Truncated sum (TSUM for short) is useful for MV-PLA's realization. This paper introduces a new class of multiple-valued logic functions that are expressed by truncated sum, differential product (DPRODUCT for short), NOT and variables, where TSUM (x, y)min (xy, p1) and DPRODUCT (x, y)max (xy(p1), 0) is newly defined as the product that is derived by applying De Morgan's laws to TSUM. We call the functions T-functios. First, this paper clarifies that a set of T-functions is not a lattice. It clarifies that Lukasiewicz implication can be expressed by TSUM and NOT. It guarantees that a set of p-valued T-functios is not complete but complete with constants. Next, the speculations of the number of T-functions for less than ten radixes are derived. For eleven or more radix p, a speculation of the number of p-valued T-functions is shown. Moreover, it compares the T-functions with B-functions. The B-functions have been defined as the functions expressed by MAX, MIN, NOT and variables. As a result, it shows that a set of T-functions includes a set of B-functions. Finally, an inclusion relation among these functional sets and normality condition is shown.

This paper proposes a repairable and diagnosable k-valued cellular array. We assume a single fault, i.e., either stuck-at-O fault or stuck-at-(k1) fault of switches occurs in the array. By building in a duplicate column iteratively, when a stuck-at-(k1) fault occurs in the array, the fault never influences the output of the array. That is, we can construct a fault-tolerant array for the stuck-at-(k1) fault. While, for the stuck-at-O fault, the diagnosing method is simple and easy because we don't have to diagnose the stuck-at-(k1) fault. Moreover, our array can be repaired easily for the fault. The comparison with other rectangular arrays shows that our array has advantages for the number of cells and the cost of the fault diagnosis.

The frequency of uterine peristalsis is closely related to the success rate of pregnancy. An ultrasonic imaging is almost always employed for the measure of the frequency. The physician subjectively evaluates the frequency from the ultrasound image by the naked eyes. This paper aims to measure the frequency of uterine peristalsis from the ultrasound image. The ultrasound image consists of relative amounts in the brightness, and the contour of the uterine is not clear. It was not possible to measure the frequency by using the inter-frame difference and optical flow, which are the representative methods of motion detection, since uterine peristaltic movement is too small to apply them. This paper proposes a measurement method of the frequency of the uterine peristalsis from the ultrasound image in the implantation phase. First, traces of uterine peristalsis are semi-automatically done from the images with location-axis and time-axis. Second, frequency analysis of the uterine peristalsis is done by Fourier transform for 3 minutes. As a result, the frequency of uterine peristalsis was known as the frequency with the dominant frequency ingredient with maximum value among the frequency spectrums. Thereby, we evaluate the number of the frequency of uterine peristalsis quantitatively from the ultrasound image. Finally, the success rate of pregnancy is calculated from the frequency based on Fuzzy logic. This enabled us to evaluate the success rate of pregnancy by measuring the uterine peristalsis from the ultrasound image.

This paper gives a survey of intelligent computational techniques in medical and health care system. First, we briefly describe diagnosable techniques in medical image processing. Next, we demonstrate two ultrasonic surgery support systems for orthopedic and rectum cancer surgeons. In them, intelligent computational technique plays a primary role. Third, computational techniques are introduced in human health care system. Usually, this goal is not to apply clinical treatment but to home use to pay consciousness to health. In it, a simple ECG and respiration meter are introduced with a mat sheet which detects heart rate and respiration. Finally, a medical big data application is introduced, that is, body weight prediction is shown based on autoregressive model. Thus, we show that intelligent computing is effective and essential in modern medical and health care system.

This paper describes the benefit of utilizing the unary function generators in a multiple-valued Programmable Logic Array (PLA). We will clarify the most suitable PLA structure in terms of the array size. The multiple-valued PLA considered here has a structure with two types of function generators (literal and unary function generators), a first-level array and a second-level array. On investigating the effectiveness to reduce the array size, we can pick up four form PLAs: MAX-of-TPRODUCT form, MIN-of-TSUM form, TSUM-of-TPRODUCT form and TPRODUCT-of-TSUM form PLAs among possible eight form PLAs constructing from the MAX, MIN, TSUM and TPRODUCT operators. The upper bound of the array sizes with v UGs is derived as (log2ppv + p(n-v) + 1) pn-1 to realize any n-variable p-valued function. Next, experiments to derive the smallest array sizes are done for 10000 randomly generated functions and 21 arithmetic functions. These results conclude that MAX-of-TPRODUCT form PLA is the most useful in reducing the array size among the four form PLAs.

An idea of optimal output permutation of multiple-valued sum-of-products expressions is presented. The sum-of-products involve the TSUM operator on the MIN of window literal functions. Some bounds on the maximum number of implicants needed to cover an output permuted function are clarified. One-variable output permuted functions require at most p1 implicants in their minimal sum-of-products expressions, where p is the radix. Two-variable functions with radix between three and six are analyzed. Some speculations of maximum number of the implicants could be established for functions with higher radix and more than 2-variables. The result of computer simulation shows that we can have a saving of approximately 15% on the average using permuting output values. Moreover, we demonstrate the output permutation based on the output density as a simpler method. For the permutation, some speculation is shown and the computer simulation shows a saving of approximately 10% on the average.

The purpose of this paper is to establish an access control system by using only fingerprint identification. In order to minimize the identification time, we propose a new fingerprint classification suitable for a personal computer, and the real machine by using the classification is introduced. Our classification is implemented by only cores which are one of the features on fingerprint pattern. Therefore, it classifies all fingerprints into one of 11 classes rapidly on a personal computer. In the machine, an input fingerprint is classified and compared with ones registered in the same class. If both the input fingerprint and the registered one match, the person is allowed entry to the restricted area. Simulation results show that 443 fingerprint patterns (45 persons) are classified completely and rapidly. And the machine is effective and useful as identifier for home and room security.

To promote effective guidance by health checkup results, this paper predict a likelihood of developing lifestyle-related diseases from health check data. In this paper, we focus on the fluctuation of hemoglobin A1c (HbA1c) value, which deeply connected with diabetes onset. Here we predict incensement of HbA1c value and examine which kind of health checkup item has important role for HbA1c fluctuation. Our experimental results show that, when we classify the subjects according to their gender and triglyceride (TG) fluctuation value, we will effectively evaluate the risk of diabetes onset for each class.

In this paper, we describe a new ultrasonic oscillosensor and its application in a biological information measurement system. This ultrasonic sensor has a cylindrical tank of 26 mm (diameter)20 mm (height) filled with water and an ultrasonic probe. It detects the vibration of the target object by obtaining echo signals reflected from the water surface. This sensor can noninvasively detect the vibration of a patient by placing it under a bed frame. We propose a recognition system for humans in bed. Using this sensor, we could determine whether or not a patient is in the bed. Moreover, we propose a heart rate monitoring system using this sensor. When our system was tested on four volunteers, we successfully detected a heart rate comparable to that in the case of using an electrocardiograph. Fuzzy logic plays a primary role in the recognition. Consequently, this system can noninvasively determine whether a patient is in the bed as well as their heart rate using a constraint-free and compact device.

To enhance fault tolerance ability of the feedforward neural networks (NNs for short) implemented in hardware, we discuss the learning algorithm that converges without adding extra neurons and a large amount of extra learning time and cycles. Our algorithm modified from the standard backpropagation algorithm (SBPA for short) limits synaptic weights of neurons in range during learning phase. The upper and lower bounds of the weights are calculated according to the average and standard deviation of them. Then our algorithm reupdates any weight beyond the calculated range to the upper or lower bound. Since the above enables us to decrease the standard deviation of the weights, it is useful in enhancing fault tolerance. We apply NNs trained with other algorithms and our one to a character recognition problem. It is shown that our one is superior to other ones in reliability, extra learning time and/or extra learning cycles. Besides we clarify that our algorithm never degrades the generalization ability of NNs although it coerces the weights within the calculated range.

In this paper we propose a learning algorithm to enhance the fault tolerance of feedforward neural networks (NNs for short) by manipulating the gradient of sigmoid activation function of the neuron. We assume stuck-at-0 and stuck-at-1 faults of the connection link. For the output layer, we employ the function with the relatively gentle gradient to enhance its fault tolerance. For enhancing the fault tolerance of hidden layer, we steepen the gradient of function after convergence. The experimental results for a character recognition problem show that our NN is superior in fault tolerance, learning cycles and learning time to other NNs trained with the algorithms employing fault injection, forcible weight limit and the calculation of relevance of each weight to the output error. Besides the gradient manipulation incorporated in our algorithm never spoils the generalization ability.

We describe a new ultrasonography system, which can identify an implant position in bone. Although conventional X-ray fluoroscopy can visualize implants, it has the serious disadvantage of X-ray exposure. Therefore, we developed a system for orthopedic surgery that involves no X-ray exposure. Barriers to the development of the system were overcome using an ultrasonic instrument and fuzzy logic techniques. We located distal transverse screw holes in an intramedullary nail during surgery for femur fracture. The screw hole positions are identified by calculating two fuzzy degrees of intensity and the variance. Results allow this system to identify the screw hole positions within an error of 1.43 mm, an error ratio adequate for clinical surgical practice.

This paper describes Kleenean coefficients that are a subset of Kleenean functions for use in representing multiple-valued logic functions. A conventional multiple-valued sum-of-products expression uses product terms that are the MIN of literals and constants. In this paper, a new sum-of-products expression is allowed to sum product terms that also include variables and complements of variables. Since the conventional sum-of-products expression is complete, so also is the augmented one. A minimization method of the new expression is described besed on the binary Quine-McCluskey algorithm. The result of computer simulation shows that a saving of the number of implicants used in minimal expressions by approximately 9% on the average can be obtained for some random functions. A result for some arithmetic functions shows that the minimal solutions of MOD radix SUM, MAX and MIN functions require much fewer implicants than those of the standard sum-of-products expressions. Thus, this paper clarifies that the new expression has an advantage to reduce the number of implicants in minimal sum-of-products expressions.

In this letter we propose an interpolation technique for low-quality fingerprint images for highly reliable feature extraction. To improve the feature extraction rate, we extract fingerprint features by referring to both the interpolated image obtained by using a directional Laplacian filter and the high-contrast image obtained by using histogram equalization. Experimental results show the applicability of our method.

This paper presents a feature extraction model MAGNET' to find the deepest point of branched sulcus. Our model demonstrates magnetic principle and consists of four types of ideal magnetic poles: an N-pole and three S-poles. According to attractive or repulsive Coulomb forces between their poles, one of the S-poles is pushed to the deepest point of the sulcus. First, we explain our model on the simple sulcus model. Second, we apply it to the sulcus with implicit branches. Our model can detect the target points in every branch. Then an example to realize the model on a synthetic image is introduced. We apply our model to human brain MR images and human foot CT images. Experimental results on human brain MR images show that our method enable us to successfully detect the points.

In this paper, we propose a method to design ternary cellular arrays by using Ternary Decision Diagrams (TDD's). Our cellular array has a rectangular structure composed of ternary switch cells. The ternary functions represented by TDD's are realized by mapping the TDD's to the arrays directly. That is, both the nodes and the edges in the TDD are realized by some sets of the cells. Since TDD's can represent easily multiple-output functions without large memory requirements, our arrays are wuitable for the realization of multiple-output functions. To evaluate our method, we apply our method to some benchmark circuits, and compare our arrays with the ternary PLA's. The experimental results show that our arrays have the advantage for their sizes, especially in the realization of symmetric functions. The results also clarify that the size of our arrays depends on the size of TDD's.

This paper presents a rough sets-based method for clustering nominal and numerical data. This clustering result is independent of a sequence of handling object because this method lies its basis on a concept of classification of objects. This method defines knowledge as sets that contain similar or dissimilar objects to every object. A number of knowledge are defined for a data set. Combining similar knowledge yields a new set of knowledge as a clustering result. Cluster validity selects the best result from various sets of combined knowledge. In experiments, this method was applied to nominal databases and numerical databases. The results showed that this method could produce good clustering results for both types of data. Moreover, ambiguity of a boundary of clusters is defined using roughness of the clustering result.

This letter shows that an m-valued majority function is realized by m-1 two-valued threshold functions with common weight vector. A necessary and sufficient condition for an m-valued logical function to be a majority function is provided by using the concept of asummability.

A method is proposed for realizing any k-valued n-variable function with a celluler array, which consists of linear arrays (called input arrays) and a rectangular array (called control array). In this method, a k-valued n-variable function is divided into kn-1 one-variable functions and remaining (n1)-variable function. The parts of one-variable functions are realized by the input arrays, remaintng the (n1)-variable function is realized by the control array. The array realizing the function is composed by connecting the input arrays with the control array. Then, this array requires (kn2)kn-1 cells and the number is smaller than the other rectangular arrays. Next, a ternary cell circuit and a literal circuit are actually constructed with CMOS transistors and NMOS pass transistors. The experiment shows that these circuits perform the expected operations.

This paper shows the best operators for sum-of-products expressions. We first describe conditions of functions for product and sum operations. We examine all two-variable functions and select those that meet the conditions and then evaluate the number of product terms needed in the minimum sum-of-products expressions when each combination of selected product and sum functions is used. As a result of this, we obtain three product functions and nine sum functions on three-valued logic. We show that each of three product functions can express the same functions and MODSUM function is the most suitable for reduction of product terms. Moreover, we show that similar results are obtained on four-valued logic.