The document describes modeling and simulation of hardware designs in VHDL. It discusses writing test benches to simulate and verify models. Test benches generate stimulus, apply it to the model, and monitor the output responses. Stimulus can be created using repetitive patterns or vectors. Vectors can be stored in tables or files. Assertions are used to check output matches expectations. Monitoring behavior involves applying a vector, sampling output after a delay, and verifying it matches the expected value.
This is an intermediate conversion course for C++, suitable for second year computing students who may have learned Java or another language in first year.
This is an intermediate conversion course for C++, suitable for second year computing students who may have learned Java or another language in first year.
Aaa ped-23-Artificial Neural Network: Keras and TensorfowAminaRepo
We will focus in this part on two important libraries for ANN: Tensorflow and Keras. Both of them propose two types of model creation. We will use the high level API of tenshorflow, and the sequential models of Keras.
We will introduce you to some basic important concept related to tensorflow, and we will present you tensorboard. The later one is used to visualize, among other things, quantitative values related to a training process.
[Notebook](https://colab.research.google.com/drive/13KlhoNvYmeRZTZ-TLKAtW3rOkFzQVGYC)
Yellowbrick: Steering machine learning with visual transformersRebecca Bilbro
In machine learning, model selection is a bit more nuanced than simply picking the 'right' or 'wrong' algorithm. In practice, the workflow includes (1) selecting and/or engineering the smallest and most predictive feature set, (2) choosing a set of algorithms from a model family, and (3) tuning the algorithm hyperparameters to optimize performance. Recently, much of this workflow has been automated through grid search methods, standardized APIs, and GUI-based applications. In practice, however, human intuition and guidance can more effectively hone in on quality models than exhaustive search.
This talk presents a new Python library, Yellowbrick, which extends the Scikit-Learn API with a visual transfomer (visualizer) that can incorporate visualizations of the model selection process into pipelines and modeling workflow. Yellowbrick is an open source, pure Python project that extends Scikit-Learn with visual analysis and diagnostic tools. The Yellowbrick API also wraps matplotlib to create publication-ready figures and interactive data explorations while still allowing developers fine-grain control of figures. For users, Yellowbrick can help evaluate the performance, stability, and predictive value of machine learning models, and assist in diagnosing problems throughout the machine learning workflow.
In this talk, we'll explore not only what you can do with Yellowbrick, but how it works under the hood (since we're always looking for new contributors!). We'll illustrate how Yellowbrick extends the Scikit-Learn and Matplotlib APIs with a new core object: the Visualizer. Visualizers allow visual models to be fit and transformed as part of the Scikit-Learn Pipeline process - providing iterative visual diagnostics throughout the transformation of high dimensional data.
Static variable is explained using simple terms and example followed by conclusion. An application is demonstrated using simple example. The examples provided in slide can be taken as a reference by c,c++,java,php students.
This lab introduces students to the concepts of polymorphism, early binding, late binding, abstract classes, and virtual class functions. This will be done in the context of performing calculations on basic geometrical shapes. Polymorphism is a very powerful extension of inheritance, and by using pointers to the base class, it allows access to derived class objects and their functions based on the context that they are called in.
The lab will require the creation of a base geometric class, called Shape, and two sub classes, Circle and Rectangle, that are derived public from the class Shape. From there, objects of both the Circle and the Rectangle classes will be created, as will an array of pointers to the base class Shape. By using the instantiated objects and the object pointers, both static and dynamic binding will be demonstrated.
Deliverables
1. Submit a single NotePad file containing the source code for all the lab files to the Week 7 Dropbox. Your source code should use proper indentation and be error free. Be sure that your last name and the lab number are part of the file name: for example, YourLastName_Lab5.txt.
Each program should include a comment section that includes, at a minimum, your name, the lab and exercise number, and a description of what the program accomplishes.
Aaa ped-22-Artificial Neural Network: Introduction to ANNAminaRepo
Finally we will talk about Artificial Neural Networks (ANN) . In this part we will focus on the building blocks of ANN's. We will describe the perceptron and its learning rules. We will talk in more details about the gradient descent algorithm.
We will also define an important concept in ANN which is : the back-propagation algorithm. For our examples we will use two libraries: neurolab and scikit-learn libraries.
[Notebook](https://colab.research.google.com/drive/1CqYwu9NzeXuUNeR8RmkDplUd71DHWNod)
java notes, object oriented programming using java, java tutorial, lecture notes, java programming notes, java example programs, java programs with explanation, java source code with output, java programs, java coding, java codes, java slides, java notes,command line arguments in java, java command line arguments notes, java command line arguments notes,different types of command line arguments in Java,command line arguments with an example, command line arguments in Java
The following resources come from the 2009/10 BEng in Digital Systems and Computer Engineering (course number 2ELE0065) from the University of Hertfordshire. All the mini projects are designed as level two modules of the undergraduate programmes.
The objectives of this module are to demonstrate, within an embedded development environment:
• Processor – to – processor communication
• Multiple processors to perform one computation task using parallel processing
This project requires the establishment of a communication protocol between two 68000-based microcomputer systems. Using ‘C’, students will write software to control all aspects of complex data transfer system, demonstrating knowledge of handshaking, transmission protocols, transmission overhead, bandwidth, memory addressing. Students will then demonstrate and analyse parallel processing of a mathematical problem using two processors. This project requires two students working as a team.
Aaa ped-23-Artificial Neural Network: Keras and TensorfowAminaRepo
We will focus in this part on two important libraries for ANN: Tensorflow and Keras. Both of them propose two types of model creation. We will use the high level API of tenshorflow, and the sequential models of Keras.
We will introduce you to some basic important concept related to tensorflow, and we will present you tensorboard. The later one is used to visualize, among other things, quantitative values related to a training process.
[Notebook](https://colab.research.google.com/drive/13KlhoNvYmeRZTZ-TLKAtW3rOkFzQVGYC)
Yellowbrick: Steering machine learning with visual transformersRebecca Bilbro
In machine learning, model selection is a bit more nuanced than simply picking the 'right' or 'wrong' algorithm. In practice, the workflow includes (1) selecting and/or engineering the smallest and most predictive feature set, (2) choosing a set of algorithms from a model family, and (3) tuning the algorithm hyperparameters to optimize performance. Recently, much of this workflow has been automated through grid search methods, standardized APIs, and GUI-based applications. In practice, however, human intuition and guidance can more effectively hone in on quality models than exhaustive search.
This talk presents a new Python library, Yellowbrick, which extends the Scikit-Learn API with a visual transfomer (visualizer) that can incorporate visualizations of the model selection process into pipelines and modeling workflow. Yellowbrick is an open source, pure Python project that extends Scikit-Learn with visual analysis and diagnostic tools. The Yellowbrick API also wraps matplotlib to create publication-ready figures and interactive data explorations while still allowing developers fine-grain control of figures. For users, Yellowbrick can help evaluate the performance, stability, and predictive value of machine learning models, and assist in diagnosing problems throughout the machine learning workflow.
In this talk, we'll explore not only what you can do with Yellowbrick, but how it works under the hood (since we're always looking for new contributors!). We'll illustrate how Yellowbrick extends the Scikit-Learn and Matplotlib APIs with a new core object: the Visualizer. Visualizers allow visual models to be fit and transformed as part of the Scikit-Learn Pipeline process - providing iterative visual diagnostics throughout the transformation of high dimensional data.
Static variable is explained using simple terms and example followed by conclusion. An application is demonstrated using simple example. The examples provided in slide can be taken as a reference by c,c++,java,php students.
This lab introduces students to the concepts of polymorphism, early binding, late binding, abstract classes, and virtual class functions. This will be done in the context of performing calculations on basic geometrical shapes. Polymorphism is a very powerful extension of inheritance, and by using pointers to the base class, it allows access to derived class objects and their functions based on the context that they are called in.
The lab will require the creation of a base geometric class, called Shape, and two sub classes, Circle and Rectangle, that are derived public from the class Shape. From there, objects of both the Circle and the Rectangle classes will be created, as will an array of pointers to the base class Shape. By using the instantiated objects and the object pointers, both static and dynamic binding will be demonstrated.
Deliverables
1. Submit a single NotePad file containing the source code for all the lab files to the Week 7 Dropbox. Your source code should use proper indentation and be error free. Be sure that your last name and the lab number are part of the file name: for example, YourLastName_Lab5.txt.
Each program should include a comment section that includes, at a minimum, your name, the lab and exercise number, and a description of what the program accomplishes.
Aaa ped-22-Artificial Neural Network: Introduction to ANNAminaRepo
Finally we will talk about Artificial Neural Networks (ANN) . In this part we will focus on the building blocks of ANN's. We will describe the perceptron and its learning rules. We will talk in more details about the gradient descent algorithm.
We will also define an important concept in ANN which is : the back-propagation algorithm. For our examples we will use two libraries: neurolab and scikit-learn libraries.
[Notebook](https://colab.research.google.com/drive/1CqYwu9NzeXuUNeR8RmkDplUd71DHWNod)
java notes, object oriented programming using java, java tutorial, lecture notes, java programming notes, java example programs, java programs with explanation, java source code with output, java programs, java coding, java codes, java slides, java notes,command line arguments in java, java command line arguments notes, java command line arguments notes,different types of command line arguments in Java,command line arguments with an example, command line arguments in Java
The following resources come from the 2009/10 BEng in Digital Systems and Computer Engineering (course number 2ELE0065) from the University of Hertfordshire. All the mini projects are designed as level two modules of the undergraduate programmes.
The objectives of this module are to demonstrate, within an embedded development environment:
• Processor – to – processor communication
• Multiple processors to perform one computation task using parallel processing
This project requires the establishment of a communication protocol between two 68000-based microcomputer systems. Using ‘C’, students will write software to control all aspects of complex data transfer system, demonstrating knowledge of handshaking, transmission protocols, transmission overhead, bandwidth, memory addressing. Students will then demonstrate and analyse parallel processing of a mathematical problem using two processors. This project requires two students working as a team.
Test automation principles, terminologies and implementationsSteven Li
A general slides for test automation principle, terminologies and implementation
Also, the slides provide an example - PET, which is a platform written by Perl, but not just for Perl. It provides a general framework to use.
Using Monte Carlo Simulation in Project Estimates by Akram Najjar
The PMI Lebanon is glad to announce that Akram Najjar is the speaker for the a lecture titled “Using Monte Carlo Simulation in Project Estimates” delivered on Thursday, 28 July 2016
Lecture Outline
* Why are single point estimates unreliable and what is the alternative?
* What are distributions and how do we extract random samples from them (using Excel)? Two costing examples.
* How to setup a Monte Carlo Simulation model in a spreadsheet?
* Two PM examples (in detail)
* How to statistically analyze the thousands of runs to reach reliable estimates?
Lecture Objectives
* A Project Manager usually knows how certain parameters (such as duration, resource rates or quantities) behave. However, the PM can almost never define reliable single point estimates for these parameters. The result: many projects fail due to unreliable estimates. The alternative? The PM has to use his/her knowledge of how specific parameters behave statistically. For example, the PM knows that a specific task’s duration is distributed according to the bell shaped curve OR that another is uniformly distributed (flat variation), or triangular, or Beta-PERT, etc. The PM can then use Monte Carlo Simulation (MCS) to arrive at statistically significant and robust results. Monte Carlo Simulation (MCS) is a technique that relies on two processes. Process 1 aims at developing a spreadsheet model that calculates the critical path or the total cost, etc. The calculation is setup in a single row (or Run). This row is then duplicated a large number of times (thousands). Process 2 aims at inserting Excel functions in each of the parameters (durations, costs). In each row (or Run), such functions will provide a sample drawn from a statistical distribution that properly describes the behavior of that parameter. For example, a specific duration follows a Normal (Bell) distribution with an Average A and a Standard Deviation S. The model will then generate for each run and for that duration a different value that conforms with the bell shaped curve as defined (A and S). Each of these thousands of runs will provide the PM with a different “simulation” of the duration or the total cost, etc. By statistically analyzing the thousands of results, the PM can arrive at a robust and reliable estimate. Proprietary Add On’s for Monte Carlo Simulation in Microsoft Project are available. However, it is easy, free and more flexible to use native Microsoft functions to carry out the full simulation. The talk covered all the steps needed for such simulations giving several examples
How do you determine the test coverage of your application per project? Do you have strategies in place? Do you know when to implement methods to examine your test coverage? How do you know enough is enough?
Jay and Jean Ann discuss the meaning of test coverage and use 4 concepts to help determine when "enough is enough" providing approaches on how to discover the data required to make a informed decisions on what to test, where to test, what testing is missing and how much testing is needed based on scope of project. By breaking down the types of coverages, Jay and Jean Ann built some guiding strategies which testers can immediately apply to their testing projects with more confidence in achieving a stronger sense of quality in their efforts. Take back a stronger understanding of test coverage which makes test design more thorough and answers a higher level of quality.
** Presentation given at CAST 2014 by Jay Philips & Jean Ann Harrison
IOSR Journal of Electrical and Electronics Engineering(IOSR-JEEE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electrical and electronics engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electrical and electronics engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
UNDERSTANDING WHAT GREEN WASHING IS!.pdfJulietMogola
Many companies today use green washing to lure the public into thinking they are conserving the environment but in real sense they are doing more harm. There have been such several cases from very big companies here in Kenya and also globally. This ranges from various sectors from manufacturing and goes to consumer products. Educating people on greenwashing will enable people to make better choices based on their analysis and not on what they see on marketing sites.
ENVIRONMENT~ Renewable Energy Sources and their future prospects.tiwarimanvi3129
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Presented by The Global Peatlands Assessment: Mapping, Policy, and Action at GLF Peatlands 2024 - The Global Peatlands Assessment: Mapping, Policy, and Action
"Understanding the Carbon Cycle: Processes, Human Impacts, and Strategies for...MMariSelvam4
The carbon cycle is a critical component of Earth's environmental system, governing the movement and transformation of carbon through various reservoirs, including the atmosphere, oceans, soil, and living organisms. This complex cycle involves several key processes such as photosynthesis, respiration, decomposition, and carbon sequestration, each contributing to the regulation of carbon levels on the planet.
Human activities, particularly fossil fuel combustion and deforestation, have significantly altered the natural carbon cycle, leading to increased atmospheric carbon dioxide concentrations and driving climate change. Understanding the intricacies of the carbon cycle is essential for assessing the impacts of these changes and developing effective mitigation strategies.
By studying the carbon cycle, scientists can identify carbon sources and sinks, measure carbon fluxes, and predict future trends. This knowledge is crucial for crafting policies aimed at reducing carbon emissions, enhancing carbon storage, and promoting sustainable practices. The carbon cycle's interplay with climate systems, ecosystems, and human activities underscores its importance in maintaining a stable and healthy planet.
In-depth exploration of the carbon cycle reveals the delicate balance required to sustain life and the urgent need to address anthropogenic influences. Through research, education, and policy, we can work towards restoring equilibrium in the carbon cycle and ensuring a sustainable future for generations to come.
WRI’s brand new “Food Service Playbook for Promoting Sustainable Food Choices” gives food service operators the very latest strategies for creating dining environments that empower consumers to choose sustainable, plant-rich dishes. This research builds off our first guide for food service, now with industry experience and insights from nearly 350 academic trials.
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3. Abstract
describes the environment necessary for simulating hardware
models written in VHDL.
These models can be tested by writing test bench models which can
themselves be described in VHDL.
Some approaches to writing test bench models .
4. Simulation
• Decide what type of simulation will be performed.
• what values would be used during simulation.
• Would all objects (signals and variables) take values '0' or 'V only, or would
they take values '0', 'V, 'V', or 'Z', or use other logic levels?
• number of values used during simulation will define the basic types used to
model hardware(prior to writing any model), the predefined types that the
language provides are not sufficient to model values such as "U'
(undefined) and 'Z' (high-impedance).
• The language, however, does provide the capability of creating user-
defined types that can be used to model the values required for a
particular simulation. In certain cases, it may be sufficient to model
hardware using the predefined types of the language, for example, when
modeling hardware at an abstract level where all data may be represented
strictly as integers
5. Example
• we shall assume that we are interested in performing a four-value simulation, 'U'
to represent undefined, '0' to represent logic 0, 'Y to represent logic I, and 'Z' to
represent high-impedance. The basic types are, therefore, defined as:
• The leftmost value of the MVL type is defined to be a 'U' so that all objects that
are defined to be of this base type will have an initial value of 'U' at start of
simulation. "This accurately models real hardware.
• These new basic types are still insufficient to model hardware. What is lacking is a
set of operations that can operate on these types. Most of the operators in the
language are defined for the predefined types only.
• Therefore, to allow operations on these new types, we need to provide
overloaded operator functions for all the language operators that are needed to
operate on these types. Often, it is useful to put all such information into a
package.
10. Writing a Test Bench
• A test bench is a model that is used to exercise and verify the
correctness of a hardware model . A test bench has three main
purposes:
• Generate stimulus for simulation
(waveforms)1
• Apply this stimulus to the entity under
test and to monitor the output
responses
2
• monitor output responses with
expected known values3
12. Waveform Generation
Main approaches in generating stimulus values:
1. create waveforms and apply stimulus at certain discrete time
intervals
2. generate stimulus based on the state of the entity, that is, based on
the output response of the entity.
Two types of waveforms are typically needed :
1. Repetitive Patterns
2. Using Vectors
13. 1-Repetitive Patterns
• A repetitive pattern with a constant on-off delay can be created using
a concurrent signal assignment statement.
• - Signal A is assumed to be of type BIT.A <= not A after 20 ns;
14. • A clock with varying on-off period can be created using a process
statement
15. A problem
both of these approaches is that if the host environment provides no control
over the amount of time simulation is to be performed, simulation would go on
until time is equal to TIME'HIGH causing a lot of simulation time to be wasted.
Solution
One way to avoid this is to put an assertion statement in the process that will
cause the assertion to fail after a specific time. Here is such an example of an
assertion statement.
• Assertion would fail when simulation time exceeds 1000 ns and simulation would stop
(assuming that the simulator stops on getting a severity level of ERROR).
16. Another Ways To Generate Clock
• An alternate way to generate a clock with a varying on-off period is
by using a conditional signal assignment statement. Here is an
example.
17. Another Ways To Generate Clock(Cont.)
• A clock that is phase delayed from another clock can be generated by
using the DELAYED predefined attribute. For example:
18. Another Ways To Generate Clock(Cont.)
• Two overlapping clocks can be generated in a similar manner by making sure that
the time specified for the DELAYED attribute is larger than the clock's on-period.
• A sequential set of values can also be generated for a signal by using multiple
waveforms in a concurrent signal assignment statement. For example,
19. Another Ways To Generate Clock(Cont.)
• The previous waveform can be made to repeat itself by placing the
signal assignment statement inside a process statement along with a
wait statement. The following example of a two-phase clock shows
such a repeated waveform.
20. 2-Using Vectors
• Another way to apply stimulus to a set of signals is to store the set of
vectors in a constant table or in an ASCII file.
• Example :stores the input vectors in a table
INPUT_VECTORS (1-5)
1001
1000
0010
0000
0110
21. • Assume that the entity under test has four inputs. A, B, C, and D. If
the vectors are to be applied at regular time intervals, a generate
statement can be used as shown :
If the vectors were to be applied at arbitrary intervals, a concurrent
signal assignment statement with multiple waveforms can be used.
22. • Example :stores the input vectors in an ASCII file as a sequence of
values.
23. A complete test bench that uses the waveform application method is
shown next. The entity being simulated is called DIV. The output value is
written into a file for later comparison.
24. • Consider an entity in which the objective is to compute the factorial of an
input number. The handshake mechanism between the entity under test
and the test bench model is shown bellow
• RESET input to the entity resets the
factorial model to an initial state.
• START signal is set after the DATA input is
applied.
• DONE signal is set when computation is
complete,
• FAC_OUT and EXP_OUT outputs signals
to indicate output
• . The resulting factorial value is [ FAC.OUT *
2^EXP_OUT ].
Hands on Example
25. The test bench model applies the data, sets the START signal, waits for DONE signal,
and then applies the next input data. Assertion statements are used to make sure that
the values appearing at the output are correct.
27. Monitoring Behavior
• Another common way to monitor the output response of an entity is to
apply a vector, sample the output after a specific time and then verify to
make sure that the output response matches the expected values.
• Remember :In the test bench model for the FACTORIAL entity, we saw
how the test bench can monitor the behavior of the entity and apply
different patterns based on the output response .
28. Example
The input and output vectors are stored in tables. Alternately,
they could have been read from an ASCII file.
29. • In this test bench example, the application of test vectors is expressed
as a process. After a test vector is applied, the process suspends for
time (CYCLE_TIME - STROBE_DELAY), samples the output and then
checks to see if the expected output is equal to the specified output
vector. The process then suspends for STROBE_DELAY time before
reapplying the next vector.