In this paper, we propose an adaptive lattice-transversal joint (LTJ) filter structure that is quite suitable for the practical implementation of the acoustic echo canceller. The structure maintains fast convergence of the lattice structure and low computational complexity of the transversal structure simultaneously. It is particularly more efficient in memory usage than any other existing fast-convergent algorithm for the acoustic echo cancellation.

We present a cascade neural network for blind source extraction. We propose a family of unconstrained optimization criteria, from which we derive a learning rule that can extract a single source signal from a linear mixture of source signals. To prevent the newly extracted source signal from being extracted again in the next processing unit, we propose another unconstrained optimization criterion that uses knowledge of this signal. From this criterion, we then derive a learning rule that deflates from the mixture the newly extracted signal. By virtue of blind extraction and deflation processing, the presented cascade neural network can cope with a practical case where the number of mixed signals is equal to or larger than the number of sources, with the number of sources not known in advance. We prove analytically that the proposed criteria both for blind extraction and deflation processing have no spurious equilibria. In addition, the proposed criteria do not require whitening of mixed signals. We also demonstrate the validity and performance of the presented neural network by computer simulation experiments.

A constant modulus adaptive array algorithm is derived using analysis and synthesis filter banks to permit adaptive digital beamforming for wideband signals. The properties of the CMA adaptive array using the filter banks are investigated. This array would be used to realize adaptive digital beamforming when this is difficult by means of ordinary (that is, non-subband) processing due to the limited speed of signal processor operations. As an actual application, we present a beamspace adaptive array structure that combines the analysis and synthesis filter banks with RF-domain multibeam array antennas, such as those utilizing optical signal processing.

This paper presents a neural network-based control system for Adaptive Noise Control (ANC). The control system derives a secondary signal to destructively interfere with the original noise to cut down the noise power. This paper begins with an introduction to feedback ANC systems and then describes our adaptive algorithm in detail. Three types of noise signals, recorded in destroyer, F16 airplane and MR imaging room respectively, were then applied to our noise control system which was implemented by software. We obtained an average noise power attenuation of about 20 dB. It was shown that our system performed as well as traditional DSP controllers for narrow-band noise and achieved better results for nonlinear broadband noise problems. In this paper we also present a hardware implementation method for the proposed algorithm. This hardware architecture allows fast and efficient field training in new environments and makes real-time real-life applications possible.

In this paper, we propose two new pipelined adaptive digital filter architectures. The architectures are based on an equivalent expression of the least mean square (LMS) algorithm. It is shown that one of the proposed architectures achieves the minimum output latency, or zero without affecting the convergence characteristics. We also show that, by increasing the output latency be one, the other architecture can be obtained which has a shorter critical path.

The phase and frequency commands of a rotating radar system, that utilizes the frequency scanning and phase shifters to steer the beam in the azimuth and elevation directions, respectively, are derived in terms of the angles of the ground based coordinate system. The frequency equation derived is approximated to a simple form to reduce the calculation time for real time multi-function radar systems. It is shown that the approximate frequency commands are in good agreement with the exact ones if the range of the azimuth scanning is not too wide.

This paper introduces an adaptive distributed routing algorithm for the faulty star graph. The algorithm is based on that the n-star graph has uniform node degree n-1 and is n-1-connected. By giving two routing rules based on the properties of nodes, an optimal routing function for the fault-free star graph is presented. For a given destination in the n-star graph, n-1 node-disjoint and edge-disjoint subgraphs, which are derived from n-1 adjacent edges of the destination, can be constructed by this routing function and the concept of Breadth First Search. When faults are encountered, according to that there are n-1 node-disjoint paths between two arbitrary nodes, the algorithm can route messages to the destination by finding a fault-free subgraphs based on the local failure information (the status of all its incident edges). As long as the number f of faults (node faults and/or edge faults) is less than the degree n-1 of the n-star graph, the algorithm can adaptively find a path of length at most d+4f to route messages successfully from a source to a destination, where d is the distance between source and destination.

This paper describes methods used in the design of a high speed burst modem applied for mobile communication systems. The modem has burst mode operations including burst mode AGC (automatic gain control), burst mode BTR (bit timing recovery), adaptive equalization, and diversity based on a selection algorithm to achieve a higher performance in multipath fading channels. Moreover, the performance of the burst modem, which is developed using analog signal processing devices, DSPs (digital signal processors), and FPGAs (field programmable gate arrays), is analyzed experimentally. Results show that the modem can suppress irreducible BER values below 1. 0e-6 and attains a 2 dB implicit diversity gain over multipath fading channels modeled by a two-ray impulse response system with independent Rayleigh fading.

The applications of adaptive arrays based on the digital beam forming technique are receiving wide attention. However, the adaptive control algorithm of the array antennas under TDMA systems is not well considered even though it requires particular consideration due to its intermittent bursts. In this paper, first, we propose a novel DOA (Direction of Arrival) estimation method which is suitable for TDMA bursts. Second, the HA (Howells-Applebaum) adaptive array based on the proposed DOA estimation method will be constructed for the terminals in the mobile communication systems using non-geostationary satellites . Then the performance of the DOA estimation method and the HA adaptive array will be examined through computer simulations and experiments.

For usage in fading environments, the idea of utilizing radio signal processing with software programmable devices promises greater flexibility and functionality. However, to realize these benefits very high performance DPSs and AD converters are required, especially for adaptive antennas with CDMA. One potential method of reducing the implementation complexity is to employ multicarrier (MC) modulation as it allows parallel signal processing in addition to its benefits in combating fading and ISI effects. This paper proposes a system utilizing MC DS/CDMA and a recursive adaptive array antenna processing algorithm for estimating the array response vector (array manifold) and hence the optimal weights. In order to evaluate this system performance results obtained through computer simulations are presented. The proposed MC system improves the accuracy of the direction-of-arrival (DOA) estimation of the signal by 2 degrees as compared with a single carrier system. It is also shown that the proposed adaptive array antenna algorithm for MC DS/CDMA reduces the number of iterations of the power method significantly and allows parallel processing of the adaptive algorithm.

A new radar system is presented, which consists of one main radar and cooperative plural transponders. The transponders are integrated in the respective retrodirective antennas which are arranged beyond the horizon in such a manner as they surround the main radar. An algorithm for determining the three-dimensional target position is given. Computer simulations have been made for different target positions by assuming measurement errors. A target whose monostatic radar cross section is small or has been specially reduced by absorbing materials could be detected by this system if it is properly constructed.

This paper presents a new algorithm that can solve the problem of selecting appropriate update step size in the LMS algorithm. The proposed algorithm, called a Complementary Pair LMS (CP-LMS) algorithm, consists of two adaptive filters with different update step sizes operating in parallel, one filter re-initializing the other with the better coefficient estimates whenever possible. This new algorithm provides the faster convergence speed and the smaller steady-state error than those of a single filter with a fixed or variable step size.

In this paper, we consider the selection of analysis filters used in the delayless subband adaptive digital filter (SBADF) and propose to use simple analysis filters to reduce the computational complexity. The coefficients of filters are determined using the components of the first order Hadamard matrix. Because coefficients of Hadamard matrix are either 1 or -1, we can analyze signals without multiplication. Moreover, the conditions for convergence of the proposed method is considered. It is shown by computer simulations that the proposed method can converge to the Wiener filter.

As open-ended distributed systems and mobile computing systems have spread widely, the need for software which can adapt itself to the dynamic change of runtime environments increases. We call the ability of the software dynamic adaptability. We designed and implemented a language LEAD that provides an architecture for dynamic adaptability. The basic idea is to introduce the mechanism which changes procedure invocation dynamically according to the states of runtime environments. Using LEAD, we can easily realize 1) the highly extensible dynamically adaptable applications, and 2) the introduction of the dynamic adaptability into existing applications.

We present a mechanism, named the law of the jungle (LOJ), to improve the Kohonen learning. The LOJ is used to be an adaptive vector quantizer for approximating nonstationary probability distribution functions. In the LOJ mechanism, the probability that each node wins in a competition is dynamically estimated during the learning. By using the estimated win probability, "strong" nodes are increased through creating new nodes near the nodes, and "weak" nodes are decreased through deleting themselves. A pair of creation and deletion is treated as an atomic operation. Therefore, the nodes which cannot win the competition are transferred directly from the region where inputs almost never occur to the region where inputs often occur. This direct "jump" of weak nodes provides rapid convergence. Moreover, the LOJ requires neither time-decaying parameters nor a special periodic adaptation. From the above reasons, the LOJ is suitable for quick approximation of nonstationary probability distribution functions. In comparison with some other Kohonen learning networks through experiments, only the LOJ can follow nonstationary probability distributions except for under high-noise environments.

Recently, studies and developments of Collision Warning System (CWS) have attracted widely a particular attention among various wave applications at 60 GHz frequency band. Major performance of the CWS is given by the maximum detection range, the minimum detectable width and accuracy of detection distance. Since the maximum detection range and the minimum detectable width depend on a size of a target, it is important to know Radar Cross Section (RCS) of the target. RCS data should be measured so-called far field condition (greater than a few hundreds meters), while CWS is usually used in the range of 20-100 m. Therefore, a measurement of the reflection characteristics in shorter distance should be necessarily known. In this study, we present reflection characteristics of a medium size truck and a passenger car measured in open field at 60 GHz frequency. It is concludingly described that the reflection characteristics of the passenger car and the medium size truck are 13-18 dBm2 and 15-32 dBm2, respectively, for a case of horizontal polarization.

In land mobile communication, CMA (Constant Modulus Algorithm) has been studied to reduce multipath fading effect. By this method, the transmitted power is not used efficiently since all the multipath components have the same information. To make use of received power efficiently, we propose a Blind Multiple Beam Adaptive Array. It has the following three feature points. First, we use CMA which can reduce the multipath fading effect to some extent without training signal. Second, LMS algorithm which can capture the multipath components which are separated from the reference signal by some extent. Third, we use FDF (Fractional Delay Filter) and TED (Timing Error Detector) loop which can detect and compensate fractional delay. As a result of utilizing the multipath components which is suppressed by CMA, the proposed technique achieves better performance than CMA adaptive array.

Huynen has already provided a method to decompose a Mueller matrix in order to retrieve detailed target information in a polarimetric radar system. However, this decomposition sometimes fails in the presence of small error or noise in the elements of a Mueller matrix. This paper attempts to improve Huynen's decomposition method. First, we give the definition of stable decomposition and present an example, showing a problem of Huynen's approach. Then two methods are proposed to carry out stable decompositions, based on the nonlinear least square method and the Newton's method. Stability means the decomposition is not sensitive to noise. The proposed methods overcomes the problems on the unstable decomposition of Mueller matrix, and provides correct information of a target.

Two-dimensional (2-D) adaptive digital filters (ADFs) for 2-D signal processing have become a fascinating area of the adaptive signal processing. However, conventional 2-D FIR ADF's require a lot of computations. For example, the TDLMS requires 2N2 multiplications per pixel. We propose a new 2-D adaptive filter using the FFTs. The proposed adaptive filter carries out the fast convolution using overlap-save method, and has parallel structure. Thus, we can reduce the computational complexity to O(log2N) per pixel.

The adaptive associative memory proposed by Ma is used to construct a new model of semantic network, referred to as associative semantic memory (ASM). The main novelty is its computational effectiveness which is an important issue in knowledge representation; the ASM can do inference based on large conceptual hierarchies extremely fast-in time that does not increase with the size of conceptual hierarchies. This performance cannot be realized by any existing systems. In addition, ASM has a simple and easily understandable architecture and is flexible in the sense that modifying knowledge can easily be done using one-shot relearning and the generalization of knowledge is a basic system property. Theoretical analyses are given in general case to guarantee that ASM can flawlessly infer via pattern segmentation and recovery which are the two basic functions that the adaptive associative memory has.