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The historical training or exam data of an athlete produced in the past sport exercise or test activities have provided a promising way to objectively and accurately evaluate the real-time sport performance of the athlete. However, the continuous generation of sport training or exam data has placed a heavy transmission and processing burden on the traditional centralized data processing paradigm (e.g., cloud platform). Considering this drawback, a decentralized blockchain-based athlete sport data transmission and utilization solution is proposed in this research work. Moreover, the available athlete sport data produced in past sport exercise or test activities is often sparse and time-related, which call for a robust and time-aware data fusion and processing solution. In this situation, HMM model is employed in this article to cope with the data sparsity and dynamics and further make accurate sports performance prediction for athletes accordingly. Finally, we design a set of experiments on a real-world dataset to validate the feasibility of our proposal in terms of effectiveness and efficiency.

This paper proposes a novel phonography-based method for Fetal Breathing Movement (FBM) detection by its excitation sounds. It requires significantly less effort than the current procedures, and it allows long-term measurement, even at home. More than 50 pregnancies in the third trimester were examined, for a minimum of 20 minutes, taking synchronous long-term measurements using a commercial phonocardiographic fetal monitor and a 3D ultrasound machine. To analyze the gained chaotic signal, the frequency band was split into single test-frequencies in the 15-35 Hz frequency band, and their signal-free (silent) zones were regarded as the starting point (SP) of the next motions. The analysis made other disturbing signals, such as fetal hiccups, trunk rotation and limb movements, or maternal heart beats, distinguishable. The dominant test-frequencies of the analysis were predicted by a Hidden Markov Model (HMM). The SPs of the motion units (episodes) were determined by some features of the FBM, applying weighting factors. The recorded material lasted for 16 hours altogether (with nearly 3.5 hours of FBM). Based on the results of HMM method, nearly 7500 FBM episodes were identified in the phonogram signal with an average length of 0.96±0.13 seconds. The procedure for phonography-based breathing movement detection can be combined with a fetal heart activity measurement, and thus allows very intensive, long-term monitoring of the fetus.

The wireless power terminals are deployed in harsh public places and lack strict control, facing security problems. Thus, they are faced with security problems such as illegal and counterfeit terminal access, unlawful control of connected terminals, etc. The intrusion detection system based on machine learning and artificial intelligence significantly improve the terminal side’s abnormal detection capacity. In this article, we aim at identifying the abnormal behavior of wireless power terminals based on a double Hidden Markov Model (HMM), which solves the computational complexity problem caused by high dimensions in intrusion detection systems using a single HMM. The lower-layer HMM is used to identify the discrete single network abnormal behavior. Simultaneously, the upper-layer can obtain more extended period attack behavior in multiple independent abnormal events identified by the low-level. The experiment results indicate that the intrusion detection system using proposed double HMM can effectively detect the terminal’s abnormal behavior and identify the network attack behavior for an extended period.

This paper investigates the problem of sliding mode controller design based on output feedback for discrete-time Markov jump systems. Considering the typical asynchronous phenomenon for jump systems, here we adopt the Hidden Markov Model (HMM) to quantify the asynchronous degree. Based on this, a static output feedback sliding surface is constructed. Sufficient conditions in terms of bilinear matrix inequalities are proposed ensuring the sliding motion dynamic to be stochastically stable and its $\mathcal {H}_\infty $ performance for the considered discrete time Markov jump systems. Moreover, the reachability of the sliding mode surface is ensured by a predesigned control law. The whole design scheme is presented by using an iterative algorithm. Finally, simulation results are presented to illustrate the effectiveness of the design methodology.

The rapid development of computer technology and artificial intelligence is affecting people’s daily lives, where language is the most common way of communication in people’s daily life. To apply the emotion information contained in voice signals to artificial intelligence products after analysis, this article proposes a design based on voice emotion recognition for aging intelligent home products with RBF. The authors first aimed at a smart home design, and based on the problem of weak adaptability and learning ability of the aging population, a speech emotion recognition method based on a hybrid model of Hidden Markov/Radial Basis Function Neural Network (HMM/RBF) is proposed. This method combines the strong dynamic timing modeling capabilities of the HMM model and the strong classification decision-making ability of the RBF model, and by combining the two models, the speech emotion recognition rate is greatly improved. Furthermore, by introducing the concept of the dynamic optimal learning rate, the convergence speed of the network is reduced to 40.25s and the operation efficiency is optimized. Matlab’s simulation tests show that the recognition speed of the HMM/RBF hybrid model is 9.82–12.28% higher than that of the HMM model and the RBF model alone, confirming the accuracy and superiority of the algorithm and model.

BackgroundMetagenomics can provide important insight into microbial communities. However, assembling metagenomic datasets has proven to be computationally challenging. Current methods often assemble only fragmented partial genes.ResultsWe present a novel method for targeting assembly of specific protein-coding genes. This method combines a de Bruijn graph, as used in standard assembly approaches, and a protein profile hidden Markov model (HMM) for the gene of interest, as used in standard annotation approaches. These are used to create a novel combined weighted assembly graph. Xander performs both assembly and annotation concomitantly using information incorporated in this graph. We demonstrate the utility of this approach by assembling contigs for one phylogenetic marker gene and for two functional marker genes, first on Human Microbiome Project (HMP)-defined community Illumina data and then on 21 rhizosphere soil metagenomic datasets from three different crops totaling over 800 Gbp of unassembled data. We compared our method to a recently published bulk metagenome assembly method and a recently published gene-targeted assembler and found our method produced more, longer, and higher quality gene sequences.ConclusionXander combines gene assignment with the rapid assembly of full-length or near full-length functional genes from metagenomic data without requiring bulk assembly or post-processing to find genes of interest. HMMs used for assembly can be tailored to the targeted genes, allowing flexibility to improve annotation over generic annotation pipelines. This method is implemented as open source software and is available at https://github.com/rdpstaff/Xander_assembler.

A common step in the analysis of multiparent populations (MPPs) is genotype reconstruction: identifying the founder origin of haplotypes from dense marker data. This process often makes use of a probability model for the pattern of founder alleles along chromosomes, including the relative frequency of founder alleles and the probability of exchanges among them, which depend on a model for meiotic recombination and on the mating design for the population. While the precise experimental design used to generate the population may be used to derive a precise characterization of the model for exchanges among founder alleles, this can be tedious, particularly given the great variety of experimental designs that have been proposed. We describe an approximate model that can be applied for a variety of MPPs. We have implemented the approach in the R/qtl2 software, and we illustrate its use in applications to publicly available data on Diversity Outbred and Collaborative Cross mice. [ABSTRACT FROM AUTHOR]

Weather radars are capable of detecting and displaying storm‐related turbulence as well as precipitation rate with fine spatial resolution and reliable quality. Especially, weather radars can enable the detection of atmospheric conditions in the vicinity of cities and thereby help to notify the strong winds. This article investigates the application of weather radar measurements for predicting strong wind and presents a new method for very short‐term storm prediction. In the proposed method, hidden Markov model (HMM) is used to classify atmospheric conditions to "potentially stormy" and "non‐stormy" states using the available radar data, and semi‐Markov theory is used to estimate the probability of storm occurrence with time. In fact, the probability of transition between "potentially stormy," "non‐stormy," and "stormy" states is modeled by a semi‐Markov model, to find the unconditional probability of storm occurrence with time. The model is implemented with the use of the data of Tehran C‐band weather radar and anemometer of Tehran international airport. Verification results show that the precision (forecast accuracy) is around 0.19 and the recall is around 0.67 in the presented classification method. [ABSTRACT FROM AUTHOR]

One of the most commonly reported disabilities is vision loss, which can be diagnosed by an ophthalmologist in order to determine the visual system of a patient. This procedure, however, usually requires an appointment with an ophthalmologist, which is both time-consuming and expensive process. Other issues that can arise include a lack of appropriate equipment and trained practitioners, especially in rural areas. Centered on a cognitively motivated attribute extraction and speech recognition approach, this paper proposes a novel idea that immediately determines the eyesight deficiency. The proposed system uses an adaptive filter bank with weighted mel frequency cepstral coefficients for feature extraction. The adaptive filter bank implementation is inspired by the principle of spectrum sensing in cognitive radio that is aware of its environment and adapts to statistical variations in the input stimuli by learning from the environment. Comparative performance evaluation demonstrates the potential of our automated visual acuity test method to achieve comparable results to the clinical ground truth, established by the expert ophthalmologist's tests. The overall accuracy achieved by the proposed model when compared with the expert ophthalmologist test is 91.875%. The proposed method potentially offers a second opinion to ophthalmologists, and serves as a cost-effective pre-screening test to predict eyesight loss at an early stage. [ABSTRACT FROM AUTHOR]

In general, the safety critical systems are zero error tolerance systems, designed with the high precision approach and with maximum perfection. Hence the authors attempted to create a flawless design by analyzing the various components including error occurrence at low-level design of making software. In view of this, the migration of design defects is quantified from origin to multiple states through hidden markov model approach. Here the probabilistic natures of selected defects by observing the operation of anti-lock braking system in various scenarios are modeled. It is observed that this model supports in identifying and quantifying the behavioral properties of selected errors while interacting with subsystems. The behavior of software is determined in terms of hidden state sequence. The sensitivity and precision quotient are measured for goodness-of-fit. This approach of early analysis of software hidden design errors will enhance the precision in producing any of the safety critical systems in practice. [ABSTRACT FROM AUTHOR]

Recognition of the emotional content in speech has increased the attention of researchers and various techniques have been proposed to identify the emotion of the speech. This is the survey paper for the classification of emotions methods. Three important aspects of the speech recognition system are in the selection of the features of the speech, second is the appropriate classification method, and the last is the preparation of the emotion speech database. This paper also indicates the possible ways to improve the speech emotion recognition system. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We developed PyLAE, a new tool for determining local ancestry along a genome using whole-genome sequencing data or high-density genotyping experiments. PyLAE can process an arbitrarily large number of ancestral populations (with or without an informative prior). Since PyLAE does not involve estimating many parameters, it can process thousands of genomes within a day. PyLAE can run on phased or unphased genomic data. We have shown how PyLAE can be applied to the identification of differentially enriched pathways between populations. The local ancestry approach results in higher enrichment scores compared to whole-genome approaches. We benchmarked PyLAE using the 1000 Genomes dataset, comparing the aggregated predictions with the global admixture results and the current gold standard program RFMix. Computational efficiency, minimal requirements for data pre-processing, straightforward presentation of results, and ease of installation make PyLAE a valuable tool to study admixed populations. [ABSTRACT FROM AUTHOR]

Abstract Point of interest (POI) recommendation can benefit users and merchants. It is a very important and popular service in modern life. In this paper, we aim to study the next new POI recommendation problem with the consideration of privacy preserving in edge computing. The challenge lies in capturing the transition patterns between POIs precisely and meanwhile protecting users’ location. In this paper, first, we propose to model users’ check-in sequences with their latent states based on HMM, and EM algorithm is used to estimate the parameters of the model. Second, we propose to protect users’ location information by a weighted noise injection method. Third, we predict users’ next movement according to his current location based on Forward algorithm. Experimental results on two large-scale LBSNs datasets show that our proposed model without noise injection can achieve better recommendation accuracy than several state-of-the-art techniques, and the proposed weighted noise injection approach can achieve better performance on privacy preserving than traditional one with a little cost on accuracy.

Clinical researchers use prognostic modeling techniques to identify a-prior patient health status and characterize progression patterns. It is highly desirable to predict future health condition especially to implement preventive and intervention strategies in pre-diabetic individuals. Hidden Markov Model (HMM) and its variants are a class of models that provide predictions concerning future condition by exploiting sequences of clinical measurements obtained from a longitudinal sample of patients. Despite the advantages of using these models for prognostic modeling, it still face barriers and significant challenges, to effectively learn dynamic interactions, when using irregularly sampled longitudinal Electronic Medical Records (EMRs) data. Newton's divide difference method (NDDM) is a classical approach for handling irregular data in terms of divided difference. However, as it is polynomial approximation technique, it suffers with Runge Phenomenon. The problem can be even more severe when the interval is a bit extended. Therefore, to tackle this problem, we proposed a novel approximation method based on NDDM as a component with HMM in order to estimate the 8 years risk of developing Type 2 Diabetes Mellitus (T2DM) in a particular individual. The proposed method is evaluated on real world clinical data obtained from CPCSSN. The results demonstrated that our proposed technique has the ability to exploit the available irregularly sampled EMRs data for effective approximation and improved prediction accuracy.

Hill musculoskeletal model (HMM) is commonly used to estimate human motion intentions. HMM utilizes electromyography (EMG) signals as the nonlinear model input to obtain muscle forces or torques. However, due to the fact that it contains many physiological parameters that are difficult to measure, HMM is generally applied in simple continuous intention estimation of a single joint. In this work, we aimed at recognizing shoulder and elbow joints angles and their angular velocities continuously in real time. Firstly, we used MYO armband as the EMG sensor. Then, a reasonable prediction model was deduced based on HMM and human dynamics to realize online continuous recognition of the four angles and angular velocities of shoulder and elbow joints. Nonlinear autoregressive with external input neural network (NARX) replaced the prediction equation. In addition, the framework of state space model was completed by constructing an observation equation. Thus, the closed-loop characteristic was realized to eliminate the influence of cumulative error and ensure good estimation performance. Experimental results verified the feasibility and accuracy of the algorithm. For predefined trajectory and random trajectory separately, the RMSE were 0.955 and 1.15 (degree) for angles estimation and 2.8, 3.40 for angular velocities (degree/s). Compared with the normally used back-propagation neural network (BPNN), the method proposed in this paper obviously got more accurate and smooth results.

Condition-Based Maintenance (CBM), based on sensors, can only be reliable if the data used to extract information are also reliable. Industrial metrology plays a major role in ensuring the quality of the data collected by the sensors. To guarantee that the values collected by the sensors are reliable, it is necessary to have metrological traceability made by successive calibrations from higher standards to the sensors used in the factories. To ensure the reliability of the data, a calibration strategy must be put in place. Usually, sensors are only calibrated on a periodic basis; so, they often go for calibration without it being necessary or collect data inaccurately. In addition, the sensors are checked often, increasing the need for manpower, and sensor errors are frequently overlooked when the redundant sensor has a drift in the same direction. It is necessary to acquire a calibration strategy based on the sensor condition. Through online monitoring of sensor calibration status (OLM), it is possible to perform calibrations only when it is really necessary. To reach this end, this paper aims to provide a strategy to classify the health status of the production equipment and of the reading equipment that uses the same dataset. A measurement signal from four sensors was simulated, for which Artificial Intelligence and Machine Learning with unsupervised algorithms were used. This paper demonstrates how, through the same dataset, it is possible to obtain distinct information. Because of this, we have a very important feature creation process, followed by Principal Component Analysis (PCA), K-means clustering, and classification based on Hidden Markov Models (HMM). Through three hidden states of the HMM, which represent the health states of the production equipment, we will first detect, through correlations, the features of its status. After that, an HMM filter is used to eliminate those errors from the original signal. Next, an equal methodology is conducted for each sensor individually and using statistical features in the time domain where we can obtain, through HMM, the failures of each sensor.

Summary We developed PyLAE, a new tool for determining local ancestry along a genome using whole-genome sequencing data or high-density genotyping experiments. PyLAE can process an arbitrarily large number of ancestral populations (with or without an informative prior). Since PyLAE does not involve estimating many parameters, it can process thousands of genomes within a day. PyLAE can run on phased or unphased genomic data. We have shown how PyLAE can be applied to the identification of differentially enriched pathways between populations. The local ancestry approach results in higher enrichment scores compared to whole-genome approaches. We benchmarked PyLAE using the 1000 Genomes dataset, comparing the aggregated predictions with the global admixture results and the current gold standard program RFMix. Computational efficiency, minimal requirements for data pre-processing, straightforward presentation of results, and ease of installation make PyLAE a valuable tool to study admixed populations. Availability and implementation The source code and installation manual are available at https://github.com/smetam/pylae.

Presently, it is still challenging to obtain satisfied identification results for long-distance safety monitoring with fiber distributed acoustic sensor (DAS) in practical complicated burying environments. Thus, extracting increasingly abundant features has always been the direction of DAS signal recognition. This paper proposes a new recognition method using an end-to-end mCNN-HMM combined model, which can identify the vibration sources more correctly by simultaneously extracting multi-scale structural features and the sequential information of the DAS signals. A modified multi-scale convolution neural network (mCNN) is designed to automatically extract the DAS signals' local structural features from a multilevel perspective and their relationship in the proposed model. A hidden Markov model (HMM) is then used to mine the sequential information of the whole sample's previously extracted features. The test results based on real field data show that it outperforms the HMM model with the all-around hand-crafted features, the CNN-HMM model, and the MS-CNN-HMM model in both the feature extraction ability and the recognition accuracy in the case of little increase in time consumption. Moreover, the Euclidean distance between the posterior probabilities classified correctly and incorrectly is proposed to evaluate the test samples' feature distinguishability for different recognition models. Then the feature extraction capabilities of the models can be measured in an objective parameter. [ABSTRACT FROM AUTHOR]

Copyright of Plastics Science & Technology / Suliao Ke-Ji is the property of Plastics Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

INTRODUCTION: In this modern era of internet and technology natural language processing task has emerged as one of the major research area in computer science. Grammatical error detection and correction system assists to detect and correct syntactic errors present in written text. OBJECTIVES: In this research article, author investigate the applicability of stochastic approach for the development of grammatical error detection and correction system for Punjabi language. METHOD: Author used corpus based stochastic approach to developed the system. The corpus used was taken from Indian language corpora initiative. RESULTS: On testing, the developed system shows a precision as 82.5%, recall as 89%. and f-measure as 85%. The results of the proposed system outperform the existing rule based system that shows precision of 76.79%, recall of 87.08%, and F-measure of 81.61%. CONCLUSION: author concluded that for syntax analysis stochastic approach can perform better than rule based approach. [ABSTRACT FROM AUTHOR]

Background: We present ARCHes, a fast and accurate haplotype-based approach for inferring an individual's ancestry composition. Our approach works by modeling haplotype diversity from a large, admixed cohort of hundreds of thousands, then annotating those models with population information from reference panels of known ancestry. Results: The running time of ARCHes does not depend on the size of a reference panel because training and testing are separate processes, and the inferred population-annotated haplotype models can be written to disk and reused to label large test sets in parallel (in our experiments, it averages less than one minute to assign ancestry from 32 populations using 10 CPU). We test ARCHes on public data from the 1000 Genomes Project and the Human Genome Diversity Project (HGDP) as well as simulated examples of known admixture. Conclusions: Our results demonstrate that ARCHes outperforms RFMix at correctly assigning both global and local ancestry at finer population scales regardless of the amount of population admixture. [ABSTRACT FROM AUTHOR]

Animals continuously interact with their environment through behavioral decisions, rendering the appropriate choice of movement speed and directionality an important phenotypic trait. Anthropogenic activities may alter animal behavior, including movement. A detailed understanding of movement decisions is therefore of great relevance for science and conservation alike. The study of movement decisions in relation to environmental and seasonal cues requires continuous observation of movement behavior, recently made possible by high‐resolution telemetry. We studied movement traits of 13 capercaillie (Tetrao urogallus), a mainly ground‐moving forest bird species of conservation interest, over two summer seasons in a Swedish windfarm using high‐resolution GPS tracking data (5‐min sampling interval). We filtered and removed unreliable movement steps using accelerometer data and step characteristics. We explored variation in movement speed and directionality in relation to environmental and seasonal covariates using generalized additive mixed models (GAMMs). We found evidence for clear daily and seasonal variation in speed and directionality of movement that reflected behavioral adjustments to biological and environmental seasonality. Capercaillie moved slower when more turbines were visible and faster close to turbine access roads. Movement speed and directionality were highest on open bogs, lowest on recent clear‐cuts (<5 y.o.), and intermediate in all types of forest. Our results provide novel insights into the seasonal and environmental correlates of capercaillie movement patterns and supplement previous behavioral observations on lekking behavior and wind turbine avoidance with a more mechanistic understanding. [ABSTRACT FROM AUTHOR]

Statistical dependence between hypotheses poses a significant challenge to the stability of large scale multiple hypotheses testing. Ignoring it often results in an unacceptably large spread in the false positive proportion even though the average value is acceptable (Fan et al., J Amer Statist Assoc 107(499): 1019-1035, 2012; Owen J R Stat Soc Ser B 67(3): 411–426, 2005; Qiu et al., Stat Appl Genet Mol Biol 4: 32, 2005 and Schwartzman and Lin Biometrika 98(1): 199–214, 2011). However, the statistical dependence structure of data is often unknown. Using a generic signal-processing model, Bayesian multiple testing, and simulations, we demonstrate that the variance of the false positive proportion can be substantially reduced even under unknown short range dependence. We do this by modeling the data generating process as a stationary ergodic binary signal process embedded in noisy observations. We derive conditional probabilities needed for the Bayesian multiple testing by incorporating nearby observations into a second order Taylor series approximation. Simulations under general conditions are carried out to assess the validity and the variance reduction of the approach. Along the way, we address the problem of sampling a random Markov matrix with specified stationary distribution and lower bounds on the top absolute eigenvalues, which is of interest in its own right. [ABSTRACT FROM AUTHOR]