• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.35S no.12
• /
• pp.17-26
• /
• 1998

Ultrasonic sensors are widely used in various applications due to advantages of low cost, simplicity in construction, mechanical robustness, and little environmental restriction in usage. But for the application of object recognition, ultrasonic sensors exhibit several shortcomings of poor directionality which results in low spatial resolution of objects, and specularity which gives frequent erroneous range readings. The time-of-flight(TOF) method generally used for distance measurement can not distinguish small object patterns of plane, corner or edge. To resolve the problem, an increased number of the sensors in the forms of a linear array or 2-dimensional array of the sensors has been used. Also better resolution has been obtained by shifting the array in several steps using mechanical actuators. Also simple patterns are classified based on analyzing signal reflections. In this paper we propose a method of a sensor array system with improved capability in pattern distinction using electronic circuits accompanying the sensor array, and intelligent algorithm based on neuro-fuzzy processing of data fusion. The circuit changes transmitter output voltages of array elements in several steps. A set of different return signals from neighborhood sensors is manipulated to provide enhanced pattern recognition in the aspects of inclination angle, size and shift as well as distance of objects. The results show improved resolution of the measurements for smaller targets.

• Journal of the Institute of Convergence Signal Processing
• /
• v.10 no.1
• /
• pp.7-12
• /
• 2009

In this paper, we propose a directional feature extraction method for off-line handwritten numerals by using the morphological operations. Direction features are obtained from four directional line images, each of which contains horizontal, vertical, right-diagonal and left-diagonal lines in entire numeral lines. Conventional method for extracting directional features uses Kirsch masks which generate edge-shaped double line images for each direction, whereas our method uses directional erosion operations and generate single line images for each direction. To apply these directional erosion operations to the numeral image, preprocessing steps such as thinning and dilation are required, but resultant directional lines are more similar to numeral lines themselves. Our four [$4{\times}4$] directional features of a numeral are obtained from four directional line images through a zoning method. For obtaining the higher recognition rates of the handwrittern numerals, we use the multiple feature which is comprised of our proposed feature and the conventional features of a kirsch directional feature and a concavity feature. For recognition test with given features, we use a multi-layer perceptron neural network classifier which is trained with the back propagation algorithm. Through the experiments with the CENPARMI numeral database of Concordia University, we have achieved a recognition rate of 98.35%.

• Journal of the Institute of Convergence Signal Processing
• /
• v.19 no.3
• /
• pp.139-146
• /
• 2018

Underwater acoustic communication can be applied to various area such as scientific, commercial and military survey using Autonomous Underwater Vehicles and Unmanned Underwater Vehicles. Underwater communication is studying very actively by advanced country like United States. But differ from wireless communication in the air, underwater acoustic communication has some difficult problems, ISI(Inter Symbol Interference) due to multipath and limit of transmission bandwidth due to slow propagation of sound wave. In this paper, SC-FDE and OFDM transmission system for the cancellation of ISI in conjunction with underwater acoustic channel modeling are applied to the underwater simulation of communication. The performance of these methods in the simulation guide to possibility of adopting in underwater acoustic communication algorithm. For this purpose, we compare SER performance of SC-FDE with that of OFDM for modelled underwater channel. Underwater channel is generated by Bellhop model. Simulation results show above 5dB SNR gain at 10-3 SER. And it demonstrate SC-FDE is efficient method for underwater acoustic communication.

• Journal of the Institute of Convergence Signal Processing
• /
• v.8 no.1
• /
• pp.58-62
• /
• 2007

Digital communication is important for reliability and mobilization of the multi-channel communication systems. Transmitting and receiving data for the mobilization should be possible in anywhere and in anytime. And this system must be designed light weight small size and low power. One are essential technology for implementing the mobile wireless communication system on the age of ubiquotos. Requirements in constructing such communication field are followings. At first data transmitting and receiving should be carried out by a simple command. Second, the device should be designed as hand-hold type and low power consumption. Third, data communication should be reliable. As one of examples, car to car system which is popular in the market is introduced here, All traffic information in highway is transmitted from one car to another by using this system which can prevent possible traffic accident. This paper shows the design of a digital data communication system with CC1020 chip. This CC1020 makes easy frequency selection and easy switch from the transmit mode to the receive mode by simple setting of a memory register in the chip. The transmit power of this system is designed 10dBm and its communication range is about 100m. The power supplied this system is 3V considered as low power. The sleep mode can be easily entered during transmit mode or receive mode. We shows the program algorithm of CC1020 and interface circuit between MCU and CC1020. We shows the Photo of the CC1020 Module and Atmega128 Module.. We analysed the receiver rate with this system.

• Journal of the Institute of Convergence Signal Processing
• /
• v.5 no.4
• /
• pp.256-262
• /
• 2004

A key component of an autonomous mobile robot is to localize itself and build a map of the environment simultaneously. In this paper, we propose a vision-based localization and mapping algorithm of mobile robot using fisheye lens. To acquire high-level features with scale invariance, a camera with fisheye lens facing toward to ceiling is attached to the robot. These features are used in mP building and localization. As a preprocessing, input image from fisheye lens is calibrated to remove radial distortion and then labeling and convex hull techniques are used to segment ceiling and wall region for the calibrated image. At the initial map building process, features we calculated for each segmented region and stored in map database. Features are continuously calculated for sequential input images and matched to the map. n some features are not matched, those features are added to the map. This map matching and updating process is continued until map building process is finished, Localization is used in map building process and searching the location of the robot on the map. The calculated features at the position of the robot are matched to the existing map to estimate the real position of the robot, and map building database is updated at the same time. By the proposed method, the elapsed time for map building is within 2 minutes for 50㎡ region, the positioning accuracy is ±13cm and the error about the positioning angle of the robot is ±3 degree for localization.

• Journal of the Institute of Convergence Signal Processing
• /
• v.12 no.2
• /
• pp.76-82
• /
• 2011

In this paper, we propose the technique to detect the location and size of the small target in multi-resolution image using cubic facet model. The input image is reduced by the multi-resolution and we obtain the multi-resolution images. We apply the facet model and the local maxima conditions to the multi-resolution images of each level. And then, we detect the location of the small target. We estimate that the location at the maximum of the $D_2$ which means the local maxima value of the facet model in the multi-resolution images is the location of the small target. We can detect the small target of the various size about the multi-resolution images of each level. In this paper, we experimented in the various infrared images with the small target. The method using the typical facet model applies a mask. However, the proposed method applies a mask in the multi-resolution images. We verified to vary the mask size and differ the size of the small target. The proposed algorithm can detect the location and size of the small target.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.46 no.11
• /
• pp.66-76
• /
• 2009

The motion estimation in video encoding system occupies the biggest part. So, we require the motion estimator with efficient structure for real-time operation. And for motion estimator's implementation, it is desired to design hardware module of an exclusive use that perform the encoding process at high speed. This paper proposes motion estimation detection block(MED), 41 SADs(Sum of Absolute Difference) calculation block, minimum SAD calculation and motion vector generation block based on parallel processing. The parallel processing can reduce effectively the amount of the operation. The minimum SAD calculation and MED block uses the pre-computation technique for reducing switching activity of the input signal. It results in high-speed operation. The MED and 41 SADs calculation blocks are composed of adder tree which causes the problem of critical path. So, the structure of adder tree has changed the most commonly used ripple carry adder(RCA) with carry skip adder(CSA). It enables adder tree to operate at high speed. In addition, as we enabled to easily control key variables such as control signal of search range from the outside, the efficiency of hardware structure increased. Simulation and FPGA verification results show that the delay of MED block generating the critical path at the motion estimator is reduced about 19.89% than the conventional strukcture.

• Journal of the Institute of Convergence Signal Processing
• /
• v.21 no.1
• /
• pp.13-20
• /
• 2020

In shipyards, internal materials are assembled after designing and manufacturing each ship's block. Internal material assembly means the installation of parts and equipment except ship's body. In this process, if the assembly of pipes and equipment existing in the block is not done correctly during the assembly between blocks, this causes a lot of costs. In addition, even if the assembly of the internal materials already completed, the production efficiency of the ship is reduced due to rework when problems such as space arrangement of the internal materials occurs. Therefore, this study introduces space arrangement and inspection system before and after hull outfitting work based on coexistence reality technology using 3D design drawing to solve these problems. The various coexistence reality algorithms and inspection systems developed and introduced in this study are based on AR service, which has never been introduced in Korea. So it will be widely applicable to various manufacturing industries using design drawings such as automobiles and architectures as well as ship building process.

• Journal of the Institute of Convergence Signal Processing
• /
• v.22 no.4
• /
• pp.185-192
• /
• 2021

Various methods have been proposed to determine the defect detection of welding bead, and recently sensor data and image data inspection have been steadily announced. There are advantages that sensor data inspection is highly accurate, and two-dimensional-based image data inspection is able to determine the position of the welding bead. However, when analyzing only with sensor data, it is difficult to determine whether the welding has been performed at the correct position. On the other hand, the image data inspection does not have high accuracy due to noise and measurement errors. In this paper, we propose a method that can complement the shortcomings of each inspection method and increase its advantages to improve accuracy and speed up inspection by fusing sensor data inspection which are average current, average volt, and mixed gas data, and image data inspection methods and is implemented as software. In addition, it is intended to allow users to conveniently and intuitively analyze and grasp the results by performing analysis using a graphical user interface(GUI) and checking the data and inspection results used for the inspection. Sensor inspection is performed using the characteristics of each sensor data, and image data is inspected by applying a morphology geodesic active contour algorithm. The experimental results showed 98% accuracy, and when performing the inspection on the four image data, and sensor data the inspection time was about 1.9 seconds, indicating the performance of software that can be used as a real-time inspector in the welding process.

• Journal of the Institute of Convergence Signal Processing
• /
• v.23 no.3
• /
• pp.166-172
• /
• 2022

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.