A simple digital circuit based on the probabilistic cellular automata is proposed whose temporal evolution generates 1/f noise over many frequency decades. The N cells with internal states form a one-dimensional network and probabilistically interact with nearest-neighbor ones. The internal state of the cell is either the stable state or the unstable state. Each cell obeys simple rules as follows. When the excitatory signal is applied to the cell in the stable state, the state changes to the unstable state. On the other hand, when the state is unstable, the state changes to the stable state, and then the cell generates the excitatory signal. The excitatory signal is applied to the cell which is randomly chosen between the right side cell and the left side cell. The edge condition of the network is open, so that the excitatory signal can leave both the first edge and the last edge. The excitatory signal is randomly added to the first edge of the network at intervals of T time. Then the sequential interactions may occur like avalanche breakdown. After the interactions, the network goes to the equilibrium state. Considering that the breakdown happen simultaneously and assigning the stable state and the unstable state to 0 and 1, respectively, one can get the random pulse stream on the internal state of each cell. The power spectra of pulse streams are Lorentzian with various pole frequencies. The probability distribution of the pole frequency is inversely proportional to the frequency, i. e. , obeys Zipf law. Then the total sum of the internal states of all cells fluctuates following 1/f power law. The frequency range following 1/f power law can be easily varied by changing the number of the cells for the summation. A prototype generator using 15 cells generates 1/f noise over 3 frequency decades. This simple circuit is composed of only full adders and needs not complex components such as multipliers. Fine-tuning of any parameters and precise components also are not needed. Therefore integration into one chip using standard CMOS process is easy.

Fuzzy value is a Fuzzy set the α-cuts of which are closed intervals. Let [0,1] be the set of Fuzzy values on [0,1]. We introduce two kinds of metric D and D1 in it, and investigate some topological properties.

This paper proposes the Dual Zipfian Model addressing how to describe HTTP access trends in large-scale data communication networks, and discusses how to design the capacity of address cache tables in an edge router of the networks. We show that destination addresses of packets can be characterized by two types of Zipf's law. Fundamental concept of the Dual Zipfian Model is in complementary use of these laws, and we can derive the relationship between the number of accesses and the number of destination addresses. Experimental results show that the relation gives a good approximation. Applying this relation, we derive cache hit probabilities of the address cache table that incorporates high-speed address resolution. Using the probabilities, design issues including the capacity of the cache tables and aging algorithms of cache entries are also discussed.

Since the development of odor sensing system is required in many fields, we have been developing the system using QCM (Quartz Crystal Microbalance, 10 MHz AT-cut) array and neural-network pattern recognition. Since it is important to obtain stable sensor responses, a sensing film of lipid blended with PVC (polyvinyl chloride) was studied here. First, we studied the stability of various sensing films e. g. , phospholipids, GC liquid stationary phase materials and others in odor sensing system. It was found that most of lipids were stable, while PEG 200, octadecane and DBP were not stable materials. Second, we studied to obtain a stable QCM sensor using a sensing film blended with PVC. 4 plasticizers blended with PVC were systematically characterized, analyzing the pattern of QCM sensor responses by multivariate analysis. It was found that the pattern of DOPP was very different from that of PVC. Thus, we adopted DOPP as plasticizer and the stable sensor response was obtained using a lipid film blended with both PVC and DOPP. Finally, we studied the influence of sensing film materials on the stability of QCM sensors. It was found that the loss of sensing film mass after many vapor exposures depended upon the molecular weight.

We have improved the optical beam induced resistance change (OBIRCH) system so as to detect (1) a current path as small as 10-50 µA from the rear side of a chip, (2) current paths in silicide lines as narrow as 0. 2 µm, (3) high-resistance Ti-depleted polysilicon regions in 0. 2 µm wide silicide lines, and (4) high-resistance amorphous thin layers as thin as a few nanometers at the bottoms of vias. All detections were possible even in observation areas as wide as 5 mm 5 mm. The physical causes of these detections were characterized by focused ion beam and transmission electron microscopy.

In this paper, we propose a new cooperative search algorithm based on pheromone communication for solving the Vehicle Routing Problems. In this algorithm, multi-agents can partition the problem cooperatively and search partial solutions independently using pheromone communication, which mimics the communication method of real ants. Through some computer experiments the cooperative search of multi-agents is confirmed.

Access control is a key technology for providing data security in database management systems (DBMSs). Recently, various authorization models for object-oriented databases (OODBs) have been proposed since authorization models for relational databases are insufficient for OODBs because of the characteristics of OODBs, such as class hierarchies, inheritance, and encapsulation. Generally, an authorization is modeled as a set of rights, where a right consists of at least three components s, o, t and means that subject s is authorized to perform operation t on object o. In specifying authorizations implicitly, inference rules are useful for deriving rights along the class hierarchies on subjects, objects, and operations. An access request req=(s,o,t) is permitted if a right corresponding to req is given explicitly or implicitly. In this paper, we define an authorization model independent of any specific database schemas and authorization policies, and also define an authorization specification language which is powerful enough to specify authorization policies proposed in the literature. Furthermore, we propose an efficient access control method for an authorization specified by the proposed language, and evaluate the proposed method by simulation.

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.

Subharmonically Injection-locked oscillators (ILO's) with very wide injection-locking ability are presented. Two types of ILO MMIC's with this ability are proposed. The oscillation frequency tuning function of the ILO MMIC is very useful for expansion of the injection locking range at higher subharmonics. One consists of a shunt varactor diode inserted into the oscillation loop, and the other incorporates a vector-combining configuration with in-phase divider and 90 degree hybrid. Using three-dimensional MMIC's technology which can offer miniature and high-density passive circuits, the vector-combining type ILO is formed in a very compact area of 1. 7 mm2. Fabricated 20 GHz-band ILO achieves a wide tuning ranges of 870 MHz, resulting in a very wide locking range for higher subharmonics. The wide frequency tuning ability also reduces phase noise, shortens a locking time and compensates the center frequency deviation against temperature, as well as increasing locking range. The measured results show that the ILO configuration is extremely suitable for realizing simple, fully monolithic and low phase noise millimeter-wave frequency synthesizers.

This paper describes two dimensional (2D) equalization scheme of orthogonal coding multi-carrier CDMA for reverse link of mobile communication systems. The purpose of the 2D equalization is the reduction of Multiple Access Interference (MAI) which is caused by the random access and the different propagation path from each mobile station. The orthogonal coding multi-carrier CDMA multiplexes all mobile stations' data by Code Division Multiplexing (CDM). The 2D coding scheme spreads a preamble signal at time (in subchannel signals) and frequency (between subchannel signals) domains. The 2D decoding scheme estimates transmission delay time and instantaneous fading frequency from preamble signal for individual mobile stations and compensate the received data using these estimation values to reduce MAI.

This paper describes a neuro-based optimization algorithm for three dimensional (3-D) rectangular puzzles which are the problems to arrange the irregular-shaped blocks so that they perfectly fit into a fixed three dimensional rectangular shape. First, the fitting function of the 3-D block, which means the fitting degree of each irregular block to the neighboring block and the rectangular configuration, is described. Next, the energy function for the 3-D rectangular puzzles is proposed, where the horizontal rotation of the block is also considered. Finally, our optimization method is applied to several examples using the 3-D analog neural array and it is shown that our algorithm is useful for solving 3-D rectangular puzzles.

This paper describes a theoretical foundation of fuzzy morphological operations and architectural extension of the shared-weight neural network (SWNN). The network performs shift-invariant filtering using fuzzy-morphological operations for feature extraction. The nodes in the feature extraction stage employ the generalized-mean operator to implement fuzzy-morphological operations. The parameters of the SWNN, weights, morphological structuring element and fuzziness, are optimized by the error back-propagation (EBP) training method. The parameter values of the trained SWNN are then implanted into the extended SWNN (ESWNN) which is a simple convolution neural network. The ESWNN architecture dramatically reduces the amount of computation by avoiding segmentation process. The neural network is applied to automatic recognition of a vehicle in visible images. The network is tested with several sequences of images that include targets ranging from no occlusion to almost full occlusion. The results demonstrate an ability to detect occluded targets, while trained with non-occluded ones. In comparison, the proposed network was superior to the Minimum-Average Correlation filter systems and produced better results than the ordinary SWNN.

This paper proves a general sampling theorem, which is an extension of Shannon's classical theorem. Let o be a closed subspace of square integrable functions and call o a signal space. The main aim of this paper is giving a necessary and sufficient condition for unique existence of the sampling basis {Sn}o without band-limited assumption. Using the general sampling theorem we rigorously discuss a frequency domain treatment and a general signal space spanned by translations of a single function. Many known sampling theorems in signal spaces, which have applications for multiresolution analysis in wavelets theory are corollaries of the general sampling theorem.

In this paper, an FPGA (Field Programmable Gate Array)-implementable digital chaos circuit with nonlinear mapping function learning ablility is proposed. The features of this circuit are user-programmability of the mapping functions by on-chip supervised learning, robustness of chaos signal generation based on digital processing, and high-speed and low-cost thanks to its FPGA implementation. The circuit design and analysis are presented in detail. The learning dynamics of the circuit and the quantitization effect to the quasi-chaos generation are analyzed by numerical simulations. The proposed circuit is designed by using an FPGA CAD tool, Verilog-HDL. This confirmed that the one-dimensional chaos circuit block (except for SRAM's) is implementable on a single FPGA chip and can generate quasi-chaos signals in real time.

A function approximation scheme for image restoration is presented to resolve conflicting demands for smoothing within each object and differentiation between objects. Images are defined by probability distributions in the augmented functional space composed of image values and image planes. According to the fuzzy Hough transform, the probability distribution is assumed to take a robust form and its local maxima are extracted to yield restored images. This statistical scheme is implemented by a feedforward neural network composed of radial basis function neurons and a local winner-takes-all subnetwork.

This letter presents a novel variable-rate vector quantizer (VQ) design algorithm, which is a hybrid approach combining a genetic algorithm with the entropy-constrained VQ (ECVQ) algorithm. The proposed technique outperforms the ECVQ algorithm in the sense that it reaches to a nearby global optimum rather than a local one. Simulation results show that, when applied to the image coding, the technique achieves higher PSNR and image quality than those of ECVQ algorithm.

This paper presents an evolutionary scheduling scheme for solving the job shop scheduling problem (JSSP) and other combinatorial optimization problems. The approach is based on a genetized-knowledge genetic algorithm (gkGA). The basic idea behind the gkGA is that knowledge of heuristics which are used in the GA is also encoded as genes alongside the genetic strings, referred to as chromosomes. Furthermore, during the GA selection, weaker heuristics die out while stronger ones survive for a given problem instance. We evaluate our evolutionary scheduling scheme based on the gkGA approach using well known benchmark instances for the JSSP. We observe that the gkGA based scheme is shown to consistently outperform the scheme based on ordinary GAs. In addition the gkGA-based scheme removes the problem of instance dependency.

We have developed and fabricated an LSI called the FMPP-VQ64. The LSI is a memory-based shared-bus SIMD parallel processor containing 64 PEs, intended for low bit-rate image compression using vector quantization. It accelerates the nearest neighbor search (NNS) during vector quantization. The computation time does not depend on the number of code vectors. The FMPP-VQ64 performs 53,000 NNSs per second, while its power dissipation is 20 mW. It can be applied to the mobile telecommunication system.

We introduce a concept of regularization into Genetic Algorithms (GAs). Conventional GAs include no explicit regularizing operations. However, the regularization is very effective in solving ill-posed problems. So, we propose a method of regularization to apply GAs to ill-posed problems. This regularization is a kind of consensus operation among neighboring individuals in GAs, and plays the role of `smoothing the solution. ' Our method is based on the evaluation of macroscopic fitness, which is a new fitness criterion. Conventional fitness of an individual in GAs is defined only from the phenotype of the individual, whereas the macroscopic fitness of an individual is evaluated from the phenotypes of the individual and its neighbors. We tested our regularizing operation by means of experiments with an elastic image mapping problem, and showed the effectiveness of the regularization.

In this letter, a design method of linear-phase paraunitary filter banks is proposed for an odd number of channels. In the proposed method, a non-linear unconstrained optimization process is assumed to be applied to a lattice structure which makes the starting guess of design parameters simple. In order to avoid insignificant local minimum solutions, a recursive initialization procedure is proposed. The significance of our proposed method is verified by some design examples.