Hidden Markov Model Technique Research Articles

Asynchronous Control for Discrete-Time Hidden Markov Jump Power Systems.

In this article, the stabilization problem of discrete-time power systems subject to random abrupt changes is studied via asynchronous control. In this regard, the transient faults in the power lines, and subsequent switching of associated circuit breakers are modeled as a Markov chain. Based on this, the power systems are described as discrete-time Markov jump systems. The focus is mainly to design the control for Markov jump-based power systems (MJPSs) when modes of the control asynchronously run with the modes of power systems. To do this, a hidden Markov model technique is used to characterize the nonsynchronization between the control and system. By constructing the mode-dependent stochastic Lyapunov function, the sufficient conditions are acquired in the form of linear matrix inequalities (LMIs), which ensure not only the stochastic stability of the resulting hidden MJPSs but also the existence of the desired control. Finally, the simulation example reveals the efficiency of the designed control law.

Ward_p_HMM: A Shilling Attack Detection Technique Using Ward_p Method and Hidden Markov Model

Collaborative Filtering Recommender Systems ( CFRSs ) are widely employed in several applications because of its satisfying performance in the customized recommendation. Recent studies show that CFRSs are at risk of shilling attacks where attackers inject shilling profiles into the system. Malicious user injected ratings not only severely impact genuineness of recommendations but also user's trustworthiness within recommendation systems. Existing unsupervised clustering technique uses Ward method which is an iterative method of low scalability . For addressing this issue, in this work an unsupervised SA detection technique named WardpHMM has been proposed. It uses Ward p and Hidden Markov Model (HMM). In this proposed method HMM is used to measure difference of user’s rating behavior. It generates User Suspicious Degree (USD) of each user by analyzing user’s Suspicious Degree Range of Items (SDRI) and User’s Matching Degree (UMD). Then Ward p method is applied to merge users based on USD and to acquire group of Attack Users (AUs). For performance analysis of the proposed technique Amazon-ratings sample dataset has been used. The performance comparison shows that proposed Ward p HMM technique outperforms baseline technique with respect to precision, recall and F1-score .

Detection of Bryde's whale short pulse calls using time domain features with hidden Markov models

Passive acoustic monitoring (PAM) is generally usedto extract acoustic signals produced by cetaceans. However, the large data volume from the PAM process is better analyzed using an automated technique such as the hidden Markovmodels (HMM). In this paper, the HMM is used as a detection and classification technique due to its robustness and low time complexity. Nonetheless, certain parameters, such as the choice of features to be extracted from the signal, the frame duration, and the number of states affect the performance of the model. Theresults show that HMM exhibits best performances as the number of states increases with short frame duration. However, increasing the number of states creates more computational complexity in the model. The inshore Bryde's whales produce short pulse calls with distinct signal features, which are observable in the time-domain. Hence, a time-domain feature vector is utilized to reduce the complexity of the HMM. Simulation results also show that average power as a time-domain feature vector provides the best performance compared to other feature vectors for detecting the short pulse call of inshore Bryde's whales based on the HMM technique. More so, the extracted features such as the average power, mean, and zero-crossing rate, are combined to form a single 3-dimensional vector (PaMZ). The PaMZ-HMM shows improved performance and reduced complexity over existing feature extraction techniques such as Mel-scale frequency cepstral coefficients (MFCC) and linear predictive coding (LPC). Thus, making the PaMZ-HMM suitable for real-time detection.

Learning to Learn Sequential Network Attacks Using Hidden Markov Models

The global surge of cyber-attacks in the form of sequential network attacks has propelled the need for robust intrusion detection and prediction systems. Such attacks are difficult to reveal using current intrusion detection systems, since each individual attack phase may appear benign when examined outside of its context. In addition, there are challenges in building supervised learning models for such attacks, since there are limited labelled datasets available. Hence, there is a need for updating already built models to specific operational environments and for addressing the concept drift. Hidden Markov models (HMMs) is a popular framework for sequential modelling, however, in addition to the above challenges, the model parameters are difficult to optimise. This paper proposes a transfer learning (TL) approach that exploits already learned knowledge, gained from a labelled source dataset, and adapts it on a different, unlabelled target dataset. The datasets may be from a different but related domain. Five unsupervised HMM techniques are developed utilising a TL approach and evaluated against conventional machine learning approaches. Baum-Welch (BW), Viterbi training, gradient descent, differential evolution (DE) and simulated annealing, are deployed for the detection of attack stages in the network traffic, as well as, forecasting both the next most probable attack stage and its method of manifestation. Specifically, for the prediction of the three next most likely states and observations, TL with DE achieved a maximum accuracy improvement of 48.3%, and 27.4%, respectively. Finally, the actual detection prediction for the three next most probable states and methods of manifestation reaches 78.9% and 96.3% using TL with BW and DE, respectively.

Residential Consumer-Centric Demand Side Management

Energy management systems (EMS) are mainly price driven with minimal consumer interaction. To improve the effectiveness of EMS in the context of demand response, an alternative EMS control framework driven by resident behavior patterns is developed. Using hidden Markov modeling techniques, the EMS detects consumer behavior from real-time aggregate consumption and a pre-built dictionary of reference models. These models capture variations in consumer habits as a function of daily living activity sequence. Following a training period, the system identifies the best fit model which is used to estimate the current state of the resident. When a request to activate a time-shiftable appliance is made, the control agent compares grid signals, user convenience constraints, and the current consumer state estimate to predict the likelihood that the future aggregate load exceeds a consumption threshold during the operating cycle of the requested device. Based on the outcome, the control agent initiates or defers the activation request. Using three consumer reference models, a case study assessing EMS performance with respect to model detection, state estimation, and control as a function of consumer comfort and grid-informed consumption constraints is presented. A tradeoff analysis between comfort, consumption threshold, and appliance activation delay is demonstrated.

An enhanced multi-channel bacterial foraging optimization algorithm for MIMO communication system

ABSTRACTChannel estimation and optimisation are the main challenging tasks in Multi Input Multi Output (MIMO) wireless communication systems. In this work, a Multi-Channel Bacterial Foraging Optimization Algorithm approach is proposed for the selection of antenna in a transmission area. The main advantage of this method is, it reduces the loss of bandwidth during data transmission effectively. Here, we considered the channel estimation and optimisation for improving the transmission speed and reducing the unused bandwidth. Initially, the message is given to the input of the communication system. Then, the symbol mapping process is performed for converting the message into signals. It will be encoded based on the space–time encoding technique. Here, the single signal is divided into multiple signals and it will be given to the input of space–time precoder. Hence, the multiplexing is applied to transmission channel estimation. In this paper, the Rayleigh channel is selected based on the bandwidth range. This is the Gaussian distribution type channel. Then, the demultiplexing is applied on the obtained signal that is the reverse function of multiplexing, which splits the combined signal arriving from a medium into the original information signal. Furthermore, the long-term evolution technique is used for scheduling the time to channels during transmission. Here, the hidden Markov model technique is employed to predict the status information of the channel. Finally, the signals are decoded and the reconstructed signal is obtained after performing the scheduling process. The experimental results evaluate the performance of the proposed MIMO communication system in terms of bit error rate, mean squared error, average throughput, outage capacity and signal to interference noise ratio.

Sign Transition Modeling and a Scalable Solution to Continuous Sign Language Recognition for Real-World Applications

We propose a new approach to modeling transition information between signs in continuous Sign Language Recognition (SLR) and address some scalability issues in designing SLR systems. In contrast to Automatic Speech Recognition (ASR) in which the transition between speech sounds is often brief and mainly addressed by the coarticulation effect, the sign transition in continuous SLR is far from being clear and usually not easily and exactly characterized. Leveraging upon hidden Markov modeling techniques from ASR, we proposed a modeling framework for continuous SLR having the following major advantages, namely: (i) the system is easy to scale up to large-vocabulary SLR; (ii) modeling of signs as well as the transitions between signs is robust even for noisy data collected in real-world SLR; and (iii) extensions to training, decoding, and adaptation are directly applicable even with new deep learning algorithms. A pair of low-cost digital gloves affordable for the deaf and hard of hearing community is used to collect a collection of training and testing data for real-world SLR interaction applications. Evaluated on 1,024 testing sentences from five signers, a word accuracy rate of 87.4% is achieved using a vocabulary of 510 words. The SLR speed is in real time, requiring an average of 0.69s per sentence. The encouraging results indicate that it is feasible to develop real-world SLR applications based on the proposed SLR framework.

Analysis and Design of Principal Component Analysis and Hidden Markov Model for Face Recognition

Biometric detection is considered as an important tool for states to use to strengthen the safety measures. Biometric increases robustness of the biometric system against many attacks and solve the problem of non-universality. Since facial image is the mandatory biometric identifier this proposed work focuses on the use of facial image. Face authentication involves extracting characteristics set such as eyes, nose, mouth from a two dimensional image of the user face and matching it with the templates stored in the database. Facial recognition is a difficult task because of the fact that the face is variable social organ which displays a variety of expressions. The proposed method is for facial recognition for both images and moving video using Principal Component Analysis (PCA), includes Hidden Markov Model (HMM) technique and Gaussian mixture model (GMM) and Artificial Neural Network (ANN), Since HMM technique is a powerful tool for statistical natural image processing and videos. PCA is a statistical procedure which uses an orthogonal transformation. Face recognition techniques dependent on parameters like background noise, lighting, eyes moments, lips and position of key features. Moreover, the face patterns are divided into numerous small scale states and recombined to obtain the recognition result. The experimental results are obtained from this proposed work has been achieved the performance parameters 99.83% of false rejection rate (FRR) and 0.62% of false acceptance rate (FAR) and an accuracy of 96% is implemented using Matlab2012A.

E-Content Based UNL Deconverter Framework for Tamil and Hindi Language

The enormous growth in E-content information in the web requires the content to be available in the natural language. The demand in converting the E-content from the one natural language to another natural language have been increased recently. E-content information was obtained in the target language from the Interlingua representation such as Universal Networking Language (UNL) rather than from the source language. A framework towards conversion of a UNL expression related E-content into the tamil and hindi language was presented in this paper. This approach uses the dictionary to select target language words for UWs in the UNL expression. The morphological rules were created to modify the headwords owing to the target language. Also the Hidden Markov Model technique has been adopted to define the word order in the generated sentence. Keywords: E-content, tamil, hindi, deconverter, dictionary, morphological rules, Hidden Markov Model.

Data-driven techniques for detecting dynamical state changes in noisily measured 3D single-molecule trajectories.

Optical microscopes and nanoscale probes (AFM, optical tweezers, etc.) afford researchers tools capable of quantitatively exploring how molecules interact with one another in live cells. The analysis of in vivo single-molecule experimental data faces numerous challenges due to the complex, crowded, and time changing environments associated with live cells. Fluctuations and spatially varying systematic forces experienced by molecules change over time; these changes are obscured by “measurement noise” introduced by the experimental probe monitoring the system. In this article, we demonstrate how the Hierarchical Dirichlet Process Switching Linear Dynamical System (HDP-SLDS) of Fox et al. [IEEE Transactions on Signal Processing 59] can be used to detect both subtle and abrupt state changes in time series containing “thermal” and “measurement” noise. The approach accounts for temporal dependencies induced by random and “systematic overdamped” forces. The technique does not require one to subjectively select the number of “hidden states” underlying a trajectory in an a priori fashion. The number of hidden states is simultaneously inferred along with change points and parameters characterizing molecular motion in a data-driven fashion. We use large scale simulations to study and compare the new approach to state-of-the-art Hidden Markov Modeling techniques. Simulations mimicking single particle tracking (SPT) experiments are the focus of this study.

Stochastic Analysis of Variation in Disruption with Changing Traffic Patterns in IEEE 802.11-based networks

The idea of using indoor WLAN Access Points from vehicles has been explored in various previous works. Since WLANs are not inherently designed to support high mobility applications, they introduce frequent disruptions in the network services when used in vehicular environments. The objective of this paper is to investigate whether the vehicular traffic patterns affect this undesired disruption in WLAN-based vehicular communication. Stochastic models are first developed to describe connectivity conditions in normal and dense traffic conditions based on experimental data. Hidden Markov model techniques measure the variations in estimated disruption with changing traffic conditions.

Exploring the Rhythm of the Palestrina Style

This article presents a general-purpose formalism for modeling musical syntax as a probabilistic musical grammar. The formal probabilistic framework offers a precise yet flexible characterization of musical style as structure and process. Moreover, the grammar can be built algorithmically from a sample of musical examples, using a statistical grammar induction technique known as hidden Markov models (HMMs). The two fundamental assumptions of HMMs—termed finite memory and stationarity—are analyzed to show that the framework is expressive enough to capture a broad range of syntactic constraints in music. It is argued that the HMM technique draws its power from the ability to identify hidden structures that are important for shaping the musical surface. Thus, HMM-based grammar induction offers a practical, accurate, and methodologically sound tool for fine-grained modeling of musical style. As a concrete illustration, this article builds a formal grammar of rhythm for the Palestrina style. The grammar’s structure and components are carefully explained, and the formalism is compared with existing approaches to style characterization. Many traditional counterpoint rules are shown to naturally correspond with the grammar’s formal structure and are thus supported or refined. Other rules are disconfirmed or shown to lie outside the formalism’s scope. The long-standing problem of Renaissance meter is discussed in light of these results. Thus, through the Palestrina case study, the problem of grammar induction is framed in terms of traditional concerns in music scholarship in order to motivate application of the technique, illustrate its usefulness, and place it in a historical and methodological context within music theory research.

Blockwise HMM computation for large-scale population genomic inference

A promising class of methods for large-scale population genomic inference use the conditional sampling distribution (CSD), which approximates the probability of sampling an individual with a particular DNA sequence, given that a collection of sequences from the population has already been observed. The CSD has a wide range of applications, including imputing missing sequence data, estimating recombination rates, inferring human colonization history and identifying tracts of distinct ancestry in admixed populations. Most well-used CSDs are based on hidden Markov models (HMMs). Although computationally efficient in principle, methods resulting from the common implementation of the relevant HMM techniques remain intractable for large genomic datasets. To address this issue, a set of algorithmic improvements for performing the exact HMM computation is introduced here, by exploiting the particular structure of the CSD and typical characteristics of genomic data. It is empirically demonstrated that these improvements result in a speedup of several orders of magnitude for large datasets and that the speedup continues to increase with the number of sequences. The optimized algorithms can be adopted in methods for various applications, including the ones mentioned above and make previously impracticable analyses possible. Software available upon request. Supplementary data are available at Bioinformatics online. yss@eecs.berkeley.edu.

Classification via the Shadow Region in SAR Imagery

The use of a target's shadow in synthetic aperture radar (SAR) imaging has garnered much attention for automated target recognition (ATR) applications. A technique of hidden Markov modeling (HMM) of the shadow profile is developed here. The basic HMM technique is refined using ensemble averaging, mission-based model selection criteria, multi-look scenarios, and data fusion. The algorithms are tested using DARPA's moving and stationary target acquisition and recognition (MSTAR) data. One of the drawbacks of using SAR shadows is that there exist certain, yet limited, target-radar configurations where the shadow simply does not robustly provide discriminatory target information. This limitation, however, can be easily overcome by imaging a target at multiple poses. With two orthogonal looks, the shadow-only classifier was seen to have an average classification performance of over 90% for a five target system. Additionally, the output of the shadow-only classifier is illustrated to be independent of a scattering center based classifier. All of the results indicate that the shadows provide useful discriminatory information that can be used to advance recognition capabilities in SAR ATR applications.

Some applications of nonlinear and non-Gaussian state–space modelling by means of hidden Markov models

Nonlinear and non-Gaussian state–space models (SSMs) are fitted to different types of time series. The applications include homogeneous and seasonal time series, in particular earthquake counts, polio counts, rainfall occurrence data, glacial varve data and daily returns on a share. The considered SSMs comprise Poisson, Bernoulli, gamma and Student-t distributions at the observation level. Parameter estimations for the SSMs are carried out using a likelihood approximation that is obtained after discretization of the state space. The approximation can be made arbitrarily accurate, and the approximated likelihood is precisely that of a finite-state hidden Markov model (HMM). The proposed method enables us to apply standard HMM techniques. It is easy to implement and can be extended to all kinds of SSMs in a straightforward manner.

Acoustic modeling problem for automatic speech recognition system: conventional methods (Part I)

In automatic speech recognition (ASR) systems, the speech signal is captured and parameterized at front end and evaluated at back end using the statistical framework of hidden Markov model (HMM). The performance of these systems depend critically on both the type of models used and the methods adopted for signal analysis. Researchers have proposed a variety of modifications and extensions for HMM based acoustic models to overcome their limitations. In this review, we summarize most of the research work related to HMM-ASR which has been carried out during the last three decades. We present all these approaches under three categories, namely conventional methods, refinements and advancements of HMM. The review is presented in two parts (papers): (i) An overview of conventional methods for acoustic phonetic modeling, (ii) Refinements and advancements of acoustic models. Part I explores the architecture and working of the standard HMM with its limitations. It also covers different modeling units, language models and decoders. Part II presents a review on the advances and refinements of the conventional HMM techniques along with the current challenges and performance issues related to ASR.

Modeling and Measuring Self-Regulated Learning in Teachable Agent Environments

Our learning-by-teaching environment has students take on the role and responsibilities of a teacher to a virtual student named Betty. The environment is designed to help students learn and understand science topics for themselves as they teach and monitor their agent. This process is supported by adaptive scaffolding and feedback through interactions with the teachable agent and a mentor agent. This paper discusses the results of a comparative study conducted in an 8th-grade science classroom, where students received two kinds of metacognitive and learning strategy feedback. We analyze student performance and learning gains as a result of the intervention. To gain further insight into student learning behaviors exhibited during the intervention, we employ a data mining methodology incorporating hidden Markov modeling and sequence mining techniques. The results illustrate both the effectiveness of the experimental agent feedback in encouraging metacognitive learning strategies and the utility of the data mining methodology.

MEASURING SELF-REGULATED LEARNING SKILLS THROUGH SOCIAL INTERACTIONS IN A TEACHABLE AGENT ENVIRONMENT

We have developed a learning environment where students teach a computer agent using visual representations, and can monitor the agent's learning progress by asking her questions and having her take quizzes. The system provides self-regulated learning and metacognitive support via dialog-embedded prompts from Betty, the teachable agent, and Mr. Davis, the mentor agent. Our primary goals have been to support learning of complex science topics in middle school classrooms and facilitate development of metacognitive skills to support future learning. In this paper, we discuss methods that we have employed for detecting and characterizing students' behavior patterns from their activity sequences on the system. In particular, we discuss a method for learning hidden Markov models (HMM) from the activity logs. We demonstrate that the HMM structure corresponds to students' aggregated behavior patterns in the learning environment. Overall, the HMM technique allows us to go beyond simple frequency and sequence analyses, such as individual activity and pre-defined pattern counts, instead using exploratory methods to examine how these activities cohere in larger patterns over time. The paper outlines a study conducted in a 5th grade science classroom, presents the models derived from the students' activity sequences, interprets the model structure as aggregate patterns of their learning behaviors, and links these patterns to students' use of self-regulated learning strategies. The results illustrate that those who teach an agent demonstrate better learning performance and better use of metacognitive monitoring behaviors than students who only learn for themselves. We also observed more advanced and focused monitoring behaviors in the students who received metacognitive strategy feedback from the mentor agent while they taught the teachable agent.

Feature distribution modelling techniques for 3D face verification

This paper shows that Hidden Markov models (HMMs) can be effectively applied to 3D face data. The examined HMM techniques are shown to be superior to a previously examined Gaussian mixture model (GMM) technique. Experiments conducted on the Face Recognition Grand Challenge database show that the Equal Error Rate can be reduced from 0.88% for the GMM technique to 0.36% for the best HMM approach.

Speech Coding and Recognition

This paper investigates the performance of a speech recognizer in an interactive voice response system for various coded speech signals, coded by using a vector quantization technique namely Multi Switched Split Vector Quantization Technique. The process of recognizing the coded output can be used in Voice banking application. The recognition technique used for the recognition of the coded speech signals is the Hidden Markov Model technique. The spectral distortion performance, computational complexity, and memory requirements of Multi Switched Split Vector Quantization Technique and the performance of the speech recognizer at various bit rates have been computed. From results it is found that the speech recognizer is showing better performance at 24 bits/frame and it is found that the percentage of recognition is being varied from 100% to 93.33% for various bit rates. Keywords—Linear predictive coding, Speech Recognition, Voice banking, Multi Switched Split Vector Quantization, Hidden Markov Model, Linear Predictive Coefficients.