Tai-Hoon Cho

Visual inspection system for the classification of solder joints

Abstract In this paper, efficient techniques for solder joint inspection have been described. Using three layers of ring-shaped LEDs with different illumination angles, three frames of images are sequentially obtained. From these images the regions of interest (soldered regions) are segmented, and their characteristic features including the average gray level and the percentage of highlights–referred to as 2D features — are extracted. Based on the backpropagation algorithm of neural networks, each solder joint is classified into one of the pre-defined types. If the output value is not in the confidence interval, the distribution of tilt angles — referred to as 3D features — is calculated, and the solder joint is classified based on the Bayes classifier. The second classifier requires more computation while providing more information and better performance. The choice of a combination of neural network and Bayes classifier is based on the performance evaluation of various classifiers. The proposed inspection system has been implemented and tested with various types of solder joints in SMDs. The experimental results have verified the validity of this scheme in terms of speed and recognition rate.

Pattern Classification Methods for Keystroke Analysis

Keystroke time intervals can be a discriminating feature in the verification and identification of computer users. This paper presents a comparison result obtained using several classification methods including k-NN (k-nearest neighbor), back-propagation neural networks, and Bayesian classification for keystroke identification. Performance of k-NN classification was best with small data samples available per user, while Bayesian classification was the most superior to others with large data samples per user. Thus, for Web-based online identification of users, it seems to be appropriate to selectively use either k-NN or Bayesian method according to the number of keystroke samples accumulated by each user

An automated visual inspection of solder joints using 2D and 3D features

In this paper, efficient techniques for solder joint inspection have been described. Using three layers of ring shaped LEDs with different illumination angles, three frames of images are sequentially obtained. From these images the regions of interest (soldered regions) are segmented, and their characteristic features including the average gray level and the percentage of highlights-referred to as 2D features-are extracted. Based on the backpropagation algorithm of neural networks, each solder joint is classified into one of the pre-defined types. If the output value is not in the confidence interval, the distribution of tilt angles-referred to as 3D features-is calculated, and the solder joint is classified based on the Bayes classifier. The second classifier requires more computation while providing more information and better performance. The proposed inspection system has been implemented and tested with various types of solder joints in SMDs. The experimental results have verified the validity of this scheme in terms of speed and recognition rate.

Automatic inspection of solder joints using layered illumination

Efficient techniques for solder joint inspection are described. Using three layers of ring shaped LEDs with different illumination angles, three frames of images are sequentially obtained. From these images the regions of interest (soldered regions) are segmented, and their characteristic features including the average gray level and the number of highlights-referred to as 2D features-are extracted. Based on the minimum distance classification rule, each solder joint is classified into one of the pre-defined types. If the classification is ambiguous, the distribution of tilt angles-referred to as 3D features-is calculated, which requires more computation while providing more information and better performance. The proposed inspection system has been implemented and tested with various types of solder joints in SMDs. The experimental results have verified the validity of this scheme in terms of speed and recognition rate.

Improved automatic gain control circuit using fuzzy logic

A problem that arises in most communication receivers concerns the wide variation in power level of the signals received at the antenna. These variations cause serious problems which are usually be solved in receiver design by using Automatic Gain Control (AGC). AGC is achieved by using an amplifier whose gain can be controlled by using external current or voltage. We have to note that the AGC circuit does not respond to rapid changes in the amplitude of input and multifrequency. Nowadays, with the development of the fuzzy theory, the advantages of the fuzzy logic are recognized widely and deeply. Applying fuzzy logic to AGC circuit is a way to enhance AGC circuit.

Improved chamfer matching using interpolated chamfer distance and subpixel search

Chamfer matching is an edge based matching technique that has been used in many applications. The matching process is to minimize the distance between transformed model edges and image edges. This distance is usually computed at the pixel resolution using a distance transform, thus reducing accuracy of the matching. In this paper, an improved approach for accurate chamfer matching is presented that uses interpolation in the distance calculation for subpixel distance evaluation. Also, instead of estimating the optimal position in subpixel using a neighborhood of the pixel position with the minimum distance, for more accurate matching, we use the Powells optimization to find the distance minimum through actual distance evaluations in subpixel. Experimental results are presented to show the validity of our approach.

Intelligent Filtering in Telerobotic System

In this paper, intelligent filtering methodology for masterarm translation signal is proposed. Fidelity and stability are contradicting factors in teleoperation. Human hand trembling filtering is one of the problems in telemanipulation field. During every operation the hand has a certain vibration that can affect the quality of teleoperation, especially in telesurgery, nanomanipuation and other precise tasks. It is very important to study the kinesthetic perception of the human and to optimize the teleoperation system accordingly. To cancel out the influence of human’s hand vibration the signal from the masterarm should be filtered. One of the feasible solutions is to use an intelligent filter, which is a very flexible instrument. Applying intelligent filtering methodology, we can use some heuristic methods to solve the filtering problem.