The most commonly used scattering parameters (S parameters) are normalized to a real reference resistance, typically 50Ω. In some cases, the use of S parameters normalized to some complex reference impedance is essential or convenient. But there are different definitions of complex-referenced S parameters that are incompatible with each other and serve different purposes. To make matters worse, different simulators implement different ones and which ones are implemented is rarely properly documented. What are possible scenarios in which using the right one matters? This tutorial-style paper is meant as an informal and not overly technical exposition of some such confusing aspects of S parameters, for those who have a basic familiarity with the ordinary, real-referenced S parameters.

This paper develops a methodology of crosstalk analysis/measurement techniques for the design and fabrication of the MEMs (Micro-ElectroMichanical system) probe card. By introducing more ground pins into the connector pins, the crosstalk characteristics can be enhanced and a design guide for the parameters, such as pin's size and pitch is proposed to satisfy the given crosstalk limitation of -30 dB for reliable high speed signal transfer. The paper also presents a novel method to characterize scattering parameters of multiport interconnect circuits with a 4-port VNA (Vector Network Analyzer). By employing the re-normalization of scattering matrices with different reference impedances at other ports, data obtained from 4-port configuration measurements can be synthesized to build a full scattering matrix of the DUT (Device-Under-Test, MEMs probe connector pins). In comparison to the conventional 2-port VNA re-normalization method, proposed technique has two advantages: saving of measuring time, and enhanced accuracy even with open-ended unmeasured ports. A good agreement of the estimated and correct S parameters verifies the validness of the proposed algorithm.

This paper employs the nature-inspired approach to investigate the ideal architecture of communication networks as large-scale and complex systems. Conventional architectures are hierarchical with respect to the functions of network operations due entirely to implementation concerns and not to any fundamental conceptual benefit. In contrast, the large-scale systems found in nature are hierarchical and demonstrate orderly behavior due to their space/time scale dependencies. In this paper, by examining the fundamental requirements inherent in controlling network operations, we clarify the hierarchical structure of network operations with respect to time scale. We also describe an attempt to build a new network architecture based on the structure. In addition, as an example of the hierarchical structure, we apply the quasi-static approach to describe user-system interaction, and we describe a hierarchy model developed on the renormalization group approach.

This paper presents Dynamic Attention Map by Ising model for face detection. In general, a face detector can not know where faces there are and how many faces there are in advance. Therefore, the face detector must search the whole regions on the image and requires much computational time. To speed up the search, the information obtained at previous search points should be used effectively. In order to use the likelihood of face obtained at previous search points effectively, Ising model is adopted to face detection. Ising model has the two-state spins; "up" and "down". The state of a spin is updated by depending on the neighboring spins and an external magnetic field. Ising spins are assigned to "face" and "non-face" states of face detection. In addition, the measured likelihood of face is integrated into the energy function of Ising model as the external magnetic field. It is confirmed that face candidates would be reduced effectively by spin flip dynamics. To improve the search performance further, the single level Ising search method is extended to the multilevel Ising search. The interactions between two layers which are characterized by the renormalization group method is used to reduce the face candidates. The effectiveness of the multilevel Ising search method is also confirmed by the comparison with the single level Ising search method.

We describe a theoretically optimal algorithm for computing the homography between two images. First, we derive a theoretical accuracy bound based on a mathematical model of image noise and do simulation to confirm that our renormalization technique effectively attains that bound. Then, we apply our technique to mosaicing of images with small overlaps. By using real images, we show how our algorithm reduces the instability of the image mapping.

We discuss optimal rotation estimation from two sets of 3-D points in the presence of anisotropic and inhomogeneous noise. We first present a theoretical accuracy bound and then give a method that attains that bound, which can be viewed as describing the reliability of the solution. We also show that an efficient computational scheme can be obtained by using quaternions and applying renormalization. Using real stereo images for 3-D reconstruction, we demonstrate that our method is superior to the least-squares method and confirm the theoretical predictions of our theory by applying bootstrap procedure.

This paper presents a new method for solving the structure-from-motion problem for optical flow. The fact that the structure-from-motion problem can be simplified by using the linearization technique is well known. However, it has been pointed out that the linearization technique reduces the accuracy of the computation. In this paper, we overcome this disadvantage by correcting the linearized solution in a statistically optimal way. Computer simulation experiments show that our method yields an unbiased estimator of the motion parameters which almost attains the theoretical bound on accuracy. Our method also enables us to evaluate the reliability of the reconstructed structure in the form of the covariance matrix. Real-image experiments are conducted to demonstrate the effectiveness of our method.

This paper presents a theoretically best algorithm within the framework of our image noise model for reconstructing 3-D from two views when all the feature points are on a planar surface. Pointing out that statistical bias is introduced if the least-squares scheme is used in the presence of image noise, we propose a scheme called renormalization, which automatically removes statistical bias. We also present an optimal correction scheme for canceling the effect of image noise in individual feature points. Finally, we show numerical simulation and confirm the effectiveness of our method.

Introducing a general statistical model of image noise, we present an optimal algorithm for computing 3-D motion from two views without involving numerical search: () the essential matrix is computed by a scheme called renormalization; () the decomposability condition is optimally imposed on it so that it exactly decomposes into motion parameters; () image feature points are optimally corrected so that they define their 3-D depths. Our scheme not only produces a statistically optimal solution but also evaluates the reliability of the computed motion parameters and reconstructed points in quantitative terms.