State-of-the-art workflows within Architecture, Engineering, and Construction (AEC) are still caught in sequential planning processes. Digital design tools in this domain often lack proper communication between different stages of design and relevant domain knowledge. Furthermore, decisions made in the early stages of design, where sketching is used to initiate, develop, and communicate ideas, heavily impact later stages, resulting in the need for rapid feedback to the architectural designer so they can proceed with adequate knowledge about design implications. Accordingly, this paper presents research on a novel integrative design framework based on a recently developed 4D sketching interface, targeted for architectural design as a form-finding tool coupled with three modules: (1) a Geometric Modelling module, which utilises Points2Surf as a machine learning model for automatic surface mesh reconstruction from the point clouds produced by sketches, (2) a Material Modelling module, which predicts the mechanical properties of biocomposites based on multiscale micromechanics homogenisation techniques, and (3) a Structural Analysis module, which assesses the mechanical performance of the meshed structure on the basis of the predicted material properties using finite element simulations. The proposed framework is a step towards using material-informed design already in the early stages of design.

Some nonlinear codes, such as Kerdock and Preparata codes, can be represented as binary images under the Gray map of linear codes over rings. This paper introduces MAP decoding of Kerdock and Preparata codes by working with their quaternary representation (linear codes over$\mathbb {Z}_{4}$) with the complexity of$\mathcal {O}(N^{2}\log _{2} N)$, where N is the code length in$\mathbb {Z}_{4}$. A sub-optimal bitwise APP decoder with good error-correcting performance and complexity of$\mathcal {O}(N\log _{2} N)$that is constructed using the decoder lifting technique is also introduced. This APP decoder extends upon the original lifting decoder by working with likelihoods instead of hard decisions and is not limited to Kerdock and Preparata code families. Simulations show that our novel decoders significantly outperform several popular decoders in terms of error rate.

Evaluating the block error rate of a digital communication system is usually done using the Monte Carlo (MC) simulation method. The main drawback of the Monte Carlo method is the need of a huge sample size for estimating low error rates. Other methods commonly used are the importance sampling technique and the quasi-analytical method. This paper introduces the metric star domain decoder model and shows that many practical decoders fall into this category. The main result is the introduction of a novel SNR-invariant quasi-analytical technique for estimating the block error rate of a communication link over the geodesic channel model (a generalization of a vast number of commonly used channels, including binary symmetric channel, BEC, and AWGN channel) with a metric star domain decoder used at the receiver. This technique outperforms the Monte Carlo and importance sampling methods in both accuracy and speed. It is shown that our quasi-analytical method is at least 103times faster than MC and 10 times faster than IS for the same accuracy.

We investigate certain optimization problems for Shannon information measures, namely, minimization of joint and conditional entropies H(X, Y), H(X|Y), H(Y|X), and maximization of mutual information I(X; Y), over convex regions. When restricted to the so-called transportation polytopes (sets of distributions with fixed marginals), very simple proofs of NP-hardness are obtained for these problems because in that case they are all equivalent, and their connection to the well-known SUBSET SUM and PARTITION problems is revealed. The computational intractability of the more general problems over arbitrary polytopes is then a simple consequence. Further, a simple class of polytopes is shown over which the above problems are not equivalent and their complexity differs sharply, namely, minimization of H(X, Y) and H(Y|X) is trivial, while minimization of H(X|Y) and maximization of I(X; Y) are strongly NP-hard problems. Finally, two new (pseudo)metrics on the space of discrete probability distributions are introduced, based on the so-called variation of information quantity, and NP-hardness of their computation is shown.

Two different methods for the soft-decision decoding of the maximum-transition-run (MTR) codes have been considered in this paper. The first one, the maximum a posteriori (MAP) algorithm, is based on the Bayesian algorithm, computing a posteriori log-likelihood ratios (LLR) using MTR codewords. The second one, the soft-decision implementation in Boolean logic circuits has been used to propagate the soft-values through the MTR decoder. The MTR code performs as an inner encoding code, in a simple concatenation of the low-density parity-check (LDPC) and the MTR. The special case of two-track two-head E2PR4 channel model was used, assuming the low level of inter-track interference (ITI) and the AWGN noise presence. The soft-decision decoding results with nearly 1.7 dB coding gain, using the MAP algorithm, while nearly 1.9 dB, using the second one approach.

A novel approach to provide unequal error protection (UEP) using rateless codes over erasure channels, named Expanding Window Fountain (EWF) codes, is developed and discussed. EWF codes use a windowing technique rather than a weighted (non-uniform) selection of input symbols to achieve UEP property. The windowing approach introduces additional parameters in the UEP rateless code design, making it more general and flexible than the weighted approach. Furthermore, the windowing approach provides better performance of UEP scheme, which is confirmed both theoretically and experimentally.

The construction of finite-length irregular LDPC codes with low error floors is currently an attractive research problem. In particular, for the binary erasure channel (BEC), the problem is to find the elements of selected irregular LDPC code ensembles with the size of their minimum stopping set being maximized. Due to the lack of analytical solutions to this problem, a simple but powerful heuristic design algorithm, the approximate cycle extrinsic message degree (ACE) constrained design algorithm, has recently been proposed. Building upon the ACE metric associated with a cycle in a code graph, we introduce the ACE spectrum of LDPC codes as a useful tool for evaluation of codes from selected irregular LDPC code ensembles. Using the ACE spectrum, we generalize the ACE constrained design algorithm, making it more flexible and efficient. We justify the ACE spectrum approach through examples and simulation results.

The construction of good, finite-length, LDPC codes is currently an attractive research area. Reducing attention to the binary erasure channel (BEC), this problem translates into the problem of finding elements of selected (irregular) LDPC code ensemble with the size of the minimal stopping set being maximized. Faced with the lack of analytical solution to this problem, simple but powerful heuristic design algorithm, an ACE constrained design algorithm, was recently introduced. Building upon the ACE metric associated with every cycle in the code graph, we introduce the ACE spectrum of LDPC code as a useful measure of success in selection of the LDPC code from selected ensemble. Using ACE spectrum, we farther generalize ACE constrained design, making it more flexible and efficient.

A new method for impulse noise removal is presented, where a robust estimator of the variance, MAD (median of the absolute deviations from the median), is modified and used to efficiently separate noisy pixels from the image details. The algorithm is free of varying parameters, requires no previous training or optimization, and successfully removes all types of impulse noise. The pixel-wise MAD concept is straightforward, low in complexity, and achieves high filtering performance.

This paper is concerned with the well-known coding technique that uses cosets of convolutional codes along with precoding to increase the free squared Euclidean distance at the output of a partial-response channel. The focus is on totally trellis-matched codes, for which the decoding trellis has the fewest number of states. Codes totally trellis-matched to the extended partial-response class 4 (EPR4) and the doubly extended partial-response class 4 (E/sup 2/PR4) channels for a variety of code rates and overall constraint lengths are sought for with respect to the squared Euclidean distance spectrum criterion. Furthermore, some previously published codes are shown to contain flawed codewords and are replaced with new ones.

The performance of maximum-likelihood decoding of trellis codes for moderate-to-high SNR is dictated by their distance spectrum. An improved version of a bidirectional algorithm is used to find cosets of convolutional codes for the EPR4 and EEPR4 channels with best (squared Euclidean) distance spectrum. Results are restricted to the class of totally trellis-matched codes. Distance spectra results for these new optimized codes as well as for some related previously published codes are tabulated. Moreover, we call attention to some codes that were erroneously tabulated in a previous paper.

The principles of a new speech scrambling concept are presented, whereby the speech components are linearly combined in contrast to their conventional permutation. A computationally and cryptographically efficient solution based on Hadamard matrices is proposed and its advantages shown. The idea is general and easily applies to all existing speech scramblers providing for both lower residual intelligibility and greater cryptanalytic efforts, maintaining the bandwidth and the speech quality.

A direct, general, and conceptually simple geometrical method for determining lower and upper bounds on the error exponent of any specific family of channel block codes is presented. It is considered that a specific family of codes is characterized by a unique distance distribution exponent. The tight linear lower bound of slope -1 on the code family error exponent represents the code family cutoff rate bound. It is always a minimum of a sum of three functions. The intrinsic asymptotic properties of channel block codes are revealed by analyzing these functions and their relationships. It is shown that the random coding technique for lower-bounding the channel error exponent is a special case of this general method. The requirements that a code family should meet in order to have a positive error exponent and at best attain the channel error exponent are stated in a clear way using the (direct) distance distribution method presented.>