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In this paper, we define expanded LRPC codes from LRPC codes with a decoding algorithm using the one of the underlying LRPC codes. Next, we propose to use these codes for cryptography by deriving two cryptosystems in a McEliece setting. in order to reduce the key sizes, we use a generator matrix in systematic form for the first scheme, and an m − order quasi-cyclic LRPC code that we define for the second scheme. The obtained code has a very poor structure and is more likely to be a random linear code. Next, we give some security parameters and compare the key sizes of our public key with the key sizes of some cryptosystems. [ABSTRACT FROM AUTHOR]

Marine oil spills are currently among the most challenging problems in marine environments. Synthetic Aperture Radar (SAR), with all-day and all-weather observation, has demonstrated great potential in oil disaster monitoring. A simple encoder-decoder network model was proposed by using SAR images for effective oil spill detection. The proposed model incorporates an optimized U-Net architecture that reduces computational requirements while maintaining detection performance. This is achieved through shortened encoder and decoder stages, depthwise separable convolutions, group normalization, and bilinear interpolation-based upsampling. To improve the model's generalization, the auto-learning rate and focal loss function are also included. Two public SAR datasets acquired by different sensors have been used to illustrate the efficiency of the proposed model. In addition, the polarimetric information has also been assessed for the detection of oil spill monitoring. The results show that the model can achieve high efficiency at a small model size. The sub-dataset with polarimetric features also achieves a high F1-score of 91.65% and an IoU of 84.59%. [ABSTRACT FROM AUTHOR]

In engineering design optimisations, the dimensionality of the design space plays a critical role in determining the number of computationally expensive evaluations required to explore the design space and reach convergence. Dimensionality reduction allows engineers to represent high-dimensional design spaces in a more compact form to reduce the computational cost while retaining design flexibility. In order to reduce the dimensions of design space for shape optimisation problems, we propose a deep learning-based architecture named DeepMorpher. Our proposed architecture is a PointNet-based encoder-decoder network, which can directly be trained on 3D point-cloud geometries, and generate simulation-ready high-quality geometries without any pre-processing or post-processing steps. Our proposed DeepMorpher can work with multiple baseline templates and allows explainability and disentanglement of learned low-dimensional latent space through sampling, interpolation and feature space visualisation. To evaluate our approach, we created an engineering dataset consisting of 3D ship hull designs. The quantitative and qualitative experimentation results of the present study demonstrate that DeepMorpher can outperform previous state-of-the-art autoencoder-based shape-generative models by several orders of magnitudes. To demonstrate the high representation capacity and compactness of learned low-dimensional latent space, we employed DeepMorpher within a simulation-based design optimisation framework to perform a real-world constrained multi-objective optimisation to find optimal ship hull designs. Highlights A deep learning-based autoencoder network for reducing the dimensionality of design space in shape optimisation is proposed. The proposed network learns an explainable and disentangled low-dimensional latent space where each dimension captures different attributes of high-dimensional input shape. The proposed network incorporates free-form deformation within the architecture to generate simulation-ready geometries with high visual quality. The efficacy of the proposed network was demonstrated inside a simulation-based design optimisation framework to perform multi-objective Bayesian optimisation using multiple baseline templates to find optimal designs. [ABSTRACT FROM AUTHOR]

The premise of effective use of high-resolution remote sensing (HRRS) images is that the data integrity and authenticity of HRRS images must be guaranteed. This paper proposes a new subject-sensitive hashing algorithm for the integrity authentication of HRRS images. This algorithm takes AGIM-net (Attention Gate-based improved M-net) proposed in this paper to extract the subject-sensitive features of the HRRS images, and uses Principal Component Analysis (PCA) based method to compress and encode the extracted features. AGIM-net is an improved U-net based on attention mechanism, adding multi-scale input in the encoder stage to extract rich image features; adding multi-scale output in the decoder stage, and suppressing the features irrelevant to the subject through Attention Gate to improve the robustness of the algorithm. Experiments show that the proposed algorithm has improved robustness compared with existing algorithms, and the tamper sensitivity and security are basically equivalent to the existing algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the synchronisation control problem for a class of discrete-time switched complex networks with time-varying delays via a coding–decoding-based approach. An observer-based control scheme is proposed for each node by utilising the outputs of complex networks. The data are transmitted in a digital manner and only the sequence of finite coded signals is sent from the observer to the controller. We design a proper coding–decoding procedure for each node to ensure that the closed-loop networks achieve the expected synchronisation performance. By applying the average dwell-time switching strategy and a uniform quantisation approach, some criteria are firstly presented to ensure the detectability of the complex networks. Then sufficient conditions are provided for the existence of desired observer-based controller, guaranteeing the synchronisation of complex networks. Finally, a numerical example is given to illustrate the effectiveness of the obtained theoretical results. [ABSTRACT FROM AUTHOR]

Demand for transmission using short block length messages has been increased recently in applications including satellite communication, mobile communication, wireless sensor networks, and machine type communications. Unacceptable performance degradation associated with the short block length Turbo codes restricted its use for applications requiring communication with short block length codewords. A novel performance improved reliability-based decoding algorithm for short block length Turbo codes has been formulated and proposed by the authors. The proposed algorithm has a coding gain of 2.45 dB at a BER of 10−3 over AWGN channel with BPSK modulation for a code rate of 1 4 . The algorithm has a channel adaptive complexity and has shown nearly 82% reduction in the decoding time complexity for the rate 1 4 Turbo code at 3 dB SNR. As an extension of this earlier work, a detailed performance analysis of the algorithm on different Turbo codes has been carried out. A four-state Turbo encoder has been used to bring out the key aspects of the level based algorithm which is driven by reliability as the key parameter. The formulated algorithm has been applied to different Turbo encoder structures namely 3GPP LTE and CCSDS Turbo codes and a detailed analysis has been carried out in this paper. Simulation results show a significant improvement in the error correction performance of short block length Turbo codes. The algorithm also leads to a marked improvement in time complexity at high SNRs. The algorithm is an attractive solution for applications requiring communication with short block lengths. [ABSTRACT FROM AUTHOR]

In this article, I look at how the modes of knowledge production in the digital age construct new colonial relations. I argue that these cybercolonial hierarchies are defined by a new elite: an artificial intelligentsia. In this regard, I discuss how the epistemic shifts in cybercolonialism redefine the role of postcolonial intellectuals in the digital age. In order to trace these shifts, I analyse the 2018 data scandal concerning Cambridge Analytica, a now defunct British political consulting firm allegedly involved in electioneering in 68 countries, to show that the power held by this artificial intelligentsia is encoded within a largely inaccessible field of computing, producing information that looks rhetorically neutral but is artificial in nature. My analysis demonstrates that this kind of knowledge production deepens the geopolitical hierarchies between the Global North and South as it bears new mechanisms of silencing. For the silenced subjects in cybercolonialism I coin the term cybaltern. The cybaltern refers to a group of people whose voices are muted and rendered unheard, paradoxically despite and because of the digital tools available to them. With this in mind, postcolonial intellectuals are given the task of decoding the discursive gaps and traps that (re)produce a condition of cybalternity. [ABSTRACT FROM AUTHOR]

The Franco-Prussian war (1870–1871) was the first major European conflict during which extensive telegraph use enabled fast communication across large distances. Field officers would therefore have to learn how to use secret codes. But training officers also raises the probability that defectors would reveal these codes to the enemy. Practically all known secret codes at the time could be broken if the enemy knew how they worked. Under Kerckhoffs' impulsion, the French military thus developed new codes, meant to resist even if the adversary knew the encoding and decoding algorithms, that were simple enough to be explained and taught to military personnel. Many of these codes were lost to history. One of the designs however, due to Major H. D. Josse, has been recovered and this article describes the features, history, and role of this particular construction. Josse's code was considered for field deployment and underwent some experimental tests in the late 1800 s, the result of which were condensed in a short handwritten report. During World War II, German forces got hold of documents describing Josse's work, and brought them to Berlin to be analyzed. A few years later these documents moved to Russia, where they have resided since. [ABSTRACT FROM AUTHOR]

High diversity gain can be achieved for orthogonal space-time block codes (OSTBCs) using rotated QAM constellations at a cost of high Maximum Likelihood (ML) decoding complexity. Thus far, low complexity ML decoding algorithms were proposed only for OSTBCs with square QAM constellations. In this paper, an algorithm has been proposed to reduce the ML decoding complexity of OSTBCs with non-square and rotated QAM constellations. The ML formulation using real-valued lattice representations is modified in such a way that the complexity depends upon the number of real values required to construct the alphabet of any constellation. Arithmetic progression and symmetry are the two elementary characteristics observed in the real numbers of the alphabets. Based on these characteristics, a new approach called Add and accumulate method has been proposed which drastically reduces the complexity to O 1 and O 3 without sacrificing the performance. [ABSTRACT FROM AUTHOR]

The phase-coding method for absolute phase measurement possesses a strong robustness because its codewords are encoded into phase domain rather than intensity. However, a misalignment problem arises between fringe order and wrapped phase in this method owing to random noises and image blurring, especially in the high-frequency fringes situation. Therefore, the measurement result is unavailable. To overcome this problem, an optimized phase-coding algorithm is proposed in the study. We use the sinusoidal patterns to generate two opposite binary masks, which can locate half of each codeword. Then, the K-means algorithm is used to correct the codewords errors. The accurate fringe order is obtained by merging two halves of codewords and the absolute phase is recovered afterward. The used method is experimentally verified by an established measurement system, and the results show the proposed method is robustness and efficiency. [ABSTRACT FROM AUTHOR]

In a digital communication system, information is sent from one place to another over a noisy communication channel. It may be possible to detect and correct errors that occur during the transmission if one encodes the original information by adding redundant bits. Low–Density Parity–Check (LDPC) convolutional codes, a member of the LDPC code family, encode the original information to improve error correction capability. In practice these codes are used to decode very long information sequences, where the information arrives in subsequent packets over time, such as video streams. We consider the problem of decoding the received information with minimum error from an optimization point of view and investigate integer programming–based exact and heuristic decoding algorithms for its solution. In particular, we consider relax–and–fix heuristics that decode information in small windows. Computational results indicate that our approaches identify near–optimal solutions significantly faster than a commercial solver in high channel error rates. Our proposed algorithms can find higher quality solutions compared with the state of the art iterative decoding heuristic. [ABSTRACT FROM AUTHOR]

Therefore, there is a clear need for innovative performance analysis and parameter design approaches to adequately realize the desired system performance and resource utilization efficiency of networked systems under coding-decoding communication mechanisms. Over the last decade, networked systems have been undergoing quick developments due mainly to their distinct merits in easy installation, maintenance convenience, and reliability assurance. [Extracted from the article]

Because of their importance in applications and their quite simple definition, Reed- Solomon codes can be explained in any introductory course on coding theory. However, decoding algorithms for Reed-Solomon codes are far from being simple and it is difficult to fit them in introductory courses for undergraduates. We introduce a new decoding approach, in a selfcontained presentation, which we think may be appropriate for introducing error correction of Reed-Solomon codes to nonexperts. In particular, we interpret Reed-Solomon codes bymeans of the degree of the interpolation polynomial of the code words and from this derive a decoding algorithm. Compared to the classical algorithms, our algorithm appears to arise more naturally from definitions and to be easier to understand. [ABSTRACT FROM AUTHOR]

As a paradigm of computation, reservoir computing has gained an enormous momentum. In principle, any sufficiently complex dynamical system equipped with a readout layer can be used for any computation. This can be achieved by only adjusting the readout layer. Owning to this inherent flexibility of implementation, new applications of reservoir computing are being reported at a constant rate. However, relatively few studies focus on sensing, and in the ones that do, the reservoir is often exploited in a somewhat passive manner. The reservoir is used to post-process the signal from sensing elements that are placed separately, and the reservoir could be replaced by other information processing system without loss of functionality of the sensor (‘reservoir computingandsensing’). An entirely different novel class of sensing approaches is being suggested, to be referred to as ‘reservoir computing for sensing’, where the reservoir plays a central role. In the State Weaving Environment Echo Tracker (SWEET) sensing approach, the reservoir functions as the sensing element if the dynamical states of the reservoir and the environment one wishes to analyze are strongly interwoven. Some distinct characteristics of reservoir computing (in particular the separability and the echo state properties) are carefully exploited to achieve sensing functionality. The SWEET approach is formulated both as a generic device setup, and as an abstract mathematical algorithm. This algorithmic template could be used to develop a theory (or a class of theories) of ‘reservoir computing for sensing’, which could provide guidelines for engineering novel sensing applications. It could also provide ideas for a creative recycling of the existing sensing solutions. For example, the Horizon 2020 project RECORD-IT (Reservoir Computing with Real-time Data for future IT) exploits the SWEET sensing algorithm for ion detection. Accordingly, the terms SWEET sensing algorithm and the RECORD-IT sensing algorithm can be used interchangeably. [ABSTRACT FROM PUBLISHER]

In this article, we consider a competing cause scenario and assume the wider family of Conway–Maxwell–Poisson (COM–Poisson) distribution to model the number of competing causes. Assuming the type of the data to be interval censored, the main contribution is in developing the steps of the expectation maximization (EM) algorithm to determine the maximum likelihood estimates (MLEs) of the model parameters. A profile likelihood approach within the EM framework is proposed to estimate the COM–Poisson shape parameter. An extensive simulation study is conducted to evaluate the performance of the proposed EM algorithm. Model selection within the wider class of COM–Poisson distribution is carried out using likelihood ratio test and information-based criteria. A study to demonstrate the effect of model mis-specification is also carried out. Finally, the proposed estimation method is applied to a data on smoking cessation and a detailed analysis of the obtained results is presented. [ABSTRACT FROM PUBLISHER]

The aim of the paper is twofold. Firstly, cyclic codes of arbitrary lengthnover the family of semigroup ringsare completely characterized in terms of ideals of the rings, whereis finite unitary commutative ring. Then, generator matrix for cyclic codes overis derived from their ideal representation. Secondly, an efficient encoding decoding algorithm is presented for polynomial cyclic codeof lengthnbased on cyclic codeover. Rigorous analyses are performed to examine the efficiency of proposed scheme. Analyses reveal that the proposed algorithm can simultaneously encode and decodemmessages of. Furthermore, the proposed technique is capable of detecting and correcting all burst errors up to lengthmd−mandfrom transmitted codewords of. The newly developed algorithm is also compared with some of the existing coding techniques. It is evident from comparison that the proposed scheme is better than existing coding techniques in Babu and Zimmermann [Decoding of linear codes over galois rings, IEEE Trans. Inform. Theory, 47 (2001)], Nagpaul and Jain [Topics in Applied Abstract Algebra, The Brooks/Cole Series in Advanced Mathematics, 2005], Shahet al.[A method for improving the code rate and error correction capability of a cyclic code, Comput. Appl. Math. 32 (2013), pp. 261–274, doi:10.1007/s40314-013-0010-1] and Shahet al.[A decoding method of an n length binary BCH code through (n+1)n length binary cyclic code, An. Acad. Brasil. Ciênc. 85 (2013), pp. 863–872]. [ABSTRACT FROM PUBLISHER]

Error-correcting codes are used to correct errors when messages are transmitted through a noisy communication channel. Bounded distance decoding (i.e. decoding up to the error-correcting capacity) is a method of correcting errors that guarantees unique decoding. Secret sharing is a cryptographic protocol that allows a secret known from a person called the dealer to be distributed tonparticipants. No single participant knows the secret but some special subsets of participants called coalitions can. In this paper, we present a multisecret-sharing scheme based on an error-correcting code where secret reconstruction is made by using bounded distance decoding of the code. This scheme is ideal in the sense that the size of each secret equals the size of any share. Its security improves on that of multisecret-sharing schemes. [ABSTRACT FROM PUBLISHER]

Stuart Hall’s “Encoding/decoding” essay sparked an on-going focus in media studies on reception and audience studies that remains theoretically robust today. Hall’s insight that audience members decode media content in multiple ways, some in line with the dominant cultural ideology and some resistant to that ideology, illuminates the phenomenon of media resistance. Media resisters significantly limit their media consumption and they do so based on their decodings of media culture—decodings, or readings, that resist normative messages about commercialism and consumption, about the natural diffusion and inherent benefits of mobile technologies and social media, and about the political landscape depicted and generated by news media. Hall’s encoding/decoding model is expanded here to include not only audiences’ decodings of specific content, but of media culture broadly. Concerns about media culture in the aggregate lead to media resisters’ practices of limiting media engagement, practices themselves that are counter hegemonic. [ABSTRACT FROM PUBLISHER]

Low-density parity-check (LDPC) codes, proposed by Gallager, emerged as a class of codes which can yield very good performance on the additive white Gaussian noise channel as well as on the binary symmetric channel. LDPC codes have gained lots of importance due to their capacity achieving property and excellent performance in the noisy channel. Belief propagation (BP) algorithm and its approximations, most notably min-sum, are popular iterative decoding algorithms used for LDPC and turbo codes. The trade-off between the hardware complexity and the decoding throughput is a critical factor in the implementation of the practical decoder. This article presents introduction to LDPC codes and its various decoding algorithms followed by realisation of LDPC decoder by using simplified message passing algorithm and partially parallel decoder architecture. Simplified message passing algorithm has been proposed for trade-off between low decoding complexity and decoder performance. It greatly reduces the routing and check node complexity of the decoder. Partially parallel decoder architecture possesses high speed and reduced complexity. The improved design of the decoder possesses a maximum symbol throughput of 92.95 Mbps and a maximum of 18 decoding iterations. The article presents implementation of 9216 bits, rate-1/2, (3, 6) LDPC decoder on Xilinx XC3D3400A device from Spartan-3A DSP family. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate joint design of quasi-cyclic low-density-parity-check (QC-LDPC) codes for coded cooperation system with joint iterative decoding in the destination. First, QC-LDPC codes based on the base matrix and exponent matrix are introduced, and then we describe two types of girth-4 cycles in QC-LDPC codes employed by the source and relay. In the equivalent parity-check matrix corresponding to the jointly designed QC-LDPC codes employed by the source and relay, all girth-4 cycles including both type I and type II are cancelled. Theoretical analysis and numerical simulations show that the jointly designed QC-LDPC coded cooperation well combines cooperation gain and channel coding gain, and outperforms the coded non-cooperation under the same conditions. Furthermore, the bit error rate performance of the coded cooperation employing jointly designed QC-LDPC codes is better than those of random LDPC codes and separately designed QC-LDPC codes over AWGN channels. [ABSTRACT FROM PUBLISHER]

Sophisticated machine learning algorithms have been successfully applied to functional neuroimaging data in order to characterize internal cognitive states. But is it possible to “mind-read” without the scanner? Capitalizing on the robust finding that the contents of working memory guide visual attention toward memory-matching objects, we trained a multivariate pattern classifier on behavioural indices of attentional guidance. Working memory representations were successfully decoded from behaviour alone, both within and between individuals. The current study provides a proof-of-concept for applying machine learning techniques to simple behavioural outputs (e.g., response times) in order to decode information about specific internal cognitive states. [ABSTRACT FROM PUBLISHER]

To transfer the medical image from one place to another place or to store a medical image in a particular place with secure manner has become a challenge. In order to solve those problems, the medical image is encrypting and compressing before sending or saving at a place. In this paper, a new block pixel sort algorithm has been proposed for compressing the encrypted medical image. The encrypted medical image acts as an input for this compression process. During the compression, encrypted secret image E12(;) is compressed by the pixel block sort encoding (PBSE). The image is divided into four identical blocks, similar to 2×2 matrix. The minimum occurrence pixel(s) are found out from every block and the positions of the minimum occurrence pixel(s) are found using the verdict occurrence process. The pixel positions are shortened with the help of a shortening process. The features (symbols and shortened pixel positions) are extracted from each block and the extracted features are stored in a particular place, and the values of these features put together as a compressed medical image. The next process of PBSE is pixel block short decoding (PBSD) process. In the decoding process, there are nine steps involved while decompressing the compressed encrypted medical image. The feature extraction value of compressed information is found out from the feature extraction, the symbols are split and the positions are shortened in a separate manner. The position is retrieved from the rescheduled process and the symbols and reconstructed positions of the minimum occurrence pixels are taken block wise. Every symbol is placed based on the position in each block: if the minimum occurrence pixel is '0', then the rest of the places are automatically allocated as '1' or if the minimum occurrence pixel is '1' the remaining place is automatically allocated as '0'. Both the blocks are merged as per order 2×2. The final output is the reconstructed encrypted medical image. From this compression method, we can achieve the high compression ratio, minimum time, less compression size and lossless compression, which are the things experimented and proved. [ABSTRACT FROM AUTHOR]

A novel technique to concatenate multiple component low density parity check (LDPC) codes in parallel is presented. Multiple Parallel Concatenated Gallager Codes leverage the advantage of breaking an equivalently long LDPC code into multiple small (lower-complexity) LDPC codes to offer scalability and scope for improving performance in practical implementation for delay-sensitive and resource-constrained applications. The construction as well as two proposed decoding techniques for MPCGCs is evaluated. It is shown that by optimizing the construction of the component LDPC codes and the number of decoding iterations at various channel conditions, the performance of MPCGCs can be significantly improved and their complexity reduced. [ABSTRACT FROM AUTHOR]

For any prime p, we consider p-ary linear codes obtained from the row span of incidence matrices of line graphs of triangular graphs and adjacency matrices of their line graphs. We determine parameters of the codes, their automorphism groups and exhibit permutation decoding sets (PD-sets) for partial permutation decoding. [ABSTRACT FROM AUTHOR]

In this article, we propose a high-speed decoding algorithm for binary BCH codes that can correct up to 7 bits in error. Evaluation of the error-locator polynomial is the most complicated and time-consuming step in the decoding of a BCH code. We have derived equations for specifying the coefficients of the error-locator polynomial, which can form the basis for the development of a parallel architecture for the decoder. This approach has the advantage that all the coefficients of the error locator polynomial are computed in parallel (in one step). The roots of error-locator polynomial can be obtained by Chien's search and inverting these roots gives the error locations. This algorithm can be employed in any application where high-speed decoding of data encoded by a binary BCH code is required. One important application is in Flash memories where data integrity is preserved using a long, high-rate binary BCH code. We have synthesized generator polynomials for binary BCH codes (error-correcting capability, s ) that can be employed in Flash memory devices to improve the integrity of information storage. The proposed decoding algorithm can be used as an efficient, high-speed decoder in this important application. [ABSTRACT FROM AUTHOR]