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BPS Mahila Vishwavidyalaya Recruitment
BPS Mahila Vishwavidyalaya Recruitment 2016: A current job notification has been transmitted as BPS Mahila Vishwavidyalaya Recruitment. Application forms are invited from hard-working and dedicated aspirants for filling up the post of Teaching Assistants (PGT). Candidates who want to grow their career in government sector may apply by submitting their application form and attending walk-in-interview on the specific date which is mentioned below.
Bhagat Phool Singh Mahila Vishwavidyalaya was founded in August, 2006. It was located in Khanpur Kalan, Sonipat, Haryana, India. It is also known as Bhagat Phool Singh Women's University. To get more details about BPS Mahila Vishwavidyalaya Vacancy you can scroll down this page which is well mentioned by the team of www.privatejobshub.in
BPS Mahila Vishwavidyalaya Recruitment
Name of the Organizaton
BPS Mahila Vishwavidyalaya
Post Name
Teaching Assistants
Education Qualification
Post Graduation/NET/ Ph.D Degree
Selection Procedure
Date of Walk-In
Check Below
Required Qualification:
Candidates should have passed Post Graduation/NET/ Ph.D Degree from a recognized University/Institute.
Post Graduate jobs for Job seekers: Get Details
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Selection Process:
The selection of the candidate for the BPS Mahila Vishwavidyalaya Vacancies will be on the basis of their presentation in the interview which will be decided by the organization.
Interview Questions and Answers for cracking Interview Round: Visit here
Salary Details:
- Selected candidates for the above mentioned post will get the salary of Rs. 19440/- per month will be given.
- No TA / DA will be provided to candidates for appearing in the Interview.
How to Apply For BPS Mahila Vishwavidyalaya Recruitment?
Candidates, who find themselves eligible for the above posts, must follow these given steps:-
- Firstly candidates have visit the official website that is www.bpswomenuniversity.ac.in
- Now, hit on the "Advertisement for the post of Teaching Assistant(PGT) in University Campus School" link which is available on the "Job Opening" section from the homepage.
- Then, read the notification carefully and prepare an application form.
- Fill the application form with the required details such as name, address, documents, certificates, etc
- Now, close the application form with all attested copies of documents, certificates and bring the application form to the address which is mentioned below.
- Candidates must bring the original documents and certificates for verification.
10 Communication Skills Employers Need in an Employee: Read Now
Venue of Interview:
The Principal office. Univ.
Campus School, Khanpur Kalan
Important Dates and Time:
- Walk-in-Date: 30th of May, 2016
- Time: 11:00 am onwards
You can go through the Official Notification to get more details about BPS Mahila Vishwavidyalaya Recruitment 2016. You can collect all the latest updates related to Bhagat Phool Singh Mahila Vishwavidyalaya Recruitment by subscribing our free e-mail service or you can like us on face book and follow us Google Plus.
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Today's Devotional
Powerful PatienceHow can you trust God more even as you wait on Him?
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Hey everybody. My name is Mirek, I'm seventeen and I'm going to trade school. Live in VsetÃÂn, in Czech Republic (small city near Slovakia). My hobbytes are God, music, friends, and more ... :) And I play in one worship band called ''3M''.
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Who do you think is the most beautiful person in the World?
hard to say but the most beautiful girl i know irl is already taken :^(
What kind of music calms you down?
calm indie rock/alt rock/orchestral/chillstep/chiptune/vaporwave music
What's your favorite font?
holy shit this is a great question for me. i occasionally work with fonts when doing shit in photoshop for civ mods, and i always greatly enjoy fucking around with fonts. i'm more of a standard and conservative type of guy when it comes to fonts, so nothing overly fancy.
serif: new aster is to times new roman what helvetica is to arial - a better look for a default and overused font while still remaining similar enough to be easily recognised and familiar. i love its italics and its capital letters with their wide diagonal strokes. i also like garamond and trajan.
sans serif: i'm going to say the same as before, helvetica is always a safe bet. i like it so much that whenever i see arial text on display anywhere, i think "oh man, that would look much better with helvetica". i also have a thing for the google fonts - roboto and open sans look great. i also quite like good ol' franklin gothic and myriad. i haven't really used akzidenz grotesk much but i think it looks fine too.
slab serif: roboto slab.
calligraphy: pristina is my default calligraphic font, but for a less detailed and decorated look i tend to go with ar cena.
jesus this was a long reply. im not even a graphic designer
Which foreign country do you dislike the most?
sweden probably, and i like to ironically hate on czechs
Which is your favorite TV show?
got but house of cards comes close
(those are the only two i've ever closely followed)
What's the last photo you took on your phone? Post it!
i would but it has my full name on it so have this second last pic i took


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High school girls basketball: Sophomore Abbie O'Neill's confidence leads Weber to success vs. Davis
KAYSVILLE — There was a time when Abbie O'Neill was afraid to take an open shot.
"The beginning of the season it was kind of hard to do," said the sophomore, who hit some huge shots for the Weber Warriors as they kept their playoff hopes alive with an upset of No. 5 ranked Davis, 46-45, Tuesday night. "But I was not very good."
Rather than simply let a more experienced teammate carry the load, however, O'Neill said she spent her Saturdays this winter fixing what wasn't working.
"Every Saturday we have practice or shootaround, and I just shot and shot and shot," she said. "I set specific goals every week and just tried to work on those things."
The result has been a better shot and more confidence — things that are critical if the Warriors hope to make it to the 5A state tournament.
Before players like O'Neill, Hannah Charlton and Maren Ulrich started to play with confidence, the offensive load was carried by the team's only returning varsity starter — Dixie-bound Erin Reichle.
She was averaging nearly 20 points a game, but the Warriors were struggling to stay above .500. That's because all defenses had to do was slow Reichle and they could beat the Warriors.
That strategy doesn't work anymore.
"Davis tried a box-and-one (defense) on us, and then (Charlton) hit a 3. And then we spread it out a little more and hit a layup. And then we're out of it," said head coach Rick Stoeckl. "The other girls stepped up, and that's what we need to do."
The problem at the beginning of the year — for many teams — is that when young players struggle, it can hurt their confidence. In turn, those inexperienced players are less willing to shoot — even if they're wide open.
O'Neill admitted she felt badly when she missed.
"I'm pretty hard on myself," she said.
But she and her teammates did something about it.
Reichle isn't just grateful the players around her worked hard and now take risks in games, she's proud of them.
"It's so much easier when everyone steps up," said Reichle, who led all scorers with 23 points. "I feel like it's just a confidence thing, and they've gotten stronger throughout the season."
She praised O'Neill's defensive efforts as much as her offensive help. The sophomore finished the game with eight points, but one of those was a layup that tied the game with 2:35 left in the fourth quarter. Then Reichle's 11-for-13 shooting from the free-throw line — with seven of those coming in the fourth quarter — sealed the win for the Warriors.
Charlton finished with nine points, while Reichle earned six steals. Ulrich had five rebounds, while Hanna Buswell grabbed seven rebounds.
Davis led most of the first quarter until Reichle tied it up with a 3-point shot just before time expired. Weber led most of the second quarter until Morgan Hartvigsen hit a 3-point shot just five seconds before halftime to give the Darts a 25-24 lead.
Points were tough to come by for both teams in the third quarter with Weber leading 32-30 heading into the fourth quarter.
Stoeckl said the fact that the Warriors led and managed to earn the victory after letting so many fourth-quarter leads slip away this season was reason to believe the team could accomplish its goal of winning the remaining region games on its schedule.
"We have to win to make the tournament," he said. "I'm as proud of this team as I've been of any team I've had."
Jessica Richardson led the Darts with 18 points and three blocked shots. Natalie Mecham added 11 points, while Hartvigsen added 8 points.
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X-Men-Based "Legion" Ordered to Series on FX
TV, Comic Books
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Writer Jason McNamara will be at Mission Comics (3520 20th Street between Mission and Valencia) in San Francisco tonight for a pre-release party for The Martian Confederacy: From Mars With Love, which comes out next week.
"Specialty cocktails will be spilled, books will be signed and an author will read" he said in an email. "Come and be dazzled by my tales of child slave labor and horny robots."
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Free G Gundam MP4 Video Download
clips from the series G Gundam Take This! MY LOVE! MY ANGER! AND ALL MY SORROW! SHINNING FINGER!!
First opening to the Super Robot Gundam anime.
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Domon Kasshu, pilot of Shining Gundam, vs Argo Gulskii, pilot of Bolt Gundam in Neo Russia.
G Gundam - Shining Gundam's True Form
Domon is tricked into thinking his brother, Kyoji and his Dark Gundam, are back. Which in turn leads to Domon revealing Shining Gundam's true form.
The last moments of Domon's brother, Kyoji. Episode 44: Schwarz Rests in Grace! Domon's Tearful Attack.
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A short clip from episode 48 of G Gundam when the Shuffle Alliance members attack the Dark Gundam together. I do not own the rights to Mobile Fighter G ...
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Neo Japan's Domon Kasshu and Burning Gundam face off against Neo Sweden's Allenby Beardsley and Noble Gundam.
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Full version of first G Gundam opening Also, check out League of Legends! Link: http://signup.leagueoflegends.com/?ref=4eb618edc6021793248612.
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The death of Master Asia. Episode 45.
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Wow, I didn't know it was so involved!
You could say to a chick, "The blue whale is the largest animal to ever live. Heh, second largest actually. Let me know you the first." and then you unzip your fly. Heh.
You mean, uhm, rape?
Now this is something we can all get behind.
My goal: Get herpes from a stranger.
Obviously, the first thing necessary to getting back in shape is buying a bunch of expensive knick-knacks.
Finally, a look at the candidate's long-delayed tax returns.
fall in! *takes hit from a bong hanging around his neck*
Welcome to the interactive movie experience of the cyber-future!
Wtf are these weird things you people keep posting. are they a kind of pastry
There are hundreds of stories happening on the World Wide Web. Let me tell you, that's a very wide web. Our goal at Weekend Web is to bring you the latest headlines from around the Internet. We go into the very bowels of message boards everywhere and find out what millions of online citizens have to say.
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Toshiba OCZ RD400 PCIe NVME SSD Review
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Guru3D Rig of the Month - April 2016
ADATA S107 Superior USB flash drive review
by Hilbert Hagedoorn on: 03/16/2012 11:39 AM | Source | 0 comment(s)
It's time for another stick drive review here at Guru3D.com, well a USB 3.0 stick that is. The storage unit is all USB 2.0 and 3.0 compatible and it's called the S107 from ADATA's 'SUPERIOR' series of products. We test an easy to carry around 16GB version of this model. This flash drive will get you read performance of roughly 100MB/sec peaks while writing just under 25 MB/sec.
Check out the article right here.
« Deepcool GAMMAXX 400 budget CPU cooler · ADATA S107 Superior USB flash drive review · MSI R7970 Lightning Graphics Card Announced »


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Non-fiction writer and journalist Charles Bowden has died, aged 69.
Bowden was found dead at his home in Las Cruces, New Mexico on Saturday (30Aug14). His close friend Ray Carroll says he had been battling an undisclosed illness and died in his sleep.
The writer penned a number of non-fiction books including Murder City, Down by the River, and Blues for the Cannibals. He was awarded the Lannan Literary Award for Nonfiction in 1996, and also received a United States Artists Fellowship Grant for Artistic Excellence in 2010 in recognition of his work.
Bowden was also an investigative journalist who contributed to magazines including GQ, Esquire, and Mother Jones. He spent a decade writing about drug wars in the Mexican border city of Juarez.
An autopsy will be carried out at the University of New Mexico in Albuquerque.
A sleekly made thriller with a sparky sense of humour, this is also a rare action movie that has something important to say.
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'Dunkirk' is slated for a July 21st, 2017 release, and features an impressive and growing cast list.
Here's a test I use when I review a kid friendly movie like On the...


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We will be in St. Thomas on a cruis ship stop, and want to go to St. John to snorkel. Does anyone know what time the last ferry is from St. John to St. Thomas - our ship leaves at 6 PM. Do we need to go to Red Hook to get the ferry? Thanks!
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Ferry from St. Thomas to St. John
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The ferrys leave Red Hook 6:30 AM & 7:30 AM, then hourly 8:00 AM to midnight.
They leave Cruz Bay back to St. Thomas hourly 6:00 AM to 11:00 PM.
The ferries to St. John leave downtown Charlotte Amalie 9:00 AM, 11:00 AM, 1:00 PM, 3:00 PM, 4:00 PM & 5:30 PM.
To return to downtown Charlotte Amalie from St. John, they leave Cruz Bay at 7:15 AM, 9:15 AM, 11:15 AM, 1:15 PM, 2:15 PM & 3:45 PM.
The fare to Cruz Bay is $3.00 one way (plus taxi to Red Hook & takes about 20 minutes on the ferry) & from downtown Charlotte Amalie is $7.00 one way & takes approximately 45 minutes.
I'd recommend the downtown ferry. Prettier ride. The drive to Red Hook shows the ugliest part of St. Thomas which is NOT the whole island.
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Those rates are now $5 each way from Red Hook an $10 from downtown. I too recommend downtown. You can catch the 9:00 or 11:00 over and the 3:45 back and be back by 4:30 with ample time for some last minute shoppig and getting back to your ship.
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The rates did go up? Thanks for the correction. I haven't been over there with the parking situation in Red Hook.
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Thanks so much for your help! We'll try downtown this time!
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no question, use the charlotte amalie ferry right in town. although the ride is longer (it really only took 1/2 hour) wouldn't you rather be on a boat?
both ferries put you right in town cruz bay same place and you can hook up a trip there.
ferries run a little less frequently so be aware. only on the hour. it will work for you tho i am sure. get there early! if you catch the 3:45 you'll be in charlotte amalie no later than 4:30 plenty of time to get to the boat. but not enough time to shop if that is something you are interested in....
suggest you try and stay away from trunk bay, really crowded.
you may want to hook up with a boat that takes you to many beaches. just make sure one of the destinations is honeymoon beach it's part of caneel bay....
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Petrol gauge broken, anxiety and tempers flaring, young lovers Rex and Saskia pull in at a service station on their way to a holiday the South of France to refuel. As soon as they stop the tension is relieved. Rex buries two coins in a crack at the base of fence post as a secret sign of their love: Saskia goes off to buy a couple of cold drinks and vanishes. Eight years later Rex is still haunted by her. Then one day he sees scrawled in the grime on the roof of a yellow car parked below his window two lines: REX YOU ARE SO SWEET SANDRA and WHEN I WRITE THIS IT SHOWS THE PAIN...And the obsession rages in his blood again.
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|Bloomsbury Publishing
|Publication date
|1 Sep 2003
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When/If TSHTF, if you have the ability to stay away from cities, don't underestimate...ATTN PREPPERS
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Some of you (like me) have places to get to away from populated areas. Some of you are good woodsmen and women. Hunters etc...Prepers.
Even if you have all your preps in order. Land to grow food. Protection. Hunting and fishing gear. Gas and stabilizer. Everything you could think of to keep you and your family going strong when/if TSHTF, never underestimate the value of a pack of hounds. The more the merrier.
I have 2 beagles, a 7 year old female with 7 good years in the woods tracking, and a 1 year old pup who has been in the woods a great number of hours since she was 8 weeks old.
I'll try to get at least 2 more if TSHTF. Together as part of your family they offer hunting companionship, protection, an alert system, and a 1st line of defense. If you treat them well they'll always be there for you.
If you can, get some hounds.
I've had beagles and they are barkers, I guess like most hounds.
I guess if you can train them to be quiet when you need them to be it'll be ok.
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|By PR Newswire
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|August 4, 2014 11:00 AM EDT
SAN FRANCISCO, Aug. 4, 2014 /PRNewswire/ -- Kabam, the leader in the western world for free-to-play games for traditional players, today announced it is working with Undead Labs to develop Moonrise, a brand new role playing game (RPG) for mobile devices.
This deal marks the first time the two companies have jointly worked on a game. Kabam is a global leader in the free-to-play and mobile games space creating such durable franchises as Kingdom of Camelot, Dragons of Atlantis, and The Hobbit: Kingdoms of Middle-earth each generating more than $100 million in revenue. Undead Labs is the creator of State of Decay, the innovative zombie survival-fantasy game that was the fastest selling original game in Xbox Live Arcade history, and is now among the top sellers of all time on that platform.
"We are thrilled to partner with Undead Labs. They are a veteran team that has worked on some of the greatest game franchises of all time," said Aaron Loeb, senior vice president of Kabam's North American Studios. "Their new creation, Moonrise, will bring a truly next generation mobile experience to gamers around the world."
Seattle-based Undead Labs was founded by games industry veteran Jeff Strain, whose career in games includes work at Blizzard Entertainment programming for StarCraft and Diablo, as well as forming the team that kicked off development of World of Warcraft. Following that, Strain co-founded ArenaNet, where he was programmer and executive producer for the Guild Wars franchise, which went on to sell more than six million units.
"We're a gamer-focused studio with a great console pedigree," said Strain. "And Kabam is a world-class publisher focused on delivering console-quality games on mobile devices. It's a perfect match."
Moonrise will have its worldwide debut at the PAX Prime convention on August 29, 2014 in Seattle, Washington.
With offices throughout the U.S. as well as China, South Korea, England, Germany and Canada, Kabam delivers hit games to global audiences to create long-lasting franchises. Kabam games are routinely among the top 25 grossing games on the app store charts, and in 2013 Kabam had two of the top ten grossing apps in the App Store. Kabam closed 2013 with more than $360 million in revenues, a 100 percent increase over 2012.
In addition to hit first-party games, Kabam is uniquely positioned in the free-to-play industry with deep Hollywood partnerships that produce hit mobile titles such as The Hobbit: Kingdoms of Middle-earth, Fast & Furious 6: The Game as well as the forthcoming The Hunger Games. Kabam also has a robust publishing platform that delivers hit games from Asia to global audiences.
For more information on Moonrise, visit http://moonrise-game.com/.
About Kabam
Kabam is the leader in the western world for free-to-play games for traditional players with first and third party published titles available on mobile devices via the Apple AppStore, Google Play, Amazon Appstore, and on the Web via Facebook, Yahoo, Kabam.com and other platforms. Kabam is the fastest growing Internet media company in the San Francisco Bay Area and the 17th fastest growing company overall in the U.S., according to Deloitte LLP. Kabam's 2013 revenues exceeded $360 million, a 100 percent increase over 2012. The company is profitable and cash-flow positive, and has created four titles that have grossed more than $100M each in their life. Kabam's Kingdoms of Camelot franchise has grossed more than $250 million in less than four years, making it one of the top 10 strategy franchises of all time. In 2012, Kabam Publishing was launched to provide third-party developers access to Kabam's technology platform, distribution channels, data analytics and best practices that Kabam's in-house development teams use to bring wildly successful games to players worldwide. Kabam has approximately 800 employees around the world, with corporate headquarters in San Francisco. The company's investors include Google, Warner Brothers, MGM, Intel, Canaan Partners, Redpoint Ventures, Pinnacle Ventures and others. More information about Kabam can be found at www.kabam.com.
About Undead Labs
Undead Labs was founded in late 2009 by Jeff Strain around a crew of talented industry veterans, with a mission to take online gaming in bold new directions. The Lab's first game, the zombie survival-fantasy title, State of Decay, launched in June 2013 and sold over a million copies in its first six weeks, making it the fastest selling original IP ever to hit Xbox Live Arcade. Based on the continued success of the game, in January 2014 Undead Labs and Microsoft announced a multi-year, multi-title partnership to continue development of the State of Decay franchise. A second partnership with leading mobile publisher Kabam was announced in August of 2014, with the beautiful new role-playing game Moonrise coming soon to mobile devices.
To learn more about Undead Labs, please go to http://undeadlabs.com.
Media Contacts:
Carol Quito
[email protected]
(415) 987-7611
Undead Labs
Sanya Weathers
[email protected]
Alex Munson
Rogers & Cowan
[email protected]
(310) 854-8297
SOURCE Kabam
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Bring a buddy.4.52 / 5.00 7,556 Views
Yugi and Kaiba have a duel to the death!4.49 / 5.00 28,762 Views
Vampire Pixel in the Neon City
A sinister deal is underway in the underbelly of Neon City4.38 / 5.00 9,617 Views
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'Jeopardy!' Hero Villain Arthur Chu's Winning Streak Ends
"Jeopardy!" controversial contestant Arthur Chu has finally lost the game after 11 straigth wins. The so-called hero villain's winning streak ended on Wednesday, March 12, but he managed to take home $297,200 from his 12-day reign.
Chu was defeated by Diana Peloquin of Ann Arbor, Mich. who won $15,700 that day. She's the only contestant with the right answer, George VI, for the question: "He was the last male monarch who had not previously been Prince of Wales."
Chu only had 6,400 in winnings heading into Final Jeopardy and bet it all for the said question. He came in third at the end of the game with zero dollars. He then took to Twitter to express his feelings after the episode aired, writing, "Thanks for all the love guys. Hard to be hurt by a loss I've known was coming since Nov but seeing others react makes it real again."
He also commented on the winner, Peloquin, "Also Diana is beginning to remind me of Katniss Everdeen and that raises my villain hackles," adding, "Diana is dangerously cute. Cute girls always worry me."
He went on announcing his upcoming TV appearance on "Good Morning America" and "Live! with Kelly and Michael", tweeting, "Just have to console myself with being a guest on #GoodMorningAmerica and #LiveWithKellyAndMichael and almost $300,000."
Chu ranks third in total cash won in the history of the game show. He made headlines in early February for his untraditional strategy, including jumping around the board to find the Daily Doubles instead of completing categories from top to bottom.
Ken Jennings, who competed on the show in 2004, still holds the record for the most win, 74 consecutive victories with total cash winnings of $2.5 million.
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text:
|Publication number
|Publication type
|Application number
|Publication date
|Dec 4, 2007
|Filing date
|Jan 7, 2004
|Priority date
|Mar 10, 2003
|Fee status
|Also published as
|Publication number
|Timothy R Littlefield, Jeanne K Pomatto, George E. Kechter
|Original Assignee
|Cranian Technologies, Inc
|Export Citation
|BiBTeX, EndNote, RefMan
|Patent Citations (13), Non-Patent Citations (11), Referenced by (24), Classifications (21), Legal Events (5)
|External Links: USPTO, USPTO Assignment, Espacenet
The following related patent applications are being filed on even date herewith and are assigned to a common assignee: Ser. No. 10/753,013. Cranial Remodeling Device Database by T. Littlefield and J. Pomatto now U.S. Pat. No. 7,177,461: Ser. No. 10/753,118, Automatic Selection of Cranial Remodeling Device Configuration by T. Littlefield and J. Pomatto now U.S. Pat. No. 7,242,798: Ser. No. 10/735,006. Automatic Selection of Cranial Remodeling Device Trim Lines by T. Littlefield and J. Pomatto now U.S. Pat. No. 7,127,101: Ser. No. 10/752,800. Cranial Remodeling Device Manufacturing System by T. Littlefield and J. Pomatto now U.S. Pat. No. 7,142,701. The disclosures of the above-identified applications are incorporated herein.
This invention pertains to a system and method for producing data representative of desired three dimensional shapes from corresponding data representative of first three dimensional shapes, in general, and to a system and method for automatically producing data representative of a modified three dimensional head shapes from corresponding data representative of a deformed head shape.
Cranial remodeling is utilized to correct for deformities in the head shapes of infants. Prior to the development of the Dynamic Orthotic CranioplastySM method of cranial remodeling by Cranial Technologies, Inc, the assignee of the present invention, the only viable approach for correction of cranial deformities was surgical correction of the shape of the cranium. Dynamic Orthotic CranioplastySM utilizes a treatment program in which a cranial remodeling band is custom produced for each infant to be treated. The band has an internal shape that produces the desired shape of the infant's cranium.
In the past, the cranial remodeling band was produced by first obtaining a full size and accurate model of the infants actual head shape. This first model or shape was then modified to produce a second or desired head shape. The second or desired head shape is used to form the cranial remodeling band for the infant. The first shape was originally produced as a cast of the infant's head. The second shape was similarly produced as a cast of the head. In the past the second or desired shape was obtained by manually modifying the first shape to form the desired shape.
Various arrangements have been considered in the past to automate the process of producing cranial remodeling devices. In some of the prior arrangements a scanner is utilized to obtain three dimensional data of an infant's head. Such arrangements have the disadvantage in that scanners cannot obtain instantaneous capture of data of the entirety of an infant's head. In addition, it is proposed to utilize expert systems to operate on scanned data to produce an image of a modified head shape from which a cranial remodeling device may be fabricated. However, because each head shape is unique, even the use of an expert system may not present an optimized solution to developing modified shapes suitable for producing a cranial remodeling device.
In accordance with the principles of the invention, a method for automatically producing data representative of a desired head shape from data representative of a deformed head is provided. The method includes a step of processing captured data for the deformed head utilizing Principal Component Analysis (PCA) to generate PCA data representative of the deformed head. The method also includes the steps of providing the PCA data as input to a neural network; and utilizing the neural network to process the PCA data to provide data representative of a corresponding modified head shape.
In accordance with another aspect of the invention the method includes training the neural network to generate data representative of deformed shapes to corresponding modified shapes
In the illustrative embodiment of the invention the captured data comprises first data points represented as Cartesian coordinates. The first data points are utilized to produce second data representative of lengths of rays of predetermined orientation.
The number of second data points is chosen to be a predetermined number, n. An n�n covariance matrix is computed. Eigenvalues and eigenvectors are computed for the covariance matrix. The eigenvectors for a predetermined number of the largest eigenvalues are selected to define PCA shapes as the PCA data. In the illustrative embodiment the predetermined number of the largest eigenvalues is 64.
A system for automatically producing data representative of a modified head shape from data representative of a deformed head, in accordance with the principles of the invention comprises one or more processors operable to process captured data for a deformed head utilizing Principal Component Analysis (PCA) to generate PCA data representative of the deformed head. The one or more processors are further operated to provide the PCA data as input to a neural network. The one or more processors are operated to utilize the neural network to process the PCA data to provide data representative of a corresponding modified head shape.
In accordance with one aspect of the invention the one or more processors operates to train the neural network to generate data representative of modified shapes for corresponding deformed shapes.
In one embodiment of the invention a first database comprises a first plurality of first sets of captured data, each first set comprising captured data for a corresponding one first head shape. A second database comprises a second plurality of second sets of captured data; each second set comprises captured data for a modified head shape for a corresponding one of the first head shapes. A processor is operable with a neural network program to train the neural network program with the first plurality of first sets of captured data and the second plurality of second sets of captured data such that the neural network operates on a set of captured data for a first head shape to produce a corresponding modified head shape.
In accordance with an aspect of the invention a support vector machine program is operated to train the neural network program. In the illustrative embodiment of the invention the support vector machine comprises a least squares support vector machine.
In accordance with other aspects of the invention, the method and apparatus produce data representative of a modified three dimensional shape from data representative of a first three dimensional shape.
In accordance with another aspect of the invention, a database for use in generating PCA coefficients and for training a neural network comprises a first plurality of first sets of captured data, each comprising captured data for a corresponding one first head shape; and a second plurality of second sets of captured data each comprising captured data for a modified head shape for a corresponding one of said first head shapes.
In accordance with another aspect of the invention each first set of captured data is obtained from a capture of a cast model of a first head shape. Each second set of captured data is obtained from a capture of a cast model of a corresponding modified head shape.
In accordance with another aspect of the invention the captured data for each first set of captured data comprises a plurality of Cartesian coordinates each corresponding to a point on the corresponding one first head shape. The captured data for each second set of captured data comprises a second plurality of Cartesian coordinates each corresponding to a point on a corresponding one modified head shape.
In accordance with another aspect of the invention, captured data for each first set of captured data comprises a plurality of ray lengths defining the three dimensional shape of the corresponding first head shape. Captured data for each second set of captured data comprises a plurality of ray lengths defining the three dimensional shape of the corresponding modified head shape.
In accordance with another aspect of the invention captured data of each first set is converted from one coordinate system into data comprising a plurality of ray lengths defining the three dimensional shape.
The invention will be better understood from a reading of the following detailed description taken in conjunction with the drawing figures in which like designations are utilized to identify like elements, and in which:
Turning to
An additional unique resource is that databases of additional information corresponding to each infant have been developed by the assignee of the present invention. That database includes information that identifies the type of cranial remodeling device for each infant head shape as well as the style of the cranial remodeling device and features selected for incorporation into the cranial remodeling device to provide for appropriate suspension and correction of the deformity. Still further, each cranial remodeling device has trim lines that are uniquely cut so as to provide for appropriate suspension and functionality as well as appearance. A further database developed by the assignee of the present invention has trim line data for each-cranial remodeling device that has been previously fabricated corresponding to the casts for unmodified heads.
In a first embodiment of the invention, shown as system 200 in
In accordance with one aspect of the invention, each unmodified or first head shape is digitally captured at step 005 as shown in
In accordance with one aspect of the illustrative embodiment the first and second data stored comprises Cartesian coordinates for a plurality of points on the surface of the corresponding shape.
In the illustrative embodiment shown in
In accordance with an aspect of the illustrative embodiment of the invention, neural network 300 is "trained", as explained below, so that captured data of a first or unmodified shape 301 shown in
Turning to
The data from each captured first and second image is represented using the same number of data points. In addition, all captured images are consistently aligned with each other.
The captured data for all head shapes represented in the databases 203, 205 of are aligned in a consistent way. The consistent alignment or image orientation has two separate aspects: alignment of all captured modified images with each other as shown at step 405; and alignment of each unmodified captured image with the corresponding modified captured image as shown at step 407. Alignment of unmodified and modified captured images ensures that the neural network will consistently apply modifications. Alignment of the modified captured images with one another allows PCA to take advantage of the similarities between different cast shapes.
The casts from which the captured images are obtained do not include facial details. Typically the face portion is merely a plane. The position of the face plane is really a result of deformity. To align the shapes a manually iterative visualization process is utilized. This approach "solved" half of the alignment issue—aligning all of the modified images with each other so that the principal components analysis could take advantage of the similarities between these shapes.
To align each unmodified captured image with its corresponding captured modified image, alignment of the face planes is was utilized. For the most part, the face planes represent a portion of the shapes that are not modified from the unmodified to the modified head shape and provide consistency. An additional attraction to this approach is that there on the head actual casts, there is writing on the face planes and this writing is visible in the texture photographs that can be overlaid onto the captured images. Alignment of face planes and writing provides a precise registration of the unmodified and modified captured images.
An automated approach to this alignment was developed using several of the first captured images. The automated alignment works where texture photographs are well focused and clear. In other cases automated alignment is supplemented with alignment by selecting "freehand" points on both the modified and unmodified images using commercially available software and then using the registration tool of that software. This approach aligned all unmodified captures with modified captures so that corrections would be consistently applied.
The capture data for both the unmodified and modified head shapes are normalized at step 409 for training the neural network. As part of the normalization, a scale factor is stored for each for each normalized head or shape set.
As indicated at step 411, PCA is utilized with the aligned shapes to determine PCA coefficients. Because PCA uses the same set of basis vectors (shapes) to represent each head (only the coefficients in the summation are changed), each captured image is represented using the same number of data points. For computational efficiency, the number of points should be as small as possible.
The original digitized data for first and second shapes stored in databases 203, 205 represent each point on a surface using a three-dimensional Cartesian coordinate system. This approach requires three numbers per point; the x, y, and z distances relative to an origin. In representing the head captures, we developed a scheme that allows us to represent the same information using only one number per data point. Mathematically the approach combines cylindrical and spherical coordinate systems. It should be noted that to obtain three dimensional representations of an object, a spherical coordinate system may be utilized. However, in the embodiment of the invention, the bottom of the shape is actually the neck of the infant, and is not of interest.
Conceptually this approach is similar to a novelty toy known as "a bed of nails." Pressing a hand or face against a grid of moveable pins pushes the pins out to form a 3D copy on the other side of the toy. This approach can be thought of as a set of moveable pins protruding from a central body that is shaped like a silo—a cylinder with a hemisphere capped to the top. The pins are fixed in their location, except that they can be drawn into or out of the central body. Using this approach, all that is needed to describe a shape is to give the amount that each pin is extended.
To adequately represent each head shape, a fixed number of approximately 5400 data points are utilized. To represent each of the captures using a fixed number of data points, the distance that each "pin" protrudes is computed. This is easily achieved mathematically by determining the point of intersection between a ray pointing along the pin direction and the polygons provided by data from the infrared imager. A set of such "pins" were selected as a reasonable compromise between accuracy of the representation and keeping the number of points to a minimum for efficiency in PCA. The specific set of points was copied from one of the larger cast captures. This number was adequate for a large head shape, so it would also suffice for smaller ones. A commercially available program used to execute this "interpolation" algorithm requires as input the original but aligned capture data and provides as output the set of approximately 5400 "pin lengths" that represent the shape.
Using the consistent alignment and the consistent array of data points described above, the normalized data for each captured cast was interpolated onto this "standard grid." Computing the covariance matrix produced an n�n matrix, where "n" is the number of data points. As the name implies, this covariance matrix analyzes the statistical correlations between all of the "pin lengths." Computing the eigenvalues and eigenvectors of this large covariance matrix provides PCA basis shapes. The PCA shapes are the eigenvectors associated with the largest 64 eigenvalues of the covariance matrix. This approach of computing basis shapes using the covariance matrix makes optimal use of the correlations between all the data points used on a standard grid.
PCA analysis allows cast shapes to be represented using only 64 PCA coefficients.
Neural networks are an example of computational tools known as "Learning Machines" and are able to learn any continuous mathematical mapping.
As those skilled in the art will understand, learning machines such as neural networks are distinguished from expert systems, in which programmers are utilized to program a system to perform the same branching sequences of steps that a human expert would perform. In effect, an expert system is an attempt to clone the knowledge base of an expert, whereas, a neural network is taught to "think" or operate based upon results that an expert might produce from certain inputs.
Neural network 300 is constructed of computational neurons each connected to others by numbers that simulate strengths of synaptic connections. These numbers are referred to as "weights."
Training refers to modification of weights used in the neural network so that the desired processing task is "learned". Training is achieved by using PCA coefficients for captured data representative of unmodified casts of infant heads as inputs to the network 300 and modifying weights of hidden layers until the output of the neural network matches PCA coefficients for the captured data representative of corresponding modified casts. Repeating this training thousands of times over the entire set of data representing the unmodified and corresponding captured shapes produces a neural network that achieves the desired transformation as well as is statistically possible. In the illustrative embodiment, several hundred pairs of head casts were utilized to train neural network 300 at step 415.
Testing on additional data from pairs of casts that the neural network was not trained with, or "verification testing", was utilized in the illustrative embodiment to ensure that the neural network 300 has learned to produce the appropriate second shape from first shape captured data and has not simply "memorized" the training set. Once this training of neural network 300 is complete, as measured by the average least squares difference between the PCA coefficients produced by the network and those from the modified cast shapes, the PCA coefficient weights are "frozen" and the network is simply a computer program like any other computer program and may be loaded onto any appropriate computer.
A commercially available software toolbox was used to develop the learning machine. The particular type of learning machine produced is called a Support Vector Machine (SVM), specifically a Least Squares Support Vector Machine (LS-SVM). Just like the neural networks described above, the SVM "learns" its processing task by modifying "weights" through a "training" process. But in addition to weights, the LS-SVM requires a user to specify "hyper parameters." For the radial basis function (RBF) type of LS-SVM used in this work, there are two hyper parameters: � (gamma) and � (sigma). The relative values of these parameters control the smoothness and the accuracy of the processing task. This concept is very similar to using different degree polynomials in conventional curve fitting.
An unmodified or first shape represented by captured data is processed utilizing a principal components analysis algorithm. The resulting PCA representation is processed by a neural network 300 to produce a second or modified PCA representation of a modified shape.
In the system of the illustrative embodiment of the invention cross-validation was used to choose the hyper-parameters. Data is randomly assigned to four groups. Three of the four groups were used to train the LS-SVM, and the remaining set was used to measure the performance in predicting the PCA coefficients for the modified head shapes. In turn, each of the four groups serves as the test set while the other three are used for training. The group assignment/division was repeated two times, so a total of eight training and test sets were analyzed (four groups with two repetitions). This process was repeated for a grid of (�, �)-pairs ranging from 0-200 on both variables. The range was investigated using a 70�70 grid of (�, �)-pairs, so a total of 4900 neural nets were tested for each of the first 38 PCA coefficients. From this computationally intensive assessment, hyper-parameters were determined and validated for the first 38 PCA coefficients and extrapolated those results to select hyper-parameters for the remaining 26. Further "tuning" of the remaining 26 PCA hyper-parameters is unlikely to produce significant improvement in the final results because the first PCA coefficients are the most influential on the solution. The table shown in
Once hyper parameters were tuned, the LS-SVM models generally produced errors of less than two percent for the PCA coefficients of the modified casts in test sets (during cross-validation). Applying these tuned models to head casts that were not part of the cross-validation or training sets also generated excellent results.
There is a surprising variability of the head shapes as represented by cast shapes. Modified casts are not simply small changes to a consistent "helmet shape." Each is uniquely adapted to the unmodified shape that it is intended to correct. Being so strongly coupled to the unmodified shapes makes these modified casts surprisingly different from one another. Of the several hundred casts that we analyzed, each is unique.
Interpolating the aligned shapes also provided surprises and challenges. The "bed of nails" concept is very effective in reducing the size of the data sets and providing a consistent representation for PCA. It helps reduce the number of data sets that would have otherwise been required to train a larger neural network. Instead of representing each data point by three components, each data point is represented by one component thereby reducing the number of data sets significantly. By utilizing this approach, the process is speeded up significantly.
Returning to
Turning now to
System 800 includes a computer 801 which may any one of a number of commercially available computers. Computer 801 has a display 823 and an input device 825 to permit visualization of data and control of operation of system 800.
Direct head image capture is a desirable feature that is provided to eliminate the need to cast the children's head. An image capturing digitizer 202 is provided that provides substantially instantaneous image captures of head shapes. The digitized image 821 a is stored in a memory 821 by computer 801. Computer 801 utilizes a data conversion program 807 to normalize data, store the normalized data in memory 821 and its scaling factor, and to convert the normalized, captured data to "bed of nails" data as described above. Computer 801 stores the modified, normalized data 821 b in memory 821. Computer 801 utilizing an alignment program 809 to align modified data 821 b to an alignment consistent with the alignments described above and to store the aligned data in an unmodified shapes database 803. Computer 801 obtains PCA coefficients and weightings from a database 813 and utilizes neural network 300 and a support vector machine 817 to operate on the data for a first shape stored in memory 821 a to produce data for a modified or second shape that is then stored in memory 821 b. The data for the modified shape stored in memory 821 b may then be utilized to fabricate a cranial remodeling device or band for the corresponding head.
The invention has been described in terms of illustrative embodiments. It will be apparent to those skilled in the art that various changes and modifications can be made to the illustrative embodiments without departing from the spirit or scope of the invention. It is intended that the invention include all such changes and modifications. It is also intended that the invention not be limited to the illustrative embodiments shown and described. It is intended that the invention be limited only by the claims appended hereto.
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|U.S. Classification
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|Aug 13, 2004
Owner name: CRANIAL TECHNOLOGIES, INC., ARIZONA
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text:
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