This paper formulates an unsupervised algorithm for symbolization of signal time series to capture the embedded dynamic behavior. The key idea is to convert time series of the digital signal into a string of (spatially discrete) symbols from which the embedded dynamic information can be extracted in an unsupervised manner (i.e., no requirement for labeling of time series). The main challenges h...

A robust method for symbol recognition is presented that utilizes a compact signature based on a modified Hough Transform (HT) and knowledge-based hierarchical neural network structure. Relative position and orientation information is extracted from a symbol image using a modified Hough Transform (HT). This information is transformed and compressed into a compact, 1-D signature vector that is i...

A blind equalizer attempts to compensate the inter-symbol interference caused by a communication channel without the knowledge of the transmitted sequence. Linear prediction-error lters (PEF) can be used to obtain the equalized symbols. These equalizers are derived assuming a white information sequence. In real communication systems, channel encoding is commonly used to enable the detection and...

Recurrent Neural Networks (RNN) are a type of statistical model designed to handle sequential data. The model reads a sequence one symbol at a time. Each symbol is processed based on information collected from the previous symbols. With existing RNN architectures, each symbol is processed using only information from the previous processing step. To overcome this limitation, we propose a new kin...

OBJECTIVE Consumers tend to have the perception that healthy equals less tasty. This study aimed to identify whether information provided by the Keyhole symbol, a widely used front-of-package symbol in Nordic countries to indicate nutritional content, and percent daily values (%DVs) affect Norwegian adolescents' perception of the healthiness of snacks and their intention to buy them. DESIGN T...