IIRC, the front of the car is to the right in this diagram, I'm not quite sure anymore. But yes, the numbers coincide with the cylinders, so there is a way in which you can align the diagram with your engine.
Anonymous on 23 Apr : 21:10
Hey Bjoern. I have a question about your wiring diagram on the MAF conversion. Is the diagram of the injector wiring from the top view of the engine? Because I'm guessing the injector numbers don't coinside with the cylinder numbers? Thanks
The auto tranny is a problem, because you have a very limited choice of computers. Unfortunately, I have no idea about auto trannies and can't help you there. Conversion to standard is a big deal and not for the faint of heart. I bought a book on Ford EFI which I found to be very helpful and I learned a lot from it. I think it was this one: [link]
hey bjoern, ive had your maf conversion in my favorites forever... i finally rebuilt the engine, but put in a lumpier cam, along with some other minor mods, so now basically, it runs, but with the old map sensor, not so good. thats alot of technical words, and all ive worked on up to this point is oldies with carbs and no EFI, but im learning alot and im loving it. i have one difference, but i dont think it bothers anything, i have an automatic transmission, so if i install the maf system out of a newer pickup it may want to read the e40d when i just have an aod... unless maybe i can find one with a standard transmission?? i dunno, im getting so confused with all this technology, i would like to do it step by step just as you did. can you help me somehow?
Poster presented at the 2005 Neuroscience meeting in Washington, DC. Abstract: It is commonly believed that animals in general and insects in particular are mere input/output machines: if one only knew all their sensory input, one could predict the behavioral output they would produce. This basic tenet not only guides basic neurobiological research but has been the foundation for a great many robotic applications. Our results contradict this view and instead suggest that the brain spontaneously initiates behavioral activity at the same time it is computing input in order to generate output. Our mathematical evaluation detected an ordered but probabilistic temporal structure in spontaneous behavior, indicating a built-in, evolutionarily conserved spontaneity generator in the brain. We hypothesize that this generator can function independently of environmental input and that it evolved to generate flexible behavior in a complex world.
Since insects are so commonly used as a metaphor for the robot-like behavior of animals, we used tethered fruit flies ( Drosophila) and foraging honeybees for our study. The flies fly stationarily, attached by head and thorax to a device (torque meter) which measures their tendency to make left or right turns (yaw torque). The flies� environment was made entirely featureless for any of the fly�s senses. Hence, any behavior produced by the flies must be spontaneous and the behavior�s temporal structure will tell us something about the underlying structure of the generator in the brain which produced it. According to the robot-hypothesis, any variability in behavior without environmental input should reflect random noise, much like the hiss of static from a radio tuned between stations. Similarly, we studied the flight paths of bees searching for their hive after displacement to an area lacking natural landmarks. Bees were tagged with harmonic radar transponders and their positions recorded while in flight. These bees were searching for the hive after they had been displaced by 200 m in an area which lacked natural landmarks. The analyses included Geometric Random Inner Products (GRIP), distribution analysis computing L�vy exponents, the computation of correlation dimensions and nonlinear forecasting (simplex-projection and S-map procedure).
The idea behind the experiments was that, even in insects, any ordered structure of such spontaneous behavior would have a significant impact on our understanding of basic function of all brains. Previous results have implicated deterministic chaos in certain behavior patterns of freely-moving animals. Systems exhibiting deterministic chaos appear to be random, even though the model of the system is deterministic in the sense that it is well-defined and does not contains any random parameters. Such systems are thus orderly in some sense; this technical use of the word chaos is at odds with common parlance, which suggests complete disorder. The distinction between random noise and deterministic chaos is difficult but important, because the former points to extrinsic, uncontrollable origins of variability, whereas the latter indicates intrinsic origins. Reducing measurement error and other sources of noise will lead to a significant increase in predictability for a brain where the main source of variability stems from noise. In contrast, noise reductions will only marginally change the variability of the output of a chaotic brain whose output is fundamentally indeterministic, despite the deterministic rules that govern it. Our analysis shows that random noise cannot account for the temporal structure of the behavior we analyzed. Instead, the results suggest an underlying mechanism for spontaneous behavior initiation, which evolved to generate probabilistic behavior patterns. Such patterns have been previously shown to outcompete random and deterministic patterns in a number of ecological situations. In the real world, predator avoidance and prey catching behavior spring to mind as other obvious beneficiaries from indeterminacy. One can easily conceive how �getting out of a rut� would also be difficult with only pre-programmed �responses� � �thinking out of the box�, creativity and freedom are required, generated entirely from within; in other words: spontaneity.
Future research in this area should include an application of our analysis in a range of other animal and human data and a genetic disruption of targeted fly-brain areas to look for the biological substrate underlying the spontaneity generator.
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