Abstract. A novel approach for using an embedded processor to aid in deterministic testing of the other components of a system-on-a-chip (SOC) is presented. The tester loads a prog...
We present a selective encoding method that reduces test data volume and test application time for scan testing of Intellectual Property (IP) cores. This method encodes the slices ...
Automated pedestrian detection, counting, and tracking have received significant attention in the computer vision community of late. As such, a variety of techniques have been inve...
Automatic assessment of programming exercises is typically based on testing approach. Most automatic assessment frameworks execute tests and evaluate test results automatically, bu...
: This paper deals with a progressive learning method for symbol recognition which improves its own recognition rate when new symbols are recognized in graphic documents. We propos...
The most basic assumption used in statistical learning theory is that training data and test data are drawn from the same underlying distribution. Unfortunately, in many applicati...
In this paper we analyze the application of parallel and sequential evolutionary algorithms (EAs) to the automatic test data generation problem. The problem consists of automatica...
Supervised learning uses a training set of labeled examples to compute a classifier which is a mapping from feature vectors to class labels. The success of a learning algorithm i...
The mismatch between training and test environmental conditions presents a challenge to speech recognition systems. In this paper, we investigate an approach for matching the dist...
Shot boundary detection is one of the most fundamental processes in video analysis, and it requires high detection accuracy and high-speed processing. This paper proposes a method...