In this paper, the magnetic field properties and the dosimetry at intermediate frequency (21 kHz) for an induction heater are investigated with the coil model, which is prescribed as substitute source model in the European standard EN50366 (CENELEC). The accuracy of the magnetic field vectors and the values of the induced current density, which are achieved with the coil model, are compared with the results of a realistic model of the induction heater obtained from the equivalent source model. It is shown that the coil model coincides well for the magnitude of the magnetic field strength around the induction heater. On the other hand, the dominant field vector of the coil model differs significantly from the real induction heater, which leads to induced current densities in the body model which are three time larger. Owing to these results, the applicability of the coil model prescribed in the EN50366 is confirmed for the induction heater.

Since congestion is very likely to happen in the Internet, locating congested areas (path segments) along a congested path is vital to appropriate actions by Internet Service Providers to mitigate or prevent network performance degradation. We propose a practical method to locate congested segments by actively measuring one-way end-to-end packet losses on appropriate paths from multiple origins to multiple destinations, using a network tomographic approach. Then we conduct a long-term experiment measuring packet losses on multiple paths over the Japanese commercial Internet. The experimental results indicate that the proposed method is able to precisely locate congested segments. Some findings on congestion over the Japan Internet are also given based on the experiment.

Voice activity detection (VAD) determines whether a slice of waveform is voice or silence. The proposed VAD algorithm applying Elliptical Basis Function (EBF) neural networks uses k-means clustering and least mean square for the update algorithm. The error rates achieved by the EBF network have superior performance to those of G.729 Annex B and RBF.

A novel frequency domain training sequence and the corresponding carrier frequency offset (CFO) estimator are proposed for orthogonal frequency division multiplexing (OFDM) systems over frequency-selective fading channels. The proposed frequency domain training sequence comprises two types of pilot tones, namely distinctively spaced pilot tones with high energies and uniformly spaced ones with low energies. Based on the distinctively spaced pilot tones, integer CFO estimation is accomplished. After the subcarriers occupied by the distinctively spaced pilot tones and their adjacent subcarriers are nulled for the sake of interference cancellation, fractional CFO estimation is executed according to the uniformly spaced pilot tones. By exploiting a predefined lookup table making the best of the structure of the distinctively spaced pilot tones, computational complexity of the proposed CFO estimator can be decreased considerably. With the aid of the uniformly spaced pilot tones generated from Chu sequence with cyclically orthogonal property, the ability of the proposed estimator to combat multipath effect is enhanced to a great extent. Simulation results illustrate the good performance of the proposed CFO estimator.

We have proposed a neural network called the Lagrange programming neural network with polarized high-order connections (LPPH) for solving the satisfiability problem (SAT) of propositional calculus. The LPPH has gradient descent dynamics for variables and gradient ascent dynamics for Lagrange multipliers, which represent the weights of the clauses of the SAT. Each weight wr increases according to the degree of unsatisfaction of clause Cr. This causes changes in the energy landscape of the Lagrangian function, on which the values of the variables change in the gradient descent direction. It was proved that the LPPH is not trapped by any point that is not a solution of the SAT. Experimental results showed that the LPPH can find solutions faster than existing methods. In the LPPH dynamics, a function hr(x) calculates the degree of unsatisfaction of clause Cr via multiplication. However, this definition of hr(x) has a disadvantage when the number of literals in a clause is large. In the present paper, we propose a new definition of hr(x) in order to overcome this disadvantage using the "min" operator. In addition, we extend the LPPH to solve the constraint satisfaction problem (CSP). Our neural network can update all neurons simultaneously to solve the CSP. In contrast, conventional discrete methods for solving the CSP must update variables sequentially. This is advantageous for VLSI implementation.

A recursive-type positive integer code is proposed. It prefixes the information about the length of the component of the codeword recursively. It is an asymptotically optimal code. The codeword length for a positive integer n is shorter than n bits in almost all of sufficiently large positive integers, where n is the log-star function.

In general, a corner model with best- and worst-case delay conditions is used in static timing analysis (STA). The best- and worst-case delays of a stage are defined as the fastest and slowest delays from a cell input to the next cell input. In this paper, we present a methodology for determining the parameters that yield the best- and worst-case delays when interconnect structural parameters have the minimum and maximum values with process variations. We also present analysis results of our circuit model using the methodology. The min and max conditions for the time constant are found to be (+Δw, +Δt, +Δh) & (-Δw, -Δt, -Δh), respectively. Here, +Δ or -Δ means the max or min corner value of each parameter variation, where w is the width, t is the interconnect thickness, and h is the height. Best and worst conditions for delay time are as follows: 1) given a circuit with an optimum driver, dense interconnects, and small branch capacitance, the best and worst conditions are respectively (-Δw, +Δt, +Δh) & (+Δw, +Δt, -Δh), 2) given driver and/or via resistances that are higher than the interconnect resistance, dense interconnects, and small branch capacitance, they are (-Δw, -Δt, +Δh) & (+Δw, +Δt, -Δh), and 3) for other conditions, they are (+Δw, +Δt, +Δh) & (-Δw, -Δt, -Δh). Moreover, if there must be only one condition each for the best- and worst-case delays, they are (+Δw, +Δt, +Δh) & (-Δw, -Δt, -Δh).

The k-edge-connectivity augmentation problem for a specified set of vertices of a graph with degree constraints, kECA-SV-DC, is defined as follows: "Given an undirected multigraph G = (V,E), a specified set of vertices S ⊆V and a function g: V → Z+ ∪{∞}, find a smallest set E' of edges such that (V,E ∪ E') has at least k edge-disjoint paths between any pair of vertices in S and such that, for any v ∈ V, E' includes at most g(v) edges incident to v, where Z+ is the set of nonnegative integers." This paper first shows polynomial time solvability of kECA-SV-DC and then gives a linear time algorithm for 2ECA-SV-DC.

We consider a watermark application to assist in the integrity maintenance and verification of the associated images. There is a great benefit in using WM in the context of authentication since it does not require any additional storage space for supplementary metadata, in contrast with cryptographic signatures, for instance. However there is a fundamental problem in the case of exact authentication: How to embed a signature into a cover message in such a way that it would be possible to restore the watermarked cover image into its original state without any error? There are different approaches to solve this problem. We use the watermarking method consisting of modulo addition of a mark and investigate it in detail. Our contribution lies in investigating different modified techniques of both watermark embedding and detection in order to provide the best reliability of watermark authentication. The simulation results for different types of embedders and detectors in combination with the pictures of watermarked images are given.

A component connection enables a component to use the functionality of other components directly, without generating adapters or other mechanisms at run-time. In conventional component connection models, the connection between components, particularly third-party components, is very costly for code reuse because the component source code must be modified if the types of requester-side and provider-side are different. This paper proposes a new component model, built upon an existing component architecture, which abandons a component service type and connects components based on a method type collection of the provider and requester components. Our model enables flexible connections owing to relaxed component matching, in which the system that implements our model automatically converts values of parameters, return values, and exceptions between required methods and provided ones within a well-defined range. As a result of experimental evaluations, it is found that our model is superior to conventional models in terms of the component-use cost and the capability of changing connections.

In this letter, we propose a novel packet detection and symbol timing synchronization algorithm for MB-OFDM UWB system over multi-path channel. To find more reliable and exact symbol timing, we utilize the merits of the auto- and cross-correlation at the same time. The auto-correlator can gather the energy of the received signal which is distorted by multipath fading. And, the cross-correlator can estimate symbol timing to sample level precision. We define the detection and symbol timing metric as the moving sum of autocorrelation of the pre-computed cross-correlated signals over zero pad duration. Simulation results show that the proposed algorithm can detect the presence of a signal with less than -4 dB SNR and find DFT window with at most 0.1 dB SNR reduction upon the receipt of two symbols.

We address the problem of dealing with large collections of data, and investigate the use of automatically constructing domain specific category hierarchies to improve text classification. We use two well-known techniques, the partitioning clustering method called k-means and loss function, to create the category hierarchy. The k-means method involves iterating through the data that the system is permitted to classify during each iteration and construction of a hierarchical structure. In general, the number of clusters k is not given beforehand. Therefore, we used a loss function that measures the degree of disappointment in any differences between the true distribution over inputs and the learner's prediction to select the appropriate number of clusters k. Once the optimal number of k is selected, the procedure is repeated for each cluster. Our evaluation using the 1996 Reuters corpus, which consists of 806,791 documents, showed that automatically constructing hierarchies improves classification accuracy.

In software development, comprehensive software reviews and testings are important activities to preserve high quality and to control maintenance cost. However it would be actually difficult to perform comprehensive software reviews and testings because of a lot of components, a lack of manpower and other realistic restrictions. To improve performances of reviews and testings in object-oriented software, this paper proposes a novel model for detecting cost-prone classes; the model is based on Mahalanobis-Taguchi method--an extended statistical discriminant method merging with a pattern recognition approach. Experimental results using a lot of Java software are provided to statistically demonstrate that the proposed model has a high ability for detecting cost-prone classes.

A hybrid evolutionary neuro-fuzzy system (HENFS) is proposed in this paper, where the weighted Gaussian function (WGF) is used as the membership function for improved premise construction. With the WGF, different types of the membership functions (MFs) can be accommodated in the rule base of HENFS. A new hybrid algorithm of random optimization (RO) algorithm incorporated with the least square estimation (LSE) is presented. Based on the hybridization of RO-LSE, the proposed soft-computing approach overcomes the disadvantages of other widely used algorithms. The proposed HENFS is applied to chaos time series identification and industrial process modeling to verify its feasibility. Through the illustrations and comparisons the impressive performances for unknown system identification can be observed.

We propose a definitional question answering system that extracts phrases using syntactic patterns which are easily constructed manually and can reduce the coverage problem. Experimental results show that our phrase extraction system outperforms a sentence extraction system, especially for selecting concise answers, in terms of recall and precision, and indicate that the proper text unit of answer candidates and the final answer has a significant effect on the system performance.

Energy efficiency of cache memories is crucial in designing embedded processors. Reducing energy consumption in the instruction cache is especially important, since the instruction cache consumes a significant portion of total processor energy. This paper proposes a new instruction cache architecture, named Partitioned Instruction Cache (PI-Cache), for reducing dynamic energy consumption in the instruction cache by partitioning it to smaller (less power-consuming) sub-caches. When the proposed PI-Cache is accessed, only one sub-cache is accessed by utilizing the temporal/spatial locality of applications. In the meantime, other sub-caches are not accessed, leading to dynamic energy reduction. The PI-Cache also reduces dynamic energy consumption by eliminating the energy consumed in tag lookup and comparison. Moreover, the performance gap between the conventional instruction cache and the proposed PI-Cache becomes little when the physical cache access time is considered. We evaluated the energy efficiency by running a cycle accurate simulator, SimpleScalar, with power parameters obtained from CACTI. Simulation results show that the PI-Cache improves the energy-delay product by 20%-54% compared to the conventional direct-mapped instruction cache.

Since a human object is an important element of the moving pictures being processed by mobile terminals, establishing a human object extraction method encourages dissemination of new applications. In accordance with the requirement of mobile applications, this paper proposes a low-cost human object extraction method, which consists of a face object and a hair object extraction based on their color information and a simple body extraction utilizing the position information of the face object. In the proposed method, skin color and hair color are estimated through color space segmentation, and a human object is effectively extracted by using a radial active contour model. Simulation results of the human object extraction with the use of XScale processor claims that QCIF 15 fps video sequences can be processed in real time.

Two compact and low loss dual-mode filters are proposed by using degenerate modes of slotted triangular microstrip patch resonators. The geometrical size and radiation loss of the triangular patch are reduced simultaneously by loading both horizontal and vertical slots. The resonant frequencies of two degenerate modes can be easily controlled by varying the dimensions and positions of the slots. A two-pole dual-mode filter operating at 3.94 GHz with a fractional bandwidth of 4.3% is designed, fabricated, and measured. The measured results verify well the theoretical predictions.

In this paper, a practical clock-scheduling engine is introduced. The minimum feasible clock-period is obtained by using a modified Bellman-Ford shortest path algorithm. Then an optimum cost clock-schedule is obtained by using a bipartite matching algorithm. It also provides useful information to circuit synthesis tools. The experiment to a circuit with about 10000 registers and 100000 signal paths shows that a result is obtained within a few minutes. The computation time is almost linear to the circuit size in practice.

Rendering realistic trees is quite important for simulating a 3D natural scene. Separating the trunk from its background is the first step toward the 3D model construction of the tree. In this paper, a three-phase algorithm is developed to extract the trunk structure of the tree and hence segment the trunk from the image. Some experiments were conducted and results confirmed the feasibility of proposed algorithm.