In this paper, we present a vision system for a depalletizing robot which recognizes carton objects. The algorithm consists of the extraction of object candidates and a labeling process to determine whether or not they actually exist. We consider this labeling a combinatorial optimization of labels, we propose a new labeling method applying Genetic Algorithm (GA). GA is an effective optimization method, but it has been inapplicable to real industrial systems because of its processing time and difficulty of finding the global optimum solution. We have solved these problems by using the following guidelines for designing GA: (1) encoding high-level information to chromosomes, such as the existence of object candidates; (2) proposing effective coding method and genetic operations based on the building block hypothesis; and (3) preparing a support procedure in the vision system for compensating for the mis-recognition caused by the pseudo optimum solution in labeling. Here, the hypothesis says that a better solution can be generated by combining parts of good solutions. In our problem, it is expected that a global desirable image interpretation can be obtained by combining subimages interpreted consistently. Through real image experiments, we have proven that the reliability of the vision system we have proposed is more than 98% and the recognition speed is 5 seconds/image, which is practical enough for the real-time robot task.

This paper proposes an efficient clustering algorithm for region merging. To speed up the search of the best pair of regions which is merged into one region, dissimilarity values of all possible pairs of regions are stored in a heap. Then the best pair can be found as the element of the root node of the binary tree corresponding to the heap. Since only adjacent pairs of regions are possible to be merged in image segmentation, this constraints of neighboring relations are represented by sorted linked lists. Then we can reduce the computation for updating the dissimilarity values and neighboring relations which are influenced by the merging of the best pair. The proposed algorithm is applied to the segmentations of a monochrome image and range images.

Mathematical morphology is inheriently suitable for range image processing because it can deal with the shape of a function in a natural and intuitive way. In this paper, a new approach to the extraction of the corner-edge-surface structure from 3D range images is proposed. Morphological operations are utilized for segmenting range images into smooth surface regions and high-variation surface regions, where the high-variation surface regions are further segmented into regions of edge type and regions of corner type. A new 3D feature, HV-skeleton, can be extracted for each high-variation surface region. The HV-skeletons can be thought of as the skeletons of high-variation surface regions and are useful for feature matching. The 3D features extracted by our approach are invariant to 3D translations and rotations, and can be utilized for higher-level vision tasks such as registration and recognition. Experimental results show that the new 3D feature extraction method works well for both simple geometric objects and complex shaped objects such as human faces.

In this study, an extraction method of failure sound signal which is strongly contaminated by noise is investigated by genetic algorithm and statistical tests of the frequency domain for the failure diagnosis of machinery. In order to check the extraction accuracy of the failure signal and obtain the optimum extraction of failure signal, the "existing probability Ps (t*k) of failure signal" and statistical information Iqp are defined as the standard indices for evaluation of the extraction results. It has been proven by practical field data and application of the inspection and diagnosis robot that the extraction method discussed in this paper is effective for detection of a failure and distinction of it's origin in the diagnosis of machinery.

In arithmetic units multiplication is a very important operation. It is a common approach to use the modified Booth's algorithm to reduce the number of partial products in a multiplication and speed it up. In this letter we show two circuits that fuse the usually separate functions of generating the partial products and selecting them. The circuits designed in DPL (Double Pass-transistor Logic) are bigger in MOS transistors, but are faster and, function at higher frequencies than a typical CMOS implementation. One of our circuits also has lower power consumption.

Ordered binary decision diagrams (OBDDs) have been widely used in many CAD applications as efficient data structures for representing and manipulating Boolean functions. For the efficient use of the OBDD, it is essential to find a good variable order, because the size of the OBDD heavily depends on its variable order. Dynamic variable reordering is a promising solution to the variable ordering problem of the OBDD. Dynamic variable reordering with the sifting algorithm is especially effective in minimizing the size of the OBDD and reduces the need to find a good initial variable order. However, it is very time-consuming for practical use. In this paper, we propose two new implementation techniques for fast dynamic variable reordering. One of the proposed techniques reduces the number of variable swaps by using the lower bound of the OBDD size, and the other accelerates the variable swap itself by recording the node states before the swap and the pivot nodes of the swap. By using these new techniques, we have achieved the speed-up ranging from 2.5 to 9.8 for benchmark circuits. These techniques have reduced the disadvantage of dynamic variable reordering and have made it more attractive for users.

The performance of decision-feedback equalization combined with maximum-likelihood detection (DFE/ML) using the fixed-delay-tree-search/decision feedback (FDTS/DF) algorithm was estimated analytically in terms of the length of the feedback-filter and the depth of the ML-detector. Performance degradation due to error propagation in the feedback-loop and in the ML-detector was taken into account by using a Markov process analysis. It was quantitatively shown that signal-to-noise-ratio (SNR) performance in high-density magnetic recording channels can be improved by combining an ML-detector with a feedback-filter and that the error propagation in the DFE channel can be reduced by using an ML-detector. Finally, it was found that near-optimum performance with regard to channel SNR and error propagation can be achieved, over the channel density range from 2 to 3, by increasing the sum of the feedback-filter length and the ML-detector depth to six bits.

Genetic Algorithm (GA) is the method of approaching optimization problem by modeling and simulating the biological evolution. As the genetic algorithm is rather time consuming, the use of a parallel genetic algorithm can be advantage. This paper describes new methods for fine-grained parallel genetic algorithm using a multiprocessor system FIN. FIN has a VLSI-oriented interconnection network, and is constructed from a viewpoint of fractal geometry so that self-similarity is considered in its configuration. The performance of the proposed methods on the Traveling Salesman Problem (TSP), which is an NP-hard problem in the field of combinatorial optimization, is compared to that of the simple genetic algorithm and the traditional fine-grained parallel genetic algorithm. The results indicate that the proposed methods yield improvement to find better solutions of the TSP.

The tandem structure of a matched filter (MF) and a maximum likelihood sequence estimator (MLSE) using the Viterbi algorithm (VA) has been considered to be an optimal receiver for digital pulse-amplitude sequences in the presence of intersymbol interference (ISI) and additive white Gaussian noise (AWGN). An adaptive array antenna has the capability of filtering received signals in the spatial domain as well as in the temporal one. In this paper, we propose a receiver structure using an adaptive array antenna, a digital filter and the VA that is spatially and temporally optimal for multi-user detection in a direct sequence code division multiple access (DS/CDMA) environment. This receiver uses a tapped delay line (TDL) array antenna and the VA, which provides a maximum likelihood sequence estimate from the spatially and temporally whitened matched filter (ST-WMF) output. Performance of the proposed receiver is evaluated by theoretical analysis and computer simulations.

Analyzing multipath propagation structure is important to develop anti-fading techniques for high-speed digital radio systems. Several techniques have been employed to measure delay profiles and/or arrival angles. This paper presents a simultaneous estimation method of delay times and arrival angles of indoor multipath waves. We obtain frequency-domain data at different receiving antenna positions using a network analyzer. We estimate the propagation parameters by means of a two-dimensional MUSIC algorithm. In order to obtain reliable results, a two-dimensional discrete inverse Fourier transform and a gating technique are employed before the MUSIC algorithm. Simulation and experimental results show that the proposed method can estimate the propagation parameters properly.

An interactive painting system for generating calligraphy words is developed. Its significant advantage lies in effective rendering functions that synthesize kasure and nijimi textures due to the effects of brush and absorbent painting paper. The system enables users to generate high quality and realistic calligraphy words.

Single-reference and multi-reference noise canceller (SRNC and MRNC) performances are investigated based on correlation between signal and noise. Exact relations between these noise canceller performances and signal-noise correlation have not been well discussed yet. In this paper, the above relations are investigated based on theoretical, analysis and computer simulation. The normalized LMS (NLMS) algorithm is employed. Uncorrelate, partially correlated, and correlated signal and noise combinations are taken into account. Computer simulation is carried out, using real speech, white noise, real noise sound, sine wave signals, and their combinations. In the SRNC problem, spectral analysis is applied to derive the canceller output power spectrum. From the simulation results, it is proven that the SRNC performance is inversely proportional to the signal-noise correlation as expected by the theoretical analysis. From the simulation results, the MRNC performance is more sensitive to the signal-noise correlation than that of SRNC. When the signal-noise correlation is high, by using a larger number of adaptive filter taps, the noise is reduced more, and, the signal distortion is increased. This means the signal components included in the noise are canceled exactly.

Signal processing antennas have been studied not only for interference suppression but also for high-resolution estimation of radio environment such as directions-of-arrival of incident signals. These two applications are based on the common technique, that is, null steering. This tutorial paper reviews the MUSIC algorithm which is one of the typical high-resolution techniques. Examining the eigenvector beam patterns, we demonstrate that the high-resolution capability is realized by steering nulls. The considerations will be useful for understanding the high-resolution techniques in the signal processing antennas. We then describe a modified version of MUSIC (Root MUSIC). We show the performance and robustness of the method. Furthermore, we introduce radar target identification and two-dimensional radar target imaging. These study fields are new applications of the signal processing antennas, to which a great deal of attention has been devoted recently.

This paper is concerned with synthesizing VLSI array processors from iterative algorithms. Our primary objective is to obtain the highest processor efficiency but not the shortest completion time. Unlike most of the previous work that assumes the index space of the given iterative algorithm to be boundless, the proposed method takes into account the effects of the boundaries of the index space. Due to this consideration, the pseudo-dependence relations are excluded, and most of the independent computations can therefore be uniformly grouped. With the method described in this paper, the index space is partitioned into equal-size blocks and the corresponding computations are systematically and uniformly mapped into processing elements. The synthesized VLSI array processors possess the attractive feature of very high processor efficiency, which, in general, is superior to what is derived from the conventional linear transformation methods.

This paper proposes a new algorithm called the fast Projection algorithm, which reduces the computational complexity of the Projection algorithm from (p+1)L+O(p3) to 2L+20p (where L is the length of the estimation filter and p is the projection order.) This algorithm has properties that lie between those of NLMS and RLS, i.e. less computational complexity than RLS but much faster convergence than NLMS for input signals like speech. The reduction of computation consists of two parts. One concerns calculating the pre-filtering vector which originally took O(p3) operations. Our new algorithm computes the pre-filtering vector recursively with about 15p operations. The other reduction is accomplished by introducing an approximation vector of the estimation filter. Experimental results for speech input show that the convergence speed of the Projection algorithm approaches that of RLS as the projection order increases with only a slight extra calculation complexity beyond that of NLMS, which indicates the efficiency of the proposed fast Projection algorithm.

One of the major open issues in neural network research includes a Network Designing Problem (NDP): find a polynomial-time procedure that produces minimal structures (the minimum intermediate size, thresholds and synapse weights) of multilayer threshold feed-forward networks so that they can yield outputs consistent with given sample sets of input-output data. The NDP includes as a sub-problem a Network Training Problem (NTP) where the intermediate size is given. The NTP has been studied mainly by use of iterative algorithms of network training. This paper, making use of both rate distortion theory in information theory and linear algebra, solves the NDP mathematically rigorously. On the basis of this mathematical solution, it furthermore develops a mathematical solution Procedure to the NDP that computes the minimal structure straightforwardly from the sample set. The Procedure precisely attains the minimum intermediate size, although its computational time complexity can be of non-polynomial order at worst cases. The paper also refers to a polynomial-time shortcut to the Procedure for practical use that can reach an approximately minimum intermediate size with its error measurable. The shortcut, when the intermediate size is pre-specified, reduces to a promising alternative as well to current network training algorithms to the NTP.

A new algorithm for multiple-precision modular multiplication is proposed. It is fast and uses a small amount of main memory, and hence, is useful for application of a public-key cryptosystem to small computers, such as card computers.

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.

In this paper, we study the following node-to-node fault tolerant routing problem: In the presence of up to n-1 faulty nodes, find a fault-free path which connects any two non-faulty nodes s and t in an n-connected graph. For node-to-node fault tolerant routing in n-dimensional hypercubes Hn, we give an algorithm which finds a fault-free path s t of length at most in O(n) time, where d(s, t) is the distance between s and t. We also show that a fault-free path s t in Hn of length at most d(s, t)2i, 1i, can be found in time. For node-to-node fault tolerant routing in n-dimensional star graphs Gn, we give an algorithm which finds a fault-free path s t of length at most min{d(Gn)3, d(s, t)6} in O(n) time, where is the diameter of Gn. It is previously known that, in Hn, a fault-free path s t of length at most d(s, t) for d(s, t)n and at most d(s, t)2 for d(s, t)n can be found in O(d(s, t)n) time, and in Gn, a fault-free path s t of length at most min{d(Gn)1, d(s, t)4}can be found in O(d(s, t)n) time. When the time efficiency of finding the routing path is more important than the length of the path, the algorithms in this paper are better than the previous ones.

The systematic treatment of speech-spectrum transformation can be obtained in terms of algebraic topology and Morse theory. Some properties of homotopy-equivalence in the transformation of 1- and 2-dimensional speech spectrum are discussed.