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I am a huge advocate of natural products and in particular, essential oils to treat skin conditions and stimulate the senses. Since this is an area of great interest to me, I have thoroughly researched the effects of essential oils and was more than happy to oblige when Beautystat.com asked me to test and review a new aromatherapy product, ADORAtherapy "Smart" Gal on the Go Mood Boost™ spray.
"Smart" Gal on the Go Mood Boost™ spray has an uplifting and energizing scent with top notes of lemon and grapefruit. Both of these essential oils have been used in aromatherapy treatment at the Mayo Clinic to reduce stress and anxiety as well as alleviate nausea. The scent of grapefruit has been shown reduce food cravings and boost metabolism according to a study performed at Osaka University in Japan.
For the past two days I have skipped my coveted afternoon energy drink and opted instead for a quick spritz of my "Smart" Gal on the Go Mood Boost™ spray. The aroma instantly dispelled my desire for a cat nap and made it easier to focus on the task at hand and see it through to completion. I am definitely enjoying this product and interested in trying the 6 additional sprays in the NEW Gal on Go Mood Boost™ collection:
- Seductive: Top notes of sandalwood and ginger. Benefits: Aphrodisiac, grounding, balancing
- Beautiful: Top notes of blood orange and jasmine. Benefits: Harmonizes emotions, enhances creativity, uplifting
- Balanced: Top notes of rose and gardenia. Benefits: Balance and harmony, calming, elevates social outlook
- Inspired: Top notes of peppermint and chamomile. Benefits: Stimulating and purifying, refreshing, relieves fatigue
- Awake: Top notes of Tulsi (Holy Basil) and frankincense. Benefits: emotional and spiritual balance, energizing, mental clarify
- Peaceful: Top notes of lavender and Rosewood. Benefits: Calming and soothing, balancing, meditation and tranquility.
NEW Gal on Go Mood Boost™ — 7 sprays let you be ready to take life on without slowing you down! Make sure you're in a great mood when you take on the day!
In celebration of these NEW mood-boosting aromatic collections, Adora is embarking on a "Brick and Click" distribution strategy adventure, backed by an exciting Indiegogo campaign. And the success of this campaign can only be achieved with YOUR help!
The formulas have been combined and the packaging designed and approved, but now, ADORAtherapy™ needs YOUR help to expand its retailer reach (by selling to mid-tier retailers, mass drugstore chains and lingerie boutiques) and produce its NEW product launches.
The ADORAtherapy™ site, www.adoratherapy.com, also has plans of re-launching and building new pages: Ask Adora, and Adora TV, in which new, original content will be created to share positive messages about managing your moods, naturally.
You can help ADORAtherapy™ expand its line and bring its all-natural, mood-changing aromatic sprays to the masses. Be the first to say you found a great new company with a great mission. Be the first to try out the NEW Mood Boosts™. Be the first to tell your friends about ADORAtherapy™ and make a difference in their lives.
If you pledge a certain amount today by going to their Indiegogo site by clicking —> HERE, ADORAtherapy™ will send you the following prizes:
- PLEDGE $5 or more — "Adore Yourself™ " Wristband, made of 100% silicone
- PLEDGE $15 or more — Gal on the Go Mood Boost™ in your choice of 7 scents
- PLEDGE $30 or more — Travel & Office Good Mood Boost™ (120 ml size/10 ml)
- PLEDGE $35 or more — Gal On The Go Mood Boost™ Spray & Screen-Printed T-Shirt
- PLEDGE $45 or more — Gal On The Go Mood Boost™ Spray & Two Tone Deluxe Tote Bag
Go to their Indiegogo site to see the other PRIZES you can WIN —> HERE!
Skip the unhealthy energy drinks and change your outlook and your life with ADORAtherapy Mood Boost™!
This sample was provided courtesy of http://www.BeautyStat.com.
To view a video demo of this product visit Vimeo here:
For more information on beauty products, trends and makeup artistry visit www.spadelic.com


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23 April, 2014@8:47 am
2 comments
On April 19, 1994, Columbia Records released the debut album of a 20-year-old from Queensbridge Houses in New York City. It was deft, wise, deadly serious and matched the babyface with unparalleled promise to beats made by the era's preeminent producers.
Two decades after Illmatic, Nas sat down with Microphone Check for a conversation that moved from his love for Ali Shaheed Muhammad's group, A Tribe Called Quest, to music journalism ("If you're Sade, it doesn't matter. She does what she does. But for all of us, journalism is a huge deal.") hearing himself on the radio for the first time and his audience:
"My surroundings. The hip-hop community also," says Nas. "So that meant I made it for other rappers, I made it for other MCs, I made it for other hip-hop groups. I made it for artists, singers, people in the arts — that's who I made it for. But it comes from the street, so my surroundings wrote that album. I made it for them."
Nas also reveals that Swizz Beatz, No I.D., and Timbaland are working with him on his next album.
If Ali was inclined to utilize a video camera, I believe folks would be more inclined to check this…
Oh and insert my complaint about the producers for Nas' new album here _______________. No Preemo, Pete Rock? Large Professor? SMH…
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Rated 5 out of 5 by 1 reviewer.
Rated 5 out of 5 by Bobbo A good match with the black "Trilogy" dinnerware. After viewing the photos online, I guess I was a little worried about the robustness of this flatware set. Once I received them, I found them to be quite sturdy and to have a nice heft to them. I am very happy with them. September 30, 2013
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Kelis' new album Flesh Tone hits stores today and has already been tipped as one of 2010′s "Best Bets" by Billboard. The David Guetta-produced single "Acapella" is already an international No. 1 hit, while the festive "4th of July (Fireworks)" is a club banger that mixes R&B soulfulness and electro breaks with a rip-roaring wobbly bass riff. Kelis says of the album, "there's a physicality behind it – it's about blood, muscle, heart. I was pregnant when I recorded 75 or 80 percent of this music… And that became a real driving force for me. She also worked with label head will.i.am, Alle, Benny Benassi, Free School, Boys Noize and Burns to achieve her club-ready sound.
Kelis Video: "4th of July (Fireworks)"
Check out the explosive video for Kelis' new single, "4th of July (Fireworks)," the follow-up single to the chart-smashing "Acapella," from her 6th album, Flesh Tone, set for release July 6th.


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Course Description Masters Level
McCormick Theological Seminary
Course Number and Title: MIN 404 Reflection on the Practice of Ministry
Faculty: Joanne Lindstrom & Adjuncts
Term: Fall
Course Description:
Authentic ministry comes out of an encounter with God. In God's presence, we discern where God is calling/leading which clarifies our skills for ministry and out of which our theology of ministry flows. The first of a two-course sequence taken in conjunction with a field placement, MIN 404 focuses specifically on theological reflection on the practice of ministry as it relates to ongoing vocational discernment and pastoral formation.
Course Objectives:
By the end of the class, students will have:
•Reflected on their experience of ministry through the following lenses:
-spiritual practices – seeking to know God
-ministry skills - being known by God and God's people
-vocational discernment – hearing God's leading
-theology - knowing about God
-personal formation – knowing oneself
•Identified and discussed the emotional challenges of beginning a new ministry
•Practiced awareness of self as one being formed for ministry
•Examined approaches to leading small groups in their ministry settings
•Practiced disciplined observation of the culture and context of their field sites
•Identified learning needs that will shape the work of our second semester together.
Masters Program Learning Outcomes
The learning outcomes established for each degree program are below. The ones that are underlined are those addressed by the course above. Because a course may be taken by students in any masters degree program, outcomes in more than one degree program may be addressed by this course.
M.Div. Learning Outcomes
MTS Learning Outcomes
MA in Discipleship Development Learning Outcomes
MA in Urban Ministry Learning Outcomes
Primary Texts:
Hamman, Jaco. 2007. Becoming a Pastor: Forming Self and Soul for Ministry. Cleveland: Pilgrim Press. ISBN: 978-0-8298-1749-2
Wimberly, Edward P. 1997. Recalling Our Own Stories: Spiritual Renewal for Religious Caregivers. San Francisco: Jossey-Bass. ISBN: 0-7879-0363-9
An additional text may be chosen. Selected articles, book chapters and resources will be available electronically through Moodle.
Approximate amount of work per week students can expect (reading, writing, etc.)
75pp – 100pp of reading per each of the 8 class sessions
2-3 hours written reflection
Course Assignments and Projects
A mixture of reflection papers and short weekly responses via Moodle.
A coupled course project which can be completed during Fall or Spring.
[1] http://mccormick.edu/assets/MIN 404 Reflection on the Practice of Ministry syllabus.doc


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Welcome to the Faroese morphology project
This is the home page of the Faroese morphology project.
The project combines a morphological and morphophonological transducer with the set of lexemes found in Føroysk orðabók into a grammatical parser for Faroese.
The project is, or rather, will at some stage be, documented here.
Interactive programs
|Analyzing Faroese text
|Generating Faroese paradigms
|Generating Faroese words
Similar projects elsewhere
Our Faroese parser is not the only project of its kind. If you find our project interesting you may perhaps also want to have a look at two similar projects:
by Trond Trosterud


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Photos: Europe Retains Ryder Cup
Posted by Galen Nathanson on 09/30/2012 5:19pm PDT tags: photo
MEDINAH, Ill.—Erasing some of their worst Ryder Cup memories, the Europeans wore the image of Seve Ballesteros on their sleeves and played their hearts out Sunday at Medinah to match the greatest comeback in history and head home with that precious gold trophy.
Europe got its payback for Brookline. Martin Kaymer gave German golf redemption for Kiawah Island.
Jose Maria Olazabal squeezed his eyes and fought back tears when Kaymer holed a 6-foot par putt—about the same length that countryman Bernhard Langer missed at Kiawah in 1991—to beat Steve Stricker and give Europe the point it needed to keep the cup.
[insertSlideshowjava xml="http://director.denverpost.com/images.php?album=1865" api="dpmc"]


text: The neo-rock and roll vibe of studded stilettos, ripped jeans and Sergeant Pepper jackets at Christophe Decarnin's red-hot showing for Balmain marked a fierce start to Paris Fashion Week. Attendees were also garbed in music-related street styles including lots of hard-edged leather and metal combinations. One jacket in particular, embellished all-over with gold studs stood out in a crowd!


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Top Definition
A now dirty town in gwinnett county, northeast of atlanta, that has slowly declined in the past 20 years from being a haven for upper middle class families, to a haven for the mexican mafia.
"I live in Lawrenceville, Ga. Have you heard of it?"
by benny tee March 18, 2009


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A Real Best Friend
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A Dog's life...
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I think Noah is going to have a little trouble breeding the lions...
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Element of surprise!
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Black Friday
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They're Not Quite As SMART As They Think They Are
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Get to tha shoppar!
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Important meeting taking place across the street...
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When the waiter asks how the food is...
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I love manatees because they are so majestic.
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Laugh more daily


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The annual Big Splash takes place this Saturday at Avery Aquatic Center as top-ranked Stanford (19-1, 5-0 MPSF) hosts No. 4 California at 5 p.m. Saturday's contest between the will be broadcast live on the Pac-12 Network with Jason Knapp and Chris Dorst on the call.
The Stanford-California women's water polo rivalry has been a one-sided affair over the past 14 years, as the Cardinal has won the past 36 meetings and not lost to the Golden Bears since March 26, 2000. On that day, Stanford dropped a 6-3 decision to California, but since then has captured the next 36 matchups, including one already this season, as the Cardinal beat the Golden Bears 14-8 in the UCI Invitational third-place game back on Feb. 23.
Saturday's Big Splash will also serve as Stanford's Senior Day in which the Cardinal's five seniors will be honored pregame. Seniors Kaley Dodson, Annika Dries, Kaitlyn Lo, Lexie Ross and Kelsey Suggs will be recognized for their contributions to the Cardinal over the past four years, and in Dries' case, five years. Dries, who took a leave of absence in 2012 as she prepared for the Olympic Games, is a 2011 Peter J. Cutino Award winner, while the entire quintet has helped bring a pair of NCAA titles (2011, 2012) to The Farm with a chance at a third in 2014.
Stanford's offensive power has been nearly unmatched this season, and its output is represented by a very diverse group of scorers, as Stanford boasts five 30-goal scorers. Junior driver Kiley Neushul leads the pack with 41 goals, while fellow drivers Maggie Steffens (37 goals) and Anna Yelizarova (32) are also over the 30-goal mark. Two-meter players Ashley Grossman and Dries give the Cardinal an unmatched inside threat, scoring 38 and 36 goals so far this season, respectively.
In her first season as the Cardinal's No. 1 goalie, sophomore Gabby Stone has proven equal to the task of facing some of the nation's top competition. The La Jolla, Calif. native ranks fourth in the MPSF with a 6.13 goals-against average and has anchored the Cardinal defense in key wins over top teams such as USC, UCLA and California. Against USC on March 30, Stone made 12 saves in Stanford's 9-5 win, and followed that with seven saves, including a crucial stop of a Bruin penalty shot, in April 11's 9-8 win at No. 2 UCLA.
Stanford clinched the No. 1 seed at April 25-27's MPSF Championship with last Saturday's win at Cal State Bakersfield. With the top seed in hand, the Cardinal receives a bye into next Saturday's semifinal at 2:30 p.m.
California (17-6, 2-3 MPSF) slipped into at tie at No. 4 with Arizona State in the Varsity Top 20 Poll this week after suffering a 6-5 upset Saturday against the Sun Devils. Since putting together a six-game winning streak over the first three weeks of February, the Golden Bears have gone 4-5 over their next nine games heading into Saturday's clash with Stanford. California is led in the pool by the the MPSF's leading scorer, Dora Antal, who averages 3.15 goals per game and has 63 goals total. In the cage, Madeline Trabucco owns a goals-against average of 8.00.


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WEST POINT (AP) — West Point police say a man has been found shot to death in his bed and the victim's 14-year-old son is a suspect in the case.
Police tell WTVA-TV that 41-year-old Randy Kelly was found Thursday morning with a gunshot wound to the head. Kelly was pronounced dead on the scene.
Police Chief Tim Brinkley says the man's son is the prime suspect, but his name is not being released because of his age.
Investigators say a woman, whose name was withheld, was there when officers arrived. She directed officers to the body and told them that the victim's son was missing.
Police say the boy was found later at an undisclosed location and was treated for what appeared to be a self-inflicted knife wound.


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The sounds of Kanye booed on stage at Bonnaroo are fading, but people are still talking about the mixed reaction the rapper received when he closed out the event over the weekend. Kanye was both booed and cheered by the fans in attendance at the event. His new wife Kim Kardashian was in attendance at the concert, and she managed to shock fans with her see through top. Web Pro News reported the latest on Kanye's performance this weekend on June 16.
Kanye West was not randomly booed off the stage. The rapper headline the concert event for their Friday night performances. The performance started off 10 minutes late because the rapper didn't appear on time. He also cut his performance 20 minutes short. After he finished singing, he went on a rant about his skills and how he is the best. He bashed the press, and he compared himself to some of the best musicians of the past.
In fact, he said that he was better than Jim Morrison and Jimi Hendrix. That is when the crowd of 90,000 began to boo the rapper. Those in attendance did not like West's rant at all. Those rants are usually reserved for his Twitter account. Following the event, fans that attended ventured to social media, and many felt that Kanye West was booed off the stage, but it sounds like Kanye's rant was booed off the stage. One does have to wonder if the rapper had stuck to singing if he would have received the boos from the crowd.
Kanye is known for stirring things up. He is not one that plays nice. He says what he wants to say when he wants to say it. His rants are well known. His shocking behavior is not new. It was thought that his relationship with Kim Kardashian would mellow out the rapper some, but that has not happened. Fatherhood didn't mellow him out any either. He is still the same outspoken Kanye that really doesn't seem to care what anyone thinks of him.
He was 'booed off the stage' at Bonnaroo, but by Sunday he was back at home with Kim and baby North to celebrate Father's Day. The day was also extra special because it was the first birthday of the infant. According to Philly.com, West spent the day with Kim and North watching sports on television.
Kim also went to her Instagram to share a photo of father and daughter. Kanye and North are napping in the photo snapshot. Kim said the following to caption the photo: "This is what life is about! Our baby girl turned 1 today! We played so hard they passed out while we were watching the game! "Happy Father's Day to the best daddy in the world! The way you love our daughter and protect her makes me filled with so much love!"
It is clear that Kanye doesn't care about being booed at Bonnaroo. This is not the first time it has happened at that event. He appeared at the event in 2008, and he was booed by concert goers at that event as well.


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On Saturday, the Drudge Report banner linked to the report, "NSA Hacks Chinese Servers," and related stories. Alone, the reports were enough to cause the Obama administration to squirm at the potential international uproar the disclosure may cause. In addition, the report also places First Lady Michelle Obama in a most uncomfortable position, in China to spread goodwill under a cloud of United States' spying scandal on the country she is visiting.
As Michelle Obama appears to be successfully winning her way into the hearts of the Chinese people, it doesn't help her for it to be revealed that the National Security Agency (NSA) has pried its way into the sealed industrial heart of China's telecommunications giant Huawei. The untimely disclosure of NSA documents was published by the New York Times as well Der Spiegel almost simultaneously on March 22, 2014.
The report provokes a host of questions. At this time, there appears to be no information available about whether the release of the two publications were coordinated or whether the Obama administration attempted to delay the story until Michelle returned or even if the Obama administration was aware the story would break while Michelle was in China. If the Obama administration was aware, that knowledge would give Michelle's statement, linked on the Drudge Report that "Barack 'has dragged me kicking and screaming into things that I wanted no part of'" new meaning.
It's possible that even if the Obama administration tried to kill the story a lack of influence with international news would have stopped the efforts cold. In any case, Michelle Obama is in the hot seat far away from home. As this is being published, the Obama administration is most likely preparing to get out in front with their official version of this story soon.
Stories coming out of China claiming that the staff of the hotel which is housing Michelle's entourage of 70 may be regretting that their hotel was chosen to accommodate the visit of the First Lady pale in comparison to the potential international embarrassment which may follow the release of these Edward Snowden documents. There are sure to be additional developments to this story. Already Der Spiegel is promising the release of a longer version of this story will appear in German on Monday.
In the meantime, can the Drudge Report be faulted for linking to a story that was already out there? The Drudge Report reach may equal or even surpass that of The New York Times and a link on Drudge undeniably has power. Still once the story hit the internet, there was no hope of containing it nor of protecting Michelle Obama from the repercussions.
Over on his Twitter site, Drudge took particular issue with another statement of the First Lady. He tweeted, "Michelle O in Beijing: Internet 'universal right'. Except in USA, where her Homeland Security shuts dozens of websites without court order!!"
Related Articles:
- Jimmy Carter fears NSA: 'I believe if I send an email, it will be monitored'
- Drudge: 'Critical investigative journalism is all but dead,' killed by DOJ spying
- Drudge Report: 'Obama doesn't dare limit the NSA, They know all his secrets!'
- Drudge slams Dianne Feinstein's government spying 'pity party,' tells her to 'Join the club!'
- Drudge Report: Matt shames 'free people' for letting NSA spy on them


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This is what Colorado is like this week
Submitted by Kris Thompson on
For those not living in Colorado (or at least the front range) this is what our bike paths look like
and this
News Item:
1 Comment
What's the state of the
Submitted by Anonymous (not verified) on
What's the state of the velodrome this time around? Talk about kicking someone while they're down. Very unfortunate.
Points: -3


text:
General Electric selling Apollo 11-esque moon boot sneakers
Forty-five years after Neil Armstrong took a giant leap for mankind, General Electric has taken a small step — into shoe design.
The company announced Thursday it will sell commemorative sneakers, dubbed The Missions, designed with some of the same materials that were used in Armstrong's boot when he made that famous footprint on the moon.
The shoes will only be available from online retailer JackThreads and they go on sale at the exact same time Armstrong took that first step out of Apollo 11 — 4:18 p.m. July 20. To commemorate the year, they are priced at $196.90.
Find out more with this Newsy video.


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The Norway Pavilion at Walt Disney World's Epcot could be looking a lot more like the Kingdom of Arendelle as soon as October, if a new report is to be believed.
The biggest change coming to the Norway Pavilion is a Frozen-themed ride, which would replace the aging "Maelstrom" ride, which explores Norway's history and culture. According to a Norwegian news report, the Maelstrom ride could shut down as soon as October to make way for construction on the new Frozen ride.
However, it seems Disney may have plans for a more extensive Frozen overhaul of the entire Norway Pavilion. The Norwegian news report (you can watch the video here, but be sure to turn on English subtitles) claims Disney is looking to convert the Norway Pavilion to be focused almost exclusively on Frozen.
Besides cashing in on the massive success of the film, it seems Disney wants a financil investment from the Norwegian government to help refurbish the pavilion in order to keep the focus on the country -- and not Frozen. (Several Norwegian companies and the Norwegian government originally helped fund the creation of the Norway Pavilion at Epcot as a way to promote tourism. Norway contributed $200,000 annually from 1992-2002 to help maintain the pavilion.)
According to the Norwegian news report, Disney is starting to make plans for additional changes to the Norway Pavilion, beyond remaking the "Maelstrom" ride. Plans include eliminating most of the shops in the Norway Pavilion to make room for a Frozen meet-and-greet, and other Frozen-related activities.


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Here is a calendar of events for the weekend of April 12-13. Please check the individual websites for additional information and last minute changes.
April 12, 10:00 a.m. – 5:00 p.m. – CityLit Project and the Enoch Pratt Free Library join together for the annual CityLit Festival. The event takes place inside the Central Library at 400 Cathedral Street. This is a free event.
April 12, 11:00 a.m. – The Eastern Shore Writers Association meets in Chestertown, Maryland. Lunch is available for a $20 charge. The meeting is free. This month's meeting is held at the Chester River Yacht & Country Club.
April 12, 7:00 p.m. – BSFS, the Baltimore Science Fiction Society, has a Balticon Planning Meeting followed by their business meeting. This meeting takes place at the BSFS building in Baltimore.
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(AP Photo/Bob Edme)
At a recent general membership meeting of the Miami Valley Youth Soccer Association (MVYSA), area select soccer coaches gathered to hear the latest updates from the league and finalize the divisions where there teams will compete this coming spring season. Dan Cullin, a representative of the Dayton Area Soccer Officials Association (DASOA) was on hand to speak to the coaches about the relationship between referees, coaches and fans. A particular emphasis was placed on the plight of first year referees.
As any parent of a select soccer player can probably tell you, there are often times when parents are asked to help officiate by serving as line judges during a match. This often results from lack of qualified referees available to officiate. One of the major challenges is referee retention. Just go to any match, pick a random day of your choice, and observe the sideline behavior of the fans - and some coaches. It doesn't take long to understand why, as Mr. Cullin states, "25 - 50% of referees don't come back after their first year." During many games, the problem of fans being antagonistic toward the referee becomes so bad that the referee is forced to stop play and ask the coach to address the situation. Responsibility for the fans behavior resides with the coaches. They are expected to handle the situation so that the match may resume under more peaceful conditions. Part of the challenge here is that coaches and fans must realize that "we are on the same side" says Mr. Cullin. "We are there for the kids."
The area of officiating that seems to come under the most scrutiny is in the calling of fouls. Often there are fans, and coaches, who consistently feel that the referee doesn't call the game both ways, that they tend to favor one side or the other. And, as you can probably guess, it's usually the other team that is favored! Mr. Cullin explains that one of the reasons for this is that a lot of people don't understand that there are four types of fouls:
- The one's you see and referee sees
- The one's you see and the referee doesn't see
- The one's the referee sees and you don't see
- The one's neither you or the referee see
This is a particular challenge for first year referees. Just like anyone starting a new endeavor, expertise comes with experience and education. The process of developing quality officials; and, for that matter, players and coaches alike requires time. There are going to be successes and failures. The key is in learning from the failures to become a better official. Just like our young players first handling the ball, if we scream and harass them they will lose interest. Yes, the officials are being paid, but honestly not enough to deal with that. So, they choose to do something else and the situation never improves.
Here's how you can help.
Be patient. Mistakes are going to be made. Some calls will be made and others missed. Get over it, you just missed a play on the field while you were yelling at the ref.
Be courteous. When mistakes are made, either chalk it up to learning or find an appropriate time to respectfully ask the referee to clarify the call.
Follow the Process. When there are legitimate concerns about officiating (perhaps a referee allows a match to get too physical or speaks inappropriately to a player or spectator) there is a formal process that the coach can follow to file a complaint.
If you can't beat 'em, join 'em! Be proactive and get involved. Take the entry level officials course and be part of the game. You can learn more about the game, serve the community and make a little cha-ching on the side!
The entry level referee course is being offered from 6:00pm - 9:00pm on February 16, 23 and March 2 at the Huber Heights Athletic Foundation located at 5367 Fishburg Road. The cost is $60 and includes licensing, instructional materials, referee uniform and equipment. For more information, check out the Ohio South State Referee Committee website at www.ossrc.com or e-mail Ed Shoemaker at email@example.com .


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- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Table of Contents
International Journal of Distributed Sensor Networks
Volume 2012 (2012), Article ID 938521, 16 pages
A Scalable Multi-Target Tracking Algorithm for Wireless Sensor Networks
School of Electrical Engineering and Computer Science, ASRI, Seoul National University, Seoul 151-744, Republic of Korea
Received 21 March 2012; Accepted 31 May 2012
Academic Editor: Hasan Cam
Copyright © 2012 Songhwai Oh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Multi-target tracking is a representative real-time application of sensor networks as it exhibits different aspects of sensor networks such as event detection, sensor information fusion, multihop communication, sensor management, and real-time decision making. The task of tracking multiple objects in a wireless sensor network is challenging due to constraints on a sensor node such as short communication and sensing ranges, a limited amount of memory, and limited computational power. In addition, since a sensor network surveillance system needs to operate autonomously without human operators, it requires an autonomous real-time tracking algorithm which can track an unknown number of targets. In this paper, we develop a scalable real-time multi-target tracking algorithm that is autonomous and robust against transmission failures, communication delays, and sensor localization error. The algorithm is based on a rigorous probabilistic model and an approximation scheme for the optimal Bayesian filter. In particular, an extensive simulation study shows that there is no performance loss up to an average localization error of 0.7 times the separation between sensors and the algorithm tolerates up to 50% lost-to-total packet ratio and 90% delayed-to-total packet ratio. The proposed algorithm has been successfully applied to real-time multi-target tracking problems using wireless sensor networks.
1. Introduction
In wireless sensor networks, many inexpensive and small sensor-rich devices are deployed to monitor and control our environment [1, 2]. Each device, called a sensor node, is capable of sensing, computation, and communication. Sensor nodes form a wireless adhoc network for communication. The limited supply of power and other constraints, such as manufacturing costs and limited package sizes, limit the capabilities of each sensor node. For example, a typical sensor node has short communication and sensing ranges, a limited amount of memory, and limited computational power. However, the abundant number of spatially spread sensors will enable us to monitor changes in our environment accurately despite the inaccuracy of each sensor node.
Multi-target tracking is a representative real-time application of sensor networks as it exhibits different aspects of sensor networks such as event detection, sensor information fusion, communication, sensor management, and real-time decision making. The applications of tracking using sensor networks include surveillance, search and rescue, disaster response system, pursuit evasion games [3], distributed control [4], spatiotemporal data collection, and other location-based services [5]. Two well-known multi-target tracking algorithms are joint probabilistic data association (JPDA) filter [6] and multiple hypothesis tracker (MHT) [7, 8]. But, considering the limited computational power and memory space of each sensor node, they are not feasible for sensor networks due to their exponential time and space complexities. As a result, many new tracking algorithms for sensor networks have been developed recently.
Many target tracking algorithms developed for sensor networks are designed for single-target tracking [10–21] and some of these algorithms are applied to track multiple targets using classification [10–12] or heuristics, such as the nearest-neighbor filter (NNF) used in [13]. The NNF [6] processes the new measurements in some predefined order and associates each with the target whose predicted position is closest, thereby selecting a single association. Although effective under benign conditions, the NNF gives order-dependent results and breaks down under more difficult circumstances. Some algorithms are designed for multi-target tracking [22–24] where the complexity of the data association problem inherent to multi-target tracking is avoided by classification [22, 23] or heuristics [22, 24]. The associations between measurements and targets are not generally known in multi-target tracking. The data association problem is to determine which measurements were generated by which targets. When tracking targets of a similar type or when reliable classification information is not available, the classification-based tracking algorithm behaves as the NNF. Considering the fact that the complexity of the data association problem is NP-hard [25, 26], a heuristic approach breaks down under difficult circumstances. Furthermore, the measurement inconsistencies common in sensor networks, such as false alarms and missing measurements due to missing detection or packet loss, are not fully addressed in many algorithms. On the contrary, the multi-target tracking algorithm developed in this paper is based on a rigorous probabilistic model and based on a true approximation scheme for the optimal Bayesian filter.
We can categorize a target tracking algorithm for sensor networks by its computational structure: centralized [11, 14, 15], hierarchical [16, 17], or distributed [10, 12, 13, 18–24]. Since each sensor can only sense a small region around it, and its measurements are noisy and inconsistent, measurements only from a single sensor and its neighboring sensors are not sufficient to initiate, maintain, disambiguate, and terminate tracks of multiple targets. It requires measurements from distant sensors to initiate, maintain, disambiguate, and terminate tracks of multiple targets. But considering the communication load and delay when exchanging measurements between distant sensors, a completely distributed approach is not feasible for real-time applications. On the other hand, a completely centralized approach is not robust and scalable. Hence, we consider a hierarchical architecture to minimize the communication load and delay while being robust and scalable. In fact, our algorithm has been successfully demonstrated using a large-scale outdoor sensor network deployment and used to solve a real-time control problem [27].
Markov chain Monte Carlo data association (MCMCDA) presented in [9] can track an unknown number of targets in real-time and is an approximation to the optimal Bayesian filter. It has been shown that MCMCDA is computationally efficient compared to the multiple hypothesis tracker (MHT) [7] and outperforms MHT with heuristics (i.e., pruning, gating, clustering, -scan-back logic and -best hypotheses) under extreme conditions, such as a large number of targets in a dense environment, low detection probabilities, and high false alarm rates [9]. MCMCDA is suitable for sensor networks since it can autonomously initiate and terminate tracks. Since transmission failure is another form of missing observation, MCMCDA is robust against transmission failures. MCMCDA performs data association based on both current and past observations, so delayed observations, that is, out-of-sequence measurements, can be easily combined with previously received observations to improve the accuracy of estimates. Furthermore, MCMCDA requires less memory as it maintains only the current hypothesis and the hypothesis with the highest posterior. It does not require the enumeration of all or some of hypotheses as in [7, 28]. In this paper, we extend the MCMCDA algorithm to sensor networks in a hierarchical manner so that the algorithm becomes scalable. To our knowledge, the algorithm presented in this paper is the first general real-time scalable multi-target-tracking algorithm for sensor networks which can systematically track an unknown number of targets in the presence of false alarms and missing observations and is robust against transmission failures, communication delays, and sensor localization error.
We consider a simple shortest-path routing scheme on a sensor network. The transmission failures and communication delays of the network are characterized probabilistically. We assume the availability of a small number of special nodes, supernodes, that are more capable than regular nodes in terms of computational power and communication range. Examples of a supernode include high-bandwidth sensor nodes such as iMote and BTnode [29], gateway nodes such as Stargate, Intrinsyc Cerfcube, and PC104 [29], and the Tier-2 nodes used in the experiment of [27]. Each node is assigned to its nearest supernode and nodes are grouped by supernodes. We call the group of sensor nodes formed around a supernode a "tracking group". When a node detects a possible target, it communicates with its neighbors and observations from the neighboring sensors are fused and sent to its supernode. Each supernode receives the fused observations from its tracking group and executes the tracking algorithm. Each supernode communicates with neighboring supernodes when a target moves away from its range. Lastly, the tracks estimated by supernodes are combined hierarchically. Although a specific sensor network model is used for the performance evaluation, the algorithm is applicable for different routing algorithms and sensor models, for example, distributed air traffic control [30].
As mentioned earlier, the algorithm presented in this paper is used in [27]. Although the overview of the algorithm is described in [27], the focus of [27] is to describe a new control system architecture using sensor networks and an outdoor experiment. In this paper, we describe the algorithm in detail and study the characteristics of the algorithm under different conditions in simulation. The paper also includes experimental results from an indoor experiment with 45 sensors and ourdoor experiments described in [27]. While a wireless sensor network generates noisy, inconsistent, and bursty measurements, the algorithm described in this paper successfully estimated the number of targets and tracks of targets in real-time.
The remainder of this paper is structured as follows. The sensor network model assumed in this paper is described in Section 2. In Section 3, the multi-target tracking problem, the MCMCDA algorithm, and the hierarchical extension of MCMCDA are described. The simulation and experiment results are given in Sections 4 and 5. Frequently appearing symbols are listed in Abbreviations section.
2. Sensor Network Model
Let be the number of sensor nodes, including both supernodes and regular nodes, deployed over the surveillance region . We assume that each supernode can communicate with its neighboring supernodes. Let be the location of the th sensor node and let . Let be a communication graph such that if and only if node can communicate with node . Let be the number of supernodes and let be the position of the th supernode, for . Let be the assignment of each sensor to its nearest supernode such that if . For a node , if , the shortest path from to in is denoted by . For each supernode , its tracking group contains a set of nodes .
Let be the sensing range. If there is an object at , a sensor can detect the presence of the object. Each sensor records the sensor's signal strength, where , , and are constants specific to the sensor type, and we assume that are normalized such that has the standard Gaussian distribution. This signal-strength based sensor model (1) is general for sensors available in sensor networks, such as acoustic and magnetic sensors, and has been used frequently [18, 23, 31]. For each , if , where is a threshold set for appropriate values of detection and false-positive probabilities, the node transmits to its neighboring nodes, which are at most away from , and listens to incoming messages from its neighborhood. Note that this approach is similar to the leader election scheme in [18] and we assume that the transmission range of each node is larger than . However, this approach may cause some missing observations if there is more than one object in this disk of radius . A better approach to fuse local data is required and we will address this issue in our future work. For the node , if is larger than all incoming messages, , and , then the position of an object is estimated as The estimate corresponds to the computation of a center of mass of the sensing nodes weighed by measured signal strengths. The node transmits to the supernode via the shortest path . If is not the largest compared to the incoming messages, the node does nothing and goes back to the sensing mode. Although each sensor cannot give an accurate estimate of object's position, as more sensors collaborate, the accuracy of estimates improves as shown in Figure 1. The collaboration of sensors makes the system more robust against node failures and we can increase the detection probability and decrease the false alarm rate by collaboration.
A transmission along the edge fails independently with probability and the message never reaches a supernode. Notice that the transmission failure includes failures from retransmissions. We can consider transmission failure as another form of a missing observation. If is the number of hops required to relay data from a sensor node to its supernode, the probability of successful transmission decays exponentially as increases. To overcome this problem, we use independent paths to relay data if the reporting sensor node is hops away from its supernode. The probability of successful communication from the reporting node to its supernode can be computed as where . See Figure 6(a) for an example of the ratio between the number of lost packets and the number of total packets as a function of the transmission failure rate .
We assume each node has the same probability of delaying a message. If is the number of (additional) delays on a message originating from the sensor , that is, the number of sampling intervals elapsed since the required arrival time at the supernode , is distributed as We are modeling the number of (additional) delays by the negative binomial distribution. A negative binomial random variable represents the number of failures before reaching the fixed number of successes from Bernoulli trials. In our case, it is the number of delays before successful delay-less transmissions. See Figure 7(a) for an example of the ratio between the number of delayed packets, and the number of total packets as a function of the communication delay rate .
When the network is heavily loaded, the independence assumptions on transmission failure and communication delay may not hold. However, the model is realistic under the moderate conditions, and we have chosen it for its simplicity.
3. Multi-Target Tracking
3.1. Problem Formulation
Let be the duration of surveillance. Let be the number of targets that appear in the surveillance region during the surveillance period. Each target moves in for some duration . Notice that the exact values of and are unknown. Each target arises at a random position in at , moves independently around until , and disappears. At each time, an existing target persists with probability and disappears with probability . The number of targets arising at each time over has a Poisson distribution with a parameter where is the birth rate of new targets per unit time, per unit volume, and is the volume of . The initial position of a new target is uniformly distributed over .
Let be the discrete-time dynamics of the target , where is the dimension of the state variable, and let be the state of the target at time for . The target moves according to where are white-noise processes. The white-noise process is included to model nonrectilinear motions of targets. When a target is present, a noisy observation (or measurement) of the state of the target is measured with a detection probability . Notice that, with probability , the target is not detected and we call this a missing observation. There are also false alarms, and the number of false alarms has a Poisson distribution with a parameter , where is the false alarm rate per unit time, per unit volume. Let be the number of observations at time , including both noisy observations and false alarms. Let be the th observation at time for , where is the dimension of each observation vector. Each target generates a unique observation at each sampling time if it is detected. Let be the observation model. Then the observations are generated as follows: where are white noise processes and is a random process for false alarms. We assume that the targets are indistinguishable in this paper, but if observations include target type or attribute information, the state variable can be extended to include target type information as done in [32].
The main objective of the multi-target tracking problem is to estimate , and , for , from noisy observations.
Let be all measurements at time and be all measurements from to . Let be a collection of partitions of such that, for , , where is a set of false alarms and is a set of measurements from target for . Note that is also known as a joint association event in literature. More formally, is defined as follows: (1); (2) and for ; (3) is a set of false alarms; (4) for and ; (5) for . An example of a partition is shown in Figure 2. Here, is the number of tracks for the given partition . We call a track when there is no confusion although the actual track is the set of estimated states from the observations . This is because we assume there is a deterministic function that returns a set of estimated states given a set of observations. A track is assumed to contain at least two observations since we cannot distinguish a track with a single observation from a false alarm, assuming . For special cases in which or , the definition of can be adjusted accordingly.
Let be the number of targets at time , be the number of targets terminated at time and be the number of targets from time that have not terminated at time . Let be the number of new targets at time , be the number of actual target detections at time , and be the number of undetected targets. Finally, let be the number of false alarms. Using the Bayes rule, it can be shown that the posterior of is: where is the likelihood of observations given , which can be computed based on the chosen dynamic and measurement models. For example, the computation of for the linear dynamic and measurement models can be found in [9]. Our formulation of (7) is similar to MHT [28], and the derivation of (7) can be found in [28]. The parameters , , , and have been widely used in many multi-target-tracking applications [6, 28]. Our experimental and simulation experiences show that our tracking algorithm is not sensitive to changes in these parameters in most cases. In fact, we used the same set of parameters for all our experiments.
There are two major approaches to solve the multi-target-tracking problem [9]: maximum a posteriori (MAP) and Bayesian approaches. The MAP approach finds a partition of observations such that is maximized and estimates the states of the targets based on this partition. A Bayesian approach called minimum mean square error (MMSE) finds an estimate which minimizes the expected square error. For instance, is the MMSE estimate for the state of target . However, when the number of targets is not fixed, a unique labeling of each target is required to find under the MMSE approach. In this paper, we take the MAP approach to the multi-target-tracking problem for its convenience.
3.2. Markov Chain Monte Carlo Data Association Algorithm
In this section, we explain an MCMC sampler designed to solve the multi-target-tracking problem. MCMC is a general method to generate samples from a distribution by constructing a Markov chain whose states are and whose stationary distribution is . If we are at state , we propose following the proposal distribution . The move is accepted with an acceptance probability where otherwise the sampler stays at , so that the detailed balance is satisfied. If we make sure that is irreducible and aperiodic, then converges to its stationary distribution by the ergodic theorem [33].
The MCMC data association (MCMCDA) algorithm is described in Algorithm 1. MCMCDA is an MCMC algorithm whose state space is described in Section 3.1, and whose stationary distribution is the posterior (7). The proposal distribution for MCMCDA consists of five types of moves. They are (1) birth/death move pair; (2) split/merge move pair; (3) extension/reduction move pair; (4) track update move; and (5) track switch move. The MCMCDA moves are graphically illustrated in Figure 3. For a detailed description of each move, see [9]. The inputs for MCMCDA are the set of all observations , the number of samples , and the initial state . The acceptance probability is defined in (8) where = from (7). In Algorithm 1, we use MCMC to find a solution to a combinatorial optimization problem. So it can be considered as simulated annealing at a constant temperature.
In addition, the memory requirement of the algorithm is at its bare minimum. Instead of keeping all , we simply keep the partition with the maximum posterior, . Notice that, in MCMC, the construction of is done on the fly according to the proposal distribution and there is no need to store previously visited states.
The Markov chain designed by Algorithm 1 is irreducible and aperiodic [9]. In addition, the transitions described in Algorithm 1 satisfy the detailed balance condition since it uses the Metropolis-Hastings kernel (8). Hence, by the ergodic theorem, the chain converges to its stationary distribution [33].
3.3. Online Hierarchical MCMCDA
At each supernode, we implement the online MCMCDA algorithm with a sliding window of size using Algorithm 1. This online implementation of MCMCDA is suboptimal because it considers only a subset of past measurements. But since the contribution of older measurements to the current estimate is much less than recent measurements, it is still a good approximation. At each time step, we use the previous estimate to initialize MCMCDA and run MCMCDA on the observations belonging to the current window. Supernode maintains a set of observations , where is the current time and is the th fused measurement received by supernode . At time , the observations at time are removed from and a new set of observations is appended to . Any delayed observations are inserted into the appropriate slots. Then, each supernode initializes the Markov chain with the previously estimated tracks and executes Algorithm 1 on . Once a target is found, the next state of the target is predicted. If the predicted next state belongs to the surveillance area of another supernode, the target's track information is passed to the corresponding supernode. These newly received tracks are then incorporated into the initial state of MCMCDA for the next time step.
Since each supernode maintains its own set of tracks, there can be multiple tracks of a single object maintained by different supernodes. To make the algorithm fully hierarchical, we do track-level data association to combine tracks from different supernodes. Let be the set of tracks maintained by supernode . Let be the combined observations only from the established tracks. We form a new set of tracks from while making sure that constraints defined in Section 3.1 are satisfied. Then we run Algorithm 1 on this combined observation set with the initial state . An example of hierarchical tracking and track-level data association is shown in Section 4.5.
4. Simulation Results
For simulations below, we consider the surveillance over a rectangular region on a plane, . The state vector is , where is a position in along the usual and axes and is a velocity vector. The linear dynamic and measurement models are used where is a sampling interval, and are white Gaussian noises with zero mean and covariance and (set according to Figure 1), respectively, and We assume a sensor grid, in which the separation between sensors is normalized to 1. So the unit length in simulation is the length of the sensor separation. In all simulations, and . For the sensor model, we use , , , and .
Since the number of targets is not fixed, it is difficult to measure the performance of an algorithm using a standard criterion such as the mean square error. Hence, we use two separate metrics to measure performance: the estimation error in the number of targets , and the estimation error in position . Let be the number of targets at time and be the estimated number of targets at time . We define as The computation of is done when it makes sense. At any , there can be at most common tracks. We find matches between true tracks and estimated tracks based on positions at . For each match , let and be the position of the true track and the estimated track at time , respectively. We define as Both and are normalized with respect to the number of targets for easier comparison.
We first evaluate the effect of the sensing range and empirically find that there is an optimal value at which the estimation error is minimized. Then we illustrate the robustness of our algorithm against sensor localization error, transmission failures, and communication delays. We then give an example of surveillance using wireless sensor networks and demonstrate how the hierarchical MCMCDA algorithm works.
4.1. Sensing Range
When localizing a single target, we can minimize the localization error by allowing more sensors to collaborate, which is equivalent to increasing as shown in Figure 1. But when there is more than one target, this is no longer true, since observations from different targets can collide, giving missing observations and observations away from target positions. Figure 4 shows the estimation errors and when 10 targets appear and disappear at random times and . For slow-speed vehicles, ; for medium-speed vehicles; for high-speed vehicles. For each vehicle type, five different scenarios are used. When , the sensors do not completely cover the surveillance region and do not detect targets at all times, hence the estimation error is higher. But as we increase beyond , estimation errors increase, since there are more collisions among observations of different targets. The estimation errors are low for high-speed vehicles since it is easier to disambiguate crossing targets. From Figure 4, we find that is a good range for all types of vehicles and it is used in simulations below. Notice that when , for each sensor , there are 28 neighboring sensors which are at most away from . We can also interpret this result in terms of sensor density for a fixed value of . Hence, once the surveillance region is fully covered by sensors, a further increase in density does not improve the estimation error.
4.2. Sensor Localization Error
The localization of sensor nodes in an adhoc wireless sensor network, without expensive hardware such as the global positioning system (GPS), is a challenging problem [34]. Hence, an algorithm which utilizes sensor positions needs to be robust against the sensor localization error. Suppose that the true position of sensor node is and , where are Gaussian noises with zero mean and covariance . Figure 5 shows the estimation errors from tracking 10 targets as functions of the sensor localization error . It shows that the algorithm is robust against the sensor localization error and, for , the algorithm performs as if there is no sensor localization error. This is remarkable since corresponds to the case in which the average localization error is 0.707 times the separation between sensors. Notice that is always under 0.18, so the algorithm finds most tracks for all . But gets larger at high , since the target position estimates are based on incorrect node positions. Considering the fact that is computed from the norm of a vector in , is mostly due to the sensor localization error.
4.3. Transmission Failures
To assess the effects of transmission failures alone, we assume that there are no delayed observations, no false alarms, and no missing detections. A single supernode is placed at the center. As mentioned earlier, transmission failures are missing observations and Figure 6(a) shows the ratio between the number of lost packets and the number of total packets as a function of the transmission failure rate . As increases, we lose more packets and, at , we lose all packets. Figure 6 shows the estimation errors and the algorithm performs well for . The estimation error is low at high since most of packets are lost at high , making the data association problem easier. Notice that when more than 50% of packets are lost (see Figure 6(a)). It shows that our algorithm is very robust against transmission failures and tolerates up to 50% lost-to-total packet ratio. Hence, for given , we can find the maximum radius of a tracking group, measured by the number of hops from a supernode, such that , sustaining the performance of the algorithm. For example, if , , that is, the most distant node can be 38 hops away from a supernode. But one must consider the communication delay which increases with the number of hops.
4.4. Communication Delays
To assess the effects of communication delays alone, we assume that there are no transmission failures, no false alarms, and no missing observations. Figure 7(a) shows the ratio between the number of delayed packets and the number of total packets as a function of the communication delay rate . As increases to 1, all packets are delayed. Since , we do not receive all the delayed packets and the ratio between the number of delayed packets that are eventually received and the number of packets is shown in a dotted line in Figure 7(a). The estimation errors are shown in Figure 7. It shows a good performance for . At , 90% of packets are delayed and 72% of packets have delays less than . Hence, the system tolerates up to 90% delayed-to-total packet ratio.
4.5. An Example of Surveillance with Sensor Networks
In this section, we give an example of surveillance with sensor networks. The surveillance region is divided into four quadrants and sensors in each quadrant form a tracking group, where a supernode is placed at the center of each quadrant. The scenario is shown in Figure 8(a). We used , , and . The surveillance duration is increased to . Figure 8(b) shows the observations received by supernodes. There were a total of 1,174 observations and 603 observations were false alarms. A total of 319 packets out of 1,174 packets were lost due to transmission failures and 449 packets out of 855 received packets were delayed. The tracks estimated by the algorithm are shown in Figure 8(c). Figure 8(c) shows the tracks estimated locally by supernodes while Figure 8(d) shows the tracks estimated by the track-level data association step. Figure 8(d) shows that the track-level data association step corrects mistakes made by supernodes due to missing observations at the tracking group boundaries. The ability to correct mistakes made by a supernode is another strength of our algorithm. The algorithm is written in C++ and MATLAB and run on a PC with a 2.6-GHz Intel Pentium 4 processor. It takes less than 0.06 seconds per supernode, per simulation time step.
5. Experiments
5.1. Indoor Experiments
As a proof of concept, we implemented a scaled down tracking scenario on a 9-by-5 sensor network testbed (see Figure 9). The tracking scenario was that of two crossing targets moving at constant speed. Due to the small physical size of the network, there is a single supernode and hence no track-level data association. Also, to retain enough distinct data points to form meaningful tracks, we did not aggregate sensor readings as in (2).
The sensor node platform used for our experiment was the mica2dot [35], an embedded, low-power, wireless platform running TinyOS [36] which had a 4 MHz ATMega128 processor with 4 kB of RAM and 128 kB of program memory, and a CC1000 radio that provided up to 38.4 kbps transmission rate. Each node was equipped with a Honeywell HMC1002 2-axis magnetometer to sense moving targets. See Figure 9(a). Our moving targets were modified RC-cars called COTSBOTs [37], which had a 1-inch (2.54 cm) diameter, 0.125-inch (3.175 mm) thick neodymium magnet strapped to the top of the car.
The sensor nodes trigger a detection event when where is the current detection time, and are the , and -axis magnetometer readings, and is the window size. We chose this detection function because of its low computation requirement and robust performance, despite a tendency for the bias to drift on the magnetometers (see [38] for more details). We used and in our experiments.
We used the minimum-transmission-routing protocol [39] to route the sensor readings back to the supernode for calculations. This algorithm has better end-to-end reliability than shortest path routing, ensuring fewer packets are dropped from transmission errors. We performed multiple experiments with different target trajectories and velocities, all with comparable performance. The result from one of these experiments is shown in Figure 11 and snapshots are shown in Figure 10. The experiments showed that the algorithm performed well in the presence of false alarms and missing observations.
5.2. Outdoor Experiments
In this section, we present an outdoor demonstration described in [27] which is based on the proposed hierarchical multi-target-tracking algorithm for solving the multi-target-tracking problem. In addition to outdoor experiments shown here, the MCMCDA algorithm has been successfully applied to track vehicles, people, and ground robots in urban environments [40, 41]. The experiment was performed on a large-scale, long-term, outdoor sensor network testbed deployed on a short grass field at UC Berkeley's Richmond Field Station (see Figure 12). A total of 557 sensor nodes were deployed and 144 of these nodes were allotted for the tracking and PEG experiments. However, six out of the 144 nodes used in the experiment were not functioning on the day of the demo, reflecting the difficulties of deploying large-scale, outdoor systems. The sensor nodes were deployed at approximately 4- to 6-meter spacing in a grid. Each sensor node was elevated using a camera tripod to prevent the passive infrared (PIR) sensors from being obstructed by grass and uneven terrain (see Figure 12(a)). The true locations of the nodes were measured during deployment using differential GPS and stored in a table at the base station for reference. However, in the experiments the system assumed the nodes were placed exactly on a 5-meter spacing grid to highlight the robustness of the system against localization error.
In order to solve the multi-target tracking and pursuit-evasion game, we have proposed a hierarchical real-time control system LochNess which is shown in Figure 13. LochNess is composed of seven layers: the sensor networks, the multisensor fusion (MSF) module, the multi-target tracking (MTT) modules, the multi-track-fusion (MTF) module, the multi-agent coordination (MAC) module, the path planner module, and the path follower modules.
Under this architecture, sensors are spread over the surveillance region and form an adhoc network. The sensor network detects moving objects in the surveillance region and the MSF module converts the sensor measurements into target position estimates (or reports) using spatial correlation. We consider a hierarchical sensor network and the Tier-2 nodes are supernodes considered in this paper. Fused measurements are delievered to the Tier-2 nodes and the MTT module in each Tier-2 node estimates the number of evaders, the positions and velocities of the evaders, and the estimation error bounds. Each Tier-2 node communicates with its neighboring Tier-2 nodes when a target moves away from the region monitored by its tracking group. Lastly, the tracks estimated by the Tier-2 nodes are combined hierarchically by the MTF module at the base station.
The estimates computed by the MTF module are then used by the MAC module to estimate the expected capture times of all pursuer-evader pairs. Based on these estimates, the MAC module assigns one pursuer to one evader by solving the bottleneck assignment problem [42] such that the estimated time to capture the last evader is minimized. Once the assignments are determined, the path planner module computes a trajectory for each pursuer to capture its assigned evader in the least amount of time without colliding into other pursuers. Then, the base station transmits each trajectory to the path-following controller of the corresponding pursuer. The path-following controller modifies the pursuer's trajectory on the fly to avoid any obstacles sensed by the pursuer's on-board sensors. The MTT and MTF modules are based on the proposed hierarchical multi-target-tracking algorithm. For more detail about the description about the system used in the experiments, see [27].
We demonstrated the system on one, two, and three human targets, with targets entering the field at different times. In all three experiments, the tracking algorithm correctly estimated the number of targets and produced correct tracks. Furthermore, the algorithm correctly disambiguated crossing targets in the two-target and three-target experiments without classification labels on the targets. Figure 14(a) shows an example with three humans walking through the field. The three humans entered and exited the field around time 10 and 80, respectively. During the experiment, the algorithm correctly rejected false alarms and compensated for missing detections. There were many false alarms during the span of the experiments. Though not shown in the figures, the algorithm dynamically corrected previous track hypotheses as it received more sensor readings. A live picture of this experiment is shown in Figure 12(b). Figures 14(b) and 14(c) show the multi-target-tracking results with two persons walking through the field. Note that the MTT module is able to correctly disambiguate the presence of two targets (right panel of Figure 14(c)) using past measurements, despite the fact that the MSF module reports the detection of a single target (upper left panel of Figure 14(b)).
6. Conclusions
In this paper, a scalable hierarchical multi-target-tracking algorithm for wireless sensor networks is presented. The algorithm is based on the efficient MCMCDA algorithm, and it is suitable for autonomous real-time surveillance in sensor networks. This new multi-target-tracking algorithm can initiate and terminate tracks and requires a small amount of memory. The task of tracking is done hierarchically for real-time operation by forming a tracking group around a supernode and later combining tracks from different supernodes. In order to reduce the communication overhead, observations are first fused locally and then transmitted to its supernode. The algorithm is robust against sensor localization error and there is no performance loss up to the average localization error of 0.7 times the separation between sensors. The algorithm is also robust against transmission failures and communication delays. In particular, the algorithm tolerates up to 50% lost-to-total packet ratio and 90% delayed-to-total packet ratio. The extensive simulation and experimental results show that the algorithm is well suited for wireless sensor networks where transmission failures and communication delays are frequent.
AbbreviationsList of Symbols
|Assignment of node to supernode
|Number of targets
|Number of steps in MCMCDA
|Number of sensor nodes
|Number of supernodes
|Target detection probability
|Probability of delaying a message
|Transmission failure probability
|Probability of successful end-to-end communication
|Target disappearance probability
|Surveillance region
|Sensing range of a sensor node
|Shortest path from node to
|Time duration of surveillance
|Uniform distribution
|Volume of the surveillance region
|Sliding window size for online MCMCDA
|Sensor parameters
|Detection threshold,
|Birth rate of new targets per unit time and volume
|False alarm rate per unit time and volume
|Set of measurements from target
|Collection of joint association events
|Joint association event
|Initial state for MCMCDA.
The author would like to thank Phoebus Chen for his contribution to the indoor experiment. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2010-0013354). A preliminary version of this work was presented at the IEEE International Conference on Robotics and Automation, 2005 [43].
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Senior Class prank leaves lasting impression
Make-up & Photo Shop free Model, Gisele Bundchen
Photo Shoot gone wrong for wedding party
Ewww!!! Does your Pizza Delivery guy eat your toppings?
Visit NBCNews.com for breaking news, world news, and news about the economy
Who gets your tip at Starbucks?!
Bribing your kids to eat veggies.
Bacon flavored Man Candles?!
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Airfare sales & false assumptions
Man makes highest Mt. Everest BASE jump
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Winners were announced in the March/April 2013 issue of Yankee Magazine. Find out which photos took top honors.
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Thome Farms invites you to try 'The Potting Bench.' Plant your purchases here in your pots (or ours) and keep your home mess-free. Don't want to get your hands dirty? No problem! We can also plant your flowers for you!
Thome Farms can help you create huge, gorgeous baskets to beautify your porch, deck or yard. We are located on State Route 58 in Amherst, ¼ miles south off Rt. 113.


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Physical Science
Mineral Science Kit - 15 mineral specimens from ToysmithChildren will learn to look for luster, (the way a mineral reflects light), hardness (measured on the Mohs scale), and color (with a streak test). Affordable, with just enough variety to whet the young geologist's appetite. Learn More
Light & Sound Energy BallProvide a safe, dramatic, hands-on demonstration of an electrical circuit. Two exterior metal strips are the circuit terminals. Children join hands to form the circuit. Batteries included. 1½" dia. Learn More
Glass Prism - Functional prism for experiments with lightChildren can set this prism in a sunny window and see for themselves what Newton discovered — a beam of visible light can be split into a rainbow when it shines through a glass prism like this. Learn More
All the Colors of the Rainbow - A Rookie Read-About Science bookDelightful photos and close-up diagrams introduce younger children to the wonders of light, color, and rainbows. Simple text for early readers explains refraction, color, and the visible light spectrum. Paperback, 32 pages. Learn More
Jumbo Thermometer - Weather observation toolYour children can measure ambient temperature on Fahrenheit and centigrade scales, then perhaps record and graph their observations. A fascinating way to exercise observation, math, and writing skills. Clear, easy-to-read numbers; sturdy plastic. Easy to mount; 15". Learn More
Solid: Size, Color, Shape Sort - Young scientist hands-on physics activity setA great addition to your science curriculum, this hands-on physics activity set includes 3 activities, so that related activities can be rotated. Reinforces the observation that solids come in different sizes, colors, and shapes. Learn More
Solid: Rough & Smooth Sort - Young scientist hands-on physics activity setA great addition to your science curriculum, this hands-on physics activity set gives children a chance to exercise familiar sensorial vocabulary (rough, smooth) as they sort fascinating items of varying textures under the appropriate label. Learn More
Solid: Rigid, Plastic, Elastic Sort - Young scientist hands-on physics activity setA great addition to your science curriculum, this hands-on physics activity set offers a brilliant way for children to notice how solids respond differently to stress, an important physics (and engineering) distinction. Learn More
The Mystery of MagnetsGreat photos of children working with magnets, combined with simple text for early readers, make this photo-based fact book a good introduction to magnets and their properties. Paperback, 16 pages Learn More
Iron Filings - 12 oz. from Dowling MagnetsSee magnetic fields in action! Manipulating iron filings with a magnet offers elementary-age children a fun hands-on experience making magnetic field patterns. Shaker-top dispenser bottle allows the filings to be dispensed as needed; 12 oz. Ages 8 and up. Learn More
Alnico Bar Magnets - Hands-on physical scienceThis magnet creates especially strong fields, with dramatic repulsion and attraction properties. Great For polarity experiments. (3" x ½" x ¼"); poles are marked. Learn More
Alnico Horseshoe Magnet - 2" horseshoe magnet from Dowling MagnetsThis horseshoe magnet is perfect for a magnetic/non-magnetic "fishing" game. It creates especially strong fields, with dramatic repulsion and attraction properties. (1¼" x 2" x ¼"). Aluminum-nickel-cobalt alloy. ¼" x 2" x ¼". Learn More
I Wonder Why Zippers Have Teeth - and other questions about inventionsThis graphic format fact book answers questions children love to ask about inventions and inventors. Lively question and answer approach, incredible information, and appealing artwork. A favorite resource. Paperback, 32 pages. Learn More
Solids, Liquids, and Gases - Rookie Read-About ScienceAppealing photos show concrete examples of solids, liquids, and gases that children can touch, see, taste, or smell in their environment. A good introduction to forms of matter with simple text for early readers. Picture glossary, index. Paperback, 32 pages. Learn More
Bubbles, Rainbows & Worms - Science Experiments for Preschool ChildrenA well-organized treasury of hard-to-find experiments for the youngest children. The emphasis is on open-ended activities designed to inspire lots of follow-up research. Step-by-step with simple explanations. A must! Learn More
Acrylic Refracting Prism - From AccoutrementsOver 300 years ago, Sir Isaac Newton concluded that a prism separates white light into colored light. Now children can see for themselves—a beam of light split into a multi-colored rainbow. 4½" x 1⅜" x ⅝". Learn More


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While Paul Rudd's name has been rumored to be on the short list for the lead role in "Ant-Man," recent reports contend that Rudd is now the frontrunner for the role of Henry Pym. According to a recent rundown by The Hollywood Reporter, Rudd is at the top of the short list, but there's no news as to who will play his "30-ish girlfriend," though "Parks and Recreation" star Rashida Jones has been rumored to be under consideration.
Opens July 31, 2015
A new bit of viral marketing for "X-Men: Days of Future Past" connects Magneto to the Kennedy assassination. The Bent Bullet viral website has a detailed full article as well as a video -- though there's a particularly fun reference to Marvel Comics' "What If…?" comic book series in the body of the text piece.
"It was a different time," explains "Scary" Carey Morrison, a veteran radio personality. Morrison has hosted the conspiracy theory show What If...? since 1977. "Back then, if you talked about folks with superpowers, you might as well be talking about little green moon men. Most didn't believe."
Opens May 23, 2014
MTV spoke with Chris Cooper about his role as Norman Osborn in the upcoming "Amazing Spider-Man" sequel, which he characterized as "extremely odd."
"'[Amazing] Spider-Man 2' was an introduction to Norman, and -- you'll see -- an extremely odd introduction, which apparently will lead to something more," Cooper told MTV.
Additionally, the Official "Amazing Spider-Man 2" Twitter account revealed the trailer for the film will debut December 5.
WORLDWIDE TRAILER PREMIERE of #AmazingSpiderMan 2 >> DECEMBER 5. Watch @GMA that morning for a sneak peek! #GMAmazing pic.twitter.com/xmOmv5O1ZN— Spider-Man (@SpiderManMovie) November 25, 2013
Opens May 2, 2014
The BBC has posted a plethora of new "Doctor Who" content including a deleted scene from "The Day of the Doctor" and the first trailer for the upcoming Christmas special, which signals the exit of Matt Smith.
Tate on YouTube has posted an interview with Christopher Nolan where "The Dark Knight Trilogy" director discusses how he was influenced by the art of Francis Bacon.
Got any rampant rumors or weird comic book stuff? Be sure to drop us an email or sling us a webline on Twitter!


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Mike Faulk. Remember the name. He's the son of Lee--former driver of theSlender U NASCAR Cup and Busch cars and now a car builder--and AmbyFaulk, who now sees her son following in her husband's tire tracks.
"I remember when I was around six years old going to watch my dad racein the All American Challenge Series (later NASCAR All Pro). I grew upgoing to Orlando Speed World and New Smyrna on Friday and Saturdaynights," Faulk recalls.
He first climbed into a race car in Bill Beltz's Pure Stock at nearbyBronson, and in the ensuing season won nine races. Then he moved to LateModels and competed in the S.A.R.A. Series where he was Rookie of Yearand finished Third in points in 2000.
From there it was pick and choose until, under Lee's guidance, heentered the Florida Pro Series full time for 2003.
Enter the Reutimanns, David and Buzzie. "I had first met David Reutimannthrough my dad at a local short-track race in Florida," says Faulk. "Iwas a youngster, but Dad was racing and I was spotting for him."
Although from different venues, Buzzie and Lee also crossed pathsfrequently.
"Buzzie is known for his open-wheel background, and my dad was more intothe Late Model scene, so they didn't get a chance to race togethermuch," Faulk says.
However, David started driving the Cowherd Motorsports Late Model in2000 after the senior Faulk was in the car, which is currently driven byJames Powell.
"That relationship didn't last very long, but we became closer friendswith David," recalls Faulk.
Did they ever. So much so that when Reutimann left the 00 car last year,he recommended Faulk as his replacement, but with the results mentionedearlier.
"I'm used to me and Dad loading up and going to the races, which is howwe've done it my whole career," Faulk says. "So it will be fun to bondwith a new team and new people at the Hooters ProCup races.
"Even without TV, Hooters ProCup seems to be better promoted and thepublic is more aware of the series. There are certainly more fans in thestands. Hooters is corporate friendly, and that always helps to attractcurrent and prospective sponsors and their clients. The ProCup Series isthe main attraction for Hooters Racing, so naturally it's broadly andpowerfully promoted. The series also attracts the best and mostexperienced drivers in the country, and winning at this level wouldreally mean something to me personally."
Buzzie's official position will be Mike's crewchief. Young Faulk andBuzzie have proven they communicate well together, and Buzzie'sknowledge and experience is an obvious advantage for the team. Buzziewill of course consult with both teams regarding race car setups, tires,shocks, motors, gears, and so on.
Lee is the general manager of MB3 Motorsports. He will oversee allaspects of both teams on and off the track.
Michael and Michelle Burton from Ocala, Florida, are the owners of theteam, and Larry Goolsby from nearby Williston, Florida, is the driver ofthe second team car.
What about that all-important sponsorship for the new No. 53 car?
"Team owner Michael Burton is heavily involved with companies such asHerbalife, Mybodyshape.com, and several others, which will play a keyrole in corporate sponsorship," Faulk says.
Reutimann, the Truck Series driver, is married to Lisa with daughterEmilia, so the pair's outside interests took quite a fork in the road in2004--Reutimann to the trucks and Faulk to the Southeast Series--asFaulk attests.
"Since he is married and I am single, our interests aren't exactly thesame as I'm sure you can imagine," Faulk says. "I don't really know whatDavid likes to do in his spare time--which he doesn't really have any ofanymore. As for myself, though, I like water-skiing, basketball,fishing, and I always keep time to the side for girls.
"We do speak, probably three times a week, depending upon schedules, andwe make time to visit one another personally when close by," says Faulk."I call David not only about car setups and tracks, but about the racingbusiness in general. He's a great friend and helps whenever and howeverhe can."


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7 Dec 17:08 2009
Using FMPP Output Tags separately
alebu <aleboo <at> gmail.com>
Hi all, I need some guidelines about FMPP API. I am interested in collecting data for FMPP engine using my own code. In general, I only interested in those wanderfull tags, that allows to create multiple outputs from single template. The questions are: 1) What objects are responsible for actual generation part of code? 2) How to configure them ? I was looking at code and I saw that TemplateEnvironment has some tags definitions, and Engine is using it somehow. 3) How to specify to search templates in classpath (it is important for me). Also, if templates uses some macros and additional includes, then it must be specified too. It is probably more Freemarker question, but still... In few words, i want to write a code that: 1) Takes some data (as freemarker content) 2) Instanciate FMPP Engine, configures it to use those tags and search templates and other Freemarker specific resources in classpath. 3) Processes data. Here I need to correctly handle output. Or maybe it is worth to somehow move those fmpp specific tags into freemarker environment? In this case, how can it be done? General overview of solution will be fine for me, just point me please on the right directions (classes, execution chain, etc). Thanks in advance! ------------------------------------------------------------------------------(Continue reading)


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Brentson Andre Buckner
Position: DT-DE-NT
Height: 6-2 Weight: 305 lbs.
Born: September 30, 1971 in Columbus, GA
High School: Carver
College: Clemson (school history)
Drafted by the Pittsburgh Steelers in the 2nd round (50th overall) of the 1994 NFL Draft.
Weighted Career AV (100-95-...): 53 (1285th overall since 1950)
Promote your website or business by sponsoring this page for $5 on Pro-Football-Reference.com. Your message will replace this ad.


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Mark C. Murphy
Adjunct Professor of Law
B.A., University of Texas; M.A., Ph.D., University of Notre Dame
B.A., University of Texas; M.A., Ph.D., Notre Dame. After spending the first two years of his career at the University of Hawaii, Professor Murphy came...Continue Reading
B.A., University of Texas; M.A., Ph.D., Notre Dame. After spending the first two years of his career at the University of Hawaii, Professor Murphy came to Georgetown, where he is now Professor of Philosophy.
His research interests include moral, political, and legal philosophy and the history of early modern philosophy. His research focuses on natural law theory both in its historical manifestations and as a live option for jurisprudence and the theory of practical reasoning. He is the author of Natural Law and Practical Rationality (Cambridge, 2001), An Essay on Divine Authority (Cornell, 2002), Natural Law in Jurisprudence and Politics (Cambridge, 2006), and Philosophy of Law (Blackwell, 2006), and is editor of Alasdair MacIntyre (Cambridge, 2003). He has also written a number of papers on Hobbes's moral, political, and legal theory, among which are "Was Hobbes a Legal Positivist?" (Ethics, 1995), "Deviant Uses of 'Obligation' in Hobbes' Leviathan" (History of Philosophy Quarterly, 1994), "Hobbes on the Evil of Death" (Archiv fur Geschichte der Philosophie, 2000), and "Desire and Ethics in Hobbes's Leviathan: A Response to Professor Deigh" (Journal of the History of Philosophy, 2000).