This paper proposes an adaptive noise canceller (ANC) with low signal-distortion for human-robot communication. The proposed ANC has two sets of adaptive filters for noise and crosstalk; namely, main filters (MFs) and subfilters (SFs) connected in parallel thereto. To reduce signal-distortion in the output, the stepsizes for coefficient adaptation in the MFs are controlled according to estimated signal-to-noise ratios (SNRs) of the input signals. This SNR estimation is carried out using SF output signals. The stepsizes in the SFs are determined based on the ratio of the primary and the reference input signals to cope with a wider range of SNRs. This ratio is used as a rough estimate of the input signal SNR at the primary input. Computer simulation results using TV sound and human voice recorded in a carpeted room show that the proposed ANC reduces both residual noise and signal-distortion by as much as 20 dB compared to the conventional ANC. Evaluation in speech recognition with this ANC reveals that with a realistic TV sound level, as good recognition rate as in the noise-free condition is achieved.

In recent years, there has been an increased focus on the mechanics of information transmission in spiking neural networks. Especially the Noise Shaping properties of these networks and their similarity to Delta-Sigma Modulators has received a lot of attention. However, very little of the research done in this area has focused on the effect the weights in these networks have on the Noise Shaping properties and on post-processing of the network output signal. This paper concerns itself with the various modes of network operation and beneficial as well as detrimental effects which the systematic generation of network weights can effect. Also, a method for post-processing of the spiking output signal is introduced, bringing the output signal more in line with conventional Delta-Sigma Modulators. Relevancy of this research to industrial application of neural nets as building blocks of oversampled A/D converters is shown. Also, further points of contention are listed, which must be thoroughly researched to add to the above mentioned applicability of spiking neural nets.

This paper deals with minimum spanning tree problems where each edge weight is a fuzzy random variable. In order to consider the imprecise nature of the decision maker's judgment, a fuzzy goal for the objective function is introduced. A novel decision making model is constructed based on possibility theory and on a stochastic programming model. It is shown that the problem including both randomness and fuzziness is reduced to a deterministic equivalent problem. Finally, a polynomial-time algorithm is provided to solve the problem.

This paper proposes resource management in Layer 1 Virtual Private Networks (VPNs). We have been proposing Layer 1 VPNs that provide layer 1 services to multiple customers over the single optical network with per VPN control and management capabilities. We have proposed two resource management models for Layer 1 VPNs, which constitute different class of services. One is the shared model, where resources are shared among VPNs. The other is the dedicated model, where resources are explicitly pre-assigned to each VPN. In this paper, after introducing an overview of Layer 1 VPNs, we evaluate several path computation algorithms for these two models focusing on the multi layer network scenario. In the shared model, there are several existing studies for non-VPN cases, but considerations for VPN cases are not investigated. This paper evaluates algorithms originally proposed for non-VPN cases for use in VPN cases. Simulation results show that the path computation algorithm that works as saving layer 1 resources achieves better resource sharing effect. In the dedicated model, the problem is identical to non-VPN cases. There is one conventional algorithm, but amount of available resources is not well considered. We propose a novel path computation algorithm. Simulation results show effectiveness of our proposed algorithm against the conventional algorithm. Furthermore, resource usage efficiency of two resource management models is compared. We analyze and propose applicability of resource management models.

In spatial multiplexing systems using multiple antennas, the error-rate performance is heavily dependent on the residual channel estimation error. In this letter, we propose a design method that uses the genetic algorithms to optimize training sequences for accurate channel estimation.

An estimation method of source location of undesired electromagnetic wave from electronic devices by using the MUSIC algorithm is proposed. The MUSIC algorithm can estimate the direction of arrival accurately, however, the estimation error is large in the case of short range multiple coherent sources. In order to overcome this problem, a method to improve the estimation accuracy is presented. Experimental results show that the proposed method can reduce the maximum estimation error from 7 cm of the conventional method to 2 cm.

This paper presents two novel blind set-theoretic adaptive filtering algorithms for suppressing "Multiple Access Interference (MAI)," which is one of the central burdens in DS/CDMA systems. We naturally formulate the problem of MAI suppression as an asymptotic minimization of a sequence of cost functions under some linear constraint defined by the desired user's signature. The proposed algorithms embed the constraint into the direction of update, and thus the adaptive filter moves toward the optimal filter without stepping away from the constraint set. In addition, using parallel processors, the proposed algorithms attain excellent performance with linear computational complexity. Geometric interpretation clarifies an advantage of the proposed methods over existing methods. Simulation results demonstrate that the proposed algorithms achieve (i) much higher speed of convergence with rather better bit error rate performance than other blind methods and (ii) much higher speed of convergence than the non-blind NLMS algorithm (indeed, the speed of convergence of the proposed algorithms is comparable to the non-blind RLS algorithm).

In this paper, a design alternative for guaranteeing short-term QoS in the E-PON (Ethernet-Passive Optical Network) OLT (Optical Line Terminal) node is studied. A scheduling algorithm called Short-term QoS Deficit Round Robin (SQ-DRR) is proposed to guarantee tunable deterministic QoS constraints for multimedia applications over E-PON. The major appealing aspect of the scheduler is that it guarantees delay constraint for short-term aggregate burst traffic violating pre-contracted descriptors at the same time without loosing long-term fairness. We then evaluate the scheduler performance with and without admission control scheme under non-stationary long-range dependence (LRD) traffic. The simulation results indicate that the SQ-DRR performs well in dynamic burst traffic conditions.

This paper proposes an efficient two-phase optimization approach for a compact W-band double-plane stepped rectangular waveguide filter design, which combines genetic algorithms (GAs) with the simplified transmission-line model and full-wave analysis. Being more efficient and robust than the gradient-based method, the approach can lead to a compact waveguide filter design. Numerical results show that the resultant waveguide filter design with 4 sections (total length 19.6 mm) is sufficient to meet the design goal and provides comparable performance to that with 8 sections (total length 35.6 mm) by the Chebyshev synthesis approach. Based on the present approach, nineteen compact high-pass waveguide filters have been implemented and measured at the W-band with satisfactory performance.

This paper describes a method for separating a target sound from other noise arriving in a single direction when the target cannot, therefore, be separated by directivity control. Microphones are arranged in a line toward the sources to form null sensitivity points at given distances from the microphones. The null points exclude non-target sound sources on the basis of weighting coefficients for microphone outputs determined by blind source separation. The separation problem is thereby simplified to instantaneous separation by adjustment of the time-delays for microphone outputs. The system uses a direct (i.e. non-iterative) algorithm for blind separation based on second-order statistics, assuming that all sources are non-stationary signals. Simulations show that the 2-microphone system can separate a target sound with separability of more than 40 dB for the 2-source problem, and 25 dB for the 3-source problem when the other sources are adjacent.

This paper describes an estimation of the computational and memory complexities of Greengard-Rokhlin's Fast Multipole Algorithm (GRFMA). GRFMA takes a quad tree structure and six calculation processes. We consider a perfect a-ary tree structure and the number of floating-point operations for each calculation process. The estimation for both complexities shows that the perfect quad tree is the best and the perfect binary tree is the worst. When we apply GRFMA to the computation of realistic problems, volume scattering are the best case and surface scattering are the worst case. In the worst case, the computational and memory complexities of GRFMA are O(Llog2 L) and O(Llog L), respectively. The computational complexity of GRFMA is higher than that of the multilevel fast multipole algorithm.

In this letter, a new computation method for the noise parameters of a linear noisy two-port network is introduced. A new error function, which considers noise figure and source admittance error simultaneously, is proposed to estimate the four noise parameters. The global optimization of the error function is searched directly by using a genetic algorithm.

An algorithm for the sound projection using multiple sources is presented. The source strength vector is obtained by using a fast estimation approach instead of the conventional eigenvalue decomposition (EVD) method. The computation load is therefore greatly reduced, which makes the algorithm more efficient in practical applications.

Personal Communication Network (PCN) is an emerging wireless network that promises many new services for the telecommunication industry. The high speed backbone network (ATM or WDM) is one possible approach to provide broadband wireless transmission with PCN's using the ATM switching networks for interconnection of PCN cells. The wireless ATM backbone network design problem is that of allocating backbone links among ATM switches to reduce the effects of terminal mobility on the performance of ATM-based PCN's. In this paper, the wireless ATM backbone network design (WABND) problem is formulated and studied. The goal of the WABND is to minimize the location update cost under constraints. Since WABND is NP-hard, a heuristic algorithm and a genetic algorithm are proposed to solve it. These algorithms are used to find the close-to-optimal solution. Simulated results show that the proposed algorithms are able to achieve good performance.

In this paper, the 1-D real-valued discrete Gabor transform (RDGT) proposed in our previous work and its relationship with the complex-valued discrete Gabor transform (CDGT) are briefly reviewed. Block time-recursive RDGT algorithms for the efficient and fast computation of the 1-D RDGT coefficients and for the fast reconstruction of the original signal from the coefficients are then developed in both the critical sampling case and the oversampling case. Unified parallel lattice structures for the implementation of the algorithms are studied. And the computational complexity analysis and comparison show that the proposed algorithms provide a more efficient and faster approach for the computation of the discrete Gabor transforms.

It is well known that the trellises of lattices can be employed to decode efficiently. It was proved in [1] and [2] that if a lattice L has a finite trellis under the coordinate system , then there must exist a basis (b1,b2,,bn) of L such that Wi=span() for 1in. In this letter, we prove this important result in a completely different method, and give an efficient method to compute all bases of this type.

This paper presents a technique for designing a dielectric Electronically Steerable Parasitic Array Radiator (Espar) antenna to achieve miniaturization of the conventional Espar antenna. The antenna's size is reduced by immersing the central active element in a dielectric cylinder, mounting the surrounding planar parasitic elements at the circumference of the cylinder, and decreasing the radius of the ground skirt to that of the parasitic elements. An example of a polycarbonate (εr = 2.9 + j0.006) Espar antenna operating at 2.484 GHz is optimised by using a genetic algorithm in conjunction with an FEM-based cost function. The designed antenna generates a half-power beam width of 78and a main lobe that elevates at an angle of only 5from the horizontal plane. The designed antenna is also fabricated and measured. Good agreement between the measurement and simulation results is obtained. We reduce the size of the designed Espar antenna to 1/8 the size of its conventional counterpart while achieving a 12improvement in half-power beam width.

MBF (Microwave Beam Forming) antennas are beam forming antennas that perform pattern control in RF, for a low-cost design suitable for mobile terminals. An MBF antenna has only a single output port, since this antenna consists of an array antenna, microwave phase shifters, and a power combiner. Because of this simple configuration, MBF antennas cannot adopt conventional beam forming algorithms that require both phase and amplitude control, and signal observation of each antenna element. In this paper, mutual coupling matrix estimation and null forming methods are presented for MBF antennas. It is shown that the mutual coupling matrix can be estimated by changing the antenna weight instead of signal observation of each antenna element. It is also shown that phase-only null forming, including mutual coupling effect, can be done by the optimum phase perturbations. Numerical and experimental results show the performance of these algorithms.

In many electromagnetic field problems, matrix equations were always deduced from using the method of moment. Among these matrix equations, some of them might require a large amount of computer memory storage which made them unrealistic to be solved on a personal computer. Virtually, these matrices might be too large to be solved efficiently. A fast algorithm based on a Toeplitz matrix solution was developed for solving a bordered Toeplitz matrix equation arising in electromagnetic problems applications. The developed matrix solution method can be applied to solve some electromagnetic problems having very large-scale matrices, which are deduced from the moment method procedure. In this paper, a study of a computationally efficient order-recursive algorithm for solving the linear electromagnetic problems [Z]I = V, where [Z] is a Toeplitz matrix, was presented. Upon the described Toeplitz matrix algorithm, this paper derives an efficient recursive algorithm for solving a bordered Toeplitz matrix with the matrix's major portion in the form of a Toeplitz matrix. This algorithm has remarkable advantages in reducing both the number of arithmetic operations and memory storage.

The recent sight system requires high stabilization functions for the longer range of observation and the higher kill probability. To this end, it is necessary to compensate rotational disturbances which are not stabilized with the conventional 2-axes stabilization system. This paper proposes a simple method on the rotational motion estimation for the stabilization of the sight system.