Iiideung terms VCC), integrated inductor. phase iioise .A I3 ST R A CT I NTR ODU CTI ON A t all RF wireless applications where we have to iriiplement VCOs the resonators are critical parts of the systcm. Using off-chip solutions \\'ill cause problems with packaging, the pwxiitics of the packages will be part of the rcsoiiator circuitry so their accurate modeling is ii must [ I ] . Furthermore the package wdl c;iIisc unwanted couphgs between the rcsonator pins where large RF currents can be prcscnt and other parts of the circuit. On-Chip resonators are good candidates to avoid packaging probkms. The most common intcgratcci rcsona[ors contain simple intcgratcd spirals which suffer from the low quality factor dui. to the ohmic and substrate losses. Their typical Q is around 4-5 at 2GHz on high ohmic silicon substrates. [2]To r-cducc the substrate losses we employed a special structure to block the parasitic eddy currmts in the substrate responsible for the stitxtratc losscs. Our method resulted in 15% increase in the quality factor of the spirals which enabled us to achieve improoved phase noise performdnce with this hlly integrated oscillator circuit.

The correlated biochemical and electrical engineering based development of the entropy theory was formalized for the parameterization of path information,i.e. storage measures. Similarly, the computer and neural network path delays were developed. Both path parameters were illustrated for the computer networking topological representation of two neural function organs and their interconnection, i.e., the star, the Hippocampus computer network representation, and the wheel with a hub, the Cerebellum computer network representation. The information measures illustrated the topologically motivated data compression and connectivity differences. The following computer networking issues are realized in the topological neural network conversion to a functional computer network derivation of path delay and storage parameters; load, real time requests for various topological structures, concurrency in centralized and distributed networks, deadlock and livelock control, and contention resolution. Category theory is utilized as the mathematical formulation for the definition of simulation in this software theory. I . MATHEMATICAL FRAMEWORK FOR COMPUTER NETWORKING/NEURALDuring the past ten years, neural networking, which exhibits characteristics linked to the operation of the human brain intelligence states, has surfaced as a research area developing parallel to computer networking. The interaction between disciplines of electrical engineering, physics and biochemistry with the interdisciplinary idea of neural prosthetic hardware and software replacement components, and its connection to computer networking, was suggested by Niznik [14], Niznik and Newcomb [16,17] and Niznik, Hoss, and Watts [15,18]. The distinct interaction theory concept represents the transfer of theoretical and heuristic concepts between the disciplines of electrical engineering, computer networking and neural networking to extend their definition of network operation and control. Category theory is the basic structure realized for the interaction of the mathematical techniques of iterative software systems. The overall illustration of this statement occurs in the characteristic terminology and parameters which comprise the category. Simulation in the context of a category is defined as "a mapping where Machine B simulates the activity of Machine A if one step in Machine B and the halting of Machine A forces Machine B to halt" [19]. Simulation is the methodology for programmatically specifying and evaluating the performance of processes in computer networks and functions that the neural networks comprise. Therefore, category theory formalizes the finite identification of an infinite number of network paths, whose analogy is the infinite number of sentences, and the finite number of rules to create the sentences in a simulation language. TOPOLOGICAL FORMALISMSIn [18] Niznik, Hoss and Watts have implemented the theoretical basis for the development of the Robotic Quantization Controlled Sensory[RQCS] System, a section of the robotic neural network. In this...