An n-detection testing for stuck-at faults can be used not only for delay fault testing but also for detection of unmodeled faults. We have developed a hybrid BIST circuit; that is, a method consisting of a shift register with partial rotation and a procedure that selects test vectors from ATPG ones. This testing method can perform at-speed testing with high stuck-at fault coverage. During the at-speed testing, a subset of the ATPG vectors is input by using a low-speed tester. Computer simulations on ISCAS'85, ISCAS'89, and ITC'99 circuits are conducted for n = 1, 2, 3, 5, 10, and 15. The simulation results show that the amount of test vectors can be reduced to ranging from 52.3% to 0.9% in comparison with that of the ATPG vectors. As a result, the proposed method can reduce the cost of at-speed testing.

In this paper, we propose a method to reconstruct current distributions in the human brain from neuromagnetic measurements. The proposed method is based on the weighted lead-field synthetic (WLFS) filtering technique with the weighting factors calculated from the results of previous source space scanning. In this method, in addition to the depth normalization technique, weighting factors of the WLFS are determined by the cost values previously calculated based on the multiple signal classification (MUSIC) scan. We performed computer simulations of this method under noisy measurement conditions and compared the results to those obtained with the conventional WLFS method. The results of the simulations indicate that the proposed method is effective for the reconstruction of the current distributions in the human brain using magnetoencephalographic (MEG) measurements, even if the signal-to-noise ratio of the measured data is relatively low. We applied the proposed method to the magnetoencephalographic data obtained during a mental image processing task that included object recognition and mental rotation operations. The results suggest that the proposed method can extract the neural activity in the extrastriate visual region and the parietal region. These results are in agreement with the results of previous positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) studies.

Rectangular/circular-to-radial waveguide tra-nsformers through a ring slot have been proposed for the feeder of radial line slot antennas (RLSAs) in millimeter wave application. Rotating electric modes are excited by a set of ring slot and perturbation dog bone slot. Basic operation is observed in 12 GHz band. Concentric array radial line slot antennas fed by these transformers are fabricated and the antenna gain of 26.9 dBi with the efficiency more than 60% is measured. The applicability for millimeter wave is verified for 38 GHz band RLSA fed by the rectangular waveguide. The measured gain of the antenna is 22.5 dBi with the efficiency of 53% with the diameter of 46mm and 26.4 dBi with 61% with the diameter of 66mm.

In this paper we develop a CORDIC-based floating-point vectoring algorithm which reduces significantly the amount of microrotation steps as compared to the conventional algorithm. The overhead required to accomplish this is minimized by the introduction of an angle selection function which considers only a few of the total amount of bits used to represent the vector being rotated. At the same time, the cost of individual microrotations is kept low by the utilization of a fast rotations angle base.

We present two estimation methods for camera rotation from two images obtained by the active camera before and after rotation. Based on the representation of the projected rotation group, quasi moment features are constructed. Camera rotation can be estimated by applying the singular value decomposition (SVD) or Newton's method to tensor quasi moment features. In both cases, we can estimate 3D rotation of the active camera from only two projected images. We also give some experiments for the estimation of the actual active camera rotation to show the effectiveness of these methods.

In this paper, a token-controlled network with exhaustive service strategy is analyzed. The mean and variance of service time of a station, and the mean token rotation time on the network are derived under the condition that the buffer capacity of each station is individually finite. For analysis, an extended stochastic Petri-net model of a station is presented. Then, by analyzing the model, the mean service time of a station and the mean token rotation time are derived, as functions of the given network parameters such as the total number of stations on the network, the arrival rate of frames, the transmission rate of frames, and the buffer capacity. The variance of service time of a station is also derived. By examining derived results, it is shown that they exactly describe the actual operations of the network. In addition, computer simulations with sufficient confidence intervals help to validate the results.

We first demonstrate a self-pulsation phenomenon in a semiconductor ring laser(SRL). Not only self-mode-locked optical pulse but self-Q-switched optical pulse can be observed in a SRL. Furthermore, experimental results show that the repetition period of the Q-switched optical pulse train can be controlled by the injection current to a SRL.

A novel method for the radiated immunity test is proposed. The method is to generate controlled electromagnetic fields applying in arbitrary directions to an under test. The fields rotate at a low speed controlled electrically so that the immunity characteristics may be known in more detail. The primal characteristics of the fields generated by a trial benchtop setup are investigated.

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 describes a classification method for rotated and scaled textured images using invariant parameters based on spectral-moments. Although it is well known that rotation invariants can be derived from moments of grey-level images, the use is limited to binary images because of its computational unstableness. In order to overcome this drawback, we use power spectrum instead of the grey levels to compute moments and adjust the integral region of moment evaluation to the change of scale. Rotation and scale invariants are obtained as the ratios of the different rotation invariants on the basis of a spectral-moment property with respect to scale. The effectiveness of the approach is illustrated through experiments on natural textures from the Brodatz album. In addition, the stability of the invariants with respect to the change of scale is discussed theoretically and confirmed experimentally.

It becomes essential in practice to improve a processing rate and to divide an image into small segments adjusting a limited memory, because image filing systems handle large images up to A1 size. This paper proposes a new method of an automatic skew normalization, comprising a high-speed skew detection and a distortion-free dividing rotation. We have evaluated the proposed method from the viewpoints of the processing rate and the accuracy for typed documents. As results, the processing rate is 2. 9 times faster than that of a conventional method. A practical processing rate for A1 size documents can be achieved under the condition that the accuracy of a normalized angle is controlled within 0. 3 degrees. Especially, the rotation with dividing can have no error angle, even when the A1 size documents is divided into 200 segments, whereas the conventional method cause the error angle of 1. 68 degrees.

This paper focuses on competitive learning algorithms for WTA (winner-take-all) networks which perform rotation invariant pattern classification. Although WTA networks may theoretically be possible to achieve rotation invariant pattern classification with infinite memory capacities, actual networks cannot memorize all input data. To effectively memorize input patterns or the vectors to be classified, we present two algorithms for learning vectors in classes (LVC1 and LVC2), where the cells in the network memorize not only weight vectors but also their firing numbers as statistical values of the vectors. The LVC1 algorithm uses simple and ordinary competitive learning functions, but it incorporates the firing number into a coefficient of the weight change equation. In addition to all the functions of the LVC1, the LVC2 algorithm has a function to utilize under-utilized weight vectors. From theoretical analysis, the LVC2 algorithm works to minimize the energy of all weight vectors to form an effective memory. From computer simulation with two-dimensional rotated patterns, the LVC2 is shown to be better than the LVC1 in learning and generalization abilities, and both are better than the conventional Kohonen self-organizing feature map (SOFM) and the learning vector quantization (LVQ1). Furthermore, the incorporation of the firing number into the weight change equation is shown to be efficient for both the LVC1 and the LVC2 to achieve higher learning and generalization abilities. The theoretical analysis given here is not only for rotation invariant pattern classification, but it is also applicable to other WTA networks for learning vector quantization.

This paper presents efficient algorithms for updating moving octrees with real-time performance. The first algorithm works for octrees undergoing both translation and rotation motion; it works efficiently by compacting source octrees into a smaller set of cubes (not necessarily standard octree cubes) as a precomputation step, and by using a fast, exact cube/cube intersection test between source octree cubas and target octree cubes. A parallel version of the algorithm is also described. Finally, the paper presents an efficient algorithm for the more limited case of octree translation only. Experimental results are given to show the efficiency of the algorithms in comparison to competing algorithms. In addition to being fast, the algorithms presented are also space efficient in that they can produce target octrees in the linear octree representation.

Cellular Neural Networks (CNNs) are nonlinear dynamic array processors with mainly local interconnections. In most of the applications, the local interconnection pattern, called cloning template, is translation invariant. In this paper, an optimal ring-coding method for rotation invariant description of given set of objects, is introduced. The design methodology of the templates based on the ring-codes and the synthesis of CNN analogic algorithms to detect standing and moving objects in a rotationally invariant way, discussed in detail. It is shown that the algorithms can be implemented using the CNN Universal Machine, the recently invented analogic visual microprocessor. The estimated time performance and the parallel detecting capability is emphasized, the limitations are also thoroughly investigated.

This paper presents a simple and efficient method for estimation of parameters useful for textured image analysis. On the basia of a 2-D Wold-like decomposition of homogenenous random fields, the texture field can be decomposed into a sum of two mutually orthogonal components: a deterministic component and an indeterministic component. The spectral density function (SDF) of the former is a sum of 1-D or 2-D delta functions. The 2-D autocorrelation function (ACF) of the latter is fitted to the assumed anisotropic ACF that has an elliptical contour. The parameters representing the ellipse and those representing the delta functions can be used to detect rotation angles and scaling factors of test textures. Specially, rotation and scaling invariant parameters, which are applicable to the classification of rotated and scaled textured images, can be estimated by combining these parameters. That is, a test texture can be correctly classified even if it is rotated and scaled. Several computer experiments on natural textures show the effectiveness of this method.

A radial line slot antenna (RLSA) is a high gain and high efficiency planar antenna proposed for DBS subscribers. Spirally arrayed slots are excited by a cylindrical wave with the rotational symmetry. In a small sized antenna where large slot coupling is adopted, aperture efficiency reduction due to rotational asymmetry associated with a spiral arrangement of the slots becomes notable. Authors proposed a RLSA with a concentric slot arrangement excited by a rotating mode in order to enhance the rotational symmetry. This is the first report of the normal operation of a rotating mode RLSA fed by a cavity resonator. The experiments confirm the basic operation of this novel antenna; the gain of 27.8dBi and the efficiency of 68% is measured at 11.85GHz for the RLSA with 0.24mφ.

A radial line slot antenna (RLSA) is a slotted waveguide planar array for the direct broadcast from satellite (DBS) subscriber antennas. A single-layered RLSA (SL-RLSA) is excited by a radially outward traveling wave. The antenna efficiency of more than 85% has already been realized. These antennas are designed on the assumption of perfectly rotationally symmetrical traveling wave excitation; the slot design is based upon the analysis of a slot pair on the rectangular waveguide model with periodic boundary walls. However, the slots perturb the inner field and the actual antenna operation is not perfectly symmetrical. This causes the efficiency reduction especially for very small size antenna. This paper presents a fundamental analysis of the inner field of the radial waveguide. It is impossible to analyze all the slot pairs in the aperture as it is and only the slots in the inner few turns are considered since these provide dominant perturbation. The calculated results are verified by the experiments and reasonable agreement is demonstrated. Some design policies are suggested for enhancing the rotational symmetry.

In this paper we discuss a neural network model that can recognize patterns rotated at various angles. The model employs copy learning, a learning method entirely different from those used in conventional models. Copy-Learning is an effective learning method to attain the desired objective in a short period of time by making a copy of the result of basic learning through the application of certain rules. Our model using this method is capable of recognizing patterns rotated at various angles without requiring mathematical preprocessing. It involves two processes: first, it learns only the standard patterns by using part of the network. Then, it copies the result of the learning to the unused part of the network and thereby recognizes unknown input patterns by using all parts of the network. The model has merits over the conventional models in that it substantially reduces the time required for learning and recognition and can also recognize the rotation angle of the input pattern.

To investigate necessary conditions for the object recognition by simulations using neural network models is one of ways to acquire suggestions for understanding the neuronal representation of objects in the brain. In the present study, we trained a three layered neural network to form a geometrical feature representation in its output layer using back-propagation algorithm. After training using 73 learning examples, 65 testing patterns made by various combinations of above features could be recognized with the network at a rate of 95.3% appropriate response. We could classify four types of hidden layer units on the basis of effects on the output layer.

This paper introduces a modeling of the human rotation invariant recognition mechanism at the neural level. In the model, mechanisms of memory search and mental rotation are realized in the process of minimizing the energy of a bi-directional connection network. The thrust of the paper is to explain temporal mental activities such as successive memory retrievals and continuous mental rotation in terms of state transitions of collective neurons based on nonequilibrium dynamics. We conclude that regularities emerging in the dynamics of intermittent chaos lead the recognition process in a structural and meaningful way.