Problem Of Decoding Research Articles

Early identification or broken promises? A comparison of literacy assessment performance in 112 Swedish third graders

The national standardised literacy assessment (NSLA) for Swedish Language was introduced in 2009 as a grade-three compulsory assessment and includes the assessment of reading ability. It was introduced as a measure of relatively early identification of reading difficulties among nine-years old students. The primary objective of this study was to examine whether the NSLA is able to identify students with word decoding problems from a sample of third graders (N = 112; n = 57 girls; n = 55 boys) attending six schools in a Swedish municipality. Eleven students (10%) performed below the cut-off value for word decoding ability in students of this age group. Only three of these students were identified as not achieving the goals posed by the NSLA. In contrast, eight students with low word decoding ability managed to meet the NSLA requirements. Gender differences were observed, since all of the students who passed the NSLA, notwithstanding having low performance in WD, were boys. Although the NSLA was specifically introduced at this level to identify weakness in reading at an early stage, the results of this study indicate that approximately three quarters of students with poor word decoding ability may remain unidentified by the NSLA. These findings call into question the validity of the NSLA in recognising pupils in need of additional support in reading.

A tensor-product-kernel framework for multiscale neural activity decoding and control.

Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain's motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensor-product-kernel-based framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering. In a sensory stimulation experiment, the tensor-product-kernel decoder outperforms decoders that use only a single neural data type. In addition, an adaptive inverse controller for delivering electrical microstimulation patterns that utilizes the tensor-product kernel achieves promising results in emulating the responses to natural stimulation.

Color in Medical Imaging

Color in medical imaging (coloring of monochrome images and parametric coding) is discussed. The disadvantages of color image decoding are noted. In recent years, there has been significant progress in medical color imaging. This has raised the problem of decoding of color images. In addition to conventional imaging methods, the goal of this work was to discuss the parametric method of visualization of hidden color images.

A note on BDD problems with [formula omitted]-gap

In CRYPTO 2009, Lyubashevsky and Micciancio presented reductions BDD1/2γ (Bounded Distance Decoding Problem) ⩽ uSVPγ (Unique Shortest Vector Problem) ⩽ BDD1/γ, and posed an open problem whether the reduction gap can be closed. This paper concerns bounded distance decoding (BDD) problems for lattices with large λ2-gap. In the presence of larger λ2-gap, better reductions from BDD to uSVP and exact SVP are obtained. Some result can be regarded as a partial solution to the open problem.

Universal Decoding for Arbitrary Channels Relative to a Given Class of Decoding Metrics

We consider the problem of universal decoding for arbitrary unknown channels in the random coding regime. For a given random coding distribution and a given class of metric decoders, we propose a generic universal decoder whose average error probability is, within a sub-exponential multiplicative factor, no larger than that of the best decoder within this class of decoders. Since the optimum, maximum likelihood (ML) decoder of the underlying channel is not necessarily assumed to belong to the given class of decoders, this setting suggests a common generalized framework for: (i) mismatched decoding, (ii) universal decoding for a given family of channels, and (iii) universal coding and decoding for deterministic channels using the individual-sequence approach. The proof of our universality result is fairly simple, and it is demonstrated how some earlier results on universal decoding are obtained as special cases. We also demonstrate how our method extends to more complicated scenarios, like incorporation of noiseless feedback, and the multiple access channel.

The Learning Algorithms of Coupled Discrete Hidden Markov Models

A hidden Markov model (HMM) encompasses a large class of stochastic process models and has been successfully applied to a number of scientific and engineering problems, including speech and other pattern recognition problems, and biological sequence analysis. A major restriction is found, however, in conventional HMM, i.e., it is ill-suited to capture the interactions among different models. A variety of coupled hidden Markov models (CHMMs) have recently been proposed as extensions of HMM to better characterize multiple interdependent sequences. The resulting models have multiple state variables that are temporally coupled via matrices of conditional probabilities. This paper study is focused on the coupled discrete HMM, there are two state variables in the network. By generalizing forward-backward algorithm, Viterbi algorithm and Baum-Welch algorithm commonly used in conventional HMM to accommodate two state variables, several new formulae solving the 2-chain coupled discrete HMM probability evaluation, decoding and training problem are theoretically derived.

Error Correction for Index Coding With Side Information

A problem of index coding with side information was first considered by Birk and Kol in 1998. In this study, a generalization of index coding scheme, where transmitted symbols are subject to errors, is studied. Error-correcting methods for such a scheme, and their parameters, are investigated. In particular, the following question is discussed: given the side information hypergraph of index coding scheme and the maximal number of erroneous symbols δ , what is the shortest length of a linear index code, such that every receiver is able to recover the required information? This question turns out to be a generalization of the problem of finding a shortest length error-correcting code with a prescribed error-correcting capability in the classical coding theory. The Singleton bound and two other bounds, referred to as the α-bound and the κ -bound, for the optimal length of a linear error-correcting index code (ECIC) are established. For large alphabets, a construction based on concatenation of an optimal index code with a maximum distance separable classical code is shown to attain the Singleton bound. For smaller alphabets, however, this construction may not be optimal. A random construction is also analyzed. It yields another inexplicit bound on the length of an optimal linear ECIC. Further, the problem of error-correcting decoding by a linear ECIC is studied. It is shown that in order to decode correctly the desired symbol, the decoder is required to find one of the vectors, belonging to an affine space containing the actual error vector. The syndrome decoding is shown to produce the correct output if the weight of the error pattern is less or equal to the error-correcting capability of the corresponding ECIC. Finally, the notion of static ECIC, which is suitable for use with a family of instances of an index coding problem, is introduced. Several bounds on the length of static ECICs are derived, and constructions for static ECICs are discussed. Connections of these codes to weakly resilient Boolean functions are established.

Contributions of phonological awareness, phonological short-term memory, and rapid automated naming, toward decoding ability in students with mild intellectual disability

Reading decoding ability is a fundamental skill to acquire word-specific orthographic information necessary for skilled reading. Decoding ability and its underlying phonological processing skills have been heavily investigated typically among developing students. However, the issue has rarely been noticed among students with intellectual disability who commonly suffer from reading decoding problems. This study is aimed at determining the contributions of phonological awareness, phonological short-term memory, and rapid automated naming, as three well known phonological processing skills, to decoding ability among 60 participants with mild intellectual disability of unspecified origin ranging from 15 to 23 years old. The results of the correlation analysis revealed that all three aspects of phonological processing are significantly correlated with decoding ability. Furthermore, a series of hierarchical regression analysis indicated that after controlling the effect of IQ, phonological awareness, and rapid automated naming are two distinct sources of decoding ability, but phonological short-term memory significantly contributes to decoding ability under the realm of phonological awareness.

A Comparison of the Language Skills of ELLs and Monolinguals Who Are Poor Decoders, Poor Comprehenders, or Normal Readers

The overall objective of this article is to examine how oral language abilities relate to reading profiles in English language learners (ELLs) and English as a first language (EL1) learners, and the extent of similarities and differences between ELLs and EL1s in three reading subgroups: normal readers, poor decoders, and poor comprehenders. The study included 100 ELLs and 50 EL1s in Grade 5. The effect of language group (ELL/EL1) and reading group on cognitive and linguistic skills was examined. Except for vocabulary, there was no language group effect on any measure. However, within ELL and EL1 alike, significant differences were found between reading groups: Normal readers outperformed the two other groups on all the oral language measures. Distinct cognitive and linguistic profiles were associated with poor decoders and poor comprehenders, regardless of language group. The ELL and EL1 poor decoders outperformed the poor comprehenders on listening comprehension and inferencing. The poor decoders displayed phonological-based weaknesses, whereas the poor comprehenders displayed a more generalized language processing weakness that is nonphonological in nature. Regardless of language status, students with poor decoding or comprehension problems display difficulties with various aspects of language.

Multi-scale classification of disease using structural MRI and wavelet transform

Recently, multivariate analysis algorithms have become a popular tool to diagnose neurological diseases based on neuroimaging data. Most studies, however, are biased for one specific scale, namely the scale given by the spatial resolution (i.e. dimension) of the data. In the present study, we propose to use the dual-tree complex wavelet transform to extract information on different spatial scales from structural MRI data and show its relevance for disease classification. Based on the magnitude representation of the complex wavelet coefficients calculated from the MR images, we identified a new class of features taking scale, directionality and potentially local information into account simultaneously. By using a linear support vector machine, these features were shown to discriminate significantly between spatially normalized MR images of 41 patients suffering from multiple sclerosis and 26 healthy controls. Interestingly, the decoding accuracies varied strongly among the different scales and it turned out that scales containing low frequency information were partly superior to scales containing high frequency information. Usually, this type of information is neglected since most decoding studies use only the original scale of the data. In conclusion, our proposed method has not only a high potential to assist in the diagnostic process of multiple sclerosis, but can be applied to other diseases or general decoding problems in structural or functional MRI.

What Makes Different People's Representations Alike: Neural Similarity Space Solves the Problem of Across-subject fMRI Decoding

A central goal in neuroscience is to interpret neural activation and, moreover, to do so in a way that captures universal principles by generalizing across individuals. Recent research in multivoxel pattern-based fMRI analysis has led to considerable success at decoding within individual subjects. However, the goal of being able to decode across subjects is still challenging: It has remained unclear what population-level regularities of neural representation there might be. Here, we present a novel and highly accurate solution to this problem, which decodes across subjects between eight different stimulus conditions. The key to finding this solution was questioning the seemingly obvious idea that neural decoding should work directly on neural activation patterns. On the contrary, to decode across subjects, it is beneficial to abstract away from subject-specific patterns of neural activity and, instead, to operate on the similarity relations between those patterns: Our new approach performs decoding purely within similarity space. These results demonstrate a hitherto unknown population-level regularity in neural representation and also reveal a striking convergence between our empirical findings in fMRI and discussions in the philosophy of mind addressing the problem of conceptual similarity across neural diversity.

A Novel Approach for Turbo Decoding In ISI Channel

codes are family of forward error correcting codes, whose performance is near Shannon limit. Turbo decoding is based on the maximum a-posterior algorithm (MAP) algorithm. In this paper, the problem of turbo decoding in ISI channel is studied. A Super-trellis structure method has been presented and modified turbo decoding is suggested. Two methods have been suggested for turbo decoding in ISI channel. In the first method, we take all possible combinations of output of encoder-2 and in method-2, output of each encoder is passed through channel filter independently. Method-2 performs better than method-1 but requires higher bandwidth. The improvement in performance is demonstrated through simulations. Keywordscode, Intersymbol interferance, ISI channel, Iterative decoding, Turbo code.

BICM Decoding of Jammed OFDM Transmissions Using the EM Algorithm

Wireless communications over unlicensed frequency bands are expected to suffer from significant co-channel interference, which inevitably limits the error-rate performance. In this letter, an orthogonal frequency-division multiplexing system employing bit-interleaved coded-modulation is considered and the problem of reliable decoding in the presence of unknown narrowband interference (NBI) is addressed. The proposed solution operates in an iterative fashion according to the expectation-maximization algorithm and is derived by modeling the interference power on each subcarrier as a random variable with an inverse gamma distribution. Computer simulations indicate that the resulting scheme is effective against NBI and, compared to existing alternatives, it achieves a better trade-off in terms of error rate performance, computational complexity and system overhead.

A Unified Framework for Linear-Programming Based Communication Receivers

It is shown that a large class of communication systems which admit a sum-product algorithm (SPA) based receiver also admit a corresponding linear-programming (LP) based receiver. The two receivers have a relationship defined by the local structure of the underlying graphical model, and are inhibited by the same phenomenon, which we call 'pseudoconfigurations'. This concept is a generalization of the concept of 'pseudocodewords' for linear codes. It is proved that the LP receiver has the 'maximum likelihood certificate' property, and that the receiver output is the lowest cost pseudoconfiguration. Equivalence of graph-cover pseudoconfigurations and linear-programming pseudoconfigurations is also proved. A concept of 'system pseudodistance' is defined which generalizes the existing concept of pseudodistance for binary and nonbinary linear codes. It is demonstrated how the LP design technique may be applied to the problem of joint equalization and decoding of coded transmissions over a frequency selective channel, and a simulation-based analysis of the error events of the resulting LP receiver is also provided. For this particular application, the proposed LP receiver is shown to be competitive with other receivers, and to be capable of outperforming turbo equalization in bit and frame error rate performance.

Teaching children with dyslexia to spell in a reading-writers’ workshop

To identify effective treatment for both the spelling and word decoding problems in dyslexia, 24 students with dyslexia in grades 4 to 9 were randomly assigned to treatments A (n=12) or B (n=12) in an after-school reading-writers' workshop at the university (thirty 1-h sessions twice a week over 5 months). First, both groups received step 1 treatment of grapheme-phoneme correspondences (gpc) for oral reading. At step 2, treatment A received gpc training for both oral reading and spelling, and treatment B received gpc training for oral reading and phonological awareness. At step 3, treatment A received orthographic spelling strategy and rapid accelerated reading program (RAP) training, and treatment B continued step 2 training. At step 4, treatment A received morphological strategies and RAP training, and treatment B received orthographic spelling strategy training. Each treatment also had the same integrated reading-writing activities, which many school assignments require. Both groups improved significantly in automatic letter writing, spelling real words, compositional fluency, and oral reading (decoding) rate. Treatment A significantly outperformed treatment B in decoding rate after step 3 orthographic training, which in turn uniquely predicted spelling real words. Letter processing rate increased during step 3 RAP training and correlated significantly with two silent reading fluency measures. Adding orthographic strategies with "working memory in mind" to phonics helps students with dyslexia spell and read English words.

Improved multi-pass fast correlation attacks with applications

In this paper we propose two new algorithms for multi-pass fast correlation attacks on stream ciphers. The first algorithm aims at fast symbol-wise decoding in the circumstances that the noise is not very high and we have little resource for pre-computation. The second algorithm deals with the practical decoding problem in the high noise and limited keystream cases. The new algorithms are applicable to arbitrary form LFSR and compare favorably to the previously known algorithms in the scenarios under consideration. As applications, we demonstrate new key recovery attacks on one-level Bluetooth E0 and LILI-128, respectively. Given 237-bit keystream and 228-byte memory, our attack against one-level E0 needs 235.1 operations. Given 224-bit keystream and 224.5-byte memory, our attack on LILI-128 has time complexity 270.6 operations.

NP-hardness of decoding quantum error-correction codes

Though the theory of quantum error correction is intimately related to the classical coding theory, in particular, one can construct quantum error correction codes (QECCs) from classical codes with the dual containing property, this does not necessarily imply that the computational complexity of decoding QECCs is the same as their classical counterparts. Instead, decoding QECCs can be very much different from decoding classical codes due to the degeneracy property. Intuitively, one expect degeneracy would simplify the decoding since two different errors might not and need not be distinguished in order to correct them. However, we show that general quantum decoding problem is NP-hard regardless of the quantum codes being degenerate or non-degenerate. This finding implies that no considerably fast decoding algorithm exists for the general quantum decoding problems, and suggests the existence of a quantum cryptosystem based on the hardness of decoding QECCs.

Minimum Bayes Risk decoding and system combination based on a recursion for edit distance

Abstract In this paper we describe a method that can be used for Minimum Bayes Risk (MBR) decoding for speech recognition. Our algorithm can take as input either a single lattice, or multiple lattices for system combination. It has similar functionality to the widely used Consensus method, but has a clearer theoretical basis and appears to give better results both for MBR decoding and system combination. Many different approximations have been described to solve the MBR decoding problem, which is very difficult from an optimization point of view. Our proposed method solves the problem through a novel forward–backward recursion on the lattice, not requiring time markings. We prove that our algorithm iteratively improves a bound on the Bayes risk.

Reconstruction of the Reference Signal in DVB-T-based Passive Radar

Reconstruction of the Reference Signal in DVB-T-based Passive RadarIn the paper the problem of decoding of digital television signal and its reconstruction for the purpose of using it in passive radar is presented. The main focus is the reconstruction of the signal using a general purpose receiver, not dedicated to digital television signal reception. The performance of the proposed method is verified on simulated and real-life signals.

Non-Binary Decoder Diversity for Dense or Locally-Dense Parity-Check Codes

In this paper, a new and promising framework, called "non-binary decoder diversity", is presented based on the observation that different non-binary Tanner graphs of the same binary code, decoded with a non-binary belief-propagation decoder, can have distinct convergence behaviors and fixed points. The goal of this work is to propose a decoder with linear complexity in the blocklength, and with performance close to maximum-likelihood decoding. This framework is especially interesting for binary codes which are dense or locally-dense, and for which the usual binary iterative decoders perform far from the optimum curves. By using the diversity brought by decoding distinct Tanner graphs of the same code, the proposed technique has very good decoding performance for three very different test cases which are known to be complex decoding problems: (i) near maximum-likelihood decoding (MLD) of BCH codes on the BPSK-AWGN channel, (ii) performance results which outperform bounded distance decoding of BCH codes over a binary symmetric channel (BSC), and finally (iii) decoding performance better than the BCJR-based turbo-decoder for parallel duo-binary turbo-codes.