A rigorous and simple method is proposed for analyzing guided modes of metallic electromagnetic crystal waveguides. The method is a combination of generalized reflection and transmission matrices and the mode-matching technique. Fast convergence, low computer cost, and high calculating precision are main advantages of the proposed method. This method can easily avoid the relative convergence phenomena than a classical mode-matching method, and the proposed formulation is very suitable to analyzing multilayered problems with very low computer cost. The existence of H-polarized modes in metallic electromagnetic crystal waveguides has been verified.

How to mitigate the influence of unfair testimonies remains an open issue in the research of rating systems. Methods have been proposed to filter the unfair testimonies in order to mitigate the influence of unfair testimonies. However, existing methods depend on assumptions that ratings follow a particular distribution to carry out the testimony filtering. This constrains them in specific rating systems and hinders their applications in other reputation systems. Moreover, existing methods do not scale well with the increase of testimony number due to their iterative nature. In this paper, a novel entropy-based method is proposed to measure the testimony quality, based on which unfair testimonies are further filtered. The proposed method does not require the assumption regarding the rating distribution. Moreover, it scales linearly with the increase of the testimony number. Experimental results show that the proposed method is effective in mitigating the influence of various types of unfair testimonies.

We present a bit-parallel squarer for GF(2n) defined by an irreducible trinomial xn +xk +1 using a shifted polynomial basis. The proposed squarer requires TX delay and at most n/2 XOR gates, where TX is the delay of one XOR gate. As a result, the squarer using the shifted polynomial basis is more efficient than one using the polynomial basis except for k=1 or n/2.

We present a novel precomputed radiance transfer method for efficient relighting under all-frequency environment illumination. Environment illumination is represented as a set of environment lights. Each environment light comprises a direction and an intensity. In a preprocessing step, the environment lights are clustered into several clusters, taking into account only the light directions. By experiment, we confirmed that the environment lights can be clustered into a much smaller number of clusters than their original number. Given any environment illumination, sampled as an environment map, an efficient relighting is then achieved by computing the radiance using the precomputed clusters. The proposed method enables relighting under very high-resolution environment illumination. In addition, unlike previous approaches, the proposed method can efficiently perform relighting when some regions of the given environment illumination change.

For obtaining a higher level of generalization capability in supervised learning, model parameters should be optimized, i.e., they should be determined in such a way that the generalization error is minimized. However, since the generalization error is inaccessible in practice, model parameters are usually determined in such a way that an estimate of the generalization error is minimized. A standard procedure for model parameter optimization is to first prepare a finite set of candidates of model parameter values, estimate the generalization error for each candidate, and then choose the best one from the candidates. If the number of candidates is increased in this procedure, the optimization quality may be improved. However, this in turn increases the computational cost. In this paper, we give methods for analytically finding the optimal model parameter value from a set of infinitely many candidates. This maximally enhances the optimization quality while the computational cost is kept reasonable.

This investigation proposes a modified equivalent circuit of single complementary split-ring resonator (CSRR) in planar transmission media and a dual-mode ring bandpass filter (BPF) that uses periodic CSRRs to suppress the spurious response. The proposed modified equivalent circuit consists of lumped elements that can be easily extracted from the measured S parameters. The proposed dual-mode ring BPF has exhibits a wide stopband characteristic owing to the bandgap resonant characteristic of CSRRs in the harmonic frequency of the dual-mode ring BPF. Good agreement with EM simulation and measurement is demonstrated.

This article presents the analysis of the Bragg scattering phenomenon which has been observed in the images of machine-planted rice paddies acquired by the JERS-1 L-band synthetic aperture radar (SAR). The simultaneous measurements of rice plants were made at the SAR data acquisition times. Large differences of 20-25 dB in image intensity between the transplanting and ripening stages are found to be dependent on the planting direction and bunch separation. This selective image enhancement is a result of the Bragg resonance backscatter due to the double-bounce of incident L-band microwave between the flooded water surface and periodically planted bunches of rice plants. Support for the idea of double-bounce scattering is provided by the decomposition analysis of L-band and X-band polarimetric Pi-SAR data; and a simple numerical simulation based on the physical optics model shows fairly good agreement with the JERS-1 SAR data. The results presented in this paper is mainly of academic interest, but a suggestion can be made on the selection of suitable microwave band for monitoring rice fields.

To provide basic considerations for the realization of methods for predicting the electromagnetic (EM) radiation from a printed circuit board (PCB) with plural signal traces driven in the even-mode, the characteristics of the EM radiation resulting from two signal traces on a PCB are investigated experimentally and by numerical modeling. First, the frequency responses of common-mode (CM) current and far-electric field as electromagnetic interference (EMI) are discussed. As the two traces are moved closer to the PCB edge, CM current and far-electric field increase. The frequency responses in the two signal trace case can be identified using insights gained from the single trace case. Second, to understand the details of the increase in CM current, the distribution of the current density on the ground plane is calculated and discussed. Although crosstalk ensues, the rule for PCB design is to keep two high-speed traces on the interior of the PCB whenever possible, from the point of view of EM radiation. Finally, an empirical formula to quantify the relationship between the positions of two traces and CM current is provided and discussed by comparing four different models. Results calculated with the empirical formula and finite-difference time-domain (FDTD) modeling are in good agreement, which indicates the empirical formula may be useful for developing EMI design guidelines.

We extend a graph spectral method for extracting clusters from graphs representing pairwise similarity between data to hypergraph data with hyperedges denoting higher order similarity between data. Our method is robust to noisy outlier data and the number of clusters can be easily determined. The unsupervised method extracts clusters sequentially in the order of the majority of clusters. We derive from the unsupervised algorithm a semi-supervised one which can extract any cluster irrespective of its majority. The performance of those methods is exemplified with synthetic toy data and real image data.

A distributed denial-of-service (DDoS) attack presents a very serious threat to the stability of the Internet. In a typical DDoS attack, a large number of compromised hosts are amassed to send useless packets to jam a victim or its Internet connection, or both. Defense against DDoS attacks as well as identification of their sources comprise demanding challenges in the realm of Internet security studies. In this paper, effective measures are proposed for detecting attacks in routers through the use of queuing models, which help detect attacks closer to the attack sources. Utilizing these measures, an effective DDoS attack detection and packet-filtering scheme is proposed. The suggested approach is a cooperative technique among routers intended to protect the network from persistent and severe congestion arising from a rapid increase in attack traffic. Through computer simulations, it is shown that the proposed scheme can trace attacks near to the attack sources, and can effectively filter attack packets.

From the viewpoint of a low-power pipeline ADC design, a comparison between two conventional power reduction techniques is discussed. The comparison shows that the amplifier sharing technique has an advantage in terms of the power reduction effect. To confirm the advantage, a test chip of 10-bit 80-MSPS ADC using the amplifier sharing technique is fabricated. The test chip dissipates 55 mW at 80 MSPS (Mega Sample Per Second).

In this paper, we proposed a new Two-Dimensional Linear Discriminant Analysis (2DLDA) method, based on Two-Dimensional Principle Component Analysis (2DPCA) concept. In particular, 2D face image matrices do not need to be previously transformed into a vector. In this way, the spatial information can be preserved. Moreover, the 2DLDA also allows avoiding the Small Sample Size (SSS) problem, thus overcoming the traditional LDA. We combine 2DPCA and our proposed 2DLDA on the Two-Dimensional Linear Discriminant Analysis of principle component vectors framework. Our framework consists of two steps: first we project an input face image into the family of projected vectors via 2DPCA-based technique, second we project from these space into the classification space via 2DLDA-based technique. This does not only allows further reducing of the dimension of feature matrix but also improving the classification accuracy. Experimental results on ORL and Yale face database showed an improvement of 2DPCA-based technique over the conventional PCA technique.

In the field of industrial manufacturing, visual pattern inspection is an important task to prevent the inclusion of incorrect parts. There have been demands for such methods able to handle factors caused by positional and rotational alignment, and illumination changes. In this paper, we propose a discrimination method called Trinarized broad-edge and Gray-scale Hybrid Matching (TGHM). The method is highly reliable due to gray-scale cross correlation which has a high pattern discrimination efficiency, with high-speed position and rotation alignment using the characteristics of trinarized broad-edge representation which has high data compressibility and illumination-resistant variability. In an example in which the method is applied to mis-collation inspection equipment of a bookbinding machine, it is confirmed that the processing speed is 24,000 sheets/hour, the error detection rate is 100.0%, and the mis-alarm rate is less than 0.002%, and it is verified that the method is practical.

The IEEE802.11e is a standard developed by a Task Group E of the IEEE802.11 working group and defines a MAC protocol, which provides EDCA (enhanced distributed channel access) and HCCA (HCF controlled channel access) to support differentiation service over WLAN (wireless LAN). In IEEE802.11e WLAN, real-time application such as VoIP (Voice over IP) can have more chances to access the WM (wireless medium) than non real-time application. In addition to QoS support in WLAN, power consumption is a critical issue when WLAN is used in handheld devices. For power saving in the use of real-time applications like VoIP under EDCA, U-APSD (Unscheduled Automatic Power Save Delivery) was proposed in [2] and [4]. In fact, it can save power consumption and works well when it is used for bi-directional voice connections generated at constant bit rate. However, when it is used for real-time applications like ON-OFF traffic, buffering delay at AP (access point) increases. To reduce the buffering delay, this paper proposes two mechanisms. Simulation results show that they can alleviate buffering delay generated at AP.

Kernel-based learning algorithms have been successfully applied in various problem domains, given appropriate kernel functions. In this paper, we discuss the problem of designing kernel functions for binary regression and show that using a bell-shaped cosine function as a kernel function is optimal in some sense. The rationale of this result is based on the Karhunen-Loeve expansion, i.e., the optimal approximation to a set of functions is given by the principal component of the correlation operator of the functions.

This paper presents a robust object tracking method under pose variation and partial occlusion. In practical environment, the appearance of objects is changed dynamically by pose variation or partial occlusion. Therefore, the robustness to them is required for practical applications. However, it is difficult to be robust to various changes by only one tracking model. Therefore, slight robustness to variations and the easiness of model update are required. For this purpose, Kernel Principal Component Analysis (KPCA) of local parts is used. KPCA of local parts is proposed originally for the purpose of pose independent object recognition. Training of this method is performed by using local parts cropped from only one or two object images. This is good property for tracking because only one target image is given in practical applications. In addition, the model (subspace) of this method can be updated easily by solving a eigen value problem. Performance of the proposed method is evaluated by using the test face sequence captured under pose, partial occlusion, scaling and illumination variations. Effectiveness and robustness of the proposed method are demonstrated by the comparison with template matching based tracker. In addition, adaptive update rule using similarity with current subspace is also proposed. Effectiveness of adaptive update rule is shown by experiment.

The noise robustness of an audio fingerprinting system is one of the most important issues in music information retrieval by the content-based audio identification technique. In a real environment, sound recordings are commonly distorted by channel and background noise. Recently, Philips published a robust and efficient audio fingerprinting system for audio identification. To extract a robust and efficient audio fingerprint, Philips applied the first derivative (differential) to the frequency-time sequence of the perceptual filter-bank energies. In practice, however, the noise robustness of Philips' audio fingerprinting scheme is still insufficient. In this paper, we introduce an extension method of the audio fingerprinting scheme for the enhancement of noise robustness. As an alternative to frequency filtering, a type of band-pass filter, instead of a high-pass filter, is used to achieve robustness to background noise in a real situation. Our experimental results show that the proposed filter improves the noise robustness in audio identification.

In 2005, Yong and Lee proposed a buyer-seller fingerprinting protocol using symmetric and commutative encryptions. They claimed that their protocol was practical and anonymous since they used symmetric and commutative encryptions. However, an attacker can get the content embedded with one or more honest buyers' fingerprints using man-in-the-middle attack. In this letter, we point out the weakness and propose methods for improving to their protocol.

Diseases in the gastro-intestinal track are becoming more prevalent. In order to diagnose a patient, the various signals of the digestive organ, such as temperature, pH, and pressure, can offer helpful information. The variation of a pressure signal of the gastro-intestinal track can offer information about digestive troubles or provide clues about diseases. This paper presents a wireless system for the pressure monitoring system, which includes a swallow-type pressure capsule and the external receiving system. A transmitter, a transmitting antenna (Helix), a receiver, and a receiving antenna (Loop) were designed and fabricated in consideration of the MPE, power consumption, system size, signal to noise ratio and modulation method. The wireless system designed and implemented for the pressure monitoring system was verified by in-vivo experiments. As a result, we found each organ has its own characteristic pressure fluctuation.

An unsupervised adaptive signal processing method of principal components analysis (PCA) neural networks (NN) based on signal eigen-analysis is proposed to permit the eigenstructure analysis of lower signal to noise ratios (SNR) direct sequence spread spectrum (DS) signals. The objective of eigenstructure analysis is to estimate the pseudo noise (PN) of DS signals blindly. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, which duration is two periods of PN sequence. Then an autocorrelation matrix is computed and accumulated by these signal vectors one by one. Lastly, the PN sequence can be estimated by the principal eigenvector of autocorrelation matrix. Since the duration of temporal window is two periods of PN sequence, the PN sequence can be reconstructed by the first principal eigenvector only. Additionally, the eigen-analysis method becomes inefficient when the estimated PN sequence is long. We can use an unsupervised adaptive method of PCA NN to realize the PN sequence estimation from lower SNR input DS-SS signals effectively.