This definition implies that a device can only be called a “robot” if it contains a movable mechanism, influenced by sensing, planning, and actuation and control components. It does not imply that a minimum number of these components must be implemented in software, or be changeable by the “consumer” who uses the device; for example, the motion behavior can have been hard-wired into the device by the manufacturer.

 

So, the presented definition, as well as the rest of the material in this part of the Book, covers not just “pure” robotics or only “intelligent” robots, but rather the somewhat broader domain of robotics and automation. This includes “dumb” robots such as: metal and woodworking machines, “intelligent” washing machines, dish washers and pool cleaning robots, etc. These examples all have sensing, planning and control, but often not in individually separated components. For example, the sensing and planning behavior of the pool cleaning robot have been integrated into the mechanical design of the device, by the intelligence of the human developer.

 

Robotics is, to a very large extent, all about system integration, achieving a task by an actuated mechanical device, via an “intelligent” integration of components, many of which it shares with other domains, such as systems and control, computer science, character animation, machine design, computer vision, artificial intelligence, cognitive science, biomechanics, etc. In addition, the boundaries of robotics cannot be clearly defined, since also its “core” ideas, concepts and algorithms are being applied in an ever increasing number of “external” applications, and, vice versa, core technology from other domains (vision, biology, cognitive science or biomechanics, for example) are becoming crucial components in more and more modern robotic systems.

 

This part of the WEBook makes an effort to define what exactly is that above-mentioned core material of the robotics domain, and to describe it in a consistent and motivated structure. Nevertheless, this chosen structure is only one of the many possible “views” that one can want to have on the robotics domain.

 

In the same vein, the above-mentioned “definition” of robotics is not meant to be definitive or final, and it is only used as a rough framework to structure the various chapters 

 

Components of robotic systems

 

 

 

 

 

 

 

 

This figure depicts the components that are part of all robotic systems. The purpose of this Section is to describe the semantics of the terminology used to classify the chapters in the WEBook: “sensing”, “planning”, “modeling”, “control”, etc.

 

The real robot is some mechanical device (“mechanism”) that moves around in the environment, and, in doing so, physically interacts with this environment. This interaction involves the exchange of physical energy, in some form or another. Both the robot mechanism and the environment can be the “cause” of the physical interaction through “Actuation”, or experience the “effect” of the interaction, which can be measured through “Sensing”.

 

Robotics as an integrated system of control interacting with the physical world.

 

Sensing and actuation are the physical ports through which the “Controller” of the robot determines the interaction of its mechanical body with the physical world. As mentioned already before, the controller can, in one extreme, consist of software only, but in the other extreme everything can also be implemented in hardware.

 

Within the Controller component, several sub-activities are often identified:

 

Modelling. The input-output relationships of all control components can (but need not) be derived from information that is stored in a model. This model can have many forms: analytical formulas, empirical look-up tables, fuzzy rules, neural networks, etc.

 

The name “model” often gives rise to heated discussions among different research “schools”, and the WEBook is not interested in taking a stance in this debate: within the WEBook, “model” is to be understood with its minimal semantics: “any information that is used to determine or influence the input-output relationships of components in the Controller.”

 

The other components discussed below can all have models inside. A “System model” can be used to tie multiple components together, but it is clear that not all robots use a System model. The “Sensing model” and “Actuation model” contain the information with which to transform raw physical data into task-dependent information for the controller, and vice versa.

 

Planning. This is the activity that predicts the outcome of potential actions, and selects the “best” one. Almost by definition, planning can only be done on the basis of some sort of model.

 

Regulation. This component processes the outputs of the sensing and planning components, to generate an actuation setpoint. Again, this regulation activity could or could not rely on some sort of (system) model.

 

The term “control” is often used instead of “regulation”, but it is impossible to clearly identify the domains that use one term or the other. The meaning used in the WEBook will be clear from the context.

 

Scales in robotic systems

 

The above-mentioned “components” description of a robotic system is to be complemented by a “scale” description, i.e., the following system scales have a large influence on the specific content of the planning, sensing, modelling and control components at one particular scale, and hence also on the corresponding sections of the WEBook.

 

Mechanical scale. The physical volume of the robot determines to a large extent the limites of what can be done with it. Roughly speaking, a large-scale robot (such as an autonomous container crane or a space shuttle) has different capabilities and control problems than a macro robot (such as an industrial robot arm), a desktop robot (such as those “sumo” robots popular with hobbyists), or milli micro or nano robots.

Spatial scale. There are large differences between robots that act in 1D, 2D, 3D, or 6D (three positions and three orientations).

 

Time scale. There are large differences between robots that must react within hours, seconds, milliseconds, or microseconds.

 

Power density scale. A robot must be actuated in order to move, but actuators need space as well as energy, so the ratio between both determines some capabilities of the robot.

 

System complexity scale. The complexity of a robot system increases with the number of interactions between independent sub-systems, and the control components must adapt to this complexity.

 

Computational complexity scale. Robot controllers are inevitably running on real-world computing hardware, so they are constrained by the available number of computations, the available communication bandwidth, and the available memory storage.

 

Obviously, these scale parameters never apply completely independently to the same system. For example, a system that must react at microseconds time scale can not be of macro mechanical scale or involve a high number of communication interactions with subsystems.

 

Background sensitivity

 

Finally, no description of even scientific material is ever fully objective or context-free, in the sense that it is very difficult for contributors to the WEBook to “forget” their background when writing their contribution. In this respect, robotics has, roughly speaking, two faces: (i) the mathematical and engineering face, which is quite “standardized” in the sense that a large consensus exists about the tools and theories to use (“systems theory”), and (ii) the AI face, which is rather poorly standardized, not because of a lack of interest or research efforts, but because of the inherent complexity of “intelligent behaviour.” The terminology and systems-thinking of both backgrounds are significantly different, hence the WEBook will accomodate sections on the same material but written from various perspectives. This is not a “bug”, but a “feature”: having the different views in the context of the same WEBook can only lead to a better mutual understanding and respect.

 

Research in engineering robotics follows the bottom-up approach: existing and working systems are extended and made more versatile. Research in artificial intelligence robotics is top-down: assuming that a set of low-level primitives is available, how could one apply them in order to increase the “intelligence” of a system. The border between both approaches shifts continuously, as more and more “intelligence” is cast into algorithmic, system-theoretic form. For example, the response of a robot to sensor input was considered “intelligent behaviour” in the late seventies and even early eighties. Hence, it belonged to A.I. Later it was shown that many sensor-based tasks such as surface following or visual tracking could be formulated as control problems with algorithmic solutions. From then on, they did not belong to A.I. any more.

 

 

 

Robotics Technology

 

Most industrial robots have at least the following five parts:

 

Sensors, Effectors, Actuators, Controllers, and common effectors known as Arms.

 

Many other robots also have Artificial Intelligence and effectors that help it achieve Mobility.

 

This section discusses the basic technologies of a robot. Click one of the links above or use the navigation bar menu on the far right.

 

Robotics Technology – Sensors

 

Most robots of today are nearly deaf and blind.  Sensors can provide some limited feedback to the robot so it can do its job.  Compared to the senses and abilities of even the simplest living things, robots have a very long way to go.

 

The sensor sends information, in the form of electronic signals back to the cfontroller.  Sensors also give the robot controller information about its surroundings and lets it know the exact position of the arm, or the state of the world around it.

Sight, sound, touch, taste, and smell are the kinds of information we get from our world.  Robots can be designed and programmed to get specific information that is beyond what our 5 senses can tell us. For instance, a robot sensor might “see” in the dark, detect tiny amounts of invisible radiation or measure movement that is too small or fast for the human eye to see.

 

Here are some things sensors are used for:

 

Physical Property

 Technology

 

Contact Bump, Switch

Distance Ultrasound, Radar, Infra Red

Light Level Photo Cells, Cameras

Sound Level microphones

Strain Strain Gauges

Rotation Encoders

Magnetism Compasses

Smell Chemical

Temperature Thermal, Infra Red

Inclination Inclinometers, Gyroscope

Pressure Pressure Gauges

Altitude Altimeters

 

    Sensors can be made simple and complex, depending on how much information needs to be stored.  A switch is a simple on/off sensor used for turning the robot on and off.  A human retina is a complex sensor that uses more than a hundred million photosensitive elements (rods and cones).  Sensors provide information to the robots brain, which can be treated in various ways.  For example, we can simply react to the sensor output: if the switch is open, if the switch is closed, go. 

 

Levels of Processing

 

    To figure out if the switch is open or closed, you will need to measure the voltage going through the circuit, that’s electronics.  Now lets say that you have a microphone and you want to recognize a voice and separate it from noise; that’s signal processing.  Now you have a camera, and you want to take the pre-processed image and now you need to figure out what those objects are, perhaps by comparing them to a large library of drawings; that’s computation.  Sensory data processing is a very complex thing to try and do but the robot needs this in order to have a “brain”.  The brain has to have analog or digital processing capabilities, wires to connect everything, support electronics to go with the computer, and batteries to provide power for the whole thing, in order to process the sensory data.  Perception requires the robot to have sensors (power and electronics), computation (more power and electronics, and connectors (to connect it all). 

 

Switch Sensors

 

 Switches are the simplest sensors of all.  They work without processing, at the electronics (circuit) level.  Their general underlying principle is that of an open vs. closed circuit.  If a switch is open, no current can flow; if it is closed, current can flow and be detected.  This simple principle can (and is) used in a wide variety of ways.

 

Switch sensors can be used in a variety of ways:

 

contact sensors: detect when the sensor has contacted another object (e.g., triggers when a robot hits a wall or grabs an object; these can even be whiskers)

 

limit sensors: detect when a mechanism has moved to the end of its range

 

shaft encoder sensors: detects how many times a shaft turns by having a switch click (open/close) every time the shaft turns (e.g., triggers for each turn, allowing for counting rotations)

 

   There are many common switches: button switches, mouse switches, key board keys, phone keys, and others.  Depending on how a switch is wired, it can be normally open or normally closed.  This would of course depend on your robot’s electronics, mechanics, and its task.  The simplest yet extremely useful sensor for a robot is a “bump switch” that tells it when it’s bumped into something, so it can back up and turn away. Even for such a simple idea, there are many different ways of implementation.

 

Light Sensors

 

Switches measure physical contact and light sensors measure the amount of light impacting a photocell, which is basically a resistive sensor.  The resistance of a photocell is low when it is brightly illuminated, i.e., when it is very light; it is high when it is dark.  In that sense, a light sensor is really a “dark” sensor.  In setting up a photocell sensor, you will end up using the equations we learned above, because you will need to deal with the relationship of the photocell resistance photo, and the resistance and voltage in your electronics sensor circuit.  Of course since you will be building the electronics and writing the program to measure and use the output of the light sensor, you can always manipulate it to make it simpler and more intuitive.  What surrounds a light sensor affects its properties.  The sensor can be  shielded and positioned in various ways.  Multiple sensors can be arranged in useful configurations and isolate them from each other with shields.

 

Just like switches, light sensors can be used in many different ways:

 

Light sensors can measure:

 

light intensity (how light/dark it is)

 

differential intensity (difference between photocells)

 

break-beam (change/drop in intensity)

 

Light sensors can be shielded and focused in different ways

 

Their position and directionality on a robot can make a great deal of difference and impact

 

Polarized light

 

“Normal” light emanating from a source is non-polarized, which means it travels at all orientations with respect to the horizon.  However, if there is a polarizing filter in front of a light source, only the light waves of a given orientation of the filter will pass through.  This is useful because now we can manipulate this remaining light with other filters; if we put it through another filter with the same characteristic plane, almost all of it will get through.  But, if we use a perpendicular filter (one with a 90-degree relative characteristic angle), we will block all of the light.  Polarized light can be used to make specialized sensors out of simple photocells; if you put a filter in front of a light source and the same or a different filter in front of a photocell, you can cleverly manipulate what and how much light you detect. 

 

Resistive Position Sensors

 

    We said earlier that a photocell is a resistive device.  We can also sense resistance in response to other physical properties, such as bending.  The resistance of the device increases with the amount it is bent.  These bend sensors were originally developed for video game control (for example, Nintendo Powerglove), and are generally quite useful.  Notice that repeated bending will wear out the sensor.  Not surprisingly, a bend sensor is much less robust than light sensors, although they use the same underlying resistive principle.

 

Potentiometers

 

    These devices are very common for manual tuning; you have probably seen them in some controls (such as volume and tone on stereos).  Typically called pots, they allow the user to manually adjust the resistance.  The general idea is that the device consists of a movable tap along two fixed ends.  As the tap is moved, the resistance changes.  As you can imagine, the resistance between the two ends is fixed, but the resistance between the movable part and either end varies as the part is moved.  In robotics, pots are commonly used to sense and tune position for sliding and rotating mechanisms.

 

Biological Analogs

 

All of the sensors we described exist in biological systems

 

Touch/contact sensors with much more precision and complexity in all species

 

Bend/resistance receptors in muscles 

 

Reflective Optosensors

 

    We mentioned that if we use a light bulb in combination with a photocell, we can make a break-beam sensor. This idea is the underlying principle in reflective optosensors: the sensor consists of an emitter and a detector. Depending of the arrangement of those two relative to each other, we can get two types of sensors:

 

reflectance sensors (the emitter and the detector are next to each other, separated by a barrier; objects are detected when the light is reflected off them and back into the detector)

 

break-beam sensors (the emitter and the detector face each other; objects are detected if they interrupt the beam of light between the emitter and the detector)

 

    The emitter is usually made out of a light-emitting diode (an LED), and the detector is usually a photodiode/phototransistor.

 

    Note that these are not the same technology as resistive photocells. Resistive photocells are nice and simple, but their resistive properties make them slow; photodiodes and photo-transistors are much faster and therefore the preferred type of technology.

 

What can you do with this simple idea of light reflectivity? Quite a lot of useful things:

 

object presence detection

 

object distance detection

 

surface feature detection (finding/following markers/tape)

 

wall/boundary tracking

 

rotational shaft encoding (using encoder wheels with ridges or black & white color)

 

bar code decoding

 

    Note, however, that light reflectivity depends on the color (and other properties) of a surface. A light surface will reflect light better than a dark one, and a black surface may not reflect it at all, thus appearing invisible to a light sensor. Therefore, it may be harder (less reliable) to detect darker objects this way than lighter ones. In the case of object distance, lighter objects that are farther away will seem closer than darker objects that are not as far away. This gives you an idea of how the physical world is partially-observable. Even though we have useful sensors, we do not have complete and completely accurate information.

 

    Another source of noise in light sensors is ambient light. The best thing to do is subtract the ambient light level out of the sensor reading, in order to detect the actual change in the reflected light, not the ambient light. How is that done? By taking two (or more, for higher accuracy) readings of the detector, one with the emitter on, and one with it off, and subtracting the two values from each other. The result is the ambient light level, which can then be subtracted from future readings. This process is called sensor calibration. Of course, remember that ambient light levels can change, so the sensors may need to be calibrated repeatedly.

 

Break-beam Sensors

 

    We already talked about the idea of break-beam sensors. In general, any pair of compatible emitter-detector devices can be used to produce such a sensors:

 

an incandescent flashlight bulb and a photocell

 

red LEDs and visible-light-sensitive photo-transistors

 

or infra-red IR emitters and detectors

 

Shaft Encoding

 

Shaft encoders measure the angular rotation of an axle providing position and/or velocity info. For example, a speedometer measures how fast the wheels of a vehicle are turning, while an odometer measures the number of rotations of the wheels.

 

In order to detect a complete or partial rotation, we have to somehow mark the turning element. This is usually done by attaching a round disk to the shaft, and cutting notches into it. A light emitter and detector are placed on each side of the disk, so that as the notch passes between them, the light passes, and is detected; where there is no notch in the disk, no light passes.

 

If there is only one notch in the disk, then a rotation is detected as it happens. This is not a very good idea, since it allows only a low level of resolution for measuring speed: the smallest unit that can be measured is a full rotation. Besides, some rotations might be missed due to noise.

 

Usually, many notches are cut into the disk, and the light hits impacting the detector are counted. (You can see that it is important to have a fast sensor here, if the shaft turns very quickly.)

 

An alternative to cutting notches in the disk is to paint the disk with black (absorbing, non-reflecting) and white (highly reflecting) wedges, and measure the reflectance. In this case, the emitter and the detector are on the same side of the disk.

 

In either case, the output of the sensor is going to be a wave function of the light intensity. This can then be processes to produce the speed, by counting the peaks of the waves.

 

Note that shaft encoding measures both position and rotational velocity, by subtracting the difference in the position readings after each time interval. Velocity, on the other hand, tells us how fast a robot is moving, or if it is moving at all. There are multiple ways to use this measure:

 

measure the speed of a driven (active) wheel

 

use a passive wheel that is dragged by the robot (measure forward progress)

 

We can combine the position and velocity information to do more sophisticated things:

 

move in a straight line

 

rotate by an exact amount

 

Note, however, that doing such things is quite difficult, because wheels tend to slip (effector noise and error) and slide and there is usually some slop and backlash in the gearing mechanism. Shaft encoders can provide feedback to correct the errors, but having some error is unavoidable.

 

Quadrature Shaft Encoding

 

So far, we’ve talked about detecting position and velocity, but did not talk about direction of rotation. Suppose the wheel suddenly changes the direction of rotation; it would be useful for the robot to detect that.

 

An example of a common system that needs to measure position, velocity, and direction is a computer mouse. Without a measure of direction, a mouse is pretty useless. How is direction of rotation measured?

 

Quadrature shaft encoding is an elaboration of the basic break-beam idea; instead of using only one sensor, two are needed. The encoders are aligned so that their two data streams coming from the detector and one quarter cycle (90-degrees) out of phase, thus the name “quadrature”. By comparing the output of the two encoders at each time step with the output of the previous time step, we can tell if there is a direction change. When the two are sampled at each time step, only one of them will change its state (i.e., go from on to off) at a time, because they are out of phase. Which one does it determines which direction the shaft is rotating. Whenever a shaft is moving in one direction, a counter is incremented, and when it turns in the opposite direction, the counter is decremented, thus keeping track of the overall position.

 

Other uses of quadrature shaft encoding are in robot arms with complex joints (such as rotary/ball joints; think of your knee or shoulder), Cartesian robots (and large printers) where an arm/rack moves back and forth along an axis/gear.

 

Modulation and Demodulation of Light

 

We mentioned that ambient light is a problem because it interferes with the emitted light from a light sensor. One way to get around this problem is to emit modulated light, i.e., to rapidly turn the emitter on and off. Such a signal is much easier and more reliably detected by a demodulator, which is tuned to the particular frequency of the modulated light. Not surprisingly, a detector needs to sense several on-flashes in a row in order to detect a signal, i.e., to detect its frequency. This is a small point, but it is important in writing demodulator code.

 

The idea of modulated IR light is commonly used; for example in household remote controls.

 

Modulated light sensors are generally more reliable than basic light sensors. They can be used for the same purposes: detecting the presence of an object measuring the distance to a nearby object (clever electronics required, see your course notes)

 

Infra Red (IR) Sensors

 

Infra red sensors are a type of light sensors, which function in the infra red part of the frequency spectrum.  IR sensors consist are active sensors: they consist of an emitter and a receiver.  IR sensors are used in the same ways that visible light sensors are that we have discussed so far: as break-beams and as reflectance sensors.  IR is preferable to visible light in robotics (and other) applications because it suffers a bit less from ambient interference, because it can be easily modulated, and simply because it is not visible.

 

IR Communication

 

Modulated infra red can be used as a serial line for transmitting messages. This is is fact how IR modems work. Two basic methods exist:

 

bit frames (sampled in the middle of each bit; assumes all bits take the same amount of time to transmit)

 

bit intervals (more common in commercial use; sampled at the falling edge, duration of interval between sampling determines whether it’s a 0 or 1)

 

Ultrasonic Distance Sensing

 

As we mentioned before, ultrasound sensing is based on the time-of-flight principle. The emitter produces a sonar “chirp” of sound, which travels away from the source, and, if it encounters barriers, reflects from them and returns to the receiver (microphone). The amount of time it takes for the sound beam to come back is tracked (by starting a timer when the “chirp” is produced, and stopping it when the reflected sound returns), and is used to compute the distance the sound traveled. This is possible (and quite easy) because we know how fast sound travels; this is a constant, which varies slightly based on ambient temperature.

 

At room temperature, sound travels at 1.12 feet per millisecond. Another way to put it that sound travels at 0.89 milliseconds per foot. This is a useful constant to remember.

 

The process of finding one’s location based on sonar is called echolocation. The inspiration for ultrasound sensing comes from nature; bats use ultrasound instead of vision (this makes sense; they live in very dark caves where vision would be largely useless). Bat sonars are extremely sophisticated compared to artificial sonars; they involve numerous different frequencies, used for finding even the tiniest fast-flying prey, and for avoiding hundreds of other bats, and communicating for finding mates.

                                                         

Specular Reflection

 

A major disadvantage of ultrasound sensing is its susceptibility to specular reflection (specular reflection means reflection from the outer surface of the object). While the sonar sensing principle is based on the sound wave reflecting from surfaces and returning to the receiver, it is important to remember that the sound wave will not necessarily bounce off the surface and “come right back.” In fact, the direction of reflection depends on the incident angle of the sound beam and the surface. The smaller the angle, the higher the probability that the sound will merely “graze” the surface and bounce off, thus not returning to the emitter, in turn generating a false long/far-away reading. This is often called specular reflection, because smooth surfaces, with specular properties, tend to aggravate this reflection problem. Coarse surfaces produce more irregular reflections, some of which are more likely to return to the emitter. (For example, in our robotics lab on campus, we use sonar sensors, and we have lined one part of the test area with cardboard, because it has much better sonar reflectance properties than the very smooth wall behind it.)

 

In summary, long sonar readings can be very inaccurate, as they may result from false rather than accurate reflections. This must be taken into account when programming robots, or a robot may produce very undesirable and unsafe behavior. For example, a robot approaching a wall at a steep angle may not see the wall at all, and collide with it!

 

Nonetheless, sonar sensors have been successfully used for very sophisticated robotics applications, including terrain and indoor mapping, and remain a very popular sensor choice in mobile robotics.

 

The first commercial ultrasonic sensor was produced by Polaroid, and used to automatically measure the distance to the nearest object (presumably which is being photographed). These simple Polaroid sensors still remain the most popular off-the-shelf sonars (they come with a processor board that deals with the analog electronics). Their standard properties include:

 

32-foot range

 

30-degree beam width

 

sensitivity to specular reflection

 

shortest distance return

 

Polaroid sensors can be combined into phased arrays to create more sophisticated and more accurate sensors.

 

One can find ultrasound used in a variety of other applications; the best known one is ranging in submarines. The sonars there have much more focused and have longer-range beams. Simpler and more mundane applications involve automated “tape-measures”, height measures, burglar alarms, etc.

 

Machine Vision

 

So far, we have talked about relatively simple sensors. They were simple in terms of processing of the information they returned. Now we turn to machine vision, i.e., to cameras as sensors.

 

Cameras, of course, model biological eyes. Needless to say, all biological eyes are more complex than any camera we know today, but, as you will see, the cameras and machine vision systems that process their perceptual information, are not simple at all! In fact, machine vision is such a challenging topic that it has historically been a separate branch of Artificial Intelligence.

 

The general principle of a camera is that of light, scattered from objects in the environment (those are called the scene), goes through an opening (”iris”, in the simplest case a pin hole, in the more sophisticated case a lens), and impinging on what is called the image plane. In biological systems, the image plane is the retina, which is attached to numerous rods and cones (photosensitive elements) which, in turn, are attached to nerves which perform so-called “early vision”, and then pass information on throughout the brain to do “higher-level” vision processing. As we mentioned before, a very large percentage of the human (and other animal) brain is dedicated to visual processing, so this is a highly complex endeavor.

 

In cameras, instead of having photosensitive rhodopsin and rods and cones, we use silver halides on photographic film, or silicon circuits in charge-coupled devices (CCD) cameras. In all cases, some information about the incoming light (e.g., intensity, color) is detected by these photosensitive elements on the image plane.

 

In machine vision, the computer must make sense out of the information it gets on the image plane. If the camera is very simple, and uses a tiny pin hole, then some computation is required to compute the projection of the objects from the environment onto the image plane (note, they will be inverted). If a lens is involved (as in vertebrate eyes and real cameras), then more light can get in, but at the price of being focused; only objects a particular range of distances from the lens will be in focus. This range of distances is called the camera’s depth of field.

 

The image plane is usually subdivided into equal parts, called pixels, typically arranged in a rectangular grid. In a typical camera there are 512 by 512 pixels on the image plane (for comparison, there are 120 x 10^6 rods and 6 x 10^6 cones in the eye, arranged hexagonally). Let’s call the projection on the image plane the image.

 

The brightness of each pixel in the image is proportional to the amount of light directed toward the camera by the surface patch of the object that projects to that pixel. (This of course depends on the reflectance properties of the surface patch, the position and distribution of the light sources in the environment, and the amount of light reflected from other objects in the scene onto the surface patch.) As it turns out, brightness of a patch depends on two kinds of reflections, one being specular (off the surface, as we saw before), and the other being diffuse (light that penetrates into the object, is absorbed, and then re-emitted). To correctly model light reflection, as well as reconstruct the scene, all these properties are necessary.

 

Let us suppose that we are dealing with a black and white camera with a 512 x 512 pixel image plane. Now we have an image, which is a collection of those pixels, each of which is an intensity between white and black. To find an object in that image (if there is one, we of course don’t know a priori), the typical first step (”early vision”) is to do edge detection, i.e., find all the edges. How do we recognize them? We define edges as curves in the image plane across which there is significant change in the brightness.

 

A simple approach would be to look for sharp brightness changes by differentiating the image and look for areas where the magnitude of the derivative is large. This almost works, but unfortunately it produces all sorts of spurious peaks, i.e., noise. Also, we cannot inherently distinguish changes in intensities due to shadows from those due to physical objects. But let’s forget that for now and think about noise. How do we deal with noise?

 

We do smoothing, i.e., we apply a mathematical procedure called convolution, which finds and eliminates the isolated peaks. Convolution, in effect, applies a filter to the image. In fact, in order to find arbitrary edges in the image, we need to convolve the image with many filters with different orientations. Fortunately, the relatively complicated mathematics involved in edge detection has been well studied, and by now there are standard and preferred approaches to edge detection.

 

Once we have edges, the next thing to do is try to find objects among all those edges. Segmentation is the process of dividing up or organizing the image into parts that correspond to continuous objects. But how do we know which lines correspond to which objects, and what makes an object? There are several cues we can use to detect objects:

 

We can have stored models of line-drawings of objects (from many possible angles, and at many different possible scales!), and then compare those with all possible combinations of edges in the image. Notice that this is a very computationally intensive and expensive process. This general approach, which has been studied extensively, is called model-based vision.

 

We can take advantage of motion. If we look at an image at two consecutive time-steps, and we move the camera in between, each continuous solid objects (which obeys physical laws) will move as one, i.e., its brightness properties will be conserved. This hives us a hint for finding objects, by subtracting two images from each other. But notice that this also depends on knowing well how we moved the camera relative to the scene (direction, distance), and that nothing was moving in the scene at the time. This general approach, which has also been studied extensively, is called motion vision.

 

We can use stereo (i.e., binocular stereopsis, two eyes/cameras/points of view). Just like with motion vision above, but without having to actually move, we get two images, which we can subtract from each other, if we know what the disparity between them should be, i.e., if we know how the two cameras are organized/positioned relative to each other.

 

We can use texture. Patches that have uniform texture are consistent, and have almost identical brightness, so we can assume they come from the same object. By extracting those we can get a hint about what parts may belong to the same object in the scene.

 

We can also use shading and contours in a similar fashion. And there are many other methods, involving object shape and projective invariants, etc.

 

Note that all of the above strategies are employed in biological vision. It’s hard to recognize unexpected objects or totally novel ones (because we don’t have the models at all, or not at the ready). Movement helps catch our attention. Stereo, i.e., two eyes, is critical, and all carnivores use it (they have two eyes pointing in the same direction, unlike herbivores). The brain does an excellent job of quickly extracting the information we need for the scene.

 

Machine vision has the same task of doing real-time vision. But this is, as we have seen, a very difficult task. Often, an alternative to trying to do all of the steps above in order to do object recognition, it is possible to simplify the vision problem in various ways:

 

Use color; look for specifically and uniquely colored objects, and recognize them that way (such as stop signs, for example)

 

Use a small image plane; instead of a full 512 x 512 pixel array, we can reduce our view to much less, for example just a line (that’s called a linear CCD). Of course there is much less information in the image, but if we are clever, and know what to expect, we can process what we see quickly and usefully.

 

Use other, simpler and faster, sensors, and combine those with vision. For example, IR cameras isolate people by body-temperature. Grippers allow us to touch and move objects, after which we can be sure they exist.

 

Use information about the environment; if you know you will be driving on the road which has white lines, look specifically for those lines at the right places in the image. This is how first and still fastest road and highway robotic driving is done.

 

Those and many other clever techniques have to be employed when we consider how important it is to “see” in real-time. Consider highway driving as an important and growing application of robotics and AI. Everything is moving so quickly, that the system must perceive and act in time to react protectively and safely, as well as intelligently.

 

Now that you know how complex vision is, you can see why it was not used on the first robots, and it is still not used for all applications, and definitely not on simple robots. A robot can be extremely useful without vision, but some tasks demand it. As always, it is critical to think about the proper match between the robot’s sensors and the task.

 

You’ve got 5 hours for your CPC exam – but should you tackle that heart catch coding scenario or skip it? Join us at an upcoming CPC Training Camp, and gain time management skills to help you know for sure.

Don’t waste time and money on CPC exam prep courses that offer non-practical information. CPC Training Camps have been designed to provide you with only the information you need in order to successfully pass your CPC exam.

And that’s not all! 3 days are all it takes to prepare. During your 3 days of CPC exam preparation, you’ll learn exactly what the CPC exam expects you to know, what format you can expect, and what you can skip. Your AAPC approved instructor will guide you as you tackle and review practice questions so that you’re prepared for the real thing. Overall, you’ll get a first-rate, insider’s guide to passing CPC certification exam  that you won’t find anywhere else!

Here’s a little preview of what you’ll learn at your 3-Day CPC Training Camp:

• CPC Exam-Taking Tips from the Pros
• What the CPC Exam Wants You to Know About ICD-9 CM
• Are You A Modifier Master?
• Insider’s Guide to the Integumentary Questions
• Musculoskeletal, Cardiovascular & Respiratory Surgery Review
• Down There: The 40,000 and 50,000 Series
• Get Set for 60,000
• Anesthesia Coding Crash Course
• What the CPC Exam Wants You to Know about E/M?

Radiology, Path/Lab, Medicine Pointer You Can’t Afford to Miss

To reserve your spot at your nearest CPC Training Camp, call 688-458-2962.

The mini-PACS (Picture Archive and Communication System) as you are aware is a type of PACS that is specific to one type of medical imaging modality, such as computed tomography (CT) or magnetic resonance imaging (MRI). The current trend today is toward full PACS RIS integration however. This presents many advantages, as this will facilitate sharing of these images over different platforms, improving diagnoses and allowing for greater opportunities for consultations when needed.

This process is already going on at large for-profit medical centers and major university hospitals; however, for smaller health care facilities and non-profits, often the decision is more complex. This is particularly true of specialty clinics and radiology facilities that focus on one type of medical issue and a single type of modality. Is it better to go with a mini-PACS that serves present needs and deal with the problem of integration when it comes, or should the facility make the costly investment of a full PACS-RIS?

Fortunately, drops in the cost of technology have made such components as the PACS server more affordable for smaller and less well-funded clinics and health care facilities. The web-based PACS makes integration of older specialized systems much less expensive than even a few years ago; prices on a fully-functional PACS-RIS that is capable of reading, processing and storing a wide range of medical images in various modalities start at around $5000.

Granted, a lower-end PACS-RIS will have some limitations; however, it may be the best choice for smaller clinics as it will allow the facility continue to provide much-needed patient services while upgrading its equipment in stages as finances allow.

Keep in mind that due to several factors, there is currently a shortage of qualified radiologists. With a web-based PACS possible to consult with a radiologist almost anywhere in the country. It is possible to add a radiology component to an existing mini-PACS for a reasonable cost; in fact, many such specialized systems have elements  radiology imaging built-in.

If yours in a clinic that deals with a single modality, just be aware that because of economics, demographics and a greater need for efficiency, the trend is toward full PACS integration; stand-alone applications will become increasingly rare. Advance planning and some research into the various PACS options available will be of great help for smaller clinics, non-profit facilities and even private practices when it comes to preparation for full PACS integration and the moving toward the establishment of a full-service facility.

Online Health Care Degrees encompasses studies in the management, treatment and prevention of illness, or the rise thereof, in the community. Health care professions include the medical sciences, pharmaceutical, dental, nursing laboratory/ clinical science as well as allied health care professions (these are clinical healthcare professions distinct from those aforementioned e.g. professions such as radiology, abortion, midwifery, massage etc). Allied Health care professionals work in a health care team to make the health care system function.

Modern health care

Health care careers are on the rise. The baby-boomer generation from the 40’s through the 60’s (some 80 million+ individuals in North America alone) are now getting older, and the added requirement to provide health care for a booming population has caused the health care profession to skyrocket into one of the largest and most vital of service industries. Such is the importance of health care to the world today (with health related issues increasing in numbers with old age) that many health care qualifications today require less than a year to achieve- as compared to a few decades ago when health care education took years to complete.

Online Health care education

Mature students and professionals wishing to undertake education and training in any of the fields in health care today have a host of options when it comes to learning online while juggling their families and jobs. There are over 5000 degrees, associate degrees and certifications (accredited) for allied health care professions now available online from some of the 2000 institutions that offer health care education online. Allied health care is also the most popular field of education pursued online as well, with many professionals using such courses to attain CME credits or to diversify their practice portfolios.

Web 2.0 and podcast for online healthcare

Online healthcare education is now being delivered using the following means;

Web 2.0 basically means the modern internet, where students can interact with the information and other people, i.e. through blogs, webcasts, web-desktop and social networking sites (like facebook).

Podcasts are basically a way of broadcasting/ distributing information to multiple users through the means of video/ audio files and electronic copies of documents or slides which are usable on mp3/mp3-video players (not necessarily iPods as the term may suggest).

The Podcasts and Web2.0 based (blogs or RSS feeds) methods can be use to record audio-visual lectures or digital instructions of any kind and can be distributed both manually and automatically to a cell phone, PC, MP3 Player or laptop with as little hassle as possible; these lectures will allow students the luxury to go to work, attend to personal details of even relax and take time off, while still being able to progress in their coursework easily.

References

Podcasting and web 2.0 implications for healthcare. Lecture by Dr. Rodney B Murray

Resource Area:

DISCLAIMER: Above is a GENERAL OVERVIEW and may or may not reflect specific practices, courses and/or services associated with ANY ONE particular school(s) that is or is not advertised on SchoolsGalore.com.

Copyright 2009 – All rights reserved by Media Positive Communications, Inc.

Notice: Publishers are free to use this article on an ezine or website, provided the article is reprinted in its entirety, including copyright and disclaimer, and ALL links remain intact and active.

Gadolinium toxicity is a serious issue that needs to be addressed immediately in order to protect your health. If you have recently had an MRI scan completed and if you suffer from kidney problems, gadolinium toxicity is a particularly concerning issue because it can lead to a serious disease known and Nephrogenic systemic fibrosis. Not only is Nephrogenic systemic fibrosis a very painful disease, it is also potentially fatal. By taking a few immediate steps, however, you will have a better chance of successfully treating the disease and obtaining the financial compensation you deserve.

Getting the Proper Medical Attention

If you have had a gadolinium injection in order to help a medical professional obtain a clearer MRI image, you may be suffering from gadolinium toxicity. The two main signs that you are suffering from gadolinium toxicity include hardening of the skin and restricted movement. If you are experiencing any of these problems after being exposed to gadolinium, it is essential for you to receive medical attention right away. You should also ask your doctor whether or not he or she thinks the gadolinium exposure may be associated with the symptoms you are experiencing.

If you are experiencing these symptoms, it is possible that you have developed Nephrogenic systemic fibrosis. This disease leads to the hardening of your skin, joints and internal organs. As a result, you may experience a loss of mobility that is so severe that you may become wheelchair bound. You may also experience problems with your organs that can ultimately become fatal.

Obtaining Legal Help

If you are suffering from gadolinium toxicity, it is also important for you to seek legal counsel as soon as you can. It is possible that you are entitled to receive financial compensation for the Nephrogenic systemic fibrosis you have developed. By contacting an attorney that is knowledgeable about gadolinium toxicity lawsuits, you will increase your chances of obtaining financial compensation for the disease. Not only are you entitled to receive money to pay for your medical expenses, you are also entitled to receive payment for the pain and suffering you have endured and will continue to endure as a result of the Nephrogenic systemic fibrosis.

In order to help you with your case, it is important for you to keep track of what has taken place since you received a gadolinium injection. This includes documenting your symptoms as well as what medical professionals have told you about your condition. The better you document what has occurred, the stronger case your attorney will be able to create on your behalf.

In this tough market while some industries like retail and manufacturing are vulnerable, while there are some other industries which will remain ‘recession resistant’. Certain things will continue even though people do not have enough money to spend, e.g. people will continue to get sick, pay taxes and use energy. Hence, these areas are expected to thrive well even in economic downturn.

Are you working in a recession-proof business? Here is the list of careers which are expected to remain unaffected in recession:

1. Education and Training – In spite of low income kids will go to school, and many out-of-work adults may decide to continue their education or take up various vocational training to add values to their résumé. Hence the demand of skilled teachers will always be high even in collapsing economy. Along with teachers administrators, independent consultants and sales executives to supply text books and other learning materials will remain in business.
2. Healthcare and Pharmaceuticals – As long as we are alive we are prone to sickness and the need for medicines, therapies and health insurance will never cease. Thus, people like doctors, nurses, lab technicians, pharmacists, researchers, insurance agents and others will be in demand.
3. Energy and Utility – No matter how much consumers reduce consumption, they’re not going to stop using energy and they’ll still light their homes. Hence, geoscientists, nuclear power reactor operators, engineers, utility administrator, technicians and others working in this industry may relax. In fact, this industry may grow, as companies look for more efficient ways to deliver using less energy.
4. Law and Order – In practical world criminal activities will never stop. There will always be a need for security, whether it’s public assets or private assets. So a career in this field will always be secured.
5. Technology – Technology touches everyone’s life. Thus, software designers, programmers and networking and systems administrators are required to meet business needs. Yes, there may be some consolidation and right-sizing because of budget cuts, but spending in technology has to continue.
6. Government Sector and Government Contract – Even in recession public officials will be required for smooth functioning of the government. Bureaucrats, administrators, Consultants, tax collectors, and accountants are safe from layoff. Moreover, despite economic turbulence, roads must be maintained and schools must be built, so people doing these works can breathe easy.
7. Sales and Marketing – As a general rule, anyone who is a source of income for a company will be safe. In recession, companies need a talented sales force to get new customers and grow business opportunities. So salespeople have little to worry about.
8. Environmental Services – As concerns about global warming swell, more and more companies are “going green” and will hire engineers and scientists to develop “green” technology.
9. Skilled Services – Irrespective of recession skilled labor will be in demand. Plumbers, tailors, hairstylist and other skilled workers will never go jobless. Therefore, it is advisable that you don’t put all of your eggs in one basket. Develop a skill for secondary income and start freelancing.

If your job do not fall in any of the aforementioned fields then instead of panicking be smart to start thinking about recession-proofing your job. Consider the following tips to make yourself the one person that every manager would hate to lose:

• Be Financially Efficient – Think of ways to generate revenues or cut costs and be indispensable. Take initiative to put those ideas into action. Try to finish your assigned work on time and error free and then see if you can be of assistance to anyone else in your department.
• Become Visible – This is not the time to take vacation. Come to office on time and stay long. Show enthusiasm in your work and be eager to take new assignments. Make sure you’re adding value at work by going above and beyond your basic job responsibilities
• Never stop networking – The day you get a pink slip is not the day you want to start calling old colleagues, asking former bosses out to lunch, and getting in touch to say hello to all the interesting people you’ve known over the years. The time to do that is now. Remember, with networking comes opportunities.
• Don’t be a whiner – Recession is the worst time to be a whiner. Management wants people who can boost morale during tough times and not panic others by whining.
• Update your résumé and skills – Take classes and focus on enhancing your skills. Keep your choices open take headhunter calls seriously and investigate prospective employers in other, healthier industries or smaller businesses that might require your talents.

Finally, don’t worry. Economy has been through recessions before and has managed to tough them out every time. This dark time will also end, just be positive and keep your options open.

I’ve always been intrigued by the subject of intelligence. As a child my mother would refer to me as “smart,” but I quickly noticed that all parents refer to their children as smart. In time I would discover that all children are not smart, just as all babies are not cute. If that were the case, we’d have a world full of beautiful, smart people – which we don’t.

Some of us are smart; but not as smart as we think, and others are smarter than they seem, which makes me wonder, how do we define smart? What makes one person smarter than another? When do “street smarts” matter more than “book smarts”? Can you be both smart and stupid? Is being smart more influenced by genetics or one’s environment?

Then there are the issues of education, intelligence and wisdom.

What does it mean to be highly educated? What’s the difference between being highly educated and highly intelligent? Does being highly educated automatically make you highly intelligent? Can one be highly intelligent without being highly educated? Do IQs really mean anything? What makes a person wise? Why is wisdom typically associated with old age?

My desire to seek answers to these questions inspired many hours of intense research which included the reading of 6 books, hundreds of research documents, and countless hours on the Internet; which pales in comparison to the lifetime of studies and research that pioneers in the fields of intelligence and education like Howard Gardner, Richard Sternberg, Linda S. Gottfredson, Thomas Sowell, Alfie Kohn, and Diane F. Halpern whose work is cited in this article.

My goal was simple: Amass, synthesize, and define data on what it means to be smart, educated and intelligent so that it can be understood and used by anyone for their benefit.

PRENATAL CARE

With this in mind, there was not a better (or more appropriate) place to start than at the very beginning of our existence: as a fetus in the womb. There’s a reason why they call it “prenatal,” which means occurring, existing, or performed before birth.

There is mounting evidence that the consumption of food that’s high in iron both before and during pregnancy is critical to building the prenatal brain. Researchers have found a strong association between low iron levels during pregnancy and diminished IQ. Foods rich in iron include lima beans, kidney beans, pinto beans, spinach, asparagus, broccoli, seafoods, nuts, dried fruits, oatmeal, and fortified cereals.

Children with low iron status in utero (in the uterus) scored lower on every test and had significantly lower language ability, fine-motor skills, and tractability than children with higher prenatal iron levels. In essence, proper prenatal care is critical to the development of cognitive skills.

COGNITIVE SKILLS

Cognitive skills are the basic mental abilities we use to think, study, and learn. They include a wide variety of mental processes used to analyze sounds and images, recall information from memory, make associations between different pieces of information, and maintain concentration on particular tasks. They can be individually identified and measured. Cognitive skill strength and efficiency correlates directly with students’ ease of learning.

DRINKING, PREGNANCY, AND ITS INTELLECTUAL IMPACT

Drinking while pregnant is not smart. In fact, it’s downright stupid.

A study in Alcoholism: Clinical & Experimental Research has found that even light to moderate drinking – especially during the second trimester – is associated with lower IQs in offspring at 10 years of age. This result was especially pronounced among African-American rather than Caucasian offspring.

“IQ is a measure of the child’s ability to learn and to survive in his or her environment. It predicts the potential for success in school and in everyday life. Although a small but significant percentage of children are diagnosed with Fetal Alcohol Syndrome (FAS) each year, many more children are exposed to alcohol during pregnancy who do not meet criteria for FAS yet experience deficits in growth and cognitive function,” said Jennifer A. Willford, assistant professor of psychiatry at the University of Pittsburgh School of Medicine.

Paul D. Connor, clinical director of the Fetal Alcohol and Drug Unit and assistant professor in the department of psychiatry and behavioral sciences at the University of Washington has this to say about the subject:

“There are a number of domains of cognitive functioning that can be impaired even in the face of a relatively normal IQ, including academic achievement (especially arithmetic), adaptive functioning, and executive functions (the ability to problem solve and learn from experiences). Deficits in intellectual, achievement, adaptive, and executive functioning could make it difficult to appropriately manage finances, function independently without assistance, and understand the consequences of – or react appropriately to – mistakes.”

This is a key finding which speaks directly to the (psychological) definition of intelligence which is addressed later in this article.

ULTRA SOUNDS

Studies have shown that the frequent exposure of the human fetus to ultrasound waves is associated with a decrease in newborn body weight, an increase in the frequency of left-handedness, and delayed speech.

Because ultrasound energy is a high-frequency mechanical vibration, researchers hypothesized that it might influence the migration of neurons in a developing fetus. Neurons in mammals multiply early in fetal development and then migrate to their final destinations. Any interference or disruption in the process could result in abnormal brain function.

Commercial companies (which do ultrasounds for “keepsake” purposes) are now creating more powerful ultrasound machines capable of providing popular 3D and 4D images. The procedure, however, lasts longer as they try to make 30-minute videos of the fetus in the uterus.

The main stream magazine New Scientist reported the following: Ultrasound scans can stop cells from dividing and make them commit suicide. Routine scans, which have let doctors peek at fetuses and internal organs for the past 40 years, affect the normal cell cycle.

On the FDA website this information is posted about ultrasounds: 

While ultrasound has been around for many years, expectant women and their families need to know that the long-term effects of repeated ultrasound exposures on the fetus are not fully known. In light of all that remains unknown, having a prenatal ultrasound for non-medical reasons is not a good idea.

NATURE VERSUS NURTURE…THE DEBATE CONTINUES

Now that you are aware of some of the known factors which determine, improve, and impact the intellectual development of a fetus, it’s time for conception. Once that baby is born, which will be more crucial in the development of its intellect: nature (genetics) or nurture (the environment)?

Apparently for centuries, scientists and psychologists have gone back and forth on this. I read many comprehensive studies and reports on this subject during the research phase of this article, and I believe that it’s time to put this debate to rest. Both nature and nurture are equally as important and must be fully observed in the intellectual development of all children. This should never be an either/or proposition – why risk it?

A recent study shows that early intervention in the home and in the classroom can make a big difference for a child born into extreme poverty, according to Eric Turkheimer, a psychologist at the University of Virginia in Charlottesville. The study concludes that while genetic makeup explains most of the differences in IQ for children in wealthier families, environment – and not genes – makes a bigger difference for minority children in low-income homes.

Specifically, what researchers call “heritability”- the degree to which genes influence IQ – was significantly lower for poor families. “Once you’re put into an adequate environment, your genes start to take over,” Mr. Turkheimer said, “but in poor environments genes don’t have that ability.”

But there are reports that contradict these findings…sort of.

Linda S. Gottfredson, a professor of educational studies at the University of Delaware, wrote in her article, The General Intelligence Factor that environments shared by siblings have little to do with IQ. Many people still mistakenly believe that social, psychological and economic differences among families create lasting and marked differences in IQ.

She found that behavioral geneticists refer to such environmental effects as “shared” because they are common to siblings who grow up together. Her reports states that the heritability of IQ rises with age; that is to say, the extent to which genetics accounts for differences in IQ among individuals increases as people get older.

In her article she also refers to studies comparing identical and fraternal twins, published in the past decade by a group led by Thomas J. Bouchard, Jr., of the University of Minnesota and other scholars, show that about 40 percent of IQ differences among preschoolers stems from genetic differences, but that heritability rises to 60 percent by adolescence and to 80 percent by late adulthood.

And this is perhaps the most interesting bit of information, and relevant to this section of my article: With age, differences among individuals in their developed intelligence come to mirror more closely their genetic differences. It appears that the effects of environment on intelligence fade rather than grow with time.

Bouchard concludes that young children have the circumstances of their lives imposed on them by parents, schools and other agents of society, but as people get older they become more independent and tend to seek out the life niches that are most congenial to their genetic proclivities.

BREAST-FEEDING INCREASES INTELLIGENCE

Researchers from Christchurch School of Medicine in New Zealand studied over 1,000 children born between April and August 1977. During the period from birth to one year, they gathered information on how these children were fed.

The infants were then followed to age 18. Over the years, the researchers collected a range of cognitive and academic information on the children, including IQ, teacher ratings of school performance in reading and math, and results of standardized tests of reading comprehension, mathematics, and scholastic ability. The researchers also looked at the number of passing grades achieved in national School Certificate examinations taken at the end of the third year of high school.

The results indicated that the longer children had been breast-fed, the higher they scored on such tests.

TALKING TO YOUR CHILDREN MAKES A DIFFERENCE

Thomas Sowell, author of Race, IQ, Black Crime, and facts Liberals Ignore uncovered some fascinating information that every parent should take note of. He writes:

There is a strong case that black Americans suffer from a series of disadvantageous environments. Studies show time and again that before they go to school, black children are on average exposed to a smaller vocabulary than white children, in part due to socioeconomic factors.

While children from professional households typically exposed to a total of 2,150 different words each day, children from working class households are exposed to 1,250, and children from households on welfare a mere 620.

Yes, smart sounding children tend to come from educated, professional, two-parent environments where they pick-up valuable language skills and vocabulary from its smart sounding inhabitants.

Mr. Sowell continues: Black children are obviously not to blame for their poor socioeconomic status, but something beyond economic status is at work in black homes. Black people have not signed up for the “great mission” of the white middle class – the constant quest to stimulate intellectual growth and get their child into Harvard or Oxbridge

Elsie Moore of Arizona State University, Phoenix, studied black children adopted by either black or white parents, all of whom were middle-class professionals. By the age of 7.5 years, those in black homes were 13 IQ points behind those being raised in the white homes.

ACCUMULATED ADVANTAGES

At this juncture in my research it dawned on me, and should be fairly obvious to you, that many children are predisposed to being smart, educated, and intelligent, simply by their exposure to the influential factors which determine them long before they start school.

An informed mother, proper prenatal care, educated, communicative parents, and a nurturing environment in which to live, all add up to accumulated advantages that formulate intellectual abilities. As you can see, some children have unfair advantages from the very beginning.

Malcolm Gladwell, author of top-selling book Outliers, wrote that “accumulated advantages” are made possible by arbitrary rules…and such unfair advantages are everywhere. “It is those who are successful who are most likely to be given the kinds of social opportunities that lead to further success,” he writes. “It’s the rich who get the biggest tax breaks. It’s the best students who get the best teaching and most attention.”

With that in mind, we turn our attention to education and intelligence.

WHAT DOES IT MEAN TO BE WELL EDUCATED?

Alfie Kohn, author of the book What Does It Mean To Be Well Educated? poses the question, does the phrase well educated refer to a quality of schooling you received, or something about you? Does it denote what you were taught? Or what you remember?

I contend that to be well educated is all in the application; the application and use of information. Information has to be used in order to become knowledge, and as we all have heard, knowledge is power.

Most people are aware of the floundering state of education in this country on some level. We tell our children that nothing is more important than getting a “good” education, and every year, due to government budget shortfalls, teachers are laid off, classes are condensed, schools are closed, and many educational programs – especially those which help the underprivileged – are cut.

The reality is, we don’t really value education. We value it as a business, an industry, political ammunition, and as an accepted form of discrimination, but not for what it was intended: a means of enriching one’s character and life through learning.

What we value as a society, are athletes and the entertainment they offer. The fact that a professional athlete makes more money in one season, than most teachers in any region will make in their careers, is abominable. There is always money to build new sports stadiums, but never enough to give teachers a decent (and well-deserved) raise.

Ironically, the best teachers don’t go into the profession for money. They teach because it’s a calling. Most of them were influenced by a really good teacher as a student. With the mass exodus of teachers, many students are not able to cultivate the mentoring relationships that they once were able to because so many are leaving the profession – voluntarily and involuntarily – within an average of three years.

At the high school level, where I got my start, the emphasis is not on how to educate the students to prepare them for life, or even college (all high schools should be college-prep schools, right?), it was about preparing them to excel on their standardized tests. Then the controversial “exit” exams were implemented and literally, many high schools were transformed into testing centers. Learning has almost become secondary.

This mentality carries over into college, which of course there’s a test one must take in order to enroll (the SAT or ACT). This explains why so many college students are more concerned with completing a course, than learning from it. They are focused on getting “A’s” and degrees, instead of becoming degreed thinkers. The latter of which are in greater demand by employers and comprise the bulk of the self-employed. The “get-the-good-grade” mindset is directly attributable to the relentless and often unnecessary testing that our students are subjected to in schools.

Alfie Kohn advocates the “exhibition” of learning, in which students reveal their understanding by means of in-depth projects, portfolios of assignments, and other demonstrations.

He cites a model pioneered by Ted Sizer and Deborah Meier. Meier has emphasized the importance of students having five “habits of mind,” which are: the value of raising questions about evidence (”How do we know what we know?”), point of view, (”Whose perspective does this represent?”), connections (”How is this related to that?”), supposition (”How might things have been otherwise?”), and relevance (”Why is this important?”).

Kohn writes: It’s only the ability to raise and answer those questions that matters, though, but also the disposition to do so. For that matter, any set of intellectual objectives, any description of what it means to think deeply and critically, should be accompanied by a reference to one’s interest or intrinsic motivation to do such thinking…to be well-educated then, is to have the desire as well as the means to make sure that learning never ends…

HISTORY AND PURPOSE OF IQ

We’ve always wanted to measure intelligence. Ironically, when you look at some the first methods used to evaluate it in the 1800s, they were not, well, very intelligent. Tactics such as subjecting people to various forms of torture to see what their threshold for pain was (the longer you could withstand wincing, the more intelligent you were believed to be), or testing your ability to detect a high pitch sound that others could not hear.

Things have changed…or have they?

No discussion of intelligence or IQ can be complete without mention of Alfred Binet, a French psychologist who was responsible for laying the groundwork for IQ testing in 1904. His original intention was to devise a test that would diagnose learning disabilities of students in France. The test results were then used to prepare special programs to help students overcome their educational difficulties.

It was never intended to be used as an absolute measure of one’s intellectual capabilities.

According to Binet, intelligence could not be described as a single score. He said that the use of the Intelligence Quotient (IQ) as a definite statement of a child’s intellectual capability would be a serious mistake. In addition, Binet feared that IQ measurement would be used to condemn a child to a permanent “condition” of stupidity, thereby negatively affecting his or her education and livelihood.

The original interest was in the assessment of ‘mental age’ — the average level of intelligence for a person of a given age. His creation, the Binet-Simon test (originally called a “scale”), formed the archetype for future tests of intelligence.

H. H. Goddard, director of research at Vineland Training School in New Jersey, translated Binet’s work into English and advocated a more general application of the Simon-Binet test. Unlike Binet, Goddard considered intelligence a solitary, fixed and inborn entity that could be measured. With help of Lewis Terman of Stanford University, his final product, published in 1916 as the Stanford Revision of the Binet-Simon Scale of Intelligence (also known as the Stanford-Binet), became the standard intelligence test in the United States.

It’s important to note that the fallacy about IQ is that it is fixed and can not be changed. The fact is that IQ scores are known to fluctuate – both up and down during the course of one’s lifetime. It does not mean that you become more, or less intelligent, it merely means that you tested better on one day than another.

One more thing to know about IQ tests: They have been used for racist purposes since their importation into the U.S. Many of those who were involved in the importation and refinement of these tests believed that IQ was hereditary and are responsible for feeding the fallacy that it is a “fixed” trait.

Many immigrants were tested in the 1920s and failed these IQ tests miserably. As a result, many of them were denied entry into the U.S., or were forced to undergo sterilization for fear of populating America with “dumb” and “inferior” babies. If you recall, the tests were designed for white, middle class Americans. Who do you think would have the most difficulty passing them?

Lewis Terman developed the original notion of IQ and proposed this scale for classifying IQ scores:

000 – 070: Definite feeble-mindedness 
070 – 079: Borderline deficiency 
080 – 089: Dullness 
090 – 109: Normal or average intelligence 
110 – 119: Superior intelligence 
115 – 124: Above average (e.g., university students) 
125 – 134: Gifted (e.g., post-graduate students) 
135 – 144: Highly gifted (e.g., intellectuals) 
145 – 154: Genius (e.g., professors) 
155 – 164: Genius (e.g., Nobel Prize winners) 
165 – 179: High genius 
180 – 200: Highest genius 
200 -    ? : Immeasurable genius

*Genius IQ is generally considered to begin around 140 to 145, representing only 25% of the population (1 in 400).
*Einstein was considered to “only” have an IQ of about 160.

DEFINING INTELLIGENCE

Diane F. Halpern, a psychologist and past-president of the American Psychological Association (APA), wrote in her essay contribution to Why Smart People Can Be So Stupid that in general, we recognize people as intelligent if they have some combination of these achievements (1) good grades in school; (2) a high level of education; (3) a responsible, complex job; (4) some other recognition of being intelligent, such as winning prestigious awards or earning a large salary; (5) the ability to read complex text with good comprehension; (6) solve difficult and novel problems.

Throughout my research and in the early phases of this article, I came across many definitions of the word intelligence. Some were long, some were short. Some I couldn’t even understand. The definition that is most prevalent is the one created by the APA which is: the ability to adapt to one’s environment, and learn from one’s mistakes.

How about that? There’s the word environment again. We just can’t seem to escape it. This adds deeper meaning to the saying, “When in Rome, do as the Romans do.” It means recognizing what’s going on in your environment, and having the intelligence adapt to it – and the people who occupy it – in order to survive and succeed within it.

There are also many different forms of intelligence. Most notably those created by Dr. Howard Gardner, professor of education at Harvard University.

Dr. Gardner believes (and I agree) that our schools and culture focus most of their attention on linguistic and logical-mathematical intelligence. We esteem the highly articulate or logical people of our culture. However, Dr. Gardner says that we should also place equal attention on individuals who show gifts in the other intelligences: the artists, architects, musicians, naturalists, designers, dancers, therapists, entrepreneurs, and others who enrich the world in which we live.

He felt that the traditional notion of intelligence, based on IQ testing, was far too limited and created the Theories Of Multiple Intelligences in 1983 to account for a broader range of human potential in children and adults.

These intelligences are:

-Linguistic intelligence (”word smart”) 
-Logical-mathematical intelligence (”number/reasoning smart”) 
-Spatial intelligence (”picture smart”) 
-Bodily-Kinesthetic intelligence (”body smart”) 
-Musical intelligence (”music smart”) 
-Interpersonal intelligence (”people smart”) 
-Intrapersonal intelligence (”self smart”) 
-Naturalist intelligence (”nature smart”)

Not associated with Dr. Gardner, but equally respected are:

FLUID & CRYSTALLIZED INTELLIGENCE

According to About.com, Psychologist Raymond Cattell first proposed the concepts of fluid and crystallized intelligence and further developed the theory with John Horn. The Cattell-Horn theory of fluid and crystallized intelligence suggests that intelligence is composed of a number of different abilities that interact and work together to produce overall individual intelligence.

Cattell defined fluid intelligence as “…the ability to perceive relationships independent of previous specific practice or instruction concerning those relationships.” Fluid intelligence is the ability to think and reason abstractly and solve problems. This ability is considered independent of learning, experience, and education. Examples of the use of fluid intelligence include solving puzzles and coming up with problem solving strategies.

Crystallized intelligence is learning from past experiences and learning. Situations that require crystallized intelligence include reading comprehension and vocabulary exams. This type of intelligence is based upon facts and rooted in experiences. This type of intelligence becomes stronger as we age and accumulate new knowledge and understanding.

Both types of intelligence increase throughout childhood and adolescence. Fluid intelligence peaks in adolescence and begins to decline progressively beginning around age 30 or 40. Crystallized intelligence continues to grow throughout adulthood.

SUCCESSFUL INTELLIGENCE

Then there’s Successful Intelligence, which is authored by intelligence psychologist and Yale professor, Robert J. Sternberg, who believes that the whole concept of relating IQ to life achievement is misguided, because he believes that IQ is a pretty miserable predictor of life achievement.

His Successful Intelligence theory focuses on 3 types of intelligence which are combined to contribute to one’s overall success: Analytical Intelligence; mental steps or components used to solve problems; Creative Intelligence: the use of experience in ways that foster insight (creativity/divergent thinking); and Practical Intelligence: the ability to read and adapt to the contexts of everyday life.

With regard to environment, Mr. Sternberg writes in his book Successful Intelligence: Successfully intelligent people realize that the environment in which they find themselves may or may not be able to make the most of their talents. They actively seek an environment where they can not only do successful work, but make a difference. They create opportunities rather than let opportunities be limited by circumstances in which they happen to find themselves.

As an educator, I subscribe to Mr. Sternberg’s Successful Intelligence approach to teaching. It has proven to be a highly effective tool and mindset for my college students. Using Successful Intelligence as the backbone of my context-driven curriculum really inspires students to see how education makes their life goals more attainable, and motivates them to further develop their expertise. Mr. Sternberg believes that the major factor in achieving expertise is purposeful engagement.

EMOTIONAL INTELLIGENCE

In his best-selling 1995 book, Emotional Intelligence, Daniel Goleman reported that research shows that conventional measures of intelligence – IQ – only account for 20% of a person’s success in life. For example, research on IQ and education shows that high IQ predicts 10 to 25% of grades in college. The percentage will vary depending on how we define success. Nonetheless, Goleman’s assertion begs the question: What accounts for the other 80%?

You guessed it…Emotional Intelligence. What exactly is emotional intelligence? Emotional intelligence (also called EQ or EI) refers to the ability to perceive, control, and evaluate emotions. Many corporations now have mandatory EQ training for their managers in an effort to improve employee relations and increase productivity.

TACIT KNOWLEDGE aka “STREET SMARTS

You’ve heard the phrase, “Experience is the greatest teacher…”

In psychology circles knowledge gained from everyday experience is called tacit knowledge. The colloquial term is “street smarts,” which implies that formal, classroom instruction (aka “book smarts”) has nothing to do with it. The individual is not directly instructed as to what he or she should learn, but rather must extract the important lesson from the experience even when learning is not the primary objective.

Tacit knowledge is closely related to common sense, which is sound and prudent judgment based on a simple perception of the situation or facts. As you know, common sense is not all that common.

Tacit knowledge, or the lessons obtained from it, seems to “stick” both faster and better when the lessons have direct relevance to the individual’s goals. Knowledge that is based on one’s own practical experience will likely be more instrumental to achieving one’s goals than will be knowledge that is based on someone else’s experience, or that is overly generic and abstract.

BEING BOTH SMART AND STUPID

Yes, it’s possible to be both smart and stupid. I’m sure someone you know comes to mind at this precise moment. But the goal here is not to ridicule, but to understand how some seemingly highly intelligent, or highly educated individuals can be so smart in one way, and incredibly stupid in others.

The woman who is a respected, well paid, dynamic executive who consistently chooses men who don’t appear to be worthy of her, or the man who appears to be a pillar of the community, with a loving wife and happy kids, ends up being arrested on rape charges.

It happens, but why? I found the answer in Why Smart People Can Be So Stupid. 

Essentially, intellect is domain specific. In other words, being smart (knowledgeable) in one area of your life, and stupid (ignorant) in another is natural. Turning off one’s brain is quite common especially when it comes to what we desire. A shared characteristic among those who are smart and stupid, is difficulty in delaying gratification.

Olem Ayduk & Walter Mischel who wrote the chapter summarized: Sometimes stupid behavior in smart people may arise from faulty expectations, erroneous beliefs, or merely a lack of motivation to enact control strategies even when one has them. But sometimes it is an inability to regulate one’s affective states and the behavioral tendencies associated with them that leads to stupid and self-defeating behavior.

The central character in this book who many of these lessons regarding being smart and stupid revolve around is Bill Clinton and his affair with Monica Lewinksky.

WISDOM & CONCLUSION

My great grandmother, Leola Cecil, maybe had an 8th grade education at the most. By no stretch of the imagination was she highly educated. She was very observant and could “read” people with startling accuracy. Till the very end of her life she shared her “crystallized intelligence” with whomever was receptive to it.

She died at the age of 94. I often use many of her sayings as a public speaker, but most importantly, I use her philosophies to make sure that I’m being guided spiritually and not just intellectually. Many of us who are lucky enough to have a great grandparent can testify that there is something special about their knowledge. They seem to have life figured out, and a knack for helping those of us who are smart, educated and intelligent see things more clearly when we are too busy thinking.

What they have is what we should all aspire to end up with if we are lucky: wisdom.

Wisdom is the ability to look through a person, when others can only look at them. Wisdom slows down the thinking process and makes it more organic; synchronizing it with intuition. Wisdom helps you make better judgments regarding decisions, and makes you less judgmental. Wisdom is understanding without knowing, and accepting without understanding. Wisdom is recognizing what’s important to other people, and knowing that other people are of the utmost importance to you. Wisdom is both a starting point, and a final conclusion.

In the US, about 180,000 women develop it each year. Inflammatory Breast Cancer Symptoms The disease can also occur in men, although cancer of the male breast accounts for less than 1 in 100 cases. The risk of it increases with age, doubling every 10 years.

The disease is most commonly diagnosed in women over age 50. Very few women under age 30 develop it. Despite the rise in incidence, there has been a small drop in the number of deaths in the recent years and only about one-fifth of cases prove fatal. This reduction is due to improvements in treatment and the increased use of mammography for screening, which means that tumors can be detected early, when they often respond well to treatment.

Screening may reduce the number of deaths in women over age 50 by up to 4 in 10. In the US, many doctors recommend that women over age 40 have a mammogram every 1-2 years and every year over age 50. A cancerous tumor may first develop in the breast lobules (the structures in the breast that produce milk). A tumor that originates in the milk ducts may lead to Paget’s disease of the breast. Tumors may spread to other organs, such as the lungs or the liver, before being detected.

It is a cancer that originates in the breast tissue of women and men. It can spread to the lymph nodes under the arm before diagnosis. With advanced disease, metastasis can be seen in many body organs, including bone, brain, lung, liver and skin.

Causes:

The underlying cause of most is unclear. However, some risk factors have been identified, many of which suggest that the female hormone estrogen is an important factor in the development and progress of the disease. It is known that women who have their first menstrual period before age 11, or who have a late menopause, seem to be at increased risk of developing this cancer, probably because they are exposed to high levels of estrogen for longer. The number of menstrual cycles before a first pregnancy is also significant. And a woman who has her first child before age 20 has chances. Breast feeding is thought to have an additional protective effect.

Risk factors for developing it include

Lung Cancer Secrets Revealed Click here

. Early onset of menses or late menopause

. First pregnancy after age 30

. Family history of the disease

. Radiation exposure

Possible risk factors include

. High fat diet

. Excessive alcohol intake

. Estrogen replacement therapy

. Oral contraceptive use

Symptoms:

It is usually manifest as a painless lump anywhere in the breast or under the arm. Occasionally, its symptoms can be more subtle, such as:

. An inverted nipple

. Bloody discharge from the nipple

. Changes in the skin overlying the breast making it resemble the skin of an orange.

Diagnosis:

Any Breast pain or lumps felt on physical examination by a woman or her physician and any lumps found on mammography (Radiography) should be considered for biopsy. Lumps seen on mammography, but not palpable on examination can be located by ultrasound or mammogram for biopsy. If a diagnosis of it is established, staging tests include:

. Liver function tests

. Alkaline phosphates test to check for bone disease

. Chest X-ray (Radiography)

. Bone Scan (Nuclear Medicine)

Complications:

Complications of it are related to areas of metastasis:

. Metastasis to bone can cause pain, bone fractures or elevated calcium levels in the blood.

. Metastasis to the brain or spinal cord can cause seizures, headaches, weakness, numbness or confusion.

. Metastasis to the lungs can cause breathing difficulty, chest pain or swelling of the face and neck.

Treatment: Self Treatment:

. A well balanced diet should be maintained. Once a diagnosis of it is made all estrogen medication should be stopped, including birth control pills.

Medical Treatment:

Many women will require additional drug therapy after surgery to prevent it from returning. Either tamoxifen (a hormonal pill) or chemotherapy (intravenous medication) may be recommended, depending on the type of tumor. More advanced case is also treated with chemotherapy or hormonal therapy.

Surgical Treatment:

Two alternative initial treatments for it are:

. Lumpectomy with lymph node dissection followed by radiation therapy to the breast.

. Mastectomy (mastectomy, partial or mastectomy, modified radical)

Prevention:

Early detection of it by regular beast self-examination and regular mammography (Radiography) screening is important. A low – fat diet and moderate alcohol intake may be important. Some researchers theorize that exercise for preadolescent girls may be helpful as it delays the age of onset of menstruation.

lung cancer treatment breakthroughs Click here

Harley Street is possibly the worldâ??s most famous area for medical practice. For around 150 years medical practitioners have been setting up practices in Harley Street and the surrounding areas. Around the 1960â??s doctors began moving to this area of London, and some set up practices from their homes. This began a trend that transformed the area into the centre of excellence it has become. Once a few doctors began to work in the area it opened the flood gates. The success of these doctors encouraged other to move to the area too, and it almost became necessary for the best private doctors to work in the area, as it became the go-to place for those seeking quality private healthcare. This reputation continues to this day, with around 1,500 medical practitioners in and around Harley Street, across a broad range of different services. These days they include chiropractors, psychiatrists, and cosmetic surgeons.

The London Clinic is the most well known organisation in Harley Street, although most of its services now run nearby on the corner of Devonshire Place and Marylebone Road. The London Clinic is a private healthcare organisation which was established in 1932.

Harley Streetâ??s reputation has meant the term â??Harley Street ofâ? has been used to describe other street around the world. So now letâ??s have a look at the Harley Streetâ??s of the North, Australia, Enniskillen and Ramsgate.

Welbeck Street is in the West End of London, two block west of Harley Street, and also has a reputation for healthcare. A large part of this is due to its closeness to Harley Street. Because of its fame, property price and rent is high in Harley Street, so many practitioners unable to afford this have instead opted to practice close to, but not in, Harley Street. Welbeck Street is the best example of this. Itâ??s most famous medical institution is The London Welbeck Hospital. In the 20th century it was a nationally know maternity hospital, but possibly as a sign of the times it now specialises in cosmetic surgery. The Welbeck Street Hospital and the British Institute of Radiology are two other well know institutions on the street. The Welbeck Street Hospital specialised in diseases of the nervous system, while the British Institute of Radiology is the oldest radiology society in the world, and dates back to the 1800â??s.

Rodney Street in Liverpool is commonly known as the â??Harley Street of the Northâ?, due to the large number of doctors based there. It originated from the 1780â??s and was mostly developed in the 1820â??s. During this period it mostly housed the wealthy and former Prime Minister William Ewart was born there. A number of medical consulting rooms reside in Rodney Street, and anything from cosmetic surgeons to dentists practice there. It is still seen as an area for medical expertise to this day.

Macquarie Street in Sydney, Australia was once nicknamed â??the Harley Street of Sydneyâ?. Like much of the city the name bares that of Lachlan Macquarie, who was the governor of Sydney from 1810 until 1821. He was responsible for much of the early building of the City, including this street. Until around 20 years ago there were a large number of medical practices there, although this has mostly now been taken over by business premises, as the street runs through the City just east of the centre. One famous medical building that does remain though is the Sydney Hospital, which is the oldest hospital in Australia. It has been around since 1788 and at its present site since 1811. It specialises in ophthalmology and hand surgery, but also houses an emergency department.

Two other places that have had areas compared with Harley Street (though on much smaller scales) are Darling Street in Enniskillen in Ireland, and Chapel Place in Ramsgate in England.

Enniskillen is a town of approximately 13,000 people in the province of Ulster. It is a Georgian town with Victorian influences. Within the town lies Darling Street, which was referred to as, â??the Harley Street of Enniskillenâ? in the 1840â??s. This comes from the fact that nine of the eleven doctors surgeries in the town where based in this street.

Finally there is â??the Harley Street of Ramsgateâ?, Chapel Place. Ramsgate is in the South-East of England on the peninsular of the Isle of Thanet in Kent. It is a seaside town of around 40,000 people. Chapel Place was a highly sort after area in the late 18th and early 19th century. Royalty and other wealthy people settled in the area around this time. Its Georgian buildings were once home to the surgeries of many doctors and dentists, hence itâ??s association with Harley Street. This is no longer the case though, and it is now populated by businesses and flats.

Andrew Marshall ©