Browsing by Subject "HMM"

Now showing 1 - 4 of 4

Open Access
A method for automatic scaling of ionograms and electron density reconstruction
(IEEE, 2021-10-19) Gök, Gökhan; Alp, Y. K.; Arıkan, Orhan; Arıkan, F.
Ionogram scaling is the process of reconstructing electron density with respect to height by using the measurements of a remote sensing instrument known as ionosonde. In this study, a novel two stage ionogram scaling technique, ISED, is proposed. In the first stage, Hidden Markov Models (HMMs) are used to identify the actual ionospheric reflections in the ionosonde measurements. In the second stage, an IRI-Plas model based optimization problem is solved to obtain the vertical profile that generates the best least squares fit to the reflections identified in the first stage. To show the performance of ISED in global scale, experiments are conducted on 14,812 ionograms recorded at the three different stations which are Pruhonice in Czech Republic, Eielson in USA and Sao Luis in Brazil. Application of ISED to raw ionograms indicate 97.6% of the cases, ISED provides accurate electron density reconstructions, which is an improvement about 8.7% over ARTIST, most commonly used ionogram scaling technique.
Open Access
A novel and robust parameter training approach for HMMs under noisy and partial access to states
(Elsevier BV, 2014-01) Ozkan, H.; Akman, A.; Kozat, S. S.
This paper proposes a new estimation algorithm for the parameters of an HMM as to best account for the observed data. In this model, in addition to the observation sequence, we have partial and noisy access to the hidden state sequence as side information. This access can be seen as "partial labeling" of the hidden states. Furthermore, we model possible mislabeling in the side information in a joint framework and derive the corresponding EM updates accordingly. In our simulations, we observe that using this side information, we considerably improve the state recognition performance, up to 70%, with respect to the "achievable margin" defined by the baseline algorithms. Moreover, our algorithm is shown to be robust to the training conditions. © 2013 Elsevier B.V.
Open Access
Online learning under adverse settings
(2015-05) Özkan, Hüseyin
We present novel solutions for contemporary real life applications that generate data at unforeseen rates in unpredictable forms including non-stationarity, corruptions, missing/mixed attributes and high dimensionality. In particular, we introduce novel algorithms for online learning, where the observations are received sequentially and processed only once without being stored, under adverse settings: i) no or limited assumptions can be made about the data source, ii) the observations can be corrupted and iii) the data is to be processed at extremely fast rates. The introduced algorithms are highly effective and efficient with strong mathematical guarantees; and are shown, through the presented comprehensive real life experiments, to significantly outperform the competitors under such adverse conditions. We develop a novel highly dynamical ensemble method without any stochastic assumptions on the data source. The presented method is asymptotically guaranteed to perform as well as, i.e., competitive against, the best expert in the ensemble, where the competitor, i.e., the best expert, itself is also specifically designed to continuously improve over time in a completely data adaptive manner. In addition, our algorithm achieves a significantly superior modeling power (hence, a significantly superior prediction performance) through a hierarchical and self-organizing approach while mitigating over training issues by combining (taking finite unions of) low-complexity methods. On the contrary, the state-of-the-art ensemble techniques are heavily dependent on static and unstructured expert ensembles. In this regard, we rigorously solve the resulting issues such as the over sensitivity to source statistics as well as the incompatibility between the modeling power and the computational load/precision. Our results uniformly hold for every possible input stream in the deterministic sense regardless of the stationary or non-stationary source statistics. Furthermore, we directly address the data corruptions by developing novel versatile imputation methods and thoroughly demonstrate that the anomaly detection -in addition to being stand alone an important learning problem- is extremely effective for corruption detection/imputation purposes. To that end, as the first time in the literature, we develop the online implementation of the Neyman-Pearson characterization for anomalies in stationary or non-stationary fast streaming temporal data. The introduced anomaly detection algorithm maximizes the detection power at a specified controllable constant false alarm rate with no parameter tuning in a truly online manner. Our algorithms can process any streaming data at extremely fast rates without requiring a training phase or a priori information while bearing strong performance guarantees. Through extensive experiments over real/synthetic benchmark data sets, we also show that our algorithms significantly outperform the state-of-the-art as well as the most recently proposed techniques in the literature with remarkable adaptation capabilities to non-stationarity.
Open Access
Searching for complex human activities with no visual examples
(2008) Ikizler, N.; Forsyth, D.A.
We describe a method of representing human activities that allows a collection of motions to be queried without examples, using a simple and effective query language. Our approach is based on units of activity at segments of the body, that can be composed across space and across the body to produce complex queries. The presence of search units is inferred automatically by tracking the body, lifting the tracks to 3D and comparing to models trained using motion capture data. Our models of short time scale limb behaviour are built using labelled motion capture set. We show results for a large range of queries applied to a collection of complex motion and activity. We compare with discriminative methods applied to tracker data; our method offers significantly improved performance. We show experimental evidence that our method is robust to view direction and is unaffected by some important changes of clothing. © 2008 Springer Science+Business Media, LLC.