Coupled-mode theory

Article Abstract:

Coupled mode theory is presented in an historical perspective, then described in terms of microwave, optoelectronics and fiber optics advancements so that it is seen how the coupled mode formalism is derived from a variational principle for system frequencies and then applied to boundary conditions in space. An energy orthogonal mode case demonstrates that two modes with positive energies coupled results in resonance frequency splitting. Opposite energies creates an unstable case if the systems natural frequencies are not very different. If energies are not orthogonal, usually the result of the coupling, the description is different. Refining energy in nonorthogonal cases requires more systematic methods for determining coupling matrices. Coupling modes in space has more numerous physical phenomena because of the variations in energies and power flows. All of the cases are derived from a variational principle shown in a passive lossfree electromagnetic structure case.

Electromagnetic fields, Technical, Frequency modulation, Waveforms, Space Planning, Timing, Theorem Proving, Models of Computation, Coupling

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Complex temporal sequence learning based on short-term memory

Article Abstract:

We design neural networks to learn, recognize, and reproduce complex temporal sequence, with short-term memory (STM) modeled by units comprising recurrent excitatory connections between two neurons (a dual neuron model). The output of a neuron has graded values instead of binary ones. By applying the Hebbian learning rule at each synapse and a normalization rule among all synaptic weights of a neuron, we show that a certain quantity, called the input potential, increases monotonically with sequence presentation, and that the neuron can only be fired when its input signals are arranged in a specific sequence. These sequence-detecting neurons form the basis for our model of complex sequence recognition, which can tolerate distortions of the learned sequences. A recurrent network of two layers is provided for reproducing complex sequences.

Pattern recognition (Computers), Neural networks, Brain, Models, Pattern Recognition, Neural Network

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Thresholding based on learning theory

Article Abstract:

Learning theory is applied to the real-time thresholding of gray-level images on a line-by-line basis under either vertical or horizontal scanning. Each scan line is isolated as a single entity, and the dynamic threshold value of each pixel is iteratively calculated. Applications include: developing real-time algorithms that can be implemented in hardware and location of objects in the image.

Industrial research, Research and Development, Scanning, Computer Learning, Scene Analysis, Thresholding

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