Viterbi Algorithm Research Articles

Decision Feedback Equalization-Based Low-Complexity Interference Cancellation and Signal Detection Technique Based for Non-Orthogonal Signaling

FTN signalling is an effective communication method that achieves a high spectral efficiency. However, employing a symbol rate faster than the Nyquist rate disrupts the orthogonality between symbols, leading to unavoidable inter-symbol interference (ISI). To mitigate the effects of ISI, interference cancellation and signal detection processes are essential for FTN receivers. Conventional ISI reduction techniques often utilize trellis-based algorithms. However, as the number of states increases due to additional interference symbols, the complexity of these algorithms grows exponentially. To address this challenge, this paper explores a matrix computation-based interference cancellation technique tailored for FTN communication systems, aiming to significantly reduce the complexity of the ISI mitigation process. To execute ISI cancellation and signal detection more precisely, the proposed technique includes iterative interference cancellation and a signal detection process. When six interference symbols are considered, the complexity of the proposed technique is reduced by 97% compared with that of the conventional Viterbi algorithm. Furthermore, in the case of τ = 0.85, the performance of the proposed technique is about 1 dB better than that of the Viterbi algorithm at BER = 10−4.

AMBV: An Optimized Generic Viterbi Algorithm for Bayesian Networks

AMBV: An Optimized Generic Viterbi Algorithm for Bayesian Networks

Three-State Hidden Markov Model for Spectrum Prediction in Cognitive Radio Networks

The exponential growth and proliferation of wireless devices for different wireless applications have led to the emergence of cognitive radio network (CRN) for optimal utilization of scarce spectrum resources. However, these resources have grossly been under-utilized due to the inaccurate spectrum predictions. Existing spectrum occupancy and prediction techniques which rely on 2-state hidden Markov model (HMM) results in false alarm or missed detection caused by noisy or incomplete observable effects. In this paper, a 3-state HMM spectrum occupancy and prediction technique in CRNs is proposed. The transmission, emission and initial state probabilities of the proposed 3-state HMM parameters were derived based on the three canonical problems associated with HMM. The evaluation, decoding and learning problems were solved using Forward algorithm, Viterbi algorithm and the Baum-Welch algorithm, respectively. The performance of the proposed 3-state HMM spectrum prediction technique was evaluated using prediction accuracy, probability of detection and spectrum utilization efficiency. The simulation results obtained revealed that the 3-state HMM outperformed the 2-state HMM spectrum prediction technique by 24.1% in prediction accuracy.

Joint Decoding Technique for Collision Resolution in Non-orthogonal Multiple Access Environment

Multiple access technologies have grown hand in hand from the first generation to the 5th Generation (5G) with both performance and quality improvement. Non-Orthogonal Multiple Access (NOMA) is the recent multiple access technology adopted in the 5G communication technology. Capacity requirements of wireless networks have grown to a large extent with the penetration of ultra-high-definition video transmission, Internet of Things (IoT), and virtual reality applications taking ground in the recent future. This paper develops the Physical Layer Network Coding (PNC) for collision resolution in a NOMA environment with two users. Traditionally NOMA uses Successive Interference Cancellation (SIC) for collision resolution. While additionally a decoding algorithm is added along with SIC to improve the performance of the collision resolution. MATLAB-based simulation is developed on the NOMA environment with two users using Viterbi coding, Low-Density Parity Check (LDPC), and Turbo coding. Performance parameters of Bit Error Rate (BER) and throughput are compared for these three algorithms. It is observed that the Turbo coding performed better among these three algorithms both in the BER and throughput. The BER obtained from the SIC- Turbo is found to be performing well with an increase of about 14% from the ordinary SIC implementation. The performance of the collision resolution has increased by 13% to 14% when joint decoding techniques are used and thus increasing the throughput of the NOMA paradigm.

The Semantic Function of Modern Chinese “Negation + X” Modal Words Based on Communication Technology and Big Data Corpus

The semantic function of modern Chinese “negation + X” modal words based on communication technology and big data corpus has gained wide attention. As the basis of SOA architecture, Web services provide the key resources for worldwide information transfer and information sharing with their characteristics of loose coupling, platform independence, and data exchange without additional support from third-party hardware and software. However, along with the popularization and improvement of Web service technology, the number and types of Web services in the Internet are also increasing massively, and there are a large number of Web services with various functions, quality and granularity. Therefore, how to quickly and accurately discover Web services that satisfy users’ query requests from a large and complex set of services has become a critical problem to be solved in the current Web service discovery research. Based on the real corpus, this paper analyzes the similarities and differences in the semantic functions of modern Chinese “negation + X” modal words by combining lexicalization and grammatization, cognitive linguistics, systemic functional grammar and other related theories. The experimental results demonstrate that the model is designed for automatic annotation of semantic word classes, and the annotation algorithm based on the hidden horse model, combined with the Viterbi algorithm based on dynamic programming, achieves a correct rate of 94.3% in the closed test and 89.1% in the open test despite the small size of the training corpus and severe data sparsity, and the model fitting effect meets the dynamic expectations.

A TDOA sequence estimation method of underwater sound source based on hidden Markov model

To address the time difference of arrival (TDOA) estimation problem in the passive positioning system with wideband underwater motion sound sources and distributed hydrophones, a hidden Markov model-based (HMM) TDOA sequence estimation method is proposed in this paper. The method estimates the TDOA with multi-frame output of cross-correlated signals received on hydrophones. The transfer equation of the TDOA is established as a first-order hidden Markov process by analyzing the motion characteristics of the moving sound source and delays obtained from different hydrophones. Dynamic assignment of the HMM parameters is proposed to address the inconsistent change rate of the TDOA. We then achieve an HMM expression of the TDOA sequence by fitting the transfer equation and dynamic assignment of parameters into the HMM. Then, the Viterbi algorithm (VA) is applied to distinguish the optimal sequence of the TDOA among ambiguous estimations. To deal with the problem of data loss or unreliable issues caused by interferences, a data prediction algorithm which could produce possible time delays is added to VA to avoid the impact of outliers on the estimation results. By utilizing multi-frame processing, the proposed method reduces the signal-to-noise ratio (SNR) requirement of single frames since it does not require accurate estimations of TDOA for each frame. Moreover, the method adapts to a lower SNR, which has significant advantages in terms of whole sequence estimation compared with common methods. The results from the simulations and lake experiments validated the proposed TDOA sequence estimation method.

A VVC Dependent Quantization Optimization Based on the Parallel Viterbi Algorithm and Its FPGA Implementation

A VVC Dependent Quantization Optimization Based on the Parallel Viterbi Algorithm and Its FPGA Implementation

Discrete Dynamic Modeling Analysis of Badminton Games Based on Viterbi Algorithm in College Badminton Physical Education

Discrete Dynamic Modeling Analysis of Badminton Games Based on Viterbi Algorithm in College Badminton Physical Education

Prediction maintenance based on vibration analysis and deep learning — A case study of a drying press supported on a Hidden Markov Model

The main objective of this paper is to describe a methodology that was developed to support maintenance decision-making methods based on equipment condition. Condition-Based Maintenance allows to increase equipment availability and maximize investments. This is mainly due to the prevention of unexpected equipment downtime. By avoiding turning on/off industrial equipment, production flows are more efficient, allowing manufacturers to improve the quality of the end-product. The industry aims more and more to correspond satisfying customer expectations. We argue that the methodology developed in this paper adds value to the existing literature, namely because the fact that it is possible to anticipate the state of an equipment without a large amount of support data. In other words, although one could find information gaps regarding the occurrence of failures, it was possible to accurately assess the state of the equipment. This approach is robust, as it can be used in distinct equipment with different sensors, making this methodology generalizable for Condition-Based Maintenance. The paper presents the validation of the preceding through a case study on drying presses in the paper industry. To do so, three states were adopted, namely: “Proper function”; “Alert state”; and “Equipment failure”. The methodology follows a series of steps, going through the collection of values from vibration sensors, imputation of values using Deep Artificial Neural Networks through on-line sensors, until reaching the last stage of classification carried out by the Hidden Markov Model. Through optimized observations from the previous steps, it was possible to define the hidden states through the Viterbi algorithm, which corresponds to the health states of the equipment. Additionally, it was possible to demonstrate that the proposed methodology can accurately characterize the condition states of the equipment based on the data obtained and can be generalized to other types of equipment.

Applying the Viterbi algorithm to planetary-mass black hole searches

Applying the Viterbi algorithm to planetary-mass black hole searches

Enhancing air traffic operational efficiency by reducing network scale

The growing demand for air travel has led to the saturation of air traffic networks. Conventional methods of adding routes to alleviate congestion and reduce delays may not achieve the desired effect and even degrade system performance. In this paper, we explore the application of Braess’s Paradox in the reduction of air traffic networks. This counterintuitive phenomenon shows that adding new connections to a network can actually increase the overall network pressure. This study uses Hidden Markov methods and the Viterbi algorithm to match air traffic flow with routes, a machine learning approach and a mathematical method to construct cost functions for flight time and traffic volume, and finally uses genetic algorithm and the A* algorithm to detect Braess’s Paradox edges. We uses ADS-B data from the busy month of July 2019 for a case study of the air traffic network over the UK airspace. The results show that Braess’s Paradox is also applicable to multi-flight level air route networks. Removing such network edges can improve system performance. In one day’s case, the total flight time of the day’s traffic volume decreased from 11509.24 minutes to 10459.97 minutes. This equates to an average savings of 4.99 minutes of flight time per flight, which is significant in controlling delay performance.

Likelihood and decoding problems for mixed space hidden Markov model

Abstract A Hidden Markov Model (HMM) is a couple of stochastic processes: A hidden Markov process and an observed emission process. Generally, the HMMs are used to study the hidden behavior of random systems through some observed emission sequences generated by the phenomenon under study. In this frame-work, we propose to solve the likelihood and the decoding problems of HMMs whose state space is composed of a continuous part and a discrete part. We adapt forward, backward and Viterbi algorithms to the case of our proposal. Numerical examples and Monte Carlo simulations are considered to show the efficiency and the adaptation of the algorithms for the proposed model.

An Enhanced Hidden Markov Model for Map-Matching in Pedestrian Navigation

Map-matching is a core functionality of pedestrian navigation applications. The localization errors of the global positioning systems (GPSs) in smartphones are one of the most critical factors that limit the large-scale deployment of pedestrian navigation applications, especially in dense urban areas where multiple road segments exist within the range of GPS errors, which can be increased by tall buildings neighboring each other. In this paper, we address two issues of practical importance for map-matching based on the Hidden Markov Model (HMM) in pedestrian navigation systems: large localization error in the initial phase of map-matching and HMM breaks in open field traversals. A heuristic method to determine the probability of initial states of the HMM based on a small number of GPS data received during the short warm-up period is proposed to improve the accuracy of initial map-matching. A simple but highly practical method based on a heuristic evaluation of near-future locations is proposed to prevent the malfunction of the Viterbi algorithm within the area of open fields. The results of field experiments indicate that the enhanced HMM constructed via the proposed methods achieves significantly higher map-matching accuracy compared to that of state of the art.

Noninvasive Prenatal Diagnosis of SEA-Thalassemia by Combining 1000 Genomes Database and Relative Haplotype Dosage

To explore a noninvasive method for diagnosis of SEA-thalassemia and to investigate whether the regional factors affect the accuracy of this method. The method involved using a public database and bioinformatics software to construct parental haplotypes for proband and predicting fetal genotypes using relative haplotype dosage. We screened and downloaded sequencing data of couples who were both SEA-thalassemia carriers from the China National Genebank public data platform, and matched the sequencing data format with that of the reference panel using Ubuntu system tools. We then used Beagle software to construct parental haplotypes, predicted fetal haplotypes by relative haplotype dosage. Finally, we used Hidden Markov Model and Viterbi algorithm to determine fetal pathogenic haplotypes. All noninvasive fetal genotype diagnosis results were compared with gold standard gap-PCR electrophoresis results. Our method was successful in diagnosing 13 families with SEA-thalassemia carriers. The best diagnostic results were obtained when Southern Chinese Han was used as the reference panel, and 10 families showed full agreement between our noninvasive diagnostic results and the gap-PCR electrophoresis results. The accuracy of our method was higher when using a Chinese Han as the reference panel for haplotype construction in the Southern Chinese Han region as opposed to Beijing Chinese region. The combined use of public databases and relative haplotype dosage for diagnosing SEA-thalassemia is a feasible approach. Our method produces the best noninvasive diagnostic results when the test samples and population reference panel are closely matched in both ethnicity and geography. When constructing parental haplotypes with our method, it is important to consider the effect of region in addition to population background alone.

Improvement of post BER performance in computer communication on Viterbi algorithm

Computer communication is the pivotal exchange of information and data among computing devices, exerting substantial influence over network communication quality. The conventional approach has typically centred on enhancing Bit Error Rate (BER) performance to bolster information quality during the transmission process. However, our research pioneers a novel strategy, focusing on post-BER improvement. Our innovative method prioritizes enhancing information quality at the receiver's end by implementing a unique approach. We employ the Viterbi algorithm to elevate the quality of individual words, ensuring accurate word recognition and precise comprehension. The ultimate goal is to provide end-users with an unambiguous and accurate understanding of transmitted words. Our simulation results conclusively demonstrate the effectiveness of this approach. Following the reception of words, we successfully attain the correct word information with remarkable accuracy. This approach transforms word-level information processing, safeguarding against inaccuracies and ambiguities that may otherwise compromise the integrity of data transfer. In summary, our research introduces a paradigm shift in optimizing information quality within computer communication networks. By focusing on post-BER improvements and harnessing the power of the Viterbi algorithm, we enable the receiver to decode words with precision and clarity. This breakthrough promises to enhance the overall quality and reliability of information exchange, further solidifying the foundation of modern computer communication.

Innovative non-contact r-r intervals estimation using viterbi algorithm with Squared Branch Metric (VSBM)

Non-contact heartbeat detection by using Doppler sensor is a critical component of remote health monitoring systems, enabling continuous and unobtrusive monitoring of an individual’s cardiovascular health. In this paper, we reported an innovative approach for non-contact heartbeat detection using the Viterbi algorithm, leveraging the distribution of the difference of two adjacent R-R intervals (RRIs). RRIs represent the time between successive peaks in the electrocardiogram (ECG) signal and are fundamental in analyzing heart rate variability, mental stress conditions and heart diseases. Numerous non-contact Doppler sensor-based methods have been proposed for heartbeat detection, leveraging the evaluation of RRIs without physical device attachment. However, challenges arise from unwanted peaks introduced by respiration and slight body movements, even when the subject remains motionless with normal breathing. This study presented an innovative approach for selecting heartbeat peaks utilizing the Viterbi algorithm with the squared difference of two adjacent RRIs as the Branch metric (BM). Our preliminary experiments reveal that the difference between two adjacent RRIs closely follows a Gaussian distribution. Building upon this observation, we considered the Viterbi algorithm with Squared Branch Metric (VSBM) to estimate the heartbeat accurately. To assess the accuracy of our peak selection method, we conducted experiments comparing it with two existing peak detection methods: (i) Doppler output after Low-Pass Filter (LPF)-based method and (ii) Spectrogram-based method. Our results demonstrate that the proposed VSBM method is effective to detect the heartbeat accurately for each peak detection method. Furthermore, we compared the performance of “Spectrogram + VSBM” outperforms the “Doppler output after LPF + VSBM” method by the Root-Mean-Square Error (RMSE) of RRIs.

Partial Sequence Labeling With Structured Gaussian Processes.

Existing partial sequence labeling models mainly focus on a max-margin framework that fails to provide an uncertainty estimation of the prediction. Furthermore, the unique ground-truth disambiguation strategy employed by these models may include wrong label information for parameter learning. In this article, we propose structured Gaussian processes for partial sequence labeling (SGPPSL), which encodes uncertainty in the prediction and does not need extra effort for model selection and hyperparameter learning. The model employs factor-as-piece approximation that divides the linear-chain graph structure into the set of pieces, which preserves the basic Markov random field structure and effectively avoids handling a large number of candidate output sequences generated by partially annotated data. Then, confidence measure is introduced in the model to address different contributions of candidate labels, which enables the ground-truth label information to be utilized in parameter learning. Based on the derived lower bound of the variational lower bound of the proposed model, variational parameters and confidence measures are estimated in the framework of alternating optimization. Moreover, a weighted Viterbi algorithm is proposed to incorporate confidence measures to sequence prediction, which considers label ambiguity arose from multiple annotations in the training data and thus helps improve the performance. SGPPSL is evaluated on several sequence labeling tasks and the experimental results show the effectiveness of the proposed model.

Method for changing the redundancy of sequential concatenated code

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.

P445 Wireless capsule endoscopy reading support for Crohn’s disease patients using deep neural networks with an active learning method

Abstract Background Artificial intelligence (AI) systems, in particular neural networks (NN), have been developed to detect lesions in wireless capsule endoscopy (WCE) in patients with Crohn’s disease (CD) in order to ease their reading. Despite acceptable performance on individual images, these tools do not analyze entire videos and require a large amount of labeled data. Active learning (AL) methods have already been used, proving their ability to limit labeling cost without reducing NN performance. The aim of this study was to evaluate several AL models in order to train an AI system able to differentiate normal and pathological entire videos with a limited labeled dataset. Methods Firstly, 76 WCEs of CD patients were used to automate the detection of the first and last image of the small bowel. They were identified by an expert and then used for training, validation and test of a NN. The model associated with the Viterbi algorithm predicted boundaries location and was compared with expert annotations. Secondly, 4286 images from the Crohn-IPI database¹ were used to train another NN to classify pathological or normal images. An image was considered pathological if it contained erythema, oedema, ulceration or stenosis. Four AL models with several selection criteria² were tested on a limited labeled dataset and their classification performance (normal/pathological image) was evaluated. Finally, 42 WCEs were used to assess performance of a pre-trained AL model to predict the normal or pathological nature of entire videos. Results Overall, the model detected the pylorus compared with experts with a median deviation (IQR) of 3 images (66.5) and the last ileal image with a median deviation of 98 images (336). A history of ileocolic resection non-significantly impaired the last ileal image location, with a median difference of 203 images (812, p=0.59). None of the four AL models showed superiority in classifying pathological or normal image, with a sensitivity of 60% and a specificity of 98%. Finally, the AL pre-trained NN assessed the pathological/normal status of entire videos with a sensitivity of 83% and a specificity of 50%. Conclusion Our study suggests the feasibility and efficiency of NN to identify the first and last image of the small bowel on CD patients WCE. Performances of the four AL models were similar but remained sufficient given the small amount of labeled data used. Prediction of the pathological/normal status of entire videos by AI systems seems feasible, but requires optimization to obtain acceptable results for current practice. 1. De Maissin A et al. Multi-expert annotation of Crohn's disease(...) neural network, Endosc IntOpen(2021) 2. Wang K et al. Cost-Effective Active Learning for Deep Image Classification, IEEE (...) Technology(2017)

Tensor-Based Viterbi Algorithms for Collaborative Cloud-Edge Cyber-Physical-Social Activity Prediction

With the rapid development and application of smart city, Cyber-Physical-Social Systems (CPSS) as its superset is becoming increasingly important, and attracts extensive attentions. For satisfying the smart requirements of CPSS design, a cloud-edge collaborative CPSS framework is first proposed in this paper. Then Coupled-Hidden-Markov-Model (CHMM) and tensor algebra are used to improve existing activity prediction methods for providing CPSS with more intelligent decision support. There are three key features (timing, periodicity and correlation) implied in CPSS data from multi-edge, which affects the accuracy of activity prediction. Thus, these features are synthetically integrated into improved Tensor-based CHMMs (T-CHMMs) to enhance the prediction accuracy. Based on the multi-edge CPSS data, three Tensor-based Viterbi Algorithms (TVA) are correspondingly proposed to solve the prediction problem for T-CHMMs. Compared with traditional matrix-based methods, the proposed TVA could more accurately compute the optimal hidden state sequences under given observation sequences. Finally, the comprehensive performances of proposed models and algorithms are validated on three open datasets by self-comparison and other-comparison. The experimental results show that the proposed methods is superior to the compared three classical methods in terms of F1 measure, average precision and average recall.