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Background HH-suite is a widely used open source software suite for sensitive sequence similarity searches and protein fold recognition. It is based on pairwise alignment of profile Hidden Markov models (HMMs), which represent multiple sequence alignments of homologous proteins. Results We developed a single-instruction multiple-data (SIMD) vectorized implementation of the Viterbi algorithm for profile HMM alignment and introduced various other speed-ups. These accelerated the search methods HHsearch by a factor 4 and HHblits by a factor 2 over the previous version 2.0.16. HHblits3 is ∼10× faster than PSI-BLAST and ∼20× faster than HMMER3. Jobs to perform HHsearch and HHblits searches with many query profile HMMs can be parallelized over cores and over cluster servers using OpenMP and message passing interface (MPI). The free, open-source, GPLv3-licensed software is available at https://github.com/soedinglab/hh-suite . Conclusion The added functionalities and increased speed of HHsearch and HHblits should facilitate their use in large-scale protein structure and function prediction, e.g. in metagenomics and genomics projects.

In the field of azimuth tracking of the underwater target, the spatial and frequency joint track-before-detect methods are widely used. Among them, estimating the frequency and azimuth states of line spectrum signals in the time series of frequency azimuth (FRAZ) spectrum is considered a direct and practical approach. However, existing processing methods generally face the challenge of high computational complexity. To deal with this problem, this paper proposed an improved method. This method transforms the FRAZ spectrum into a spectrum of azimuth-azimuth variation with fewer cells. It achieves compensation for changes in azimuth and frequency caused by target motion to some extent by taking the maximum value after multiplying the shifted spectra of different azimuths at adjoining times. Based on this transformation, a hid-den Markov model (HMM) with azimuth and azimuth variation as states is established, and the Viterbi algorithm is used to track the azimuth of underwater acoustic targets iteratively. The results of processing the simulation data demonstrate that the proposed method significantly re-duces computational complexity and improves tracking stability.

A fastIMM-extended Viterbi (fastIMM-EV) algorithm-based maneuvering target tracking method is proposed for the real-time tracking of ground maneuvering targets by a ballistic acoustic array, which firstly adopts the extended Viterbi interactive multi-model (IMM-EV) algorithm to select the best model from a given model set to match the maneuvering target motion pattern; secondly, the α – β filter and α – β – γ filter are used to replace the 2D or 3D Kalman filter in the traditional IMM algorithm, respectively, to form the fastIMM-EV algorithm, which nearly improves the algorithm efficiency, and at the same time, for the switching problem of different fastIMM-EV modules, a target maneuver recognition parameter is defined as the switching factor of the fastIMM-EV module, so that fastIMM-EV to switch the module when the target maneuver occurs; finally, the MATLAB simulation test results verify the practicality and high efficiency of the algorithm in this paper compared with different IMM target tracking methods.

Chinese word segmentation is an important research direction in related research on elementary mathematics knowledge extraction. The speed of segmentation directly affects subsequent applications, and the accuracy of segmentation directly affects corresponding research in the next step. In the machine learning methods for extracting basic mathematical knowledge points, the Conditional Random Field (CRF) model implements new word discovery well, and is increasingly used in knowledge extraction of basic mathematics. This article first introduces the traditional CRF process of named entity recognition. Then, an improved algorithm CRF++for conditional field model is proposed. Since the recognition rate of named entities based on traditional machine learning methods is not high, a post-processing method for entity recognition that automatically generates a dictionary is proposed. After identifying mathematical entities, a pruning strategy combining Viterbi algorithm and rules is proposed to achieve a higher recognition rate of elementary mathematical entities. Finally, several methods of disambiguation after entity recognition are introduced.

Cyclotron radiation emission spectroscopy (CRES) is a modern approach for determining charged particle energies via high-precision frequency measurements of the emitted cyclotron radiation. For CRES experiments with gas within the fiducial volume, signal and noise dynamics can be modelled by a hidden Markov model. We introduce a novel application of the Viterbi algorithm in order to derive informational limits on the optimal detection of cyclotron radiation signals in this class of gas-filled CRES experiments, thereby providing concrete limits from which future reconstruction algorithms, as well as detector designs, can be constrained. The validity of the resultant decision rules is confirmed using both Monte Carlo and Project 8 data.

Discovering radio pulsars in compact binaries, whose orbital periods Pb satisfy Pb ≲ 1 day, is computationally challenging, because the time-dependent pulse frequency fp(t) is strongly Doppler modulated by the binary motion. Here we present a new, fast, semi-coherent detection scheme based on a hidden Markov model (HMM) combined with a maximum-likelihood matched filter, the Schuster periodogram. The HMM scheme complements traditional acceleration searches by dividing fp(t) into piecewise-constant blocks and tracking the block-to-block evolution efficiently using dynamic programming. Monte Carlo simulations show that the new method can detect compact binaries with flux densities S ≥ 0.50 mJy and orbital periods Pb ≥ 0.012 day under observing conditions (e.g., cadence) typical of radio pulsar surveys, with and without impulsive, narrowband radio-frequency interference. The new method is fast; it employs the classic Viterbi algorithm to solve the HMM recursively. The central processing unit runtime scales nominally as Trun≈2.8NB(NT/102)(NQlnNQ/104ln104)s for NB subbands, NT coherent segments, and NQ frequency bins.

During the operation of information transmission systems errors occur in the transmitted data due to the influence of various negative factors. To correct errors the codes are currently used that can withstand both single and multiple errors. From the point of view of the ability to correct multiple errors, concatenated codes are highly efficient. When constructing a sequential concatenated code, coding is carried out first by the external, then by the internal code. A significant disadvantage of such coding is a significant increase in redundancy. It is proposed to regulate redundancy by encoding with the internal code only a certain part of the bits from the output of the external code encoder. The study showed the effectiveness of this method, which made it possible to change the parameters of the cascade code within a wide range. A method was proposed and tested to increase the corrective ability of selective coding by multiplying symbols decoded by an external code by coefficients that are different for symbols that have and have not been encoded by an internal code. Decoding of the external code was carried out according to the Viterbi algorithm. The performance of the proposed method has been confirmed experimentally.

It is relatively difficult to obtain the real-time information of the football position in the connected area formed by the ball and the line of the stadium in the football match video. This paper proposes a method for identifying the football trajectory. The least squares algorithm is applied to the football movement trajectory recognition function, and the Viterbi algorithm detection is screened according to the movement recognition function, and the football movement trajectory information is complemented for the error of the football movement in the football video. Finally, the results of an example show that the method proposed in this paper can accurately identify the changes in the motion trajectory in the video.

Finding communities of connected individuals in complex networks is challenging, yet crucial for understanding different real-world societies and their interactions. Recently attention has turned to discover the dynamics of such communities. However, detecting accurate community structures that evolve over time adds additional challenges. Almost all the state-of-the-art algorithms are designed based on seemingly the same principle while treating the problem as a coupled optimization model to simultaneously identify community structures and their evolution over time. Unlike all these studies, the current work aims to individually consider this three measures, i.e. intra-community score, inter-community score, and evolution of community over time. Here, we adopt a new perspective towards detecting the evolution of community structures. The proposed method realizes the decomposition of the problem into three essential components; searching in: intra-community connections, inter-community connections, and community evolution. A multi-objective optimization problem is defined to account for the different intra and inter community structures. Further, we formulate the community evolution problem as a Hidden Markov Model in an attempt to dexterously track the most likely sequence of communities. Then the new model, called Hidden Markov Model-based Multi-Objective evolutionary algorithm for Dynamic Community Detection (HMM-MODCD), uses a multi-objective evolutionary algorithm and Viterbi algorithm for formulating objective functions and providing temporal smoothness over time for clustering dynamic networks. The performance of the proposed algorithm is evaluated on synthetic and real-world dynamic networks and compared against several state-of-the-art algorithms. The results clearly demonstrate the effectiveness of the proposed algorithm to outperform other algorithms.

An algorithm used to extract HMM parameters is revisited. Most parts of the extraction process are taken from implemented Hidden Markov Toolkit (HTK) program under the name HInit. The HMM model is introduced briefly based on the theory of Markov Chain. We schematically outline the Viterbi method implemented by HInit. The iterative formal definition of the method which directs computer implementation is reviewed. We also illustrate the method calculation precisely using manual calculation and extensive graphical illustration. The distribution of observation probability used is simply independent Gaussians. The performance of the algorithm for phone recognition rate on small Indonesian vocabulary is 80%, while the result is near perfect 95% for word recognition.