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Journal articles on the topic 'Coding gain'

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Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Kojima, Toshiharu, Akinori Fujimura, Makoto Miyake, Tadashi Fujino, Hideo Yoshida, and Atsuhiro Yamagishi. "Concatenated coding scheme with variable coding gain." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 76, no. 3 (1993): 91–104. http://dx.doi.org/10.1002/ecjc.4430760310.

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2

Hong, Sungho, Brian Nils Lundstrom, and Adrienne L. Fairhall. "Intrinsic Gain Modulation and Adaptive Neural Coding." PLoS Computational Biology 4, no. 7 (July 18, 2008): e1000119. http://dx.doi.org/10.1371/journal.pcbi.1000119.

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3

Jeruchim, M. "On the Coding Gain for Degraded Channels." IEEE Transactions on Communications 34, no. 5 (May 1986): 492–96. http://dx.doi.org/10.1109/tcom.1986.1096559.

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4

Evangelista, G. "The coding gain of multiplexed wavelet transforms." IEEE Transactions on Signal Processing 44, no. 7 (July 1996): 1681–92. http://dx.doi.org/10.1109/78.510616.

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5

Soman, A. K., and P. P. Vaidyanathan. "Coding gain in paraunitary analysis/synthesis systems." IEEE Transactions on Signal Processing 41, no. 5 (May 1993): 1824–35. http://dx.doi.org/10.1109/78.215302.

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6

Kok, C. W., and T. Q. Nguyen. "Multirate filter banks and transform coding gain." IEEE Transactions on Signal Processing 46, no. 7 (July 1998): 2041–44. http://dx.doi.org/10.1109/78.700978.

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7

Lee, H., and S. Lee. "Visual Entropy Gain for Wavelet Image Coding." IEEE Signal Processing Letters 13, no. 9 (September 2006): 553–56. http://dx.doi.org/10.1109/lsp.2006.874464.

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8

Srinath, Koteshwar Pavan, and Balaji Sundar Rajan. "Fast-Decodable MIDO Codes With Large Coding Gain." IEEE Transactions on Information Theory 60, no. 2 (February 2014): 992–1007. http://dx.doi.org/10.1109/tit.2013.2292513.

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9

Scagliola, Michele, Fernando Perez-Gonzalez, and Pietro Guccione. "Gain-Invariant Dirty Paper Coding for Hierarchical OFDM." IEEE Transactions on Communications 59, no. 12 (December 2011): 3323–34. http://dx.doi.org/10.1109/tcomm.2011.101011.100544.

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10

Calvagno, G., G. A. Mian, and R. Rinaldo. "Computation of the coding gain for subband coders." IEEE Transactions on Communications 44, no. 4 (April 1996): 475–87. http://dx.doi.org/10.1109/26.489094.

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11

Serrano, Eduardo, Thomas Nowotny, Rafael Levi, Brian H. Smith, and Ramón Huerta. "Gain Control Network Conditions in Early Sensory Coding." PLoS Computational Biology 9, no. 7 (July 18, 2013): e1003133. http://dx.doi.org/10.1371/journal.pcbi.1003133.

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12

Fujiwara, Juri, Philippe N. Tobler, Masato Taira, Toshio Iijima, and Ken-Ichiro Tsutsui. "Segregated and Integrated Coding of Reward and Punishment in the Cingulate Cortex." Journal of Neurophysiology 101, no. 6 (June 2009): 3284–93. http://dx.doi.org/10.1152/jn.90909.2008.

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Reward and punishment have opposite affective value but are both processed by the cingulate cortex. However, it is unclear whether the positive and negative affective values of monetary reward and punishment are processed by separate or common subregions of the cingulate cortex. We performed a functional magnetic resonance imaging study using a free-choice task and compared cingulate activations for different levels of monetary gain and loss. Gain-specific activation (increasing activation for increasing gain, but no activation change in relation to loss) occurred mainly in the anterior part of the anterior cingulate and in the posterior cingulate cortex. Conversely, loss-specific activation (increasing activation for increasing loss, but no activation change in relation to gain) occurred between these areas, in the middle and posterior part of the anterior cingulate. Integrated coding of gain and loss (increasing activation throughout the full range, from biggest loss to biggest gain) occurred in the dorsal part of the anterior cingulate, at the border with the medial prefrontal cortex. Finally, unspecific activation increases to both gains and losses (increasing activation to increasing gains and increasing losses, possibly reflecting attention) occurred in dorsal and middle regions of the cingulate cortex. Together, these results suggest separate and common coding of monetary reward and punishment in distinct subregions of the cingulate cortex. Further meta-analysis suggested that the presently found reward- and punishment-specific areas overlapped with those processing positive and negative emotions, respectively.

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13

Engelmann, J. B., G. S. Berns, and B. W. Dunlop. "Hyper-responsivity to losses in the anterior insula during economic choice scales with depression severity." Psychological Medicine 47, no. 16 (June 7, 2017): 2879–91. http://dx.doi.org/10.1017/s0033291717001428.

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BackgroundCommonly observed distortions in decision-making among patients with major depressive disorder (MDD) may emerge from impaired reward processing and cognitive biases toward negative events. There is substantial theoretical support for the hypothesis that MDD patients overweight potential losses compared with gains, though the neurobiological underpinnings of this bias are uncertain.MethodsTwenty-one unmedicated patients with MDD were compared with 25 healthy controls (HC) using functional magnetic resonance imaging (fMRI) together with an economic decision-making task over mixed lotteries involving probabilistic gains and losses. Region-of-interest analyses evaluated neural signatures of gain and loss coding within a core network of brain areas known to be involved in valuation (anterior insula, caudate nucleus, ventromedial prefrontal cortex).ResultsUsable fMRI data were available for 19 MDD and 23 HC subjects. Anterior insula signal showed negative coding of losses (gain > loss) in HC subjects consistent with previous findings, whereas MDD subjects demonstrated significant reversals in these associations (loss > gain). Moreover, depression severity further enhanced the positive coding of losses in anterior insula, ventromedial prefrontal cortex, and caudate nucleus. The hyper-responsivity to losses displayed by the anterior insula of MDD patients was paralleled by a reduced influence of gain, but not loss, stake size on choice latencies.ConclusionsPatients with MDD demonstrate a significant shift from negative to positive coding of losses in the anterior insula, revealing the importance of this structure in value-based decision-making in the context of emotional disturbances.

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14

Kordiš, Dušan, and Janez Kokošar. "What Can Domesticated Genes Tell Us about the Intron Gain in Mammals?" International Journal of Evolutionary Biology 2012 (May 30, 2012): 1–7. http://dx.doi.org/10.1155/2012/278981.

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Domesticated genes, originating from retroelements or from DNA-transposons, constitute an ideal system for testing the hypothesis on the absence of intron gain in mammals. Since single-copy domesticated genes originated from the intronless multicopy transposable elements, the ancestral intron state for domesticated genes is zero. A phylogenomic approach has been used to analyse all domesticated genes in mammals and chordates that originated from the coding parts of transposable elements. A significant amount of intron gain was found only in domesticated genes of placental mammals, where more than 70 cases were identified. De novo gained introns show clear positional bias, since they are distributed mainly in 5′ UTR and coding regions, while 3′ UTR introns are very rare. In the coding regions of some domesticated genes up to 8 de novo gained introns have been found. Surprisingly, the majority of intron gains have occurred in the ancestor of placental mammals. Domesticated genes could constitute an excellent system on which to analyse the mechanisms of intron gain. This paper summarizes the current understanding of intron gain in mammals.

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15

Naima, SOFI, FATIMA Debbat, and Fethi Tarik Bendimerad. "Performance Improvement of MIMO-OSTBC System with BCH-TURBO Code In Rayleigh Fading Channel." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 3 (September 1, 2018): 898. http://dx.doi.org/10.11591/ijeecs.v11.i3.pp898-907.

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Recently, OSTBCs has become a widespread technique for signal transmission over wireless channels because of their diversity gain, but there are not designed to achieve an additional coding gain. Hence, OSTBCs must be concatenated with an external code which allows a significant coding gain.FEC (forward error correction) is a technique used for detecting and possibly correcting errors that can occur when messages are transmitted through a digital communication system, also for rendering the information more reliable. Thus, with staffing these coding techniques that are able to reach Shannon limits, in MIMO systems, better performances can be achieved by taking advantages of diversity and coding gains. The objective of this paper is to compare different FEC codes in Rayleigh fading channel and propose an appropriate code for MIMO-OSTBC systems. The simulation results reveal the performance of the proposed model

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16

Samčović, Andreja. "Analysis of Coding Gain and Optimal Bit Allocation in Motion-Compensated Video Compression." Journal of Electrical Engineering 63, no. 2 (March 1, 2012): 129–32. http://dx.doi.org/10.2478/v10187-012-0020-z.

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Analysis of Coding Gain and Optimal Bit Allocation in Motion-Compensated Video CompressionThis paper describes mathematical frameworks on temporal and spatial predictive processing in the motion-compensated video compression. Firstly, the coding gain over intra coding is derived, regarding the bit allocation algorithm and Lagrange multiplier method. The optimal ordering of three different picture types (I, P and B pictures) is clarified according to image source characteristics. Secondly, a novel framework with the block-based multihypothesis motion-compensated optimal coding gain and bit allocation are derived in a closed-form expression.

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17

Andrew, J. P. "Coding gain and spatial localisation properties of discrete wavelet transform filters for image coding." IEE Proceedings - Vision, Image, and Signal Processing 142, no. 3 (1995): 133. http://dx.doi.org/10.1049/ip-vis:19951938.

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18

Prashanthi, V. "NETWORK CODING GAIN OPTIMIZATION IN WIRELESS AD-HOC NETWORKS." International Journal of Advanced Research in Computer Science 8, no. 7 (August 20, 2017): 185–88. http://dx.doi.org/10.26483/ijarcs.v8i7.4151.

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19

Chou, Yao-Tang, Wen-Liang Hwang, and Chung-Lin Huang. "Gain–shape optimized dictionary for matching pursuit video coding." Signal Processing 83, no. 9 (September 2003): 1937–43. http://dx.doi.org/10.1016/s0165-1684(03)00112-9.

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20

Ma, Hong. "Free programming sites for librarians to gain coding skills." Technical Services Quarterly 33, no. 1 (December 30, 2015): 99–100. http://dx.doi.org/10.1080/07317131.2015.1093857.

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21

Soman, A. K., and P. P. Vaidyanathan. "Generalized polyphase representation and application to coding gain enhancement." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 41, no. 9 (1994): 627–30. http://dx.doi.org/10.1109/82.326593.

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22

Djokovic, I., and P. P. Vaidyanathan. "On optimal analysis/synthesis filters for coding gain maximization." IEEE Transactions on Signal Processing 44, no. 5 (May 1996): 1276–79. http://dx.doi.org/10.1109/78.502341.

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23

Tarokh, V., and I. F. Blake. "Trellis complexity versus the coding gain of lattices. I." IEEE Transactions on Information Theory 42, no. 6 (1996): 1796–807. http://dx.doi.org/10.1109/18.556675.

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24

Tarakh, V., and I. F. Blake. "Trellis complexity versus the coding gain of lattices. II." IEEE Transactions on Information Theory 42, no. 6 (1996): 1808–16. http://dx.doi.org/10.1109/18.556676.

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25

Weeks, W., and R. E. Blahut. "The capacity and coding gain of certain checkerboard codes." IEEE Transactions on Information Theory 44, no. 3 (May 1998): 1193–203. http://dx.doi.org/10.1109/18.669282.

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26

Juin-Hwey Chen and A. Gersho. "Gain-Adaptive Vector Quantization with Application to Speech Coding." IEEE Transactions on Communications 35, no. 9 (September 1987): 918–30. http://dx.doi.org/10.1109/tcom.1987.1096884.

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27

Wu, Jun, and Xiao Bo Wu. "An FPGA Implementation of TCM Cascade Space Time Block Code." Applied Mechanics and Materials 195-196 (August 2012): 901–3. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.901.

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Trellis coded (TCM) modulation can obtain the coding gain without increase the transmission power and the bandwidth but it can not obtain diversity gain, and space-time block code (STBC) can provide diversity gain in a simple encoding and decoding way, though its coding gain is not very satisfied. This article will achieve a STBC-class networking trellis coded modulation scheme based on FPGA to further study the performance of the concatenated code.

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28

Kuznetsov, Alexandr, Oleg Oleshko, and Kateryna Kuznetsova. "ENERGY GAIN FROM ERROR-CORRECTING CODING IN CHANNELS WITH GROUPING ERRORS." Acta Polytechnica 60, no. 1 (March 2, 2020): 65–72. http://dx.doi.org/10.14311/ap.2020.60.0065.

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Abstract. This article explores the a mathematical model of the a data transmission channel with errors grouping. We propose an estimating method for energy gain from coding and energy efficiency of binary codes in channels with grouped errors. The proposed method uses a simplified Bennet and Froelich’s model and allows leading the research of the energy gain from coding for a wide class of data channels without restricting the way of the length distributing the error bursts. The reliability of the obtained results is confirmed by the information of the known results in the theory of error-correcting coding in the simplified variant.

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29

Kaimkhani, Naveed Ali, Zhe Chen, and Fuliang Yin. "Evolution of Diversity Gain with and without Coding Gain in MIMO for Emerging Wireless Networks." International Journal of Computer Theory and Engineering 9, no. 1 (2017): 32–37. http://dx.doi.org/10.7763/ijcte.2017.v9.1107.

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30

Fujii, Masahiro, Makoto Itami, and Kohji Itoh. "Performance evaluation of diversity gain and coding gain in coded orthogonal multi-carrier modulation systems." European Transactions on Telecommunications 15, no. 3 (May 2004): 201–6. http://dx.doi.org/10.1002/ett.966.

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31

Palmer, Colin J., Nathan Caruana, Colin W. G. Clifford, and Kiley J. Seymour. "Adaptive sensory coding of gaze direction in schizophrenia." Royal Society Open Science 5, no. 12 (December 2018): 180886. http://dx.doi.org/10.1098/rsos.180886.

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Schizophrenia has been associated with differences in how the visual system processes sensory input. A fundamental mechanism that regulates sensory processing in the brain is gain control, whereby the responses of sensory neurons to a given stimulus are modulated in accordance with the spatial and temporal context. Some studies indicate an impairment of certain cortical gain control mechanisms in schizophrenia in low-level vision, reflected, for instance, in how the visual appearance of a stimulus is affected by the presence of other stimuli around it. In the present study, we investigated higher-level, social vision in schizophrenia, namely the perception of other people's direction of gaze (i.e. a type of face processing). Recent computational modelling work indicates that perceptual aftereffects—changes in perception that occur following repeated exposure to faces that display a specific direction of gaze—are indicative of two distinct forms of gain control involved in the coding of gaze direction across sensory neurons. We find that individuals with schizophrenia display strong perceptual aftereffects following repeated exposure to faces with averted gaze, and a modelling analysis indicates similarly robust gain control in the form of (i) short-term adjustment of channel sensitivities in response to the recent sensory history and (ii) divisive normalization of the encoded gaze direction. Together, this speaks to the typical coding of other people's direction of gaze in the visual system in schizophrenia, including flexible gain control, despite the social–cognitive impairments that can occur in this condition.

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32

Padmaja, C., and B. L. Malleswari. "Performance improvement of 4G OFDM systems using CTSTC techniques." International Journal of Engineering & Technology 7, no. 2.21 (April 20, 2018): 131. http://dx.doi.org/10.14419/ijet.v7i2.21.11850.

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The concatenation of channel coding and diversity schemes are essential in the 4G communication systems to improve the reliable data rate transmission. To address Bit Error Rate performance enhancement, the paper presents the coding gain and diversity gain benefits using the proposed CTSTC scheme by adding modified Turbo features and Space Time encoding features. Simulation results of are provided using MATLAB and compared the results with convolutional coded Space Time Coding technique.

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33

Kimura, Toshitaka, and Hiroaki Gomi. "Time-domain coding of somatosensory reflex gain during arm movements." Neuroscience Research 58 (January 2007): S92. http://dx.doi.org/10.1016/j.neures.2007.06.1102.

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34

Gerson, Ira Alan. "Provision of speech coder gain information using multiple coding modes." Journal of the Acoustical Society of America 103, no. 4 (April 1998): 1700. http://dx.doi.org/10.1121/1.421372.

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35

Labeau, F. "Synthesis filters design for coding gain in oversampled filter banks." IEEE Signal Processing Letters 12, no. 10 (October 2005): 697–700. http://dx.doi.org/10.1109/lsp.2005.855549.

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36

Sabin, Michael J. "Fixed-shape adaptive-gain vector quantization for speech waveform coding." Speech Communication 8, no. 2 (June 1989): 177–83. http://dx.doi.org/10.1016/0167-6393(89)90043-5.

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37

YOKOTA, Y. "Optimal Quantization Noise Allocation and Coding Gain in Transform Coding with Two-Dimensional Morphological Haar Wavelet." IEICE Transactions on Information and Systems E88-D, no. 3 (March 1, 2005): 636–45. http://dx.doi.org/10.1093/ietisy/e88-d.3.636.

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38

Di Renzo, Marco, Michela Iezzi, and Fabio Graziosi. "On Diversity Order and Coding Gain of Multisource Multirelay Cooperative Wireless Networks With Binary Network Coding." IEEE Transactions on Vehicular Technology 62, no. 3 (March 2013): 1138–57. http://dx.doi.org/10.1109/tvt.2012.2229476.

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39

LIN, CHUNYU, YAO ZHAO, and CE ZHU. "TWO-STAGE MULTIPLE DESCRIPTION IMAGE CODING USING TCQ." International Journal of Wavelets, Multiresolution and Information Processing 07, no. 05 (September 2009): 665–73. http://dx.doi.org/10.1142/s0219691309003185.

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In this paper, we incorporate Trellis Coded Quantization (TCQ) into a two-stage multiple description coding structure to obtain granular gain over two-stage multiple description Scalar Quantizer (SQ). Analysis and experiment on Gaussian signal show that the performance of the proposed scheme can achieve larger gain than that of the two-stage SQ scheme because of better performance of TCQ. The proposed scheme for image coding is shown to be more effective than other relevant multiple description image coding schemes in terms of central-side-distortion rate performance.

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40

Watanabe, Hiroshi, and Yutaka Suzuki. "Structure and coding characteristics of adaptive gain/shape vector quantizer for low bit-rate motion picture coding." Electronics and Communications in Japan (Part I: Communications) 74, no. 6 (June 1991): 1–12. http://dx.doi.org/10.1002/ecja.4410740601.

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41

Minallah, Nasru, Khadem Ullah, Jaroslav Frnda, Laiq Hasan, and Jan Nedoma. "On the Performance of Video Resolution, Motion and Dynamism in Transmission Using Near-Capacity Transceiver for Wireless Communication." Entropy 23, no. 5 (May 1, 2021): 562. http://dx.doi.org/10.3390/e23050562.

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This article investigates the performance of various sophisticated channel coding and transmission schemes for achieving reliable transmission of a highly compressed video stream. Novel error protection schemes including Non-Convergent Coding (NCC) scheme, Non-Convergent Coding assisted with Differential Space Time Spreading (DSTS) and Sphere Packing (SP) modulation (NCDSTS-SP) scheme and Convergent Coding assisted with DSTS and SP modulation (CDSTS-SP) are analyzed using Bit Error Ratio (BER) and Peak Signal to Noise Ratio (PSNR) performance metrics. Furthermore, error reduction is achieved using sophisticated transceiver comprising SP modulation technique assisted by Differential Space Time Spreading. The performance of the iterative Soft Bit Source Decoding (SBSD) in combination with channel codes is analyzed using various error protection setups by allocating consistent overall bit-rate budget. Additionally, the iterative behavior of SBSD assisted RSC decoder is analyzed with the aid of Extrinsic Information Transfer (EXIT) Chart in order to analyze the achievable turbo cliff of the iterative decoding process. The subjective and objective video quality performance of the proposed error protection schemes is analyzed while employing H.264 advanced video coding and H.265 high efficient video coding standards, while utilizing diverse video sequences having different resolution, motion and dynamism. It was observed that in the presence of noisy channel the low resolution videos outperforms its high resolution counterparts. Furthermore, it was observed that the performance of video sequence with low motion contents and dynamism outperforms relative to video sequence with high motion contents and dynamism. More specifically, it is observed that while utilizing H.265 video coding standard, the Non-Convergent Coding assisted with DSTS and SP modulation scheme with enhanced transmission mechanism results in Eb/N0 gain of 20 dB with reference to the Non-Convergent Coding and transmission mechanism at the objective PSNR value of 42 dB. It is important to mention that both the schemes have employed identical code rate. Furthermore, the Convergent Coding assisted with DSTS and SP modulation mechanism achieved superior performance with reference to the equivalent rate Non-Convergent Coding assisted with DSTS and SP modulation counterpart mechanism, with a performance gain of 16 dB at the objective PSNR grade of 42 dB. Moreover, it is observed that the maximum achievable PSNR gain through H.265 video coding standard is 45 dB, with a PSNR gain of 3 dB with reference to the identical code rate H.264 coding scheme.

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42

Budisin, S., and V. Jovanovic. "Bounds on the Asymptotic Coding Gain of Long Binary Block Codes." IEEE Transactions on Communications 35, no. 1 (January 1987): 113–14. http://dx.doi.org/10.1109/tcom.1987.1096671.

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43

Vaidyanathan, P. P. "Orthonormal and biorthonormal filter banks as convolvers, and convolutional coding gain." IEEE Transactions on Signal Processing 41, no. 6 (June 1993): 2110–30. http://dx.doi.org/10.1109/78.218140.

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44

Subramanian, Abhay T., and Andrew Thangaraj. "Path Gain Algebraic Formulation for the Scalar Linear Network Coding Problem." IEEE Transactions on Information Theory 56, no. 9 (September 2010): 4520–31. http://dx.doi.org/10.1109/tit.2010.2054270.

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45

Vehkalahti, Roope. "The Coding Gain of Real Matrix Lattices: Bounds and Existence Results." IEEE Transactions on Information Theory 56, no. 9 (September 2010): 4359–66. http://dx.doi.org/10.1109/tit.2010.2054690.

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46

Marcellin, M. W., and A. Bilgin. "Quantifying the parent-child coding gain in zero-tree-based coders." IEEE Signal Processing Letters 8, no. 3 (March 2001): 67–69. http://dx.doi.org/10.1109/97.905942.

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47

Sampson, D., and M. Ghanbari. "Fast lattice-based gain-shape vector quantisation for image-sequence coding." IEE Proceedings I Communications, Speech and Vision 140, no. 1 (1993): 56. http://dx.doi.org/10.1049/ip-i-2.1993.0009.

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48

Yang, Xican, Jian Li, Changliang Xie, and Li Li. "Throughput gain of random wireless networks with Physical-layer Network Coding." Tsinghua Science and Technology 17, no. 2 (April 2012): 161–71. http://dx.doi.org/10.1109/tst.2012.6180041.

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49

Wei, H., and L. Hanzo. "Coding against spreading gain optimisation of nonbinary BCH coded CDMA system." Electronics Letters 41, no. 14 (2005): 816. http://dx.doi.org/10.1049/el:20051702.

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50

Lightstone, M., and S. K. Mitra. "Optimal variable-rate mean-gain-shape vector quantization for image coding." IEEE Transactions on Circuits and Systems for Video Technology 6, no. 6 (1996): 660–68. http://dx.doi.org/10.1109/76.544737.

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