Weird ringing noise in ear nhs,electro acupuncture for tinnitus relief,cause of subjective tinnitus 2014,tenis de futbol 2016 wikipedia - For Begninners

Good thing they’re all in the dictionary–or we might think that they were invented for the pages of a fairytale! You probably think you should be able to read English with zero problems since you’ve mastered all the important grammar rules,  slang words and idioms. Download: This blog post is available as a convenient and portable PDF that you can take anywhere. Even native English speakers are constantly learning new words that they never knew existed!
Just understanding one more word will help you make your way through the minefield that is be English. The more words that you can use comfortably and correctly, the more ways you have to say exactly what you mean. A word that really sounds like what it means, hullaballoo (noun) is the loud noises and shouting that people make when they’re angry. You may already know that words that end in phone or phony are related to sound in some way. Nothing to do with whips or snaps, say whippersnapper (noun) quickly and you’ll create a funny and harsh sound!
Although this term is a little bit old-fashioned today, it’ll certainly make people smile if you use it.
The meaning has changed over the years, and today it’s used for a young person who’s too confident and perhaps a little cheeky! Created from the meaningless sound that turkeys make, gobbledygook (noun) was originally an American English word.
Gibberish (noun) means the same: nonsense words and phrases that sound like English but have little meaning. Have you ever listened to somebody trying to talk about something that they know absolutely nothing about? Poppycock actually came from the Dutch word pappekak, which is made from pap (soft) and kak (poop!). Mainly used in North American English, if somebody’s talking lots of gibberish, gobbledegook, and poppycock, they may be trying to discombobulate (verb) another person. Are you trying to find just the right word for someone who’s very bad-tempered and grumpy?
How about if you want to describe that someone’s lazy and has no enthusiasm or determination? It’s easy to break this word into two parts – woe (extreme sadness) and begone (an old-fashioned word that means surrounded by something).
Very new when compared to all the others on the list, the word Frankenfood (noun) came into existence in the 1990’s.
Once you’ve learned all these great new words, make sure that you actually learn them so that you too can make them part of your regular conversations. This doesn’t have to be anything fancy – just a small notebook where you write all new words as you learn them.
If you find some words very difficult to remember, try to write the word on a post-it note and stick it somewhere where you’ll see it often.
If you liked this post, something tells me that you’ll love FluentU, the best way to learn English with real-world videos.
Learning English becomes fun and easy when you learn with movie trailers, music videos, news and inspiring talks. To recap (here and here), our goal is to build high precision robot systems using cheap components, and now we have actually tried a control installation. We built a Lego bench top visual feedback test rig to see how accurate we could get visual feedback in previous work. We achieved high accuracy by using the visual servoing platform developed at Lagadic University (VISP). So with a high accuracy optical feedback mechanism prototyped, it was time to test its ability to control a real(ly crap) robot. To make proportional control you can simply turn the joint on and off really quickly (pulse width modulation), and adjust the ratio of off to on per duty cycle. Building a GUI off the bat is important so you have a panic button to click when later the robot goes haywire (guaranteed). The final component of the system is the connector between the optical feedback and the motor control system.
We need some maths formalisms before we can pose the control problem ready for adaptive control.
Now we have the formulation of the dynamics of the arm, to control it, we have to choose a motor command u to move the arm towards the desired state. Here x-x_desired reflects the error in state, and f(x,u) the change in state for a particular motor command.
The above equation is not collapsing the error into a single scalar value, so commonly people will take the root mean squared error over the above. So once you have built a training set, chosen meta-parameters for LWPR, fitted g(), and tested it does indeed predict well on held out data, you are left with a function that takes state + motor commands [x, u] and predicts the state change, &#7819. The initial system reads optical feedback as fast as possible (30fps) which is provided as a homography matrix, and the optimizes what to do next. Its worth noting that you want training set data collection, and the application of learnt control should to share as much common code as possible.
After we generated a training set and then fitted the data with LWPR and applied it, we got terrible performance! Before we actually got to control the robot we had problems with how fast we could solve the control optimization (the min_u(J(x,u)) bit). As you can see we tried 4 different numerical optimizing routines from scipy before we found one that was blazing fast (tnc, written in C with a python wrapper).
What you see is that the state responds to the motor command 3 – 5 states after the motor command is applied. Latency is a massive problem in control, because it defines a theoretic limit to how fast you can control the system. Only one of the motor commands exhibited variable latency which was the swivel joint at the base of the robot.
What happens is that when the base swivel joint changes direction, an extra distance has to be traversed before the gearing re-engages.
What you see is instability on the homography when the arm starts moving, and a wave like overtures to the homography over time. To account for latency and instability in the feedback we choose to wait for three consecutive readings of the homography before deciding what applying a given motor commmand did. A big drawback of this approach is that now we can only control the arm in discrete steps, and we don’t really know how long wait_for_stable_homography() will take to settle, but it does lead to much cleaner data and therefore an easier problem. It took a little while to successfully learn the data set, the LWPR documentation helped enormously, eventually we were able to get a reasonable setting for all the parameters and could turn on online adaptive control (i.e. In early runs the optimization would choose massive motor commands and would try to hammer the robot arm into the ground at maximum speed (I said haywire behaviour was guaranteed).
For large u the penulty term rises quadratically and makes large us undesirable to the optimizer. That solved our final problem and the adaptive control could be turned on and the robot leaned!
When you think about it, our training set is of consecutive readings taken from a single trajectory the robot arm took during random movements.
When the robot is switch on with adaptive control, it does a very bad job at controlling itself at first.
You can clearly see the changes in position as we commanded the robot around (this data was collected during Edinburgh mini-Makerfaire).
Zooming in to one of the steady spikeless portions we can measure the real accuracy of the the arm.
The performance was not the 0.1mm accuracy hoped for, but the software stack is doing what it can given the limited hardware.
First, we need to reduce all the unpredictability, particularly in the low level PWM control.


A lot of our problems come from the hardware itself, such as backlash and general susceptibility to vibrations. Tom Larkworthy and Tom Joyce (mainly worked on Runesketch but was of considerable moral support) This entry was posted in Mechanical, Software. Even if you never use some new words, you won’t be left wondering “what?” if you hear them used by other people. Make them part of your active vocabulary (the words that you use on a regular basis) and have fun with English! Hear the fantastic sounds they make, and you’ll certainly agree that these weird words will add some more life to your sentences and make people stop and listen!
Imagine two of your friends having a minor disagreement over something and making quite a bit of noise – doesn’t kerfuffle sound like a great way to describe the situation? Imagine birds screeching, alarm bells ringing and babies screaming…and you’ve got yourself a cacophony!
It’s been part of the English language since the 17th century and is a mixture of two terms. It’s a perfect word to use for an inquisitive child who just can’t stop questioning and correcting their parents! Like, you know that what they’re saying is completely untrue, yet they insist on continuing to talk? So, woebegone means “surrounded by sadness.” It comes from Middle English, English that was used during the Middle Ages.
Nothing to do with lollies or gags, it actually means to be idle and lazy or to waste time.
GM foods are those that have been scientifically altered in some way, that haven’t grown naturally. The existing approach to precision machinery is to spend lots of money on precision steel components and more or less control the machine open loop (without feedback). While this robot is not dangerous, its still horrible hearing the robot fight its own internal gearing, and also it can mess up an experiment if it manages to changes its dynamics but putting itself under internal strain. Now, the tried and tested approach is model-based control, where you measure the moving parts and build a mathematical model of the system you are trying to control. This is simple stuff, but masked in control specific nomenclature which can be a little hard to follow even if you are mathematician. Actions we denote as u, which again is a vector quantity (we can move many joints at the same time all at different speeds). In our system we also can’t measure the joint angles directly, so we use the homography matrix as x instead.
There is rich literature in control relating to the fact that moving the system as fast as possible might not be the optimal policy, because you will might pickup a large velocity and overshoot. To use that in the control framework, at a given point in time, you know the current state, x, and you vary the motor commands in order to minimize the cost function, J (above) i.e. Well that’s the theory anyway, but in practice there are a whole load of issues not accounted for! You want to avoid collecting data in a different control loop because the statistics you learn won’t apply in a new software context. The robot just did a nose dive into the ground, it did not seem to have learnt anything sensible. We further improved performance by feeding the previous answer to the optimization as the initial condition for the next time step. Using a spreadsheet of the training data you can quickly look at the correlations between variables on a scatter plot or time series.
As we are performing regression immediately, the learner has been given an impossible function to learn, and it just learns the mean value of nothing.
To control a system fast you either need to be able to predict the system very accurately, or integrate feedback very fast.
This manifests itself as the motor command taking a longer time to affect the state variables. In our case it would be easier to add a spring to the base and preload the join mechanically. Basically, moving shakes the camera alot, whcih can change the values of the homography drastically, and it takes a little while to settle down. We flattened homography into an 8 dim state vector (I would flatten it to a 6 dim one given my time again), so from a random initialisation, the probability that the initial state is anywhere near where the collected training data is quite low. The control routine is an optimization problem seeded with the previous step’s answer, so when the robot updates its dynamics drastically, the iterative optimization routine has to do more iterations before finding the optimal solution. However, you can also see massive spasms which we think is the proportional control having a pause during pulse width modulation (or the usb bus acting up and blocking the PWM loop).
The mean is subtracted so we can see the behaviour of both coordinates around a mean of zero. Its written in C++ and did not segment fault during the several hours of running during Makerfaire.
By creating a shared space to do this, we provide access to better facilities than we could each have at home, as well as opportunities to collaborate, learn, and socialise.
You can impress your friends with your knowledge and get a great new job by demonstrating your skills in a big interview.
It probably came from either Scottish Gaelic or from Celtic Irish, the languages that were used historically in Scotland and Ireland. So it’ll make sense to know that a ragamuffin is a person who wears dirty and scruffy clothes – clothes that are just like rags! It’s not known where the word came from, but many people believe it was taken from either a similar Spanish or Swedish word. You could also have small cards with new words that you take with you to different places – you can then refresh your memory when sitting on the train or bus, waiting for a friend in a coffee shop on your lunch break … anywhere! The hardware cost about ?48, and it took us 6 weekends to connect up cutting edge open source research software.
I think the system can achieve more than that but we don’t have the equipment around to test in more detail.
A naive implementation is pretty straightforward, but we found out later on the learning system worked better if all duty cycles completed before switching to a new speed. Having a GUI makes subsequent manoeuvring of the robot for later experiments a breeze, so its worthwhile investing in a decent user interface early on. You are looking to define the relationship between the feedback signals (in our case a homography matrix from findHomography() ) and the motor commands (a vector of joint commands).
In this style of control, a learning algorithm learns the statistical relationship between feedback and motor commands using a training set.
The first important concept is that motor commands do different things depending on the pose of the robot.
Applying an action changes the state smoothly at some rate (we assume the system is continuously differentiable which why we can control it and also why we need proportional control to get the formalisms to work).
This does not pose a problem, as the problem formulation is very general and independent of coordinate system.
If we are considering velocity we might change the formulation to include acceleration, but regardless optimal control tends to have to consider a series of motor commands a number of time steps into the future, perhaps solving the equations using dynamic programming. The reason for expressing it as explicit cost function is that it is useful to tweak J to accommodate different behaviours such as minimizing energy (we do this later to avoid some undesirable effects), whilst still keeping the same optimization framework. Regression is the statistical technique of relating one set of variables to another, y_r = g(x_r). I have glossed over a few details like data normalization, but the pragmatic documentation that accompanies LWPR is very good at explaining how do everything, so read that for the full approach. With the robot arm randomly moving it tended to hit the floor fairly quickly so we had to add a safety feature that if the grid pattern was going out of view, specific joints moved it back into the centre of the screen. Thus, our loop had switches to decide whether we were generating random control vectors or applying optimizations, as well as whether we were saving data to file or applying online adaptation (or neither, or both). At time step 10 the learner would be trying to learn that an a positive u has no effect on state.
We are using cheap hardware, so prediction of the system is not going to be great, thus we want our control loop fast.


The reason why this doesn’t happen with the other joints is they are under force in a consistent direction due to gravity, thus the gears are always engaged on one side (called preloading).
But seeing as this is just exploratory experimentation we just abandoned trying to control the base joint. The wave pattern is the vibrations in the arm, although the homograph maths projects that into a weird space which is harder to visualise with a 2D plot. The problem stemmed from the fact that sometimes the partially learnt dynamics would suggests all directions did not improve the cost function, so any motor command the optimizer would consider as bad.
The trajectory the training data traced in 8 dimensional space is pretty much far away from everywhere because an 8 dimensional space scales aggressively compared to line features (see the curse of dimensionality). Thus, learning some thing drastically new like the cost gradient is in the opposite direction than previously thought really does make the loop slow down the process the new optimal move to make. Using the rough estimate that 95% of samples lie between 2 sigmas from the mean, we can say that 95% of the time our arm was within 2mm of its target.
Yet despite these challenges, the software does a good a job under those severe constraints. The proportional control should delegated to an embedded system with reliable timings instead (e.g. With a trajectory planner we could try to achieve reduction in error with smooth accelerations (no sudden movements and hopefully stabler homography feedback). Backlash is not an expensive problem to solve (put springs everywhere and preload), so demanding a low backlash system for control is not in direct opposition to cheap precision, but it is something that needs to be thought of before choosing a platform.
Shame its a pig to compile ?? The next step for us is to abandon the robot arm (its too crap even for our skills), and try repeating these experiments on a stiffer platform (our homebrew CNC at the lab, its currently used to play music). It’s usually used for children, and you may also sometimes hear it used to describe scruffy-looking animals. The other term was used for young people who lived on the street and did bad things, like stealing and tricking people. Write the word in the first column and a short definition (in English) in the second column. There’s no need to feel discombobulated if you hear gibberish and gobbledegook, and don’t be woebegone – learning new words can be easy once you start!
This approach made sense when we did not have cheap methods of precision feedback, but now we have cheap cameras and cheap computation (thanks smart phones), an alternative method for obtaining precision could be to just to use dodgy mechanics and closed loop control (feedback). Thus, the estimation of a circle’s centre is really the result of a calculation involving all edge pixels in the camera image (so there is redundancy).
In addition, latency in the USB system meant an unpredictability in the usb commands had to be accounted for (as much as possible). I have been reusing the same python GUI (wxPython) I made for a 3D plotter a few years ago so it does not take long to customize a clone for new robots, you can find the code in armcontrol.py.
Its not usually that easy to formulate, in our case we would need the kinematic model of the arm e.g. We went one step further and used an *online* adaptive control which also continuously refines its model from every movement it ever makes. In general terms, we need a notation to denote the *state* of the system which is written x (the pose normally). We don’t do this for simplicity, but also as you will see in the experiment section there were mechanical issues that prevented us doing this anyway. To turn estimating the plant into a regression problem we just concatenate the motor commands with the state variable, x_r = [x, u] and try to fit a curve to y_r = &#7819.
Roughly, we twiddle all of LWPRs parameters to minimise the cross-validation error after learning on training data. Although the full homography matrix is hard to interpret by eye roughly speaking the righthand side is the offset coordinates which you can condition upon easily.
With LWPR every iteration you can also re-estiamte g() with new data as it comes in, although its not necessary in early stages of development (online learning).
Note that in this example the most extreme values of the states (17,33) lie where motor command has returned to zero!
Even worse, this diagram has variable latency which is actually another (unpleasant) phenomena going on. So even if we ignore the latency problem, the camera shakes inject a hell of a lot of noise into the system which will give the regression a hard time.
Due to the local nature of LWPR, a massive motor command lies outside the explored space and so it predicts 0 for large u. This meant the robot hadn’t got a good estimate of its dynamics in the vicinity of its starting location. A nice feature of this software stack is that it is all the control is in feedback space and we never had to write down the motion equations of the robot arm, thus this software could be run on a quite a different robot with minimal changes. It would be hugely preferable to continuously move the arm around whilst integrating optical feedback on the fly, however, we would have to tackle the latency issue as well. You can use the third column for making notes, sample sentences and any additional meaning in your own language.
Furthermore, we are using a 10 x 10 grid of circles, so a lot of spatial information is fused over the entire image, so the accuracy of the results is much greater than the camera’s accuracy on a single pixel. I had the good fortune of being taught robotics by Sethu Vijayakumar, one of the academics behind the state-of-the-art online adpative controllers, LWPR. The bold typeface indicates the variable is vector (a list of numbers) rather than a scalar (a single number). Our solution for control simply involved choosing a motor command that minimized the distance between where we were and where we wanted to go.
0 is naively a better choice as far as the optimizer is concerned, so that is what it was picking. I do think camera latency might be a common issue so we should definitely try to tackle that in our next iteration.
With vision you can just slap markers on a mechanical part and off you go (with the appropriate software).
By using many redundant spatial features you end up with sub-pixel accuracy, and that is the main effect that gets us ridiculously good accuracy cheaply.
If you use a different robot with a different usb chip, you can listen to dmesg when you plug in the device to discover the specific number for the idVendor field yourself.
We are too lazy to do the maths, and anyway, that system would only work if the mathematical model is correct. The cool features about this algorithm is that it performs dimension reduction on the fly (so can do control even in high dimensional feedback systems), learns non-linear regression, and regularizes itself (so no over fitting) and manages it all in constant time per update!
We don’t actually know how the actions are going to change the state over time, so we represent the system under control as a black box function f. After learning the new direction to move the robot tends to then overshoot its target and does another pause before reversing direction. I hope this article will guide people towards making use of these valuable public domain resources. Lagadic’s software unique selling point is the extensive engineering on the specialised circle tracker (its fast).
So we could not install that on a different robot without redoing all the sums again (very tedious). Clearly moving the elbow joint when the arm is fully extended has a different effect on the end position than when the arm is retracted. This ringing settles down over time as it learns its space properly and it does a pretty good job at keeping the homograph values where it is aiming.
We used an adaptive control scheme so at no point was robotic geometry measured, and instead the software *learnt* how to move the robot from experience alone.
Its also open source and has python bindings but does the heavy lifting in C++, ergo, it’s fast as f***.



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