Pentop Computing Is More Than A Kids' Toy

Do you interest in a pen which combine a lot of functions inside. And it works like a small AI device and let’s go and see what it can do:
The Fly Pentop Computer depends on a number of technologies to do its thing. Most of these are inside the computer itself. The Fly uses technology from the Anoto Group AB in Sweden, a spin-off from telecommunications giant Ericsson. Anoto doesn't manufacture hardware, rather owns and licenses the specs for pen-based computer pattern recognition hardware. It also licenses the software required for pen-based computer pattern recognition. Anoto licensees who plan to produce products must see to it that required pattern recognition hardware is produced and integrated with Anoto's software. Licensees must also create or purchase hardware and software to support specific functions to be included in their pen-based computers.
The figure below is a slightly modified diagram from Anoto that shows how the technology works. Note that the Fly PenTop doesn't come with a cap as the diagram shows. Rather, the top of the device has a receptacle for plug-in application cartridges (see below). Aside from the top of the pen, the diagram is a pretty good representation of the Anoto technology as implemented by LeapFrog. Follow along on the diagram as I explain what's up inside and outside of a Fly computer.

Pattern recognize technology in stock market

FX Solutions' industry-leading Charting Package may be a useful thing for those people who are interested in stock market. It using pattern recognizes technology to make a chart and how it work is in below:
Charting with Pattern Recognition
FX Solutions' industry-leading Charting Package provides Pattern Recognition, which identifies common market patterns, such as Three White Soldiers and Inverted Hammers, based on the trader's selection. The identified patterns help the trader to better recognize potential trading opportunities.The Charting Package also allows traders to trade directly from the charts; the Buy/Sell toolbar has real-time prices.
Multiple Stops and Limits
The GTS platform includes an exclusive feature - the ability to attach up to five stop loss and limit orders to each trade. This feature is a key risk management tool. As an example, traders with a 10 lot position can leave 5 orders for 2 lots each.

A new way to help computers recognize patterns

Aleix Martinez, assistant professor of electrical and computer engineering at Ohio State, explained what all these areas of research have in common: pattern recognition.

He designs computer algorithms to replicate human vision, so he studies the patterns in shape and color that help us recognize objects, from apples to friendly faces. But much of today's research in other areas comes down to finding patterns in data -- identifying the common factors among people who develop a certain disease, for example.
In fact, the majority of pattern recognition algorithms in science and engineering today are derived from the same basic equation and employ the same methods, collectively called linear feature extraction, Martinez said.

But the typical methods don't always give researchers the answers they want. That's why Martinez has developed a fast and easy test to find out in advance which algorithms are best in a particular circumstance. "You can spend hours or weeks exploring a particular method, just to find out that it doesn't work," he said. "Or you could use our test and find out right away if you shouldn't waste your time with a particular approach." The research grew out of the frustration that Martinez and his colleagues felt in the university's Computational Biology and Cognitive Science Laboratory, when linear algorithms worked well in some applications, but not others.
reference:http://www.physorg.com/news10223.html

Pattern Recognition Versus Recall

Human memory is incredibly bad. In fact, it is so bad that you probably don’t remember what you ate for breakfast just a few days ago. The interesting thing is that human brainpower is pretty impressive; we are outstanding at pattern matching and problem solving. Memory has everything to do with search.
You often search because you have poor memory. But, it isn’t so poor that you are a blank slate, tabula rasa. No, instead, you have a clue and you are buying more clues with every search you do. Let’s cut to the core of this.
Your ability to pattern match and recognize is outstanding, but your ability to dredge up old memories is awful. This is recognition versus recall at work.
Put into a simple example, you are great with faces but you just can’t remember names. Which face? Any face! By the way, you aren’t special or unique. You are like every other human on the planet. You are better with faces than names.
Why?
Show Me and Don’t Make Me Think!
The answer is simple. When you see a face you match it against what you know. You are matching patterns. You are capitalizing on your ability to recognize faces. But, you can’t remember names because you have poor recall. You can’t dredge up the name that goes with that face. Recognition in this case is a face-to-face matching exercise. Folks, that is pretty easy.

SDK Technology

Sophisticated Algorithm Provides Versatile and Accurate Recognition Using Still or Video Images from a Wide Variety of Cameras Vilnius, Lithuania - June 4, 2007 - Neurotechnologija, a company widely recognized for their high-precision biometric identification technologies, today announced their entry into the AI and Robotics market with the introduction of the new SentiSight Software Development Kit (SDK) object recognition technology. This technology will be using in AI product and intelligence robots. Designed for the development of computer-based vision systems, the SentiSight algorithm provides versatile, fast and accurate 2D and 3D object recognition for use in a wide variety of applications, including image search engines, security systems, manufacturing and robot and machine vision. SentiSight object recognition technology is tolerant to object scale, rotation and pose and works with still and video images from most digital cameras, including Webcams. It can process video streams in real time, enabling its use in real-time applications such as autonomous robot navigation, parts identification on an assembly line or road sign recognition in a moving vehicle.
SentiSight SDK enables fully automatic and manual object learning as well as simultaneous multiple object detection and recognition. Using a live camera, series of still images or video, SentiSight first learns an object by extracting specific features or descriptors of the object from different sides, distances from the camera and angles of view. This enables SentiSight to develop a 2D or 3D object model that can be stored (e.g. in a database). When that same object is later presented in a photograph, video, on the Web or from a real-time live video camera, the SentiSight algorithm compares the new images to the existing object model, recognizes the object and outputs the object's name and coordinates.

http://www.neurotechnologija.com/sentisight.html.

Unusual Fish---species of Pseudoplatystoma

A revision of the South American tiger shovelnose catfish genus Pseudoplatystoma recognizes eight valid species.In the revision, which is published inthe most recent issue of the journal Zootaxa, Uriel Buitrago-Suárez and Brooks Burr recognize eight valid species of Pseudoplatystoma, of which three are described as new: P. corruscans, P. fasciatum, P. magdaleniatum, P. metaense, P. orinocoense, P. punctifer, P. reticulatum, and P. tigrinum.The authors examined a large number of specimens from throughout South America and revalidated two species (P. punctifer and P. reticulatum) in addition to describing three new species (P. magdaleniatum, P. metaense and P. orinocoense). The eight species are distinguished from each other by colour pattern and differences in bone structure.The exact number of species out of these eight imported for the aquarium trade is unknown since the large majority of the fish imported for the trade are very young fish that are difficult to identify with certainty. According to the authors: “...slight pattern and shape differences may indicate that more than one species is imported.”

Pseudoplatystoma corruscans
Pseudoplatystoma corruscans is known from the Paraná and São Francisco rivers in Argentina, Brazil, Paraguay and Uruguay. It is distinguished from congeners in having the sides of the body covered with large spots distributed regularly in six to eight rows and four to thirteen pale vertical bars. Other distinguishing characters include: adipose fin with 5–10 or no spots, caudal fin with few spots, 44–47 vertebrae and surrounding region of both dorsal and ventral procurrent caudal rays with no spots.The maximum recorded size of this species is 1140 mm TL.

This just one type of that species of Pseudoplatystoma, do you get interest in fishes

more information please click and you can share your hobby in comments TQ:

http://www.practicalfishkeeping.co.uk/pfk/pages/item.php?news=1291

Why A Journal for Patterns Recognised?

Put your fingers on your closed eyelids and press gently: you can 'see' in the dark. We can look at a stone or a wall and observe what Leonardo da Vinci described as: "several things like landscapes, battles, clouds, uncommon attitudes, humorous faces, draperies, &c. Out of this confused mass of objects, the mind will be furnished with abundance of designs and subjects perfectly new". Pattern recognition is a subject of huge importance. Nobody is sure how big exactly. Our minds are constantly involved in the unintentional bringing forth of patterns, even, as the sensory deprivation tank shows, without stimulus. It was Samuel Taylor Coleridge who famously identified this ability to make something out of nothing as the source behind 'imagination' as opposed to mere 'fancy'; a distinction between copying existing patterns a little different and recognizing entirely new ones.

By repairing and augmenting the senses, man has always sought to create new tools to improve the ability to recognise patterns. Science introduced spectacles, telescopes, microscopes, x-rays. and statistics are scientific examples of such tools; altered states, dream interpretation, automatism, the cut-up and the dream machine are just a small collection of fringe methods to recognise patterns different.

The story of the research of butterfly wings

Do you like butterfly? If the answer is yes, please read this story.
Earlier this century, scientists recognized that each butterfly wing pattern is a variation on a common theme. Every design is a composite of several discrete elements--spots, stripes, and borders--whose relative positions on the wing remain largely constant, even though the size, shape, color, and number of these elements vary independently of one another. Some of the thousands of living butterfly species--swallowtails, for example sport a rich assortment of design elements on their wings. Others--such as the sulphurs--have just a few. Butterfly wing markings have many functions, such as camouflage, mimicry, and perhaps attracting a mate. The role of eyespots appears to be to deceive predators: any bird about to snap up a juicy morsel is likely to pause when confronted by a startling flash of big "eyes" or at least to be confused and focus its attack on the wings, where damage is less serious than a direct hit on the soft body.
Every butterfly wing starts out as a flat disk of cells in the caterpillar. The disk grows during the caterpillar stage, and by the time the larva encloses itself in the chrysalis a blueprint of the future wing pattern has been drafted. Color development, however, takes place in the chrysalis. Fifteen years ago, Nijhout (butterfly expert) showed in an elegant series of microsurgery experiments that the position of eyespots in the buckeye is decided just before the caterpillar forms a chrysalis, while the colored rings around the eyespot are painted many days later, just before the adult emerges.
Using a collection of fruit fly genes as bait we can fished out corresponding genes from the thousands of genes in butterfly DNA. (Radioactively labeled fly genes can be used to locate and isolate their counterparts from butterfly DNA.) Finding a gene, however, is not the same as demonstrating its function. Using molecular probes marked with fluorescent chemicals and a high-powered microscope, we next looked to see which cells glowed when the probes stuck to the disks, revealing where genes were "turned on," or activated. Genes are activated in patterns: "on" in some cells; "off" in others. These patterns of gene activity are the earliest sign in animal tissue of future morphological changes. If any of our candidate genes had a role in determining butterfly wing patterns, we hoped this technique would enable us to catch them red-handed.
what do you think in this story especially how the wing pattern use in search different type of butterfly? if you have any different opinion you can share in comments.
reference: http://findarticles.com/p/articles/mi_m1134/is_n1_v106/ai_19318725

How VIPR works

Today we are going to discuss the detail of VIPR (visual pattern recognition). At first the VIPR technology is relate to the neural network technology. Because the VIPR technology will collect the key elements and combine them together and it will allows the ViPR technology to achieve a high level of performance.
Here is an example to show how this technology works: First, is the choice of descriptors it uses to encode unique visual patterns such as the corner of an object or the print on a label. As the most distinct regions (called features) are localized in the image, unique descriptors are computed for each of them. Several hundred such features are automatically extracted and stored in a database to describe the unique patterns in each image. as you can see the below image the VIPR technology will detect and collect the most useful element and according to those elements it will compare and identify the image and it will show the result if the information was matched. and this technology can also be used in different position, distances, partial occlusion and different angle. (see below examples)

reference: http://www.evolution.com/core/ViPR/

Example of a recognized image with the features highlighted.

ViPR (visual pattern recognition)

Bandai, one of the world’s largest toy companies, and Evolution Robotics, the leading provider of breakthrough robotic technologies for consumer products, announced the release of a new home telepresence robot powered by Evolution Robotics’ ViPR® visual pattern recognition technology.
ViPR technology is a latest technology being used in IT products.
A ViPR-enabled device can automatically detect and recognize visual patterns using low- or high-end camera sensors. The algorithms that make up the technology are particularly robust and provide an unprecedented level of reliability even with heavy distortions that can be introduced by the imaging device, a wide range of lighting conditions, and pattern occlusions.
Sony Corporation’s Entertainment Robot Company, for example, has licensed the ViPR technology for their Robotic pet dog, AIBO®, to allow users to command the dog by showing it cards having different visual patterns printed on them, and also enable the dog to autonomously find its charging station by recognizing a visual pattern printed on it.
ViPR technology can be purchased for development as part of the ERSP™ 3.0 SDK. The SDK is available for Windows or Linux. It can also be licensed as a standalone technology to be included in your products.

(ie. LaneHawk™, a new loss-prevention product for retailers, also uses ViPR technology to recognize grocery items by analyzing the printed patterns on their box, instead of using the barcode.)

reference: http://edageek.com/2006/11/27/evolution-robotics-vipr-bandai-nettansor-robot/

Pattern recognition in ghost map

According to the latest comes from Steven Johnson he shows off his new inventory ----Ghost map. And he creates his map in his blog. Using this map it will shows the most recent locations that a blog has covered. And this function has it’s own name----Outside.in.
Pattern Recognition - Plotting the data on the map was obviously key. Without it they would not have been able to center in on that single pump.
Today neighborhoods are still dense and geodata is becoming available (see GeoCommons for an example), but it is difficult to know who else is talking in your area. This realization is what led to the creation of Outside.in, a place for local amateurs to connect and potentially enable future Snow's & Whitehead's. Outside.in has a good start on this goal. They are building the framework of the site to fill out in the future with more data. As they start to aggregate more sources and expand to more cities they will start to become a valuable resource to locals and tourists. BTW, they're hiring.

link: http://radar.oreilly.com/archives/2007/06/where_20_outsid.html

Art's Pattern Language

This is an extremely speculative post exploring multimodal perception in artists. It was primed by this fascinating paper written by " a multimedia conceptual artist (...) working on a series of projects that explore the nature of rainbows and the music of waterfalls in relation to the forgotten universal language of Solresol, invented by Jean François Sudre in the mid-nineteenth century" (surely the guy has organized his whole career to contend for the Hype Curriculum World Prize) and by Chris Chatham's no less interesting reflections on visual pattern recognition in musicians.


James Peel, Goldberg Variations Series, after Bach, Variations no. 4, 2005.

The Formula for Creative Meetings

Do your creative meetings multiply, or are they divisive? Are you the lowest common denominator in a brainstorm or the highest common factor when a breakthrough occurs?
One day I will address all of this and more in a pamphlet provisionally entitled The Mathematics of Creativity.
Maybe.
But having just spent 13 hours being hosed down by PowerPoint while floating along on the soothing corporate speak of a Management Consultant-led brand workshop, I was inspired to bring some maths to the eternal question of “why are some meetings so crap” (or “poorly leveraged interventions” in consultant speak).
Actually this one wasn’t crap, it was a good, productive sharing of information and initial opinions.

The formula is created by doug hall.

C = (DxS)/F
Where :
C = quality of creative output (note “quality”, not “quantity”)
D = diversity of people in the meeting
S = amount of stimulation experienced by people in the meeting
F = fear level of people in the meeting
Now my particular beef is not the normal agency whinge that management consultants don’t understand creativity (darling), but more a worry that they don’t seem to know how to push for high levels of creativity in meetings. For them a meeting is a meeting and is run like all meetings. I think creative agencies could teach them a trick or two.
But now is not the time to go all creative and fluffy, but to illustrate my point with some hard numbers.



To simplify things, let’s say that each of Doug’s variables of Diversity, Stimulation and Fear can have three settings low, normal or good. And let’s quantify that on a three point scale of 1, 2 and 3. Now, with those numbers, a superficial understanding of the creative dynamic might say that a good meeting is just 50% (3/2) more productive than an average meeting.
But applying Doug’s formula, the meeting I experienced was creatively “average” in three distinct ways and, as I shall mathematically show in a moment, fell well short of its creative potential.

Pattern recognition studies

In nowadays, pattern recognition is the research area that studies the operation and design of systems that recognize patterns in data. It encloses subdisciplines like discriminant analysis, feature extraction, error estimation, cluster analysis (together sometimes called statistical pattern recognition), grammatical inference and parsing (sometimes called syntactical pattern recognition). Important application areas are image analysis, character recognition, speech analysis, man and machine diagnostics, person identification and industrial inspection. the analysis of security it prove that the recognition technologies is the most useful technology to improve the security. it will identify the voice and image using this technique and it will try to match the correct answer when it detect the frequency of the voice and it will also can make a big help for companie and persons. Because it trying to stop health care fraud say they have a new weapon: computer programs that can flag potential fraud even before medical claims are paid. and in the other way companies can save far more money by stopping claims before they are paid than trying to get fraudsters to pay back money.

Technique in Robot

Honda, for example, has for a decade been developing humanoid robots leading up to the present (aptly-named) Asimo. Asimo is the size of a human 10-year-old, and its body mass index (mass divided by square of height) is only slightly above that of a healthy human. There are, of course, other robot development programmes (humanoid and otherwise), but they are for the most part military or similar in emphasis; for scientific work, Asimo’s interest is in its civilian approach and also in the direction of its perceptual development – it will, among other things, respond intelligently to human gesture, posture and movement. I don’t seriously suggest that Asimo is yet the manifestation of Asimov’s dream, nor does it really matter whether this is the way forward: what matters is that it demonstrates the feasibility of onboard perceptual intelligence based on sophisticated algorithmic pattern recognition. In limited ways, Asimo responds to the external world as a human of two to five years might; in some other ways, it demonstrates how such responses might be managed in ways not analogous to the human.

Recognising the future

Pattern recognition technologies are of central importance to this. Just as a biological entity acts not on the raw data received from eyes, but on the analyses and hypotheses derived from it, the data from telescopes, microscopes, endoscopes, and every other sort of artificial eye, will be mediated through pattern recognition algorithms designed to make it machine usable.

The essence of pattern recognition is a simple one. Take a collection of examples from a known object; identify the features that appear to be constant in different instances of that object; use those features to identify, at a pragmatically acceptable level of statistical error, other instances of the same object. In some cases, the detection of the pattern itself is the object of the exercise (for example, when monitoring modulations of the hydrogen line in the search for extraterrestrial intelligence) but, even then, if achieved, it will inevitably be applied to subsequent identification of other cases. In this case I’m concerned with those technological applications of pattern recognition that seek to mimic the human senses, with a particular emphasis on visual identification.

http://www.scientific-computing.com/features/feature.php?feature_id=3