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The Eden Project was used as a filming location for the 2002 James Bond film, "Die Another Day" (starring Pierce Brosnan).
On 2 July 2005 The Eden Project hosted the "Africa Calling" concert of the Live 8 concert series.
It has also provided some plants for the British Museum's Africa garden.
In 2005, the Project launched "A Time of Gifts" for the winter months, November to February.
This features an ice rink covering the lake, with a small café/bar attached, as well as a Christmas market.
Cornish choirs regularly perform in the biomes.
On 6 December 2007, the Eden Project invited people all over Cornwall to try to break the world record for the biggest ever pub quiz as part of its campaign to bring £50 million of lottery funds to Cornwall.
In December 2007, the project failed in its bid for £50 million of funding, after the Big Lottery Fund popular vote, when it received just 12.07% of the votes, the lowest for the four projects being considered.
Eden wanted the money for Edge, a proposed desert biome that was going to look at people and plants living on the edge today and the solutions that they have come up with to the challenge of living within limits.
In December 2009, much of the project, including both greenhouses, became available to navigate through Google Street View.
The Eden Trust revealed a trading loss of £1.3 million for 2012-13,on a turnover of £25.4 million.
The Eden Project had posted a surplus of £136,000 for the previous year.
In 2014 Eden accounts showed a surplus of £2 million.
The World Pasty Championships have been held at the Eden Project since 2012, an international competition to find the best Cornish pasties and other pasty-type savoury snacks.
The Eden Project is said to have contributed over £1 billion into the Cornish economy.
========,2,Eden Sessions.
Since 2002, the Project has hosted a series of musical performances, called the Eden Sessions.
Artists have included Amy Winehouse, James Morrison, Muse, Lily Allen, Snow Patrol, Pulp, Brian Wilson and The Magic Numbers.
2008's summer headliners were: The Verve, Kaiser Chiefs, and KT Tunstall.
Oasis were also set to play in the summer of 2008, but the concert was postponed because Noel Gallagher was unable to perform after breaking three ribs in a stage invasion incident several weeks before.
The concert was instead played in the summer of 2009.
2010 saw performances from artists including Mika, Jack Johnson, Mojave 3, Doves, Paolo Nutini, Mumford & Sons, and Martha Wainwright.
The 2011 sessions were headlined by The Flaming Lips, Primal Scream, Pendulum, Fleet Foxes and Brandon Flowers with support from The Horrors, The Go!
Team, OK Go, Villagers, and The Bees.
The 2012 Eden sessions were headlined by: Tim Minchin, Example, Frank Turner, Chase & Status, Plan B, Blink-182, Noah and the Whale, and The Vaccines.
The 2013 Eden Sessions were headlined by: Kaiser Chiefs, Jessie J, Eddie Izzard, Sigur Rós, and The xx.
The 2014 Eden Sessions were headlined by: Dizzee Rascal, Skrillex, Pixar in Concert, Ellie Goulding and Elbow.
The 2015 Eden Sessions were headlined by: Paolo Nutini, Elton John, Paloma Faith, Motörhead, The Stranglers, Spandau Ballet and Ben Howard.
The 2016 Eden Sessions were headlined by: Lionel Richie, Tom Jones, PJ Harvey, Manic Street Preachers and Jess Glynne.
========,1,preface.
Linear filters process time-varying input signals to produce output signals, subject to the constraint of linearity.
This results from systems composed solely of components (or digital algorithms) classified as having a linear response.
Most filters implemented in analog electronics, in digital signal processing, or in mechanical systems are classified as causal, time invariant, and linear signal processing filters.
The general concept of linear filtering is also used in statistics, data analysis, and mechanical engineering among other fields and technologies.
This includes non-causal filters and filters in more than one dimension such as those used in image processing; those filters are subject to different constraints leading to different design methods.
========,2,Impulse response and transfer function.
A linear time-invariant (LTI) filter can be uniquely specified by its impulse response "h", and the output of any filter is mathematically expressed as the convolution of the input with that impulse response.
The frequency response, given by the filter's transfer function ***formula***, is an alternative characterization of the filter.
Typical filter design goals are to realize a particular frequency response, that is, the magnitude of the transfer function ***formula***; the importance of the phase of the transfer function varies according to the application, inasmuch as the shape of a waveform can be distorted to a greater or lesser extent in the process of achieving a desired (amplitude) response in the frequency domain.
The frequency response may be tailored to, for instance, eliminate unwanted frequency components from an input signal, or to limit an amplifier to signals within a particular band of frequencies.
The impulse response "h" of a linear time-invariant causal filter specifies the output that the filter would produce if it were to receive an input consisting of a single impulse at time 0.
An "impulse" in a continuous time filter means a Dirac delta function; in a discrete time filter the Kronecker delta function would apply.
The impulse response completely characterizes the response of any such filter, inasmuch as any possible input signal can be expressed as a (possibly infinite) combination of weighted delta functions.
Multiplying the impulse response shifted in time according to the arrival of each of these delta functions by the amplitude of each delta function, and summing these responses together (according to the superposition principle, applicable to all linear systems) yields the output waveform.
Mathematically this is described as the convolution of a time-varying input signal "x(t)" with the filter's impulse response "h", defined as:
The first form is the continuous-time form, which describes mechanical and analog electronic systems, for instance.
The second equation is a discrete-time version used, for example, by digital filters implemented in software, so-called "digital signal processing".
The impulse response "h" completely characterizes any linear time-invariant (or shift-invariant in the discrete-time case) filter.
The input "x" is said to be "convolved" with the impulse response "h" having a (possibly infinite) duration of time "T" (or of "N" sampling periods).
Filter design consists of finding a possible transfer function that can be implemented within certain practical constraints dictated by the technology or desired complexity of the system, followed by a practical design that realizes that transfer function using the chosen technology.
The complexity of a filter may be specified according to the order of the filter.
Among the time-domain filters we here consider, there are two general classes of filter transfer functions that can approximate a desired frequency response.
Very different mathematical treatments apply to the design of filters termed infinite impulse response (IIR) filters, characteristic of mechanical and analog electronics systems, and finite impulse response (FIR) filters, which can be implemented by discrete time systems such as computers (then termed "digital signal processing").
========,3,Infinite impulse response filters.
Consider a physical system that acts as a linear filter, such as a system of springs and masses, or an analog electronic circuit that includes capacitors and/or inductors (along with other linear components such as resistors and amplifiers).
When such a system is subject to an impulse (or any signal of finite duration) it responds with an output waveform that lasts past the duration of the input, eventually decaying exponentially in one or another manner, but never completely settling to zero (mathematically speaking).
Such a system is said to have an infinite impulse response (IIR).
The convolution integral (or summation) above extends over all time: T (or N) must be set to infinity.
For instance, consider a damped harmonic oscillator such as a pendulum, or a resonant L-C tank circuit.
If the pendulum has been at rest and we were to strike it with a hammer (the "impulse"), setting it in motion, it would swing back and forth ("resonate"), say, with an amplitude of 10 cm.
After 10 minutes, say, the pendulum would still be swinging but the amplitude would have decreased to 5 cm, half of its original amplitude.
After another 10 minutes its amplitude would be only 2.5 cm, then 1.25 cm, etc.
However it would never come to a complete rest, and we therefore call that response to the impulse (striking it with a hammer) "infinite" in duration.
The complexity of such a system is specified by its order "N".
N is often a constraint on the design of a transfer function since it specifies the number of reactive components in an analog circuit; in a digital IIR filter the number of computations required is proportional to N.
========,3,Finite impulse response filters.
A filter implemented in a computer program (or a so-called digital signal processor) is a discrete-time system; a different (but parallel) set of mathematical concepts defines the behavior of such systems.
Although a digital filter can be an IIR filter if the algorithm implementing it includes feedback, it is also possible to easily implement a filter whose impulse truly goes to zero after N time steps; this is called a finite impulse response (FIR) filter.
For instance, suppose one has a filter that, when presented with an impulse in a time series:
outputs a series that responds to that impulse at time 0 until time 4, and has no further response, such as:
Although the impulse response has lasted 4 time steps after the input, starting at time 5 it has truly gone to zero.
The extent of the impulse response is "finite", and this would be classified as a fourth-order FIR filter.
The convolution integral (or summation) above need only extend to the full duration of the impulse response T, or the order N in a discrete time filter.
========,3,Implementation issues.
Classical analog filters are IIR filters, and classical filter theory centers on the determination of transfer functions given by low order rational functions, which can be synthesized using the same small number of reactive components.
Using digital computers, on the other hand, both FIR and IIR filters are straightforward to implement in software.
A digital IIR filter can generally approximate a desired filter response using less computing power than a FIR filter, however this advantage is more often unneeded given the increasing power of digital processors.
The ease of designing and characterizing FIR filters makes them preferable to the filter designer (programmer) when ample computing power is available.
Another advantage of FIR filters is that their impulse response can be made symmetric, which implies a response in the frequency domain that has zero phase at all frequencies (not considering a finite delay), which is absolutely impossible with any IIR filter.
========,2,Frequency response.
The frequency response or transfer function ***formula*** of a filter can be obtained if the impulse response is known, or directly through analysis using Laplace transforms, or in discrete-time systems the Z-transform.
The frequency response also includes the phase as a function of frequency, however in many cases the phase response is of little or no interest.
FIR filters can be made to have zero phase, but with IIR filters that is generally impossible.
With most IIR transfer functions there are related transfer functions having a frequency response with the same magnitude but a different phase; in most cases the so-called minimum phase transfer function is preferred.
Filters in the time domain are most often requested to follow a specified frequency response.
Then, a mathematical procedure finds a filter transfer function that can be realized (within some constraints), and approximates the desired response to within some criterion.
Common filter response specifications are described as follows:
***LIST***.
========,3,IIR transfer functions.
Since classical analog filters are IIR filters, there has been a long history of studying the range of possible transfer functions implementing various of the above desired filter responses in continuous time systems.
Using transforms it is possible to convert these continuous time frequency responses to ones that are implemented in discrete time, for use in digital IIR filters.
The complexity of any such filter is given by the "order" N, which describes the order of the rational function describing the frequency response.
The order N is of particular importance in analog filters, because an N order electronic filter requires N reactive elements (capacitors and/or inductors) to implement.
If a filter is implemented using, for instance, biquad stages using op-amps, N/2 stages are needed.
In a digital implementation, the number of computations performed per sample is proportional to N. Thus the mathematical problem is to obtain the best approximation (in some sense) to the desired response using a smaller N, as we shall now illustrate.
Below are the frequency responses of several standard filter functions that approximate a desired response, optimized according to some criterion.
These are all fifth-order low-pass filters, designed for a cutoff frequency of .5 in normalized units.
Frequency responses are shown for the Butterworth, Chebyshev, inverse Chebyshev, and elliptic filters.
As is clear from the image, the elliptic filter is sharper than the others, but at the expense of ripples in both its passband and stopband.
The Butterworth filter has the poorest transition but has a more even response, avoiding ripples in either the passband or stopband.