A new method is proposed for estimating a single complex sinusoid and its parameters (frequency and amplitude) from measurements corrupted by white noise. This method is called the ECKF-SVD method, which is derived by applying an extended complex Kalman filter (ECKF) to a nonlinear stochastic system whose state variables consist of the AR coefficient (a function of frequency) and a sample of the original signal. Proof of the stability is given in the case of a single sinusoid. Simulations demonstrate that the proposed ECKF-SVD method is effective for estimating a single complex sinusoid and its frequency under a low signal-to-noise ratio (SNR). In addition, the amplitude estimation by means of the ECKF-SVD method is also discussed.

In this paper a novel narrow-band bandpass filter with an output pair of analytic signals is presented. Since it is based on the complex analog filter, both synthesis and response characteristics of this filter are different from conventional bandpass filters. In the design of this filter, the frequency shift method is employed and the conventional lowpass to bandpass frequency transformation is not required. The analysis and examples show that the output signal pair of the proposed filter possesses same filtering characteristics and a 90 degree phase shifting characteristics in the passband. Therefore, the proposed filter will be used for a single sideband (SSB) signal generator without quadrature generator.

Spectral transform methods have been widely studied for classification and analysis of logic functions. Spectral methods have also been used for logic synthesis, and by use of BDDs, practical-sized synthesis problems have been solved. Wavelet theory has recently attracted the attention of researchers in the signal processing field. The Haar function is used in both spectral methods and in signal processing to obtain spectral coefficients of logic functions of signals. In this paper spectral transform-based analysis of neural nets verifying signal processing and discrete function is presented. A neural net element is defined as a discrete function with multi-valued input signals and multi-valued or binary outputs. The multi-valued variable is realized as a variable (V, W) formed by a pair of a binary value and a multi-value pulse width. The multi-valued encoding is used with the multi-valued Haar function to give meanings to the wavelet coefficients from the view of Boolean algebra. A design example shows that these conceptually different concepts are closely related.

A new multiple-valued current-mode (MVCM) integrated circuit using a switched current-source control technique is proposed for a 1.5 V-supply high-speed arithmetic circuit with low-power dissipation. The use of a differential logic circuit (DLC) with a pair of dual-rail inputs makes the input voltage swing small, which results in a high driving capability at a lower supply voltage, while having large static power dissipation. In the proposed DLC using a switched current control technique, the static power dissipation can be greatly reduced because current sources in non-active circuit blocks are turned off. Since the gate of each current source is directly controlled by using a multiphase clock whose technique has been already used in dynamic circuit design, no additional transistors are required for currentsource control. As a typical example of arithmetic circuits, a new 1.5 V-supply 5454-bit multiplier based on a 0.8µm standard CMOS technology is also designed. Its performance is about 1.3 times faster than that of a binary fastest multiplier under the normalized power dissipation. A prototype chip is also fabricated to confirm the basic operation of the proposed MVCM integrated circuit.

Dynamic Channel Assignment (DCA), which improves the efficiency of channel use in cellular mobile communication systems, requires finding an available channel for a new call after the call origination. This causes the delay which is defined as the time elapsing between call origination and completion of the channel search. For system planning, it is important to evaluate the delay characteristic of DCA because the delay corresponds to the waiting time of a call and influences service quality. It is, however, difficult to theoretically analyze the delay characteristics except its worst case behavior. The time delay of DCA has not been theoretically analyzed. The objective of this paper is analyzing the distribution and the mean value of the delay theoretically. The theoretical techniques in this paper are based on the techniques for analyzing the blocking rate performance of DCA.

A clock-feedthrough (CFT) compensation technique using a dummy cell is valid when the CFT current from a switched-current (SI) memory cell is signal-independent. Based on this idea, a SI dummy cell appropriate for the S2I cell is developed. Simulations show that the CFT rejection ratio as high as 60dB is attainable over the temperature range from -30 to 80 with this architecture. The CFT-compensated SI cell proposed here is, therefore, quite usuful for high-accuracy, current-mode signal processing.

In recent years, fractal processes have played important roles in various application fields. Since a 1/f process possesses the statistical self-similarity, it is considered sa a main part of fractal signal modeling. On the other hand, noise reduction is often needed in real-world signal processing. Hence, we propose an enhancement algorithm for 1/f signal disturbed by white noise. The algorithm is based on constrained minimization in a wavelet domain: the power of 1/f signal distortion in the wavelet domain is minimized under a constraint that the power of residual noise in the wavelet domain is smaller than a threshold level. We solve this constrained minimization problem using a Lagrangian equation. We also consider a setting method of the Lagrange multiplier in the proposed algorithm. In addition, we will confirm that the proposed algorithm with this Lagrange multiplier setting method obtains better enhancement results than the conventional algorithm through computer simulations.

We discuss a new decoder for the multiple-valued signed-digit number, using a current-mode CMOS transistor-oriented circuit structure. In this paper, a new decoding method with the selective summation of a redundantly represented addend "O = [-1 r]" is proposed, where r is the radix and the addend is applied to each digit with a negative value and any consecutively higher digit takes which has a value of O. A newly designed literal linear circuit is realized, which has a current-switch function that makes independently the short path when each digit has a value of O. Through the parallel connections of these current swiches, the same addend signal at the lower digit is transmitted in a higher speed, The decoder circuit is tested by using the general circuit simulation software SPICE and the circuit characteristics of the selective summation of a redundantly represented O addend and the output results of the SD decoding operation were simulated. We also evaluated the decoder circuit in terms of the processing speed and the circuit size.

Modern database systems have to support complex data objects, which appear in advanced data models such as object-oriented data models and nested relational data models. Set-valued objects are basic constructs to build complex structures in those models. Therefore, efficient processing of set-valued object retrieval (simply, set retrieval) is an important feature required of advanced database systems. Our previous work proposed a basic scheme to apply superimposed coded signature files to set retrieval and showed its potential advantages over the B-tree index based approach using a performance analysis model. Retrieval with signature files is always accompanied by mismatches called false drops, and proper control of the false drops is indispensable in the signature file design. This study intensively analyzes the false drops in set retrieval with signature files. First, schemes to use signature files are presented to process set retrieval involving "has-subset," "is-subset," "has-intersection," and "is-equal" predicates, and generic formulas estimating the false drops are derived. Then, three sets of concrete formulas are derived in three ways to estimate the false drops in the four types of set retrieval. Finally, their estimates are validated with computer simulations, and advantages and disadvantages of each set of the false drop estimation formulas are discussed. The analysis shows that proper choice of estimation formulas gives quite accurate estimates of the false drops in set retrieval with signature files.

An optical array imaging system has been presented in previous articles. In this system, first, the object is illuminated with laser light sequentially from each of the array elements and the reflected field is detected as interferogram. The interferograms obtained are then spatially heterodyne-detected on a computer to extract the signal components, that is, array data. Then, the eigenvector of the largest eigenvalue is derived by applying the power method to the array data and it is beam-steered to get images of the object. The algorithm gives good images for most objects, but it fails to work for some objects. It was shown that using a subset of the array data may solve the problem, but that finding the corresponding optimum subaperture is quite time-consuming. In this paper, first, the integral equation describing the system is solved for a general class of object, to make clear the conditions for the eigenvector to form a sharp beam. Second, the imaging algorithm is sped up to a great degree by optimizing only the illuminating aperture in a coarse fashion. Third, the rate of convergence of the power method is adaptively estimated in the algorithm to make the eigenvector derivation reliable. Finally the improved algorithm is investigated using both computer-generated and experimentally obtained array data.

A neural network approach called the "Gradual Neural Network (GNN)" for the time slot assignment problem in the TDM multicast switching system is presented in this paper. The goal of this NP-complete problem is to find an assignment of packet transmission requests into a minimum number of time slots. A packet can be transmitted from one source to several destinations simultaneously by its replication. A time slot represents a switching configuration of the system with unit time for each packet transmission through an I/O line. The GNN consists of the binary neural network and the gradual expansion scheme. The binary neural network satisfies the constraints imposed on the system by solving the motion equation, whereas the gradual expansion scheme minimizes the number of required time slots by gradually expanding activated neurons. The performance is evaluated through simulations in practical size systems, where the GNN finds far better solutions than the best existing altorithm.

Event-related are the kind of signals that are time-related to a given event. In this work, we will study the effect of bias and overlapping noise on Fourier linear combiner (FLC)-based filters, and its implication on filtering event-related signals. We found that the bias alters the weights behaviour, and therefore the filter output, and we discuss solutions to the problem of spectral overlap.

Electric fishes generate an AC electric field around themselves by the electric organ in the tail. Spatial distortion of the field by nearby objects is detected by an electroreceptor array located an over the body surface to localize the object electrically when other senses such as vision and mechanosense are useless. Each fish has its own 'frequency band' for its electric organ discharges, and jamming of the electrolocation system occurs when two fish with similar discharge frequencies encounter. To avoid janmming, the fish shift their discharge frequencies in appropriate directions. A computational algorithm for this electrical behavior and its neuronal implementation by the brain have been discovered. The design features of the system, however, are rather complex for this simple behavior and cannot be readily explained by functional optimization processes during evolution. To gain insights into the origin of the design features, two independently evolved electric fish species which perform the same behavior are compared. Complex features of the neuronal computation may be explained by the evolutionary history of neuronal elements.

This paper describes novel techniques for analyzing power MOSFETs. Since the gate width of power MOSFETs is much larger than that of power MESFETs or HJFETs, an appropriate device design to suppress matching circuit losses is needed. These losses and the intrinsic device characteristics are analyzed employing the proposed techniques, which are based on large-signal simulations. Also, new formulas describing the dependence of saturated output power on gate width are derived to perform loss-minimized design. These techniques are applied to the design of power MOSFETs for GSM cellular telephones. As a result, an output power of 35.5 dBm with a power-added efficiency of 55% and a power gain of 10.5 dB at 900 MHz have been achieved.

This paper compares signal classification performance of multilayer neural networks (MLNNs) and linear filters (LFs). The MLNNs are useful for arbitrary waveform signal classification. On the other hand, LFS are useful for the signals, which are specified with frequency components. In this paper, both methods are compared based on frequency selective performance. The signals to be classified contain several frequency components. Furthermore, effects of the number of the signal samples are investigated. In this case, the frequency information may be lost to some extent. This makes the classification problems difficult. From practical viewpoint, computational complexity is also limited to the same level in both methods.IIR and FIR filters are compared. FIR filters with a direct form can save computations, which is independent of the filter order. IIR filters, on the other hand, cannot provide good signal classification deu to their phase distortion, and require a large amount of computations due to their recursive structure. When the number of the input samples is strictly limited, the signal vectors are widely distributed in the multi-dimensional signal space. In this case, signal classification by the LF method cannot provide a good performance. Because, they are designed to extract the frequency components. On the other hand, the MLNN method can form class regions in the signal vector space with high degree of freedom.

Some kind of practical problems such as security verification of cryptographic protocols can be described as a problem to accomplish a given purpose by using limited operations and limited materials only. To model such problems in a natural way, unification problems under constrained substitutions have been proposed. This paper is a collection of results on the decidability and the computational complexity of a syntactic unification problem under constrained substitutions. A number of decidable, undecidable, tractable and intractable results of the problem are presented. Since a unification problem under constrained substitutions can be regarded as an order-sorted unification problem with term declarations such that the number of sorts is only one, the results presented in this paper also indicate how the intractability of order-sorted unification problems is reduced by restecting the number of sorts to one.

The Multiple input-output INverse/filtering Theorem (MINT) proves that N + 1 inverse filters are necessary to precisely control sound at N points in a space, and gives the minimum orders of such filters. In this paper, we propose the Indefinite MINT Filters (IMFs) for adding one or more control points to the above framework without increasing the number of inverse filters. Although the controllability of the new point is not sufficient, that of the other points is still maintained high enough by the principle of the MINT. In a two point sound control (using two inverse filters), the IMFs could reduce the squared error to the desired sound up to - 10 dB at the second point which is not controlled by the MINT.

In audio-frequency magnetotelluric surveys, electromagnetic radiation from worldwide thunderstorm activity is used as an energy source for geophysical exploration. Owing to its origin, such a signal is inherently transient and short lived. Therefore, special care should be taken in the detection and processing of this transient signal because the interval of time between two successive transient events contains almost no information as far as the audio frequency magnetotellurist is concerned. In this paper, a wavelet transform detection, processing and analysis technique is developed. A complex-compactly-supported wavelet, known as the Morlet wavelet, is selected as the mother wavelet. With the Morlet wavelet, lightning transients can be easily identified in the noisy recordings and the magnetotelluric impedance tensor can be computed directly in the wavelet transform domain. This scheme has been tested on real data collected in the archipelago of Svalbard, Norway as well as on five sets of synthetic data contaminated with various kinds of noise. The results show the superior performance of the wavelet transform transient detection and analysis technique.

In this paper,we propose general fast one dimensional (1-D) and two dimensional (2-D) slant transform algorithms. By introducing simple and structural permutations, the heavily computational operations are centralized to become standardized and localized processing units. The total numbers of multiplications for the proposed fast 1-D and 2-D slant transforms are less than those of the existed methods. With advantages of convenient description in formulation and efficient computation for realization, the proposed fast slant transforms are suitable for applications in signal compression and pattern recognition.

New network survivability measures are developed and compared with conventional ones. The advantages of using multiple survivability measures, including the new ones, are discussed. The measures are illustrated and interpreted through several numerical examples. We also show how survivability can be included as a constraint in network optimization models.