Optical label processing is expected to reduce power consumption in label switching network nodes. Previously, we proposed passive waveguide circuits for the recognition of BPSK labels with a theoretically infinite contrast ratio. The recognizable label number was limited to four and eight for 4-bit and 8-bit BPSK labels, respectively. In this paper, we propose methods to increase the recognizable label number. The proposed circuits can recognize eight and sixteen labels of 4-bit BPSK codes with a contrast ratio of 4.00 and 2.78, respectively. As 8-bit BSPK codes, 64, 128, and 256 labels can be recognized with a contrast ratio of 4.00, 2.78, and 1.65, respectively. In recognition of all encoded labels, that is, 16 and 256 labels for 4-bit and 8-bit BPSK labels, a reference signal is employed to identify the sign of the optical output signals. The effect of phase deviation and loss along the optical waveguides of the devices is also discussed.

Optical processing of optical labels is expected for increasing processing speed in network routers. We previously proposed optical waveguide circuits for recognition of optical QAM codes by detecting a null output port. The circuits are based on a recognition circuit for QPSK codes. In the device, however, optical or electrical inverters with large dynamic range are required. In this paper, we propose optical circuits to recognize optical QAM codes by maximum output with a post-processor consisting of thresholders and logical circuits. The recognition function of the waveguide circuit is numerically proved by FD BPM.

This paper consists of two parts. The first part is devoted to comparative study on handwritten ZIP code numeral recognition using seventeen typical feature vectors and seven statistical classifiers. This part is the counterpart of the sister paper Handwritten Postal Code Recognition by Neural Network - A Comparative Study" in this special issue. In the second part, a procedure for feature synthesis from the original feature vectors is studied. In order to reduce the dimensionality of the synthesized feature vector, the effect of the dimension reduction on classification accuracy is examined. The best synthesized feature vector of size 400 achieves remarkably higher recognition accuracy than any of the original feature vectors in recognition experiment using a large number of numeral samples collected from real postal ZIP codes.