Basecalling using hidden Markov models

Basecalling using hidden Markov models

In this paper we propose hidden Markov models to model electropherograms from DNA sequencing equipment and perform basecalling. We model the state emission densities using artificial neural networks, and modify the Baum–Welch reestimation procedure to perform training. Moreover, we develop a method that exploits consensus sequences to label training data, thus minimizing the need for hand label...

Intrusion Detection Using Evolutionary Hidden Markov Model

Intrusion detection systems are responsible for diagnosing and detecting any unauthorized use of the system, exploitation or destruction, which is able to prevent cyber-attacks using the network package analysis. one of the major challenges in the use of these tools is lack of educational patterns of attacks on the part of the engine analysis; engine failure that caused the complete training,  ...

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Radar Target Detection Using Hidden Markov Models

Standard radar detection process requires that the sensor output is compared to a predetermined threshold. The threshold is selected based on a-priori knowledge available and/or certain assumptions. However, any knowledge and/or assumptions become inadequate due to the presence of multiple targets with varying signal return and usually non stationary background. Thus, any fixed predefined thres...

Person Movement Prediction Using Hidden Markov Models

Abstract: Ubiquitous systems use context information to adapt appliance behavior to human needs. Even more convenience is reached if the appliance foresees the user’s desires and acts proactively. This paper introduces Hidden Markov Models, in order to anticipate the next movement of some persons. The optimal configuration of the model is determined by evaluating some movement sequences of real...