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Journal articles on the topic 'Multiple-Model coding'
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Qing, Linbo, Xiaohai He, Xianfeng Ou, and Rui Lv. "Distributed Video Coding Based on Multiple-source Correlation Model." Applied Mathematics & Information Sciences 7, no. 4 (July 1, 2013): 1609–14. http://dx.doi.org/10.12785/amis/070447.
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Zhou, Yugang, and Wai-Yip Chan. "E-model based comparison of multiple description coding and layered coding in packet networks." European Transactions on Telecommunications 18, no. 7 (2007): 661–68. http://dx.doi.org/10.1002/ett.1168.
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Zhang, Jinlin, Jun Shao, Yun Ling, Min Ji, Guiyi Wei, and Bishan Ying. "Efficient multiple sources network coding signature in the standard model." Concurrency and Computation: Practice and Experience 27, no. 10 (July 25, 2014): 2616–36. http://dx.doi.org/10.1002/cpe.3322.
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WANG, NING, and LIZHONG PENG. "BALANCED MULTIPLE DESCRIPTION SUBBAND CODING." International Journal of Wavelets, Multiresolution and Information Processing 09, no. 04 (July 2011): 571–86. http://dx.doi.org/10.1142/s0219691311004225.
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Multiple description coding is one of the source coding techniques used to alleviate the problems of packet loss in network. The objective is to encode a source into two (or more) bitstreams supporting two quality levels of decoding: A high-quality reconstruction decoded from the two bitstreams together, and two lower ones, but still acceptable, decoded from either of the two bitstreams individually. Most of the earlier works concentrated on the trade-off between redundancy and average side distortion, which is insufficient to evaluate the system performance. In this paper, we discuss the difference in quality between the two side reconstructions, which is defined as the balance eccentric modulus of the coding system. A scenario to design balanced multiple description coding system is presented. Application to the popular first-order autoregressive model yields encouraging results.
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Bai, Yang, Xuan Guang, and Raymond W. Yeung. "Multiple Linear-Combination Security Network Coding." Entropy 25, no. 8 (July 28, 2023): 1135. http://dx.doi.org/10.3390/e25081135.
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In this paper, we put forward the model of multiple linear-combination security multicast network coding, where the wiretapper desires to obtain some information about a predefined set of multiple linear combinations of the source symbols by eavesdropping any one (but not more than one) channel subset up to a certain size r, referred to as the security level. For this model, the security capacity is defined as the maximum average number of source symbols that can be securely multicast to all sink nodes for one use of the network under the linear-combination security constraint. For any security level and any linear-combination security constraint, we fully characterize the security capacity in terms of the ratio of the rank of the linear-combination security constraint to the number of source symbols. Also, we develop a general construction of linear security network codes. Finally, we investigate the asymptotic behavior of the security capacity for a sequence of linear-combination security models and discuss the asymptotic optimality of our code construction.
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Li, Shizheng, and Aditya Ramamoorthy. "Multiple-Source Slepian-Wolf Coding Under a Linear Equation Correlation Model." IEEE Transactions on Communications 60, no. 9 (September 2012): 2402–7. http://dx.doi.org/10.1109/tcomm.2012.070912.110062.
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Swanson, H. Lee. "Verbal coding deficits in learning-disabled readers: A multiple stage model." Educational Psychology Review 1, no. 3 (September 1989): 235–77. http://dx.doi.org/10.1007/bf01320136.
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Greenberg, Jane, Shaun Trujillo, and Ketan Mayer-Patel. "YouTube: Applying FRBR and Exploring the Multiple Description Coding Compression Model." Cataloging & Classification Quarterly 50, no. 5-7 (June 2012): 742–62. http://dx.doi.org/10.1080/01639374.2012.681273.
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Wang, Cui Yan, Wei Sheng Du, and Jun Wang. "Optimization Algorithm Study for Multiple-Constrained and Multiple-Objective Job-Shop Tool Dynamic Distribution." Applied Mechanics and Materials 551 (May 2014): 612–16. http://dx.doi.org/10.4028/www.scientific.net/amm.551.612.
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It’s an NP problem for distributing tools for job-shop tasks when the schedules were formulated, and it belongs to multiple-constrained and multiple-objective problem. Based on adaptive weight approach, the restriction and multiple objective problems were solved. The optimization dynamic distribution model for this problem was established. Then heuristic and self-adaptive genetic algorithm was presented. In order to express the dynamic of the distribution result, Two-dimensional coding technology was adopted, a new coding rule combining dispatching rule was designed. The results show that hybrid self-adaptive genetic algorithm based on adaptive weight approach forms well for multiple-constrained and multiple-objective job-shop tool dynamic distribution.
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Lee, Bum-Jik, Young-Hoon Joo, and Jin-Bae Park. "A DNA Coding-Based Interacting Multiple Model Method for Tracking a Maneuvering Target." Journal of Korean Institute of Intelligent Systems 12, no. 6 (December 1, 2002): 497–502. http://dx.doi.org/10.5391/jkiis.2002.12.6.497.
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Standage, Dominic, and Martin Paré. "Slot-like capacity and resource-like coding in a neural model of multiple-item working memory." Journal of Neurophysiology 120, no. 4 (October 1, 2018): 1945–61. http://dx.doi.org/10.1152/jn.00778.2017.
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For the past decade, research on the storage limitations of working memory has been dominated by two fundamentally different hypotheses. On the one hand, the contents of working memory may be stored in a limited number of “slots,” each with a fixed resolution. On the other hand, any number of items may be stored but with decreasing resolution. These two hypotheses have been invaluable in characterizing the computational structure of working memory, but neither provides a complete account of the available experimental data or speaks to the neural basis of the limitations it characterizes. To address these shortcomings, we simulated a multiple-item working memory task with a cortical network model, the cellular resolution of which allowed us to quantify the coding fidelity of memoranda as a function of memory load, as measured by the discriminability, regularity, and reliability of simulated neural spiking. Our simulations account for a wealth of neural and behavioral data from human and nonhuman primate studies, and they demonstrate that feedback inhibition lowers both capacity and coding fidelity. Because the strength of inhibition scales with the number of items stored by the network, increasing this number progressively lowers fidelity until capacity is reached. Crucially, the model makes specific, testable predictions for neural activity on multiple-item working memory tasks. NEW & NOTEWORTHY Working memory is the ability to keep information in mind and is fundamental to cognition. It is actively debated whether the storage limitations of working memory reflect a small number of storage units (slots) or a decrease in coding resolution as a limited resource is allocated to more items. In a cortical model, we found that slot-like capacity and resource-like neural coding resulted from the same mechanism, offering an integrated explanation for storage limitations.
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Zhang, Xiaoqiang, and Yangming Hu. "Multiple-image encryption algorithm based on the 3D scrambling model and dynamic DNA coding." Optics & Laser Technology 141 (September 2021): 107073. http://dx.doi.org/10.1016/j.optlastec.2021.107073.
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Wagatsuma, H., and Y. Yamaguchi. "3P244 Multiple sequence learning in theta phase coding model in T-maze alternation task." Seibutsu Butsuri 44, supplement (2004): S250. http://dx.doi.org/10.2142/biophys.44.s250_4.
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Grimes, David B., and Rajesh P. N. Rao. "Bilinear Sparse Coding for Invariant Vision." Neural Computation 17, no. 1 (January 1, 2005): 47–73. http://dx.doi.org/10.1162/0899766052530893.
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Recent algorithms for sparse coding and independent component analysis (ICA) have demonstrated how localized features can be learned from natural images. However, these approaches do not take image transformations into account. We describe an unsupervised algorithm for learning both localized features and their transformations directly from images using a sparse bilinear generative model. We show that from an arbitrary set of natural images, the algorithm produces oriented basis filters that can simultaneously represent features in an image and their transformations. The learned generative model can be used to translate features to different locations, thereby reducing the need to learn the same feature at multiple locations, a limitation of previous approaches to sparse coding and ICA. Our results suggest that by explicitly modeling the interaction between local image features and their transformations, the sparse bilinear approach can provide a basis for achieving transformation-invariant vision.
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Samann, Fars, and Thomas Schanze. "Multiple parallel hidden layers autoencoder for denoising ECG signal." Current Directions in Biomedical Engineering 8, no. 2 (August 1, 2022): 161–64. http://dx.doi.org/10.1515/cdbme-2022-1042.
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Abstract Deep learning with multiple hidden layers denoising autoencoders (MHL-DAE) is commonly used to denoise images and signals through dimension reduction. Here, we explore the potential of multiple parallel hidden layers denoising autoencoder (MPHL-DAE) to denoise complex bio-signals, like electrocardiogram (ECG). A merge layer, e.g., average layer is considered as the output of the proposed model by combining the outputs of the parallel hidden layers. The parallel hidden layers in the coding layer with activation function of different scale a, e.g., are considered to capture distinct features of the input. The lower/upper number of the required hidden neurons of the coding layer are estimated using data driven approach via singular values decomposition (SVD). The results show that the proposed MPHL-DAE model achieve better/similar SNR improvement compared to MHL-DAE with suitable scale for various noise levels respectively.
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McNamee, Daniel C., Kimberly L. Stachenfeld, Matthew M. Botvinick, and Samuel J. Gershman. "Compositional Sequence Generation in the Entorhinal–Hippocampal System." Entropy 24, no. 12 (December 8, 2022): 1791. http://dx.doi.org/10.3390/e24121791.
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Neurons in the medial entorhinal cortex exhibit multiple, periodically organized, firing fields which collectively appear to form an internal representation of space. Neuroimaging data suggest that this grid coding is also present in other cortical areas such as the prefrontal cortex, indicating that it may be a general principle of neural functionality in the brain. In a recent analysis through the lens of dynamical systems theory, we showed how grid coding can lead to the generation of a diversity of empirically observed sequential reactivations of hippocampal place cells corresponding to traversals of cognitive maps. Here, we extend this sequence generation model by describing how the synthesis of multiple dynamical systems can support compositional cognitive computations. To empirically validate the model, we simulate two experiments demonstrating compositionality in space or in time during sequence generation. Finally, we describe several neural network architectures supporting various types of compositionality based on grid coding and highlight connections to recent work in machine learning leveraging analogous techniques.
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Sarfraz, Fahad, Elahe Arani, and Bahram Zonooz. "Sparse Coding in a Dual Memory System for Lifelong Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (June 26, 2023): 9714–22. http://dx.doi.org/10.1609/aaai.v37i8.26161.
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Efficient continual learning in humans is enabled by a rich set of neurophysiological mechanisms and interactions between multiple memory systems. The brain efficiently encodes information in non-overlapping sparse codes, which facilitates the learning of new associations faster with controlled interference with previous associations. To mimic sparse coding in DNNs, we enforce activation sparsity along with a dropout mechanism which encourages the model to activate similar units for semantically similar inputs and have less overlap with activation patterns of semantically dissimilar inputs. This provides us with an efficient mechanism for balancing the reusability and interference of features, depending on the similarity of classes across tasks. Furthermore, we employ sparse coding in a multiple-memory replay mechanism. Our method maintains an additional long-term semantic memory that aggregates and consolidates information encoded in the synaptic weights of the working model. Our extensive evaluation and characteristics analysis show that equipped with these biologically inspired mechanisms, the model can further mitigate forgetting. Code available at \url{https://github.com/NeurAI-Lab/SCoMMER}.
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Prasad, Binod Kumar, and Goutam Sanyal. "Multiple Hidden Markov Model Post Processed with Support Vector Machine to Recognize English Handwritten Numerals." International Journal on Artificial Intelligence Tools 27, no. 05 (August 2018): 1850019. http://dx.doi.org/10.1142/s0218213018500197.
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This paper presents rotation and size invariant English numerals recognition system with, competitive recognition rate. The novelty of this paper is the introduction of two unique methods of feature extraction namely Pixel Moment of Inertia (PMI) and Delta Distance Coding (DDC). The proposed Multiple Hidden Markov Model (MHMM) is a two tier model to neutralize the effect of two very frequent writing styles of numerals ‘4’ and ‘7’ on their recognition rates. The novelty of PMI is that it finds moment of all the pixels of a specified zone about the central pixel and not about geometrical centroid of image area. In this paper, PMI has been observed to have an upper hand over centroidal MI. DDC is a new technique of curvature coding, based on distance from a reference level and is similar to the logic behind Delta modulation scheme in Digital Communications. Thus, the current paper correlates two digital domains namely, Digital Image Processing and Digital Communications. Support Vector Machine differentiates two close output classes obtained from classification with MHMM. The overall recognition accuracy rate of 99.17% has been achieved based on MNIST database.
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Humphrey, Sam, Alastair Kerr, Magnus Rattray, Caroline Dive, and Crispin J. Miller. "A model of k-mer surprisal to quantify local sequence information content surrounding splice regions." PeerJ 8 (November 4, 2020): e10063. http://dx.doi.org/10.7717/peerj.10063.
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Molecular sequences carry information. Analysis of sequence conservation between homologous loci is a proven approach with which to explore the information content of molecular sequences. This is often done using multiple sequence alignments to support comparisons between homologous loci. These methods therefore rely on sufficient underlying sequence similarity with which to construct a representative alignment. Here we describe a method using a formal metric of information, surprisal, to analyse biological sub-sequences without alignment constraints. We applied our model to the genomes of five different species to reveal similar patterns across a panel of eukaryotes. As the surprisal of a sub-sequence is inversely proportional to its occurrence within the genome, the optimal size of the sub-sequences was selected for each species under consideration. With the model optimized, we found a strong correlation between surprisal and CG dinucleotide usage. The utility of our model was tested by examining the sequences of genes known to undergo splicing. We demonstrate that our model can identify biological features of interest such as known donor and acceptor sites. Analysis across all annotated coding exon junctions in Homo sapiens reveals the information content of coding exons to be greater than the surrounding intron regions, a consequence of increased suppression of the CG dinucleotide in intronic space. Sequences within coding regions proximal to exon junctions exhibited novel patterns within DNA and coding mRNA that are not a function of the encoded amino acid sequence. Our findings are consistent with the presence of secondary information encoding features such as DNA and RNA binding sites, multiplexed through the coding sequence and independent of the information required to define the corresponding amino-acid sequence. We conclude that surprisal provides a complementary methodology with which to locate regions of interest in the genome, particularly in situations that lack an appropriate multiple sequence alignment.
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Nguyen, Tam The, and Tung Thanh Nguyen. "PERSONA: A personalized model for code recommendation." PLOS ONE 16, no. 11 (November 16, 2021): e0259834. http://dx.doi.org/10.1371/journal.pone.0259834.
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Code recommendation is an important feature of modern software development tools to improve the productivity of programmers. The current advanced techniques in code recommendation mostly focus on the crowd-based approach. The basic idea is to collect a large pool of available source code, extract the common code patterns, and utilize the patterns for recommendations. However, programmers are different in multiple aspects including coding preferences, styles, levels of experience, and knowledge about libraries and frameworks. These differences lead to various usages of code elements. When the code of multiple programmers is combined and mined, such differences are disappeared, which could limit the accuracy of the code recommendation tool for a specific programmer. In the paper, we develop a code recommendation technique that focuses on the personal coding patterns of programmers. We propose Persona, a personalized code recommendation model. It learns personalized code patterns for each programmer based on their coding history, while also combines with project-specific and common code patterns. Persona supports recommending code elements including variable names, class names, methods, and parameters. The empirical evaluation suggests that our recommendation tool based on Persona is highly effective. It recommends the next identifier with top-1 accuracy of 60-65% and outperforms the baseline approaches.
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Notom, Ajaykumar, Sarvagya Mrinal, and Prandkar Parag. "Systematic-RLNC Based Secure and QoS Centric Routing Scheme for WSNs." Journal of Telecommunications and Information Technology 4, no. 2019 (December 30, 2019): 55–64. http://dx.doi.org/10.26636/jtit.2019.136219.
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In this paper a highly robust and efficient systematic-random linear network coding (S-RLNC) routing scheme is proposed. Unlike classic security systems, the proposed S-RLNC transmission model incorporates an advanced pre-coding and interleaving concept followed by multigeneration mixing (MGM) based data transmission to assure secure and QoS efficient communication. The proposed S-RLNC MGM based routing scheme exhibits higher throughput (99.5-100%) than the existing NCC-ARQ-WSN protocol (80%). Unlike NCC-ARQ-WSN, the proposed model incorporates multiple enhancements, such as RLNC concept, systematic network coding, MGM concept, IBF provision and redundant packet optimization. Combined, all these optimizations have strengthened the proposed S-RLNC MGM to exhibit optimum performance for secure and QoS-centric communication over WSNs.
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Ren, Weizheng, Wei He, and Yansong Cui. "An Improved Fast Affine Motion Estimation Based on Edge Detection Algorithm for VVC." Symmetry 12, no. 7 (July 8, 2020): 1143. http://dx.doi.org/10.3390/sym12071143.
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As a newly proposed video coding standard, Versatile Video Coding (VVC) has adopted some revolutionary techniques compared to High Efficiency Video Coding (HEVC). The multiple-mode affine motion compensation (MM-AMC) adopted by VVC saves approximately 15%-25% Bjøntegaard Delta Bitrate (BD-BR), with an inevitable increase of encoding time. This paper gives an overview of both the 4-parameter affine motion model and the 6-parameter affine motion model, analyzes their performances, and proposes improved algorithms according to the symmetry of iterative gradient descent for fast affine motion estimation. Finally, the proposed algorithms and symmetric MM-AMC flame of VTM-7.0 are compared. The results show that the proposed algorithms save 6.65% total encoding time on average, which saves approximately 30% encoding time of affine motion compensation.
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Yan, Yijia, Neil Burgess, and Andrej Bicanski. "A model of head direction and landmark coding in complex environments." PLOS Computational Biology 17, no. 9 (September 27, 2021): e1009434. http://dx.doi.org/10.1371/journal.pcbi.1009434.
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Environmental information is required to stabilize estimates of head direction (HD) based on angular path integration. However, it is unclear how this happens in real-world (visually complex) environments. We present a computational model of how visual feedback can stabilize HD information in environments that contain multiple cues of varying stability and directional specificity. We show how combinations of feature-specific visual inputs can generate a stable unimodal landmark bearing signal, even in the presence of multiple cues and ambiguous directional specificity. This signal is associated with the retrosplenial HD signal (inherited from thalamic HD cells) and conveys feedback to the subcortical HD circuitry. The model predicts neurons with a unimodal encoding of the egocentric orientation of the array of landmarks, rather than any one particular landmark. The relationship between these abstract landmark bearing neurons and head direction cells is reminiscent of the relationship between place cells and grid cells. Their unimodal encoding is formed from visual inputs via a modified version of Oja’s Subspace Algorithm. The rule allows the landmark bearing signal to disconnect from directionally unstable or ephemeral cues, incorporate newly added stable cues, support orientation across many different environments (high memory capacity), and is consistent with recent empirical findings on bidirectional HD firing reported in the retrosplenial cortex. Our account of visual feedback for HD stabilization provides a novel perspective on neural mechanisms of spatial navigation within richer sensory environments, and makes experimentally testable predictions.
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Serlin, Ronald C., and Joel R. Levin. "Teaching How to Derive Directly Interpretable Coding Schemes for Multiple Regression Analysis." Journal of Educational Statistics 10, no. 3 (September 1985): 223–38. http://dx.doi.org/10.3102/10769986010003223.
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Multiple linear regression is a versatile model for encompassing analysis of variance, analysis of covariance, and aptitude-by-treatment interaction designs. The question of how to teach the coding of levels of a qualitative variable is addressed in this paper. Although a variety of coding schemes will produce invariant omnibus statistical results for a given set of data, one’s interpretation of treatment effects and treatment differences depends on the particular code values chosen. A general procedure is presented that allows the user to generate, on an a priori basis, code values that result in directly interpretable estimates of interest.
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Dziembowski, Adrian, Dawid Mieloch, Marek Domański, Gwangsoon Lee, and Jun Young Jeong. "Spatiotemporal redundancy removal in immersive video coding." Journal of WSCG 30, no. 1-2 (2022): 54–62. http://dx.doi.org/10.24132/jwscg.2022.7.
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In this paper, the authors describe two methods designed for reducing the spatiotemporal redundancy of the video within the MPEG Immersive video (MIV) encoder: patch occupation modification and cluster splitting. These methods allow optimizing two important parameters of the immersive video: bitrate and pixelrate. The patch occupation modification method significantly decreases the number of active pixels within texture and depth video produced by the MIV encoder. Cluster splitting decreases the total area needed for storing the texture and depth information from multiple input views, decreasing the pixelrate. Both methods proposed by the authors of this paper were appreciated by the experts of the ISO/IEC JTC1/SC29/WG11 MPEG and are included in the Test Model for MPEG Immersive video (TMIV), which is the reference software implementation of the MIV standard.
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Gaitán, Miguel Gutiérrez, Gowhar Javanmardi, and Ramiro Sámano-Robles. "Orthogonal Space-Time Block Coding for Double Scattering V2V Links with LOS and Ground Reflections." Sensors 23, no. 23 (December 3, 2023): 9594. http://dx.doi.org/10.3390/s23239594.
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This work presents the performance analysis of space-time block codes (STBCs) for vehicle-to-vehicle (V2V) fast-fading channels in scenarios with modified line-of-sight (LOS). The objective is to investigate how the V2V MIMO (multiple-input multiple-output) system performance is influenced by two important impairments: deterministic ground reflections and an increased Doppler frequency (time-variant channels). STBCs of various coding rates (using an approximation model) are evaluated by assuming antenna elements distributed over the surface of two contiguous vehicles. A multi-ray model is used to study the multiple constructive/destructive interference patterns of the transmitted/received signals by all pairs of Tx–Rx antenna links considering ground reflections. A double scattering model is used to include the effects of stochastic channel components that depend on the Doppler frequency. The results show that STBCs are capable of counteracting fades produced by destructive self-interference components across a range of inter-vehicle distances and for a range of Doppler frequency values. Notably, the effectiveness of STBCs in deep fades is shown to outperform schemes with exclusive receive diversity, despite the interference created by the loss of orthogonality in time-varying channels with a moderate increase of Doppler frequency (mainly due to higher vehicle speeds, higher frequency or shorter time slots). Higher-order STBCs with rate losses are also evaluated using an approximation model, showing interesting gains even for low coding rate performance, particularly when accompanied by a multiple antenna receiver. Overall, these results can shed light on how to exploit transmit diversity in time-varying vehicular channels with modified LOS.
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Doiron, Brent, Anne-Marie M. Oswald, and Leonard Maler. "Interval Coding. II. Dendrite-Dependent Mechanisms." Journal of Neurophysiology 97, no. 4 (April 2007): 2744–57. http://dx.doi.org/10.1152/jn.00988.2006.
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The rich temporal structure of neural spike trains provides multiple dimensions to code dynamic stimuli. Popular examples are spike trains from sensory cells where bursts and isolated spikes can serve distinct coding roles. In contrast to analyses of neural coding, the cellular mechanics of burst mechanisms are typically elucidated from the neural response to static input. Bridging the mechanics of bursting with coding of dynamic stimuli is an important step in establishing theories of neural coding. Electrosensory lateral line lobe (ELL) pyramidal neurons respond to static inputs with a complex dendrite-dependent burst mechanism. Here we show that in response to dynamic broadband stimuli, these bursts lack some of the electrophysiological characteristics observed in response to static inputs. A simple leaky integrate-and-fire (LIF)-style model with a dendrite-dependent depolarizing afterpotential (DAP) is sufficient to match both the output statistics and coding performance of experimental spike trains. We use this model to investigate a simplification of interval coding where the burst interspike interval (ISI) codes for the scale of a canonical upstroke rather than a multidimensional stimulus feature. Using this stimulus reduction, we compute a quantization of the burst ISIs and the upstroke scale to show that the mutual information rate of the interval code is maximized at a moderate DAP amplitude. The combination of a reduced description of ELL pyramidal cell bursting and a simplification of the interval code increases the generality of ELL burst codes to other sensory modalities.
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HUNG, CHUN-PO. "GENERAL CODING METHOD ON RANDOM SELF-SIMILAR TREE COMPOSED OF MULTIPLE BASIC PATTERNS." Fractals 17, no. 03 (September 2009): 283–98. http://dx.doi.org/10.1142/s0218348x09004442.
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This work discusses the random self-similar tree generation by employing multiple basic patterns, and investigates the character code and standardization algorithm for multiple basic patterns. With reference to the wide range of various basic patterns in natural shapes, the general coding method and the corresponding algorithm to calculate the topological distance is developed for random self-similar tree with multiple basic patterns. To assess the adaptability of the process, the general coding method is applied to transfer the generated river network to a code series and the corresponding algorithm for calculating topological distance of the links is used to determine the width function of the pattern. Finally, the width-function based geomorphologic instantaneous unit hydrograph (WF-GIUH) model is then applied to estimate the runoff of the Po-bridge watershed in northern Taiwan. The results reveal that the random self-similar tree with multiple basic patterns proposed in this study can be implemented successfully to calculate hydrologic responses.
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Kantor, Jiří, and Libuše Ludíková. "The Families of Students with Multiple Disabilities in the Educational Process." SOCIETY, INTEGRATION, EDUCATION. Proceedings of the International Scientific Conference 3 (May 17, 2015): 115. http://dx.doi.org/10.17770/sie2015vol3.373.
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<p><em>This paper focuses on several aspects of the relationship between families of students with multiple disabilities and the educational process. Quality-based methodology was used for the research due to a lack of sufficient theoretical background in this area. The objective of the paper was to elucidate the significance of education of students with multiple disabilities for their families and to describe the process of cooperation between these families and the teachers. This paper uses data and outputs from a qualitative analysis of education of students with severe multiple disabilities (semi-structured interviews with 45 teachers of students with multiple disabilities, observation of 4 students in the educational process and an analysis of 30 samples of educational documents using open, axial and selective coding). The resulting theory was combined with the results of a survey focused on the cooperation between the families and the teachers in order to gain a saturated data sample. This survey used semi-structured interviews with 5 teachers of students with multiple disabilities. The data from these interviews were analysed by means of open coding, compared with the outputs of the previous research and a theoretical model for educational intervention with the families of students with multiple disabilities was constructed and described. This model offers ideas for educational practice as well as further research.</em></p>
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Zhang, Lei, Shengyuan Zhou, Tian Zhi, Zidong Du, and Yunji Chen. "TDSNN: From Deep Neural Networks to Deep Spike Neural Networks with Temporal-Coding." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1319–26. http://dx.doi.org/10.1609/aaai.v33i01.33011319.
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Continuous-valued deep convolutional networks (DNNs) can be converted into accurate rate-coding based spike neural networks (SNNs). However, the substantial computational and energy costs, which is caused by multiple spikes, limit their use in mobile and embedded applications. And recent works have shown that the newly emerged temporal-coding based SNNs converted from DNNs can reduce the computational load effectively. In this paper, we propose a novel method to convert DNNs to temporal-coding SNNs, called TDSNN. Combined with the characteristic of the leaky integrate-andfire (LIF) neural model, we put forward a new coding principle Reverse Coding and design a novel Ticking Neuron mechanism. According to our evaluation, our proposed method achieves 42% total operations reduction on average in large networks comparing with DNNs with no more than 0.5% accuracy loss. The evaluation shows that TDSNN may prove to be one of the key enablers to make the adoption of SNNs widespread.
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Abadi, Alireza Khatoon, Keyvan Yahya, Massoud Amini, Karl Friston, and Dietmar Heinke. "Excitatory versus inhibitory feedback in Bayesian formulations of scene construction." Journal of The Royal Society Interface 16, no. 154 (May 2019): 20180344. http://dx.doi.org/10.1098/rsif.2018.0344.
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The selective attention for identification model (SAIM) is an established model of selective visual attention. SAIM implements translation-invariant object recognition, in scenes with multiple objects, using the parallel distributed processing (PDP) paradigm. Here, we show that SAIM can be formulated as Bayesian inference. Crucially, SAIM uses excitatory feedback to combine top-down information (i.e. object knowledge) with bottom-up sensory information. By contrast, predictive coding implementations of Bayesian inference use inhibitory feedback. By formulating SAIM as a predictive coding scheme, we created a new version of SAIM that uses inhibitory feedback. Simulation studies showed that both types of architectures can reproduce the response time costs induced by multiple objects—as found in visual search experiments. However, due to the different nature of the feedback, the two SAIM schemes make distinct predictions about the motifs of microcircuits mediating the effects of top-down afferents. We discuss empirical (neuroimaging) methods to test the predictions of the two inference architectures.
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Chang, Howard Y. "Genome Regulation by Long Non-Coding RNAs." Blood 122, no. 21 (November 15, 2013): SCI—29—SCI—29. http://dx.doi.org/10.1182/blood.v122.21.sci-29.sci-29.
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Abstract The discovery of extensive transcription of long noncoding RNAs (lncRNAs) provide an important new perspective on the centrality of RNA in gene regulation. Discussions will cover genome-scale strategies to discover and characterize lncRNAs. An emerging theme from multiple model systems is that lncRNAs form extensive networks of ribonucleoprotein (RNP) complexes with numerous chromatin regulators, and both control and target these enzymatic activities to appropriate locations in the genome. Consistent with this notion, long non-coding RNAs can function as modular scaffolds to specify higher order organization in RNP complexes and in chromatin states. More recently, lncRNAs have been found to control post-transcriptional processes and signal transduction as well. The importance of these modes of regulation is underscored by the newly recognized roles of long RNAs in developmental patterning and cancer. Disclosures: Chang: RaNA Therapeutics: Membership on an entity’s Board of Directors or advisory committees.
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Ibrahim, Sarmad K., and Nasser N. Khamiss. "A New Wireless Generation Technology for Video Streaming." Journal of Computer Networks and Communications 2019 (April 21, 2019): 1–9. http://dx.doi.org/10.1155/2019/3671826.
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With the exponential rise in the volumes of video traffic in cellular networks, there is an urgent need for improving the quality of video delivery. This research proposes a mobile generation model based on the updated technologies of the fourth- and fifth-generation mobile systems, which is called Proposed Generation (Pro-G). This model uses wider bandwidth and advanced adaptive modulation and coding. It also incorporates the method of the adaptive video streaming of multiple video data rates by using the transcoding technique, which is called H.265 proposed (H.265 pro). Thus, both methods are tested to provide a large number of users of video/data application with more speed and best quality. A comparison with 4G technology is done to assign the development regarding number of users with data rate. The suggested video coding shows how much the overall system is more reliable over the congested channel than conventional video coding technologies such as high-efficiency video coding (HEVC/H.265) and advanced video coding (AVC/H.264). The results showed that the proposed method of transmitting wireless data is better than the LTE-ADV method. In this method, the rate of data transfer increases by 29% compared with LTE-ADV, while the bit rate saving was increased to 13% in the proposed video coding compared with that in the H.265.
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Wang, Chao, and Changxi Ma. "Multi-objective optimization of customized bus routes based on full operation process." Modern Physics Letters B 34, no. 25 (June 2, 2020): 2050266. http://dx.doi.org/10.1142/s0217984920502668.
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In order to rationally formulate a customized bus route plan to improve the operation efficiency of customized buses, in view of the problem of ignoring the distance between the boarding area and the alighting area, and setting it as a fixed value when performing custom bus route optimization modeling, as well as solving the problem of multiple custom bus parking lots. This paper proposes a method based on the NSGA-II algorithm using a three-stage hybrid coding method to solve. First, according to real life, the entire operation process of customized buses is divided into four phases. Second, based on the four stages, a multi-objective optimization model of customized bus routes that satisfies multiple parking lots, multiple cars, and multiple boarding and alighting stations is constructed to pursue the minimum total travel time of passengers and minimizes the operating costs of customized bus companies. Third, the NSGA-II algorithm is employed to solve the model, including three-stage hybrid coding, segmented crossover, and segmented mutation operators. Finally, the local road network in Lanzhou City is adopted for simulation research. The results show that it is feasible to use NSGA-II algorithm to solve the problem of customizing bus routes for multiple parking lots, multiple vehicles, and multiple boarding and alighting stations. The research results are of great value for exploring the optimization methods of customized bus routes and improving the operating efficiency of custom buses.
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Lee, Taesik, and Dongsan Jun. "Fast Mode Decision Method of Multiple Weighted Bi-Predictions Using Lightweight Multilayer Perceptron in Versatile Video Coding." Electronics 12, no. 12 (June 15, 2023): 2685. http://dx.doi.org/10.3390/electronics12122685.
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Versatile Video Coding (VVC), the state-of-the-art video coding standard, was developed by the Joint Video Experts Team (JVET) of ISO/IEC Moving Picture Experts Group (MPEG) and ITU-T Video Coding Experts Group (VCEG) in 2020. Although VVC can provide powerful coding performance, it requires tremendous computational complexity to determine the optimal mode decision during the encoding process. In particular, VVC adopted the bi-prediction with CU-level weight (BCW) as one of the new tools, which enhanced the coding efficiency of conventional bi-prediction by assigning different weights to the two prediction blocks in the process of inter prediction. In this study, we investigate the statistical characteristics of input features that exhibit a correlation with the BCW and define four useful types of categories to facilitate the inter prediction of VVC. With the investigated input features, a lightweight neural network with multilayer perceptron (MLP) architecture is designed to provide high accuracy and low complexity. We propose a fast BCW mode decision method with a lightweight MLP to reduce the computational complexity of the weighted multiple bi-prediction in the VVC encoder. The experimental results show that the proposed method significantly reduced the BCW encoding complexity by up to 33% with unnoticeable coding loss, compared to the VVC test model (VTM) under the random-access (RA) configuration.
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Yu, Long, Yuliang Lu, Xuehu Yan, and Yongqiang Yu. "MTS-Stega: Linguistic Steganography Based on Multi-Time-Step." Entropy 24, no. 5 (April 22, 2022): 585. http://dx.doi.org/10.3390/e24050585.
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Generative linguistic steganography encodes candidate words with conditional probability when generating text by language model, and then, it selects the corresponding candidate words to output according to the confidential message to be embedded, thereby generating steganographic text. The encoding techniques currently used in generative text steganography fall into two categories: fixed-length coding and variable-length coding. Because of the simplicity of coding and decoding and the small computational overhead, fixed-length coding is more suitable for resource-constrained environments. However, the conventional text steganography mode selects and outputs a word at one time step, which is highly susceptible to the influence of confidential information and thus may select words that do not match the statistical distribution of the training text, reducing the quality and concealment of the generated text. In this paper, we inherit the decoding advantages of fixed-length coding, focus on solving the problems of existing steganography methods, and propose a multi-time-step-based steganography method, which integrates multiple time steps to select words that can carry secret information and fit the statistical distribution, thus effectively improving the text quality. In the experimental part, we choose the GPT-2 language model to generate the text, and both theoretical analysis and experiments prove the effectiveness of the proposed scheme.
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Yeon, Jihye, Stephen M. Miller, and Wipawee Dejtisakdi. "New Synthetic Operon Vectors for Expressing Multiple Proteins in the Chlamydomonas reinhardtii Chloroplast." Genes 14, no. 2 (January 31, 2023): 368. http://dx.doi.org/10.3390/genes14020368.
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Microalgae are a promising platform for generating valuable commercial products, including proteins that may not express well in more traditional cell culture systems. In the model green alga Chlamydomonas reinhardtii, transgenic proteins can be expressed from either the nuclear or chloroplast genome. Expression in the chloroplast has several advantages, but technology is not yet well developed for expressing multiple transgenic proteins simultaneously. Here, we developed new synthetic operon vectors to express multiple proteins from a single chloroplast transcription unit. We modified an existing chloroplast expression vector to contain intercistronic elements derived from cyanobacterial and tobacco operons and tested the ability of the resulting operon vectors to express two or three different proteins at a time. All operons containing two of the coding sequences (for C. reinhardtii FBP1 and atpB) expressed the products of those genes, but operons containing the other two coding sequences (C. reinhardtii FBA1 and the synthetic camelid antibody gene VHH) did not. These results expand the repertoire of intercistronic spacers that can function in the C. reinhardtii chloroplast, but they also suggest that some coding sequences do not function well in the context of synthetic operons in this alga.
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Marquardt, Kyle L. "How and how much does expert error matter? Implications for quantitative peace research." Journal of Peace Research 57, no. 6 (November 2020): 692–700. http://dx.doi.org/10.1177/0022343320959121.
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Expert-coded datasets provide scholars with otherwise unavailable data on important concepts. However, expert coders vary in their reliability and scale perception, potentially resulting in substantial measurement error. These concerns are acute in expert coding of key concepts for peace research. Here I examine (1) the implications of these concerns for applied statistical analyses, and (2) the degree to which different modeling strategies ameliorate them. Specifically, I simulate expert-coded country-year data with different forms of error and then regress civil conflict onset on these data, using five different modeling strategies. Three of these strategies involve regressing conflict onset on point estimate aggregations of the simulated data: the mean and median over expert codings, and the posterior median from a latent variable model. The remaining two strategies incorporate measurement error from the latent variable model into the regression process by using multiple imputation and a structural equation model. Analyses indicate that expert-coded data are relatively robust: across simulations, almost all modeling strategies yield regression results roughly in line with the assumed true relationship between the expert-coded concept and outcome. However, the introduction of measurement error to expert-coded data generally results in attenuation of the estimated relationship between the concept and conflict onset. The level of attenuation varies across modeling strategies: a structural equation model is the most consistently robust estimation technique, while the median over expert codings and multiple imputation are the least robust.
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Youssef, M. I., A. E. Emam, and M. Abd Khalifa. "ICI and PAPR enhancement in MIMO-OFDM system using RNS coding." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (April 1, 2019): 1209. http://dx.doi.org/10.11591/ijece.v9i2.pp1209-1219.
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<p><span>The Inter-Carrier-Interference (ICI) is considered a bottleneck in the utilization of Multiple-Input-Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, due to the sensitivity of the OFDM towards frequency offsets which lead to loss of orthogonality, interference and performance degradation. In this paper Residue Numbers as a coding scheme is impeded in MIMO-OFDM systems, where the ICI levels is measured and evaluated with respect to conventional ICI mitigation techniques implemented in MIMO-OFDM. The Carrier-to-Interference Ratio (CIR), the system Bit-Error-Rate (BER) and the Complementary Cumulative Distribution Function (CCDF) for MIMO-OFDM system with Residue Number System (RNS) coding are analyzed and evaluated. The results had demonstrated a performance of transmission model with and without RNS.</span></p>
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Yang, Fengrui, Xiaosi Wu, and Luwei Zhao. "Knowledge dialogue generation with multi-head attention mechanism." Journal of Physics: Conference Series 2078, no. 1 (November 1, 2021): 012013. http://dx.doi.org/10.1088/1742-6596/2078/1/012013.
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Abstract An increasing number of research efforts are focusing on knowledge dialogue generation. Less attention is focused on increasing knowledge diversity in generated responses. A model of knowledge selection guided by a multi-head attention mechanism is proposed. First, the current input discourse and knowledge content are input into the Bi-GRU module to obtain the coding vector, and then obtain multiple aspects of semantics from the user input discourse coding vector based on the multi-head attention mechanism, so as to select different knowledge. A punishment item method is proposed to force different attention to focus on different aspects, and finally, use the user input and selected knowledge for the decoding stage. Experiments with manual and automated evaluations have proven that the model is superior to the baseline model compared to previous work.
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Tscshantz, Alexander, Beren Millidge, Anil K. Seth, and Christopher L. Buckley. "Hybrid predictive coding: Inferring, fast and slow." PLOS Computational Biology 19, no. 8 (August 2, 2023): e1011280. http://dx.doi.org/10.1371/journal.pcbi.1011280.
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Predictive coding is an influential model of cortical neural activity. It proposes that perceptual beliefs are furnished by sequentially minimising “prediction errors”—the differences between predicted and observed data. Implicit in this proposal is the idea that successful perception requires multiple cycles of neural activity. This is at odds with evidence that several aspects of visual perception—including complex forms of object recognition—arise from an initial “feedforward sweep” that occurs on fast timescales which preclude substantial recurrent activity. Here, we propose that the feedforward sweep can be understood as performing amortized inference (applying a learned function that maps directly from data to beliefs) and recurrent processing can be understood as performing iterative inference (sequentially updating neural activity in order to improve the accuracy of beliefs). We propose a hybrid predictive coding network that combines both iterative and amortized inference in a principled manner by describing both in terms of a dual optimization of a single objective function. We show that the resulting scheme can be implemented in a biologically plausible neural architecture that approximates Bayesian inference utilising local Hebbian update rules. We demonstrate that our hybrid predictive coding model combines the benefits of both amortized and iterative inference—obtaining rapid and computationally cheap perceptual inference for familiar data while maintaining the context-sensitivity, precision, and sample efficiency of iterative inference schemes. Moreover, we show how our model is inherently sensitive to its uncertainty and adaptively balances iterative and amortized inference to obtain accurate beliefs using minimum computational expense. Hybrid predictive coding offers a new perspective on the functional relevance of the feedforward and recurrent activity observed during visual perception and offers novel insights into distinct aspects of visual phenomenology.
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Choi, Minkyu, and Jun Tani. "Predictive Coding for Dynamic Visual Processing: Development of Functional Hierarchy in a Multiple Spatiotemporal Scales RNN Model." Neural Computation 30, no. 1 (January 2018): 237–70. http://dx.doi.org/10.1162/neco_a_01026.
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This letter proposes a novel predictive coding type neural network model, the predictive multiple spatiotemporal scales recurrent neural network (P-MSTRNN). The P-MSTRNN learns to predict visually perceived human whole-body cyclic movement patterns by exploiting multiscale spatiotemporal constraints imposed on network dynamics by using differently sized receptive fields as well as different time constant values for each layer. After learning, the network can imitate target movement patterns by inferring or recognizing corresponding intentions by means of the regression of prediction error. Results show that the network can develop a functional hierarchy by developing a different type of dynamic structure at each layer. The letter examines how model performance during pattern generation, as well as predictive imitation, varies depending on the stage of learning. The number of limit cycle attractors corresponding to target movement patterns increases as learning proceeds. Transient dynamics developing early in the learning process successfully perform pattern generation and predictive imitation tasks. The letter concludes that exploitation of transient dynamics facilitates successful task performance during early learning periods.
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Baek, Youngkyun, Dazhi Yang, and Yibo Fan. "Understanding second grader’s computational thinking skills in robotics through their individual traits." Information Discovery and Delivery 47, no. 4 (November 18, 2019): 218–28. http://dx.doi.org/10.1108/idd-09-2019-0065.
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Purpose This study aims to investigate the relationship between the personal traits and computational thinking skills of second graders within the context of robotics activities. Design/methodology/approach Through literature review, a research model and hypotheses were tested with 122 second graders after robotic activities. Findings The hypothesized model showed that learning preference, intrinsic motivation and self-efficacy were the main predictors of coding achievement and computational thinking skills, while no direct relationship was found between learning preference, intrinsic or extrinsic motivation. The final path analysis revealed that intrinsic and extrinsic motivation predict self-efficacy, self-efficacy predicts coding achievement and coding achievement predicts computational thinking skills. Another important finding was the strong impact of self-efficacy on coding achievement, as well as computational thinking skills. Results are interpreted with reference to implications for potential methods of improving computational thinking skills when using robotics in the lower grades in elementary schools. Research limitations/implications This study not only examined these relationships but also proposed, tested and built a research model containing a wide range of personal traits based on path analysis and multiple regression analysis, which, to the best of the researchers’ knowledge, has not been investigated in the current literature. Practical implications As reflected in the final research model, self-efficacy played an important role in impacting second grader’s coding achievement and computational thinking skills. Originality/value Few studies have investigated the various relationships in the context of robotics instruction in elementary schools as in this study. Given the increasing popularity of robotics education in elementary schools, the re-examination and identification of the pivotal role of self-efficacy in predicting second graders’ learning of coding and computational thinking skills have important implications for the implementation of robotics education.
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Richardson, Kaylla, Sankari Penumaka, Jaleesa Smoot, Mansi Reddy Panaganti, Indu Radha Chinta, Devi Priya Guduri, Sucharitha Reddy Tiyyagura, John Martin, Michael Korvink, and Laura H. Gunn. "A Data-Driven Approach to Defining Risk-Adjusted Coding Specificity Metrics for a Large U.S. Dementia Patient Cohort." Healthcare 12, no. 10 (May 10, 2024): 983. http://dx.doi.org/10.3390/healthcare12100983.
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Medical coding impacts patient care quality, payor reimbursement, and system reliability through the precision of patient information documentation. Inadequate coding specificity can have significant consequences at administrative and patient levels. Models to identify and/or enhance coding specificity practices are needed. Clinical records are not always available, complete, or homogeneous, and clinically driven metrics to assess medical practices are not logistically feasible at the population level, particularly in non-centralized healthcare delivery systems and/or for those who only have access to claims data. Data-driven approaches that incorporate all available information are needed to explore coding specificity practices. Using N = 487,775 hospitalization records of individuals diagnosed with dementia and discharged in 2022 from a large all-payor administrative claims dataset, we fitted logistic regression models using patient and facility characteristics to explain the coding specificity of principal and secondary diagnoses of dementia. A two-step approach was produced to allow for the flexible clustering of patient-level outcomes. Model outcomes were then used within a Poisson binomial model to identify facilities that over- or under-specify dementia diagnoses against healthcare industry standards across hospitalizations. The results indicate that multiple factors are significantly associated with dementia coding specificity, especially for principal diagnoses of dementia (AUC = 0.727). The practical use of this novel risk-adjusted metric is demonstrated for a sample of facilities and geospatially via a U.S. map. This study’s findings provide healthcare facilities with a benchmark for assessing coding specificity practices and developing quality enhancements to align with healthcare industry standards, ultimately contributing to better patient care and healthcare system reliability.
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Pokkuluri, Kiran Sree, Ramesh Babu Inampudi, and S. S. S. N. Usha Devi Nedunuri. "IN-MACA-MCC: Integrated Multiple Attractor Cellular Automata with Modified Clonal Classifier for Human Protein Coding and Promoter Prediction." Advances in Bioinformatics 2014 (July 15, 2014): 1–7. http://dx.doi.org/10.1155/2014/261362.
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Protein coding and promoter region predictions are very important challenges of bioinformatics (Attwood and Teresa, 2000). The identification of these regions plays a crucial role in understanding the genes. Many novel computational and mathematical methods are introduced as well as existing methods that are getting refined for predicting both of the regions separately; still there is a scope for improvement. We propose a classifier that is built with MACA (multiple attractor cellular automata) and MCC (modified clonal classifier) to predict both regions with a single classifier. The proposed classifier is trained and tested with Fickett and Tung (1992) datasets for protein coding region prediction for DNA sequences of lengths 54, 108, and 162. This classifier is trained and tested with MMCRI datasets for protein coding region prediction for DNA sequences of lengths 252 and 354. The proposed classifier is trained and tested with promoter sequences from DBTSS (Yamashita et al., 2006) dataset and nonpromoters from EID (Saxonov et al., 2000) and UTRdb (Pesole et al., 2002) datasets. The proposed model can predict both regions with an average accuracy of 90.5% for promoter and 89.6% for protein coding region predictions. The specificity and sensitivity values of promoter and protein coding region predictions are 0.89 and 0.92, respectively.
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Yang, Shuai, Kuang Zhang, Xumin Ding, Guohui Yang, and Qun Wu. "Tailoring the scattering properties of coding metamaterials based on machine learning." EPJ Applied Metamaterials 8 (2021): 15. http://dx.doi.org/10.1051/epjam/2021006.
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Diverse electromagnetic (EM) responses of coding metamaterials have been investigated, and the general research method is to use full-wave simulation. But if we only care its scattering properties, it is not necessary to perform full-wave simulation, which is usually time-consuming. Machine learning has significantly impelled the development of automatic design and optimize coding matrix. Based on metamaterial particle that has multiple response and genetic algorithm which is coupled with the scattering pattern analysis, we can optimize the coding matrix quickly to tailor the scattering properties without conducting full-wave simulation a lot of times for optimization. Since the coding matrix control of each particle allow modulation of EM wave, various EM phenomena can be achieved easier. In this paper, we proposed two reflective unitcells with different reflection phase, and then a semi-analytical model is built up for unitcells. To tailor the scattering properties, genetic algorithm normally based on binary coding, is coupled with the scattering pattern analysis in order to optimize the coding matrix. Finally, simulation results are compared with the semi-analytical calculation results and it is found that the simulation results agree very well with the theoretical values.
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Fan, Jin Hong. "Transmission Scheme of MIMO Two-Way Relaying Systems Based on Symmetric Channel." Applied Mechanics and Materials 325-326 (June 2013): 452–55. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.452.
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There are fading and noise in MIMO two-way relaying system. A novel transmission scheme was proposed. The scheme performs equivalent to a 2×2 V-BLAST MIMO(Multiple-Input Multiple-Output) and a two 2×1 AlAMOUTI MISO(Multiple-Input Single-Output) in multiple-access phase and broadcast phase respectively. Network coding and minimum mean square error detection are employed at the relay. Binary phase shift keying and 8 phase shift keying modulation is used. The performance is analyzed under the assumption of a symmetric relay channel model. Simulation results show that the proposed transmission scheme can facilitate more reliable transmission.
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Arifin, Ajib Setyo, and Tomoaki Ohtsuki. "Water-Filling Solution for Distributed Estimation of Correlated Data in WSN MIMO System." ISRN Sensor Networks 2013 (September 18, 2013): 1–7. http://dx.doi.org/10.1155/2013/345457.
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We consider the distributed estimation of a random vector signal in a power constraint wireless sensor network (WSN) that follows a multiple-input and multiple-output (MIMO) coherent multiple access channel model. We design linear coding matrices based on linear minimum mean-square error (LMMSE) fusion rule that accommodates spatial correlated data. We obtain a closed-form solution that follows a water-filling strategy. We also derive a lower bound to this model. Simulation results show that when the data is more correlated, the distortion in terms of mean-square error (MSE) degrades. By taking into account the effects of correlation, observation, and channel matrices, the proposed method performs better than equal power method.
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Rantelobo, Kalvein, Hendro Lami, and Wirawan Wirawan. "Video Transmission using Combined Scalability Video Coding over MIMO-OFDM Systems." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 2 (November 1, 2016): 390. http://dx.doi.org/10.11591/ijeecs.v4.i2.pp390-396.
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<p><em>The needs of efficient bandwidth utilization and method to handle bandwidth fluctuation condition of wireless channel have become fundamental problems in video transmission. This research</em><em> proposed </em><em>Combined Scalable Video Coding (CSVC) that refers to Joint Scalable Video Model (JSVM), i.e. development of video coding H.264/AVC by exploiting scalable combination method using Medium Grain Scalability (MGS) on wireless channel of MIMO-OFDM (Multiple Input Multiple Output – Orthogonal Frequency Division Multiplexing) technology. Th</em><em>e</em><em> research shows that the scalable combination method can be implemented on the scenario for wireless transmission on multicast network. Experimental results show</em><em> </em><em>that the delivered quality is close to the alternative traditional simulcast delivery mechanism in MIMO-OFDM systems.</em></p>
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Dannenberg, Holger, Andrew S. Alexander, Jennifer C. Robinson, and Michael E. Hasselmo. "The Role of Hierarchical Dynamical Functions in Coding for Episodic Memory and Cognition." Journal of Cognitive Neuroscience 31, no. 9 (September 2019): 1271–89. http://dx.doi.org/10.1162/jocn_a_01439.
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Behavioral research in human verbal memory function led to the initial definition of episodic memory and semantic memory. A complete model of the neural mechanisms of episodic memory must include the capacity to encode and mentally reconstruct everything that humans can recall from their experience. This article proposes new model features necessary to address the complexity of episodic memory encoding and recall in the context of broader cognition and the functional properties of neurons that could contribute to this broader scope of memory. Many episodic memory models represent individual snapshots of the world with a sequence of vectors, but a full model must represent complex functions encoding and retrieving the relations between multiple stimulus features across space and time on multiple hierarchical scales. Episodic memory involves not only the space and time of an agent experiencing events within an episode but also features shown in neurophysiological data such as coding of speed, direction, boundaries, and objects. Episodic memory includes not only a spatio-temporal trajectory of a single agent but also segments of spatio-temporal trajectories for other agents and objects encountered in the environment consistent with data on encoding the position and angle of sensory features of objects and boundaries. We will discuss potential interactions of episodic memory circuits in the hippocampus and entorhinal cortex with distributed neocortical circuits that must represent all features of human cognition.