Viterbi Algorithm Research Articles

Efficient SOVA decoding and enhanced early termination mechanism based on new attenuation factors

This paper proposes two attenuation factors (AF’s) to improve the performance of the soft-output Viterbi algorithm (SOVA) as well as to enhance the early termination mechanism in turbo decoding. The mean square difference between the systematic bipolar coded symbols and the a-posteriori information is used to estimate the first AF. The second AF is computed online for each iteration based on the correlation coefficient between the extrinsic and a-priori information instead of intrinsic information as customary to calculate in literature. The second factor is used in the early termination (ET) scheme which is practically useful to terminate iterations when there is no significant improvement is achieved. In addition, a method for offline computing the AF’s that cover a specific range of signal-to-noise power ratio is provided which results in a reduction of utilization and latency estimates with a shallow degradation in performance. The results show that the proposed scheme outperforms the previous related works by about 0.2 dB at bit error rate (BER) of 10 -5 using interleaver depth of 512 and reducing the average number of iterations (ANI) by about 3 iterations.

An Efficient Viterbi Algorithm for Communication System

The Viterbi decoding algorithm is widely used in areas like Decoding convolutional codes in satellite communication, digital TV, wireless local area networks, mobile relay. Also, the method is used in the development of Automatic Speech Recognition (ASR) and storage systems that work automatically. For Viterbi decoder-based architectures with low latency and complexity, which proposes error detection techniques that are effective. This paper explores the Viterbi algorithm which has two types of approaches for two types of subparts. Important aspects of any communication system are area/power consumption and throughput /efficiency. Minimization of these aspects is the need for an efficient system. This paper explores unwanted logical block reduction by modifying the present logical block. This paper explores signature-based approaches which result in acceptable efficiency. Also, another approach is used to achieve error detection in permanent and transient faults. This error detection is achieved by recomputing with encoded operands. Encoding means the use of shifting operation or the use of rotation operation. This approach makes the system slightly efficient. The proposed approaches can be based on reliability and the efficiency objective.

Blood and breath alcohol concentration from transdermal alcohol biosensor data: estimation and uncertainty quantification via forward and inverse filtering for a covariate-dependent, physics-informed, hidden Markov model* *This research supported in part by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) Grant No. R01AA026368. Shao was supported in part by a Grant from USC’s Women in Science and Engineering (WiSE)

Transdermal alcohol biosensors that do not require active participation of the subject and yield near continuous measurements have the potential to significantly enhance the data collection abilities of alcohol researchers and clinicians who currently rely exclusively on breathalyzers and drinking diaries. Making these devices accessible and practical requires that transdermal alcohol concentration (TAC) be accurately and consistently transformable into the well-accepted measures of intoxication, blood alcohol concentration/breath alcohol concentration (BrAC). A novel approach to estimating BrAC from TAC based on covariate-dependent physics-informed hidden Markov models (HMMs) with two emissions is developed. The hidden Markov chain serves as a forward full-body alcohol model with BrAC and TAC, the two emissions, assumed to be described by a bivariate normal which depends on the hidden Markovian states and person-level and session-level covariates via built-in regression models. An innovative extension of hidden Markov modeling is developed wherein the HMM framework is regularized by a first-principles PDE model to yield a hybrid that combines prior knowledge of the physics of transdermal ethanol transport with data-based learning. Training, or inverse filtering, is effected via the Baum–Welch algorithm and 256 sets of BrAC and TAC signals and covariate measurements collected in the laboratory. Forward filtering of TAC to obtain estimated BrAC is achieved via a new physics-informed regularized Viterbi algorithm which determines the most likely path through the hidden Markov chain using TAC alone. The Markovian states are decoded and used to yield estimates of BrAC and to quantify the uncertainty in the estimates. Numerical studies are presented and discussed. Overall good agreement between BrAC data and estimates was observed with a median relative peak error of 22% and a median relative area under the curve error of 25% on the test set. We also demonstrate that the physics-informed Viterbi algorithm eliminates non-physical artifacts in the BrAC estimates.

56-Gb/s/λ C-band DSB IM/DD PAM-4 40-km SSMF transmission employing a multiplier-free MLSE equalizer.

Chromatic dispersion, which introduces pattern-dependent inter-symbol interference (ISI), appears to be a long-standing performance limiting problem in optical intensity modulation direct detection (IM/DD) transmission systems. In this paper, we propose a multiplier-free maximum likelihood sequence estimation (MLSE) equalizer for C-band double-sideband IM/DD transmission. It models the IM/DD channel with dispersion-induced ISI and Gaussian noise. A look-up table is applied to record ISI for transition probability calculation and the Viterbi algorithm for decision sequence acquisition. Specifically, to reduce the number of multipliers, a refined construction of Viterbi algorithm based on tentative path decisions is adopted, which compresses the complexity of branch metric calculation to less than 1/4 for PAM-4 format. Moreover, approximation calculation is employed to realize multiplier-free hardware implementation, which greatly reduces the hardware consumption. The proposed MLSE equalizer offers superior performance and lower complexity over conventional equalizers. In the experimental verification, we experimentally demonstrate a C-band 56-Gb/s double-sideband 4-level pulse amplitude modulation (PAM-4) IM/DD transmission over 40-km standard single mode fiber exploiting the proposed refined MLSE without any optical amplifier, filter or dispersion managed modules at the receiver end, achieving a bit-error-ratio of 2.65×10-4, which is 2.28 orders of magnitude lower than the scheme using Volterra nonlinear equalizer.

A multi‐agent based mechanism for collaboratively detecting distributed denial of service attacks in internet of vehicles

AbstractDistributed denial of service (DDoS) attacks have become a hidden danger in the development of the internet of vehicles (IoV). DDoS attacks for TCP protocol are studied to improve the information security environment of IoV. For the distribution characteristics of DDoS attacks, an information sharing and collaborative detection mechanism based on multi‐agent is proposed. Considering the relationship between the features of adjacent moments in the TCP communication, the DDoS detection model based on hidden Markov model is built, and the Viterbi algorithm is improved for the problem of the false alarm in the observation sequence. The optimal communication strategy among agents is determined by deep reinforcement learning, and fusion algorithm is designed to improve the current strategy of agents. Three groups of comparative experiments are designed and analyzed. The simulation results show that proposed algorithms are effective.

(Mis)Information on Digital Platforms: Quantitative and Qualitative Analysis of Content From Twitter and Sina Weibo in the COVID-19 Pandemic.

Misinformation about COVID-19 on social media has presented challenges to public health authorities during the pandemic. This paper leverages qualitative and quantitative content analysis on cross-platform, cross-national discourse and misinformation in the context of COVID-19. Specifically, we investigated COVID-19-related content on Twitter and Sina Weibo-the largest microblogging sites in the United States and China, respectively. Using data from 2 prominent microblogging platform, Twitter, based in the United States, and Sina Weibo, based in China, we compared the content and relative prevalence of misinformation to better understand public discourse of public health issues across social media and cultural contexts. A total of 3,579,575 posts were scraped from both Sina Weibo and Twitter, focusing on content from January 30, 2020, within 24 hours of when WHO declared COVID-19 a "public health emergency of international concern," and a week later, on February 6, 2020. We examined how the use and engagement measured by keyword frequencies and hashtags differ across the 2 platforms. A 1% random sample of tweets that contained both the English keywords "coronavirus" and "covid-19" and the equivalent Chinese characters was extracted and analyzed based on changes in the frequencies of keywords and hashtags and the Viterbi algorithm. We manually coded a random selection of 5%-7% of the content to identify misinformation on each platform and compared posts using the WHO fact-check page to adjudicate accuracy of content. Both platforms posted about the outbreak and transmission, but posts on Sina Weibo were less likely to reference topics such as WHO, Hong Kong, and death and more likely to cite themes of resisting, fighting, and cheering against coronavirus. Misinformation constituted 1.1% of Twitter content and 0.3% of Sina Weibo content-almost 4 times as much on Twitter compared to Sina Weibo. Quantitative and qualitative analysis of content on both platforms points to lower degrees of misinformation, more content designed to bolster morale, and less reference to topics such as WHO, death, and Hong Kong on Sina Weibo than on Twitter.

Estimating Interference with a Two-Dimensional Viterbi Algorithm for Bit-Patterned Media Recording

Bit-patterned media recording (BPMR) is proposed as a candidate for future magnetic data storage to overcome superparamagnetism. The distance between magnetic islands in BPMR must be reduced to increase the areal density (AD). As magnetic islands become closer, two-dimensional (2D) interference is increased, including intersymbol interference (ISI) based on the down-track direction and intertrack interference (ITI) from the cross-track direction. We propose an estimator to predict interference from neighboring (upper and lower) tracks. This estimator exploits the 2D Viterbi algorithm (VA) with reduced states. We removed the interference from the neighboring track and applied a simple 1D VA to detect the received signal. The simulation results show that our model performs better than previously proposed models.

Improving Serial Detection Using MAP Algorithm for Bit-Patterned Media Recording

Bit-patterned media recording (BPMR) is a promising technology for increasing the areal density (AD) of magnetic data storage systems. In BPMR, the non-magnetic zone is reduced to increase AD, which causes serious problems such as two-dimensional (2D) interference and track mis-registration (TMR). Therefore, in previous studies, a serial detection, which exploits the Viterbi algorithm (VA), was proposed to combat 2D interference. Because the VA lacks prior information from the inner detector to the outer detector, in this study, we use the maximum a posteriori (MAP) algorithm to supply prior information from the inner detector to the outer detector. Simulation results show that the proposed model can improve the bit error ratio (BER) performance of serial detection, especially in cases where the BPMR suffers from TMR effects.

An analysis on potential renewable energy system using triangular tile pasting as a Markov Chain in terms of Maximal Clique.

Transformation is an art of wavering chattels from one location to the other location or one zone to the other zone or one pattern to the other pattern. Designing any regular or irregular polygonal structures especially in tile pasting plays decisive portrayal of procreation. Tile pasting is a technique of gluing tiles in sequence of specific nature, and subsidize collection of structures to the constructors. The sequence of tile pasting can be analyzed and the best sequential path will be determined using Viterbi Algorithm. The pictorial representation of Viterbi algorithm is the trellis path that hand out more strength to the algorithm which shows the sequential order of tile pasting effectively. This paper will also be initiated to find the renewable energy produced by gluing triangular tiles of different polygonal structures and gives an intention to identify the most effective structure among the shapes discussed.

Efficient parallel viterbi algorithm for big data in a spark cloud computing environment

The aim of this paper is to present a parallel distributed version of Viterbi algorithm that combines the advantages of Spark, the big data framework, and hidden Markov models to solve the decoding problem for large scale multidimensional data. The scope of the paper includes a review of hidden Markov models, a study of decoding problem, a presentation of related work, and a discussion of previously proposed implementations. The main part of the paper consists of a description of development and implementation of a parallel distributed Viterbi algorithm in a cloud computing environment, followed by a description of evaluation experiments of the presented algorithm. The results showed that the proposed algorithm is faster, with high scalability and no deterioration in forecast accuracy is observed.

Information source tracing method of electronic commerce supply chain based on blockchain

In order to overcome the problems of long tracing time and poor tracing accuracy in traditional source tracing methods, this paper proposes a blockchain-based electronic commerce supply chain source tracing method. First, use IoT equipment to collect transaction data, store it in the cluster server in the form of blockchain, build electronic commerce supply chain traceability information base and determine the traceability query scheme. Secondly, the traceability state probability matrix is constructed to obtain the source tag variables. Then, the tracing function is constructed, and the tracing parameters are modified by Baum Welch algorithm. Finally, the Viterbi algorithm is used to obtain the tag matching function, and the recursive method is used to solve the source tracing results. The implementation results show that the tracing time of this method is only 50 s, and the tracing accuracy is as high as 99.8%, indicating that the source tracing effect is good.

Stock Price Prediction Based on Discrete Hidden Markov Chain

Stock forecasting has always been the center of the financial market. This paper uses the Discrete Hidden Markov Model (HMM) to predict stock price. The initial data of stock price is denoised by wavelet, and the denoised data is processed as the input data of HMM, and the number of implicit states that make the model most robust is determined by using OEHS criteria. The model is trained by Baum- Welch-Algo algorithm to obtain the correlation matrix parameters, and then the implicit state with the maximum probability is obtained by the Viterbi algorithm. This paper also introduces the Voting Strategy to improve the probability of accurate prediction. Finally, according to the Observation emission matrix, the probability distribution of the return rate of the next day is obtained, and the stock price is obtained. The accuracy of the predicted value is tested by MAPE. The experimental results show that the Discrete Hidden Markov model can better predict the future stock price trend. The accuracy of the predicted value was tested by MAPE, and the value was 0.0284.

Automatic Information Extraction for Financial Events by Integrating BiGRU and Attention Mechanism

In this paper, an information extraction method for financial events written in Chinese is proposed. The core entities of the causes and results, as well as the verbs and conditions are extracted from the financial events reported by websites. The method takes the original words and the part-of-speech of words as two inputs. BERT encoder is utilized to transform the original sentences to word-embedding vectors, which then are send to BiGRU to extract the sematic features. And a full-connected network is overlapped on BiGRU to reduce the impact of “covariate shift”. For the second input, the original sentences are cut by Chinese cut-word tool ‘jieba’ to get the part-of-speech of words, which are then transformed by self-attention mechanism to get global dependencies. The two outputs for word-embedding vectors and part-of-speech of words are combined and then decoded by CRF. Finally, the Viterbi algorithm is utilized to get the best sequences. The experiment results validate the effectiveness of the proposed method.

A Wind Speed Correction Method Based on Modified Hidden Markov Model for Enhancing Wind Power Forecast

Short-term wind power forecast (WPF) depends highly on the wind speed forecast (WSF), which is the prime contributor to the forecasting error. To achieve more accurate WPF results, this article proposes a wind speed correction method to improve the WSF result obtained by using the weather research and forecasting (WRF) model. First, the WRF model is constructed to forecast the wind speed, and its performance is analyzed. Second, a novel hidden Markov model (HMM) is developed to explore both the temporal autocorrelation of WSF error and the nonlinear correlation between the WSF result and the error. In the model, the fuzzy C-means cluster is introduced to properly divide the hidden state space of HMM and the emission probability of HMM is improved as continuous by the kernel density estimation (KDE) to make full use of the observation information. The proposed HMM model is better at wind speed correction through modification. Third, the HMM is solved by the Viterbi algorithm and the minimum mean-square error regulation to correct the predicted wind speed. Finally, the deterministic and probabilistic WPF results are obtained by using another KDE model, the proposed method is demonstrated to be superior to the benchmarks in case studies.

Automatic tracking for seismic horizons using convolution feature analysis and optimization algorithm

Seismic horizon tracking is a fundamental aspect of seismic data interpretation. However, seismic horizons are typically obtained using manual tracking or a combination of manual tracking and traditional auto-tracking techniques, either of which is a time-consuming and error-prone process. To improve the efficiency and the accuracy of seismic horizon tracking, we developed a convolution feature analysis method on the basis of the traditional coherent technology combined with the Viterbi algorithm, and proposed a method for auto-tracking seismic horizons in complex exploration areas. Firstly, the local seismic waveform data of the target horizon passing through the drilling area have been extracted as the convolution kernel (i.e. the standard seismic trace). Then, the waveform data of each seismic trace in the whole region have been treated with the convolution to obtain the convolution feature in a sliding time window (i.e, the similarity seismic attribute profile). Finally, the convolution feature data have been taken as the inputs, and constraint optimization is performed for the automatic tracking of the seismic horizon. It has the ability to search for the maximum value by integrating the maximum similarity forward and searching for the shortest path method on synthetic and real seismic data. The obtained results show that the proposed method performs effectively for seismic horizons auto-tracking of low signal-to-noise ratio seismic data in complex exploration areas, and also traces the seismic horizons with good continuity and high accuracy. • Convolution feature analysis and optimization algorithm. • Auto-tracking seismic horizons in complex exploration areas. • The traced horizon exhibit good continuity and high accuracy.

Joint Range-Doppler Estimation Based on Multipulse Processing of Composite Hyperbolic Frequency Modulated Waveforms

We herein investigate the joint estimation of range and Doppler based on multipulse processing of composite hyperbolic frequency-modulated (HFM) waveforms. The composite HFM waveform solves the range bias problem in the HFM waveform by using the mapping relationship between the radial velocity and the peak-to-peak interval in the outputs of matched filters. However, the result may be inaccurate in the presence of multiple false peaks. In this letter, we fit matched filter outputs into a hidden Markov model by utilizing the kinetic model of the target. We can subsequently solve the peak-pair selection problem and perform the joint range-Doppler estimation by using the Viterbi algorithm. The proposed method, by multipulse processing, lowers the signal-to-noise ratio (SNR) requirement, and exact detection of the target in each return pulse is no longer necessary. We prove the effectiveness of the proposed method via numerical simulations, and the simulation results demonstrate that this method outperforms existing methods in low SNR environments.

Human Micro-Doppler Frequency Estimation Using CESP-Based Viterbi Algorithm

Accurate acquisition of human micro-Doppler (m-D) frequencies remains a challenging task, which can help us to identify the target of interest and provide valuable information about its motion dynamics. In this letter, a novel theoretical method to estimate the m-D frequencies of human specific scattering parts from continuous-wave radar echo is proposed with a united application of a modified Viterbi algorithm and a nonlinear prediction technology. Although the proposed algorithm achieves a limited improvement in the estimation accuracy and produces better human m-D frequency curves, this algorithm suppresses the frequency ambiguity effectively. The employed search method enhances upon traditional ones and improves the efficiency of finding the optimal paths considerably. Experimental results demonstrate the superior performance of the proposed method.

A Novel Deep Reinforcement Learning Based Relay Selection for Broadcasting in Vehicular Ad Hoc Networks

VANETs (Vehicular Ad hoc NETworks) are considered among the world’s largest networks. These networks are providing multiple services like infotainment applications, safety services, driver assistance, and even video on demand. On one hand, VANETs are characterized by their random topology and dynamic behavior that varies in urban context, and which highly changes in highways. On the other hand, diffusing information is a fundamental task to deliver multiple services. Thus, the broadcasting task is a challenging problem and need more investigation. In fact, to achieve this task, artificial intelligence and learning based computing seem to be one of the most appropriate options that best fits the dynamic behavior of VANETs. Accordingly, in this paper we propose a novel hybrid relay selection technique to perform the broadcasting task based on a reinforcement learning method. Our proposition is initially to combine an artificial neural network-based classification applied to select forwarding nodes, and in the second phase, we apply the Viterbi algorithm as a reinforcement tool to refine the first classification. To measure the performance of our contribution, we adopt a grid map scenario with varied traffic densities. Afterwards, we analyze and compare the simulation results with other methods in the literature based on different parameters such as the success rate, the data loss, the saved rebroadcasts, and the delay. We conclude by proving that the proposed technique combining deep learning along with reinforcement learning outperforms other recently proposed broadcasting schemes based on the results which show that the new solution increased the success rate by 16%, the saved rebroadcasts by 20%, and reduced the delay by 23%.

Data-driven model for ship encounter intention inference in intersection waters based on AIS data

Frequent collision accidents of ships in intersection waters have caused huge casualties and property losses. Unclear encounter intention, poor communication, or inaccurate judgment of the encounter intention are often the major causes of ships falling into dangerous and urgent situations, leading to collision accidents. There are few methods and models for automatically inferring ship encounter intention. In this study, an intelligent model driven by AIS data is proposed to infer the ship encounter intention in intersection waters. The Hidden Markov Model (HMM) is adopted to formulate the encounter process and perform intention inference. The encounter intentions, including crossing, overtaking and head-on, are modeled as unobservable states of the formulated HMM. The observable measures of HMM extracted from AIS data, include the relative distance, relative speed, and course difference between two ships. Subsequently, the Forward-Backward algorithm is employed to obtain the model parameters and the Viterbi algorithm is exploited to estimate the hidden state with the highest probability, resulting in the inferred intention. The main advantage of the proposed model is its ability to capture the spatial-temporal characteristics of the encounter process, that is, the spatial interaction between ships and the dynamic evolution of states of the encounter process. The AIS data collected from the Lantau Strait intersection waters are adopted to verify the effectiveness of the proposed model. The experimental results reveal that the model can achieve an inference accuracy of 95%, 91.33%, and 92.67% for crossing, overtaking, and head-on, respectively. Moreover, it has real-time performance that ensures the encounter intentions can be recognized at an early stage, which is very critical for the safe navigation of any ships encountered. Our results show that our model can infer the encounter intentions in a timely manner and with high accuracy.

Design of ACS Architecture Using FinFET and CNTFET Devices for Low-Power Viterbi Decoder Using Asynchronous Techniques for Digital Communication Systems

Viterbi algorithm is the most popular algorithm used to decode the convolution code, but its computational complexity increases exponentially with the increasing constraint length due to a large number of Trellis transitions. However, high constraint length is necessary to improve the accuracy of the decoding process for the high rate convolution code. In particular, the Add-Compare-Select (ACS) module of the Viterbi Decoder will have large numbers of trellis states and trellis transitions with increased constraint lengths, which give rise to high hardware complexity and large power consumption. As the performance of the Viterbi decoder mainly depends on its efficient implementation of the ACS module, in the literature, several methods are presented for the implementation of ACS for the Viterbi decoder. The methods based on Precharge Half Buffer (PCHB) and Weak Conditioned Half Buffer, Shannon’s decomposition circuits, body-biased pseudo-NMOS logic and Quasi Delay Insensitive (QDI) timing model performance is analyzed. The methods are implemented using CMOS technology. In this paper, FinFET and CNTFET-based ACS implementation is performed. From the analysis, it has been found that the Carbon Nanotube-based implementation is better in performance when compared to the CMOS and FinFET technology. The proposed QDI model and retiming circuits for ACS block operate above 1[Formula: see text]GHz with high driving current and low power.