This document provides an introduction and overview of NI Multisim simulation software. It discusses key features like schematic capture, simulation, and PCB layout. The software includes a library of over 13,000 electronic components that can be used in circuits. It also allows integration with hardware through NI ELVIS and data analysis using LabVIEW. Application areas of the software include academic teaching, instrument control, and embedded system prototyping by combining simulation with real-time data acquisition.
PYTHON AND DATA SCIENCE FOR INVESTMENT PROFESSIONALSQuantUniversity
Join CFA Institute and QuantUniversity for an information session about the upcoming CFA Institute Professional Learning course: Python and Data Science for Investment professionals.
This workshop will look into ways to create synthetic data from lending club loan record datasets alongside comparing characteristics and statistical properties of real and synthetic datasets. There will also be discussions into building machine learning models for predicting interest rates using real and synthetic datasets and evaluating the performance and discuss the advantages and disadvantages of using synthetic datasets as a proxy for real datasets
A survey on various technologies available for Smart lab based on Internet of...IJSRD
This paper explores some approaches to harnessing the IoT in teaching field. The Internet of Things (IoT) is a fast emerging system of physical sensors and connected devices, enabling an advanced information gathering, interpretation and monitoring. Smart Lab is still in need of an efficient attendance system which takes attendance in real time. Various Research papers are summarized in this paper. This paper describes the concept of development of Smart Lab which takes attendance by using RFID technology. Then it improves the efficiency of attendance taking system by analyzing the reading range of RFID system. The Smart Lab concept also monitors and controls the temperature and humidity of the computer system.
PYTHON AND DATA SCIENCE FOR INVESTMENT PROFESSIONALSQuantUniversity
Join CFA Institute and QuantUniversity for an information session about the upcoming CFA Institute Professional Learning course: Python and Data Science for Investment professionals.
This workshop will look into ways to create synthetic data from lending club loan record datasets alongside comparing characteristics and statistical properties of real and synthetic datasets. There will also be discussions into building machine learning models for predicting interest rates using real and synthetic datasets and evaluating the performance and discuss the advantages and disadvantages of using synthetic datasets as a proxy for real datasets
A survey on various technologies available for Smart lab based on Internet of...IJSRD
This paper explores some approaches to harnessing the IoT in teaching field. The Internet of Things (IoT) is a fast emerging system of physical sensors and connected devices, enabling an advanced information gathering, interpretation and monitoring. Smart Lab is still in need of an efficient attendance system which takes attendance in real time. Various Research papers are summarized in this paper. This paper describes the concept of development of Smart Lab which takes attendance by using RFID technology. Then it improves the efficiency of attendance taking system by analyzing the reading range of RFID system. The Smart Lab concept also monitors and controls the temperature and humidity of the computer system.
This workshop will look into ways to create synthetic data from lending club loan record datasets alongside comparing characteristics and statistical properties of real and synthetic datasets. There will also be discussions into building machine learning models for predicting interest rates using real and synthetic datasets and evaluating the performance and discuss the advantages and disadvantages of using synthetic datasets as a proxy for real datasets
How to build containerized architectures for deep learning - Data Festival 20...Antje Barth
When it comes to AI data scientists/engineers tend to focus on tools. Though the data platform that enables these tools is equally important, it’s often overlooked. In fact, 90% of the effort required for success in ML is not the algorithm – it’s the data logistics. In this workshop we will talk about common architecture blueprints to integrate AI in your data centers and how the right data platform choice can make all the difference in launching your AI use case into production! Presented at Data Festival Munich, 2019.
Synthetic data generation for machine learningQuantUniversity
As machine learning becomes more pervasive in the industry, data scientists and quants are realizing the challenges and limitations of machine learning models. One of the primary reasons machine learning applications fail is due to the lack of rich, diverse and clean datasets needed to build models. Datasets may have missing values, may not incorporate enough samples for all use cases (for example: availability of fraudulent transaction records to train a model) and may not be easily sharable due to privacy concerns. While there are many data cleansing techniques to fix data-related issues and we can always try and get new and rich datasets, the cost is at times prohibitive and at times impractical leading many institutions to abandon machine learning and go back to rule-based methods.
Synthetic data sets and simulations are used to enrich and augment existing datasets to provide comprehensive samples while training machine learning problems. In addition, synthetic datasets can be used for comprehensive scenario analysis, missing value filling and privacy protection of the datasets when building models. The advent of novel techniques like Deep Learning has rekindled interest in using techniques like GANs and Encoder-Decoder architectures in financial synthetic data generation.
In this workshop, we will discuss the state of the art in Synthetic data generation and will illustrate the various techniques and methods that can be used in practice. Through examples using QuSynthesize & QuSandbox, we will demonstrate how these techniques can be realized in practice.
RAPIDS is a suite of open source software libraries and APIs gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs.In this workshop, we will:
1. Introduce Rapids.ai & GPUs
2. Illustrate why GPUs are critical for machine learning and AI applications
3. Demonstrate common machine learning algorithms such as Regression, KNN,SGD etc. using RAPIDS on the QuSandbox
BigDataPilotDemoDays - I BiDaaS Application to the Manufacturing Sector WebinarBig Data Value Association
The new data-driven industrial revolution highlights the need for big data technologies to unlock the potential in various application domains. To this end, BDV PPP projects I-BiDaaS, BigDataStack, Track & Know and Policy Cloud deliver innovative technologies to address the emerging needs of data operations and applications. To fully exploit the sustainability and take full advantage of the developed technologies, the projects onboarded pilots that exhibit their applicability in a wide variety of sectors. In the Big Data Pilot Demo Days, the projects will showcase the developed and implemented technologies to interested end-users from the industry as well as technology providers, for further adoption.
UberCloud HPC Experiment Introduction for Beginnershpcexperiment
UberCloud HPC Experiment Introduction for Beginners.
What is the HPC Experiment
How the HPC Experiment works
How to participate in the HPC Experiment
And an example project
This workshop will look into ways to create synthetic data from lending club loan record datasets alongside comparing characteristics and statistical properties of real and synthetic datasets. There will also be discussions into building machine learning models for predicting interest rates using real and synthetic datasets and evaluating the performance and discuss the advantages and disadvantages of using synthetic datasets as a proxy for real datasets
How to build containerized architectures for deep learning - Data Festival 20...Antje Barth
When it comes to AI data scientists/engineers tend to focus on tools. Though the data platform that enables these tools is equally important, it’s often overlooked. In fact, 90% of the effort required for success in ML is not the algorithm – it’s the data logistics. In this workshop we will talk about common architecture blueprints to integrate AI in your data centers and how the right data platform choice can make all the difference in launching your AI use case into production! Presented at Data Festival Munich, 2019.
Synthetic data generation for machine learningQuantUniversity
As machine learning becomes more pervasive in the industry, data scientists and quants are realizing the challenges and limitations of machine learning models. One of the primary reasons machine learning applications fail is due to the lack of rich, diverse and clean datasets needed to build models. Datasets may have missing values, may not incorporate enough samples for all use cases (for example: availability of fraudulent transaction records to train a model) and may not be easily sharable due to privacy concerns. While there are many data cleansing techniques to fix data-related issues and we can always try and get new and rich datasets, the cost is at times prohibitive and at times impractical leading many institutions to abandon machine learning and go back to rule-based methods.
Synthetic data sets and simulations are used to enrich and augment existing datasets to provide comprehensive samples while training machine learning problems. In addition, synthetic datasets can be used for comprehensive scenario analysis, missing value filling and privacy protection of the datasets when building models. The advent of novel techniques like Deep Learning has rekindled interest in using techniques like GANs and Encoder-Decoder architectures in financial synthetic data generation.
In this workshop, we will discuss the state of the art in Synthetic data generation and will illustrate the various techniques and methods that can be used in practice. Through examples using QuSynthesize & QuSandbox, we will demonstrate how these techniques can be realized in practice.
RAPIDS is a suite of open source software libraries and APIs gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs.In this workshop, we will:
1. Introduce Rapids.ai & GPUs
2. Illustrate why GPUs are critical for machine learning and AI applications
3. Demonstrate common machine learning algorithms such as Regression, KNN,SGD etc. using RAPIDS on the QuSandbox
BigDataPilotDemoDays - I BiDaaS Application to the Manufacturing Sector WebinarBig Data Value Association
The new data-driven industrial revolution highlights the need for big data technologies to unlock the potential in various application domains. To this end, BDV PPP projects I-BiDaaS, BigDataStack, Track & Know and Policy Cloud deliver innovative technologies to address the emerging needs of data operations and applications. To fully exploit the sustainability and take full advantage of the developed technologies, the projects onboarded pilots that exhibit their applicability in a wide variety of sectors. In the Big Data Pilot Demo Days, the projects will showcase the developed and implemented technologies to interested end-users from the industry as well as technology providers, for further adoption.
UberCloud HPC Experiment Introduction for Beginnershpcexperiment
UberCloud HPC Experiment Introduction for Beginners.
What is the HPC Experiment
How the HPC Experiment works
How to participate in the HPC Experiment
And an example project
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
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AKS UNIVERSITY Satna Final Year Project By OM Hardaha.pdf
Ni Multisim.pptx
1. BITS Pilani
Hyderabad Campus
NI MULTISIM -
An Introduction to Electronics Workbench
Staff In-charge
Dr. R N Ponnalagu
Dr. Karum Chappanda
4-Jul-23
Teaching Assistants:
Madhusudan B Kulkarni, Samit Kumar Ghosh and Sharda Sharma
ELECTRONICS & INSTRUMENTATION LAB
II B.TECH II SEMESTER 2019-20
2. BITS Pilani
Hyderabad Campus
Outline
i. Introduction
ii. Parameters
iii. Tool Box
iv. Virtual Vs Real Components
v. Application Areas
Introduction
NIMultisim
3. BITS Pilani, Hyderabad Campus
3
NI Multisim & Ultiboard is world’s most popular software for
learning electronics and having 1,80,000 industrial and
academic users. It includes as follows:
Multisim simulation and capture
Ultiboard PCB layout
Multisim MCU Module controller simulation
Multisim software integrates powerful SPICE simulation and
schematic capture entry into highly intuitive electronics lab on
the PC. NI provides software that has become a teaching and
learning tool of choice for thousands of educators.
Introduction(1/2)
4. BITS Pilani, Hyderabad Campus
4
Multisim (Simulation & Capture):
Multisim is an intuitive, drag-and-drop schematic capture and
simulation program that educators and students can use to create
complete circuits containing both analog and digital components.
It also adds microcontroller unit co-simulation capabilities, so
you can include an MCU, programming in assembly code, within
your SPICE-model circuit.
Ultiboard ( PCB Layout) :
With Ultiboard, students can gain exposure to the physical
implementation and manufacturing of circuits on PCBs.
Students can import the Multisim schematic into Ultiboard with
a single mouse click.
Introduction(2/2)
7. BITS Pilani, Hyderabad Campus
7
1. Multisim- Schematics: Easy-to-use schematics, simple click and drag,
3D animated parts and wire drag without breaking connections.
2. Multisim- Virtual Breadboard: Breadboard techniques, synchronized
with schematic and Wiring report for NI ELVIS (step 5)
3. Multisim- Simulation: 13,000+part library, 22+ NI ELVIS
Virtual instruments, Microcontroller simulation.
4. Ultiboard- PCB Layout: Integrated with Multisim, Flexible interface,
3D view, Design rule check and Built-in auto routing.
5. NI ELVIS- Test: Instrumentation, Data acquisition and
prototyping
6. LabVIEW- Compare: Automatically import like Multisim virtual data
and NI ELVIS measured data. Compare ideal and real data.
Parameters
17. BITS Pilani
Hyderabad Campus
Outline
i. Introduction
ii. Parameters
iii. Tool Box
iv. Virtual Vs Real Components
v. Application Areas
VirtualComponentsVs.RealComponents
18. BITS Pilani, Hyderabad Campus
18
Virtual vs.Real Components
Real components have a specific value that cannot be changed
and a footprint used for circuit board layout.
Virtual components are for simulation only.
For our purposes, there isalmost no difference between the
component types.
19. BITS Pilani
Hyderabad Campus
Outline
i. Introduction
ii. Parameters
iii. Tool Box
iv. Virtual Vs Real Components
v. Application Areas
MultisimApplicationAreas
20. BITS Pilani, Hyderabad Campus
20
Acquiring Data and Process Signals: Measure any
sensor, perform advanced analysis, display data and
custom user interfaces.
Automating Test and Validation Systems: Automate the
validation and control multiple instruments.
Academic Teaching: Apply an interactive, hands-on
approach to learning, combine algorithm design with real
time datameasurements.
Instrument Control: Automated data collection,
analyze anddisplay signals.
Embedded Monitoring and Control Systems: Reuse ASCII
C and HDL code, prototype with FPGA technology.
MultisimApplication Areas