in length and has to change for more than Ks = 50 µV for the ST level function (and for more than qrs = 0.5 SD and st = 0.4 SD for QRS and ST Mahalanobis distance functions) ... waveform in the data set Therefore, we chose to distribute the waveforms at random among the different experts (such that each waveform was measured by one expert only) For each ECG waveform,...
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Advanced Methods and Tools for ECG Data Analysis - Part 9 ppsx
... waveform annotations using the supervised estimator given in (11.18) For Gaussian observation models, the mean and variance of the full set of signal samples were computed for each model state For ... particular, the Q and Toff single-beat segmentation errors of 5.5 ms and 12.4 ms for lead II, and 3.3 ms and 9.5 ms for lead V2, are significantly better than the corresponding errors for the HMM with ... constraints for hidden Markov models 11.9 Duration Modeling for Robust Segmentations A significant limitation of the standard HMM is the manner in which it models state durations For a given...
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Advanced Methods and Tools for ECG Data Analysis - Part 10 pps
... Figures 13.14 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 ... Unsupervised Learning for ECG Classification Figure 13.15 SOM-based data visualization for sleep apnea data set: (a) U-matrix; and (b) label map ‘‘1’’ stands for Class Normal and ‘‘2’’ represents ... parameters for the GSOM-based results reported in this chapter are as follows: SF = 0.001, N0 = for the ECG beat data set and N0 = for sleep apnea data set, initial learning rate, LR(0), = 0.5 and the...
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![[new] ensuring query correctness and completeness for outsourced databases](https://media.store123doc.com/images/document/2015_01/29/small_oyb1422505634.jpg)
[new] ensuring query correctness and completeness for outsourced databases
... Khanh Query assurance for outsourced databases:state-of-the-art [2] Tran Khanh Dang Ensuring correctness, completeness, and freshness for Outsourced Tree-Indexed Data Information Resource Management ... Resource Management Journal, 2008 [3] Tran Khanh Dang Security Protocols for Outsourcing Database Services Information and Security: An International Journal, ProCon Ltd.,Sofia, Bulgaria, ISSN ... Nội Dung Dịch vụ Outsourcing Database Đảm bảo tính bảo mật cho Outsourced Database Đảm bảo kết cho việc truy vấn Outsourced Database Một số phương pháp Chữ ký điện tử...
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Modeling and detection for holographic data storage
... channel models for the HDS While accurate models for the HDS are available; their computational complexities are prohibitively high On the other hand, a notably efficient channel model for the ... Symbols and Abbreviations αc BCJR forward recursion for column detector αr BCJR forward recursion for row detector βc BCJR backward recursion for column detector βr BCJR backward recursion for row ... the data, it is straightforward to extend such 1-D equalizers to their 2-D counterparts Chugg et al [9] and Keskinoz and Kumar [15] investigate the design and performance of linear minimum mean- square...
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How to: Calculate Mode, Median and Mean Averages ppt
... dividing The formula for the Mean average is: Ta÷Na=Ma In other words, the total of the amounts divided by the number of amounts you have equals the Mean average For example, the Mean average ... etc EXERCISES FOR MEAN Try these for practice! Find the Mean average of: 4,3,67,23,2 34,25,14,17,46,32 456,231,253,687 Thank you for reading this manual on how to find averages of data I personally ... EXERCISES FOR MEDIAN Try these for practice! Find the Median average of: 2,4,7,7,6 3,11,43,21,56,4 4,23,43,43,26 MEAN This is the hardest average to find out...
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Tài liệu Synchronization and Linearity An Algebra for Discrete Event Systems doc
... theory and linear system theory form an ideal background For algebra, [61] for instance provides suitable background material; for probability theory this role is for instance played by [20], and for ... from manufacturing models (e.g assembly/disassembly queues, also called fork-join queues, jobshop and flowshop models, production lines, etc.) to communication and computer science models (communication ... Matrices and Invertibility Dioids of Polynomials and Power Series 4.7.1 Definitions and Properties of Formal Polynomials and Power Series 4.7.2 Subtraction and Division...
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Tài liệu Báo cáo khoa học: "A New Dataset and Method for Automatically Grading ESOL Texts" pdf
... available shared dataset for training and testing such systems and comparing their performance As it is likely that the deployment of such systems will increase, standardised and independent evaluation ... consistent, comparable and replicable set of results based entirely on the new dataset and on public-domain tools and data, whilst also experimentally motivating some novel feature types for the AA task, ... types and compared it to the SVM rank preference model The results are given in Table The rank preference model improves Pearson’s and Spearman’s correlation by 0.044 and 0.067 respectively, and...
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Tài liệu Báo cáo khoa học: "Support Vector Machines for Query-focused Summarization trained and evaluated on Pyramid data" ppt
... (Manevitz and Yousef, 2001) can learn from just positive examples, according to Yu et al (2002) they are prone to underfitting and overfitting when data is scant (which happens in The mean, median, standard ... III and e Marcu (2005)’s R-2 score was 0.071 [0.067–0.074] and R-SU4 was 0.126 [0.123–0.129] and it is better than the DUC-2005 Fisher and Roark supervised approach with an R-2 of 0.066 and an ... adjacent sentences (Fisher and Roark, 2006) and use rouge scores to initially select negative examples Conclusions and future work L.M Manevitz and M Yousef 2001 One-class SVM for document classification...
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MULTIMEDIA DATABASE APPLICATIONS: ISSUES AND CONCERNS FOR CLASSROOM TEACHING docx
... 4.3 Data Storage and Retrieval A multimedia database system includes a multimedia database management system and a multimedia database The database management system manages the multimedia database; ... used for representing and retrieving the multimedia data Many multimedia databases allow users to query a database by specifying keywords and/ or image characteristics For example: general information ... database; a multimedia database is the multimedia data being managed [14] Why is a database needed? It is for storing and retrieving data more efficiently Multimedia information (e.g., text,...
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