The document discusses survey network adjustment theory, design, and testing. It covers topics such as network adjustment algorithms, input and output data, statistical testing of results, reliability indicators, and network design considerations. Proper network design and testing is important to determine the quality and accuracy of survey results. A variety of network types are discussed, including level networks, resection, intersection, control traverse, and control networks.
This document discusses multi-sensor data fusion techniques for target tracking. It begins by stating the objectives of performing data fusion within the sampling interval to send accurate information to the tracking center. It then discusses areas of use for target tracking including military and non-military applications. The key elements of multiple target tracking are outlined such as detection, recognition, identification, tracking, and decision making. The need for multiple sensors to improve performance is explained. Different data fusion techniques and architectures including centralized, distributed and hybrid are described. The implementation of the weighted least squares method for fusing sensor data is presented along with results showing improved tracking accuracy compared to other fusion techniques.
The document discusses network design and training issues for artificial neural networks. It covers architecture of the network including number of layers and nodes, learning rules, and ensuring optimal training. It also discusses data preparation including consolidation, selection, preprocessing, transformation and encoding of data before training the network.
Dr. Robert J. Bonneau presents an overview of his program, Complex Networks / Foundations of Information Systems, at the AFOSR 2013 Spring Review. At this review, Program Officers from AFOSR Technical Divisions will present briefings that highlight basic research programs beneficial to the Air Force.
Metrology & The Consequences of Bad Measurement DecisionsRick Hogan
This document discusses the importance of metrology and the consequences of bad measurement decisions. It provides examples of failures that resulted from one or more inadequate elements: requirements that were not linked to performance, uncalibrated equipment, and improper measurement procedures. Consequences ranged from mission failures costing over $1 billion to loss of life. Ensuring measurements have good requirements, equipment, and processes is critical to making correct decisions and avoiding risk.
The document discusses building an indoor tracking system using Wi-Fi routers that can provide navigation for areas where GPS does not work. It aims to build a low battery consuming system that can locate users inside buildings. The proposed system would use Wi-Fi signal strengths from multiple routers to determine a user's location through trilateration and then provide navigation to destinations by matching the position to an indoor map. Key components discussed are positioning techniques, mapping, software requirements, and potential applications in malls, hospitals and industries.
Measurement Procedures for Design and Enforcement of Harm Claim ThresholdsPierre de Vries
Presentation at DySPAN 2017, March 2017
Paper forthcoming on IEEE Xplore
Paper authors:
Janne Riihijärvi, Petri Mähönen (RWTH Aachen University, Germany)
J. Pierre de Vries (Silicon Flatirons Centre, University of Colorado, USA)
The document summarizes research on improving automatic target identification methods for laser scanners. It presents results from testing new fuzzy classification algorithms on targets scanned with a Cyrax 2500 laser scanner. The new fuzzypos and fuzzyposfine algorithms achieved the best performance, with high identification accuracy under 1mm, for various scan angles and distances. These algorithms utilize fuzzy classification of target point clouds and provide improved and more robust automatic target identification compared to existing methods.
This document discusses multi-sensor data fusion techniques for target tracking. It begins by stating the objectives of performing data fusion within the sampling interval to send accurate information to the tracking center. It then discusses areas of use for target tracking including military and non-military applications. The key elements of multiple target tracking are outlined such as detection, recognition, identification, tracking, and decision making. The need for multiple sensors to improve performance is explained. Different data fusion techniques and architectures including centralized, distributed and hybrid are described. The implementation of the weighted least squares method for fusing sensor data is presented along with results showing improved tracking accuracy compared to other fusion techniques.
The document discusses network design and training issues for artificial neural networks. It covers architecture of the network including number of layers and nodes, learning rules, and ensuring optimal training. It also discusses data preparation including consolidation, selection, preprocessing, transformation and encoding of data before training the network.
Dr. Robert J. Bonneau presents an overview of his program, Complex Networks / Foundations of Information Systems, at the AFOSR 2013 Spring Review. At this review, Program Officers from AFOSR Technical Divisions will present briefings that highlight basic research programs beneficial to the Air Force.
Metrology & The Consequences of Bad Measurement DecisionsRick Hogan
This document discusses the importance of metrology and the consequences of bad measurement decisions. It provides examples of failures that resulted from one or more inadequate elements: requirements that were not linked to performance, uncalibrated equipment, and improper measurement procedures. Consequences ranged from mission failures costing over $1 billion to loss of life. Ensuring measurements have good requirements, equipment, and processes is critical to making correct decisions and avoiding risk.
The document discusses building an indoor tracking system using Wi-Fi routers that can provide navigation for areas where GPS does not work. It aims to build a low battery consuming system that can locate users inside buildings. The proposed system would use Wi-Fi signal strengths from multiple routers to determine a user's location through trilateration and then provide navigation to destinations by matching the position to an indoor map. Key components discussed are positioning techniques, mapping, software requirements, and potential applications in malls, hospitals and industries.
Measurement Procedures for Design and Enforcement of Harm Claim ThresholdsPierre de Vries
Presentation at DySPAN 2017, March 2017
Paper forthcoming on IEEE Xplore
Paper authors:
Janne Riihijärvi, Petri Mähönen (RWTH Aachen University, Germany)
J. Pierre de Vries (Silicon Flatirons Centre, University of Colorado, USA)
The document summarizes research on improving automatic target identification methods for laser scanners. It presents results from testing new fuzzy classification algorithms on targets scanned with a Cyrax 2500 laser scanner. The new fuzzypos and fuzzyposfine algorithms achieved the best performance, with high identification accuracy under 1mm, for various scan angles and distances. These algorithms utilize fuzzy classification of target point clouds and provide improved and more robust automatic target identification compared to existing methods.
The document discusses the key characteristics and performance parameters of measuring instruments. It describes:
1) Static characteristics relate to constant or slowly varying inputs over time and include parameters like accuracy, precision, resolution, sensitivity, and linearity.
2) Dynamic characteristics relate to rapidly varying inputs over time and are represented by differential equations.
3) Measuring instruments are evaluated based on both their static and dynamic characteristics, with static characteristics being most important for time-independent signals.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/deep-neural-network-training-diagnosing-problems-and-implementing-solutions-a-presentation-from-sensor-cortek/
Fahed Hassanat, Chief Operating Officer and Head of Engineering at Sensor Cortek, presents the “Deep Neural Network Training: Diagnosing Problems and Implementing Solutions” tutorial at the May 2023 Embedded Vision Summit.
In this presentation, Hassanat delves into some of the most common problems that arise when training deep neural networks. He provides a brief overview of essential training metrics, including accuracy, precision, false positives, false negatives and F1 score.
Hassanat then explores training challenges that arise from problems with hyperparameters, inappropriately sized models, inadequate models, poor-quality datasets, imbalances within training datasets and mismatches between training and testing datasets. To help detect and diagnose training problems, he also covers techniques such as understanding performance curves, recognizing overfitting and underfitting, analyzing confusion matrices and identifying class interaction issues.
This document discusses drive testing for GSM network development. It begins by defining drive testing as a method to measure coverage, capacity, and quality of service in a mobile network. Drive testing involves using mobile measurement equipment in a vehicle to detect network parameters along pre-defined routes. Data collected includes signal strength, quality, interference, call statistics, and location. The document then discusses the various parameters measured in drive testing and how the data is analyzed. It outlines the process for conducting drive tests including defining test routes and methods. Both idle and dedicated mode tests are discussed. The results are analyzed to evaluate network performance and identify areas for improvement.
Conducting site surveys for wlan performance and reliabilityKen Scott
This document discusses the importance of conducting wireless site surveys to assess network performance and reliability. It outlines a 5-phase process for wireless site surveys: 1) preparation, 2) on-site surveying, 3) analysis, 4) reporting, and 5) periodic re-surveying. The preparation phase involves gathering relevant details about the environment and network needs. The on-site phase involves passive and active scanning to collect wireless data. Analysis visualizes coverage, interference, and performance metrics. Reporting communicates findings and recommendations. Periodic re-surveys account for environmental and usage changes over time.
Detecting Discontinuties in Large Scale Systemsharoonmalik786
The document proposes an automated approach to help analysts identify discontinuities in large-scale system performance data. The approach involves 4 steps: 1) data preparation to filter noise, 2) metric selection using PCA, 3) anomaly detection using quadratic modeling, and 4) discontinuity identification by comparing distributions using Cohen's D effect size. The approach was tested on synthetic, ecommerce, and industrial system data and achieved high accuracy in detecting discontinuities, which were verified by experts. However, limitations include difficulty distinguishing overlapping discontinuities and sensitivity to the effect size threshold.
The document discusses K parameters used in propagation models. K1-K2 relate to path loss intercept and slope. K3-K4 relate to mobile height. K5-K6 relate to effective base station antenna height and how it affects path loss. K7 relates to diffraction effects. All K parameters must keep the same polarity as the original Okumura-Hata model. Model tuning aims to minimize standard deviation error and provide zero mean error to accurately characterize network limits and propagation effects in a given region. Continuous wave drive tests are conducted to collect data for model tuning.
This document discusses the field data requirements for validating PV module performance models. It outlines that field testing is needed to understand module performance under real operating conditions compared to standardized lab tests. Key requirements for field testing include carefully selecting and characterizing modules, using high-precision calibrated equipment, properly characterizing the test site, and processing data with harmonized methods that include calculating measurement uncertainties. Meeting all these requirements allows for more comparable data across studies and better validation of models. Typical uncertainties in field performance ratio measurements are around 4.5%, while uncertainties can be reduced to around 1.5% by standardizing testing practices.
This document discusses VLSI faults and testing. It begins by outlining the VLSI realization process from customer needs to fabrication. It then defines key terms like defects, faults, errors and describes common types of defects from fabrication. The document discusses logical fault models and the role of testing in detecting errors. It outlines different types of testing like production testing and burn-in testing. Finally, it discusses topics like design for testability, fault simulation, and benefits of testing like improved quality and economy of scale.
NSL KDD Cup 99 dataset Anomaly Detection using Machine Learning Technique Sujeet Suryawanshi
This document summarizes a presentation given on using decision trees and machine learning techniques for anomaly detection on the NSL KDD Cup 99 dataset. It discusses anomaly detection, machine learning, different machine learning algorithms like decision trees, SVM, Naive Bayes etc. and their application for intrusion detection. It then describes an experiment conducted using the decision tree algorithm on the NSL KDD Cup 99 dataset to classify network traffic as normal or anomalous. The results showed the decision tree model achieved over 98% accuracy on both the full dataset and a reduced feature set.
A wireless site survey involves planning and designing a wireless network to provide adequate wireless coverage, data rates, capacity, roaming, and quality of service. It involves assessing current WiFi performance, identifying obstacles and dead zones, and determining optimal access point locations. The key steps are reviewing floor plans, identifying coverage areas, determining the number of access points needed, testing coverage, troubleshooting issues, and rescheduling future testing as needs change. Common survey tools help map predicted coverage and place access points.
⭐⭐⭐⭐⭐ Trilateration-based Indoor Location using Supervised Learning AlgorithmsVictor Asanza
The indoor positioning system (IPS) has a wide range of applications, due to the advantages it has over Global Positioning Systems (GPS) in indoor environments. Due to the biosecurity measures established by the World Health Organization (WHO), where the social distancing is provided, being stricter in indoor environments. This work proposes the design of a positioning system based on trilateration. The main objective is to predict the positioning in both the ‘x’ and ‘y’ axis in an area of 8 square meters. For this purpose, 3 Access Points (AP) and a Mobile Device (DM), which works as a raster, have been used. The Received Signal Strength Indication (RSSI) values measured at each AP are the variables used in regression algorithms that predict the x and y position. In this work, 24 regression algorithms have been evaluated, of which the lowest errors obtained are 70.322 [cm] and 30.1508 [cm], for the x and y axes, respectively.
Published in: 2022 International Conference on Applied Electronics (AE)
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
Helping companies harness wireless innovation and expertiseJames Bemrose
ASH is a creative electronics design consultancy who are experts in wireless technologies. This presentation shows how we can help companies harness wireless innovation and expertise
Kirit Kisanbhai Gohil is seeking an entry-level position as an electronic and telecommunication engineer. He has a diploma in industrial electronics from K.J. Somaiya Polytechnic and a degree in electronics from Theem College of Engineering. His final year project involved building a gesture-controlled robot and his past experience includes working as a trainee QC electronic engineer at Pulz Electronics and currently as an RF engineer at Reliance Jio Infocomm where he performs various tasks related to network maintenance and optimization. He has strong problem-solving and technical skills.
This resume summarizes the qualifications and experience of Velmurugan S, who is seeking a position as an RF Engineer. He has over 2 years of experience in RF application engineering, testing, and troubleshooting complex systems. His responsibilities have included product testing and verification, supporting R&D engineering, and developing test methods. He is proficient with various RF and digital test equipment and instruments. Velmurugan holds a B.E. in Electronics and Communication Engineering and has participated in conferences and workshops related to RF measurements.
“Metrology is the science of measurement, including theoretical and experimental conclusions at any level of uncertainty in every area of science and technology.” Without a doubt, metrology is the foundation of all practical scientific endeavors.
Khi bạn lên kế hoạch xây dựng một phòng thí nghiệm hoặc phòng sạch mới, cần thiết lập cả các yêu cầu giám sát môi trường. Nếu bạn đợi cho đến sau khi xây dựng, các giải pháp mà bạn đưa ra có thể không đạt được kết quả mong muốn hoặc gây tốn kém. Checklist này giúp bạn tối ưu hóa quá trình lập kế hoạch, giữ cho dự án đúng tiến độ và trong phạm vi ngân sách.
Xem thêm các tài liệu khác trên kênh của Công ty Cổ phần Tư vấn Thiết kế GMP EU.
Tracking involves estimating a target's state based on uncertain sensor measurements and calculating the accuracy of the estimate. Effective tracking requires modeling target motion, sensor measurements, and measurement origin uncertainty. Tracking performance is evaluated using mean-square estimation error. Data association and filtering techniques have been developed to track maneuvering targets in clutter using multiple sensors, with information fusion improving accuracy.
This document describes the statistical methods available in East software for designing clinical trials. It lists methods for continuous, discrete, survival, and agreement data including single-arm, multiple-arm, paired/unpaired, non-inferiority/equivalence, and adaptive designs. It provides an overview of East's capabilities for simulation, sample size calculation, interim monitoring, and customizable reporting of trial design characteristics.
This document summarizes a study that analyzed different unsteady friction models used in water hammer analysis software. The study implemented several friction models in Python code and compared them based on input requirements, stability, computational efficiency, and accuracy when validated with experimental data. It was found that Vítkovský's unsteady friction model performed best. This model was then implemented in the WANDA commercial software. Testing showed the model provided more accurate simulations of transient pressure waves compared to the previous quasisteady friction approach.
This document discusses assessing normality and data transformations. It notes that many statistical methods require normality and outlines tools to assess it such as histograms, boxplots, normal quantile plots, and goodness of fit tests. It also discusses how transformations like taking the square root, logarithm, or reciprocal can be used to remove skewness and make data more normal, with the goal of meeting assumptions of statistical methods. Tukey's ladder of powers is presented as a guide for selecting transformations to reduce left or right skewness. Examples demonstrate transforming skewed data like systolic volume to achieve normality.
The document discusses the key characteristics and performance parameters of measuring instruments. It describes:
1) Static characteristics relate to constant or slowly varying inputs over time and include parameters like accuracy, precision, resolution, sensitivity, and linearity.
2) Dynamic characteristics relate to rapidly varying inputs over time and are represented by differential equations.
3) Measuring instruments are evaluated based on both their static and dynamic characteristics, with static characteristics being most important for time-independent signals.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/deep-neural-network-training-diagnosing-problems-and-implementing-solutions-a-presentation-from-sensor-cortek/
Fahed Hassanat, Chief Operating Officer and Head of Engineering at Sensor Cortek, presents the “Deep Neural Network Training: Diagnosing Problems and Implementing Solutions” tutorial at the May 2023 Embedded Vision Summit.
In this presentation, Hassanat delves into some of the most common problems that arise when training deep neural networks. He provides a brief overview of essential training metrics, including accuracy, precision, false positives, false negatives and F1 score.
Hassanat then explores training challenges that arise from problems with hyperparameters, inappropriately sized models, inadequate models, poor-quality datasets, imbalances within training datasets and mismatches between training and testing datasets. To help detect and diagnose training problems, he also covers techniques such as understanding performance curves, recognizing overfitting and underfitting, analyzing confusion matrices and identifying class interaction issues.
This document discusses drive testing for GSM network development. It begins by defining drive testing as a method to measure coverage, capacity, and quality of service in a mobile network. Drive testing involves using mobile measurement equipment in a vehicle to detect network parameters along pre-defined routes. Data collected includes signal strength, quality, interference, call statistics, and location. The document then discusses the various parameters measured in drive testing and how the data is analyzed. It outlines the process for conducting drive tests including defining test routes and methods. Both idle and dedicated mode tests are discussed. The results are analyzed to evaluate network performance and identify areas for improvement.
Conducting site surveys for wlan performance and reliabilityKen Scott
This document discusses the importance of conducting wireless site surveys to assess network performance and reliability. It outlines a 5-phase process for wireless site surveys: 1) preparation, 2) on-site surveying, 3) analysis, 4) reporting, and 5) periodic re-surveying. The preparation phase involves gathering relevant details about the environment and network needs. The on-site phase involves passive and active scanning to collect wireless data. Analysis visualizes coverage, interference, and performance metrics. Reporting communicates findings and recommendations. Periodic re-surveys account for environmental and usage changes over time.
Detecting Discontinuties in Large Scale Systemsharoonmalik786
The document proposes an automated approach to help analysts identify discontinuities in large-scale system performance data. The approach involves 4 steps: 1) data preparation to filter noise, 2) metric selection using PCA, 3) anomaly detection using quadratic modeling, and 4) discontinuity identification by comparing distributions using Cohen's D effect size. The approach was tested on synthetic, ecommerce, and industrial system data and achieved high accuracy in detecting discontinuities, which were verified by experts. However, limitations include difficulty distinguishing overlapping discontinuities and sensitivity to the effect size threshold.
The document discusses K parameters used in propagation models. K1-K2 relate to path loss intercept and slope. K3-K4 relate to mobile height. K5-K6 relate to effective base station antenna height and how it affects path loss. K7 relates to diffraction effects. All K parameters must keep the same polarity as the original Okumura-Hata model. Model tuning aims to minimize standard deviation error and provide zero mean error to accurately characterize network limits and propagation effects in a given region. Continuous wave drive tests are conducted to collect data for model tuning.
This document discusses the field data requirements for validating PV module performance models. It outlines that field testing is needed to understand module performance under real operating conditions compared to standardized lab tests. Key requirements for field testing include carefully selecting and characterizing modules, using high-precision calibrated equipment, properly characterizing the test site, and processing data with harmonized methods that include calculating measurement uncertainties. Meeting all these requirements allows for more comparable data across studies and better validation of models. Typical uncertainties in field performance ratio measurements are around 4.5%, while uncertainties can be reduced to around 1.5% by standardizing testing practices.
This document discusses VLSI faults and testing. It begins by outlining the VLSI realization process from customer needs to fabrication. It then defines key terms like defects, faults, errors and describes common types of defects from fabrication. The document discusses logical fault models and the role of testing in detecting errors. It outlines different types of testing like production testing and burn-in testing. Finally, it discusses topics like design for testability, fault simulation, and benefits of testing like improved quality and economy of scale.
NSL KDD Cup 99 dataset Anomaly Detection using Machine Learning Technique Sujeet Suryawanshi
This document summarizes a presentation given on using decision trees and machine learning techniques for anomaly detection on the NSL KDD Cup 99 dataset. It discusses anomaly detection, machine learning, different machine learning algorithms like decision trees, SVM, Naive Bayes etc. and their application for intrusion detection. It then describes an experiment conducted using the decision tree algorithm on the NSL KDD Cup 99 dataset to classify network traffic as normal or anomalous. The results showed the decision tree model achieved over 98% accuracy on both the full dataset and a reduced feature set.
A wireless site survey involves planning and designing a wireless network to provide adequate wireless coverage, data rates, capacity, roaming, and quality of service. It involves assessing current WiFi performance, identifying obstacles and dead zones, and determining optimal access point locations. The key steps are reviewing floor plans, identifying coverage areas, determining the number of access points needed, testing coverage, troubleshooting issues, and rescheduling future testing as needs change. Common survey tools help map predicted coverage and place access points.
⭐⭐⭐⭐⭐ Trilateration-based Indoor Location using Supervised Learning AlgorithmsVictor Asanza
The indoor positioning system (IPS) has a wide range of applications, due to the advantages it has over Global Positioning Systems (GPS) in indoor environments. Due to the biosecurity measures established by the World Health Organization (WHO), where the social distancing is provided, being stricter in indoor environments. This work proposes the design of a positioning system based on trilateration. The main objective is to predict the positioning in both the ‘x’ and ‘y’ axis in an area of 8 square meters. For this purpose, 3 Access Points (AP) and a Mobile Device (DM), which works as a raster, have been used. The Received Signal Strength Indication (RSSI) values measured at each AP are the variables used in regression algorithms that predict the x and y position. In this work, 24 regression algorithms have been evaluated, of which the lowest errors obtained are 70.322 [cm] and 30.1508 [cm], for the x and y axes, respectively.
Published in: 2022 International Conference on Applied Electronics (AE)
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
Helping companies harness wireless innovation and expertiseJames Bemrose
ASH is a creative electronics design consultancy who are experts in wireless technologies. This presentation shows how we can help companies harness wireless innovation and expertise
Kirit Kisanbhai Gohil is seeking an entry-level position as an electronic and telecommunication engineer. He has a diploma in industrial electronics from K.J. Somaiya Polytechnic and a degree in electronics from Theem College of Engineering. His final year project involved building a gesture-controlled robot and his past experience includes working as a trainee QC electronic engineer at Pulz Electronics and currently as an RF engineer at Reliance Jio Infocomm where he performs various tasks related to network maintenance and optimization. He has strong problem-solving and technical skills.
This resume summarizes the qualifications and experience of Velmurugan S, who is seeking a position as an RF Engineer. He has over 2 years of experience in RF application engineering, testing, and troubleshooting complex systems. His responsibilities have included product testing and verification, supporting R&D engineering, and developing test methods. He is proficient with various RF and digital test equipment and instruments. Velmurugan holds a B.E. in Electronics and Communication Engineering and has participated in conferences and workshops related to RF measurements.
“Metrology is the science of measurement, including theoretical and experimental conclusions at any level of uncertainty in every area of science and technology.” Without a doubt, metrology is the foundation of all practical scientific endeavors.
Khi bạn lên kế hoạch xây dựng một phòng thí nghiệm hoặc phòng sạch mới, cần thiết lập cả các yêu cầu giám sát môi trường. Nếu bạn đợi cho đến sau khi xây dựng, các giải pháp mà bạn đưa ra có thể không đạt được kết quả mong muốn hoặc gây tốn kém. Checklist này giúp bạn tối ưu hóa quá trình lập kế hoạch, giữ cho dự án đúng tiến độ và trong phạm vi ngân sách.
Xem thêm các tài liệu khác trên kênh của Công ty Cổ phần Tư vấn Thiết kế GMP EU.
Tracking involves estimating a target's state based on uncertain sensor measurements and calculating the accuracy of the estimate. Effective tracking requires modeling target motion, sensor measurements, and measurement origin uncertainty. Tracking performance is evaluated using mean-square estimation error. Data association and filtering techniques have been developed to track maneuvering targets in clutter using multiple sensors, with information fusion improving accuracy.
This document describes the statistical methods available in East software for designing clinical trials. It lists methods for continuous, discrete, survival, and agreement data including single-arm, multiple-arm, paired/unpaired, non-inferiority/equivalence, and adaptive designs. It provides an overview of East's capabilities for simulation, sample size calculation, interim monitoring, and customizable reporting of trial design characteristics.
This document summarizes a study that analyzed different unsteady friction models used in water hammer analysis software. The study implemented several friction models in Python code and compared them based on input requirements, stability, computational efficiency, and accuracy when validated with experimental data. It was found that Vítkovský's unsteady friction model performed best. This model was then implemented in the WANDA commercial software. Testing showed the model provided more accurate simulations of transient pressure waves compared to the previous quasisteady friction approach.
This document discusses assessing normality and data transformations. It notes that many statistical methods require normality and outlines tools to assess it such as histograms, boxplots, normal quantile plots, and goodness of fit tests. It also discusses how transformations like taking the square root, logarithm, or reciprocal can be used to remove skewness and make data more normal, with the goal of meeting assumptions of statistical methods. Tukey's ladder of powers is presented as a guide for selecting transformations to reduce left or right skewness. Examples demonstrate transforming skewed data like systolic volume to achieve normality.
This document provides an introduction to using SPSS (Statistical Package for the Social Sciences) software. It covers opening and navigating SPSS, cleaning and transforming data, descriptive statistics, graphs and charts, and saving work. The topics are demonstrated using a sample education data set. Key functions covered include selecting cases, recoding variables, descriptive statistics like frequencies and crosstabs, formatting histograms and other graphs, and performing a one-way ANOVA test. Resources for further learning SPSS are also provided.
This document provides an overview of remote sensing. It defines remote sensing as acquiring information about an object without physical contact. The history of remote sensing is outlined from early uses of photography from balloons and planes to modern satellite systems. The key principles of remote sensing are described, including the electromagnetic spectrum, energy sources, atmospheric interactions, and how radiation is absorbed, transmitted or reflected when interacting with targets. Remote sensing applications and different sensor types are also mentioned.
This document provides an overview of non-linear least squares and sparse matrix techniques. It begins with an introduction to non-linear least squares using the example of triangulation. It then discusses solutions such as normal equations, LDLT factorization, and the Levenberg-Marquardt algorithm. The document next covers sparse matrix techniques for structure from motion problems, including sparse LDLT factorization and iterative methods like conjugate gradients. It concludes by comparing direct and iterative solutions for 1D and 2D problems.
This document discusses assessing normality and data transformations. It notes that many statistical methods require normality and outlines tools to assess it such as histograms, boxplots, normal quantile plots, and goodness of fit tests. It also discusses how transformations like taking the square root, logarithm, or reciprocal can be used to remove skewness and make data more normal, with the goal of meeting assumptions of statistical methods. Tukey's ladder of powers is presented as a guide for selecting transformations to reduce left or right skewness. Examples demonstrate transforming skewed variables like systolic volume to achieve normality.
- The document discusses principles of least squares adjustment for survey measurements.
- It introduces random error adjustment to account for measurement errors by minimizing the sum of squared residuals.
- The fundamental principle of least squares states that to obtain the most probable values, the sum of squares of the residuals must be minimized.
- It presents examples to demonstrate setting up and solving least squares adjustments through normal equation matrices in both linear and nonlinear systems.
The document provides information on performing a chi-squared test for independence using contingency tables. It defines key terms like degrees of freedom and expected values. An example compares infections at 3 hospitals. It shows calculating degrees of freedom, stating hypotheses, finding the critical value, computing the test statistic, and making a decision to reject or fail to reject the null hypothesis of independence.
This document discusses statistical tests used to analyze data from different types of study designs. It provides an overview of tests for comparing two or more groups, including ANOVA and chi-square tests. It also reviews alternatives that can be used if the assumptions of those tests, like normality, are violated. Examples are given of how to calculate ANOVA by hand and how it relates to the t-test. In summary, the document reviews best practices for selecting the appropriate statistical test based on the study design, number of groups, type of outcome variable, and whether observations are independent or correlated between groups.
The document provides an overview of the chi-square distribution and how to conduct chi-square tests. It discusses when chi-square tests can be used, the assumptions of chi-square tests, and how to perform chi-square analyses including calculating expected frequencies, degrees of freedom, and comparing the chi-square statistic to critical values. Examples demonstrating chi-square tests for proportions and association are presented. SAS code for conducting chi-square tests is also shown.
This document discusses statistical techniques for comparing two sets of data, including the t-distribution for unknown standard deviations, hypothesis testing, and the Wilcoxon rank-sum test. It provides examples of using the Wilcoxon rank-sum test to compare precipitation water quality data from two sites and determine that the residential concentration is lower than the industrial concentration with a p-value of 0.024.
This document introduces two nonparametric hypothesis tests using the chi-square statistic: the chi-square test for goodness of fit and the chi-square test for independence. These tests do not require assumptions about population parameters and instead use frequency data. The chi-square test for goodness of fit tests hypotheses about the shape or proportions of a population using observed and expected frequencies. The chi-square test for independence tests hypotheses about the relationship between two variables or differences between population proportions using a matrix of observed and expected frequencies. Both tests calculate a chi-square statistic to measure discrepancy between observed and expected frequencies under the null hypothesis.
This document discusses correlation, regression, and the general linear model. It defines correlation as assessing the relationship between two variables, while regression describes how well one variable can predict another. Pearson's r standardizes the covariance between variables. Linear regression finds the best-fitting line that minimizes the residuals through the least squares method. The coefficient of determination, r2, indicates how much variance in the dependent variable is explained by the independent variable. Multiple regression extends this to include multiple independent variables. The general linear model encompasses both linear regression and multiple regression.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
1. 451-200 Survey Networks
Theory, Design and Testing
Allison Kealy
akealy@unimelb.edu.au
Department of Geomatics
The University of Melbourne
Victoria
3010
2. Survey Networks: Theory, Design and Testing
Introduction
Survey network adjustment is also
known as
Variation of coordinates
Least squares adjustment
Least squares estimation
Survey adjustment
Use routinely for survey
computations.
3. Survey Networks: Theory, Design and Testing
Advantages
Networks adjustment is widely adopted
due to
Consistent treatment of redundant
measurements
Rigorous processing of measurement
variability
Ability to statistically test and analyse
the results
4. Survey Networks: Theory, Design and Testing
Implementations
Many commercial and proprietary
network adjustment packages are
available
SkiPro
CompNET
Star*Net
TDVC, DNA
Wide variation in ease of use,
sophistication and available features
5. Survey Networks: Theory, Design and Testing
Non-Network Adjustment
Coordinate geometry computations
Also known as “COGO” packages
Simple 2D or 3D geometry computations
for radiations, intersections etc
Traverse adjustment
Known as Bowditch or traverse rules
Valid method of distributing errors
Not statistically rigorous
6. Survey Networks: Theory, Design and Testing
Input Data
Survey measurments
Horizontal angles
Vertical angles
Distances (slope and horizontal)
Level differences
GPS positions and baselines
Azimuths/bearings
Measurement precisions
7. Survey Networks: Theory, Design and Testing
Input Data (continued)
Fixed and adjustable coordinate indicators
Known coordinates of unknown stations
Approximate coordinates of unknown
stations
Auxiliary data such as
Coordinate system and datum
Atmospheric refraction
Default values for precisions etc
8. Survey Networks: Theory, Design and Testing
Algorithm – Functional Model
Describe the geometric relationship
between measurements and stations
Very well understood for conventional
measurements
GPS knowledge well established
Sets the response of station positions
to different measurement types
9. Survey Networks: Theory, Design and Testing
Algorithm – Stochastic Model
Models the statistical properties of the
measurements
Assumes a Gaussian or normal distribution
function of random error
Effectively a “weighting” of the
“importance” of different measurements
based on precision data
Precision levels are often not well estimated
10. Survey Networks: Theory, Design and Testing
Results Output
Adjusted coordinates for all stations
Precision of all coordinates
Error ellipses for all stations
Adjusted measurements
Measurement residuals
Differences between the measured and
adjusted values for any measurment
11. Survey Networks: Theory, Design and Testing
Statistical Testing Information
Unit weight precision
Also known as sigma zero (s0)
Squared quantity known as estimate of
the variance factor or unit weight
variance
Indicates overall or global quality of the
solution
t statistics for each measurement
Indicates local quality of individual
measurements
12. Survey Networks: Theory, Design and Testing
Reliability Indicators
Reliability is a measure of the
susceptibility to error
Global and local values can be
computed
Indicated by either
Redundancy numbers
Reliability factors
Generally only useful for internal
comparisons of measurements
13. Survey Networks: Theory, Design and Testing
Network Analysis
Analysis of the results of survey networks is
essential
Assessment of station coordinate precisions
against specifications is often first priority
Networks may also be tested for accuracy if
suitable independent checks are available
Testing of networks for gross errors and
other factors is mandatory
14. Survey Networks: Theory, Design and Testing
Network Testing
The estimate of the variance factor is used
as a global test of the entire survey
network
Individual measurements are locally tested
against the student t distribution
Both test distributions are independent of
the number of redundancies in the network
The confidence of the testing improves with
higher redundancy numbers
15. Survey Networks: Theory, Design and Testing
Network Testing (continued)
Global and local test values are
influenced by
Blunders or gross errors e.g. reading or
transcription errors
Systematic errors, e.g. calibration errors
or anomalous refraction
Precision errors, e.g. under or over
estimation of the repeatability of an
instrument or the influence of
environmental factors
16. Survey Networks: Theory, Design and Testing
Network Testing (continued)
An initial global test is required to
determine the likelihood of errors in
individual measurements
Local errors are tested, de-activating the
measurements with the worst t statistic and
re-processing the adjustment
Measurements are deactivated until all local
tests are acceptable or the point of
“diminishing returns” is reached
If the global test still fails then systematic
or precision errors are investigated
17. Survey Networks: Theory, Design and Testing
Network Design
Networks must be designed to suit
The survey problem
Specifications for precision and accuracy
Expectations for reliability
Limitations on physical access
Restrictions placed o time and/or cost
Availability of equipments
Availability of staff
18. Survey Networks: Theory, Design and Testing
Network Design (continued)
Network design is part experience
and part science
Experience comes from practiced
knowledge of network types, error
propagation and geometry
Scientific analysis comes from the
interpretation of error ellipses and
other indicators of network quality
19. Survey Networks: Theory, Design and Testing
Network Design (continued)
Basic network types comprise
Level networks
Resection
Intersection
Control traverse
Control networks
The choice of type is primarily based on the
survey problem, specifications for
precision/accuracy and available
equipments
20. Survey Networks: Theory, Design and Testing
Level Network
Measurement data is level differences
only
All horizontal angles must be fixed
At least one station height must be
fixed to set the vertical datum
Level differences are typically set s
proportional to the square root of the
run length
21. Survey Networks: Theory, Design and Testing
Resection
Measurement data is horizontal angles only
All coordinates of the resection targets
must be held fixed
The height of the instrument station must
be held fixed
Horizontal angle precisions are set from the
standard deviations of the means of the
multiple rounds of observations
22. Survey Networks: Theory, Design and Testing
Control Traverse
Measurement data is horizontal and
vertical angles, distances and perhaps
level differences
At least one known control station
and one reference object are needed
Precision data may be estimated from
experience or adopted from
instrument specifications
23. Survey Networks: Theory, Design and Testing
Control Networks
All measurement data types
At least one control station and one
reference object needed
Precision data may be estimated from
experience, adopted from the
instrument specifications or computed
High numerical and geometric
redundancies leading to very high
reliabilities
24. Survey Networks: Theory, Design and Testing
Steps in Survey Design
Using available information lay out possible
positions of stations
Check line of sights
Do field recce and adjust positions of
stations
Determine approximate coordinates
Compute values of observations from
coordinates
Compute standard deviation of
measurements
25. Survey Networks: Theory, Design and Testing
Steps in Survey Design
Perform least square adjustment, to
compute observational redundancy
numbers, standard deviations of
coordinates and error ellipses
Inspect the solution for weak areas
based on redundancy numbers and
ellipse shapes
Evaluate cost of survey
Write specification
26. Survey Networks: Theory, Design and Testing
Conclusions
Any survey work involves a component of
network design and almost invariably
requires testing
Efficient and appropriate network design is
a learned skill, supplemented by experience
Network testing is essential to determine
the quality of the survey
http://www.geom.unimelb.edu.au/kealyal/2
00/Teaching/net_design_test.html
27. Survey Networks: Theory, Design and Testing
Survey Network Configurations
Station coordinates can be fixed,
constrained or free
Good approximations for the free
stations are necessary for
convergence
There must be sufficient
measurements to geometrically
define all the free coordinates
28. Survey Networks: Theory, Design and Testing
Survey Network Configurations
Assuming we have sufficient station
coordinates and measurements to define the
datum, orientation and scale, station
coordinates are defined by the measurements
as follows:
Measurement type X Y H
Bearing S S No
Horizontal angle S S No
Vertical Angle W W S
Slope Distance S S W
Horizontal distance S S No
Height Difference No No Yes
29. Survey Networks: Theory, Design and Testing
Survey Network Configurations
Strength or weakness of the determination
depends on the geometry of the relationship
between the stations and the measurements
Every station can be tested for the minimum
numerical requirement to define all the
coordinates of the station
Measurement type Planimetric height
Bearing 1 0
Horizontal angle 1 0
Vertical Angle 0 1
Slope Distance 1 0
Horizontal distance 1 0
Height Difference 0 1
30. Survey Networks: Theory, Design and Testing
Externally Constrained Networks
Assume survey networks are externally constrained
Externally constrained networks contain sufficient fixed
or constrained station coordinates to define the datum,
orientation and scale of the networks
Datum
Locates network relative of coordinate system origin
three coordinates fixed, one in each dimension
Orientation
Fix the orientation of the network relative to the coordinate
system
Use bearings or planimetric coordinate of another stations
31. Survey Networks: Theory, Design and Testing
Externally Constrained Networks
Scale
Use distances to fix the scale of the network relative
to the coordinate system
Fix planimetric coordinates of another station
Minimal Constraints
32. Survey Networks: Theory, Design and Testing
Free Networks
Free or internally constrained
All stations open to adjustment
Based on initial coordinates of
stations
Datum, scale and orientation
arbitrary
33. Survey Networks: Theory, Design and Testing
Testing of Adjustments
Factors affecting adjustments
Mathematical model
Stochastic model
Gross errors
Confidence intervals
Redundant Measurements