hidden Markov model

Ridden .Markov Models (HMMs) are applied t.0 the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated on the globin family, the protein kinase catalytic domain, and the EF-hand calcium binding motif. In each case the parameters of an HMM are estimated from a training set of unaligned sequences. After the HMM is built, it is used to obtain a multiple alignment of all the training sequences. It is also used to search the . SWISS-PROT 22 database for other sequences. that are members of the given protein family, or contain the given domain. The Hi" produces multiple alignments of good quality that agree closely with the alignments produced by programs that incorporate threedimensional structural information. When employed in discrimination tests (by examining how closely the sequences in a database fit the globin, kinase and EF-hand HMMs), the '\ HMM is able to distinguish members of these families from non-members with a high degree ' of accuracy. Both the HMM and PROFILESEARCH ( a technique used to search for relationships between a protein sequence and multiply aligned sequences) perform better in these tests than PROSITE (a dictionary of sites and patterns in proteins). The HMM appecvs to have a slight advantage over PROFILESEARCH in terms of lower rates of false negatives and false positives, even though the HMM is trained using only unaligned sequences, whereas PROFILESEARCH requires aligned training sequences. Our results suggest the presence of an EF-hand calcium binding motif in a highly conserved and evolutionary preserved putative intracellular region of 155 residues in the a-l subunit of L-type calcium channels which play an important role in excitation-contraction coupling. This region has been suggested to contain the functional domains that are typical or essential for all L-type calcium channels regardless of whether they couple to ryanodine receptors, conduct ions or both. \ 8 Abbreviations HMM, hidden Marko,. ,,,dels; length from 130 to 170 residues (with few excep-' m, Expectetion-Maximization; ML. maximum tions) and two domains, the protein kinase catalytic like€ihood; MAP, maximum a posten'wi; hTL-score.

An offline cursive handwriting recognition system, based on hybrid of Neural Networks (NN) and Hidden Markov Models (HMM), is described in this paper. Applying SegRec principle, the recognizer does not make hard decision at the character segmentation process. Instead, it delays the character segmentation to the recognition stage by generating a segmentation graph that describes all possible ways to segment a word into letters. To recognize a word, the NN computes the observation probabilities for each segmentation candidates (SCs) in the segmentation graph. Then, using concatenated letter-HMMs, a likelihood is computed for each word in the lexicon by multiplying the probabilities over the best paths through the graph. We present in detail two approaches to train the word recognizer: 1). character-level training 2). word-level training. The recognition performances of the two systems are discussed. I.

In recent years, there are many great successes in using deep architectures for unsupervised feature learning from data, especially for images and speech. In this paper, we introduce recent advanced deep learning models to classify two emotional categories (positive and negative) from EEG data. We train a deep belief network (DBN) with differential entropy features extracted from multichannel EEG as input. A hidden markov model (HMM) is integrated to accurately capture a more reliable emotional stage switching. We also compare the performance of the deep models to KNN, SVM and Graph regularized Extreme Learning Machine (GELM). The average accuracies of DBN-HMM, DBN, GELM, SVM, and KNN in our experiments are 87.62%, 86.91%, 85.67%, 84.08%, and 69.66%, respectively. Our experimental results show that the DBN and DBN-HMM models improve the accuracy of EEGbased emotion classification in comparison with the state-ofthe-art methods.

In the analysis and prediction of real-world systems, two of the key problems are nonstationarity (often in the form of switching between regimes) and overfitting (particularly serious for noisy processes). This article addresses these problems using gated experts, consisting of a (nonlinear) gating network, and several (also nonlinear) competing experts. Each expert learns to predict the conditional mean, and each expert adapts its width to match the noise level in its regime. The gating network learns to predict the probability of each expert, given the input. This article focuses on the case where the gating network bases its decision on information from the inputs. This can be contrasted to hidden Markov models where the decision is based on the previous state(s) (i.e. on the output of the gating network at the previous time step), as well as to averaging over several predictors. In contrast, gated experts soft-partition the input space, only learning to model their region. This...

This paper presents an application, "LentInfo", which is a system used to provide information about programmes for the Festival Lent in Slovenia. The Festival Lent consists of different open-air theatre and music performances and raws more than 400,000 visitors per year. This application is based on a Hidden Markov Model (HMM) speech recogniser, and the dialogue construction and management is done using the CSDP (Common Spoken Dialogue Platform) dialogue management system. It is represented as a finite-state structure. The dialogue can be specified in a script using simple syntax description. The dialogue manager is multi-application oriented, so it can easily be upgraded for new applications. If some new concepts are needed, only new actions need be added to the existing ones. Currently, prompt messages are prerecorded, but it is also possible to include a speech synthesis system depending on the needs of the application. Error recovery during the dialogue is done with user confirmation of the recognised input speech. The results are presented for tests performed in the year 2001. The results are analyzed according to the phone type (fixed/mobile), signal to noise ratio, dialogue path, etc. Although some calls where carried out using mobile phones from noisy festival places, the performance of the system decreased only slightly under these conditions.

This paper presents a novel approach to recognizing driver activities using a multi-perspective (i.e., four camera views) multi-modal (i.e., thermal infrared and color) video-based system for robust and real-time tracking of important body parts. The multi-perspective characteristics of the system provides redundant trajectories of the body parts, while the multi-modal characteristics of the system provides robustness and reliability of feature detection and tracking. The combination of a deterministic activity grammar (called 'operationtriplet') and a Hidden Markov model-based classifier provides semantic-level analysis of human activity. The application context for this research is that of intelligent vehicles and driver assistance systems. Experimental results in real-world street driving demonstrate effectiveness of the proposed system.

Recognizing human action from image sequences is an active area of research in computer vision. In this paper, we present a novel method for human action recognition from image sequences in different viewing angles that uses the Cartesian component of optical flow velocity and human body shape feature vector information. We use principal component analysis to reduce the higher dimensional shape feature space into low dimensional shape feature space. We represent each action using a set of multidimensional discrete hidden Markov model and model each action for any viewing direction. We performed experiments of the proposed method by using KU gesture database. Experimental results based on this database of different actions show that our method is robust.

Traditionally, musical instrument recognition is mainly based on frequency domain analysis (sinusoidal analysis, cepstral coefficients) and shape analysis to extract a set of various features. Instruments are usually classified using k-NN classifiers, HMM, Kohonen SOM and Neural Networks. In this work, we describe a system for the recognition of musical instruments from isolated notes. We are introducing the use of a Time Encoded Signal Processing method to produce simple matrices from complex sound waveforms, for instrument note encoding and recognition. These matrices are presented to a Fast Artificial Neural Network (FANN) to perform instrument recognition with promising results in organ classification and reduced computational cost. The evaluation material consists of 470 tones from 19 musical instruments synthesized with 5 wide used synthesizers (Microsoft Synth, Creative SB Live! Synth, Yamaha VL-70m Tone Generator, Edirol Soft-Synth, Kontakt Player) and 84 isolated notes from 20 western orchestral instruments (Iowa University Database).

The reliable extraction of characteristic gait features from image sequences and their recognition are two important issues in gait recognition. In this paper, we propose a novel two-step, model-based approach to gait recognition by employing a five-link biped locomotion human model. We first extract the gait features from image sequences using the Metropolis-Hasting method. Hidden Markov Models are then trained based on the frequencies of these feature trajectories, from which recognition is performed. As it is entirely based on human gait, our approach is robust to different type of clothes the subjects wear. The model-based gait feature extraction step is insensitive to noise, cluttered background or even moving background. Furthermore, this approach also minimizes the size of the data required for recognition compared to model-free algorithms. We applied our method to both the USF Gait Challenge data set and CMU MoBo data set, and achieved recognition rate of 61 and 96%, respectively. We further studied the relationship between number of subjects within data set and the recognition rate. The results suggest that the recognition rate is significantly limited by the distance of the subject to the camera.

We present two real-time hidden Markov model-based systems for recognizing sentence-level continuous American Sign Language (ASL) using a single camera to track the user's unadorned hands. The first system observes the user from a desk mounted camera and achieves 92% word accuracy. The second system mounts the camera in a cap worn by the user and achieves 98% accuracy (97% with an unrestricted grammar). Both experiments use a 40 word lexicon.

Semantic context detection is one of the key techniques to facilitate efficient multimedia retrieval. Semantic context is a scene that completely represents a meaningful information segment to human beings. In this paper, we propose a novel hierarchical approach that models the statistical characteristics of several audio events, over a time series, to accomplish semantic context detection. The approach consists of two stages: audio event and semantic context detections. HMMs are used to model basic audio events, and event detection is performed in the first stage. Then semantic context detection is achieved based on Gaussian mixture models, which model the correlations among several audio events temporally. With this framework, we bridge the gaps between low-level features and the semantic contexts that last in a time series. The experimental evaluations indicate that the approach is effective in detecting high-level semantics.

We address the problem of protein secondary structure prediction with Hidden Markov Models. A 21-state model is built using biological knowledge and statistical analysis of sequence motifs in regular secondary structures. Sequence family information is integrated via the combination of independent predictions of homologous sequences and a weighting scheme. Prediction accuracy with single sequences reaches 65.3% and raises to 72%

In this paper, we contribute to the development of context-aware operating rooms by introducing a novel approach to modeling and monitoring the workflow of surgical interventions. We first propose a new representation of interventions in terms of multidimensional time-series formed by synchronized signals acquired over time. We then introduce methods based on Dynamic Time Warping and Hidden Markov Models to analyze and process this data. This results in workflow models combining low-level signals with high-level information such as predefined phases, which can be used to detect actions and trigger an event. Two methods are presented to train these models, using either fully or partially labeled training surgeries. Results are given based on tool usage recordings from sixteen laparoscopic cholecystectomies performed by several surgeons.

The main steps of document processing have been reviewed, especially those implemented on Arabic writing. The techniques used in this research, such as Vector Quantization (VQ), Hidden Markov Models (HMM), and Induction of Decision Trees (ID3) have been considered, as well as reviewing pre-processing and feature extraction used in Arabic writing.

In past years, there were a lot of researches made in order to provide more accurate and comfortable interaction between human and machine. Developing a system which recognizes human gestures, is an important study to improve interaction between human and machine. Sign language is a way of communication for hearing-impaired people which enables them to communicate among themselves and with other people around them. Sign language consists of hand gestures and facial expressions. During the past 20 years, researches were made to facilitate communication of hearing-impaired people with others. Sign language recognition systems are designed in various countries. This paper presents a sign language recognition system, which uses Kinect camera to obtain skeletal model. Our aim was to recognize expressions, which are used widely in Turkish Sign Language (TSL). For that purpose we have selected 15 words/expressions randomly (repeated 4 times each by 3 different signers) which belong to Turkish Sign Language. We have used 180 records in total. Videos are recorded using Microsoft Kinect Camera and Nui Capture. Joint angles and joint positions have been used as features of gesture and achieved close to 100% recognition rates.

Cytotoxic T cells (CTLs) perceive the world through small peptides that are eight to ten amino acids long. These peptides (epitopes) are initially generated by the proteasome, a multi-subunit protease that is responsible for the majority of intra-cellular protein degradation. The proteasome generates the exact C-terminal of CTL epitopes, and the N-terminal with a possible extension. CTL responses may diminish if the epitopes are destroyed by the proteasomes. Therefore, the prediction of the proteasome cleavage sites is important to identify potential immunogenic regions in the proteomes of pathogenic microorganisms (or humans). We have recently shown that NetChop, a neural network-based prediction method, is the best method available at the moment to do such predictions; however, its performance is still lower than desired. Here, we use novel sequence encoding methods and show that the new version of NetChop predicts approximately 10% more of the cleavage sites correctly while lowering the number of false positives with close to 15%. With this more reliable prediction tool, we study two important questions concerning the function of the proteasome. First, we estimate the Nterminal extension of epitopes after proteasomal cleavage and find that the average extension is relatively short. However, more than 30% of the peptides have N-terminal extensions of three amino acids or more, and thus, N-terminal trimming might play an important role in the presentation of a substantial fraction of the epitopes. Second, we show that good TAP ligands have an increased chance of being cleaved by the proteasome, i.e., the specificity of TAP has evolved to fit the specificity of the proteasome. This evolutionary relationship allows for a more efficient antigen presentation.

This paper presents the design of a FPGA-based hardware co-processor, based on the SPHINX 3 speech recognition engine from CMU; capable of performing Acoustic Modeling (AM) for medium sized vocabularies in real-time. By creating an input-driven pipeline for performing the ...

Diagnosis classifies the present state of operation of the equipment, and prognosis predicts the next state of operation and its remaining useful life. In this paper, a prognosis method for the gear faults in dc machines is presented. The proposed method uses the time-frequency features extracted from the motor current as machine health indicators and predicts the future state of fault severity using hidden Markov models (HMMs). Parameter training of HMMs generally needs huge historical data, which are often not available in the case of electrical machines. Methods for computing the parameters from limited data are presented. The proposed prognosis method uses matching pursuit decomposition for estimating state-transition probabilities and experimental observations for computing state-dependent observation probability distributions. The proposed method is illustrated by examples using data collected from the experimental setup.

Spoken document retrieval (SDR) has been extensively studied in recent years because of its potential use in navigating large multi-media collections in the near future. Considering the characteristics and monosyllabic structure of the Chinese language, the syllable-based indexing for retrieval of spoken documents in Mandarin Chinese has been investigated, and extensive experiments on retrieval of broadcast news speech collected in Taiwan were performed. This paper reports some interesting results and ®ndings obtained in this research.

As amounts of publicly available video data grow, the need to automatically infer semantics from raw video data becomes significant. In this paper, we address the use of three different techniques that support that task, namely, spatio-temporal rule-based method, hidden Markov models, and dynamic Bayesian networks. First, the application of these techniques for detection and recognition of diverse events is briefly described using two case studies (Tennis and Formula 1). We explain the relationships and differences of the three approaches, as well as benefits of their integrated use. Then the focus is moved to the main point of the paper, which is the integration of the aforementioned techniques within a database management system, which provides efficient, flexible, scalable, and domain independent content-based video retrieval. We identify and consider the most important issues when extending a traditional database management system with content-based video retrieval functionality, namely issues concerning video data models, dynamic feature extraction, and extensions of different layers of database architecture. The advantages of the integrated system are demonstrated on examples from our two case studies.

The ability to predict episodes of acute hypotension (abnormal drop in arterial blood pressure) would be of immense benefit to the healthcare community, and is therefore a focus of research in both medical and engineering domains. This paper presents the use of Hidden Markov Models to predict the onset of acute hypotension, using blood pressure measurements over time. Our use of HMMs has been motivated by their ability to characterize sequential/temporal trends in a given time signal. This lends the ability to infer the health status based on blood pressure information collected over an interval of time, rather than just instantaneous measurements. We have tested the proposed technique on standard physiological signal datasets available online and have obtained promising results. As part of a bigger project, we see potential in the proposed technique being used in real time health monitoring systems.

Current methods of coding recall, summarization, talk-aloud, and question-answering data are inherently unreliable and not effectively documented. If the process of coding protocol data could even be partially automated, this would be an im- portant scientific advance in the field of text comprehension. Twenty-four human subjects read and recalled each of four short texts. Half of the human recall data

This paper describes the lemmatisation and tagging guidelines developed for the "Spoken Dutch Corpus", and lays out the philosophy behind the high granularity tagset that was designed for the project. To bootstrap the annotation of large quantities of material (10 million words) with this new tagset we tested several existing taggers and tagger generators on initial samples of the corpus. The results show that the most effective method, when trained on the small samples, is a high quality implementation of a Hidden Markov Model tagger generator.

The basic theory of Markov chains has been known to mathematicians and engineers for close to 80 years, but it is only in the past decade that it has been applied explicitly to problems in speech processing. One of the major reasons why speech models, based on Markov chains, have not been developed until recently was the lack of a method for optimizing the parameters of the Markov model to match observed signal patterns. Such a method was proposed in the late 1960's and was immediately applied to speech processing in several research institutions. Continued refinements in the theory and implementation of Markov modelling techniques have greatly enhanced the method, leading to a wide range of applications of these models. It is the purpose of this tutorial paper to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.

This paper describes a novel hand gesture recognition system that utilizes both multi-channel surface electromyogram (EMG) sensors and 3D accelerometer (ACC) to realize user-friendly interaction between human and computers. Signal segments of meaningful gestures are determined from the continuous EMG signal inputs. Multi-stream Hidden Markov Models consisting of EMG and ACC streams are utilized as decision fusion method to recognize hand gestures. This paper also presents a virtual Rubik's Cube game that is controlled by the hand gestures and is used for evaluating the performance of our hand gesture recognition system. For a set of 18 kinds of gestures, each trained with 10 repetitions, the average recognition accuracy was about 91.7% in real application. The proposed method facilitates intelligent and natural control based on gesture interaction.

Recent efforts in audio indexing and retrieval in music databases mostly focus on melody. If this is appropriate for polyphonic music signals, specific approaches are needed for systems dealing with percussive audio signals such as those produced by drums, tabla or djembé. Most studies of drum signals transcription focus on sounds taken in isolation. In this paper, we propose several methods for drum loops transcription where the drums signals dataset reflects the variability encountered in modern audio recordings (real and natural drum kits, audio effects, simultaneous instruments, . . . ). The approaches described are based on Hidden Markov Models (HMM) and Support Vector Machines (SVM). Promising results are obtained with a 83.9% correct recognition rate for a simplified taxonomy.

Online auction, shopping, electronic billing etc. all such types of application involves problems of fraudulent transactions. Online fraud occurrence and its detection is one of the challenging fields for web development and online phantom transaction. As no-secure specification of online frauds is in research database, so the techniques to evaluate and stop them are also in study. We are providing an approach with Hidden Markov Model (HMM) and mobile implicit authentication to find whether the user interacting online is a fraud or not. We propose a model based on these approaches to counter the occurred fraud and prevent the loss of the customer. Our technique is more parameterized than traditional approaches and so,chances of detecting legitimate user as a fraud will reduce.

One of the research goals in the human-computer interaction community is to build believable Embodied Conversational Agents, that is, agents able to communicate complex information with human-like expressiveness and naturalness. Since emotions play a crucial role in human communication and most of them are expressed through the face, having more believable ECAs implies to give them the ability of displaying emotional facial expressions.

Pergamon Pattern Recognition, Vol. 28, No. 2, pp. 161 170, 1995 Elsevier Science Ltd Copyright ) 1995 Pattern Recognition Society Printed in Great Britain. All rights reserved 0031 3203 95 9.50+.00 00313203(94)000921 APPLICATION OF HIDDEN MARKOV MODELS FOR ...

In today’s age the major problem is related to the predicting user’s web page request. In the past few years the markov model is used for this problem. The effective web mining techniques like Clustering, Association rule mining and markov model having many drawbacks. We are proposing an approach for overcome that drawbacks which help us to improving search engine and helping user to find interesting web pages.

Using social science methods to induce a state of frustration in users, we collected physiological, video and behavioral data, and developed a strategy for coupling these data with real-world events. The e ectiveness of the proposed strategy was tested in a study with thirty-six subjects, where the system was shown to reliably synchronize and gather data for a ect analysis. Hidden Markov M o dels were applied to each s u bject's physiological signals of skin conductivity and blood volume pressure in an e ort to see if regimes of likely frustration could be automatically discriminated from regimes when all was proceeding smoothly. T his pattern recognition approach c orrectly classi ed these two r e gimes 67.4% of the time. Mouse-clicking behavior was also synchronized to frustration-eliciting events, and analyzed, revealing NN distinct patterns of clicking responses

This paper describes a new identity authentication technique by a synergetic use of lip-motion and speech. The lip-motion is defined as the distribution of apparent velocities in the movement of brightness patterns in an image and is estimated by computing the velocity components of the structure tensor by 1D processing, in 2D manifolds. Since the velocities are computed without extracting the speaker's lip-contours, more robust visual features can be obtained in comparison to motion features extracted from lip-contours. The motion estimations are performed in a rectangular lip-region, which affords increased computational efficiency. A person authentication implementation based on lip-movements and speech is presented along with experiments exhibiting a recognition rate of 98%. Besides its value in authentication, the technique can be used naturally to evaluate the ''liveness'' of someone speaking as it can be used in text-prompted dialogue. The XM2VTS database was used for performance quantification as it is currently the largest publicly available database (%300 persons) containing both lip-motion and speech. Comparisons with other techniques are presented.

Motivation: A problem in predicting the topography of transmembrane proteins is the optimal localization of the transmembrane segments along the protein sequences, provided that each residue is associated with a propensity of being or not being included in the transmembrane protein region. From previous work it is known that postprocessing of propensity signals with suited algorithms can greatly improve the quality and the accuracy of the predictions. In this paper we describe a general dynamic programming-like algorithm (MaxSubSeq, Maximal Sub-Sequence) specifically designed to optimize the number and length of segments with constrained length in a given protein sequence. Previous application of our algorithm, has proved its effectiveness in the optimization task of both neural network and hidden Markov models output, and in this paper we present the detailed description of MaxSubSeq. Results: We describe the application of MaxSubSeq to the location of both helical and beta strand transmembrane segments, optimizing the outputs derived with different predictive algorithms. For all-alpha transmembrane proteins we use both the standard Kyte-Doolittle (KD) hydropathy scale and the TMHMM predictor (http://www.cbs.dtu.dk/). Using a set of 188 well characterized membrane proteins, MaxSubSeq nearly doubles the correct location of transmembrane segments as compared to the standard KD hydrophobicity plot, reaching 51% accuracy. If MaxSubSeq is used to optimize the TMHMM method the accuracy increases from 68 to 72%. When used to regularize the prediction of beta transmembrane strands, obtained using both a neural network and a HMM based predictors, MaxSubSeq increases the accuracy per protein up to 72 and 73% respectively.

Sign language recognition is a very challenging research area. In this proposed system, a well trained computer system is used to recognize static hand gestures representing linguistic words. The main aim of the paper is conversion of linguistic sign languages into text and speech form. Recognized sign are also translated into Tamil and Hindi languages. It contains three processes of work. First process is pre-processing, in which the obtained images are processed through the steps like segmentation, resize, and gray conversion. Second process is region-based analysis which exploits both boundary and interior pixels of an object. Solidity, perimeter, convex hull, area, major axis length, minor axis length, eccentricity, orientation are some of the shape descriptors used as features in this process. The features derived are used to train the binary classifier first; secondly the testing images are given for classification. Knn classifiers are used for classification which provides a good result with less computation time for larger datasets. Since, the system handled a binary classifier it performed a one-versus-all kind of classification. PCNN (Pulse Coupled Neural Network) is used for pattern recognition. Third process is the hand gesture recognition.

The battery management system (BMS) is an integral part of an automobile. It protects the battery from damage, predicts battery life, and maintains the battery in an operational condition. The BMS performs these tasks by integrating one or more of the functions, such as protecting the cell, thermal management, controlling the charge-discharge, determining the state of charge (SOC), state of health (SOH), and remaining useful life (RUL) of the battery, cell balancing, data acquisition, communication with on-board and off-board modules, as well as monitoring and storing historical data. In this paper, we propose a BMS that estimates the critical characteristics of the battery (such as SOC, SOH, and RUL) using a data-driven approach. Our estimation procedure is based on a modified Randles circuit model consisting of resistors, a capacitor, the Warburg impedance for electrochemical impedance spectroscopy test data, and a lumped parameter model for hybrid pulse power characterization test data. The resistors in a Randles circuit model usually characterize the self-discharge and internal resistance of the battery, the capacitor generally represents the charge stored in the battery, and the Warburg impedance represents the diffusion phenomenon. The Randles circuit parameters are estimated using a frequency-selective nonlinear least squares estimation technique, while the lumped parameter model parameters are estimated by the prediction error minimization method. We investigate the use of support vector machines (SVMs) to predict the capacity fade and power fade, which characterize the SOH of a battery, as well as estimate the SOC of the battery. An alternate procedure for estimating the power fade and energy fade from low-current Hybrid Pulse Power characterization (L-HPPC) test data using the lumped parameter battery model has been proposed. Predictions of RUL of the battery are obtained by support vector regression of the power fade and capacity fade estimates. Survival function estimates for reliability analysis of the battery are obtained using a hidden Markov model (HMM) trained using time-dependent estimates of capacity fade and power fade as observations. The proposed framework provides a systematic way for estimating relevant battery characteristics with a high-degree of accuracy.

This paper investigates the use of facial gestures for identity recogni- tion. This is the first time that such a quantitative evaluation is conducted, comparing the analyses of 2D versus 3D dynamic data of verbal and non- verbal facial actions. Suitable data processing and feature extraction meth- ods are examined, then a number of pattern matching techniques including the Fr´

The Adaptive Safety Analysis and Monitoring (ASAM) system is a hybrid model-based software tool for assisting intelligence analysts to identify terrorist threats, to predict possible evolution of the terrorist activities, and to suggest strategies for countering terrorism. The ASAM system provides a distributed processing structure for gathering, sharing, understanding, and using information to assess and predict terrorist network states. In combination with counter-terrorist network models, it can also suggest feasible actions to inhibit potential terrorist threats. In this paper, we will introduce the architecture of the ASAM system, and discuss the hybrid modeling approach embedded in it, viz., Hidden Markov Models (HMMs) to detect and provide soft evidence on the states of terrorist network nodes based on partial and imperfect observations, and Bayesian networks (BNs) to integrate soft evidence from multiple HMMs. The functionality of the ASAM system is illustrated by way of application to the Indian Airlines Hijacking, as modeled from open sources.

7.2 Phoneme Recognition Using Time-Delay Neural Networks 395 Fig. 1. A Time-Delay Neural Network (TDNN) unit. BOG Output layer integration Hidden Layer 2 Hidden layer 1 I Input layer 15 frames 10 msec frame rate Fig. 2. The architecture of the TDNN. network, where ...

Understanding and predicting protein structures depends on the complexity and the accuracy of the models used to represent them. We have setup a Hidden Markov Model to optimally compress three dimensional (3D) conformation of protein into a structural alphabet, i.e. a library of exhaustive and representative states (describing short fragments) learnt simultaneously with connection logic. The discretization of protein backbone local conformation as a series of states results in a simplification of protein 3D coordinates into a unique unidimensional (1D) representation. We present some evidence that such approach can constitute a very relevant way to the analysis of protein architecture in particular for protein structure comparison or prediction.