Centres of envelopment and intensive movement in digital signal processing
Centres of envelopment and intensive movement in digital signal processing NL
Centres of envelopment and intensive movement in digital signal processing FR
Adrian Mackenzie

Abstract
The paper broadly concerns algorithmic processes commonly found in wireless networks, video and audio compression. The problem it addresses is how to account for the convoluted nature of the digital signal processing (DSP). Why is signal processing so complex and relatively inaccessible? The paper argues that we can only understand what is at stake in these labyrinthine calculations by switching focus away from abstract understandings of calculation to the dynamic re-configuration of space and movement occurring in signal processing. The paper works through one example in detail of this reconfigured movement in order to illustrate how digital signal processing enables different experiences of proximity, intimacy, co-location and distance. It explores how wireless signal processing algorithms envelope heterogeneous spaces in the form of hidden states, and logistical networks. Importantly, it suggests that the ongoing dynamism of signal processing could be understood in terms of intensive movement produced by a centre of envelopment. Centres of envelopment generate extensive changes, but they also change the nature of change itself. 
From sets to signals: digital signal processing
In new media art, in new media theory and in various forms of media activism, there has been so much work that seizes on the possibilities of using digital technologies to design interactions, sound, image, text, and movement that challenge dominant forms of experience, habit and selfhood. In various ways, the processes of branding, commodification, consumption, control and surveillance associated with contemporary media have been critically interrogated and challenged. 
However, there are some domains of contemporary technological and media culture that are really hard to work with. They may be incredibly important, they may be an intimate part of everyday life, yet remain relatively intractable. They resist contestation, and engagement with may even seem pointless. This is because they may contain intractable materials, or be organised in such complicated ways that they are hard to change. 
This paper concerns one such domain, digital signal processing (DSP).  I am not saying that new media has not engaged with DSP. Of course it has, especially in video art and sound art, but there is little work that helps us make sense of how the sensations, textures, and movements associated with DSP come to be taken for granted, come to appear as normal, and everyday, or how they could be contested. 
A promotional video from Intel for the UltraMobilePC (http://youtube.com/watch?v=G_FS2TiK3AI) promotes change in relation to mobile media. Intel, because it makes semiconductors, is highly invested in digital signal processing in various forms. In any case, video itself is a prime example of contemporary DSP at work. Two aspects of this promotional video for the UMPC, the UltraMobile PC, relate to digital signal processing. There is much signal processing here. It connects the individual's eyes, mouths and ears to screens that display information services of various kinds. There is also much signal processing in the wireless network infrastructures that connect all these gadgets to each other and to various information services (maps, calendars, news feeds). In just this example, sound, video, speech recognition, fibre, wireless and satellite, imaging technologies in medicine all rely on DSP. We could say a good portion of our experience is DSP-based.
This paper is an attempt to develop a theory of digital signal processing, a theory that could be used to talk about ways of contesting, critiquing, or making alternatives. The theory under development here relies a lot on two notions, ‘intensive movement’ and ‘centre of envelopment’ that Deleuze proposed in Difference and Repetition. However, I want to keep the philosophy in the background as much as possible. I basically want to argue that we need to ask: why does so much have to be enveloped or interiorised in wireless or audiovisual DSP? 
How does DSP differ from other algorithmic processes? 
What can we say about DSP? Firstly, influenced by recent software studies-based approaches (Fuller, Chun, Galloway, Manovich), I think it is worth comparing the kinds of algorithmic processes that take place in DSP with those found in new media more generally. Although it is an incredibly broad generalisation, I think it is safe to say that DSP does not belong to the set-based algorithms and data-structures that form the basis of much interest in new media interactivity or design.
DSP differs from set-based code. If we think of social software such as Flickr, Google, or Amazon, if we think of basic information infrastructures such as relational databases or networks, if we think of communication protocols or search engines, all of these systems rely on listing, enumerating, and sorting data. The practices of listing, indexing, addressing, enumerating and sorting, all concern sets. Understood in a fairly abstract way, this is what much software and code does: it makes and changes sets. Even areas that might seem quite remote from set-making, such as the 3D-projective geometry used in computer game graphics are often reduced algorithmically to complicated set-theoretical operations on shapes (polygons). Even many graphic forms are created and manipulated using set operations. 
The elementary constructs of most programming languages reflect this interest in set-making. For instance, networks or, in computer science terms, graphs, are visually represented like using lines and boxes. But in terms of code, they are presented as either edge or 'adjacency lists', like this:
    graph = {'A': ['B', 'C'],
             'B': ['C', 'D'],
             'C': ['D'],
             'D': ['C'],
             'E': ['F'],
             'F': ['C']}
http://www.python.org/doc/essays/graphs/
A graph or network can be seen as a list of lists. This kind of representation in code of relations is very neat and nice. It means that something like the structure of the internet, as a hybrid of physical and logical relations, can be recorded, stored, sorted and re-ordered in code. Importantly, it is highly open to modification and change. Social software, or Web2.0, as exemplified in websites like Facebook or YouTube also can be understood as massive deployments of set theory in the form of code. Their sociality is very much dependent on set making and set changing operations, both in the composition of the user interfaces and in the underlying databases that make constantly seek to attach new relations to data, to link identities and attributes. In terms of activism, and artwork, relations that can be expressed in the form of sets and operations on sets, are highly manipulable. They can be learned relatively easily, and they are not too difficult to work with. For instance, scripts that crawl or scrape websites have been widely used in new media art and activism.
By contrast, DSP code is not based on set-making. It relies on a different ordering of the world that lies closer to streams of signals that come from systems such as sensors, transducers, cameras, and that propagate via radio or cable. Indeed, although it is very widely used, DSP is not usually taught as part of the computer science or software engineering. The textbooks in these areas often do not mention DSP. The distinction between DSP and other forms of computation is clearly defined in a textbook of DSP:
‘Digital Signal Processing is distinguished from other areas in computer science by the unique type of data it uses: signals. In most cases, these signals originate as sensory data from the real world: seismic vibrations, visual images, sound waves, etc. DSP is the mathematics, the algorithms, and the techniques used to manipulate these signals after they have been converted into a digital form.’ (Smith, 2004) 
While it draws on some of the logical and set-based operations found in code in general, DSP code deals with signals that usually come kind of sensory data – vibrations, waves, electromagnetic radiation, etc. These signals often involve forms of rapid movement, rhythms, patterns or fluctuations. Sometimes these movements are embodied in physical senses, such as the movements of air involved in hearing, or the flux of light involved in seeing. Because they are often irregular movements, they cannot be easily captured in the forms of movement idealised in classical mechanics – translation, rotation, etc. Think for instance of a typical photograph of a city street. Although there are some regular geometrical forms, the way in which light is reflected, the way shadows form, is very difficult to describe geometrically. It is much easier, as we will see, to think of an image as a signal that distributes light and colour in space. Once an image or sound can be seen as a signal, it can undergo digital signal processing. 
What distinguishes DSP from other algorithmic processes is its reliance on transforms rather than functions. This is a key difference. The ‘transform’ deals with many values at once. This is important because it means it can deal with things that are temporal or spatial, such as sounds, images, or signals in short. This brings algorithms much closer to sensation, and to what bodies feel. While there is codification going on, since the signal has to be treated digitally as discrete numerical values, it is less reducible to the sequence of steps or operations that characterise set-theoretical coding. Here for instance is an important section of the code used in MPEG video encoding in the free software ffmpeg package:
/**
 * @file mpegvideo.c
 * The simplest mpeg encoder (well, it was the simplest!).
 */
....
/* for jpeg fast DCT */
#define CONST_BITS 14

static const uint16_t aanscales[64] = {
    /* precomputed values scaled up by 14 bits */
    16384, 22725, 21407, 19266, 16384, 12873, 8867, 4520,
    22725, 31521, 29692, 26722, 22725, 17855, 12299, 6270,
    21407, 29692, 27969, 25172, 21407, 16819, 11585, 5906,
    19266, 26722, 25172, 22654, 19266, 15137, 10426, 5315,
    16384, 22725, 21407, 19266, 16384, 12873, 8867, 4520,
    12873, 17855, 16819, 15137, 12873, 10114, 6967, 3552,
    8867, 12299, 11585, 10426, 8867, 6967, 4799, 2446,
    4520, 6270, 5906, 5315, 4520, 3552, 2446, 1247
};
...
for(i=0;i<64;i++) {
                const int j= dsp->idct_permutation[i];

                qmat[qscale][i] = (int)((uint64_t_C(1) << (QMAT_SHIFT + 14)) /
                                (aanscales[i] * qscale * quant_matrix[j]));

I don’t think we need to understand this code in detail. There is only thing I want to point out from this code: the list of ‘precomputed’ numerical values is used for ‘jpeg fast DCT’. This is a typical piece of DSP type code. It refers to the way in which video frames are encoding using Fast Fourier Transforms. The key point here is that these values have been carefully worked out in advance to scale different colour and luminosity components of the image differently. The transform, DCT (Discrete Cosine Transform), is applied to chunks of sensation – video frames – to make them into something that can be manipulated, stored, changed in size or shape, and circulated. Notice that the code here is quite opaque in comparison to the graph data structures discussed previously. This opacity reflects the sheer number of operations that have to be compressed into code in order for digital signal processing to work.
Working with DSP: architecture and geography
So we can perhaps see from the two code examples above that there is something different about DSP in comparison to the set-based processing. DSP seems highly numerical and quantified, while the set-based code is symbolic and logical. What is at stake in this difference? I would argue that it is something coming into the code from outside, something that is difficult to read in the code itself because it is so opaque and convoluted. Why is DSP code hard to understand and also hard to write? 
You will remember that I said at the outset that there are some facets of technological cultures that resist appropriation or intervention. I think the mathematics of DSP is one of those facets. If I just started explaining some of the mathematical models that have been built into the contemporary world, I think it would be shoring up or reinforcing a certain resistance to change associated with DSP, at least in its main mathematical formalisations. I do think the mathematical models are worth engaging with, partly because they look so different from the set-based operations found in much code today. The mathematical models can tell us why DSP is difficult to intervene in at a low level. 
However, I don’t think it is the mathematics as such that makes digital signal processing hard to grapple with. The mathematics is an architectural response to a geographical problem, a problem of where code can go and be in the world. I would argue that it is the relation between the architecture and geography of digital signal processing itself that we should grapple with. It is something to do about the immersion in everyday life, the proximity to sensation, the shifting multi-sensory patterning of sociality, the movements of bodies across variable distances, and the effervescent sense of impending change that animates the convoluted architecture of DSP.  
We could think of the situations in which DSP is commonly found. For instance, in the background of the scenes in the daily lives of businessmen shown in Intel’s UPMC video, lie wireless infrastructures and networks. Audiovisual media and wireless networks both use signal processing, but for different reasons. Although they seem quite disparate from each other in terms of how we embody them, they actually sometimes use the same DSP algorithms. (In other work, I have discussed video codecs.) While images are visible, wireless signals are relatively hard to sense. So they are a ‘hard case’ to analyse. We know they surround us, but we hardly have any sensation of them. A tightly packed labyrinth of digital signal processing lies between antenna and what reaches their eyes and ears. Much of what they look at and listen has passed through wireless chipsets. The chipsets, produced by Broadcom, Intel, Texas Instruments, Motorola, Airgo or Pico, are tiny (<1 cm²) fragments that support highly convoluted and concatenated paths on nanometre scales. In wireless networks such as Wi-Fi, Bluetooth, and 3G mobile phones with their billions of miniaturised chipsets, we encounter a vast proliferation of relations. What is at stake in these convoluted, compressed packages of relationality, these densely patterned architectures dedicated to wireless communication? 
Take for instance the picoChip, a latest-generation wireless digital signal processing chip, designed by a ‘fabless’ semiconductor company, picoChip Designs Ltd, in Bath, UK. The product brief describes the chip as 
‘[t]he architecture of choice for next-generation wireless. Expressly designed to address the new air-interfaces, picoChip’s multi-core DSP is the most powerful baseband processor on the market. Ideally suited to WiMAX, HSPA, UMTS-LTE, 802.16m, 802.20 and others, the picoArray delivers ten-times better MIPS/$ than legacy approaches. Crucially, the picoArray is easy to program, with a robust development environment and fast learning curve.’ (PicoChip, 2007) 
Written for electronics engineers, the key points here are that the chip is designed for wireless communication or ‘air-interface’, that its purpose is to receive and transmit information wirelessly, and that it accommodates a variety of wireless communication standards (WiMAX, HSPA, 802.16m, etc). In this context, much of the terminology of performance and low cost is familiar. The chip combines computing performance and value for money (‘ten times better MIPS/$’ – Million Instructions Per Second/$) as a ‘baseband processor’. That means that it could find its way into many different version of hardware being produced for applications that range between large-scale wireless information infrastructures and small consumer electronics applications. Only the last point is slightly surprisingly emphatic: ‘[c]rucially, the picoArray is easy to program, with a robust development environment and fast learning curve.’ Why should ease of programming be important?
And why should so many processors be needed for wireless signal processing? 
The architecture of the picoChip stands on shifting ground. We are witnessing, as Nigel Thrift writes, ‘a major change in the geography of calculation. Whereas “computing” used to consist of centres of calculation located at definite sites, now, through the medium of wireless, it is changing its shape’ (Thrift, 2004, 182). The picoChip's architecture is a respond to the changing geographies of calculation. Calculation is not carried out at definite sites, but at almost any site. We can see the picoChip as an architectural response to the changing geography of computing. The architecture of the picoChip is typical in the ways that it seeks to make a constant re-shaping of computation possible, normal, affordable, accessible and programmable. This is particularly evident in the parallel character of its architecture. Digital signal processing requires massive parallelisation: more chips everywhere, and chips that do more in parallel. The advanced architecture of the picoChip is typical of the shape of things more generally: 
‘[t]he picoArray® is a tiled processor architecture in which hundreds of processors are connected together using a deterministic interconnect. The level of parallelism is relatively fine grained with each processor having a small amount of local memory. ... Multiple picoArrayTM devices may be connected together to form systems containing thousands of processors using on-chip peripherals which effectively extend the on-chip bus structure.’ (Panesar, et al., 2006, 324)  
The array of processors shown in Figure 1, then, is a partial representation, an armature for a much more extensive diffusion of processors in wireless digital signal processing: in wireless base stations, 3G phones, mobile computing, local area networks, municipal, community and domestic Wi-Fi network, in femtocells, picocells, in backhaul, last-mile or first mile infrastructures.   
Architectures and intensive movement
It is as if the picoChip is a miniaturised version of the urban geography that contains the many gadgets, devices, and wireless and wired infrastructures. However, this proliferation of processors is more than a diffusion of the same. The interconnection between these arrays of processors is not just extensive, as if space were blanketed by an ever finer and wider grid of points occupied by processors at work shaping signals. As we will see, the interconnection between processors in DSP seeks to potentialise an intensive movement. It tries to accommodate a change in the nature of movement. Since all movement is change, intensive movement is a change in change. When intensive movement occurs, there is always a change in kind, a qualitative change.
Intensive movements always respond to a relational problem. The crux of the relational problem of wirelessness is this: how can many things (signals, messages, flows of information) occupy the same space at the same time, yet all be individualised and separate? The flow of information and messages promises something highly individualised (we saw this in the UMPC video from Intel). In terms of this individualising change, the movement of images, messages and data, and the movement of people, have become linked in very specific ways today. The greater the degree of individualization, the more dense becomes the mobility of people and the signals they transmit and receive. And as people mobilise, they drag personalised flows of communication on the move with them. Hence flows of information multiply massively, and networks must proliferate around those flows. The networks need to become more dense, and imbricate lived spaces more closely in response to individual mobility. 
This poses many problems for the architecture of communication infrastructure. The infrastructural problems of putting networks everywhere are increasingly, albeit only partially, solved by packing radio-frequency waves with more and more intricately modulated signal patterns. This is the core response of DSP to the changing geography of calculation, and to the changing media embodiments associated with it. To be clear on this: were it not for digital signal processing, the problems of interference, of unrelated communications mixing together, would be potentially insoluble. The very possibility of mobile devices and mobility depends on ways of increasing the sheer density of wireless transmissions. Radio spectrum becomes an increasingly valuable, tightly controlled resource. For any one individual communication, not much space or time can be available. And even when there is space, it may be noisy and packed with other people and things trying to communicate. Different kinds of wireless signals are constantly added to the mix. Signals may have to work their way through crowds of other signals to reach a desired receiver. Communication does not take place in open, uncluttered space. It takes place in messy configurations of buildings, things and people, which obstruct waves and bounce signals around. The same signal may be received many times through different echoes (‘multipath echo’). Because of the presence of crowds of other signals, and the limited spectrum available for any one transmission, wirelessness needs to be very careful in its selection of paths if experience is to stream rather than just buzz. The problem for wireless communication is to micro-differentiate many paths and to allow them to interweave and entwine with each other without coming into relation.
So the changing architectures of code and computation associated with DSP in wireless networks does more, I would argue, than fit in with changing geography of computing. It belongs to a more intensive, enveloped, and enveloping set of movements. To begin addressing this dynamic, we might say that wireless DSP is the armature of a centre of envelopment. This is a concept that Gilles Deleuze proposes late in Difference and Repetition. ‘Centres of envelopment’ are a way of understanding how extensive movements arise from intensive movement. Such centres crop up in ‘complex systems’ when differences come into relation: 
‘to the extent that every phenomenon finds its reason in a difference of intensity which frames it, as though this constituted the boundaries between which it flashes, we claim that complex systems increasingly tend to interiorise their constitutive differences: the centres of envelopment carry out this interiorisation of the individuating factors.’ (Deleuze, 2001, 256)
Much of what I have been describing as the intensive movement that folds spaces and times inside DSP can be understood in terms of an interiorisation of constitutive differences. An intensive movement always entails a change in the nature of change. In this case, a difference in intensity arises when many signals need to co-habit that same place and moment. The problem is: how can many such signals can move simultaneously without colliding, without interfering with each other? How can many more signals can pass by each other without taking up more space? These problems induce the compression and folding of spaces inside wireless processing, the folding that we might understand as a ‘centre of envelopment’ in action.  
The Fast Fourier Transform: transformations between time and space
I have been arguing is that the complications of the mathematics and the convoluted nature of the code or hardware used in DSP, stems from an intensive movement or constitutive difference that is interiorised. We can trace this interiorisation in the DSP used in wireless networks. I do not have time to show how this happens in detail, but hopefully one example of DSP that occurs but in the video codecs and wireless networks will illustrate how this happens in practice. 
Late in the encoding process, and much earlier in the decoding process in contemporary wireless networks, a fairly generic computational algorithm comes into action: the Fast Fourier Transform (FFT). In some ways, it is not surprising to find the FFT in wireless networks or in digital video. Dating from the mid-1960s, FFTs have long been used in to analyse electrical signals in many scientific and engineering settings. It provides the components frequencies of a time-varying signal or waveform. Hence, in ‘spectral analysis’, the FFT can show the spectrum of frequencies present in a signal. 
The notion of the Fourier transform is mathematical and has been known since the early 19th century: it is an operation that takes an arbitrary waveform and turns it into a set of periodic waves (sinusoids) of different frequencies and amplitudes. Some of these sinusoids make more important contributions to overall shape of the waveform than others. Added together again, these sine or cosine waves should exactly re-constitute the original signal. Crucially, a Fourier transform can turn something that varies over time (a signal) into a set of simple components (sine or cosine waves) that do not vary over time. Put more technically, it switches between ‘time’ and ‘frequency’ domains. Something that changes in time, a signal, becomes a set of distinct or components that can be handled separately. In a way, this analysis of a complex signal into simple static component signals means that DSP does use the set-based approaches I described earlier. Once a complex signal, such as an image, has been analysed into a set of static components, we can imagine code that would select the most important or relevant components. This is precisely what happens in video and sound codecs such as MPEG and MP3.
The FFT treats something sounds and images signals as complicated superimpositions of waveforms. The envelope of a signal becomes something that contains many simple signals. It is interesting that wireless networks tend to use this process in reverse. It deliberately takes a well-separated and discrete set of signals – a digital datastream – and turns it into a single complex signal. In contrast to the normal uses of FFT in separating important from insignificant parts of a signal, in wireless networks, and in many other communications setting, FFT is used to put signals together in such a way as to contain them in a single envelope. The FFT is found in many wireless computation algorithms because it allows many different digital signals to be put together on a single wave and then extracted from it again. 
Why would this superimposition of many signals onto a single complex waveform be desirable? Would it not increase the possibilities of confusion or interference between signals? In some ways the FFT is used to slow everything down rather than speed it up. Rather than simply spatialising a duration, the FFT as used in wireless networks defines a different way of inhabiting the crowded, noise space of electromagnetic radiation. Wireless transmitters are better at inhabiting crowded signal spectrum when they don't try to separate themselves off from each other, but actually take the presence of other transmitters into account. How does the FFT allow many transmitters to inhabit the same spectrum, and even use the same frequencies?
The name of this technique is OFDM (Orthogonal Frequency Division Multiplexing). OFDM  spreads a single data stream coming from a single device across a large number of sub-carriers signals (52 in IEEE 802.11a/g). It splits the data stream into dozens of separate signals of slightly different frequency that together evenly use the whole available radio spectrum. This is done in such a way that many different transmitters can be transmitting at the same time, on the same frequency, without interfering with each other. The advantage of spreading a single high speed data stream across many signals ('wideband') is that each individual signal can carry data at a much slower rate. Because the data is split into 52 different signals, the each signal can be much slower (1/50). That means each bit of data can be spaced apart more in time. This has great advances in urban environments where they are many obstacles to signals, and signals can reflect and echo often. In this context, the slower the data is transmitted, the better.
At the transmitter, a reverse FFT (IFFT) is used to re-combine the 50 signals onto 1 signal. That is, it takes the 50 or so different sub-carriers produced by OFDM, each of which has a single slightly different, but carefully chosen frequency, and combines them into one complex signal that has a wide spectrum. That is, it fills the available spectrum quite evenly because it contains many different frequency components. The waveform that results from the IFFT looks like 'white noise': it has no remarkable or outstanding tendency whatsoever, except to a receiver synchronised to exactly the right carrier frequency. At the receiver, this complex signal is transformed, using  FFT, back into a set of 50 separate data streams, that are then reconstituted into a single high speed stream. 
Even if we cannot come to grips with the techniques of transformation using in DSP in any great detail, I hope that one point stands out. The transformation involve 'changes in kind'. Data does not simply move through space. It changes in kind in order to move through space, a space whose geography is understood as too full of potential relations.
Conclusion
A couple of points in conclusion:
1.	The spectrum of different wireless-audiovisual devices competing to do more or less the same thing, are all a reproduction of the same. Extensive movement associated with wireless networks and digital video occur in various forms. Firstly in the constant enveloping of spaces by wireless signals, and secondly in the dense population of wireless spectrum by competing, overlapping signals, vying for market share in highly visible, well-advertised campaigns to dominate spectrum while at the same time allowing for the presence of many others. 
2.	Actually, in various ways, wirelessness puts the very primacy of extension as space-making in question. Signals seem to be able to occupy the same space at the same time, something that should not happen in space as usually understood. We can understand this by re-conceptualising movement as intensive. Intensive movement occurs in multiple ways. Here I have emphasised the constant folding inwards or interiorisation of heterogeneous movements via algorithms used in digital signal processing. Intensive movement ensues occurs when a centre of envelopment begins to interiorise differences. While these interiorised spaces are computationally intensive (as exemplified by the picoChip's massive processing power), the spaces they generate are not perceived as calculated, precise or rigid. Wirelessness is a relatively invisible, messy, amorphous, shifting sets of depths and distances that lacks the visible form and organisation of other entities produced by centres of calculation (for instance, the shape of a CAD-designed building or car). However, similar processes occur around sound and images through DSP. In fact, different layers of DSP are increasingly coupled in wireless media devices.   
3.	Where does this leave the centre of envelopment? The cost of this freeing up of movement, of mobility, seems to me to be an interiorisation of constitutive differences, not just in DSP code but in the perceptual fields and embodiment of the mobile user. The irony of the DSP is that it uses code to quantify sensations or physical movements that lie at the fringes of representation or awareness. We can't see DSP as such, but it supports our seeing and moving. It brings code quite close to the body. It can work with audio and images in ways that bring them much closer to us. The proliferation of mobile devices such as mp3 and digital cameras is one consequence of that. Yet the price DSP pays for this proximity to sensation, to sounds, movement, and others, is the envelopment I have been describing. DSP acts as a centre of envelopment, as something that tends to interiorise intensive movements, the changing nature of change, the intensive movements that give rise to it. 
4.	This brings us back to the UMPC video: it shows two individuals. Their relation can never, it seems, get very far. The provision of images, sound and wireless connectivity has come so far, that they hardly need encounter each other at all. There is something intensely monadological here: DSP is heavily engaged in furnishing the interior walls of the monad, and with orienting the monad in relation to other monads, but making sure that nothing much need pass between them. So much has already been pre-processed between, that nothing much need happen between. They already have a complete perception of their relation to the other. 
5.	On a final constructive note, it seems that there is room for contestation here. The question how is how to introduce the set-based code processes that have proven productive in other areas into the domain of DSP. What would that look like? How would it be sensed? What could it do to our sensations of video or wireless media?
References
Deleuze, Gilles. Difference and Repetition. Translated by Paul Patton, Athlone Contemporary European Thinkers. (London ; New York: Continuum, 2001).
Panesar, Gajinder, Daniel Towner, Andrew Duller, Alan Gray, and Will Robbins. 'Deterministic Parallel Processing', International Journal of Parallel Programming 34, no. 4 (2006): 323-41.
PicoChip. 'Advanced Wireless Technologies', (2007).  http://www.picochip.com/solutions/advanced_wireless_technologies.
PicoChip. 'Pc202 Integrated Baseband Processor Product Brief', (2007). http://www.picochip.com/downloads/03989ce88cdbebf5165e2f095a1cb1c8/PC202_product_brief.pdf.
Smith, Steven W. The Scientist and Engineer's Guide to Digital Signal Processing: California Technical Publishing, 2004).
Thrift, Nigel. 'Remembering the Technological Unconscious by Foregrounding Knowledges of Position', Environment & Planning D: Society & Space 22, no. 1 (2004): 175-91.