--- ---
List of Seminars. Latest seminars first.
| Seminar Date | Speaker | |
| | December 23, 2005 | Prof. Bhaskar Bagchi, ISI, Bangalore |
| | December 09, 2005 | Prof. Basudeb Datta, IISc, Bangalore |
| | November 11, 2005 | Dr. Sameer Jalnapurkar, IISc, Bangalore |
| | October 21, 2005 | V. V. K. Srinivas Kumar, IISc, Bangalore |
| | October 20, 2005 | V.S. Borkar, B. Rajeev |
| | October 18, 2005 | Dr. Harish Seshadri, Department of Mathematics, IISc |
| | October 07, 2005 | Dr. Anuradha Narasimhan, Department of Mathematics, IISc |
| | September 23-30, 2005 | Prof. S. Ramanan, Chennai Mathematical Institute and Institute of Mathematical Sciences |
| | September 23, 2005 | Prof. Alladi Sidaram, ISI, Bangalore |
| | September 19, 2005 | Mr N. Hariharan, Department of Mathematics, IISc |
| | September 15, 2005 | Annual Meeting of AvH Association |
| | September 12, 2005 | Prof. Ravi S. Kulkarni, Harishchandra Research Institute, Allahabad |
| | September 09, 2005 | Prof. Srikanth K Iyer, Department of Mathematics, IISc, Bangalore |
| | August 25, 2005 | Prof. B. V. Rajaram Bhat, Stat. Math. Unit, ISI, Bangalore |
| | August 24, 2005 | Dr. Anrib Mukhopadhyay, IMSc, Chennai |
| | August 22 & 29, 2005 | Dr. Thangadurai, School of Mathematics, Harish Chandra Research Institute, Allahabad |
| | August 17, 2005 | Dr. Deepak Sridhara, Institute for Mathematics, University of Zurich |
| | August 12, 2005 | Prof. S. Thangavelu, Department of Mathematics, IISc |
| | August 09 to 12, 2005 | Prof. Gregor Kemper, Department of Mathematics, Munich University of Technology, Germany |
| | August 02, 2005 | Dr. A. Raguram, University of Lowa |
| | July 01, 2005 | Mr. Siddharth Mishra, Research Scholar, IISc |
| | June 27, 2005 | K.B. Athereya, IISc |
| | June 22, 2005 | Dr. Karthik Ramaseshan, University of Rochester, USA |
| | June 21, 2005 | Dr. Nirmalendu Chaudhuri, Center for Mathematics & its Applications, Australian National University, Australia. |
| | June 03, 2005 | Prof. K. Varadarajan, University of Calgary, Canada |
| | May 31, 2005 | Prof. B. P. Purnaprajna, University of Kansas |
| | May 24, 2005 | Prof. Satya Deo Harish-Chandra Research Institute, Allahabad |
| | May 24, 2005 | Mr. Ashish Kumar Upadhyay, IISc |
| | May 05, 2005 | Ms.Dharmatti Sheetal Shashikant, IISc |
| | May 02, 2005 | Prof. S.D. Adhikari, H R I, Allahabad |
| | April 21, 2005 | B.Rajeev, Dept of Mathematics, IISc | | March 28, 2005 | Dr Gautam Bharali, Dept of Mathematics, University of Michigan |
| | March 18, 2005 | Siddarrha Gadgil, ISI, Bangalore |
| | March 11, 2005 | Dr.Amritanshu Prasad, Institute for Mathematicas Sciences, Chennai |
| | March 02, 2005 | Prof Rajeeva L Karandikar, ISI, NewDelhi |
| | March 02, 2005 | Prof R.Shivaji, Mississippi State University, USA |
| | February 28, 2005 | Prof A.R.Shastri, IIT, Mumbai |
| | February 22, 2005 | Lecture Series |
| | February 15, 2005 | Prof D.V. Evans, University of Bristol, UK |
| | February 08, 2005 | Dr. Clare'D Cruz, Chennai Mathematical Institute, Chennai |
| | January 28, 2005 | Rahul Jain |
| | January 20, 2005 | Prof Gerard Iooss, France |
| | January 20, 2005 | Siddhartha Mishra, IISC |
| | January 18, 2005 | Prof. Adam Koranyi, New York |
| | January 17, 2005 | Prof. Paul Binding, University of Calgary |
| | January 11, 2005 | Prof. Raghu Raghavan, Therataxis, Baltimore, USA |
| | January 11, 2005 | Prof. S. S. Abhyankar, Purdue University. |
| | January 10, 2005 | Prof. Raghu Raghavan, Therataxis, Baltimore, USA |
|
|
||
| Indian Institute of Science | Contact the Administrator | Disclaimer |


Balloch man's stunning Glencoe photo proves online smash-hit
Ross MacFarlane took the shot of the cheeky deer on his phone, and didn't realise until later how special it was.
by Fraser Clarke, https://www.dailyrecord.co.uk/authors/fraser-clarke/A Balloch man’s stunning shot of deer in Glencoe has proved a smash hit in an online photography competition.
Ross MacFarlane, a greenkeeper at The Carrick, managed to snap the cheeky deer with its tongue out on a visit earlier this year, and entered it in a competition ran by the Kingshouse Hotel - with more than 3000 people picking Ross’s work as their favourite from hundreds of entries.
And its popularity has come as a surprise to Ross - who didn’t really at the time how special the shot was.
He told the Lennox Herald: “I just took it on my iPhone, so I reckon it was just a lucky shot if I’m being honest.
“It wasn’t until I got home that I noticed and thought that it wasn’t a bad shot.
“It’s been getting shared all over the place, so I’m happy with that and a few folk have even got in touch asking to buy canvas prints of it or whatever - so I’ll see about that once things have calmed down after Christmas and stuff.”

He continued: “We go to Glencoe every February because that’s where me and my fiance, Dorothy, got engaged, and that’s where we got it.
“I entered the competition because Glencoe is a really special place for both of us and I thought it might be nice to get a wee break at this time of year, especially before Christmas and then getting everything sorted for the wedding.”
For more local news from West Dunbartonshire click here.


What you'll learn
- How to to build elements using Polymer 1.0.
- How to use elements in Polymer's iron, paper, and Google Web Component sets.
- How to use Polymer's data binding features to reduce boilerplate code.
- How to make your application working offline.
What you'll need
- Basic understanding of HTML, CSS, and web development.
- Install Webstorm or use your own editor of choice.


Excel
From Date: {{FromDate}}
ToDate: {{ToDate}}
Print Date: {{currentDate}}
Incentive Summary Report
Sr No.
Doctor Name
Total Income
TDS Amt.
Net Income
{{i+1}}
{{row.ReferrerName}}
{{row.DocTotalAmount}}
{{row.TDSAmount}}
{{row.NetTotal}}
Total :
{{summary.tot_TotIncome}}
{{summary.tot_TDSAmount}}
{{summary.tot_NetIncome}}


Preparation time
30 mins to 1 hour
Cooking time
over 2 hours
Serves
Serves 4
Using a sharp thin boning knife, remove as much meat from the lamb bones as possible. Chop finely and set aside.
Put the bones into a large pot. Roughly chop the carrots, large onion, celery and leek then place in the pot with the garlic and herbs. Cover with at least 4 litres/7 pints cold water. Bring to the boil, season lightly, then simmer gently, uncovered for up to two hours. Skim with a large tablespoon if necessary. Let the liquid simmer down until reduced by nearly two thirds. You need about 1.2 litres/2 pints of lovely sweet stock.
Strain and discard the bones etc. Ideally, cool and chill the stock then you can scrape off any fat that has settled on top.
Place the lamb meat into a large saucepan and cover with cold water. Bring slowly to the boil, skim off any scum, then immediately drain into a colander and rinse in cold running water. This scalding removes fat.
Return the meat to the pan and cover with the stock and bring to the boil. Turn down the heat and simmer partially covered, for about 30 minutes, or until tender.
Meanwhile, peel the carrots and cut into small chunks or shape into neat 'barrels'. Peel the baby onions, it helps to blanch them in boiling water for a minute for easy peeling. Add the small carrots and baby onions to the soup and cook for another five minutes.
Then add the diced potato and cook for another 10-15 minutes, until it starts to dissolve into the soup and thicken it. Finally check the seasoning, adding lots of aromatic black pepper and a little sea salt. Garnish with sliced spring onion and parsley.


Jadon Sancho responds to Man Utd transfer plea from fan after England training
Jadon Sancho has been tipped to leave Borussia Dortmund to join Man Utd - and the transfer rumours were directly addressed by a fan after England training
by Rich Jones, https://www.mirror.co.uk/authors/rich-jones/England star Jadon Sancho laughed off a fan begging him to sign for Manchester United .
Sancho, who earned his 11th England cap in Thursday's 7-0 win over Montenegro, took some time to sign autographs and pose for pictures with fans after a training session.
He was met by a supporter who directly addressed links with Manchester United as the Red Devils prepare for a January bid to bring him back to the Premier League.
Sancho was recently dropped and fined by Borussia Dortmund after returning late from the last international break, prompting hopes at Old Trafford they could convince him to make a £100m move.
"Please sign for United," a brazen supporter begged Sancho after taking a selfie, prompting him to laugh before moving on and diplomatically avoiding the question.
Sancho has emerged as one of Europe's hottest properties after his breakthrough in the Bundesliga.
He was wanted by United last year but their failure to qualify for the Champions League meant a move never materialised.
But his recent run-ins with Dortmund bosses have led to speculation they could allow him to move on earlier than planned having always been resigned to losing him at some stage in the future.
Ole Gunnar Solskjaer is keen to sign both Sancho and Norwegian striker Erling Braut Haaland when the January transfer window opens.
Yet he could face competition from Liverpool, who are also known admirers of Sancho and monitoring the situation regarding the England winger.
Former Liverpool star Dietmar Hamann recently told Sky Germany: "What I hear is that Liverpool have great interest.
"I would imagine that next summer they could become one of the biggest interested parties.
"He's from Manchester City and I do not know if he would come back again, and it's a thing for Manchester United, too, if they've ever been to the rivals."
Chelsea and former club Manchester City have also been linked with bringing Sancho back to the Premier League.


--- layout: post title: "In the News: NCSPE Excerpts and Reviews Teaching Machines" date: 2021-11-22 15:01:00 +0000 tags: image: https://s3.amazonaws.com/audreywatters/2017-11-03-news.jpg imagecredits: https://www.flickr.com/photos/62693815@N03/6277209256 ---
The NCSPE (The National Center for the Study of Privatization in Education) at Teachers College has reviewed Teaching Machines, as well as published an excerpt from the book: "readers will be glued and challenged."


Toggle navigation
PongOut
Levels
Themes
Level 1
Theme:
Basic
default
Oldschool
Basic
Save level and Close
This text is displayed if your browser does not support HTML5 Canvas.
Instructions
Placing new bricks:
Use Shift + Left mouse button to place new bricks.
Moving bricks:
Drag and drop the bricks.
Deleting bricks:
Select with Left Mouse Button and press DELETE.
Enter a new level name


PRODUCTION LINE MONITOR
Band 1
Download logs
Band 2
Download logs
Band 3
Download logs
Band 4
Download logs


Welcome to kinparse’s documentation!¶
Contents:
kinparse¶
This is a parser for KiCad schematic netlist files that are output by EESCHEMA. Just pass the parse_netlist() function a file containing a netlist and it will deliver a pyparsing object containing all the netlist’s information.
- Free software: MIT license
- Documentation: http://xesscorp.github.io/kinparse
Installation¶
This is a Python package, so you’ll need to have Python installed to use it. If you’re using linux, you probably already have Python. If you’re on Windows, you can download a Python installer from Anaconda , Active State , or even WinPython .
Once you have Python, you can install this package by opening a terminal window and typing the command:
$ easy_install kinparse
Or:
$ pip install kinparse
Usage¶
To use kinparse in a project:
from kinparse import parse_netlist
There is a single function that will parse a KiCad EESCHEMA netlist file, a file object, or a text string and return a pyparsing object
nlst = parse_netlist('my_schematic.net')
Then you can query the parsing object as shown below to get information about the netlist.
- Global netlist information:
nlst.version.val
: Netlist format version.
- Global design information:
nlst.design.source.val
: File name for the schematic that generated this netlist.nlst.design.date.val
: Date the schematic was created.nlst.design.tool.val
: Tool used to create the schematic.
- Schematic sheet information:
nlst.design.sheets
: List of sheets for the schematic.nlst.design.sheets[0].number.val
: Number of the first schematic sheet.nlst.design.sheets[0].name.val
: Name of the first schematic sheet.nlst.design.sheets[0].tstamps.val
: Time stamp of the first schematic sheet.nlst.design.sheets[0].title_block.title.val
: Title of the first schematic sheet.nlst.design.sheets[0].title_block.company.val
: Name of company.nlst.design.sheets[0].title_block.rev.val
: Design revision number.nlst.design.sheets[0].title_block.date.val
: Design date.nlst.design.sheets[0].title_block.source.val
: Design source file.nlst.design.sheets[0].title_block.comments
: List of comments.nlst.design.sheets[0].title_block.comments[0].number.val
: Number of the first comment.nlst.design.sheets[0].title_block.comments[0].value.val
: Text of the first comment.
- Library information:
nlst.libraries
: List of libraries used in the netlist.nlst.libraries[0].logical.val
: Logical name of first library.nlst.libraries[0].uri.val
: File name and location of first library.
- Components:
nlst.components
: List of components (or parts).nlst.components[0].ref.val
: Reference for the first component.nlst.components[0].value.val
: Value of the first component.nlst.components[0].tstamp.val
: Time stamp for the first component.nlst.components[0].datasheet.val
: Location of datasheet for the first component.nlst.components[0].fields
: List of fields for the first component.nlst.components[0].fields[0].name.val
: Name of first field of the first component.nlst.components[0].fields[0].text
: Value assigned to first field of the first component.nlst.components[0].libsource.lib.val
: Library containing the first component.nlst.components[0].libsource.part.val
: Part name for the first component.nlst.components[0].footprint.val
: PCB footprint for the first component.nlst.components[0].sheetpath.names.val
: Sheet name on which the first component appears.nlst.components[0].sheetpath.tstamps.val
: Time stamp for the sheet on which the first component appears.
- Library descriptions of components:
nlst.libparts
: List of part (or component) descriptions.nlst.libparts[0].lib.val
: Library containing the first component.nlst.libparts[0].part.val
: Part number of the first component.nlst.libparts[0].description.val
: Description of the first component.nlst.libparts[0].docs.val
: Document file name for the first component.nlst.libparts[0].fields
: List of fields for the first component.nlst.libparts[0].fields[0].name.val
: Name of the first field of the first component.nlst.libparts[0].fields[0].text
: Value assigned to the first field of the first component.nlst.libparts[0].pins
: List of pins for the first component.nlst.libparts[0].pins[0].num.val
: Pin number of the first pin of the first component.nlst.libparts[0].pins[0].name.val
: Pin name of the first pin of the first component.nlst.libparts[0].pins[0].type.val
: Pin type of the first pin of the first component.nlst.libparts[0].footprints
: List of footprints for the first component.nlst.libparts[0].footprints[0].fp.val
: First footprint for the first component.nlst.libparts[0].aliases
: List of aliases for the first component.nlst.libparts[0].aliases[0].text
: First alias for the first component.
- Net connections:
nlst.nets
: List of nets.nlst.nets[0].name.val
: Name of the first net.nlst.nets[0].code.val
: Code number for the first net.nlst.nets[0].nodes
: List of nodes attached to the first net.nlst.nets[0].nodes[0].ref.val
: Part reference for first node on the net.nlst.nets[0].nodes[0].pin.val
: Pin number of part for the first node on the net.
Contributing¶
Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.
You can contribute in many ways:
Types of Contributions¶
Report Bugs¶
Report bugs at https://github.com/xesscorp/kinparse/issues.
If you are reporting a bug, please include:
- Your operating system name and version.
- Any details about your local setup that might be helpful in troubleshooting.
- Detailed steps to reproduce the bug.
Fix Bugs¶
Look through the GitHub issues for bugs. Anything tagged with “bug” is open to whoever wants to implement it.
Implement Features¶
Look through the GitHub issues for features. Anything tagged with “feature” is open to whoever wants to implement it.
Write Documentation¶
kinparse could always use more documentation, whether as part of the official kinparse docs, in docstrings, or even on the web in blog posts, articles, and such.
Submit Feedback¶
The best way to send feedback is to file an issue at https://github.com/xesscorp/kinparse/issues.
If you are proposing a feature:
- Explain in detail how it would work.
- Keep the scope as narrow as possible, to make it easier to implement.
- Remember that this is a volunteer-driven project, and that contributions are welcome :)
Get Started!¶
Ready to contribute? Here’s how to set up kinparse for local development.
Fork the kinparse repo on GitHub.
Clone your fork locally:
$ git clone git@github.com:your_name_here/kinparse.git
Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:
$ mkvirtualenv kinparse $ cd kinparse/ $ python setup.py develop
Create a branch for local development:
$ git checkout -b name-of-your-bugfix-or-feature
Now you can make your changes locally.
When you’re done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox:
$ flake8 kinparse tests $ python setup.py test $ tox
To get flake8 and tox, just pip install them into your virtualenv.
Commit your changes and push your branch to GitHub:
$ git add . $ git commit -m "Your detailed description of your changes." $ git push origin name-of-your-bugfix-or-feature
Submit a pull request through the GitHub website.
Pull Request Guidelines¶
Before you submit a pull request, check that it meets these guidelines:
- The pull request should include tests.
- If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.
- The pull request should work for Python 2.7, 3.5, and 3.6.


Welcome to the blog of Cheryl Hung, a London-based software engineer, public speaker and tech executive.
She is the Director of Ecosystem at the Cloud Native Computing Foundation.
Here you will find talks and articles on Kubernetes, storage, open source, and cloud native trends.
{% include social-icons.html %}


Preparation time
over 2 hours
Cooking time
30 mins to 1 hour
Serves
Serves 4-6
Making your own Thai red curry paste takes time, but you'll be astonished at the difference. Leftover paste will keep in the fridge for about three weeks.
If using tamarind paste, a couple of hours before you want to make the curry (or preferably the night before), place the tamarind pulp into a small bowl. Add hot or boiling water to cover (if the pulp is very dry, use boiling water). Soak the tamarind until it's very soft and pliable, for a few hours or preferably overnight.
To obtain the pulp, remove the tamarind seeds and any tough fibres with your fingers. Discard. Place the pulp into a sieve set over a bowl and press the pulp through the sieve.
For the curry paste, soak the chillies in five tablespoons of hot water for 1-2 hours. (You could also put them in a microwave oven for 2-3 minutes and then let them soak for about an hour.)
Put the soaked chillies, together with their soaking liquid, into the bowl of a food processor, along with all the remaining paste ingredients in the order listed. Blend until you have a smooth paste. You may need to add a bit more water. This recipe will make about ten tablespoons of paste, so freeze or refrigerate what you don't use.
For the curry, cut the chicken breasts crossways into 3mm/1/8in thick slices.
Heat the oil in a wide, preferably non-stick, pan set over a medium-high heat.
Carefully open the can of coconut milk without disturbing it too much. Remove four tablespoons of the thick coconut cream that will have settled at the top and place in a small bowl. Stir the remaining contents of the can well and set aside.
When the pan is hot, add the coconut cream and stir. Add the curry paste and stir again. Stir fry for 3-4 minutes or until the oil separates and the paste is lightly browned.
Reduce the heat to low and add the chicken, bamboo shoots, reserved coconut milk, lime leaves (or lemon zest), fish sauce, tamarind paste (or lemon juice) and the palm sugar (or brown sugar).
Stir together well and bring to a simmer. The chicken should turn white and cook through by the time the first bubbles begin to appear. Simmer on a low heat for a further minute or two.
Just before serving, sprinkle the basil leaves over the curry, stir gently and remove from the heat. Serve with jasmine rice.


Career interests are your preferences regarding work activities
and environment. Identifying your career interest helps you make
a well-informed and more strategic decision on your career
path.
Following your career interest means your are pursuing a career
that uses your talents and aligns with your passion and
preferences. Understanding and being able to determine your
passion helps you find fulfillment and success in your chosen
profession.
In this webpage we provide tools that will enable you make the
right career decisions.
Information technology (IT) involves the use of computers, storage and networking devices to create, process, store, share and secure electronic data. It is a fast-growing field, due largely to the high demand for IT services across all industries. IT degree programs prepare students to earn potentially lucrative salaries while working for IT departments and consulting firms. The median annual wage for IT careers was $81,430 in May 2015, according to the U.S. Bureau of Labor Statistics, considerably higher than that for all occupations. Read more to learn how to get started in the field of IT, from the various types of careers available to the degrees necessary to secure employment.
Information technology degree programs vary widely depending on the institution issuing the credential. From traditional colleges and universities to accredited online and distance learning degree programs, educational opportunities are available to suit nearly any budget or schedule. Some schools offer certificate courses in specialized areas of information technology. Others offer associate, bachelor’s, or master’s degree programs designed to produce well-rounded students with broad-based knowledge in many different areas of information technology.
If you are looking for information technology degree programs, you’ll find both on-campus and distance-based choices. An online program could be a good choice if you want to enroll in a school that is far away, but not move from where you live. In fact, online information technology degrees come in many different shapes and sizes. These include hybrid programs, which are a blend of campus-based and distance learning courses, fully online programs, accelerated online options and even joint degree programs. Most online information technology degree programs also use an interactive learning management system that allows you to submit assignments, ask questions and have discussions with other students.
If you are looking for information technology degree programs, you’ll find both on-campus and distance-based choices. An online program could be a good choice if you want to enroll in a school that is far away, but not move from where you live. In fact, online information technology degrees come in many different shapes and sizes. These include hybrid programs, which are a blend of campus-based and distance learning courses, fully online programs, accelerated online options and even joint degree programs. Most online information technology degree programs also use an interactive learning management system that allows you to submit assignments, ask questions and have discussions with other students.
| No. | University | Location | Website |
|---|---|---|---|
| 1 | Nairobi University | Nairobi | University Of Nairobi |
| 2 | Mount kenya university | Nakuru | Mount Kenya University |
| 3 | Multimedia University of Kenya | Rongai | Multimedia University Of Kenya |
Bachelor of Business Administration (BBA) degree is designed to provide in-depth knowledge of a wide-range of aspects related to business operations. The program's core curriculum typically covers topics, such as accounting, marketing, finance, and more. The degree degree can take up to four years or more to cover at a traditional college, but can be earned much faster in an accelerated program. As a graduate, you could possess critical thinking skills and practical knowledge of one or more areas of specialty. The program aims to provide you with an opportunity to learn from instructors with extensive experience in business administration. Additionally, the course gives you a chance to connect with like-minded individuals from various walks of life.
Enrolling in the BBA program you can learn more about business theory and its real-world applications. In the end, you could gain an understanding of various aspects of business administration, including:
| No. | University | Location | Website |
|---|---|---|---|
| 1 | KCA university | Nairobi, Ruaraka Campus | KCA University |
| 2 | St Paul's University | Limuru | St. Paul's University |
| 3 | Daystar University | Mombasa road, Athi river | Daystar University |
Bachelor of Business Administration (BBA) degree
The Geospatial Sciences (Building Information Modelling) MSc
teaches students the theory, tools and techniques for
working with geospatial data, from acquisition to processing
to analysis. Students have the opportunity to engage and
work with academics and industrial partners on cutting-edge
research through seminars and a dissertation. The specialist
Building Information Modelling (BIM) programme will educate
students in the geometric and semantic aspects of BIM and
the integrated management of geospatial and BIM-related
data. This includes principles of surveying, 3D reality
capture, a general overview of technologies related to
mapping sciences, geo-information science and 3D geometric
modelling.
Careers
Students graduating with a degree in Geospatial Sciences
(Building Information Modelling) will be equipped for a
diverse range of roles, including BIM or surveying manager,
geospatial data scientist, geospatial software developer and
consultant. Graduates go on to work for a variety of
organisations including major infrastructure projects,
engineering consultancies, geospatial software companies and
national mapping agencies. Our students establish strong
links with industry through the industrial seminar series
and industrial research projects. Additionally, many
graduates from our programmes go on to PhD research at UCL
or elsewhere.
Employability
Students will gain a range of technical skills in geospatial
science that are highly valued by employers, including the
use of specialist software for surveying, Reality Capture,
BIM and GIS (e.g. Cyclone, PhotoScan, Revit, QGIS, FME);
programming, web and software development (e.g. Python, R,
MATLAB); geospatial data processing and management;
geospatial data visualisation and analysis.
The areas in which the knowledge and technology of Surveying are today applied include:
| No. | University | Location | Website |
|---|---|---|---|
| 1 | University Of Nairobi | Nairobi, Ruaraka Campus | University Of Nairobi |
| 2 | Technical University Of Kenya | Limuru | Technical University Of Kenya |
| 3 | JKUAT University | Juja | JKUAT University |
An accounting course will teach, besides accounting focused subjects, business mathematics, management theory and business economics and law. An accounting graduate will be able to work in different fields of accounting such as managerial accounting, financial accounting, taxation and auditing. A Bachelor in accounting provides an understanding of perspectives that form the context for business and financial management. It is a rigorous programme that prepares students for profesional accountancy roles and business & public sector management roles.
It includes a strong international element and considers the essential, underlying theory and concepts that are involved in the accounting sector on a global level, exploring accounting procedures in organizations around the world. As the world's economy keep growing there wil be an ever-increasing need for quality accounting and auditing of private sector and public sector institutions and their activities, with this you are assured of the many opportunities you are going to have once you complete your learning.
The aim of the Bachelor of Science in Accounting is to prepare students for entry into a variety of fields in accountancy, including corporate accounting, accounting information systems, and governmental accounting. Graduates will gain detailed knowledge of financial accounting and financial reporting.
If you are planning for to pursue a course in accounting it as advisable to consider to pertaking the accounting certifications which increases your career opportunities, qualifies you for more positions and also increase your earing potential. If you consider obtaining an accounting certification, expoloring some of the more popular ones can help you determine which is the best fit for you.
| No. | University | Location | Website |
|---|---|---|---|
| 1 | Strathmore University | Nairobi | Strathmore University |
| 2 | KCA university | Nairobi | KCA University |
| 3 | University of Nairobi | Nairobi | University Of Nairobi |
Engineering is the application of science and maths to solve problems. Engineers figure out how things work and use for scientific discoveries. Scientists and inventors often get credit for innovations that advance human condition, but it is the engineers who are instrumental in making these innovatons available to the global population. Engineering is one of the corner stones of STEM(science, technology, engineering, mathematics) education, an interdisciplinary curriculum designed to motivate students to learn about the evolving world. A career in engineering requires continual professional development to remain relevant and effective in today's rapidly changing global society. There are currently more than 15 million engineers across various disciplines worldwide and with the surge in technological advancements and preparedness, anyone can be a part of it.
Engineering spreads over multiple fields such as bio-engineering to the more common mechanical engineering courses. Engineering is a hands-on course that relies more on practicality and not theory based studies. However, engineering optons such as software engineering offer the rare pleasure of being a mind based field where traditional college institutions as well as highly-acclaimed online programmes strive to provide high grade content and knowledge. All is not lost though, since engineering favours both the active and passive. An engineering degree takes upto tho 5 years to acquire whereas the masters and PhDs take 2 years each.
To successfully design and engineer solutions for today's and tomorrow's rapidly changing and expanding global contexts, in which people are confronted with new opportunities and challenges each day, engineering programs should be training their students to become broad based professionals, who are aware of the actual needs, values and behaviors of the people that use their solutions in their everyday life, work or play. This paper argues that in order to built such an awareness, engineering students should acquire direct, first-hand experiences of real people in real contexts. It presents a number of techniques that can be used to gain such experiences.
Engineering opens a learner up to greater heights with the right skills and mindset. It is important to point out that engieering experiences an influx of aspiring students each and every year making it a highly competitive field where the resilient and committed survive to emerge on top. However, don not forget to have fun while you are at it. The field enables you to interact with new technology and people with various banks of knowledge to offer in your journey from an amateur aspirant to a fully-fledged engineer and put you well above most in the job market, let alone equip you with skills to solve some of the world's problems.
| No. | University | Location | Website |
|---|---|---|---|
| 1 | Jomo Kenyatta University Of Agriculture & Technology | Juja | JKUAT University |
| 2 | Nairobi University | Nairobi | University Of Nairobi |
| 3 | Baraton University | Eldoret | Baraton University |
Home Economics offers a range of exciting career options related to foods, nutrition, housing and clothing. Most home economists teach in various types of educational institutions.
Some work closely with the media to promote products. Others conduct research in an area of personal interest in this field. Most home economists supervise at least one other employee.
More specialized aspects include:
Counselling: home economics counselling is given informally and is aimed at developing every family member within the family’s income and their physical, mental and intellectual abilities. Individuals, organizations and associations are provided with information by means of projects and demonstrations, posters, audio-visual aids, pictures, pamphlets, placards, cookbooks, etc.
Journalism: the press, radio and television offer interesting opportunities for a person who is original and creative. The home economist may become a journalist on a full-time, part-time or freelance basis. Many home economists write books or columns for newspapers and magazines.
Home economists work in a very large variety of work settings. The actual setting is determined by the type, size, location and financial resources of the employer, and by the speciality, experience and education of the home economist. Some positions require evening work and extensive travel.
| No. | University | Location | Website |
|---|---|---|---|
| 1 | Kenyatta University | Kiambu | Kenyatta University |
| 2 | Mount Kenya university | Thika | Mount Kenya University |
| 3 | Daystar University | Nairobi | Daystar University |


Logging for golang microservices?
EFK?
Wojciech Barczyński - Hypatos.ai (SMACC.io) | LI | Twitter | 13 March 2019
About me
- Head of Engineering - Hypatos.ai/smacc.io (FinTech/AI)
- Before:
System Engineer && Developer Lyke (RocketInternet)
- Looking:
Tools for efficent teams
Point of view
- Software Developer
- startups && fast-moving environment
- Infra and platform should just work
- Infra and platform ~ invisible
My Goal
- Do not forget to start with monitoring
- What are the logging frameworks for Golang?
- What is the best strategy?
Why?
Monolit ;)
Why?
Microservices ;)
Observability
- Monitoring
- Logging
- Tracing
Centralized Logging
- Debugging tool
- Post-mortem
- Finding the needle
- ! High TCO
Notice: you can get a long way with grep
Logging
- Stream of discrete events
- Best: structured at the caller site
- 12factorapps: push on stdout
pkg/log
package main
import (
"log"
"fmt"
)
func main() {
log.Println("Hello World!")
log.Fatal("Buum!")
fmt.Println("You will not see me!")
}
2009/11/10 23:00:00 Hello World!
2009/11/10 23:00:00 Buum!
Program exited: status 1.
ELK stack
- Fluentd - collect
- Elasticsearch - store
- Kibana - visualize
Summary
- Monitoring saves your time
- Debugging helps when things go south
- Checking logs == debuging vs having tests
Thank you
ps. We are hiring: TESTER/QA, FRONT-DEV, PM
Fluentd + K8S = <3
More in one of the next talks


Hi, Basecamp. When I first read your job offer I instantly knew I
had to apply.
Why, you may think? I could easily say that I
love your products, that I've been a customer, but there's much
more. The reason is you're different. Let me explain how.
↓
You may be wondering what all this writing about Basecamp has to do with my applying to this job position. Well, Basecamp as a company has all the qualities in which I believe as a professional (and as an individual).
Old-School values, unbiased opinions, originality, authenticity, rationality. These principles, and the desire to build great products, are the reasons why Basecamp is where I want to work.
Now let me explain why I think I can be a good fit.
Over the past 3 years I completely devoted myself to ideating, building an selling my own products. I'm not talking about digital products, but physical ones, which are sometimes even more challenging. Some succeeded, others failed. My current entrepreneurial endeavor is Geekville, a coworking space for digital and creative professionals. The previous one was also a coworking space, The Collective.
Building and managing a coworking space means going trough the complete range of product business: design (both in the physical and digital realm), marketing & sales, pricing, admin, client acquisition, client retention, customer care, and more. While I certainly have still a lot to learn, I think this kind of first-hand experience can be a valuable asset for Basecamp.
In addition to this, I have an extensive expertise in User Interface design & Logo Design.
I worked in agencies, but they're not what I'm looking for. I worked as a freelance contractor, on products that lived and died in a couple of years or never saw the light of day altogether.
What I want today is working on meaningful, useful, long-standing products, and help selling them effectively trough good design and strategy.
I'm a User Interface Designer (output: high fidelity mockups in Photoshop/Sketch), and I have a good expertise in Logo Design, too. I'm passionate about front-end dev and coding for web/mobile in general, but - full disclosure - I'm not production-ready for this side of the business. Yet.


Save Editor (zip)
Load Editor (direct)
Save Editor (direct)
Examples:
Basic
Google Workflow
Smooth Voxel
Fectar
Load Example (website)
Generate concrete code language:
No Language available
This website collects usage analytics. Tested in Chrome. Works better with zoom level 75%
20
65
120
160
210
230
260
290
330
Factory Code
Concrete JSON
Code Generation
Toolbox JSON
Concrete Code


Good question! An ideathon is a short, intensive, workshop-like experience for students to address some of the most pressing challenges of our time. Participants work in teams and use design thinking and innovative learning practices to ideate and collaborate on possible solutions. In fact, the best ideathons combine students from many majors – a biology student working together with a linguist, a drama student and a political science major. The wider cross-section of students involved, the more perspectives there are to come up with insights that are relevant and novel.
The Ideathon is like a hackathon, minus the hacking. It’s about getting people together to try to solve a problem.
#1 You can pick any of the problem statements from the website or frame your own problem statements.
#2 Maximum 3 and minimum 1 in a group.
#3 Group members can be interdisciplinary.
#4 A team can enroll one idea only.
The ideas will be selected based on the following criteria.
For queries regarding Ideathon 2020, kindly contact the below number.
9600771096
Dr. D. SanthanaKrishnan,
Coordinator,
SRCAS - IC
created with
Website Builder Software .


Maestro Nikola:
I have no idea what you are talking about. I have nothing to say on the matter.
"Wouldn't you like to work together with Lorain in the future?"


Colleges For Hillary is a grassroots political organization founded by Laurelle Cheng (Wellesley ’15), Queenie Cheng Clemente (Obama ’08) and Paul Clemente (Senator Daniel Patrick Moynihan ’91-’92) - individuals who are passionate about getting Hillary Clinton elected as the first female President of the United States. Our focus is highly targeted at garnering support from the college student demographic.
In this day and age, popular media, the 24 hour news cycle and the opposing party look to dehumanize candidates, make snap judgments without giving candidates a fair shake. It is easy to create caricatures, to present all the foibles and failures without a counter narrative. Colleges For Hillary strives to dispel and debunk these myths, to separate fact from fiction about Hillary via both the film medium and in-person discussions with people who know her well; to help people understand Hillary not only as real person, your friend, your next-door neighbor, a wife, a mom - but also the guiding force who will lead this country into 2016 and beyond. Who is Hillary? What kind of person is she? Is she portrayed accurately in the media? These and many more questions are those that will be answered in this first of its kind forum at Wellesley College, Hillary Rodham Clinton’s alma mater.
In this endeavor we hope to offer young voters a new and unvarnished perspective on Hillary – not just as a highly accomplished political figure, an international statesperson, but also as a force for change. That Hillary is not a superhuman but a human being that above all else is looking to improve the lives of millions across the globe.
Over the coming months, Colleges For Hillary representatives across the United States will be hosting screenings of select films at their respective campuses. If you are interested in bringing this campaign to your campus, do not hesitate to reach out to<EMAIL_ADDRESS>Colleges For Hillary also supports independent filmmakers and is currently accepting new proposals for this campaign.
Stay connected for news, events and updates at www.CollegesForHillary.com, www.facebook.com/CollegesForHillary and www.twitter.com/CollegesForHRC.


| Lineup | Points | Rebounds | Assists |
|---|---|---|---|
| Charles | 0 | 0 | 0 |
| Aaron | 0 | 0 | 0 |
| Ginn | 0 | 0 | 0 |
| Steve | 0 | 0 | 0 |
| Lineup | Points | Rebounds | Assists |
|---|---|---|---|
| CLC-4-TTS | 0 | 0 | 0 |
| aaronlev | 0 | 0 | 0 |
| GC | 0 | 0 | 0 |
| SL | 0 | 0 | 0 |
This example is copied and slightly modified from the old version of this one, is based on this old WAI-ARIA specification specifying namespaces for WAI-ARIA attributes but served with text/html MIME and ItsNat. ItsNat automatically rebuilds all namespaced WAI-ARIA attributes to be recognized (with text/html MIME namespaces are ignored). Current WAI-ARIA specification is not based on namespaces to be compatible with MSIE because MSIE does not support XHTML MIME (application/xhtml+xml).
The focus of this example is not WAI-ARIA, it shows how ItsNat can automatically rebuild on load time non-HTML/XHTML namespaces in HTML/XHTML pages served with HTML MIME (text/html) for W3C browsers and remain compatible with MSIE. ItsNat does not know WAI-ARIA attributes, any non-XHTML namespace is supported by ItsNat.
This example works in W3C browsers (FireFox, Opera, WebKit based...) recognizing namespaces in markup, you can use a screen reader like ChromeVox installed on Chrome to recognize live regions. It also works in MSIE ignoring namespaces (no namespace rebuild).


WELCOME TO TechTics
TechTics is an application that can be used by Tech enthusiasts and researchers. The application is built using cloud technology (Amazon Web Services). Using the various services of AWS, the flow will be well authenticated and secure.
The application aims to be a point of interaction among the people who are researching on a particular topic (helping for the literature survey). This platform can also be useful for people who want to learn about the emerging technologies and trends.


Reports utility classes that can be converted to an
enum
. To avoid code coverage problems in
private
constructors, some coding styles mandate that utility classes are implemented as enums.


|
intprim
|
The BayesianInteractionPrimitive class is responsible for training an Interaction Primitive model from demonstrations as well as performing run-time inference. More...
Public Member Functions | |
| def | __init__ (self, basis_model, scaling_groups=None) |
| The initialization method for BayesianInteractionPrimitives. More... | |
| def | export_data (self, file_name) |
| Exports the internal state information from this model. More... | |
| def | import_data (self, file_name) |
| Imports the internal state information from an export file. More... | |
| def | init_scalers (self) |
| Internal method which initializes data scalers. | |
| def | compute_standardization (self, trajectory) |
| Iteratively fits data scalers. More... | |
| def | add_demonstration (self, trajectory) |
| Iteratively adds a demonstration to the model. More... | |
| def | get_mean_trajectory (self, num_samples=intprim.constants.DEFAULT_NUM_SAMPLES) |
| Gets the mean trajectory of all trained demonstrations. More... | |
| def | get_approximate_trajectory (self, trajectory, num_samples=intprim.constants.DEFAULT_NUM_SAMPLES, deriv=False) |
| Gets the approximated trajectory for the given demonstration. More... | |
| def | get_approximate_trajectory_derivative (self, trajectory, num_samples=intprim.constants.DEFAULT_NUM_SAMPLES) |
| Gets the approximated trajectory derivative for the given demonstration. More... | |
| def | get_probability_distribution (self, num_samples=intprim.constants.DEFAULT_NUM_SAMPLES) |
| Gets the probability distribution of the trained demonstrations. More... | |
| def | basis_transform (self, trajectory) |
| Transforms the given trajectory from measurement space into the latent basis space. More... | |
| def | basis_inverse_transform (self, x, weights, deriv=False) |
| Transforms the given basis space weights to measurement space for the given phase values. More... | |
| def | get_basis_weight_parameters (self) |
| Gets the mean and covariance for the trained demonstrations. More... | |
| def | generate_probable_trajectory_recursive (self, trajectory, observation_noise, active_dofs, num_samples=intprim.constants.DEFAULT_NUM_SAMPLES, starting_phase=None, return_variance=False, phase_lookahead=0.0) |
| Performs inference over the given trajectory and returns the most probable future trajectory. More... | |
| def | set_filter (self, filter) |
| Sets the given filter as the current filter. More... | |
| def | __init__ (self, basis_model, scaling_groups=None) |
| The initialization method for BayesianInteractionPrimitives. More... | |
| def | export_data (self, file_name) |
| Exports the internal state information from this model. More... | |
| def | import_data (self, file_name) |
| Imports the internal state information from an export file. More... | |
| def | init_scalers (self) |
| Internal method which initializes data scalers. | |
| def | compute_standardization (self, trajectory) |
| Iteratively fits data scalers. More... | |
| def | add_demonstration (self, trajectory) |
| Iteratively adds a demonstration to the model. More... | |
| def | get_mean_trajectory (self, num_samples=intprim.constants.DEFAULT_NUM_SAMPLES) |
| Gets the mean trajectory of all trained demonstrations. More... | |
| def | get_approximate_trajectory (self, trajectory, num_samples=intprim.constants.DEFAULT_NUM_SAMPLES, deriv=False) |
| Gets the approximated trajectory for the given demonstration. More... | |
| def | get_approximate_trajectory_derivative (self, trajectory, num_samples=intprim.constants.DEFAULT_NUM_SAMPLES) |
| Gets the approximated trajectory derivative for the given demonstration. More... | |
| def | get_probability_distribution (self, num_samples=intprim.constants.DEFAULT_NUM_SAMPLES) |
| Gets the probability distribution of the trained demonstrations. More... | |
| def | basis_transform (self, trajectory) |
| Transforms the given trajectory from measurement space into the latent basis space. More... | |
| def | basis_inverse_transform (self, x, weights, deriv=False) |
| Transforms the given basis space weights to measurement space for the given phase values. More... | |
| def | get_basis_weight_parameters (self) |
| Gets the mean and covariance for the trained demonstrations. More... | |
| def | generate_probable_trajectory_recursive (self, trajectory, observation_noise, active_dofs, num_samples=intprim.constants.DEFAULT_NUM_SAMPLES, starting_phase=None, return_variance=False, phase_lookahead=0.0) |
| Performs inference over the given trajectory and returns the most probable future trajectory. More... | |
| def | set_filter (self, filter) |
| Sets the given filter as the current filter. More... | |
Public Attributes | |
| basis_model | |
| scaling_groups | |
| basis_weights | |
| filter | |
| prior_fitted | |
| scalers | |
The BayesianInteractionPrimitive class is responsible for training an Interaction Primitive model from demonstrations as well as performing run-time inference.
Support for importing and exporting a trained model as well
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.__init__ | ( | self, | |
| basis_model, | |||
scaling_groups = None |
|||
| ) |
The initialization method for BayesianInteractionPrimitives.
| basis_model | The basis model corresponding to this state space. |
| scaling_groups | If provided, used to indicate which degrees of freedom should be scaled as a group. |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.__init__ | ( | self, | |
| basis_model, | |||
scaling_groups = None |
|||
| ) |
The initialization method for BayesianInteractionPrimitives.
| basis_model | The basis model corresponding to this state space. |
| scaling_groups | If provided, used to indicate which degrees of freedom should be scaled as a group. |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.add_demonstration | ( | self, | |
| trajectory | |||
| ) |
Iteratively adds a demonstration to the model.
The demonstration is decomposed into the latent space and the weights are stored internally.
| trajectory | Matrix of dimension D x T containing a demonstration, where T is the number of timesteps and D is the dimension of the measurement space. |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.add_demonstration | ( | self, | |
| trajectory | |||
| ) |
Iteratively adds a demonstration to the model.
The demonstration is decomposed into the latent space and the weights are stored internally.
| trajectory | Matrix of dimension D x T containing a demonstration, where T is the number of timesteps and D is the dimension of the measurement space. |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.basis_inverse_transform | ( | self, | |
| x, | |||
| weights, | |||
deriv = False |
|||
| ) |
Transforms the given basis space weights to measurement space for the given phase values.
| x | Vector of dimension T containing the phase values that the basis space weights should be projected at. |
| weights | Vector of dimension B containing the basis space weights. |
| deriv | True if the basis weights should be transformed with basis function derivatives, False for normal basis functions. |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.basis_inverse_transform | ( | self, | |
| x, | |||
| weights, | |||
deriv = False |
|||
| ) |
Transforms the given basis space weights to measurement space for the given phase values.
| x | Vector of dimension T containing the phase values that the basis space weights should be projected at. |
| weights | Vector of dimension B containing the basis space weights. |
| deriv | True if the basis weights should be transformed with basis function derivatives, False for normal basis functions. |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.basis_transform | ( | self, | |
| trajectory | |||
| ) |
Transforms the given trajectory from measurement space into the latent basis space.
| trajectory | Matrix of dimension D x T containing a demonstration, where T is the number of timesteps and D is the dimension of the measurement space. |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.basis_transform | ( | self, | |
| trajectory | |||
| ) |
Transforms the given trajectory from measurement space into the latent basis space.
| trajectory | Matrix of dimension D x T containing a demonstration, where T is the number of timesteps and D is the dimension of the measurement space. |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.compute_standardization | ( | self, | |
| trajectory | |||
| ) |
Iteratively fits data scalers.
This must be called on all training demonstrations before fitting, if scaling is used.
| trajectory | Matrix of dimension D x T containing a demonstration, where T is the number of timesteps and D is the dimension of the measurement space. |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.compute_standardization | ( | self, | |
| trajectory | |||
| ) |
Iteratively fits data scalers.
This must be called on all training demonstrations before fitting, if scaling is used.
| trajectory | Matrix of dimension D x T containing a demonstration, where T is the number of timesteps and D is the dimension of the measurement space. |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.export_data | ( | self, | |
| file_name | |||
| ) |
Exports the internal state information from this model.
Allows one to export a trained model and import it again without requiring training.
| file_name | The name of the export file. |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.export_data | ( | self, | |
| file_name | |||
| ) |
Exports the internal state information from this model.
Allows one to export a trained model and import it again without requiring training.
| file_name | The name of the export file. |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.generate_probable_trajectory_recursive | ( | self, | |
| trajectory, | |||
| observation_noise, | |||
| active_dofs, | |||
num_samples = intprim.constants.DEFAULT_NUM_SAMPLES , |
|||
starting_phase = None , |
|||
return_variance = False , |
|||
phase_lookahead = 0.0 |
|||
| ) |
Performs inference over the given trajectory and returns the most probable future trajectory.
This is a recursive call and the internal state of the currently set filter will be updated, so only new observations should be passed to this method each time it is called.
| trajectory | Matrix of dimension D x T containing a demonstration, where T is the number of timesteps and D is the dimension of the measurement space. |
| observation_noise | Matrix of dimension D x D containing the observation noise. |
| active_dofs | Vector of dimension \( D_o \) containing measurement space indices of the observed degrees of freedom. Note that the measurements will also contain unobserved degrees of freedom, but their values should not be used for inference. |
| num_samples | The length of the generated trajectory. If set to 1, only a single value at the current or specified phase (see starting_phase) is generated. |
| starting_phase | The phase value to start generating the trajectory from. If none, will start generating from the currently estimated phase value. |
| return_variance | True if the phase system mean and variance should be returned. False otherwise. |
| phase_lookahead | If phase_lookahead is none, this is an offset that will be applied to the currently estimated phase used to generate the trajectory. |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.generate_probable_trajectory_recursive | ( | self, | |
| trajectory, | |||
| observation_noise, | |||
| active_dofs, | |||
num_samples = intprim.constants.DEFAULT_NUM_SAMPLES , |
|||
starting_phase = None , |
|||
return_variance = False , |
|||
phase_lookahead = 0.0 |
|||
| ) |
Performs inference over the given trajectory and returns the most probable future trajectory.
This is a recursive call and the internal state of the currently set filter will be updated, so only new observations should be passed to this method each time it is called.
| trajectory | Matrix of dimension D x T containing a demonstration, where T is the number of timesteps and D is the dimension of the measurement space. |
| observation_noise | Matrix of dimension D x D containing the observation noise. |
| active_dofs | Vector of dimension \( D_o \) containing measurement space indices of the observed degrees of freedom. Note that the measurements will also contain unobserved degrees of freedom, but their values should not be used for inference. |
| num_samples | The length of the generated trajectory. If set to 1, only a single value at the current or specified phase (see starting_phase) is generated. |
| starting_phase | The phase value to start generating the trajectory from. If none, will start generating from the currently estimated phase value. |
| return_variance | True if the phase system mean and variance should be returned. False otherwise. |
| phase_lookahead | If phase_lookahead is none, this is an offset that will be applied to the currently estimated phase used to generate the trajectory. |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.get_approximate_trajectory | ( | self, | |
| trajectory, | |||
num_samples = intprim.constants.DEFAULT_NUM_SAMPLES , |
|||
deriv = False |
|||
| ) |
Gets the approximated trajectory for the given demonstration.
This is obtained by transforming the demonstration to the latent space and then projecting it back to measurement space.
| trajectory | Matrix of dimension D x T containing a demonstration, where T is the number of timesteps and D is the dimension of the measurement space. |
| num_samples | The length of the generated approximate trajectory |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.get_approximate_trajectory | ( | self, | |
| trajectory, | |||
num_samples = intprim.constants.DEFAULT_NUM_SAMPLES , |
|||
deriv = False |
|||
| ) |
Gets the approximated trajectory for the given demonstration.
This is obtained by transforming the demonstration to the latent space and then projecting it back to measurement space.
| trajectory | Matrix of dimension D x T containing a demonstration, where T is the number of timesteps and D is the dimension of the measurement space. |
| num_samples | The length of the generated approximate trajectory |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.get_approximate_trajectory_derivative | ( | self, | |
| trajectory, | |||
num_samples = intprim.constants.DEFAULT_NUM_SAMPLES |
|||
| ) |
Gets the approximated trajectory derivative for the given demonstration.
This is obtained by transforming the demonstration to a latent space composed of the basis function derivatives and then projecting it back to measurement space.
| trajectory | Matrix of dimension D x T containing a demonstration, where T is the number of timesteps and D is the dimension of the measurement space. |
| num_samples | The length of the generated approximate trajectory |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.get_approximate_trajectory_derivative | ( | self, | |
| trajectory, | |||
num_samples = intprim.constants.DEFAULT_NUM_SAMPLES |
|||
| ) |
Gets the approximated trajectory derivative for the given demonstration.
This is obtained by transforming the demonstration to a latent space composed of the basis function derivatives and then projecting it back to measurement space.
| trajectory | Matrix of dimension D x T containing a demonstration, where T is the number of timesteps and D is the dimension of the measurement space. |
| num_samples | The length of the generated approximate trajectory |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.get_basis_weight_parameters | ( | self | ) |
Gets the mean and covariance for the trained demonstrations.
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.get_basis_weight_parameters | ( | self | ) |
Gets the mean and covariance for the trained demonstrations.
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.get_mean_trajectory | ( | self, | |
num_samples = intprim.constants.DEFAULT_NUM_SAMPLES |
|||
| ) |
Gets the mean trajectory of all trained demonstrations.
| num_samples | The length of the generated mean trajectory |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.get_mean_trajectory | ( | self, | |
num_samples = intprim.constants.DEFAULT_NUM_SAMPLES |
|||
| ) |
Gets the mean trajectory of all trained demonstrations.
| num_samples | The length of the generated mean trajectory |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.get_probability_distribution | ( | self, | |
num_samples = intprim.constants.DEFAULT_NUM_SAMPLES |
|||
| ) |
Gets the probability distribution of the trained demonstrations.
| trajectory | Matrix of dimension D x T containing a demonstration, where T is the number of timesteps and D is the dimension of the measurement space. |
| num_samples | The length of the generated distribution. |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.get_probability_distribution | ( | self, | |
num_samples = intprim.constants.DEFAULT_NUM_SAMPLES |
|||
| ) |
Gets the probability distribution of the trained demonstrations.
| trajectory | Matrix of dimension D x T containing a demonstration, where T is the number of timesteps and D is the dimension of the measurement space. |
| num_samples | The length of the generated distribution. |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.import_data | ( | self, | |
| file_name | |||
| ) |
Imports the internal state information from an export file.
Allows one to import a trained model without requiring training.
| file_name | The name of the import file. |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.import_data | ( | self, | |
| file_name | |||
| ) |
Imports the internal state information from an export file.
Allows one to import a trained model without requiring training.
| file_name | The name of the import file. |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.set_filter | ( | self, | |
| filter | |||
| ) |
Sets the given filter as the current filter.
This is used to initialize and reset the filter between interactions.
| filter | The filter to set. |
| def intprim.bayesian_interaction_primitives.BayesianInteractionPrimitive.set_filter | ( | self, | |
| filter | |||
| ) |
Sets the given filter as the current filter.
This is used to initialize and reset the filter between interactions.
| filter | The filter to set. |


---
title: Roozbeh Farhoodi
layout: single
author_profile: true
header:
overlay_image: "/assets/images/background.jpg"
overlay_filter: 0 # same as adding an opacity of 0.5 to a black background
exceprt: " "
---
I am a postdoctoral fellow at University of California, San Francisco (UCSF) working on Functional modeling in brain. Previously, I was a postdoc researcher at University of Pennsylvania (UPenn) where my research focused on theory of deep learning and geometrical structure of neurons. I received my Ph.D. in mathematics from Sharif University of Technology in Tehran, Iran where I studied computational neuroscience. My research focused on developing interpertable machine learning to relate function and strcture of brain.


Email Network & Topic Change Over Time
Topics
Sara Shackleton & Susan Bailey
- volume/person
- contacts/person


Keep yourself fed by ordering online and picking food up or getting it delivered from great establishments nearby.


Take an Impuse Tour of Denver and become and instant-insider. See up-coming venues, hidden treasures and the hottest secrects in Denver.
Our tours and events are immersive and interactive. Tour behind the scenes: touch, taste, smell and feel the energy happening behind the scenes.
Our guides and hosts and master emcees, seamlessly blending humor with education while keeping everything engaging.
We are highly responsive. Reach out to cusomize an event, plan your trip or reserve a date.


\(\displaystyle\oint\mathbf{B.dl}=\)
Where \( \mathbf{B.dl} = r\mathbf{B.d\theta}\), and θ is the clockwise angle between the start location and the current location.
This simulation is aimed at providing a visual and interactive insight of Ampere's law.
You will see a closed loop around which B (magnetic field) is integrated.
The magnetic field is represented by the green arrow, the small incremental path element is the small purple arrow. As you integrate along the path, the arrows will move.
To launch the animation, press "play".
A graph of the value of B.dl is shown. The mean value of B.dl is indicated by a blue dotted line.
You will also find the value of the integral of B.dl around the loop (equivalent to the area under the curve).
Settings are modifiable. You can add new infinitely long wires of current, change the value of current for each of them, visualize the magnetic field for a single wire of current.
You can also change to other loop types (although less exact)
Enjoy!


Sets or gets the default captureRejection value for all emitters.
This symbol shall be used to install a listener for only monitoring 'error'
events. Listeners installed using this symbol are called before the regular
'error'
listeners are called.
Installing a listener using this symbol does not change the behavior once an
'error'
event is emitted, therefore the process will still crash if no
regular 'error'
listener is installed.
get role status. Active means that role is enabled. If role is not active, all incoming data to the role will be rejected
Deactivate role. All units will be notified immediately with rebuilding their state
Activate role. All units will be notified immediately with rebuilding their state
Set handler for any incoming message / request / stream request
Set handler for incoming message
Set handler for incoming request for receiving stream data
Set handler for incoming request.
Set handler for incoming request for sending stream data
Generated using TypeDoc
Role represents a service on the local Peer. It should handle incoming messages, requests and stream requests for certain functionality
This class should not be instantiated directly. It is exposed for type declaration and documentation


climate flood catchments catchment sensitivity change using precipitation model changes response hydrological mean annual ensemble risk used CMIP Figure uncertainty (ID: exposure greater flow Climate Water according future cluster types Prudhomme, number Resources models surfaces ‐year study Crooks, Research, allowances approach shown type {border-style catchments. series ./WR allowance based data estimated given px;} sensitive #ffffff;} However, national time Irish Wilby, amplitude assessment influence parameter projections river temperature –), Flood Kay, framework harmonic impact individual maximum rainfall‐runoff relative sample surface (a) Change, Hydrology, associated clusters period respectively. storage water RCP developed magnitude physical rainfall results seasonal CMIP_Sub applied available calculated cases effect extent high particularly percent potential provide (e.g., Environmental GRJ Hence, Hydrological Reynard, different generally important increases median method parameters range simulations slope (e) GCM Model additional centroid conditions estimate frequency highlight importance increase observed robust seasonality set shows similar soil ./j.jhydrol... Brown, EMs Murphy, NAM Research attributes case daily decision distribution domain effects ensemble. function identify local measure monthly peak projected regime relating use Catchment Catchments Davies, Generally, Given Ireland Public Response Using algorithm assigned axis) baseflow baseline changes. clustering design extreme fitted greatest horizons indicative key likely processes proportion px; return sets simulated subset values {font-family (%) (Figure (Table (d) ./hyp. ./s--- Changes Office PCDs Phase Project Prudhomme RCP, Sciences, adaptation address analysis behavioral cycle derived font-size identified impacts length located majority management members performance provides relatively representative scale scenario selected simulation single systems upland urban value variability (Prop) (equation BFI Boyne Climatic Coupled Earth Intercomparison NSE Processes, RCP., Steinschneider, Uncertainty average calibration capacity class corresponding coverage distinct double; elasticity examined expected features flows generalized grid horizon hydroclimatological input larger lead level memory original point precipitation, predictor present protection random runoff signatures streamflow studies total ungauged used. validation (Brown (i.e., (–) Application Binley, Broderick, Chiew, Concentration Conversely, Figures GLUE Hydrology Ireland. Letters, Matthews, Mean Nature OOB OPW Physical RCP. Risk River S). Sensitivity addition, adjusted approaches arithmetic axis border-color border-width catchments, classified conceptual considering contribute cover criterion dependent does efficiency employed equation event events examine forest global gradient highlights implications incremental index information infrastructure interseasonal longer long‐term lower moisture overall peaks points possible prediction predictors probability procedure projections. pronounced reduced reflects represent represents respect respectively, risk. scaling scenarios sensitivity. similarity size solid; span subselected successive surfaces. testing type, type. uncertainties uncertainty, variables vulnerability weather {font-style {font-weight ()) (Kay, (b) (km) (mm) ./hess--- ./nclimate Agency Bastola, Beven, Bussi, Classification Climate, Cluster Clusters Department Differences Ensemble FLATWET, Finally, Geophysical Knutti, NAM. Probabilistic Protection Representative Science Similarly, Studies Surface Table United Update WGEN Weather Works. [], affected allows analytical and/or antecedent area assess assumed bias bold centroids change. changing clustered collab: combined conditions. considered considered. constitutes contrast, courier; data‐sparse degree descriptors differ differences drainage em; error established estimation evapotranspiration example, exposed fluvial generator groundwater group highlighted hydrologic increased indicate indicates indices interactive investigate k‐means land large limited linear low magnitude. member members. membership model. modeling month multiple natural network observations opposed phase planning predicted predictors. probabilistic properties pt;} quantify reduction reflected representing required resources responses retained. sample. scenario‐neutral signal single‐phase spatial standardized statistical technique threshold upper variable various vector wetter winter


- close() (cs207rbtree.RedBlackTree.DBDB method)
- (cs207rbtree.RedBlackTree.Storage method)
- closed (cs207rbtree.RedBlackTree.Storage attribute)
- Color (class in cs207rbtree.RedBlackTree)
- commit() (cs207rbtree.RedBlackTree.DBDB method)
- (cs207rbtree.RedBlackTree.RedBlackTree method)
- commit_root_address() (cs207rbtree.RedBlackTree.Storage method)
|
- connect() (in module cs207rbtree.RedBlackTree)
- cs207rbtree (module)
- cs207rbtree.RedBlackTree (module)
- cs207rbtree.skeleton (module)
|


The Indian Air Force took off as a tiny fleet with four Westland Wapiti army co-operative by-planes, six RAF trained officers and 19 Hawai Sepoys. Today, it is the fourth largest Air Force in the world with a highly technical and specialised fighting force that safeguards our skies against enemy invasions.
A glance at the annals of its history will help us understand the glorious years of our legendary Air Force.
[8th October 1932- 1st April 1954] The Two Births
The two dates, mentioned above, are landmarks in the history of the Indian Air Force. On 8 October 1932, the Indian Air Force came into being and on 1st April 1954 Air Marshal Subroto Mukherjee, one of the founding members of the Air Force took over as the first Indian Chief of Air Staff.
The interim witnessed the most bloodiest war in the history of mankind, for the second time-World War II. The IAF lent its support to UK during the war. KK Majumdar, one of the legendary heroes of the Indian Air Force was rated one of the twelve best pilots of the Allied Air Forces. The same decade also saw the birth of an Independent India followed by the formation of the Indian Air Force, as we know it today.
[1955-1971] A Period Of Turmoil
This was the time the Indian Air Force entered a new era with the acquisition of jet aircraft. The period also saw the two Indo-Pak wars, both of which were marked by the neutralisation of Pakistani Air efforts by the Indian Air Force. The IAF also contributed to the UN Peace keeping mission in Congo as well as the Sino India Conflict, with dedication and commitment.
[1972-1990] A Time Of Progress
Over the next two decades the Indian Air Force undertook massive upgrading of its aircraft and equipment. As part of the process, the Indian Air Force introduced over twenty new aircraft types. Jaguars and several variations of the MiG were among the aircraft introduced to replace the Dakotas and the Packets. The MiG is a revolutionary aircraft that was introduced to the Indian fleet in the eighties. The same period also witnessed a number of world records set by Indian Air Force personnel. Sqn Ldr Makkar and Flt Lt RTS Chinna set a world record by bombing from their Mi-17 helicopter in Ladakh at an altitude of 5050 meters. Sqn Ldr Sanjay Thapar, was the first Indian to para jump over the South Pole. Exploring new vistas, Sqn Ldr Rakesh Sharma was the first Indian cosmonaut to venture into outer space as part of an Indo-Soviet space program.
[1990 - Present] Piloting India Into The New Millennium
The last decade of the twentieth century saw a phenomenal change in the structure of the Indian Air Force with induction of women into the Air Force for short service commissions. It was also a time when the Air Force undertook some of the most perilous operations ever undertaken. In 1999, the Indian Air Force undertook "Operation Safed Sagar", the most unique air operation undertaken by any air force in the world. An air attack was launched and won at an unprecedented height, under hostile climatic conditions against an intruding enemy. The Indian Air Force also participated in the peacekeeping mission at Somalia as part of a humanitarian operation.


The United States PostgreSQL Association affectionately known as PgUS is a IRS 501(c)(3) public charity.
Our purpose is to support the growth and education about The World's Most Advanced Open Source
Database, PostgreSQL.
By becoming a member, you will be able to:
There are many opportunities available to members including the ability to have affiliated projects (like the PostgreSQL Community Conference series), serving on our committee, getting sponsorship through grants and generally just participating in a fun and vibrant community.
Members of the PgUS are entitled to inspect the following records, some which are already publicly available while others must be requested from the Secretary (secretary@postgresql.us):
Members may also demand a special meeting if 5% of the voting members sign and date a petition to the Secretary with the purpose or purposes of the meeting. Currently, there are two regularly scheduled meetings for members. The Annual member meeting in March and the Semi-Annual meeting in October. Members are notified of exact dates at least 7 days in advance.
We appreciate your dedication to PostgreSQL and furthering our mission!
| Membership Type | Annual Dues |
|---|---|
| Professional Member | $25 |
| Student Member | $10 |
You may also simply make a donation. Thank you for your participation and consideration of our mission!
Any further questions or concerns can be directed to our secretary using the email secretary@postgresql.us.


Fingerprint: 0207CCA8118D34E9339F8403066758EF40EAA854
This website will provide information relative to StrangeLoopBTC
We are all strange loops when we look close enough.
Currently studying at the College of Western Idaho. Researching the Bitcoin protocol independently.


| category |
lists |
| pop |
- Calum Scott - You Are The Reason
- Harry Styles - Falling
- Michael Schulte - You Said You'd Grow Old with Me
|
| utaite |
- SPYAIR - Imagination
- rionos - ウィアートル(viator)
|
| classic |
- 소현의 자작곡 - 헨리X고소현
- Yo-Yo Ma - Bach: Cello Suite No. 1 in G Major, Prélude
|


20K
Members
12
Countries
3K
Hotels
72
Partners
Wisata Popular
Something You never Try
Before in This World
INDONESIA
DERATAN, BALI
INDONESIA
BROMO
INDONESIA
NUSA PENIDA
INDONESIA
SUKABUMI
Our Networks
Companies Are Trusted Us
More Than Just A Trip
They Are Loving Us
Moments were giving them
the best experience
Ardi Sahbani
" it wa Glorious and I could
not stopto say woohoo for
every singgle moment
Dankeeee "
Trip To Ubud
Ellie Goudling
" Keur hayang ulin the binggung neangan tempat untung aya NOMAD "
Trip to Bromo, Malang, Indonesia
Sherry Lagoon
" Arek umroh tapi rungsing hayang ngajien Visa untung di pang uruskeun ku NOMAD "
Trip to Sukabumi, Indonesia


You can prevent route/step navigation using regular route guards in angular.
Typically you create a service or hook up your app with some state management lib like ngrx or akita and check if routing should be allowed based on a state. Maybe the user needs to complete a certain step before advancing or correct a form error.
The link below show as simple example using forms and a simple service.
Show example


Bloomberg potentially entering 2020 field 'might be just what the doctor ordered,' former Schumer aide says
by Charles CreitzA Michael Bloomberg Democratic presidential candidacy may be just what the party needs to challenge President Trump in 2020, according to a former aide to Sen. Charles Schumer, D-N.Y.
The former New York City mayor and media mogul is expected to file paperwork this week designating himself as a candidate in Alabama's Democratic presidential primary, Fox News has learned.
On "The Ingraham Angle," Christopher Hahn praised Bloomberg's three-term tenure as mayor of America's largest city -- during which he served as a Republican, a Democrat and an independent.
"I think Mike Bloomberg was an excellent mayor for the city of New York," Hahn said.
BLOOMBERG EXPECTED TO FILE PAPERWORK TO ENTER 2020 ALABAMA DEMOCRATIC PRIMARY
"He might be just what the doctor ordered to shake this thing up right now."
Hahn, now a liberal radio host, said Bloomberg, 77, would shift the current 2020 Democratic presidential field's focus to issues important to the center of the party and people across the country.
He pointed to Andy Beshear, the Democrat who claimed victory in Tuesday's Kentucky governor's race, claiming he performed well because he appealed to moderates and "bread and butter issues."
In that vein, Hahn claimed Bloomberg would appeal to people who want to "shake up everything but keep what Obama built and build upon that."
CLICK HERE TO GET THE FOX NEWS APP
"That seems to be what Mike Bloomberg's message is going to be. It seems to be what the middle of the road wants in the Democratic primary right now."
In response, Republican strategist Lisa Boothe claimed the true test of whether Bloomberg is serious about entering the fray will be whether he submits an application to run in New Hampshire -- home to the first-in-the-nation primary next year.
She added Bloomberg may also be an interesting addition to the field, claiming former Vice President Joe Biden -- who like the ex-mayor is seen as a moderate Democrat -- has not received the grassroots donor support other candidates have.


The feasibility study aims to assess the practicality of the EPS module. The purpose of this stage is to assess the performance according to the association of the requirements and the state of the art.
As a brief remind, the PCC module is responsible for the distribution and regulation of electricity through the CubeSat. There is many requirements to fit such as the protection of the modules, the communication with the OBC and the energy distribution from PV cells to battery for the storage and/or to module for the consumption.
The MPPT will have to handle a maximum input voltage of 4V from solar panels and supply power to a 3.7 V battery. Redirecting the current to regulators providing energy to the modules (3.3V and 5V). The modules are both connected to the panels and the battery, that is why regulators will be connected in parallel to the MPPT output and the battery. An additional cell has been integrated because of the large amount of energy needed for the detumbling phase, the battery will not be enough to ensure the power supply in this time.
It has been established that a 3.3V regulated bus detumbling could be used to supply ADCS and OBC (which are on the same board), the other 5V converters will be connected to TCS and EDT. These figures are for now only indicative and will have to be changed according to the needs of each module.
The MCU is used for gathering and computing the housekeeping data, taking decisions for connecting/disconnecting users in case of failure and communicating with OBC thanks to an UART bus. The battery level of charge is harvested through an analog-digital converter.
A schematic of the description above, develops the architecture more clearly:
According to the availability on the market, the type of regulators varies as a function of the input and the output. As the battery charge regulator receives a various input from MPPT which can be superior or inferior to 3.7V, it has to work as a step-up and step-down regulator. For increasing the voltage from 3.7V to 5V the second regulator should be a step-up. As the last regulator decreases the voltage to 3.3V, it should be a step-down regulator. But, since the battery's output voltage decrease with its level of charge, the input voltage of regulators could be under 3.3V. Therefore, the last regulator should not be only a step-down regulator, it has to be a step-up/step-down voltage regulator, with a 3.3V output.
The microcontroller has to handle the energy distribution of the entire CubeSat, and has to communicate with the OBC too. Thus, it needs four digital/analog outputs (EDT, ADCS, Telecom, OBC), one BUS with a two-way communication between the microcontroller and the OBC in: UART. And two inputs with a power BUS and one another BUS which is used to send data of the battery level of charge to the microcontroller: SPI.
Generally, microcontrollers consume few energy, around the mirco ampere for the “Active mode” and around the hundreds of nano ampere for the “Off mode”. Others microcontrollers are more economical, they can be used with six different ways.
The voltage range of the microcontroller depends of the output voltage of the battery, which is 3.7V.
Almost microcontrollers have a voltage range of 2-3.6V, except for the ATMEGA1281 which has a voltage range of 2.7-5.5V.
In the light of the functions of the microcontrollers the memory didn’t have to be huge. All the microcontrollers are viable.
The sizing of this module has to be accurate and margins have to be considered to be sure of the capability of production compare to the consumption.
The energy production of the PV mainly depends of two factors which are the efficiency and the surface area of the cells. Other characteristics such as temperature, way to wire or weight have to be taken into account.
The PVs are subject to high electro magnetic radiation that cause huge variation of the temperature over time. (Around 100°C of difference between solar exposition phasis and eclipse). Then PVs shall be sized to undergo the extreme conditions of space environment.
The limited surface area on each side of the nanosatellite implies some restriction on the type of PVs available on the market and in compliance with the need of production. Efficiency and surface being related to each other, finding a compromise between them could imply an increase in the cost.
As panel's efficiency decreases over time, the capacity of production shall be computed for the beginning and the end of life. According to the allocated power budget, efficiency shall be maximized, Triple Junction solar cells are well indicated for this last condition. Generally, the solar cells designed for 1U CubeSat are between 25% and 30%.
Triple junction solar cells are used in space, our CubeSat will integrate this technology too. To save money, a choice of solar cells rather than complete solar panel has been studied.
The advantages of this solution are the flexibility on architecture, the cheaper cost, and the number of choices between different TJ solar cells
The disadvantages of this solution are that the architecture is more complicate than solar panels's architecture (due to the big number of solar cells), the sensors, wire and magnetorquers have to be add by ourselves (inducing more risks on the result), and some solar cells such as TASC cells have not coverglass on it. In this case, there is a need to add encapsulation on cells aftermarket coverglass.
tTherefore, the first option will be considered as our solution if the budget enables it. Otherwise, the second option will be considered even if it induce an increase of risk of mission failure.
You can find below the study of two kind of PVs Ultra Triple Junction Solar cell from Spectrolab, and ISIS TJ 3G30A
The results calculated are the power generated by only one face of the CubeSat. However the CubeSat has 5 sides with solar panels and between one and three sides can be under Sunlight at the same time. The number of sides which receives solar power is related to the orientation of the CubeSat. To estimate the power generated by the entire CubeSat, the calculations have to take in account the inclination of the satellite, in other words, the angles (here in spherical coordinates).
To supply the modules in energy, at least one rechargeable battery is needed. There are different types of batteries (Typically Li-Ion and NIMH). The technology, the attitude in a cold and hostile environment and the power consumption have to be considered to size the battery module.
Since the CubeSats have already a huge rate of failure (due to the hostile environment that they have to face with), the battery considered in the ECE³SAT has to be as safe as possible technology designed for space.
There is not a Power Budget for the ECE CubeSat, therefore an average of the precedent CubeSat is used:
For improving the battery lifespan, it has to be neither fully charged nor depth discharged. Precisely, the level of charge needs to be maintained between 20% and 90% of the total capacity. According to the amount of energy needed by the system, this should represent 70% of the battery capacity. Then, in consequence the battery should deliver 3.21 Wh.
To improve the lifespan of the battery, it has to be neither fully charged nor depth discharged. The level of charge needs to be maintained between 20% and 90% of the total capacity.
Moreover, in average, 2.25Wh will be needed during an eclipse according to the power budget part.
Thus, a battery will be required with 3.21Wh, or a capacity of 0.87 Ah with a voltage of 3.7 V.
It is in adequacy with the 1 Ah generally used.
The CubeSat may also consider the use of a non rechargeable battery to execute one time operation. It could be very interesting for some specific operations such as in the detumbling mode in the ECE³Sat case. So, it would a primary battery of 1Wh.
At low earth orbit, the atmosphere influence at really low level the space environment. Therefore, LEO is considered to be in vacuum conditions.
The battery designed for the CubeSat has to be able to charge and to furnish electricity to modules in these vacuum conditions.
The temperature in space is very low and it is important to take into account during the design of the modules. The battery is always confronted to the natural discharge issue, but in a cold environment this problem increases. That means that the battery has to be designed for space, and has to resist to low temperature.
The natural discharge of the battery has to be considered according to this extreme temperature. The battery should be able to work well and without too much loss for a temperature of 0 C°. This aims to improve the charge of battery and avoid the depth of discharge.