This paper presents a low-power video A/D conversion technique using features of moving pictures. Neighboring frames in typical video sequences and neighboring pixels in each video frame are highly correlated. This property is effectively used for the video A/D conversion to reduce the number of comparators and the resulting power consumption. A set of reference voltages is given to a comparator array so that the iterative A/D conversion converges in the logarithmic order of the prediction error. Simulation results using standard moving pictures showed that the average number of iterations for the A/D conversion is less than 3 for all the moving pictures tested. In the proposed 12 b A/D converter, the number of comparators can be reduced to about 1/5 compared with that of the two-step flash A/D converters, which are commonly used for video applications. The A/D converter is particularly useful for the integration to CMOS image sensors.

In this paper, an adaptive method for active control of vibration intensity in the frequency domain is proposed. In this method, vibration intensity is observed with the 4-sensor method, and the coefficients of an adaptive FIR filter for the active control is renewed with the Block Filtered-X LMS algorithm in the frequency domain. An experiment with the proposed method is performed on a simple model. As a result, the proposed method gives larger attenuation of vibration intensity than the conventional method in the high frequency region. The overall attenuation in vibration intensity in that frequency region is 14.1 dB with the proposed method, while it is 7.0 dB with the conventional method. In the lower frequency region, the reduction in vibration intensity by the proposed method is roughly equivalent to that obtained by the conventional method. An improvement may also be achieved there by setting the intervals between error sensors properly.

Computational sensor (smart sensor, vision chip in other words) is a very small integrated system, in which processing and sensing are unified on a single VLSI chip. It is designed for a specific targeted application. Research activities of computational sensor are described in this paper. There have been quite a few proposals and implementations in computational sensors. Firstly, their approaches are summarized from several points of view, such as advantage vs. disadvantage, neural vs. functional, architecture, analog vs. digital, local vs. global processing, imaging vs. processing, new processing paradigms. Then, several examples are introduced which are spatial processings, temporal processings, A/D conversions, programmable computational sensors. Finally, the paper is concluded.

Image processing about the vehicle is considered in this paper. When a vehicle is in a factory, image processing is applied for design and inspection, and when vehicle is on the road image processing is useful for Intelligent Transport Systems, which recently have been developed widely. There have been many researches and implementations using image sensors to get information for traffic control and vehicle control. The image seen from camera located beside or upon the road can be used for vehicle detection, velocity of car or car group measurement, parking car detection, etc. Moreover the image seen from camera located in vehicle can be used for preceding car detection, measurement of the distance to preceding car, obstacle detection, lane detection, etc. In this paper, studies about Image Processing for vehicle on the road are described.

Multi-input floating gate differential amplifier (FGDA) is proposed which can perform any convolution operation with differential structure and feedback loop. All operations are in the voltage mode. Only one terminal is required for the negative feedback which can suppress distortions due to mismatches of active elements. Possible applications include intelligent image sensor, where fully parallel DCT operation can be performed. A prototype chip is fabricated which is functional. A preliminary test result is reported.

We propose and experimentally confirm two approaches to improve the sensitivity of the H-type piezoelectric crystal gyroscope of LiTaO3. One is to adjust the resonant frequencies of the fz mode through additional mass control; the other is to change the driving mode from fx mode to fz mode, while the driving frequency is the resonant frequency of the fx mode. The sensitivity of the unit driving voltage is almost the same, but the threshold driving voltage level may increase more than 1,000 times, because it is far from the mechanical resonance. The high sensitivity of 0.11 pC (deg/sec) was obtained at a driving voltage of 30 Vpp.

This paper describes methods of tracking of moving objects in a cluttered background by integrating optical flow, depth data, and/or uniform brightness regions. First, a basic method is introduced which extracts a region with uniform optical flow as the target region. Then an extended method is described in which optical flow and depth are fused. A target region is extracted by Baysian inference in term of optical flow, depth and the predicted target location. This method works only for textured objects because optical flow or depth are extracted for textured objects. In order to solve this problem, uniform regions in addition to the optical flow are used for tracking. Realtime human tracking is realized for real image sequences by using a real time processor with multiple DSPs.

This paper proposes Emergent Behavior Based Architecture (EBBA) that fusions heterogeneous sensor information at the level of behavior modules. The characteristics of EBBA are as follows. i) sensor based architecture, ii) constructed by a set of concurrently executable behavior modules, iii) to have multiple methods to achieve given tasks by utilizing behavior modules, iv) a planner can control emergent behaviors. We also have developed mobile robot navigation system based on EBBA and confirmed the efficiency by experiments in the various situations.

We have developed a new autofocus method using image processing techniques. This method consists of two steps. The first step is the preset of an objective lens condition with the aid of the feedback of Z-sensor. Next, a hole pattern to be measured is detected using the pattern recognition. In the second step, the E-beam is shifted to the center of a hole pattern and scanned across the axis of a pattern. The exciting current of the objective lens is changed at constant intervals, where the center position of the range is the preset value of the Z-sensor. The best focus condition is determined based on the signal profile obtained by the autofocus scan. The measurement repeatability (3σ) can be achieved within 3. 9 nm. The percentage of success of 98. 7% can be realized in the present autofocus method.

Presently, mobile robots are navigated by means of a number of methods, using navigating systems such as the sonar-sensing system or the visual-sensing system. These systems each have their strengths and weaknesses. For example, although the visual system enables a rich input of data from the surrounding environment, allowing an accurate perception of the area, processing of the images invariably takes time. The sonar system, on the other hand, though quicker in response, is limited in terms of quality, accuracy and range of data. Therefore, any navigation methods that involves only any one system as the primary source for navigation, will result in the incompetency of the robot to navigate efficiently in a foreign, slightly-more-complicated-than-usual surrounding. Of course, this is not acceptable if robots are to work harmoniously with humans in a normal office/laboratory environment. Thus, to fully utilise the strengths of both the sonar and visual sensing systems, this paper proposes a fusion of navigating methods involving both the sonar and visual systems as primary sources to produce a fast, efficient and reliable obstacle-avoiding and navigating system. Furthermore, to further enhance a better perception of the surroundings and to improve the navigation capabilities of the mobile robot, active sensing modules are also included. The result is an active sensor fusion system for the collision avoiding behaviour of mobile robots. This behaviour can then be incorporated into other purposive behaviours (eg. Goal Seeking, Path Finding, etc. ). The validity of this system is also shown in real robot experiments.

In this work, a new algorithm for tracking the directions-of-arrival (DOA's) of moving targets by introducing a linear approximation is proposed. The targets are assumed to move with constant angular velocities within a short time and emitting continuously narrow-band signals that impinge on an array of sensors. Therefore the trajectories of targets can be approximated by linear functions of time, which consist of the DOA's and the angular velocities, within the short time. In the condition that the number of targets is known and the outputs vector of the sensors including the additive white complex Gaussian noises is observed continuously, a cost function which consists of the squared residual error vectors is defined. The estimation of the DOA's and the angular velocities of targets is performed by minimizing this cost function. By estimating both the DOA's and the angular velocities at the same time, the proposed algorithm is able to improve the tracking performance for rapidly moving targets. In computer simulations, the performance of the proposed algorithm is compared with the ESPRIT method, which is one of the typical subspace methods with super resolution.

In the field of speech recognition, many researchers have proposed speech recognition methods using auditory information like acoustic signal or visual information like shape and motion of lips. Auditory information has valid features for speech recognition, but it is difficult to accomplish speech recognition in noisy environment. On the other side, visual information has advantage to accomplish speech recognition in noisy environment, but it is difficult to extract effective features for speech recognition. Thus, in case of using either auditory information or visual information, it is difficult to accomplish speech recognition perfectly. In this paper, we propose a method to fuse auditory information and visual information in order to realize more accurate speech recognition. The proposed method consists of two processes: (1) two probabilities for auditory information and visual information are calculated by HMM, (2) these probabilities are fused by using linear combination. We have performed speech recognition experiments of isolated words, whose auditory information (22.05kHz sampling, 8-bit quantization) and visual information (30-frame/s sampling, 24-bit quantization) are captured with multi-media personal computer, and have confirmed the validity of the proposed method.

In this paper, we propose a method to construct structure models of articulated objects from multiple local observations of their motion using state transition analysis of local geometric constraints. The object model is constructed by a bottom-up approach with three levels. Each level groups sensor data with a constraint among local features observed by the sensor, and constructs the local model. If the sensor data in current model conflict, the model is reconstructed. In each level, the first level estimates a local geometric feature from the local sensor data (eg. edge, feature point) The second level estimates a rigid body from the local geometric feature. The third level estimates an object from the rigid bodies. In the third level, the constraint between rigid bodies is estimated by transition states, which are motions between rigid bodies. This approach is implemented on a blackboard system.

We demonstrate highly quick response and long distance distributed oil sensors using a newly developed eccentric core fiber (ECF). This distributed oil sensor,based on an interaction between measurand oil and evanescent-wave from the ECF, has achieved as short as 4 minutes response time by using an improved coating material and a sensing length over 17 km at a signal wavelength of 1310 nm. The observed sensitivity characteristics coincide with the calculations of the evanescent power outside the cladding and it is shown that the sensitivity can be well estimated from the amount of the outer cladding component of the evanescent power.

In the data path circuits of asynchronous systems, logical faults may first manifest as undetectable, transient wrong codewords, in spite of encoding the inputs and the outputs and proper organization which enables the faults to be propagated to the primary outputs in the form of non-codewords. Due to this, the conventional methods of concurrent error detection (CED) using the logic (voltage) monitoring is not effective. In this paper, we suggest a mixed-signal approach to achieve CED for a class of asynchronous circuits, known as self-timed circuits. First, we show that it is impossible to guarantee the CED using logic monitoring of the primary outputs in spite of proper encoding and organization of self-timed circuits. Then, we discuss different manifestations of single stuck-at faults occurring during normal operation in these circuits. Finally, we present the feasibility of achieving CED using a built-in current sensor (BICS) along with encoding techniques.

A new silicon capacitive pressure sensor with center clamped diaphragm is presented. The sensor has a silicon-glass structure and is fabricated by batch-fabrication processes. Since deformed diaphragm has a doughnut-shape, parallel-like displacement is realized and therefore better linearity of 0.7% which is half of the conventional flat diaphragm sensor is obtained. It is clarified both analytically and experimentally that the capacitive pressure sensor with center clamped diaphragm is advantageous in terms of linearity.

Micromechanisms and actuators which are 10-100 micrometers in size are studied by research groups in universities, national research institutes, and private industries in Japan. Some of them belong to a "Micromachine Technology" project lead by MITI (Ministry of International Trade and Industries). Microfabrication technologies based on both IC-compatible processes and mechanical machining are under development. Application-oriented devices in automobile, communication and information industries are also investigated. The research goal is to build a smart micro system through the integration of moving mechanisms, sensors and electronics on a chip; this is the fusion of mechanics and electronics in the microscopic world. This paper reviews recent activities in MEMS research in Japan.

It is necessery to investigate the buckling mechanism in order to obtaining good performance from various sensors composed of resistors and microbridges or membranes. Especially for flow sensors, a convex formed bridge has an advantage over a flat or concave bridge with respect to heat transfer coefficient. We have fabricated various shapes of bridges and have prepared SiNx sputtered films as the support films of microbridges and Pt sputtered or evaporated films as resistors. We have achieved deformation control for both the longitudinal axis and transverse axis of Pt/SiNx double layered microbridges by appropriate selection of the total residual stress of Pt/SiNx structures and of the stress gradient between the Pt film and SiNx film. The deformation direction of the longitudinal axis of bridges for the beam bridge (Type ) are all the same as that of cantilevers and may be predicted via the stress gradient between the Pt and SiNx filmes of the bridges. The deflection of the transverse axis of the table bridge supported by four beams (Type ) changes linearly with the total stress of the Pt/SiNx structure and the deformation changes for the transverse axis are the same as that of completely free films as predicted from the stress gradient between the Pt film and the SiNx film. The interesting result is that the deformation direction for the longitudinal axis of Type is opposite to that of Type with the same film structure. We discuss the reason for this opposition via differences in the progress of the anisotropic etching. We consider that this result will expand the range of manufacturable shapes and film structures of microbridges.

This paper presents an analog 2-dimensional discrete cosine transform (2-D DCT) processor for focal-plane image compression. The on-chip analog 2-D DCT processor can process directly the analog signal of the CMOS image sensor. The analog-to-digital conversion (ADC) is preformed after the 2-D DCT, and this leads to efficient AD conversion of video signals. Most of the 2-D DCT coefficients can be digitized by a relatively low-resolution ADC or a zero detector. The quantization process after the 2-D DCT can be realized by the ADC at the same time. The 88-point analog 2-D DCT processor is designed by switched-capacitor (SC) coefficient multipliers and an SC analog memory based on 0.35µm CMOS technology. The 2-D DCT processor has sufficient precision, high processing speed, low power dissipation, and small silicon area. The resulting smart image sensor chips with data compression and digital transmission functions are useful for the high-speed image acquisition devices and portable digital video camera systems.

A low dark current CCD linear image sensor with pixels consisting of a photodiode and a storage area has been developed. In order to suppress the dark current, the wafer process has been improved. An impurity profile of a photodiode was modified to minimize depletion width, which was monitored by the photodiode potential. Surface states under the storage gate were decreased by hydrogen annealing with plasma-deposited silicon nitride as an inter metal dielectric film. As the isolation dose decreased, the dark current both in the photodiode and in the storage region were effectively suppressed. Finally, low dark currents of 5 pA/cm2 at photodiode and 120 pA/cm2 at storage area were obtained.