Browsing by Subject "Hidden Markov Model (HMM)"

Now showing 1 - 3 of 3

Open Access
Hierarchical reasoning game theory based approach for evaluation and testing of autonomous vehicle control systems
(IEEE, 2016) Li, N.; Oyler, D.; Zhang, M.; Yıldız, Yıldıray; Girard, A.; Kolmanovsky, İ.
A hierarchical game theoretic decision making framework is exploited to model driver decisions and interactions in traffic. In this paper, we apply this framework to develop a simulator to evaluate various existing autonomous driving algorithms. Specifically, two algorithms, based on Stackelberg policies and decision trees, are quantitatively compared in a traffic scenario where all the human-driven vehicles are modeled using the presented game theoretic approach.
Open Access
A large vocabulary speech recognition system for Turkish
(1999) Yılmaz, Cemal
This thesis presents a large vocabulary isolated word speech recognition system for Turkish. The triphones modeled by three-state Hidden Markov Models (HMM) are used as the smallest unit for the recognition. The HMM model of a word is constructed by using the HMM models of the triphones which make up the word. In the training stage, the word model is trained as a whole and then each HMM model of the triphones is extracted from the word model and it is stored individually. In the recognition stage, HMM models of triphones are used to construct the HMM models of the words in the dictionary. In this way, the words that are not trained can be recognized in the recognition stage. A new dictionary model based on trie structure is introduced for Turkish with a new search strategy for a given word. This search strategy performs breadth-first traversal on the trie and uses the appropriate region of the speech signal at each level of the trie. Moreover, it is integrated with a pruning strategy to improve both the system response time and recognition rate.
Open Access
Real-time epileptic seizure detection during sleep using passive infrared sensors
(Institute of Electrical and Electronics Engineers Inc., 2019) Hanosh, O.; Ansari, R.; Younis, K.; Çetin, A. Enis
This paper addresses the problem of detecting epileptic seizures experienced by a human subject during sleep. Commonly used solutions to this problem mostly rely on detecting motion due to seizures using contact-based sensors or video-based sensors. We seek a low-cost, low-power alternative that can sense motion without making direct contact with the subject and provides high detection accuracy. We investigate the use of Passive InfraRed (PIR) sensors to sense human body motion caused by epileptic seizures during sleep which makes the body shake and causes the PIR sensor to generate an oscillatory output signal. This signal can be distinguished from that of ordinary motions during sleep using analysis with machine learning algorithms. The supervised hidden Markov model algorithm (HMM) and a 1-D and 2-D convolutional neural network (ConvNet) are used to classify the data set of the PIR sensor output into the occurrence of epileptic seizures, ordinary motions, or absence of motion. The method was tested on the PIR signals captured at 1 m from 33 recruited healthy subjects who, after watching seizure videos, either moved their body on a bed to simulate a seizure, ordinary motion, or lay still. The HMM algorithm attained 97.03% accuracy, while 1D-ConvNet and 2D-ConvNet attained an accuracy of 96.97% and 98.98%, respectively. All simulated seizures were successfully detected, with errors occurring only in distinguishing between ordinary motion and no motion, thereby demonstrating the potential for using PIR sensors in the epileptic seizure detection.