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Journal articles on the topic 'A posteriori probability decoding'

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Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Arar, Maher, Claude D'Amours, and Abbas Yongacoglu. "Simplified LLRs for the Decoding of Single Parity Check Turbo Product Codes Transmitted Using 16QAM." Research Letters in Communications 2007 (2007): 1–4. http://dx.doi.org/10.1155/2007/53517.

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Iterative soft-decision decoding algorithms require channel log-likelihood ratios (LLRs) which, when using 16QAM modulation, require intensive computations to be obtained. Therefore, we derive four simple approximate LLR expressions. When using the maximum a posteriori probability algorithm for decoding single parity check turbo product codes (SPC/TPCs), these LLRs can be simplified even further. We show through computer simulations that the bit-error-rate performance of(8,7)2and(8,7)3SPC/TPCs, transmitted using 16QAM and decoded using the maximum a posteriori algorithm with our simplified LLRs, is nearly identical to the one achieved by using the exact LLRs.

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2

Liu, Jia, Mingyu Zhang, Chaoyong Wang, Rongjun Chen, Xiaofeng An, and Yufei Wang. "Upper Bound on the Bit Error Probability of Systematic Binary Linear Codes via Their Weight Spectra." Discrete Dynamics in Nature and Society 2020 (January 29, 2020): 1–11. http://dx.doi.org/10.1155/2020/1469090.

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In this paper, upper bound on the probability of maximum a posteriori (MAP) decoding error for systematic binary linear codes over additive white Gaussian noise (AWGN) channels is proposed. The proposed bound on the bit error probability is derived with the framework of Gallager’s first bounding technique (GFBT), where the Gallager region is defined to be an irregular high-dimensional geometry by using a list decoding algorithm. The proposed bound on the bit error probability requires only the knowledge of weight spectra, which is helpful when the input-output weight enumerating function (IOWEF) is not available. Numerical results show that the proposed bound on the bit error probability matches well with the maximum-likelihood (ML) decoding simulation approach especially in the high signal-to-noise ratio (SNR) region, which is better than the recently proposed Ma bound.

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3

Shrestha, Rahul, and Roy Paily. "Memory-Reduced Maximum A Posteriori Probability Decoding for High-Throughput Parallel Turbo Decoders." Circuits, Systems, and Signal Processing 35, no. 8 (September 22, 2015): 2832–54. http://dx.doi.org/10.1007/s00034-015-0168-4.

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4

Shafieipour, Mohammad, Heng-Siong Lim, and Teong-Chee Chuah. "Decoding of Turbo Codes in Symmetric Alpha-Stable Noise." ISRN Signal Processing 2011 (March 29, 2011): 1–7. http://dx.doi.org/10.5402/2011/683972.

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This paper investigates the decoding of turbo codes in impulsive symmetric α-stable (SαS) noise. Due to the nonexistence of a closed-form expression for the probability density function (pdf) of α-stable processes, numerical-based SαS pdf is used to derive branch transition probability (btp) for the maximum a posteriori turbo decoder. Results show that in Gaussian noise, the turbo decoder achieves similar performance using both the conventional and the proposed btps, but in impulsive channels, the turbo decoder with the proposed btp substantially outperforms the turbo decoder utilizing the conventional btp. Results also confirm that the turbo decoder incorporating the proposed btp outperforms the existing Cauchy-based turbo decoder in non-Cauchy impulsive noise, while the two decoders accomplish similar performance in Cauchy noise.

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5

Wang, Li Na, and Xiao Liu. "Improved BP Decoding Algorithm for LDPC Codes." Advanced Materials Research 846-847 (November 2013): 925–28. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.925.

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In this paper, an improved belief propagation decoding algorithm was proposed for low density parity check codes. In the proposed decoding process, error bits can be detected once again after hard-decision in the conventional BP decoding algorithm. The detection criterion is based on check matrix characteristics and D-value between prior probability and posterior probability. Simulation results demonstrate the performance of the improved BP decoding algorithm outperform that of the conventional BP decoding algorithm.

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6

PARK, HEE-SEON, BONG-KEE SIN, JONGSUB MOON, and SEONG-WHAN LEE. "A 2-D HMM METHOD FOR OFFLINE HANDWRITTEN CHARACTER RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 01 (February 2001): 91–105. http://dx.doi.org/10.1142/s0218001401000757.

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In this paper we consider a hidden Markov mesh random field (HMMRF) for character recognition. The model consists of a "hidden" Markov mesh random field (MMRF) and an overlying probabilistic observation function of the MMRF. Just like the 1-D HMM, the hidden layer is characterized by the initial and the transition probability distributions, and the observation layer is defined by distribution functions for vector-quantized (VQ) observations. The HMMRF-based method consists of two phases: decoding and training. The decoding and the training algorithms are developed using dynamic programming and maximum likelihood estimation methods. To accelerate the computation in both phases, we employed a look-ahead scheme based on maximum marginal it a posteriori probability criterion for third-order HMMRF. Tested on a larget-set handwritten Korean Hangul character database, the model showed a promising result: up to 87.2% recognition rate with 8 state HMMRF and 128 VQ levels.

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7

Kim, Sang Wu, Taha Khalaf, and Sangmun Kim. "MAP Detection of Misbehaving Relay in Wireless Multiple Access Relay Networks." IEEE Communications Letters 15, no. 3 (March 2011): 340–42. http://dx.doi.org/10.1109/lcomm.2011.012511.101323.

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We propose the maximum a posteriori (MAP) detection of the misbehaving relay that injects false data or adds channel errors into the network encoder in multiple access relay networks. The proposed scheme does not require sending extra bits at the source and is optimal in the sense of minimizing the probability of incorrect detection. We derive the probability of false alarm and misdetection, taking into account the lossy nature of wireless links. The side information regarding the presence of relay misbehavior is exploited at the decoder to mitigate the relay misbehavior and enhance the reliability of decoding.

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8

Ravi Kumar, Ch, and K. Padmaraju. "Hard Decision Decoding Performance Improved Using Turbo Product Codes." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 228. http://dx.doi.org/10.14419/ijet.v7i3.12.16030.

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The performance of soft decision decoding, whose for which the design is complex, is superior to the performance of hard decision decoding. In this paper, we propose a turbo product code with a bit flip algorithm to improve the performance of hard decision decoding. The performance of hard decision decoding is improved with low complexity using multidimensional turbo product codes. The reliability of decoding in a communication system to detect and correct errors is discussed .Maximum a posterior probability (MAP) decoding is employed to improve the hard decision performance of turbo product codes with multiple dimensions. Our results include comparisons of multiple dimensions—2D, 3D, 4D, and 5D—and the number of iterations in soft and hard decision decoding.

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9

Raza, Hasnain, Syed Azhar Ali Zaidi, Aamir Rashid, and Shafiq Haider. "An area efficient and high throughput implementation of layered min-sum iterative construction a posteriori probability LDPC decoder." PLOS ONE 16, no. 3 (March 29, 2021): e0249269. http://dx.doi.org/10.1371/journal.pone.0249269.

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Area efficient and high speed forward error correcting codes decoder are the demand of many high speed next generation communication standards. This paper explores a low complexity decoding algorithm of low density parity check codes, called the min-sum iterative construction a posteriori probability (MS-IC-APP), for this purpose. We performed the error performance analysis of MS-IC-APP for a (648,1296) regular QC-LDPC code and proposed an area and throughput optimized hardware implementation of MS-IC-APP. We proposed to use the layered scheduling of MS-IC-APP and performed other optimizations at architecture level to reduce the area and to increase the throughput of the decoder. Synthesis results show 6.95 times less area and 4 times high throughput as compared to the standard min-sum decoder. The area and throughput are also comparable to the improved variants of hard-decision bit-flipping (BF) decoders, whereas, the simulation results show a coding gain of 2.5 over the best implementation of BF decoder in terms of error performance.

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10

Chen, Xianqing, and Lenan Wu. "Nonlinear Demodulation and Channel Coding in EBPSK Scheme." Scientific World Journal 2012 (2012): 1–7. http://dx.doi.org/10.1100/2012/180469.

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The extended binary phase shift keying (EBPSK) is an efficient modulation technique, and a special impacting filter (SIF) is used in its demodulator to improve the bit error rate (BER) performance. However, the conventional threshold decision cannot achieve the optimum performance, and the SIF brings more difficulty in obtaining the posterior probability for LDPC decoding. In this paper, we concentrate not only on reducing the BER of demodulation, but also on providing accurate posterior probability estimates (PPEs). A new approach for the nonlinear demodulation based on the support vector machine (SVM) classifier is introduced. The SVM method which selects only a few sampling points from the filter output was used for getting PPEs. The simulation results show that the accurate posterior probability can be obtained with this method and the BER performance can be improved significantly by applying LDPC codes. Moreover, we analyzed the effect of getting the posterior probability with different methods and different sampling rates. We show that there are more advantages of the SVM method under bad condition and it is less sensitive to the sampling rate than other methods. Thus, SVM is an effective method for EBPSK demodulation and getting posterior probability for LDPC decoding.

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11

Adamu, Mohammed Jajere, Li Qiang, Rabiu Sale Zakariyya, Charles Okanda Nyatega, Halima Bello Kawuwa, and Ayesha Younis. "An Efficient Turbo Decoding and Frequency Domain Turbo Equalization for LTE Based Narrowband Internet of Things (NB-IoT) Systems." Sensors 21, no. 16 (August 8, 2021): 5351. http://dx.doi.org/10.3390/s21165351.

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This paper addresses the main crucial aspects of physical (PHY) layer channel coding in uplink NB-IoT systems. In uplink NB-IoT systems, various channel coding algorithms are deployed due to the nature of the adopted Long-Term Evolution (LTE) channel coding which presents a great challenge at the expense of high decoding complexity, power consumption, error floor phenomena, while experiencing performance degradation for short block lengths. For this reason, such a design considerably increases the overall system complexity, which is difficult to implement. Therefore, the existing LTE turbo codes are not recommended in NB-IoT systems and, hence, new channel coding algorithms need to be employed for LPWA specifications. First, LTE-based turbo decoding and frequency-domain turbo equalization algorithms are proposed, modifying the simplified maximum a posteriori probability (MAP) decoder and minimum mean square error (MMSE) Turbo equalization algorithms were appended to different Narrowband Physical Uplink Shared Channel (NPUSCH) subcarriers for interference cancellation. These proposed methods aim to minimize the complexity of realizing the traditional MAP turbo decoder and MMSE estimators in the newly NB-IoT PHY layer features. We compare the system performance in terms of block error rate (BLER) and computational complexity.

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12

Dehaene, Guillaume P., Ruben Coen-Cagli, and Alexandre Pouget. "Investigating the representation of uncertainty in neuronal circuits." PLOS Computational Biology 17, no. 2 (February 12, 2021): e1008138. http://dx.doi.org/10.1371/journal.pcbi.1008138.

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Skilled behavior often displays signatures of Bayesian inference. In order for the brain to implement the required computations, neuronal activity must carry accurate information about the uncertainty of sensory inputs. Two major approaches have been proposed to study neuronal representations of uncertainty. The first one, the Bayesian decoding approach, aims primarily at decoding the posterior probability distribution of the stimulus from population activity using Bayes’ rule, and indirectly yields uncertainty estimates as a by-product. The second one, which we call the correlational approach, searches for specific features of neuronal activity (such as tuning-curve width and maximum firing-rate) which correlate with uncertainty. To compare these two approaches, we derived a new normative model of sound source localization by Interaural Time Difference (ITD), that reproduces a wealth of behavioral and neural observations. We found that several features of neuronal activity correlated with uncertainty on average, but none provided an accurate estimate of uncertainty on a trial-by-trial basis, indicating that the correlational approach may not reliably identify which aspects of neuronal responses represent uncertainty. In contrast, the Bayesian decoding approach reveals that the activity pattern of the entire population was required to reconstruct the trial-to-trial posterior distribution with Bayes’ rule. These results suggest that uncertainty is unlikely to be represented in a single feature of neuronal activity, and highlight the importance of using a Bayesian decoding approach when exploring the neural basis of uncertainty.

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13

Shu, Raphael, Jason Lee, Hideki Nakayama, and Kyunghyun Cho. "Latent-Variable Non-Autoregressive Neural Machine Translation with Deterministic Inference Using a Delta Posterior." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 8846–53. http://dx.doi.org/10.1609/aaai.v34i05.6413.

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Although neural machine translation models reached high translation quality, the autoregressive nature makes inference difficult to parallelize and leads to high translation latency. Inspired by recent refinement-based approaches, we propose LaNMT, a latent-variable non-autoregressive model with continuous latent variables and deterministic inference procedure. In contrast to existing approaches, we use a deterministic inference algorithm to find the target sequence that maximizes the lowerbound to the log-probability. During inference, the length of translation automatically adapts itself. Our experiments show that the lowerbound can be greatly increased by running the inference algorithm, resulting in significantly improved translation quality. Our proposed model closes the performance gap between non-autoregressive and autoregressive approaches on ASPEC Ja-En dataset with 8.6x faster decoding. On WMT'14 En-De dataset, our model narrows the gap with autoregressive baseline to 2.0 BLEU points with 12.5x speedup. By decoding multiple initial latent variables in parallel and rescore using a teacher model, the proposed model further brings the gap down to 1.0 BLEU point on WMT'14 En-De task with 6.8x speedup.

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14

Wu, Wei, Yun Gao, Elie Bienenstock, John P. Donoghue, and Michael J. Black. "Bayesian Population Decoding of Motor Cortical Activity Using a Kalman Filter." Neural Computation 18, no. 1 (January 1, 2006): 80–118. http://dx.doi.org/10.1162/089976606774841585.

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Effective neural motor prostheses require a method for decoding neural activity representing desired movement. In particular, the accurate reconstruction of a continuous motion signal is necessary for the control of devices such as computer cursors, robots, or a patient's own paralyzed limbs. For such applications, we developed a real-time system that uses Bayesian inference techniques to estimate hand motion from the firing rates of multiple neurons. In this study, we used recordings that were previously made in the arm area of primary motor cortex in awake behaving monkeys using a chronically implanted multielectrode microarray. Bayesian inference involves computing the posterior probability of the hand motion conditioned on a sequence of observed firing rates; this is formulated in terms of the product of a likelihood and a prior. The likelihood term models the probability of firing rates given a particular hand motion. We found that a linear gaussian model could be used to approximate this likelihood and could be readily learned from a small amount of training data. The prior term defines a probabilistic model of hand kinematics and was also taken to be a linear gaussian model. Decoding was performed using a Kalman filter, which gives an efficient recursive method for Bayesian inference when the likelihood and prior are linear and gaussian.In off-line experiments, the Kalman filter reconstructions of hand trajectory were more accurate than previously reported results.The resulting decoding algorithm provides a principled probabilistic model of motor-cortical coding, decodes hand motion in real time, provides an estimate of uncertainty, and is straightforward to implement. Additionally the formulation unifies and extends previous models of neural coding while providing insights into the motor-cortical code.

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15

Li, Jingjian, Wei Wang, Hong Mo, Mengting Zhao, and Jianhua Chen. "Source Symbol Purging-Based Distributed Conditional Arithmetic Coding." Entropy 23, no. 8 (July 30, 2021): 983. http://dx.doi.org/10.3390/e23080983.

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A distributed arithmetic coding algorithm based on source symbol purging and using the context model is proposed to solve the asymmetric Slepian–Wolf problem. The proposed scheme is to make better use of both the correlation between adjacent symbols in the source sequence and the correlation between the corresponding symbols of the source and the side information sequences to improve the coding performance of the source. Since the encoder purges a part of symbols from the source sequence, a shorter codeword length can be obtained. Those purged symbols are still used as the context of the subsequent symbols to be encoded. An improved calculation method for the posterior probability is also proposed based on the purging feature, such that the decoder can utilize the correlation within the source sequence to improve the decoding performance. In addition, this scheme achieves better error performance at the decoder by adding a forbidden symbol in the encoding process. The simulation results show that the encoding complexity and the minimum code rate required for lossless decoding are lower than that of the traditional distributed arithmetic coding. When the internal correlation strength of the source is strong, compared with other DSC schemes, the proposed scheme exhibits a better decoding performance under the same code rate.

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16

Zhang, Yan Fei, Ying Wang, and Rong Xi He. "Performance Simulation of an EM-Based Iterative Receiver for Underwater Acoustic OFDM System." Applied Mechanics and Materials 543-547 (March 2014): 547–53. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.547.

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In this paper, an expectation maximization (EM) algorithm based iterative receiver is utilized in the underwater acoustic OFDM system to improve the spectrum efficiency. A relatively smaller number of pilots are used to determine the initial value for the EM-based channel estimation process. The intermediate estimates of the channel state information generated by the EM algorithm are fed forward to the BCJR decoding unit to achieve the a posterior probability of the transmitted bit sequence. The a posterior probability is then fed back to the EM algorithm to estimate channel state information. The iterative receive process is expected to realize a better channel estimate and bit error rate (BER) performance. Simulation results demonstrate that the EM-based iterative underwater acoustic OFDM system has a fast convergence property, and can achieve a low mean square error (MSE) for the channel estimation and a near optimal BER performance.

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17

Deng, Xinyi, Daniel F. Liu, Mattias P. Karlsson, Loren M. Frank, and Uri T. Eden. "Rapid classification of hippocampal replay content for real-time applications." Journal of Neurophysiology 116, no. 5 (November 1, 2016): 2221–35. http://dx.doi.org/10.1152/jn.00151.2016.

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Sharp-wave ripple (SWR) events in the hippocampus replay millisecond-timescale patterns of place cell activity related to the past experience of an animal. Interrupting SWR events leads to learning and memory impairments, but how the specific patterns of place cell spiking seen during SWRs contribute to learning and memory remains unclear. A deeper understanding of this issue will require the ability to manipulate SWR events based on their content. Accurate real-time decoding of SWR replay events requires new algorithms that are able to estimate replay content and the associated uncertainty, along with software and hardware that can execute these algorithms for biological interventions on a millisecond timescale. Here we develop an efficient estimation algorithm to categorize the content of replay from multiunit spiking activity. Specifically, we apply real-time decoding methods to each SWR event and then compute the posterior probability of the replay feature. We illustrate this approach by classifying SWR events from data recorded in the hippocampus of a rat performing a spatial memory task into four categories: whether they represent outbound or inbound trajectories and whether the activity is replayed forward or backward in time. We show that our algorithm can classify the majority of SWR events in a recording epoch within 20 ms of the replay onset with high certainty, which makes the algorithm suitable for a real-time implementation with short latencies to incorporate into content-based feedback experiments.

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18

Lee, Jun, and Jaejin Lee. "Modified maximum a posteriori decoding algorithm." Electronics Letters 37, no. 11 (2001): 698. http://dx.doi.org/10.1049/el:20010486.

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19

Huang, Yifei, Mark P. Brandon, Amy L. Griffin, Michael E. Hasselmo, and Uri T. Eden. "Decoding Movement Trajectories Through a T-Maze Using Point Process Filters Applied to Place Field Data from Rat Hippocampal Region CA1." Neural Computation 21, no. 12 (December 2009): 3305–34. http://dx.doi.org/10.1162/neco.2009.10-08-893.

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Firing activity from neural ensembles in rat hippocampus has been previously used to determine an animal's position in an open environment and separately to predict future behavioral decisions. However, a unified statistical procedure to combine information about position and behavior in environments with complex topological features from ensemble hippocampal activity has yet to be described. Here we present a two-stage computational framework that uses point process filters to simultaneously estimate the animal's location and predict future behavior from ensemble neural spiking activity. First, in the encoding stage, we linearized a two-dimensional T-maze, and used spline-based generalized linear models to characterize the place-field structure of different neurons. All of these neurons displayed highly specific position-dependent firing, which frequently had several peaks at multiple locations along the maze. When the rat was at the stem of the T-maze, the firing activity of several of these neurons also varied significantly as a function of the direction it would turn at the decision point, as detected by ANOVA. Second, in the decoding stage, we developed a state-space model for the animal's movement along a T-maze and used point process filters to accurately reconstruct both the location of the animal and the probability of the next decision. The filter yielded exact full posterior densities that were highly nongaussian and often multimodal. Our computational framework provides a reliable approach for characterizing and extracting information from ensembles of neurons with spatially specific context or task-dependent firing activity.

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20

Schurgers, Curt, and Anantha Chandrakasan. "Traceback-Based Optimizations for Maximum a Posteriori Decoding Algorithms." Journal of Signal Processing Systems 53, no. 3 (May 28, 2008): 231–41. http://dx.doi.org/10.1007/s11265-007-0160-8.

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21

Leung, C. K., and F. K. Lam. "Maximum a posteriori spatial probability segmentation." IEE Proceedings - Vision, Image, and Signal Processing 144, no. 3 (1997): 161. http://dx.doi.org/10.1049/ip-vis:19971181.

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22

Green, Thomas J., William H. Payne, Vivian E. Titus, and Eric J. Van Allen. "Maximum a posteriori probability background estimation." Journal of the Acoustical Society of America 105, no. 2 (February 1999): 1365–66. http://dx.doi.org/10.1121/1.426468.

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23

Müller, J., and H. Stahl. "Speech understanding and speech translation by maximum a-posteriori semantic decoding." Artificial Intelligence in Engineering 13, no. 4 (October 1999): 373–84. http://dx.doi.org/10.1016/s0954-1810(99)00010-2.

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24

Kanda, Naoyuki, Xugang Lu, and Hisashi Kawai. "Maximum-a-Posteriori-Based Decoding for End-to-End Acoustic Models." IEEE/ACM Transactions on Audio, Speech, and Language Processing 25, no. 5 (May 2017): 1023–34. http://dx.doi.org/10.1109/taslp.2017.2678162.

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25

Tang, Xiao-Wei, Xiao-Ning Huan, and Xin-Lin Huang. "Maximum a Posteriori Decoding for KMV-Cast Pseudo-Analog Video Transmission." Mobile Networks and Applications 23, no. 2 (October 14, 2017): 318–25. http://dx.doi.org/10.1007/s11036-017-0949-z.

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26

Zribi, Amin, Sonia Zaibi, Ramesh Pyndiah, and Ammar Bouallègue. "Chase-Like Decoding of Arithmetic Codes with Applications." International Journal of Computer Vision and Image Processing 1, no. 1 (January 2011): 27–40. http://dx.doi.org/10.4018/ijcvip.2011010103.

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Motivated by recent results in Joint Source/ Channel (JSC) coding and decoding, this paper addresses the problem of soft input decoding of Arithmetic Codes (AC). A new length-constrained scheme for JSC decoding of these codes is proposed based on the Maximum a posteriori (MAP) sequence estimation criterion. The new decoder, called Chase-like arithmetic decoder is supposed to know the source symbol sequence and the compressed bit-stream lengths. First, Packet Error Rates (PER) in the case of transmission on an Additive White Gaussian Noise (AWGN) channel are investigated. Compared to classical arithmetic decoding, the Chase-like decoder shows significant improvements. Results are provided for Chase-like decoding for image compression and transmission on an AWGN channel. Both lossy and lossless image compression schemes were studied. As a final application, the serial concatenation of an AC with a convolutional code was considered. Iterative decoding, performed between the two decoders showed substantial performance improvement through iterations.

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27

Wu, Gaowei. "Support Vector Machines Based on Posteriori Probability." Journal of Computer Research and Development 42, no. 2 (2005): 196. http://dx.doi.org/10.1360/crad20050203.

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Robertson, Patrick, Peter Hoeher, and Emmanuelle Villebrun. "Optimal and sub-optimal maximum a posteriori algorithms suitable for turbo decoding." European Transactions on Telecommunications 8, no. 2 (March 1997): 119–25. http://dx.doi.org/10.1002/ett.4460080202.

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Chou, Chun Tung. "Maximum A-Posteriori Decoding for Diffusion-Based Molecular Communication Using Analog Filters." IEEE Transactions on Nanotechnology 14, no. 6 (November 2015): 1054–67. http://dx.doi.org/10.1109/tnano.2015.2469301.

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Thiruvasagam, Priya, and Kalavathi Palanisamy. "Brain Tissue Segmentation from Magnetic Resonance Brain Images Using Histogram Based Swarm Optimization Techniques." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 6 (July 27, 2020): 752–65. http://dx.doi.org/10.2174/1573405615666190318154943.

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Background and Objective: In order to reduce time complexity and to improve the computational efficiency in diagnosing process, automated brain tissue segmentation for magnetic resonance brain images is proposed in this paper. Methods: This method incorporates two processes, the first one is preprocessing and the second one is segmentation of brain tissue using Histogram based Swarm Optimization techniques. The proposed method was investigated with images obtained from twenty volumes and eighteen volumes of T1-Weighted images obtained from Internet Brain Segmentation Repository (IBSR), Alzheimer disease images from Minimum Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) and T2-Weighted real-time images collected from SBC Scan Center Dindigul. Results: The proposed technique was tested with three brain image datasets. Quantitative evaluation was done with Jaccard (JC) and Dice (DC) and also it was compared with existing swarm optimization techniques and other methods like Adaptive Maximum a posteriori probability (AMAP), Biased Maximum a posteriori Probability (BMAP), Maximum a posteriori Probability (MAP), Maximum Likelihood (ML) and Tree structure K-Means (TK-Means). Conclusion: The performance comparative analysis shows that our proposed method Histogram based Darwinian Particle Swarm Optimization (HDPSO) gives better results than other proposed techniques such as Histogram based Particle Swarm Optimization (HPSO), Histogram based Fractional Order Darwinian Particle Swarm Optimization (HFODPSO) and with existing swarm optimization techniques and other techniques like Adaptive Maximum a posteriori Probability (AMAP), Biased Maximum a posteriori Probability (BMAP), Maximum a posteriori Probability (MAP), Maximum Likelihood (ML) and Tree structure K-Means (TK-Means).

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31

Harrison, B. F., D. W. Tufts, and R. J. Vaccaro. "Fast, approximate maximum a posteriori probability parameter estimation." IEEE Signal Processing Letters 4, no. 4 (April 1997): 96–99. http://dx.doi.org/10.1109/97.566699.

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32

Sah, Dhaneshwar. "Iterative Decoding of Turbo Codes." Journal of Advanced College of Engineering and Management 3 (January 10, 2018): 15. http://dx.doi.org/10.3126/jacem.v3i0.18810.

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<p><strong> </strong>This paper presents a Thesis which consists of a study of turbo codes as an error-control Code and the software implementation of two different decoders, namely the Maximum a Posteriori (MAP), and soft- Output Viterbi Algorithm (SOVA) decoders. Turbo codes were introduced in 1993 by berrouet at [2] and are perhaps the most exciting and potentially important development in coding theory in recent years. They achieve near- Shannon-Limit error correction performance with relatively simple component codes and large interleavers. They can be constructed by concatenating at least two component codes in a parallel fashion, separated by an interleaver. The convolutional codes can achieve very good results. In order of a concatenated scheme such as a turbo codes to work properly, the decoding algorithm must affect an exchange of soft information between component decoders. The concept behind turbo decoding is to pass soft information from the output of one decoder to the input of the succeeding one, and to iterate this process several times to produce better decisions. Turbo codes are still in the process of standardization but future applications will include mobile communication systems, deep space communications, telemetry and multimedia. Finally, we will compare these two algorithms which have less complexity and which can produce better performance.</p><p><strong>Journal of Advanced College of Engineering and Management</strong>, Vol.3, 2017, Page: 15-30</p>

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Liu, Hai Sheng, and Jing Wei. "Research on Signal Detection in Linear Frequency Modulation Model." Applied Mechanics and Materials 644-650 (September 2014): 1294–97. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1294.

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In order to conduct signal detection in linear frequency modulation model, using Bayes Criterion, Neyman-Pearson criterion, Minimum error probability criterion and Maximum likelihood criterion. The experimental results show that: Neyman-Pearson criterion is a special case of the alternative Bayes detection; maximum posteriori probability criterion and minimum error probability criterion are equivalent; if equal prior probability, then the maximum a posteriori probability criterion and the maximum likelihood detection have the same probability. So in the actual operation, select appropriate criterion based on the specific conditions.

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34

Kryazhimskiy, A. V. "A Posteriori Integration of Probabilities. Elementary Theory." Theory of Probability & Its Applications 60, no. 1 (January 2016): 62–87. http://dx.doi.org/10.1137/s0040585x97t987466.

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35

Ozawa, Masanao. "Conditional probability and a posteriori states in quantum mechanics." Publications of the Research Institute for Mathematical Sciences 21, no. 2 (1985): 279–95. http://dx.doi.org/10.2977/prims/1195179625.

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Stankiewicz, Olgierd, and Marek Domański. "Depth Map Estimation based on Maximum a Posteriori Probability." IEIE Transactions on Smart Processing & Computing 7, no. 1 (February 28, 2018): 49–61. http://dx.doi.org/10.5573/ieiespc.2018.7.1.049.

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37

Richardson, A. M., and L. W. Nolte. "A posteriori probability source localization with array tilt uncertainty." Journal of the Acoustical Society of America 92, no. 3 (September 1992): 1578–82. http://dx.doi.org/10.1121/1.403899.

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38

Wu, Si, Danmei Chen, Mahesan Niranjan, and Shun-ichi Amari. "Sequential Bayesian Decoding with a Population of Neurons." Neural Computation 15, no. 5 (May 1, 2003): 993–1012. http://dx.doi.org/10.1162/089976603765202631.

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Population coding is a simplified model of distributed information processing in the brain. This study investigates the performance and implementation of a sequential Bayesian decoding (SBD) paradigm in the framework of population coding. In the first step of decoding, when no prior knowledge is available, maximum likelihood inference is used; the result forms the prior knowledge of stimulus for the second step of decoding. Estimates are propagated sequentially to apply maximum a posteriori (MAP) decoding in which prior knowledge for any step is taken from estimates from the previous step. Not only do we analyze the performance of SBD, obtaining the optimal form of prior knowledge that achieves the best estimation result, but we also investigate its possible biological realization, in the sense that all operations are performed by the dynamics of a recurrent network. In order to achieve MAP, a crucial point is to identify a mechanism that propagates prior knowledge. We find that this could be achieved by short-term adaptation of network weights according to the Hebbian learning rule. Simulation results on both constant and time-varying stimulus support the analysis.

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Pogorelov, V., and E. Chub. "Markov model of data measurement complex for track geometry car." E3S Web of Conferences 224 (2020): 02029. http://dx.doi.org/10.1051/e3sconf/202022402029.

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A stochastic model of a nonadjustable data measurement complex platform for track geometry cars is introduced. A state vector evaluation algorithm based on the approximation of a posteriori probability density by the system of a posteriori moments is also offered.

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Zhang, Yingxian, Aijun Liu, Xiaofei Pan, Shi He, and Chao Gong. "A Generalization Belief Propagation Decoding Algorithm for Polar Codes Based on Particle Swarm Optimization." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/606913.

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We propose a generalization belief propagation (BP) decoding algorithm based on particle swarm optimization (PSO) to improve the performance of the polar codes. Through the analysis of the existing BP decoding algorithm, we first introduce a probability modifying factor to each node of the BP decoder, so as to enhance the error correcting capacity of the decoding. Then, we generalize the BP decoding algorithm based on these modifying factors and drive the probability update equations for the proposed decoding. Based on the new probability update equations, we show the intrinsic relationship of the existing decoding algorithms. Finally, in order to achieve the best performance, we formulate an optimization problem to find the optimal probability modifying factors for the proposed decoding algorithm. Furthermore, a method based on the modified PSO algorithm is also introduced to solve that optimization problem. Numerical results show that the proposed generalization BP decoding algorithm achieves better performance than that of the existing BP decoding, which suggests the effectiveness of the proposed decoding algorithm.

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Loeliger, H. A., F. Lustenberger, M. Helfenstein, and F. Tarkoy. "Probability propagation and decoding in analog VLSI." IEEE Transactions on Information Theory 47, no. 2 (2001): 837–43. http://dx.doi.org/10.1109/18.910594.

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Claridge, Jessica, and Ioannis Chatzigeorgiou. "Probability of Partially Decoding Network-Coded Messages." IEEE Communications Letters 21, no. 9 (September 2017): 1945–48. http://dx.doi.org/10.1109/lcomm.2017.2704110.

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Hashimoto, T. "Bounds on a Probability for the Heavy Tailed Distribution and the Probability of Deficient Decoding in Sequential Decoding." IEEE Transactions on Information Theory 51, no. 3 (March 2005): 990–1002. http://dx.doi.org/10.1109/tit.2004.842580.

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K N, Manjunatha, and Vaibhav A Meshram. "Energy Efficient VLSI Architecture for Variable Iterative 4G LTE Turbo Decoder." International Journal of Engineering & Technology 7, no. 3 (July 16, 2018): 1535. http://dx.doi.org/10.14419/ijet.v7i3.12652.

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The Long Term Evolution (LTE) networks main objective is to support the next generation wireless communication systems. But most of the LTE approaches are suffer from decoding latency. Hence results in drop of data rate and this is not supported by the 4G LTE standards. To overcome this few parallel architectures has been introduced with the cost of power and silicon chip area. One promising decoding algorithm to overcome the decoding latency is Maximum a Posteriori (MAP) algorithm. The MAP has two computationally challenging α and β units. These two units have critical path and are to be reduced. A novel architecture for Add-Compare-Select (ACS) is proposed with clock gating techniques to reduce the unnecessary power dissipation across the recursive computational units. The proposed technique is applied with max-log MAP algorithm to precise the approximation. The overall design in implemented in a 45nm CMOS technology and results in 179.2mW of power dissipation which results in 34.6% less power compared to reported design while monitoring the moderate or same throughput level.

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Kienle, F., H. Michel, F. Gilbert, and N. Wehn. "Efficient MAP-algorithm implementation on programmable architectures." Advances in Radio Science 1 (May 5, 2003): 259–63. http://dx.doi.org/10.5194/ars-1-259-2003.

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Abstract. Maximum-A-Posteriori (MAP) decoding algorithms are important HW/SW building blocks in advanced communication systems due to their ability to provide soft-output informations which can be efficiently exploited in iterative channel decoding schemes like Turbo-Codes. Multi-standards demand flexible implementations on programmable platforms. In this paper we analyze a quantized turbo-decoder based on a Max-Log-MAP algorithm with Extrinsic Scaling Factor (ESF). Its communication performance approximate to a Turbo-Decoder with a Log-MAP algorithm and is less sensitive to quantization effects. We present Turbo-Decoder implementations on state-of-the-art DSPs and show that only a Max-Log-MAP implementation fulfills a throughput requirement of ~2 Mbit/s. The negligible overhead for the ESF implementation strengthen the use of Max-Log-MAP with ESF implementation on programmable platforms.

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van Houtum, Wim J., and Frans M. J. Willems. "Two-Dimensional Iterative Processing for DAB Receivers Based on Trellis-Decomposition." Journal of Electrical and Computer Engineering 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/394809.

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We investigate iterative trellis decoding techniques for DAB, with the objective of gaining from processing 2D-blocks in an OFDM scheme, that is, blocks based on the time and frequency dimension, and from trellis decomposition. Trellis-decomposition methods allow us to estimate the unknown channel phase since this phase relates to the sub-trellises. We will determine a-posteriori sub-trellis probabilities, and use these probabilities for weighting the a-posteriori symbol probabilities resulting from all the sub-trellises. Alternatively we can determine a dominant sub-trellis and use the a-posteriori symbol probabilities corresponding to this dominant sub-trellis. This dominant sub-trellis approach results in a significant complexity reduction. We will investigate both iterative and non-iterative methods. The advantage of non-iterative methods is that their forwardbackward procedures are extremely simple; however, also their gain of 0.7 dB, relative to two-symbol differential detection (2SDD) at a BER of10-4, is modest. Iterative procedures lead to the significantly larger gain of 3.7 dB at a BER of10-4for five iterations, where a part of this gain comes from 2D processing. Simulations of our iterative approach applied to the TU-6 (COST207) channel show that we get an improvement of 2.4 dB at a Doppler frequency of 10 Hz.

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Meng, Xiao-Li. "Decoding the H-likelihood." Statistical Science 24, no. 3 (August 2009): 280–93. http://dx.doi.org/10.1214/09-sts277c.

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Xie, Li, Valery A. Ugrinovskii, and Ian R. Petersen. "A Posteriori Probability Distances between Finite-Alphabet Hidden Markov Models." IFAC Proceedings Volumes 37, no. 21 (December 2004): 627–32. http://dx.doi.org/10.1016/s1474-6670(17)30540-2.

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Gerstoft, Peter, and Christoph F. Mecklenbräuker. "Ocean acoustic inversion with estimation of a posteriori probability distributions." Journal of the Acoustical Society of America 104, no. 2 (August 1998): 808–19. http://dx.doi.org/10.1121/1.423355.

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Huang, Chun-Rong, Pau-Choo Julia Chung, Di-Kai Yang, Hsing-Cheng Chen, and Guan-Jie Huang. "Maximum a Posteriori Probability Estimation for Online Surveillance Video Synopsis." IEEE Transactions on Circuits and Systems for Video Technology 24, no. 8 (August 2014): 1417–29. http://dx.doi.org/10.1109/tcsvt.2014.2308603.

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