This CD contains the music specially written by Edward Lee (who was at the time a Lecturer Guide) for various key occasions in the academic year of the EFL Section of the University of Westminster. Each term there was a "mini-festival" to celebrate the season, and it seemed a good idea to begin the programme with a Festival Overture.
The title track Westminster Reels was written to help lighten the wait which students had before their Induction, on arriving at the University.
festivals - among the most famous are Brahms' Academic Festival Overture I and Vaughan Williams' The Wasps. Here on the University of Westminster Cultural Programme we do not have composers of that calibre, but we do have a professional musician, and, as the old saying goes, 'A cat may look at a king!' So for the Summer Festival, 2003, Edward Lee wrote a special piece. This was followed by music for the Christmas Festival, in December 2003, for the Easter Festival in April 2004, and for the Induction of April 2004.
I am happy to see that the music has now been brought together on this CD. I hope that it will bring back happy memories of your time at Westminster. I also hope that you will find, as I have, that this is beautiful and stimulating music in its own right .
At the Inductions there is always a period while waiting for late arrivals from the testing procedure. Westminster Reels was written to entertain new students during that wait.
Basically it is a dance piece in the style of a Scottish Reel. This acknowledges the Scots ancestry of Eileen McConnell. It is meant to suggest the atmosphere during one of the Cultural Programme walks.
The piece begins with the first reel, Mistress McConnell's Romp. which is followed by the entry of Senior Guide, Reg Parks. As he is a traditional Englishman, this is signalled by Rule Britannia. The music then moves into his reel, The Barley Brew of Bromley. This is played on the piccolo, a small lively instrument, which is very appropriate to the man for a variety of reasons.
Next follows the third reel, for guide Edward Lee. This is Dominie Edward's Dram. 'Dominie' was the traditional name for a schoolteacher in Scotland, and a dram is a short of whisky, of which Edward is very fond. The three reels then combine as our heroes dance together.
Next the students set off on a walk; a steady pace is set (on the tuba) by Reg, which keeps going most of the time. The students chatter to each other. They come from different parts of the world, and at first everything seems fragmented or even chaotic, but gradually they blend together as a group until we hear the combination of their voices into Tower of Babel(the Students' Reel).
This is followed by the Chinese students. Being as ever very appreciative and forthcoming, they offer us a specially devised Shanghai Strathspey. (This is not real Chinese music, of the impressive sort which various students have introduced me to)but a comic sketch by an uninformed Englishman, which I hope conveys the happy times we have spent together. As it ends, the Spanish students, always romantic, break out into a lyrical phrase or too, and this sets off the Latin American students. They are typically very lively and cheerful in class, and so it is no surprise that they turn they walk into a party, dancing along the street in time with the steady tread of 'Mr Reg'.
By this time the listener's head may be reeling, which is the other reason for the choice of title. As the party comes toward the end of the walk, the Westminster staff join in again and we hear the various reels once more. They all dance together in a joyful polyphony.
The walk ends and, since we are in Westminster, we hear the chimes of Big Ben. At this point we realise why we have at various points heard the chime of bells. The whole piece is in fact based on the chimes of Big Ben (known as the Westminster Quarters). In fact in technical terms the piece is a set of variations on an ostinato (a repeated phrase) in the high register. An ostinato is normally played throughout a piece, but that would have been boring in this case (and the EFL section teachers are never boring!), so it is only implied for most of the time.
Although Big Ben is the name commonly applied to the clock and clock tower of the Palace of Westminster, strictly speaking it is the name of the biggest bell, which sounds the hour. It is this chime, which ends the piece, and announces the beginning of the Induction.
About the chimes: The tune is known as the Westminster Quarters. It is now the most popular melody used by a chiming clock. The chime of Big Ben is a set of variations by William Crotch (1775-1847) on the fifth and sixth measures of "I know that my redeemer liveth" by Georg Friedrich Handel, who included it in his work, The Messiah. According to tradition, the tune has words: "0 Lord our God/Be thou our guide/That by thy help/No foot may slide."
The Clock Tower of the Palace of Westminster in London is located at the northern end of the building. It is often colloquially referred to as "Big Ben", after the 13-ton bell that is its best-known feature.
This piece tries to capture the spirit of what for many English people is the most important holiday and festival of the year. It unashamedly looks back to tradition, the images of which surround us at this season.
The piece begins with a sense of expectation, and a touch of mystery. It is Christmas Eve, the time when young children excitedly look forward to the visit of Father Christmas. The house is decorated and the tinsel and tree decorations glitter in the light, while outside the night is cold and starry.
The music gives hints of the theme to come. Modal elements and the accordion suggest traditional English folk music.
The excitement builds, then midnight comes, a bell sounds, there is a moment of stillness and the sleigh bells of Santa Claus are heard in the distance.
Christmas Day comes and a warm theme in the traditional style is heard, with the accordion and tuba suggesting the old church bands and carol singers doing their rounds. The theme is repeated by a string orchestra, with a descant (the traditional Gloria) which reminds us of the church choirs and Festivals of Nine Lessons and Carols which are so popular at this time of year.
The subtitle is Italian to reflect the fact that I have used Italian musical styles as an inspiration. The Crucifixion was a Mediterranean event, and it has been said that the dark side of the ravishing beauty of the region is a long history of violence caused by the release of the most basic and powerful emotions. Thus in the piece you will hear the roar of the crowd calling for blood and the slow beat of the drum in an execution procession.
Christianity there has typically been highly charged and intoxicating to the emotions. Richly decorated churches are filled with music strongly influenced by the Italian love for emotional melody. The most famous manifestation of this has been the Neapolitan song, whose style I have used to express the grief of the women (especially his mother, the Virgin Mary) who witness the execution and later discover the empty tomb.
There is a further element which I include because I am not a Christian, writing for the Church, but an atheist, who sees the grief of this event more than the joy, and who sees a still vital lesson for us on the mutability of mass emotions - the cheering all too rapidly can turn to bloodlust. I also remember the occasions, well documented in Medieval Church records, when people did neither of these things, but largely unaware of and unconcerned with events on the great stage of the world, took the opportunity to let themselves go in a Saturnalia of wine, women and song.
So a folk band, less skilled than enthusiastic, and in the case of the trumpeter, drunkenly oblivious to being in the wrong key, weaves its way around the scene, past mourners. briefly halting the procession and disappearing into the distance for more wine and music. It marches to the beat of the deep bass drum we heard before.
The above is meant to be a piece of composed music with themes, but I enjoyed writing the main theme so much that I hve given it on a separate track.
This was the first piece written for the Cultural Programme Mini-festivals.
It sets out to evoke some of the uniquely English pleasures which can be enjoyed at this point in the year (providing at least that the weather is kind!).
It uses a string orchestra, modal elements and plenty of independent part writing. It certainly pays homage to the music of Vaughan Williams, whose music built on and celebrated our Elizabethan period, a time of great national pride. Like his music, this piece draws on traditional English folk elements. This seemed appropriate since an important motivation for overseas visitors is to experience the English (and British) heritage.


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List of Israeli universities and colleges
(Redirected from List of universities in Israel)
This is a list of universities and colleges in Israel. There are eight universities and several dozen[quantify] colleges in Israel, which are recognized and academically supervised by the Council for Higher Education in Israel. In addition, Israel founded a university in Ariel in the West Bank, which is recognized and academically supervised by the Council for Higher Education in Judea and Samaria. As many course offerings are varied, Israeli universities are considered to be of top quality, and they are inexpensive to attend.[1] Israel's quality university education is largely responsible for spurring the country's high tech boom and rapid economic development.[1]
The primary difference between a university and a college in Israel is that only a university can confer doctorate degrees, and therefore tends to be more research-oriented than the more teaching-oriented colleges.
UniversitiesEdit
Israel's universities are listed below, followed by their English acronym, establishment date, location, latest data about the number of students and the institute's academic rank of the top world universities, according to WebOMetrics[2] (top 3000), Shanghai Jiao Tong University (SJTU)[3] (top 500) and The Times Higher Education Supplement (THES)[4] (top 200):
Est. Date
World Academic Rank (WebOMetrics, SJTU, THES)
Technion - Israel Institute of Technology (IIT) 1912 Haifa 13,000 (2005) 101–150, 102–150, 301–350
Hebrew University of Jerusalem (HUJI) 1918 Jerusalem 22,600 (2003) 131, 59, 178
Weizmann Institute of Science (WIS) 1934 Rehovot 2,500 (2012) 346, 102–150, N/A
Bar-Ilan University (BIU) 1955 Ramat Gan 26,800 (2008) 570, 305–401, N/A
Tel Aviv University (TAU) 1956 Tel Aviv-Yafo 29,000 (2005) 266, 102–150, 201–250
University of Haifa (HU) 1963 Haifa 18,000 (2009) 510, 401–500, N/A
Ben-Gurion University of the Negev (BGU) 1969 Be'er Sheva 19,000 (2010) 448, 203–304, N/A
Open University of Israel (OPENU) 1974 Ra'anana 48,000 (2014) 1893, N/A, N/A
Ariel University1 (AU) 1982 Ariel 14,000 (2012) N/A, N/A, N/A
Most universities offer the full range of graduate and undergraduate degrees – bachelor's, master's and the doctorate. However, the Weizmann Institute does not offer the bachelor's degree and the Open University does not offer the doctorate.
CollegesEdit
ORT Braude College of Engineering
Other institutes of higher education that are accredited by CHEI to confer a bachelor's (and in some cases a master's) degree are known as colleges (Hebrew: מִכְלָלָה Mikhlala; pl. מכללות Mikhlalot). There are also over twenty teacher training colleges - most of which will award only the Bachelor of Education (B.Ed.).
The Colleges are:[5]
College of Law and Business, Ramat Gan
Academic College of Tel Aviv-Yafo
Afeka College of Engineering, Tel Aviv
Ashkelon Academic College
Beersheba Tehni School
Beit Zvi School of the Performing Arts, Ramat Gan
Bezalel Academy of Art and Design, Jerusalem
Carmel Academic Center, Haifa (Closed)
Center for Academic Studies, Or Yehuda
College of Management Academic Studies (COMAS), Rishon LeZion
Dan Academic Center, Petah Tikva
Kfar-Avraham Technology College, Petah Tikva (Closed)
Hadassah Academic College, Jerusalem
Haredi College of Jerusalem (Closed)
Holon Institute of Technology
Interdisciplinary Center Herzliya, Herzliya
Israel College of the Bible, Netanya
Jerusalem Academy of Music and Dance
Jerusalem College of Engineering
Jerusalem College of Technology
Jerusalem University College
Kinneret Academic College
Lander Institute, Jerusalem
Max Stern Academic College of Emek Yezreel
Mivhar College, Bnei Brak
Neri Bloomfeld School of Design and Education, Haifa
Netanya Academic College
Netanya Academic College of Law
Ono Academic College, Kiryat Ono
ORT Braude College of Engineering, Karmiel
Peres Academic Center, Rehovot
Reidman College, Tel Aviv, Jerusalem, Haifa, Beersheba, Eilat, Kinneret, and Misgav
Ruppin Academic Center
Sapir Academic College
Sam Spiegel Film and Television School, Jerusalem
Sami Shamoon College of Engineering, Beersheba and Ashdod
Sha'arei Mishpat College, Hod HaSharon
Shalem College, Jerusalem
Shenkar College of Engineering and Design, Ramat Gan
Tel-Hai Academic College
Western Galilee College, Acre
Yehuda Regional College, Kiryat Arba1
Zefat Academic College, Safed
The teacher training colleges include:[6]
Ahva Academic College, Ahva
Al-Qasemi Academic College of Education, Baqa al-Gharbiyye
Arab College for Education, Haifa
Beit Berl College, Kfar Saba
College of Technology Education,[7] Tel Aviv
David Yellin College of Education,[8] Jerusalem
Efrata College of Education,[9] Jerusalem
Emuna College of Education,[10] Jerusalem
Givat Washington College of Education, Givat Washington
Gordon College of Education, Haifa
Hemdat College of Education, Netivot
Herzog College, Alon Shvut1
Jerusalem College Michlala,[11] Jerusalem
Kaye Academic College of Education, Beersheba
Kibbutzim College,[12] Tel Aviv
Levinsky College of Education,[13] Tel Aviv
Lifshitz College of Education,[14] Jerusalem
MOFET Institute, Consortium of Colleges of Education
Moreshet Yaakov Religious College of Education,[15] Rehovot
Ohalo College, Katzrin (Golan Heights)1
Oranim Academic College, Kiryat Tivon
Ort College for Teachers of Technology,[16] Tel Aviv
Shaanan Religious College of Education,[17] Haifa
Talpiot College of Education, Tel Aviv
WIZO Haifa Academy of Design and Education, Haifa
1Located in the Israeli occupied territories
Foreign university campusesEdit
The following foreign universities maintain international branch campuses in Israel:
Brigham Young University – the BYU Jerusalem Center
Hebrew Union College-Jewish Institute of Religion
Of note, the prestigious NYU, which enrolls the largest number of Jewish students of any public or private university in the United States,[18] and is ranked in the top 34 globally in all major publications of university rankings, has a campus in Tel Aviv.
^ a b David Adler (10 Mar 2014). "Ambitious Israeli students look to top institutions abroad". ICEF. Retrieved 20 January 2015.
^ "Times Higher Education". Times Higher Education. Archived from the original on 2007-05-04.
^ Academic and Regional Colleges Israel Science and Technology
^ Teacher Training Colleges Israel Science and Technology
^ "דף זה אינו קיים". mictec.ac.il. Archived from the original on 2008-01-03.
^ Administrator. "לימודי חינוך והוראה - המכללה לחינוך דוד ילין". dyellin.ac.il.
^ "Colleges in Israel". science.co.il.
^ http://www.michlalah.edu/
^ "לימודי חינוך ואמנויות - סמינר הקיבוצים". smkb.ac.il. Archived from the original on 2009-05-25.
^ "מכללת לוינסקי לחינוך - לימודי חינוך". levinsky.ac.il.
^ "מכללת ליפשיץ". macam.ac.il. Archived from the original on 2012-12-18.
^ "אורות באזרחות". mor.ac.il. Archived from the original on 2010-02-12.
^ "שאנן - המכללה האקדמית הדתית לחינוך, חיפה". macam.ac.il.
Wikimedia Commons has media related to Universities and colleges in Israel.
The Council for Higher Education in Israel
Retrieved from "https://en.wikipedia.org/w/index.php?title=List_of_Israeli_universities_and_colleges&oldid=902687905"

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Zbyněk Irgl (* 29. listopadu 1980, Ostrava) je bývalý český hokejový útočník, který odehrál přes 600 utkání v extralize a přes 300 v KHL.

Od roku 2004 nastupoval za českou hokejovou reprezentaci. Svůj neslavnější gól dal v prodloužení čtvrtfinále proti Rusku na MS 2006.

28. listopadu 2014 mu byl nalezen nádor v ledvině. První extraligové utkání po návratu odehrál 4. 4. 2015 v pátém semifinalovém utkání proti HC Spartě Praha. Svou poslední profesionální sezónu 2020/21 odehrál za Vítkovice.

Zajímavosti
 22. října 2017 v domácím utkání proti HC Dukla Jihlava odehrál své 600. utkání v extralize.

Ocenění a úspěchy
 2005 ČHL/SHL – Utkání hvězd české a slovenské extraligy
 2007 NLA - Nejlepší střelec v playoff
 2009 KHL - Nejvíce vstřelených vítězných branek
 2012 KHL - Utkání hvězd

Prvenství

ČHL
 Debut - 20. září 1998 (HC Vítkovice proti HC Slovnaft Vsetín)
 První asistence - 24. ledna 1999 (HC Vítkovice proti HC ZPS Barum Zlín)
 První gól - 5. února 1999 (HC Vítkovice proti HC Velvana Kladno, brankáři Luboš Horčička)
 První hattrick - 17. února 2004 (HC Vítkovice proti HC Energie Karlovy Vary)

KHL
 Debut - 2. září 2008 (Salavat Julajev Ufa proti Lokomotiv Jaroslavl)
 První asistence - 4. září 2008 (Traktor Čeljabinsk proti Lokomotiv Jaroslavl)
 První gól - 12. září 2008 (Lokomotiv Jaroslavl proti HK Sibir Novosibirsk, brankáři Tomas Lawson)
 První hattrick - 22. listopadu 2011 (Barys Astana proti HK Dinamo Minsk)

Hráčská kariéra
 1998/1999 HC Vítkovice ELH, HC Femax Havířov (1. liga)
 1999/2000 HC Vítkovice ELH, HC Dukla Jihlava (1. liga)
 2000/2001 HC Vítkovice ELH, HC Slezan Opava (1. liga)
 2001/2002 HC Vítkovice ELH
 2002/2003 HC Vítkovice ELH
 2003/2004 HC Vítkovice ELH
 2004/2005 HC Vítkovice ELH
 2005/2006 HC Vítkovice Steel ELH
 2006/2007 HC Vítkovice Steel ELH, HC Davos (Švýcarsko) Mistr Švýcarské ligy
 2007/2008 Lokomotiv Jaroslavl (Rusko)
 2008/2009 Lokomotiv Jaroslavl (Rusko) (KHL)
 2009/2010 Lokomotiv Jaroslavl (Rusko) (KHL)
 2010/2011 Atlant Mytišči (Rusko) (KHL)
 2011/2012 HK Dinamo Minsk (Bělorusko) (KHL)
 2012/2013 HK Dinamo Minsk (Bělorusko) (KHL)
 2013/2014 HK Dinamo Minsk (Bělorusko) (KHL)
 2014/2015 HC Oceláři Třinec ELH
 2015/2016 HC Oceláři Třinec ELH
 2016/2017 HC Oceláři Třinec ELH –
 2017/2018 HC Oceláři Třinec ELH, HC Olomouc ELH (hostování)
 2018/2019 HC Olomouc ELH
 2019/2020 HC Olomouc ELH
 2020/2021 HC Vítkovice Ridera ELH
 2021/2022 MSK Orlová (Krajské hokejové přebory)

Reprezentace
První zápas v reprezentaci: 11. listopadu 2004 Česko – Finsko (Helsinky).

|-
| 1998
| Česko 18
| MEJ
|6||2||3||5||4
|- bgcolor="#f0f0f0"
| 2000
| Česko 20
| MSJ
|7||2||1||3||6
|-
| 2006
| Česko
| MS
|6||2||2||4||0
|- bgcolor="#f0f0f0"
| 2007
| Česko
| MS
|7||2||2||4||4
|-
| 2008
| Česko
| MS
|7||1||2||3||2
|- bgcolor="#f0f0f0"
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| Česko
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|-
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| Česko
| MS
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|- bgcolor="#e0e0e0"
! colspan="3" | Juniorská kariéra celkově
!13!!4!!4!!8!!10
|- bgcolor="#e0e0e0"
! colspan="3" | Seniorská kariéra celkově
!33!!7!!7!!14!!8
|}

Reference

Externí odkazy

Čeští hokejoví útočníci
Hokejisté KHL
Hokejisté Nationalligy A
Hokejisté Ruské superligy
Mistři světa juniorů v ledním hokeji
Čeští hokejoví reprezentanti
Hráči HC Třinec
Narození v Ostravě
Narození v roce 1980
Narození 29. listopadu
Žijící lidé
Muži
Hráči draftovaní Nashvillem Predators

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THE ROYAL TREATMENT!
Irish Castles & Countryside
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The Emerald Isle in Opulence
Get the royal treatment with this 6 or 8-night adventure through Ireland! Stay in luxurious castle accommodations and have access to your very own car, so you can see the sights without the stress. This is the Ireland you've been dreaming about!
Round-trip economy class airfare to Dublin (DUB)
Manual rental car for the duration of your trip- upgrade to automatic available
Pick up and drop off at Dublin Airport
2 nights in Cavan at Slieve Russell Hotel or Cabra Castle
1 night in the Roscommon area at Kilronan Castle or Lough Rynn Castle
2 nights in Ennis at Hotel Woodstock, or similar
1 night in Co. Meath at the Dunboyne Castle Hotel or in Co. Dublin at the Fitzpatrick Castle Hotel
Breakfast Daily
Want to add 2 nights in Dublin city? Choose the 8-night option!
Great Ways to Explore
Experience the breathtaking Irish countryside as you make your way from destination to destination.
Pay a visit to an ancient burial mound dating back over 3,000 years at Newgrange.
The 8-night option gives you the chance to explore Dublin, Ireland's capital city!
Live like royalty during your stay in each of the castles!
Treat yourself to a pint at Ireland's oldest pub, the Brazen Head in Dublin!
Experience the Emerald Isle in luxury! You'll start your adventure in Cavan, where you'll be surrounded by some of the country's most magical scenery. From elegant accommodations to cozy pubs, Cavan is a great home base to explore from.
The next leg of your journey is in breathtaking Roscommon, where you'll enjoy luxurious stays and adventurous days! Spend your time exploring the nearby castles, or enjoying a hike through the picturesque parks and forests. After one night, you'll drive to Ennis in County Clare. In Ennis, you'll be near the Cliffs of Moher, Bunratty Castle, Adare, and Limerick! From storybook castles to natural wonders, you'll be just a short drive from it all.
Your final night is spent in County Meath, in an elegant accommodation! You'll be just a short drive away from vibrant Dublin or prehistoric Newgrange - it's time for your Ireland adventure!
Show Detailed Itinerary
Please note that some package inclusions may be restricted or unavailable at time of travel. We will substitute them with similar options of equivalent value, as available.
Learn More about Cavan, Roscommon, Ennis, Dunboyne and Dublin
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Top Destinations in Ireland
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30 City Nicknames from Around the World
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Travel Guide to Dublin, Ireland
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Please note that some elements called out in the following suggested itinerary may not be included in your package. Check "What's Included" above for full details on inclusions. As we all go through the recovery together, the fluid nature of this event means some venues and attractions may have restrictions or closures. Please check online resources close to the time of travel to better plan your own day-to-day itinerary.
Day 1. Welcome to Cavan!
Arrive in Dublin and pick up your car at the rental area. Today you will make your way to Cavan, just an hour drive from the airport.
Enjoy the rest of the day at leisure and soak up the spirit of the area. Treat yourself to a delicious first-night meal and celebrate with a pint!
You'll be in one of these hotels, depending on availability
Slieve Russel Hotel Golf & Country Club
Cavan, Ireland
1 of 2 hotels
Enjoy a tranquil setting at Slieve Russel Hotel Golf & Country Club. Set on 300 acres of land, you'll find your experience to be positively relaxing! Guest rooms are furnished with free WiFi, coffee/tea making facilities, television, and more. Three onsite restaurants offer guests a variety of dining options featuring modern European flavors and light bites. Relax and unwind in the luxurious spa. Pamper yourself with a massage, body treatment, or time in the sauna. The hotel is proud to offer a variety of activities including golf lessons, a game room, a swimming pool, and more fun for the whole family!
Cabra Castle Hotel
From the moment you arrive through the acres of beautifully manicured gardens and parkland to this magnificent 19th century 4-star castle hotel, set in the Cavan countryside, you will feel welcomed and thoroughly spoiled. Set on 100 acres of parkland and garden, with the magnificent backdrop of Dun a Ri Forest Park, there is much to explore both indoors and out. Cabra Castle is one of the most unique luxury hotels in Ireland and is also one of the best-known castle wedding venues in Ireland.
Day 2. Cavan
Today's the perfect day to explore Ireland's vibrant capital city! Explore Dublin Castle, then head to the Trinity College Campus and consider paying a visit to the famous Book of Kells, followed by a walk in the library's iconic long room.
You might also want to tour the factories of your favorite Irish stouts and spirits, such as the Guinness Storehouse or Teeling Whisky Distillery. In the evening, treat yourself to a pint at Ireland's oldest pub, The Brazen Head - the perfect kickoff for your Irish adventure!
Lough Key Castle • County Roscommon
Day 3. Cavan - Roscommon
Today, you'll drive south to Roscommon. You may want to check out Roscommon Castle. Later, consider heading to Lough Key Forest, home to scenic bridges, 12th-century abbey ruins, and other historical and natural wonders.
Kilronan Castle Estate & Spa
Roscommon, Ireland
The Kilronan Castle in Roscommon is situated just 45 minutes from Sligo Airport, one hour from Knock International Airport & two hours from Dublin International Airport. Kilronan Castle is the ideal base from which to explore Ireland's rugged West Coast.
Kilronan Castle is the ancestral home of the Tenison family and the legendary Colonel King Tenison. It is one of a few Irish castle estates that can trace its history back to royal families.
This secluded castle hotel is set on the shores of Lough Meelagh in Roscommon. The castle is surrounded by over forty acres of breathtaking Irish scenery, lush green pastures, ancient forests and historical points of interest.
Lough Rynn Castle
Leitrim, Ireland
Rich in ancient character and original charm, Lough Rynn Castle still retains all of its historic splendor, with antique furnishings, open fires, wood paneling and decorative stone and glasswork. Today, the hotel combines its traditional elegance with modern comforts and facilities.
The castle bedrooms feature air conditioning and broadband internet access, while the estate's former stables and pheasantry have also been converted into luxurious accommodations. All rooms are tastefully decorated with rich, luxurious fabrics and beautifully preserved original features.
The Sandstone Restaurant offers an elegant fine-dining experience and has received an AA Rosette award. The restaurant offers a series of table d'hôte and à la carte menus, created using the finest, freshest seasonal produce from Leitrim and the surrounding counties.
Day 4. Roscommon - Ennis
Rise and shine! Today, you'll drive south to Ennis. Along the way, feel free to stop in Galway City for a bite to eat.
Upon arrival in Ennis, settle into your accommodation and explore the town!
Ennis, Ireland
Situated on the Wild Atlantic Way, Hotel Woodstock is the ideal base to explore many of Ireland's natural attractions. Situated at the edge of the beautiful medieval market town of Ennis, County Clare yet surrounded by the lush West coast countryside, Hotel Woodstock offers you the best of both worlds. Located within short driving distance of Limerick, Galway and the surfers' paradise that is Lahinch. Shannon Airport and Lahinch are only a 30-minute drive away. The Cliffs of Moher are less than 30 miles from the hotel.
Surrounded by a stunning parkland championship golf course, the hotel in Ennis is situated in 155 acres of beautiful countryside in Ennis. From the moment you step into the wide, airy foyer to be greeted by a sumptuous lounge, roaring log fire and friendly management, you'll feel the love this family-run hotel. The 4-star Woodstock Hotel offers you a range of spacious bedrooms, decorated in light modern colors, many offering you beautiful views over Woodstock Golf Course. Dining options include the relaxed intimate atmosphere of the Hotel Woodstock Bar, or the family-friendly spacious restaurant. The hotel features impressive facilities – including the newly refurbished deck-level pool, Jacuzzi, refitted sauna and brand new steam room.
Old Ground Hotel
The Old Ground Hotel is situated on the main street of Ennis, O'Connell Street. It is just 20 minutes from Shannon International Airport, the gateway to the west of Ireland.
A former manor house dating back to the 18th century, this ivy-clad residence has been totally refurbished to provide the warmth of a country home with the style of a luxury hotel. Located in the center of the attractive medieval town of Ennis, County Clare, the hotel provides an ideal starting point from which to explore the famous attractions of Ireland's west coast. The Burren, Cliffs of Moher, Ailwee Caves and Bunratty Folk Park are all within easy reach, whilst Connemara and the Aran Islands provide some spectacular options for day trips
Temple Gate Hotel
With a unique blend of warm hospitality and friendly service, the Temple Gate Hotel welcomes you to a charming family owned and run hotel in the heart of Ennis, Co. Clare. It provides the perfect location to wander the cobblestone streets of Ennis or explore the west of Ireland. Discreetly situated just off the town centre, a short walk through the warm glow of the Victorian lamps in the cobblestoned courtyard leads guests to the heart of the historical and vibrant town of Ennis.
Day 5. Ennis
Today, you may want to take a quick drive over to the one and only Cliffs of Moher!
For the rest of the day, consider visits to Bunratty Castle, Adare Village, and King John's Castle in Limerick!
Cliffs of Moher, Clare, Ireland
Day 6. Ennis - Meath
Journey east towards Meath today! Feel free to stop along the way- at any town, castle, or area of natural beauty that intrigues you.
Upon arrival, check into your accommodation. Then, choose how you want to spend your evening! You may want to check out Newgrange or the Hill of Tara - or, you may want to head back to Dublin and experience the city!
No matter how you spend your last night, make it one to remember!
Dunboyne Castle Hotel & Spa
Dunboyne, Ireland
With a sophisticated combination of traditional old-world opulence and contemporary design, Dunboyne Castle Hotel & Spa sets a new precedent and fresh interpretation for hotels around Ireland. As one of the leading 4-star hotels in Ireland, Dunboyne Castle Hotel & Spa features unique attributes that emphasize the beauty of its estate. Standing on 21 acres of mature manicured lawns and gardens, the hotel's most unique attribute is the peaceful oasis it offers guests, while also being located in such close proximity to Dublin's city center and surrounding golf courses.
The hotel is situated on the grounds of a former castle; the original castle was destroyed during the Cromwellian invasion of Ireland. The 18th-century Georgian house that we see today is at the heart of the hotel, blending seamlessly with the stylish, modern bedrooms and restaurant in the contemporary wing. The historic mansion, once home to the Lord of Dunboyne, today serves as one of Ireland's foremost exclusive hotels on Dublin's doorstep.
Its 145 large, comfortable rooms boast free WiFi, air-conditioning, HD TV, and much more. Relax at Seoid, meaning jewel in Irish, the decadent on-site spa. Unwind in the crystal steam room, enjoy an herbal sauna, pamper yourself with a mud treatment, and more. Take a stroll through the lavish garden, then indulge in a delicious meal at the Ivy Restaurant. Feast your eyes on its carefully prepared seasonal menu, then taste the award-winning flavors of the in-house chefs, who've earned 2 AA Rosettes from 2013 to date.
Fitzpatrick Castle Hotel
The 4-star Fitzpatrick Castle is known for its stylish surroundings, friendly service and breathtaking surroundings - all within easy reach of Dublin! Its 113 luxurious guest rooms and suites are decorated in traditional castle style.
Dining options include the AA Rosette award-winning P.J's Restaurant, with a menu offering creations using the finest ingredients sourced daily from the local area. A more informal option is The Dungeon Bar & Grill, housed in the former dungeon, and is a popular spot for drinks and dinner by the fireplace. Afternoon tea is served in the Library Bar.
The Fitzpatrick Castle Leisure Club features a 65-foot indoor swimming pool, on-deck whirlpool spa, and Scandinavian wood sauna and steam room.
Day 7. Depart Ireland
In accordance with your departure flight, make your way back to Dublin Airport. Be sure to leave enough time to drop off your car at the rental area.
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Vacation packages include round-trip economy class airfare on valid international carriers as noted in specific package inclusions and as indicated on booking engine calendar, which may also include code share flight agreements between carriers as indicated during the booking process. Carriers may vary based on city of origin.
Air fares are based on a 14-day advance purchase and include round-trip economy class airfare, including carrier and government-imposed taxes and fees, and the September 11th Security Fee of $5.60 per enplanement originating at a U.S. airport. Fluctuations in exchange rates may affect some taxes or fees. Other departure dates and origins are available at slightly higher rates. Fares apply to North American originating passengers only, and are not eligible for accrual of frequent flyer points/mileage. Seats are limited and fares may not be available on all flights. Child/infant discounts are not available, and stopovers are not permitted. Advance seat selection is controlled by the carrier(s), and may not be available on all flights. Baggage fees may apply; please check with carrier for specific policies.
Vacation packages are subject to availability, are capacity controlled, may not be available on every date or on all flights, and may be restricted to certain hotel room and car categories. Higher rates may also be available throughout travel period. Full payment is required at the time of reservation. Rates cannot be combined with any other discount/promotion, and are valid for new bookings only. Offers may be changed or withdrawn at any time without notice. Website contains special pricing that is exclusively for Internet sale only. Bookings made through our call center may incur a concierge fee of up to $25 per person.
Escorted vacation packages are per person based on two travelers (unless otherwise specified), and typically include hotel accommodations; transportation with professional driver/guide; sightseeing as listed, some meals, local taxes, hotel service charges and porterage of one (1) suitcase per person, due to limited capacity on motor coaches. Space is limited and prices may be restricted to certain travel dates, length of stay or room type. Driver and guide gratuities are not included, and are at the discretion of the traveler. Terms and conditions apply. Minimum child ages and handicapped traveler restrictions may apply on escorted tours; please call for details.
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text:
// -*-c++-*-

/*!
  \file field_painter.h
  \brief field painter class Header File.
*/

/*
 *Copyright:

 Copyright (C) The RoboCup Soccer Server Maintenance Group.
 Hidehisa AKIYAMA

 This code is free software; you can redistribute it and/or modify
 it under the terms of the GNU General Public License as published by
 the Free Software Foundation; either version 2, or (at your option)
 any later version.

 This code is distributed in the hope that it will be useful,
 but WITHOUT ANY WARRANTY; without even the implied warranty of
 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 GNU General Public License for more details.

 You should have received a copy of the GNU General Public License
 along with this code; see the file COPYING.  If not, write to
 the Free Software Foundation, 675 Mass Ave, Cambridge, MA 02139, USA.

 *EndCopyright:
 */

/////////////////////////////////////////////////////////////////////

#ifndef RCSSLOGPLAYER_FIELD_PAINTER_H
#define RCSSLOGPLAYER_FIELD_PAINTER_H

#include "painter_interface.h"

#include <QPen>
#include <QBrush>

class MainData;

class FieldPainter
    : public PainterInterface {
private:

    const MainData & M_main_data;

    QBrush M_field_brush;
    QPen M_line_pen;

    // not used
    FieldPainter();
    FieldPainter( const FieldPainter & );
    const FieldPainter & operator=( const FieldPainter & );
public:

    FieldPainter( const MainData & main_data );
    ~FieldPainter();

    void draw( QPainter & painter );

private:

    void readSettings();
    void writeSettings();

    void drawBackGround( QPainter & painter ) const;
    void drawLines( QPainter & painter ) const;
    void drawPenaltyAreaLines( QPainter & painter ) const;
    void drawGoalAreaLines( QPainter & painter ) const;
    void drawGoals( QPainter & painter ) const;
    void drawFlags( QPainter & painter ) const;
    void drawGrid( QPainter & painter ) const;
};

#endif

redpajama_set_name: RedPajamaGithub


text:
Harubi Templates
================

Harubi templates serve as the starting points for building harubi back-end services and applications. The templates highlight harubi approaches. However, the listed templates are not the only ways to build with harubi.

New templates will be added from time to time.

Harubi emphasizes on **model-driven development**. In fact, harubi makes it difficult to do otherwise. Harubi introduces necessary constraints so that every harubi developer will have the same good skills and approaches. Harubi codes will be consistent. Hence, harubi collaboration will be made easier.

Every software application can be formed as a collection of models. Users ***act*** on models. Hence, the **model-action** pattern is both user and developer friendly. See [Model-Action Framework](models).


text:
Aishwarya Rajesh to team up with Sivakarthikeyan once again?https://indianexpress.com/article/entertainment/tamil/aishwarya-rajesh-to-team-up-with-sivakarthikeyan-once-again-5628243/
Aishwarya Rajesh to team up with Sivakarthikeyan once again?
We hear Aishwarya Rajesh has been approached to play the leading lady in Sivakarthikeyan's next, directed by award-winning filmmaker Pandiraj.
By Entertainment Desk |Chennai | Published: March 15, 2019 2:46:18 pm
Vijay Sethupathi and Aishwarya Rajesh to star in Ka Pae Ranasingam
Aishwarya Rajesh: My gut told me Kanaa was worth taking up
Arun Vijay, Aishwarya Rajesh wrap up shooting for Mani Ratnam's Chekka Chivantha Vaanam
Aishwarya Rajesh was last seen in Sivakarthikeyan's production venture Kanaa.
Aishwarya Rajesh played the lead in Sivakarthikeyan's maiden production venture Kanaa, which was declared a hit. Though the film had scenes featuring both of them, they weren't seen as a pair. Now, we hear the Kaaka Muttai girl has been approached to play the leading lady in Sivakarthikeyan's next, directed by award-winning filmmaker Pandiraj.
According to sources, the makers are in talks with the actress, but nothing has been finalised. This project will be bankrolled by Sun Pictures, and apparently, Pandiraj was keen to have Aishwarya on board as he felt she would be appropriate for this rural romantic drama. If everything goes well, the shooting of this film will happen from June or July onwards—once Sivakarthikeyan completes Mithran's film Hero.
Tentatively titled SK-16, this film marks the third collaboration between Pandiraj and Sivakarthikeyan after Marina and Kedi Billa Killadi Ranga.
Meanwhile, Aishwarya Rajesh is busy with the Telugu version of Kanaa, Kowsalya Krishnamurthy, besides her Telugu debut, an untitled project with Vijay Deverakonda, directed by Nirmal Kumar.
Aishwarya Rajesh
Sivakarthikeyan
1 Anurag Kashyap regrets missing a chance to work on Super Deluxe
2 PM Narendra Modi biopic gets a release date
3 Delhi Crime review: Crime and Punishment


text:
Facebook met the Stop Hate For Profit Campaign, nothing came of it
Attendees claim the meeting was as frustrating and fruitless as they'd feared it would be.
SAUL LOEB/AFP/Getty Images
On Tuesday morning, Facebook's COO Sheryl Sandberg announced that the company's top leadership, including Mark Zuckerberg, would meet with prominent civil rights leaders as well as members of the Stop Hate For Profit campaign in order to brainstorm methods for hate speech policy on the platform.
Following the meeting, Engadget reports little came of it, and the campaign organizers left disappointed, and the boycott will continue.
"#StopHateForProfit didn't hear anything today to convince us that Zuckerberg and his colleagues are taking action," Jessica Gonzalez, Co-CEO of Free Press, one of the organizations behind the campaign, said in a statement. "Instead of committing to a timeline to root out hate and disinformation on Facebook, the company's leaders delivered the same old talking points to try to placate us without meeting our demands. This isn't over. We will continue to expand the boycott until Facebook takes our demands seriously."
A pointless meeting — The decision to meet with campaign organizers arrived shortly after multiple companies pulled their advertising on Facebook, including PlayStation, Lego, Canadian banks like Scotiabank, RBC, CIBC, BMO, and National Bank, Target, Microsoft, Unilever, Verizon, Pfizer, Patagonia, North Face, Levi's, Puma, Madewell, Hershey, and hundreds more. It's a financial wound and a gaping one at that.
"We meet in the context of what may be the largest social movement in US history and our nation's best and latest chance to act against the racism that has pervaded our country since before our independence," Sandberg said in her post on Tuesday morning ahead of the meeting. "It's a big moment for all of us, especially now. Much more than words, people, organizations and companies need to take action — and we at Facebook know what a big responsibility we have."
Those are the right noises to make, to be sure, but in typical Facebook form, it's just talk. The company got to this point because it continues to enable dangerous rhetoric — which is something Zuckerberg continues to overlook — but at the end of the day, Facebook is desperately trying to save itself from a boycott that is set to cost it billions and billions of dollars. As usual, it's only the fear of financial repercussions that have spurred it to action.
Contradicting Zuckerberg — It was only last week that Zuckerberg spoke in a town hall meeting about his lack of concern for the longterm effects of the boycott. "My guess is that all these advertisers will be back on the platform soon enough," he said, adding that the company is "not going to change our policies or approach on anything because of a threat to a small percent of our revenue." That's typical of Zuckerberg's hubris but doesn't align with Sandberg's comments this morning. It does, however, align perfectly with the experience reported by attendees of today's meeting.
Rashad Robinson, the president of Color of Change described the meeting in a tweet as "a disappointment," adding that Facebook's leadership "made it abundantly clear that they are not yet ready to address the vitriolic hate on their platform."
Rashad Robinson / Twitter
You can't have it both ways — Publicly, Facebook is using language and sound bites that are crafted to calm legitimately worried people. Internally, however, Facebook's leadership seems more inclined toward maintaining and even hardening its speech policy that has, time and again, enabled division, polarization, and bigotry. From pro-civil war Boogaloo adherents to Donald Trump himself openly supporting physical violence against protesters, Zuckerberg's platform seems almost perfectly designed to promote unhinged content without concern for its potential, real-world effects.
Civil rights audit, too — The company is expected to release a civil rights audit on Wednesday, too. The review spans over two years of Facebook's management of speech policies and practices. It's hard to imagine there'll be any progress after this meeting, but one thing is crystal-clear: Facebook is hurting and it's trying everything in its power to get advertisers back on board.


text:
Emergency Third Rail Power Triptych
Girls Names continue their upward trajectory by releasing an eleven minute opus called Zero Triptych. The Belfast band fly over a motorik beat that kicks up camel dust from an old trip that the Church took on Myrrh back in '85 and then blasts off into a globular cluster of some nether region. Not bad for a song inspired by three panel paintings from the middle ages. Depending on your circumstances you might remember the Church and their song Myrrh. It was an obtuse song about wine, gold, personal favors, drum kits and the birth of Christ. Zero Triptch picks up where Kilby and company trailed off with the sound of aliens landing, obliterating space and time and taking us through a wormhole that dusts you with myrrh, frankincense and gold, skips over an obelisk, a couple monoliths and then leaves them all behind.
Word on the street is that this is going to be released only as a 12″ single and won't be on their next album, so plan accordingly.
Unlikely Friends Strike Gold
Posted on February 10, 2015 by Toby
With Boat on somewhat of a hiatus and Math and Physics Club in the middle of their standard four or so years between albums what is a guy to do in the green and mossy Pacific Northwest? Well, in the case of Boat's Dave Crane you round up a new bunch of friends, call yourselves Unlikely Friends and cook up a new batch of killer pop pop songs. You will undoubtedly recognize the voice of Charles "Chaz" Bert from Math & Physics Club and you may know Chris Mac (the Indiepop King of Seattle) who runs the Jigsaw record label and mail order and is at least in three bands around town at any given time.
Solid Gold Cowboys will be easy to like if you are already a Boat fan because Crane's voice and his penchant for writing hooky pop songs. The gunslinger in this game is Bert who usually keeps things pretty mellow when singing in MAPC, but really lets loose on many of these songs adding an quantifiable effervescence into them.
The album is a combination of precise pop hooks akin to Guided By Voices and the sunny sweet bubblegum psychedelia of the Apples in Stereo. Soft Reputation and Satellite Station are the best of examples of this great combination, but that doesn't really cover it. Ride Off Into the Sunset chugs along like Love and Rockets, Gold Hills Theme nods to the dusty spaghetti western soundtrack music of Ennio Morricone and Gold Coast Marauders has the delicacy of a Left Banke song. Crane usually takes the lead vocal with Bert coming in on the chorus to put the song into the stratosphere.
Considering the backgrounds of these three (Un)likely friends it's not surprising that they got together to make a record. The unlikely part is that the peanut butter and chocolate combination of the heart on your sleeve style of Boat juxtaposed with the sweetness of Math and Physics Club is satisfying winner.
Cassette version is available from Mirror Universe Tapes
Compact Disc version is available from Jigsaw Records
If you are in Seattle, you won't want to miss Unlikely Friends record release show at the Rendezvous in Belltown, Saturday, February 21 with Ruler and Oh! Pears.
Messis On the Go
A few of you probably remember the Pop Art Toasters, the one-off project where Martin Phillips and David Kilgour made an EP that showcased the brilliance of the Dovers, West Coast Experimental Pop Art Band, the Who and the Squires. Paul Messis is an obvious fan of these bands, but instead of doing covers, he's creating his own future classics.
Mr. Messis hails from Billingshurst, West Sussex, 2013 but he sounds like he's from some time-warped 60's town. Back then guitars jangled more brightly, harmonies mattered and a turntable was in every home. Case Closed is Messis' s second solo album. He's also a member of the Higher State, has released a slew of other singles, collaborated with the Sufis as the Market Squares and has a single he released with Jessica Winter. The guy is prolific with his 12 string jangliness, but the quality of Case Closed doesn't lead you to believe he's spreading himself too thinly.
Not only does the guy write tons of songs, but he has his own label as well. It's called Market Square Records. The excellent Market Squares 7-inch was the label's first release earlier this year and their next release is a 7-inch by Roanoke, Virgina's Young Sinclairs who are every bit as jangly and Who-loving as Mr. Messis himself.
stream: Paul Messis – Cased Closed (available from Paul's bandcamp page)
[youtube http://youtu.be/falUZa85Ygc]


This type of service allows businesses with employees working from locations other than the company's office to present a professional face at all times. It also gives remote workers access to a variety of phone system features that mobile and home phones don't offer.
Virtual phone systems aren't full-fledged phone systems. Your calls are still processed on your mobile or home phone network. This means you are charged for the call on the virtual system and use up your mobile or home phone minutes.
Businesses with a large group of remote workers, or sole-proprietor businesses.
Landline systems are a reliable, time-tested solution that many companies are comfortable using.
Most phone system providers are moving away from landlines, making them more difficult not only to purchase but to repair should something break.
Large corporations that have the budget to pay for them and an in-house IT staff to run and maintain them. Necessary for businesses without high-speed internet access.


text:
Research at The Ottawa Hospital Research Institute
The Ottawa Hospital Logo
New Civic Campus Project
Doing business with The Ottawa Hospital
Doors Open Ottawa
Working for the Environment
Nursing Next
Stories of Care
Emergency Wait Times
Visiting the Hospital
Patient and Family Care Guide
Support and Follow Up
You're in my care
Departments, Programs, Clinics and Services
Cancer Program
Rehabilitation Centre
my Surgery
my ICU: The Intensive Care Unit
Our Model of Care
Professional Models
Our Health-Care Professional Team
Physician Engagement Agreement
Care policies
Patient Safety Tips
For health-care professionals
Teaching Partners
Research at The Ottawa Hospital
University of Ottawa Skills and Simulation Centre
Reasons to Work Here
Career Opportunities at The Hospital
Nursing Opportunities
How to Apply at the Hospital
Relocating to Ottawa
Renée Légaré
Executive Vice-President and Chief Human Resources Officer
rlegare@toh.ca
Renée Légaré is currently the Executive Vice-President, Human Resources at The Ottawa Hospital which is one of the largest Academic Health Science Centers in the country.
Prior to joining TOH, Renée was the Senior Vice President, People at the Canadian Air Transport Security Authority (CATSA), where she was recognized for her strong leadership skills, her passion for people and her desire to make a difference. She brings to the organization over 20 years of extensive experience in human resources such as change management, performance management and measurement, employee and labour relations, coaching, mentoring, and talent management and development. Renée earned her Master's degree in Industrial Relations from Université du Québec en Outaouais and an Honours degree in Economics from Université de Sherbrooke. She has also completed her Director's Education Program with the Institute of Corporate Directors. Renée is currently studying to receive her PHD in Industrial Relations.
Are you a patient who needs a Sign Language Interpreter or a Cultural Language Interpreter when you come to The Ottawa Hospital? Please visit our Accessibility page for more information.
Copyright 2018 The Ottawa Hospital. All rights reserved.


Exeter College holds the protection, safety, health, well-being and welfare of all of its students as paramount. We believe that all children, young people and vulnerable adults have a right to protection from abuse and to the support they need to reach their full potential.
We follow the statutory guidance in 'Keeping Children Safe in Education' (revised Sept. 2018) and 'Working Together to Safeguard Children' (revised July 2018) and recognise our responsibility under the Children's Act 1989 and section 175 of the Education Act 2002, to have due regard to our responsibility to safeguard and promote the welfare and wellbeing of children and young people.
In line with 'Keeping Children Safe in Education 2018, we believe that everyone has a responsibility to actively safeguard children, young people and vulnerable adults, whatever their role; and we are committed to a 'whole college approach' to safeguarding. To this end we will ensure that all staff are trained in Safeguarding and Child Protection and also in preventing radicalisation, in line with the statutory guidance in the Prevent Duty 2015.
We will take all child protection and safeguarding concerns seriously and will respond to them promptly, keeping the child/young person/vulnerable adult at the centre of all we do.
We will make information available on Safeguarding and Child Protection to students and parents and guardians and make sure that we keep up to date with changes in legislation.
The College provides a comprehensive wellbeing and welfare service to students. This includes support for: housing; financial support needs; medical and physical health needs and mental health and emotional wellbeing; all of which can impact on a student's health, well-being and safety.
If you are concerned about the safety of any child, young person or vulnerable adult, then please contact the Designated Safeguarding Lead or the Deputy Designated Safeguarding Lead on 01392 400449 or contact the MASH, Multi Agency Safeguarding Hub on 0345 155 1071 or email mashsecure@devon.gov.uk.
If you feel a student is at immediate risk of harm to themselves or others, please contact the emergency services on 999.
During college hours please contact a member of the college's safeguarding team on 01392 400449 to speak with a member of staff and pass on your concerns.
Operation Encompass is the reporting to schools and colleges, prior to the start of the next college day, when a child or young person has been exposed to, or involved in, any domestic incident.
Operation Encompass will ensure that a member or the college staff, known as a Key Adult, is trained to allow them to liaise with the police and to use the information that has been shared, in confidence, while ensuring that the college is able to make provisions for possible difficulties experienced by young people, or their families, who have been involved in, or exposed to, a domestic abuse incident.
As well as our DSL and DDSL we have a dedicated team of staff that work with students across the faculties, ensuring all students can access help and support throughout their time at Exeter College.


text:
var https = require('https'),
  fs = require('fs');

var options = {
  key: fs.readFileSync('test/keys/server.pem'),
  cert: fs.readFileSync('test/keys/server.crt')
};

var req = https.createServer(options, function(req, res) {
  res.setHeader("Content-Type", "text/html");
  res.writeHead(200);
  res.write("\n");
  res.write("\n\t<title>Simple HTTPS Server</title>\n\n");
  res.write("\n");
  res.write("
Hello Secure World.
");
  res.write("")
  res.end("");
}).listen(8000);


Right in the intersection between durable boot bag, handy duffle and smart backpack, stands the Hugger 30L. This IATA approved carry-on loyally follows you all the way from the airport to the mountain. The combination of roomy compartments and handy solutions makes the Hugger 30L a smart and versatile travel companion.
Weight: 1.4kg / 3.1 lbs.


text:
Start Classification Journals Subscription FIZ Karlsruhe Zentralblatt MATH
published by FIZ Karlsruhe GmbH
MathEduc
an:2016a.00244
Query form:
Help on query formulation
ME 2016a.00244
Stephan, Michelle L.; Chval, Kathryn B.; Wanko, Jeffrey J.; Civil, Marta; Fish, Michael C.; Herbel-Eisenmann, Beth; Konold, Clifford; Wilkerson, Trena L.
Grand challenges and opportunities in mathematics education research. (English)
J. Res. Math. Educ. 46, No. 2, 134-146 (2015).
Summary: Mathematics education researchers seek answers to important questions that will ultimately result in the enhancement of mathematics teaching, learning, curriculum, and assessment, working toward ensuring that all students attain mathematics proficiency and increasing the numbers of students from all racial, ethnic, gender, and socioeconomic groups who attain the highest levels of mathematics achievement. During the 2014 National Council of Teachers of Mathematics (NCTM) Research Conference, representatives from the NCTM Research Committee and the Special Interest Group for Research in Mathematics Education (SIG/RME) Executive Board met with representatives from the Directorate for Education and Human Resources at the National Science Foundation (NSF) to have an informal conversation about research in mathematics education. During that conversation, the authors learned that many fields have created lists of Grand Challenges, which are available to funding agencies as they prioritize funding efforts in those disciplines. That stimulating discussion was the impetus for this article in which the authors intend to begin a dialogue among mathematics education researchers and other stakeholders regarding the Grand Challenges and opportunities of this field. They begin with a description of the history of Grand Challenges and how they have provided a stimulus for advances in other fields, then next describe the results of a survey that the Research Committee distributed via multiple electronic mailing lists. In this survey, was solicited feedback about the characteristics of Grand Challenges for mathematics education as well as the types of challenges that educators think we currently face. Drawing on the experiences of other fields, we conclude with a proposed process for generating Grand Challenges for mathematics education. (ERIC)
Classification: D20
Keywords: research in mathematics education; barriers; financial support; researchers; stakeholders; research needs; teacher surveys; mathematics teachers; mathematics achievement
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text:
Pattern recognition system and method using neural network
United States Patent 5255342
An inner product computing unit computes inner products of an input pattern whose category is unknown, and orthogonalized dictionary sets of a plurality of reference patterns whose categories are known. A nonlinear converting unit nonlinearly converts the inner products in accordance with a positive-negative symmetrical nonlinear function. A neural network unit or a statistical discriminant function computing unit performs predetermined computations of the nonlinearly converted values on the basis of preset coefficients in units of categories using a neural network or a statistical discriminant function. A determining section compares values calculated in units of categories using the preset coefficients with each other to discriminate a category to which the input pattern belongs.
Inventors:
Nitta, Tsuneo (Yokohama, JP)
Application Number:
Export Citation:
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Kabushiki Kaisha Toshiba (Kawasaki, JP)
Primary Class:
Other Classes:
704/E15.017
International Classes:
G06K9/64; G06N3/04; G10L15/00; G10L15/16; (IPC1-7): G10L9/04
Field of Search:
381/41-46, 395/2, 395/21, 364/807
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US Patent References:
4942608 Pattern recognition apparatus using a composite similarity method 1990-07-17 Shigehara 381/43
4903306 Voice recognition using an eigenvector 1990-02-20 Nakamura 381/42
4888823 System for continuous speech recognition through transition networks 1989-12-19 Nitta et al. 381/43
4805225 Pattern recognition method and apparatus 1989-02-14 Clark 382/15
4783802 Learning system of dictionary for speech recognition 1988-11-08 Takebayashi et al. 381/43
4736429 Apparatus for speech recognition 1988-04-05 Niyada et al. 381/43
4624011 Speech recognition system 1986-11-18 Watanabe et al. 381/43
4394538 Speech recognition system and method 1983-07-19 Warren et al. 381/43
Specht et al, "Probabalistic Neural Networks for Classification, Mapping, or Associative Memory", IEEE Inter. Conf. on Neural Networks, Jul. 24-27, 1988 pp. 525-532.
Wasserman, "Neural Computing", 1989, Van Nostrand Reinhold Inc., N.Y., pp. 43-59.
Proceedings of the 9th International Conference on Pattern Recognition Rome, Nov. 14-17, 1988, pp. 1225-1229, H. Segawa et al, "A Similarity Value Transformation Method for Probabilistic Scoring".
Primary Examiner:
DOERRLER, MICHELLE
Attorney, Agent or Firm:
OBLON, MCCLELLAND, MAIER & NEUSTADT, L.L.P. (1940 DUKE STREET, ALEXANDRIA, VA, 22314, US)
Parent Case Data:
This application is a continuation of application Ser. No. 07/447,405, filed on Dec. 7, 1989, now abandoned.
1. A speech pattern recognition system comprising:
speech input means for inputting a speech pattern to be recognized;
feature extracting means for extracting feature vectors from the input speech pattern;
dictionary mean for storing a plurality of orthogonalized reference patterns, which are equal to eigenvectors calculated using Karhunen-Loeve (KL) expansion, and whose categories are known;
inner product computing means, provided for each category, for computing inner products of the input speech pattern, using said feature vectors, whose category is unknown and the orthogonalized reference patterns of a category which are stored in said dictionary means and whose category is known;
converting means, provided for each category, for nonlinearly converting the inner product of a category, which is computed by said inner product computing means, in accordance with a positive-negative symmetrical nonlinear function;
evaluation value computing means, provided for each category and each evaluation value computing means including a multi-layer neural network whose elements each include multiplier means for multiplying an input signal from a lower adjacent layer by a preset weight coefficient and being connected to all elements constituting an upper adjacent layer, for computing an evaluation value of a category using the nonlinearly converted values calculated by said converting means and weight coefficients which are preset in said neural network of a category based on a discriminatively trained Back Propagation (BP) algorithm;
comparing means for comparing the evaluation values obtained by said evaluation value computing means using said neural networks with other evaluation values; and
discriminating means for outputting a category to which the input speech pattern belongs as a recognition result using comparison results of said comparing means.
2. A computer-implemented speech pattern recognition method comprising the steps of:
inputting a speech pattern to be recognized;
extracting feature vectors from the input speech;
computing inner products of the input pattern, using said feature vectors, whose category is unknown and orthogonalized reference patterns of a category which are equal to eigenvectors calculated using Karhunen-Loeve (KL) expansion, and whose category is known;
nonlinearly converting the inner product of a category, which is computed in the computing step, in accordance with a positive-negative symmetrical nonlinear function;
computing an evaluation value from the nonlinearly converted values converted by the converting step and weight coefficients using a multiple neural network whose elements each include multiplier means for multiplying an input signal from a lower adjacent layer by a preset weight coefficients and connected to all elements constituting an upper adjacent layer in which said weight coefficients are preset for each category based on a discriminatively trained Back Propagation (BP) algorithm;
comparing in the evaluation values obtained from said neural networks in the evaluation value computing step of said categories with other evaluation values; and
discriminating and outputting a category to which the input pattern belongs as a recognition result from comparison results of the comparing step.
3. A speech pattern recognition system, comprising:
speech input means for inputting a speech to be recognized;
feature extracting means for extracting feature vectors from the input speech;
multi-layer neural networks whose elements each include multiplier means for multiplying an input signal from a lower adjacent layer by a preset weight coefficient and connected to all elements constituting an upper adjacent layer and, respectively provided for categories of speech, for receiving the feature vectors as input, and for outputting evaluation values for each category, each of said neural networks having, as weight coefficients of a hidden layer, the weight coefficients being computed based on a discriminatively trained Back Propagation (BP) algorithm, eigenvectors calculated in advance by Karhunen-Loeve (KL) expansion from known feature vectors belonging to one of said categories, a nonlinear function of an output of the hidden layer being a positive-negative symmetrical nonlinear function; and
determining means for determining a category as a recognition result from evaluation values output from the multi-layer neural networks.
4. A speech pattern recognition system as recited in claim 1, wherein said multi-layer neural network comprises:
second inner product computing means for computing second inner products using said nonlinearly converted inner products of a category from said converting means and said preset weight coefficients;
second converting means for nonlinearly converting said second inner products computed by said second inner product computing means;
means for obtaining a sum of the nonlinearly converted second inner products from said second converting means; and
third converting means for nonlinearly converting the sum of the nonlinearly converted second inner products, thereby producing said evaluation value.
The present invention relates to a pattern recognition system and method, which can accurately recognize an input pattern such as speech, character, figure, and the like and, more particularly, to an improvement in a partial space method.
In recent years, studies about pattern recognition processing for speech, character, figure, and the like have been made, and the pattern recognition processing has been receiving a great deal of attention as an important technique for realizing a natural man-machine interface.
A pattern recognition apparatus for executing pattern recognition processing of this type basically has an arrangement shown in FIG. 1. The respective units perform the following processing.
1) A feature extracting unit 1 analyzes an input pattern to obtain its feature pattern.
2) By referring to a reference pattern memory 2 which stores reference patterns obtained in units of categories of patterns to be recognized as dictionaries, similarities or distances between the reference pattern dictionaries and the feature pattern are computed by a pattern matching unit 3.
3) The collation results are determined by a determining unit 4 to obtain a recognition result of the input pattern. Note that the determining unit 4 obtains a category name of the reference pattern which has the highest similarity value (or the smallest distance value) with the input pattern as a recognition result or a recognition candidate for the input pattern. When the input pattern is a speech waveform, the feature extracting unit 1 performs Band Pass Filter (BPF) analysis or Linear Prediction Coding (LPC) analysis of the input speech, and then detects a speech interval, thus obtaining acoustic analysis data in this speech interval as an input pattern.
When the input pattern is a character image, the feature extracting unit 1 quantizes the input character image, and then extracts a character portion, thus obtaining feature data of the character pattern as an input pattern.
As a method in the pattern recognition processing, a subspace method is known. Pattern recognition using the subspace method is described in, e.g., U.S. Pat. No. 4,624,011 (Watanabe et. al.)
In the subspace method, as reference pattern dictionaries of categories, dictionaries (φ(K,m); K is category name, and m is the number of an orthogonal axis; m=1, 2, . . . , M) which are orthogonalized in advance by KL expansion in units of categories are created as orthogonalized dictionary sets. Similarities S(K) between the dictionaries and an input pattern (F) are computed according to the following equation to perform pattern matching processing: ##EQU1## where () indicates an inner product, and ││ ││ indicates a norm.
The pattern matching method according to the subspace method is widely used in pattern recognition since it can relatively easily obtain an accurate recognition result.
In the conventional pattern recognition processing using the subspace method, as shown in the abovementioned equation, the inner products of the input pattern (F) and the orthogonal axes (φ(K,m)) of the orthogonalized dictionary sets are merely accumulated, and the overall feature of the input pattern is evaluated using the accumulation value. In other words, the pattern recognition is performed not by using the individual inner products obtained between the input pattern and the orthogonal axes but by using the accumulation value representing the overall feature. For this reason, when an inner product with respect to a given orthogonal axis takes a large value, which cannot be obtained with a correct pattern, due to noise, the accumulation result of the inner products tends to become a value larger than that of the inner products for the correct pattern. When pattern matching processing is performed using the subspace method of this type, a determination error (recognition error) caused by a category other than an object to be recognized and various noise components tends to occur.
In order to solve this problem, recently, pattern matching processing using a multi-layer neural network is receiving a great deal of attention. A nonlinear discriminant function is realized by the neural network, for sequentially transmitting neurons carrying information, to extract feature data of an input pattern. The neural network of this type poses the problem of how to determine a set of coefficients for defining the way of combining outputs from lower-order layers. As an algorithm for obtaining these coefficients, a back propagation (BP) algorithm is known (NATURE Vol. 323 9, pp. 553-536 (1986, Oct) Learning representations by back-propagation errors). Various reports announced that pattern matching processing could be accurately executed upon application of the BP algorithm.
However, when a neural network is used, an enormous amount of computations for determining coefficients of each layer must be performed, and a load of computation processing is very large.
It is an object of the present invention to provide a pattern recognition system and method which can reduce a load on computation processing by utilizing advantages of a neural network.
In order to achieve the above object, according to a first aspect of the present invention, a pattern recognition system comprises: orthogonalized dictionary means for storing a plurality of reference patterns whose categories are known; inner product computing means for computing inner products of an input pattern whose category is unknown and the plurality of reference patterns which are stored in the orthogonalized dictionary means and whose categories are known; converting means for nonlinearly converting the inner products, which are computed by the inner product computing means, in accordance with a positive-negative symmetrical nonlinear function; evaluation value computing means for computing evaluation values on the basis of the nonlinearly converted values by the converting means and coefficients which are preset in units of categories; comparing means for comparing the evaluation values obtained by the evaluation value computing means in units of categories with each other; and discriminating means for discriminating a category to which the input pattern belongs on the basis of the comparison results of the comparing means.
According to a second aspect of the present invention, a pattern recognition method comprises the steps performed by a computer of: computing inner products of an input pattern whose category is unknown and orthogonalized dictionary sets of a plurality of reference patterns whose categories are known; nonlinearly converting the inner products, which are computed in the computing step, in accordance with a positive-negative symmetrical nonlinear function; computing evaluation values on the basis of the nonlinearly converted values by the converting step and coefficients which are preset in units of categories; comparing the evaluation values obtained by the evaluation value computing step in units of categories with each other; and discriminating a category to which the input pattern belongs on the basis of the comparison results of the comparing step.
FIG. 1 is a block diagram of a typical pattern recognition apparatus;
FIGS. 2A through 2D show patterns of orthogonal vectors obtained by a KL expansion;
FIG. 3 is a chart showing a nonlinear function normally used in a neural network;
FIG. 4 is a chart showing a positive-negative symmetrical nonlinear function introduced in the present invention;
FIG. 5 is a block diagram showing the first embodiment of a pattern recognition system according to the present invention;
FIG. 6 shows a neural network of the pattern recognition system shown in FIG. 5; and
FIG. 7 is a block diagram showing the second embodiment of the present invention.
According to a pattern recognition system and method of the present invention, inner products of an input pattern and orthogonalized dictionary sets are nonlinearly converted by a positive-negative symmetrical nonlinear function, and the converted values are subjected to predetermined computation processing using predetermined coefficients in units of categories. As a result, a variation in input pattern can be effectively absorbed by simple computation processing, and the input pattern can be accurately recognized.
For example, eigenvectors of a speech pattern are obtained as shown in FIGS. 2A through 2D. Eigenvectors φ1, φ2, φ3, and φ4 of first through fourth axes shown in FIGS. 2A through 2D are those of orthogonalized dictionary patterns (eigenvectors) obtained up to a tenth axis from speech patterns of a monosyllable speech/cho/([t∫o]) pronounced by 300 speakers by the KL expansion. In FIGS. 2A through 2D,  represents a positive vector value, and represents a negative vector value.
The eigenvector φ1 of the first axis expresses a feature (vector) of an average input pattern. However, since a plosive portion of /cho/([t∫o]) is obscured, the input pattern cannot be recognized to be distinguished from /sho/([∫o]). The eigenvector φ2 of the second axis mainly expresses a displacement in a frequency direction, and the eigenvector φ3 of the third axis mainly expresses a plosive portion of an affricate/cho/([t∫o]). Therefore, when the eigenvector of the third axis is referred, the input pattern can be recognized to be distinguished from /sho/([∫o]) described above. Furthermore, the eigenvector φ4 of the fourth axis mainly expresses a displacement in a time direction.
In this example, eigenvectors of fifth and subsequent axes are not illustrated. However, information carried by each eigenvector is not so clear like the eigenvectors of the first through fourth axes.
In this manner, the eigenvectors φ1, φ2, φ3, and φ4 (orthogonalized patterns) constituting an orthogonalized dictionary obtained by the KL expansion well express a variation in a speech (or character, or figure) pattern. This resembles feature extraction in lower-order layers in a multi-layer neural network.
Therefore, based on this point of view, a process of computing an inner product of an input pattern and an orthogonal vector is considered as a first (lowermost) layer in the neural network in the present invention, and a neural network for receiving the outputs from the first layer is constituted to execute pattern matching processing. As a result, coefficient computations of inputs of a large number of dimensions at the lowermost layer can be omitted. More specifically, the first layer portion is separated from the BP algorithm, and only a higher-order layer portion is independently computed.
As a result, of the neural network, the number of layers for which coefficients are computed by the BP algorithm can be reduced to 1 or 2. Therefore, pattern matching processing with a considerably reduced computation volume can be realized.
A process of combining outputs from a lower-order layer by a nonlinear conversion is provided between adjacent layers of the neural network. As a function of executing the nonlinear conversion, a sigmoid function expressed by the following formula (FIG. 3) is often used: f(x) =1/(1+e-x)
where x is the total sum of inputs. The inner products of the input pattern and the orthogonal vectors (eigenvectors φ1, φ2, φ3, and φ4) take positive/negative values, and the magnitude of the absolute value of each inner product represents a degree of contribution to the corresponding axis. Therefore, there is a new problem that the nonlinear function such as the sigmoid function cannot be directly applied.
In the present invention, a positive-negative symmetrical nonlinear function shown in FIG. 4 is adopted to perform the nonlinear conversion of the inner products.
In FIG. 4, a function A is a positive-negative symmetrical function having characteristics similar to the sigmoid function within the range of │x│≤1, and is given by: ##EQU2## A function B shown in FIG. 4 is a quadratic function given by: B; f(x) =x2
Furthermore, a function C exhibits linear characteristics in positive and negative regions, and is given by: C; f(x) =│x│
The inner products of the input pattern and the orthogonal vectors are nonlinearly converted using the positive-negative symmetrical nonlinear functions A, B, and C, thus obtaining a means for transferring the outputs to the higher-order layers of the neural network. Therefore, high-performance pattern recognition processing can be realized by a combination of the subspace method and the neural network, each of which compensates for the drawbacks of the other.
When the nonlinear conversion described above is performed in practice, the absolute values of inner products of an input pattern and orthogonal vectors are computed, and a positive-side nonlinear conversion shown in FIG. 4 may be performed for the absolute values.
After the inner products of an input pattern and the orthogonal axes are nonlinearly converted by the symmetrical nonlinear function, these values G=(S1, S2, . . . , SM) may be input to a statistical discriminant function in place of the neural network. In this case, for example, a Bayes decision rule given by the following equation is utilized to discriminate the input pattern. ##EQU3## where μK and RK are respectively the mean vector and the covariance matrix of an input pattern G obtained from learning data. T indicates transposition, and RK -1 and │RK │ are respectively the inverse matrix of the covariance matrix RK and the value of the determinant.
In the Bayes decision, values given by equation (2) are computed in units of categories, and a category providing a maximum value is discriminated as a recognition result. When a Maharanobis distance is used as a simplified method of the Bayes decision, pattern recognition processing may be performed according to the following equation: M(K) =(G-μK)T RK-1 (G-μK) . . . (3)
When the statistical discriminant function is used in place of the neural network, the same effect can be obtained.
FIG. 5 is a block diagram when pattern recognition of the present invention is applied to speech recognition.
Input speech is input to a feature extracting unit 11. The feature extracting unit 11 samples the input speech at 12 kHz to analog-to-digital convert the input speech into 12-bit digital data, and computes its power and LPC analysis parameters. This computation processing is performed under conditions that a window length is 24 msec, a frame period is 8 msec, an order of LPC analysis is 16, and the number of terms of the LPC mel-cepstrum is 16. Thereafter, the feature extracting unit 11 detects an input speech interval, and extracts power and LPC analysis parameter in this interval as a speech feature (input pattern). The unit 11 then supplies the extracted feature to an inner product computing unit 12.
The inner product computing unit 12 executes inner product computations of the input pattern obtained as described above and orthogonalized dictionary sets which are prepared in units of categories to be recognized and are stored in an orthogonalized dictionary memory 13. The inner products between the input pattern and the orthogonalized dictionary sets are supplied to a symmetrical nonlinear converting unit 14 and a similarity computing unit 15 based on the partial space method.
The symmetrical nonlinear converting unit 14 nonlinearly converts the inner products in accordance with the positive-negative symmetrical nonlinear function shown in FIG. 4, and supplies the converted values to a neural network unit 17. The similarity computing unit 15 executes similarity computation processing by the subspace method given by equation (1) for the inner products. The similarity computation result obtained by the similarity computing unit 15 is supplied to a predetermining section 16. The predetermining section 16 determines whether the value from the similarity computing unit 15 is to be output as a recognition result or is to be discriminated by the neural network unit 17.
The neural network is provided with coefficients shown in FIG. 5 in the same manner as in the orthogonalized dictionary sets. These coefficients are computed in advance in units of categories to be recognized on the basis of learning data using the above-mentioned BP algorithm. More specifically, after the learning pattern is nonlinearly converted as described above, the weight coefficients are computed based on the Back Propagation (BP) algorithm in such a way that G=(S1, S2, . . . Sn) is used as the input pattern of a fully connected multi-layer neural network which outputs 0.9 and 0.1 if the learning pattern is true and false, respectively. In this embodiment, the neural network is a multi-layer neural network each layer having a plurality of elements and each element including a multiplier for multiplying an input signal from a lower adjacent layer by a preset weight coefficient and connected to all the elements constituting the upper adjacent layer.
The neural network unit 17 is provided in units of categories, as shown in FIG. 5, and receives the nonlinearly converted values G (=(S1, S2, . . . , SM); M is the number of orthogonal axes) to compute sums of products with the coefficients along connecting lines shown in FIG. 5. The unit 17 combines the sums in units of nodes, and executes the nonlinear conversion based on the sigmoid function to obtain signals y1, y2, . . . , yL. Similarly, the unit 17 computes sums of products between these signals and the coefficients, and executes nonlinear conversion. The unit 17 then combines the converted values to obtain an output associated with a given category. Of neural network outputs for all the categories obtained in this manner, a category which provides a maximum output is discriminated as a recognition result, thereby recognizing the input pattern.
FIG. 6 shows a neural network of the speech recognition system shown in FIG. 5. The same reference numerals in the neural network in FIG. 6 denote the same parts as in FIG. 5.
Input patterns output from the feature extracting unit 11 are subjected to an inner product computation (Fφ(K,m)) given by equation (1) and are added by the inner product computing unit 12. The sum output values from the inner product computing unit 12 as the inner products are nonlinearly converted by the symmetrical nonlinear converting unit 14. The neural network unit 17 further computes inner products of weighting coefficients and the output values y1(1), y2(1), . . . , yM from the symmetrical nonlinear converting unit 14 in units of categories, computes a total sum of the inner products, and then nonlinearly converts the total sum according to the sigmoid function, thus obtaining SM(K).
The similarity computing unit 15 computes a total sum of values obtained by multiplying the output values y1(1), y2(1), . . . yM(1) from the symmetrical nonlinear converting unit 14 with "1" and values obtained by multiplying the output values y1(1), y2(1), . . . , yM(1) with "2", thus obtaining an output SS(K).
SS(K) corresponds to a score of the subspace method alone. SM(K) can be regarded as a score of the neural network alone when projective components to each subspace are input to the neural network. A determining section 18 performs primary determination based on higher-order scores (SS1, SS2, . . .) of SS(K). As a result, when a plurality of candidates are obtained, the recognition result is determined based on SM(K).
In this embodiment, neural network computations for all the categories are performed for an input. However, in a practical application, certain recognition candidates may be selected by utilizing the output from the predetermining section 16, and only higher-order candidates may be subjected to computations by the neural network unit 17. The number of layers of the neural network may be changed within the spirit and scope of the invention.
In this manner, in the pattern recognition system and method of the present invention, inner products between an input pattern and the orthogonalized dictionary sets are computed and are nonlinearly converted, and the converted values are supplied to the neural network unit 17 to execute pattern recognition processing. According to the system and method of the present invention, accurate recognition processing can be realized by the inner product computations and nonlinear conversion without performing complicated coefficient computations in earlier stages in the neural network. As a result, an input pattern can be easily and effectively recognized with high accuracy using the neural network in which coefficient computations are simplified, while drawbacks of the subspace method in terms of various noise components are effectively eliminated.
FIG. 7 is a block diagram showing the second embodiment of a pattern recognition system according to the present invention. Note that the same reference numerals in FIG. 7 denote the same parts as in FIG. 5, and a detailed description thereof will be omitted. In this embodiment, pattern recognition processing is executed using the statistical discriminant function described above in place of the neural network. In this embodiment, input speech is analyzed by a feature extracting unit 11 as in the first embodiment to obtain its power and LPC analysis parameter, and a speech interval is detected to extract a speech feature (input pattern). An inner product computing section 12 computes inner products between the input pattern and orthogonalized dictionaries prepared in units of categories and stored in an orthogonalized dictionary memory 13. The inner products are nonlinearly converted by a symmetrical nonlinear converting unit 14. Thereafter, a statistical discriminant function computing unit 19 performs predetermined coefficient computations using the nonlinearly converted values.
The statistical discriminant function computing unit 19 executes coefficient computation processing by referring to a coefficient registering unit 20 which stores mean vectors and covariance matrices obtained from learning data in units of categories in the same manner as the orthogonalized dictionary sets.
More specifically, the statistical discriminant function computing unit 19 receives the nonlinearly converted values G (=(S1, S2, . . . , SM); M is the number of orthogonal axes), and executes coefficient computation processing in accordance with equation (2) or (3) between the inputs and coefficients given from the coefficient registering unit 20, thus obtaining evaluation values. Of the evaluation values (outputs) computed in this manner, a category which provides a maximum output of all the categories (in the Bayes decision, a minimum value for the Maharanobis distance) is discriminated as a recognition result for the input pattern, thus recognizing the input pattern.
In this case, certain candidates may be selected by utilizing an output from a predetermining section like in the first embodiment without computing the statistical discriminant function for all the categories, and higher-order candidates may be subjected to discriminant function computations.
The present invention is not limited to the above embodiments.
For example, the number of axis of orthogonalized dictionaries used in inner product computations, the number of categories to be recognized, and the number of layers constituting the neural network may be determined in accordance with specifications of pattern recognition. The nonlinear function for nonlinearly converting inner products is not limited to the one described above.
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