MAP Decoder Research Articles

Exact calculation of bit error rates in communication systems with chaotic modulation

The paper explores the calculation of exact bit error rates (BERs) for some single-user chaotic-shift-keying (CSK) communications systems, in contrast to approximate Gaussian-based approximations in current use. The conventional signal-to-noise-ratio approach is shown to give only lower bounds on the BERs. An analytical Gaussian approach based on exact mean and variance of the decoder function gives inexact results. Exact BERs are given here for several CSK systems with spreading sequences from different types of chaotic map. They achieve exactness from fully exploiting the dynamical and statistical features of the systems and the results correspond theoretically to impractically large Monte Carlo simulations. A further aspect of the paper is the derivation of likelihood optimal bit decoders which can be superior to correlation decoders. The inapplicability of Gaussian assumptions is viewed through some exact distributional results for one system.

Telemetrically recorded neuronal activity in the inferior colliculus and bordering tegmentum during vocal communication in squirrel monkeys ( Saimiri sciureus)

In order to find out whether the inferior colliculus, in addition to its auditory decoding function, also has an auditory gating function in the sense that it treats self-produced sounds differently from external ones, we have explored the inferior colliculus and bordering tegmentum for neurones reacting differently to self-produced vocalizations and vocalizations produced by conspecifics. The experiments were made in the squirrel monkey ( Saimiri sciureus), using a telemetric extracellular recording technique which allowed to register neuronal activity in freely moving animals during natural vocal communication. The results show that the neurones of the central nucleus of the inferior colliculus do not react differently to self-produced and group mate vocalizations of the same type. In the external nucleus of the inferior colliculus, in addition to classical auditory neurones, neurones are found which react to the vocalizations of group mates, but not to self-produced vocalizations. In the paralemniscal area just below the inferior colliculus, there are neurones which are active during self-produced vocalization, but not during vocalization produced by other animals. The results suggest that the external nucleus of the inferior colliculus and bordering tegmentum are involved in vocalization-dependent auditory gating processes.

Efficient procedure for computing tumour target volumes on CT images

This paper describes the software development to compute the exact cancer tumour volume to compare quantitatively with the marker levels from CT scan images. The assignment involves the transportation of image files from the CART format to PC in DOS format. It also generates image decoding function, computation and displacement in Planning Target Volume. Clinical Target Volume and Shell Target Volume. MATLAB program is developed for result verification, where as VISUAL BASIC is complied and run the final Graphical-User-Interface under WINDOW environment. The procedure is efficient with accurate result.

Triple decoding of hepatitis C virus RNA by programmed translational frameshifting.

Ribosomes can be programmed to shift from one reading frame to another during translation. Hepatitis C virus (HCV) uses such a mechanism to produce F protein from the -2/+1 reading frame. We now report that the HCV frameshift signal can mediate the synthesis of the core protein of the zero frame, the F protein of the -2/+1 frame, and a 1.5-kDa protein of the -1/+2 frame. This triple decoding function does not require sequences flanking the frameshift signal and is apparently independent of membranes and the synthesis of the HCV polyprotein. Two consensus -1 frameshift sequences in the HCV type 1 frameshift signal facilitate ribosomal frameshifts into both overlapping reading frames. A sequence which is located immediately downstream of the frameshift signal and has the potential to form a double stem-loop structure can significantly enhance translational frameshifting in the presence of the peptidyl-transferase inhibitor puromycin. Based on these results, a model is proposed to explain the triple decoding activities of the HCV ribosomal frameshift signal.

A neural network model of memory system using hippocampus

AbstractWhen an episodic memory is to be transferred from short‐term memory to long‐term memory, there must be a buffer mechanism that temporarily stores the content of the short‐term memory as a mid‐term memory. It is considered that the hippocampus plays that role. In composing an episodic memory, there must be not only the association of different events without a time factor (spatial pattern), but also the association of different events containing time factors (time sequence pattern). This paper proposes a hippocampus neural network model, based on anatomical and physiological knowledge. The model features the following points. (i) By applying a learning rule where the input history is considered in the hippocampus CA3, which is regarded to have the associative recall function, it is made possible for the model to learn the time sequence pattern. (ii) The orthogonal coding function is provided in the gyrus dentatus, and the decoding function is provided in the hippocampus CA1. It is expected that the memory capacity of the hippocampus will be enlarged by providing the orthogonal coding function. It is shown by a numerical simulation that the mid‐term memory can learn spatial and time sequence patterns, and that associative recall of the memory contents can be realized. This result implies that the proposed hippocampus model can retain episodic memory as a mid‐term memory. The obtained results agree with physiological data, and demonstrate the effectiveness of the proposed model. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(6): 86–97, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.1010

Effectiveness of error correcting output coding methods in ensemble and monolithic learning machines

Error Correcting Output Coding (ECOC) methods for multiclass classification present several open problems ranging from the trade-off between their error recovering capabilities and the learnability of the induced dichotomies to the selection of proper base learners and to the design of well-separated codes for a given multiclass problem. We experimentally analyse some of the main factors affecting the effectiveness of ECOC methods. We show that the architecture of ECOC learning machines influences the accuracy of the ECOC classifier, highlighting that ensembles of parallel and independent dichotomic Multi-Layer Perceptrons are well-suited to implement ECOC methods. We quantitatively evaluate the dependence among codeword bit errors using mutual information based measures, experimentally showing that a low dependence enhances the generalisation capabilities of ECOC. Moreover we show that the proper selection of the base learner and the decoding function of the reconstruction stage significantly affects the performance of the ECOC ensemble. The analysis of the relationships between the error recovering power, the accuracy of the base learners, and the dependence among codeword bits show that all these factors concur to the effectiveness of ECOC methods in a not straightforward way, very likely dependent on the distribution and complexity of the data.

Space frequency block coded turbo-BLAST detection for MIMO-OFDM systems

A new bandwidth efficient technique that achieves high data rates over the wideband wireless channel is proposed. This method is targeted for the MIMO-OFDM system that achieves transmit diversity through space frequency block code and capacity enhancement through iterative detection and MAP decoding. The new method shows great improvement compared to the conventional V-BLAST system.

A Study on MAP decoding of Data Spread OFDM

A Study on MAP decoding of Data Spread OFDM

Channel reliability estimation for turbo decoding in rayleigh fading channels with imperfect channel estimates

In this letter we propose a simple estimation scheme of the channel reliability factor for turbo decoding in Rayleigh fading channels with imperfect channel estimates. The channel reliability factor is required for iterative MAP decoding of turbo codes and depends on the error variance of the channel estimate as well as the signal-to-noise ratio (SNR) when the channel estimation is imperfect. The proposed estimator is based on the statistical computations on the block observation of samples obtained by processing the received signal and provides an accurate estimate of the channel reliability factor for even a short block observation. Simulation results show that the use of this estimate in turbo decoding does not yield perceptible degradation against performance of the turbo decoder using the exact one.

An indirect genetic algorithm for set covering problems

This paper presents a new type of genetic algorithm for the set covering problem. It differs from previous evolutionary approaches first because it is an indirect algorithm, ie the actual solutions are found by an external decoder function. The genetic algorithm itself provides this decoder with permutations of the solution variables and other parameters. Second, it will be shown that results can be further improved by adding another indirect optimisation layer. The decoder will not directly seek out low cost solutions but instead aims for good exploitable solutions. These are then post-optimised by another hill-climbing algorithm. Although seemingly more complicated, we will show that this three-stage approach has advantages in terms of solution quality, speed and adaptability to new types of problems over more direct approaches. Extensive computational results are presented and compared to the latest evolutionary and other heuristic approaches to the same data instances.

Mechanisms for Temporal Tuning and Filtering by Postsynaptic Signaling Pathways

Networks of signaling pathways perform complex temporal decoding functions in diverse biological systems, including the synapse, development, and bacterial chemotaxis. This paper examines temporal filtering and tuning properties of synaptic signaling pathways as a possible substrate for emergent temporal decoding. A mass action kinetic model of 16 synaptic signaling pathways was used to dissect out the contribution of these pathways in linear cascades and when coupled to form a network. The model predicts two primary mechanisms of temporal tuning of pathways: a weighted summation of responses of pathways with different timings and the presence of biochemical feedback loop(s) with emergent dynamics. Regulatory inputs act differently on these two tuning mechanisms. In the first case, regulators act like a gain-control on pathways with different intrinsic tuning. In the case of feedback loops, the temporal properties of the loop itself are changed. These basic tuning mechanisms may underlie specialized temporal tuning functions in more complex signaling systems in biology.

Functional Studies of the 900 Tetraloop Capping Helix 27 of 16 S Ribosomal RNA

The 900 tetraloop (positions 898–901) of Escherichia coli 16 S rRNA caps helix 27, which is involved in a conformational switch crucial for the decoding function of the ribosome. This tetraloop forms a GNRA motif involved in intramolecular RNA–RNA interactions with its receptor in helix 24 of 16 S rRNA. It is involved also in an intersubunit bridge, via an interaction with helix 67 in domain IV of 23 S rRNA. Using a specialized ribosome system and an instant-evolution procedure, the four nucleotides of this loop were randomized and 15 functional mutants were selected in vivo. Positions 899 and 900, responsible for most of the tetraloop/receptor interactions, were found to be the most critical for ribosome activity. Functional studies showed that mutations in the 900 tetraloop impair subunit association and decrease translational fidelity. Computer modeling of the mutations allows correlation of the effect of mutations with perturbations of the tetraloop/receptor interactions.

A novel turbo decoder architecture for hand-held communication devices

Mobile communication systems, such as IMT-2000, adopt turbo codes because they provide reliable, bandwidth-efficient communication over AWGN channels. The BER of turbo codes is close to the Shannon limits. As a mobile communication device can use only a limited power supply, use of low power design techniques is essential for successful device implementation. This paper proposes an architecture for the turbo decoder which adopts a fast decoding structure by initial threshold setting such that the number of decoding iterations can be reduced without BER performance degradation. The proposed decoding structure can be easily modified for other MAP decoders by employing a simple decision module. The initial iteration threshold has been decided via simulations by varying decoder parameters (mean/normalized-variance of the log-likelihood ratio; BER of the decoder outputs). Performance validations for the proposed decoder have been performed under the IMT-2000 high-speed data transmission testbed with the channel characteristic being set to BER=10/sup -6/. Simulation results show 55%-90% reductions in the number of decoding iterations when compared with conventional turbo decoders with a fixed number of iterations.

Blind turbo multiuser detection for long-code multipath CDMA

We consider the problem of demodulating and decoding multiuser information symbols in an uplink asynchronous coded code-division multiple-access (CDMA) system employing long (aperiodic) spreading sequences, in the presence of unknown multipath channels, out-cell multiple-access interference (OMAI), and narrow-band interference (NBI). A blind turbo multiuser receiver, consisting of a novel blind Bayesian multiuser detector and a bank of MAP decoders, is developed for such a system. The effect of OMAI and NBI is modeled as colored Gaussian noise with some unknown covariance matrix. The main contribution of this paper is to develop blind Bayesian multiuser detectors for long-code multipath CDMA systems under both white and colored Gaussian noise. Such detectors are based on the Bayesian inference of all unknown quantities. The Gibbs sampler, a Markov chain Monte Carlo procedure, is then used to calculate the Bayesian estimates of the unknowns. The blind Bayesian multiuser detector computes the a posteriori probabilities of the channel coded symbols, which are differentially encoded before being sent to the channel. Being soft-input soft-output in nature, the proposed blind Bayesian multiuser detectors and the MAP decoders can iteratively exchange the extrinsic information to successively refine the performance, leading to the so-called blind turbo multiuser receiver.

Generating Decision Trees for Decoding Binaries

Tools reading binary code, like analysers, debuggers, disassemblers, etc., need to decode the target's machine code. A decision tree is often used to represent the decoding function. Manually writing a decoder is a lengthy and error-prone task. It is desirable to be able to use the vendor's instruction code manual and to easily transform the documentation into a specification that a tool can use to generate a decoder. This paper presents a novel algorithm that computes a decision tree from machine code bit patterns alone. Neither the bit fields of the machine code, nor the width of the machine command, nor the order in which the bits should be decoded need to be specified. The decoding algorithm accesses any significant bit exactly once during decoding.

Iterative MAP detection using interleaved EPR4 equalizer for magnetic recording

In this paper, we propose an iterative MAP detection method using two interleaved EPR4 linear equalizer outputs for high-density magnetic recording channels. The iterative detection is performed using two EPR4 MAP decoders that are separated by an interleaver. Simulation results show that the proposed method outperforms the conventional EPR4ML detection 7 dB when the density is S=2.5. When S=3.0, the difference in performance is more evident.

Robust speech recognition based on adaptive classification and decision strategies

We examine key research issues in adaptively modifying the conventional plug-in MAP decision rules in order to improve the robustness of the classification and decision strategies used in automatic speech recognition (ASR) systems. It is well known that the commonly adopted plug-in MAP decoder does not achieve the minimum error rate desired in ASR because the joint probability distribution of speech and language is usually not known exactly. The optimality issue becomes even more serious when there exists acoustic mismatch between training and testing conditions. We review in detail two recently proposed classification rules, namely minimax classification and Bayesian predictive classification. Both of them model classifier parameter uncertainty and modify the classification rules to satisfy some desired robustness properties. We also present an overview on a number of related techniques and discuss how these algorithms can be used to improve the robustness of speech recognizers.

Modified maximum a posteriori decoding algorithm

A modified maximum a posteriori (MMAP) decoding algorithm that uses two extrinsic information values as the thresholds to determine the log-likelihood ratio, forward recursion probability and backward recursion probability is proposed. The MMAP requires less decoding time and complexity than an MAP decoder. Each probability of the proposed algorithm is derived and compared to that obtained using the MAP algorithm.

Iterative receivers for multiuser space-time coding systems

Space-time coding (STC) techniques, which combine antenna array signal processing and channel coding techniques, are very promising approaches to substantial capacity increase in wireless channels. Multiuser detection techniques are powerful signal processing methodologies for interference suppression in CDMA systems. In this paper, by drawing analogies between a synchronous CDMA system and an STC multiuser system, we study the applications of some multiuser detection methods to STC multiuser systems. Specifically, we show that the so-called "turbo multiuser detection" technique, which performs soft interference cancellation and decoding iteratively, can be applied to STC multiuser systems in flat-fading channels. An iterative multiuser receiver and its projection-based variants are developed for both the space-time block coding (STBC) system and the space-time trellis coding (STTC) system. During iterations, extrinsic information is computed and exchanged between a soft multiuser demodulator and a bank of MAP decoders, to achieve successively refined estimates of the users' signals. Computer simulations demonstrate that the proposed iterative receiver techniques provide significant performance improvement over conventional noniterative methods in both single-user and multiuser STC systems. Furthermore, the performance of the proposed iterative multiuser receiver approaches that of the iterative single-user receiver in both STBC and STTC systems.

Finding consensus in speech recognition: word error minimization and other applications of confusion networks

We describe a new framework for distilling information from word lattices to improve the accuracy of the speech recognition output and obtain a more perspicuous representation of a set of alternative hypotheses. In the standard MAP decoding approach the recognizer outputs the string of words corresponding to the path with the highest posterior probability given the acoustics and a language model. However, even given optimal models, the MAP decoder does not necessarily minimize the commonly used performance metric, word error rate (WER). We describe a method for explicitly minimizing WER by extracting word hypotheses with the highest posterior probabilities from word lattices. We change the standard problem formulation by replacing global search over a large set of sentence hypotheses with local search over a small set of word candidates. In addition to improving the accuracy of the recognizer, our method produces a new representation of a set of candidate hypotheses that specifies the sequence of word-level confusions in a compact lattice format. We study the properties of confusion networks and examine their use for other tasks, such as lattice compression, word spotting, confidence annotation, and reevaluation of recognition hypotheses using higher-level knowledge sources.