Second-order Hidden Markov Models Research Articles

Traffic Status Prediction Based on Multidimensional Feature Matching and 2nd-Order Hidden Markov Model (HMM)

Spatiotemporal data from urban road traffic are pivotal for intelligent transportation systems and urban planning. Nonetheless, missing data in traffic datasets is a common challenge due to equipment failures, communication issues, and monitoring limitations, especially the missing not at random (MNAR) problem. This research introduces an approach to address MNAR-type missing data in traffic status prediction, utilizing a multidimensional feature sequence and a second-order hidden Markov model (2nd-order HMM). First, this approach involves extracting spatiotemporal features for the preset data sections and spatial features for the sections to be predicted based on the traffic spatiotemporal characteristics. Second, using the extracted features, distinctive road traffic features are generated for each section. Furthermore, at specific intervals within the defined time period, nearest distance feature matching is introduced to ascertain the traffic attributes of the road section under prediction. Finally, relying on the matched status results, a 2nd-order HMM is employed to forecast the traffic status for subsequent moments within the defined time period. Experiments were carried out using datasets from Shenzhen City and compared against the hidden Markov models and contrast measure (HMM-C) method to affirm the efficacy of the proposed approach.

SeqPolar: Sequence Matching of Polarized LiDAR Map With HMM for Intelligent Vehicle Localization

3D LiDAR maps are playing a more and more important role in intelligent vehicle localization. In this paper, the proposed SeqPolar localization method consists of two parts. The first part is a novel node-based LiDAR map representation method, termed polarized LiDAR map (PLM), and the second is a map matching algorithm based on a second-order hidden Markov model (HMM2) for vehicle localization. Unlike existing 3D LiDAR map, which is usually constructed by accumulating 3D LiDAR clouds collected at different times, the proposed polarized LiDAR map is generated from a set of nodes. Each node consists of three elements: a polarized LiDAR image, scene features extracted from the polarized LiDAR image, and sensor pose. The polarized LiDAR image encodes 3D coordinates and reflectivities of the 3D LiDAR cloud using a multi-channel image format, a more concise, straightforward, and structured representation of the 3D LiDAR cloud. In the localization step, we propose an HMM2-based method to match a sequence of input polarized images within the map nodes and find the nearest node from the PLM. Afterward, the vehicle is readily localized from the matched map node with 3D registration. The proposed SeqPolar localization method has been validated with the actual field dataset and the public KITTI database. Experimental results demonstrate that the proposed HMM2-based matching method can achieve up to 98% accuracy to find the nearest node from PLM. Moreover, the SeqPolar localization method based on the pre-built PLM can achieve 30-centimeter localization accuracy in average on both test datasets.

An Online Map Matching Algorithm Based on Second-Order Hidden Markov Model

Map matching is a key preprocess of trajectory data which recently have become a major data source for various transport applications and location-based services. In this paper, an online map matching algorithm based on the second-order hidden Markov model (HMM) is proposed for processing trajectory data in complex urban road networks such as parallel road segments and various road intersections. Several factors such as driver’s travel preference, network topology, road level, and vehicle heading are well considered. An extended Viterbi algorithm and a self-adaptive sliding window mechanism are adopted to solve the map matching problem efficiently. To demonstrate the effectiveness of the proposed algorithm, a case study is carried out using a massive taxi trajectory dataset in Nanjing, China. Case study results show that the accuracy of the proposed algorithm outperforms the baseline algorithm built on the first-order HMM in various testing experiments.

Exact inference for integrated population modelling.

Integrated population modelling is widely used in statistical ecology. It allows data from population time series and independent surveys to be analysed simultaneously. In classical analysis the time-series likelihood component can be conveniently approximated using Kalman filter methodology. However, the natural way to model systems which have a discrete state space is to use hidden Markov models (HMMs). The proposed method avoids the Kalman filter approximations and Monte Carlo simulations. Subject to possible numerical sensitivity analysis, it is exact, flexible, and allows the use of standard techniques of classical inference. We apply the approach to data on Little owls, where the model is shown to require a one-dimensional state space, and Northern lapwings, with a two-dimensional state space. In the former example the method identifies a parameter redundancy which changes the perception of the data needed to estimate immigration in integrated population modelling. The latter example may be analysed using either first- or second-order HMMs, describing numbers of one-year olds and adults or adults only, respectively. The use of first-order chains is found to be more efficient, mainly due to the smaller number of one-year olds than adults in this application. For the lapwing modelling it is necessary to group the states in order to reduce the large dimension of the state space. Results check with Bayesian and Kalman filter analyses, and avenues for future research are identified.

Log-Viterbi algorithm applied on second-order hidden Markov model for human activity recognition

Recognition of human activities is getting into the limelight among researchers in the field of pervasive computing, ambient intelligence, robotic, and monitoring such as assistive living, elderly care, and health care. Many platforms, models, and algorithms have been developed and implemented to recognize the human activities. However, existing approaches suffer from low-activity accuracy and high time complexity. Therefore, we proposed probabilistic log-Viterbi algorithm on second-order hidden Markov model that facilitates our algorithm by reducing the time complexity with increased accuracy. Second-order hidden Markov model is efficient relevance between previous two activities, current activity, and current observation that incorporate more information into recognition procedure. The log-Viterbi algorithm converts the products of a large number of probabilities into additions and finds the most likely activity from observation sequence under given model. Therefore, this approach maximizes the probability of activity recognition with improved accuracy and reduced time complexity. We compared our proposed algorithm among other famous probabilistic models such as Naïve Bayes, condition random field, hidden Markov model, and hidden semi-Markov model using three datasets in the smart home environment. The recognition possibility of our proposed method is significantly better in accuracy and time complexity than early proposed method. Moreover, this improved algorithm for activity recognition is much effective for almost all the dynamic environments such as assistive living, elderly care, healthcare applications, and home automation.

DMP-IOT: A distributed movement prediction scheme for IOT health-care applications

Mobility prediction in IP-based WSNs makes it possible to predict the next movement direction of mobile sensor nodes, which results in less power consumption and delay during handoff. The previous direction detection approaches need specific hardware facilities and impose considerable overhead during handoff. The advantage of DMP-IOT is distributing the learning model data around static sensors of the proposed tree, after the training phase. DMP-IOT includes a recovery mechanism that avoids disconnection of the mobile sensor node(s) to the network in case of a false prediction. The simulation results show about 25% improvement of DMP-IOT in saving power consumption and reducing handoff delay and packet loss, compared to movement direction approaches in similar works. The accuracy of the proposed movement prediction scheme is 83%, in average, which is validated by t−Student statistical test. Comparing the second-order Hidden Markov Model (HMM) with ANN reveals the superiority of the second-order HMM model in our application.

Activity Recognition in Smart Homes Based on Second-Order Hidden Markov Model

Hidden Markov Model is an important approach applied to activity recognition. In the first-order Hidden Markov Model, there is the hypothesis that the transition probability of state and the output probability of observation are only dependent on the current state of the model, which debases the precision of information extraction comparatively. In second-order Hidden Markov Model, the relevance between the current state and its previous two states is considered. Also, the relevance between the current observation and its previous state is considered. So second-order Hidden Markov Model has stronger performance of recognition of incorrect information. In my paper, second-order Hidden Markov Model is applied to activity recognition. The experiments show that our approach has higher precision than those approaches based on first-order Hidden Markov Model and based on Conditional Random Fields.

A probabilistic model for secondary structure prediction from protein chemical shifts

Protein chemical shifts encode detailed structural information that is difficult and computationally costly to describe at a fundamental level. Statistical and machine learning approaches have been used to infer correlations between chemical shifts and secondary structure from experimental chemical shifts. These methods range from simple statistics such as the chemical shift index to complex methods using neural networks. Notwithstanding their higher accuracy, more complex approaches tend to obscure the relationship between secondary structure and chemical shift and often involve many parameters that need to be trained. We present hidden Markov models (HMMs) with Gaussian emission probabilities to model the dependence between protein chemical shifts and secondary structure. The continuous emission probabilities are modeled as conditional probabilities for a given amino acid and secondary structure type. Using these distributions as outputs of first- and second-order HMMs, we achieve a prediction accuracy of 82.3%, which is competitive with existing methods for predicting secondary structure from protein chemical shifts. Incorporation of sequence-based secondary structure prediction into our HMM improves the prediction accuracy to 84.0%. Our findings suggest that an HMM with correlated Gaussian distributions conditioned on the secondary structure provides an adequate generative model of chemical shifts.

Complex Sequencing Rules of Birdsong Can be Explained by Simple Hidden Markov Processes

Complex sequencing rules observed in birdsongs provide an opportunity to investigate the neural mechanism for generating complex sequential behaviors. To relate the findings from studying birdsongs to other sequential behaviors such as human speech and musical performance, it is crucial to characterize the statistical properties of the sequencing rules in birdsongs. However, the properties of the sequencing rules in birdsongs have not yet been fully addressed. In this study, we investigate the statistical properties of the complex birdsong of the Bengalese finch (Lonchura striata var. domestica). Based on manual-annotated syllable labeles, we first show that there are significant higher-order context dependencies in Bengalese finch songs, that is, which syllable appears next depends on more than one previous syllable. We then analyze acoustic features of the song and show that higher-order context dependencies can be explained using first-order hidden state transition dynamics with redundant hidden states. This model corresponds to hidden Markov models (HMMs), well known statistical models with a large range of application for time series modeling. The song annotation with these models with first-order hidden state dynamics agreed well with manual annotation, the score was comparable to that of a second-order HMM, and surpassed the zeroth-order model (the Gaussian mixture model; GMM), which does not use context information. Our results imply that the hierarchical representation with hidden state dynamics may underlie the neural implementation for generating complex behavioral sequences with higher-order dependencies.

MicroRNA target prediction based on second-order Hidden Markov Model

MicroRNAs are one class of small single-stranded RNA of about 22 nt serving as important negative gene regulators. In animals, miRNAs mainly repress protein translation by binding itself to the 3′ UTR regions of mRNAs with imperfect complementary pairing. Although bioinformatics investigations have resulted in a number of target prediction tools, all of these have a common shortcoming—a high false positive rate. Therefore, it is important to further filter the predicted targets. In this paper, based on miRNA:target duplex, we construct a second-order Hidden Markov Model, implement Baum-Welch training algorithm and apply this model to further process predicted targets. The model trains the classifier by 244 positive and 49 negative miRNA:target interaction pairs and achieves a sensitivity of 72.54%, specificity of 55.10% and accuracy of 69.62% by 10-fold cross-validation experiments. In order to further verify the applicability of the algorithm, previously collected datasets, including 195 positive and 38 negative, are chosen to test it, with consistent results. We believe that our method will provide some guidance for experimental biologists, especially in choosing miRNA targets for validation.

Improving Speaker Identification Performance Under the Shouted Talking Condition Using the Second-Order Hidden Markov Models

Speaker identification systems perform well under the neutral talking condition; however, they suffer sharp degradation under the shouted talking condition. In this paper, the second-order hidden Markov models (HMM2s) have been used to improve the recognition performance of isolated-word text-dependent speaker identification systems under the shouted talking condition. Our results show that HMM2s significantly improve the speaker identification performance compared to the first-order hidden Markov models (HMM1s). The average speaker identification performance under the shouted talking condition based on HMM1s is. On the other hand, the average speaker identification performance based on HMM2s is.

Learning to Automatically Detect Features for Mobile Robots Using Second-Order Hidden Markov Models

In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks) are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or T-intersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.

Efficient multilingual phoneme-to-grapheme conversion based on HMM

Grapheme-to-phoneme conversion (GTPC) has been achieved in most European languages by dictionary look-up or using rules. The application of these methods, however, in the reverse process, (i.e., in phoneme-to-grapheme conversion [PTGC]) creates serious problems, especially in inflectionally rich languages. In this paper the PTGC problem is approached from a completely different point of view. Instead of rules or a dictionary, the statistics of language connecting pronunciation to spelling are exploited. The novelty lies in modeling the natural language intraword features using the theory of hidden Markov models (HMM) and performing the conversion using the Viterbi algorithm. The PTGC system has been established and tested on various multilingual corpora. Initially, the first-order HMM and the common Viterbi algorithm were used to obtain a single transcription for each word. Afterwards, the second-order HMM and the N-best algorithm adapted to PTGC were implemented to provide one or more transcriptions for each word input (homophones). This system gave an average score of more than 99% correctly transcribed words (overall success in the first four candidates) for most of the seven languages it was tested on (Dutch, English, French, German, Greek, Italian, and Spanish). The system can be adapted to almost any language with little effort and can be implemented in hardware to serve in real-time speech recognition systems.