Order and Orderlessness in Gravity\u27s Rainbow: A Dialectic

Gravity\u27s Rainbow is a notoriously unreliable text. The perspectives of the strange narrator and various characters give an account of the novel\u27s events that is clearly problematic in terms of the degree of reality that can be ascribed to various episodes: fantasies, hallucinations, and paranoid delusions are often indistinguishable from the events which may cause them or to which they may refer. To an unusual degree, then, the fundamental plot-question- What happens? -becomes a point of depa.rt u!e for a sort of textual metaphysics. Often, arguments about the significance of passages may be upstaged by arguments about the plot itself: what really happens and what is illusory? The reader faces the same difficulties that plague the characters: all seek knowledge of, or at least a coherent theory about, the fictional world of which the characters are inhabitants and the reader is a curiously stationed observer. Definitive answers are impossible; Pynchon\u27s work revels in its ambiguities. However, Gravity\u27s Rainbow is spectacular in the vastness of the fictive world it creates and chronicles, prompting a tremendous array of claims about the ways in which it functions. Thus, it seems appropriate to inquire into questions which are as fundamental in Pynchonian metaphysics as in the IJreal world. Probably the most important question is the one of whether or not ultimate order exists. Is the world of the novel orchestrated, ordered, or structured by some outside-the-System force or basic organizing principle, or is it characterized by randomness, with each event falling into a universal Poisson distribution

Wireless transfer of power by a 35-GHz metamaterial split-ring resonator rectenna

Wireless transfer of power via high frequency microwave radiation using a miniature split ring resonator rectenna is reported. RF power is converted into DC power by integrating a rectification circuit with the split ring resonator. The near-field behavior of the rectenna is investigated with microwave radiation in the frequency range between 20-40 GHz with a maximum power level of 17 dBm. The observed resonance peaks match those predicted by simulation. Polarization studies show the expected maximum in signal when the electric field is polarized along the edge of the split ring resonator with the gap and minimum for perpendicular orientation. The efficiency of the rectenna is on the order of 1% for a frequency of 37.2 GHz. By using a cascading array of 9 split ring resonators the output power was increased by a factor of 20

Decoding spoken words using local field potentials recorded from the cortical surface

Pathological conditions such as amyotrophic lateral sclerosis or damage to the brainstem can leave patients severely paralyzed but fully aware, in a condition known as 'locked-in syndrome'. Communication in this state is often reduced to selecting individual letters or words by arduous residual movements. More intuitive and rapid communication may be restored by directly interfacing with language areas of the cerebral cortex. We used a grid of closely spaced, nonpenetrating micro-electrodes to record local field potentials (LFPs) from the surface of face motor cortex and Wernicke's area. From these LFPs we were successful in classifying a small set of words on a trial-by-trial basis at levels well above chance. We found that the pattern of electrodes with the highest accuracy changed for each word, which supports the idea that closely spaced micro-electrodes are capable of capturing neural signals from independent neural processing assemblies. These results further support using cortical surface potentials (electrocorticography) in brain–computer interfaces. These results also show that LFPs recorded from the cortical surface (micro-electrocorticography) of language areas can be used to classify speech-related cortical rhythms and potentially restore communication to locked-in patients

Classification of spoken words using surface local field potentials

Cortical surface potentials recorded by electrocorticography (ECoG) have enabled robust motor classification algorithms in large part because of the close proximity of the electrodes to the cortical surface. However, standard clinical ECoG electrodes are large in both diameter and spacing relative to the underlying cortical column architecture in which groups of neurons process similar types of stimuli. The potential for surface micro-electrodes closely spaced together to provide even higher fidelity in recording surface field potentials has been a topic of recent interest in the neural prosthetic community. This study describes the classification of spoken words from surface local field potentials (LFPs) recorded using grids of subdural, nonpenetrating high impedance micro-electrodes. Data recorded from these micro-ECoG electrodes supported accurate and rapid classification. Furthermore, electrodes spaced millimeters apart demonstrated varying classification characteristics, suggesting that cortical surface LFPs may be recorded with high temporal and spatial resolution to enable even more robust algorithms for motor classification

Classification of spoken words using surface local field potentials

Cortical surface potentials recorded by electrocorticography (ECoG) have enabled robust motor classification algorithms in large part because of the close proximity of the electrodes to the cortical surface. However, standard clinical ECoG electrodes are large in both diameter and spacing relative to the underlying cortical column architecture in which groups of neurons process similar types of stimuli. The potential for surface micro-electrodes closely spaced together to provide even higher fidelity in recording surface field potentials has been a topic of recent interest in the neural prosthetic community. This study describes the classification of spoken words from surface local field potentials (LFPs) recorded using grids of subdural, nonpenetrating high impedance micro-electrodes. Data recorded from these micro-ECoG electrodes supported accurate and rapid classification. Furthermore, electrodes spaced millimeters apart demonstrated varying classification characteristics, suggesting that cortical surface LFPs may be recorded with high temporal and spatial resolution to enable even more robust algorithms for motor classification