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An adaptively hybrid method was proposed to improve the performance of fractal coding methods. First, it is found that the range blocks with large variances (RBLVs) play a crucial role in degrading decoded images, and the effect of the remaining range blocks with small variances (RBSVs) can be ignored. Then, an adaptive method was proposed to divide the range blocks into the above two categories: RBLVs and RBSVs. Second, RBLVs were designed to be encoded in an extended domain block pool (EDBP). Then, better block‐matching effect can be obtained, which will result in better decoded image quality. Further, the no‐search fractal encoding method is adopted for RBSVs to achieve faster encoding speed and fewer bits per pixel (bpp). Finally, four fractal coding methods were adopted to assess the performance of the proposed method. Experimental results show that compared with the previous methods, the PSNR quality of decoded images in the proposed method can be improved by about 0.15–0.4 dB, about 20%–35% of the total computations in encoding process can be saved, and about 0.2 bpp can be saved. Moreover, under the same decoding time, the proposed method can achieve comparable or smaller deviations regarding the decoded image. [ABSTRACT FROM AUTHOR]

Among numerous dynamic scheduling strategies for low‐density parity‐check codes, many of them are of high complexity due to repetitive computation and ordering of belief residuals. To address these challenges, two novel approaches are proposed: the derived informed variable‐to‐check residual belief propagation and the derived variable‐node and variable‐to‐check‐edge residual belief propagation for low‐density parity‐check codes. Simulation results show that the derived dynamic scheduling exhibits a substantial gain of approximately 0.15 dB in signal‐to‐noise ratio over the non‐derived one with considerably lower decoding complexity when targeting a bit‐error rate of 10−4. [ABSTRACT FROM AUTHOR]

A power line carrier (PLC) communication characteristics‐based method is proposed for single‐phase‐to‐ground fault location in neutral isolated medium voltage meshed distribution networks in this paper. The carrier signals with a time‐varying frequency and constant amplitude are processed by a set of PLC transmitters and receivers, whose placement is optimized by regarding the power network as an undirected graph. Two signal encoding and decoding algorithms for the PLC terminals are proposed to avoid using expensive timing systems between the terminals. The fault location technique is implemented by comparing the cosine similarity of amplitude attenuation and phase offset between the fault and a feature library. The node corresponding to the maximum cosine similarity of the characteristics between the present fault and the library is selected as the location of the current fault. Only one set of low‐cost PLC communication terminals and the widely available power lines are needed in the fault location system, making this approach highly practical. Numerical simulations using MATLAB/Simulink have been performed to verify the technique's feasibility. The results show that the method can accurately locate faults in neutral isolated medium voltage meshed distribution networks. Besides, the presented approach achieves a high level of accuracy in estimating transition resistance values. [ABSTRACT FROM AUTHOR]

Summary: Power lines have complex transmission characteristics and noise interference when used as information transmission channel, thus limiting their data‐carrying rate and reliability. Impulsive noise is the most dominant factor degrading the performance of communication systems over power lines. To combat the impulsive noise, polar codes are introduced into the orthogonal frequency division multiplexing (OFDM)‐based power line communication (PLC) system. Because the successive cancelation list bit‐flip (SCLF) decoding algorithm of polar codes has high decoding complexity, a low complexity segmented cyclic redundancy check (CRC)‐aided SCLF decoding is proposed to improve the decoding performance. The decoding process can be terminated early with segmented CRC, which can reduce the bit‐flip decoding complexity significantly with short and medium block lengths. Experimental results demonstrate that for various configurations, the proposed decoder outperforms the traditional CRC‐aided SCL decoder in terms of decoding performance and complexity, and the suggested polar codes‐based OFDM‐based PLC system can reduce the bit error rate of the system in the presence of impulsive noise. [ABSTRACT FROM AUTHOR]

The residual belief propagation (RBP) algorithm, which is the most classic informed dynamic scheduling strategy, achieves outstanding performance in error correction and can drastically accelerate convergence speed. However, the greedy algorithmic property of this iterative decoding will inevitably cause loss of decoding performance. To address this, a novel algorithm called the partial average bundle residual belief propagation (PABRBP) is proposed in this paper. According to the construction characteristics of a base matrix of protograph‐LDPC codes, informed dynamic scheduling (IDS) strategies are applied to an edge bundle of base matrices for the first time. This edge bundle of the base matrix can be applied to a corresponding cyclic permutation matrix. Furthermore, the update level of each bundle is determined by the value of the Partially Average Bundle Residual (PABR). Therefore, the edge message with the maximum residual in each bundle is updated in order, and the process of iterative decoding is less likely to become trapped in a local optimum. Additionally, the generation of silent nodes is reduced as much as possible. To further improve the PABRBP decoding performance for medium and long codes over the fading channel, the adjusted compensation term is periodically introduced. Analysis and simulation results show that PABRBP demonstrates a notable convergence quality and decoding performance improvement over the fading channels compared to existing state‐of‐art IDS algorithms. [ABSTRACT FROM AUTHOR]

Background: Computer‐aided diagnosis is of great significance to improve the diagnostic accuracy of pancreatic cancer that has an insidious course and does not have obvious symptoms at first. However, segmentation of pancreatic cancer is challenging because the tumors vary in size with the smallest tumor having a size of about 0.5 cm$cm$ in diameter, and most of them have irregular shapes and unclear boundaries. Purpose: In this study, we developed a deep learning architecture Multi‐Scale Channel Attention Unet (MSCA‐Unet) for pancreatic tumor segmentation and collected CT images of 419 patients from The Affiliated Hospital of Qingdao University and a public dataset. We embedded the multi‐scale network into the encoder to extract semantic information at different scales and the decoder to provide supplemental information to overcome the loss of information in the upsampling and the drift of the localized tumor due to the upsampling and skip connections. Methods: We adopted the channel attention unit after the multi‐scale convolution to emphasize the informative channels, which was observed to have the effects of accelerating the positioning process, reducing false positives, and improving the accuracy of outlining very small, irregular pancreatic tumors. Results: Our results show that our network outperformed the other current mainstream segmentation networks and achieved a Dice index of 68.03%, a Jaccard of 59.31%, and an FPR of 1.36% on the private dataset Task‐01 without data pre‐processing. Compared with the other pancreatic tumor segmentation networks on the public dataset Task‐02, our network produced the best Dice index, 80.12%, with the assistance of the data pre‐processing scheme. Conclusions: This study strategically utilizes the multi‐scale convolution and channel attention mechanism of the architecture to provide a dedicated network for segmentation of small and irregular pancreatic tumors. [ABSTRACT FROM AUTHOR]

Skin cancer, also known as melanoma, is a deadly form of skin cancer that can significantly improve survival rates when diagnosed at an early stage. It is usually diagnosed visually from dermoscopic images, and such visual assessment of skin cancer by the naked eye is a challenging and arduous task. Therefore, the detection of melanoma from dermoscopic images using trained artificial intelligence models is of great importance today. However, since melanoma is a rare disease, existing databases of skin lesions often contain highly unbalanced numbers of benign and malignant samples. In this paper, we propose a new one‐class classification‐based skin lesion classification strategy for small and unbalanced datasets. One‐class classification (OCC) is a special case of multi‐classification. OCC aims to learn a descriptive paradigm from positive class data (true data) during training and reject pseudo data (fake data) that do not conform to the paradigm during inference. OCC has great potential for application in anomaly detection problems. We have analyzed several approaches to the OCC task in recent years and propose a new design paradigm for the OCC problem, taking into account the unbalanced data set of the melanoma classification task. We have designed an improved OCC network based on this design paradigm, where the network is based on the architecture of a residual neural network, combining the coding and decoding idea of variational self‐encoder and the adversarial training idea of an adversarial neural network, using binary cross‐entropy as the loss function and introducing the channel attention mechanism. Tests on several publicly available dermatology datasets show that this improved OCC network addresses the unbalanced dataset situation in melanoma image classification to some extent while having relatively excellent performance. Compared with some traditional networks, it can obtain more stable training results and perform more consistently on complex datasets. [ABSTRACT FROM AUTHOR]

In the systems of industrial robotics and autonomous vehicles, instance segmentation is widely employed. However, manually labelling an object outline is time‐consuming. In order to reduce annotation costs, we present a weakly supervised instance segmentation method in this article. A deeply convolutional network is first used to construct multi‐scale feature maps for each object in the input image. After that, the encoder‐decoder framework with dynamic convolution is utilised to enhance model capacity and efficiency, while avoiding the issues of anchor design, proposal selection, and RoIAlign implementation. In particular, Dynamic Heads are used in the encoder to create dynamic convolution kernels, while Instance Heads are used in the decoder to provide the global feature map. With dynamic convolution, each instance can be segmented independently, reducing interference with other instances and improving segmentation accuracy. Under the supervision of projection loss and pixel point colour pairing loss, the contours of each object are finally outlined. On the PASCAL VOC and MS COCO datasets, the proposed method is competitive with more sophisticated approaches. In the VOC dataset, segmentation performance achieved 37.6% average precision with ResNet‐101 and FPN networks. The extensively visualised results demonstrate the effectiveness of the proposed encoder‐decoder framework with dynamic convolution. [ABSTRACT FROM AUTHOR]

Summary: A novel scheme for designing polar codes with specific decoding schemes in the additive white Gaussian noise channel is presented in this paper. The code construction strategy is built on the genetic algorithm (GA) where successive evolution of populations (or group of information sets) leads toward fittest candidate to attain finest error‐rates. In this work, it is shown that better error‐rates can be attained by both successive cancelation list decoding using GA with no cyclic redundancy check (CRC) and belief propagation decoding using GA with no CRC and no list, compared to existing polar decoding schemes. Our proposed polar code design scheme using GA has the ability to attain a target block error rates with the least possible SNR and using no additional CRC by exploiting least belief propagation iterations or lesser successive cancelation list list size with no self‐alterations in the decoding algorithm. [ABSTRACT FROM AUTHOR]

Accurately segmenting polyp from colonoscopy images is essential for early screening and diagnosis of colorectal cancer. In recent years, with the proposed encoder‐decoder architecture, many advanced methods have been applied to this task and have achieved significant improvements. However, accurate segmentation of polyps has always been a challenging task due to the irregular shape and size of polyps, the low contrast between the polyp and the background in some images, and the influence of the environment such as illumination and mucus. In order to tackle these challenges, we propose a novel uncertain area attention and cross‐image context extraction network for accurate polyp segmentation, which consists of the uncertain area attention module (UAAM), the cross‐image context extraction module (CCEM), and the adaptive fusion module (AFM). UAAM is guided by the output prediction of the adjacent decoding layer, and focuses on the difficult region of the boundary without neglecting the attention to the background and foreground so that more edge details and uncertain information can be captured. CCEM innovatively captures multi‐scale global context within an image and implicit contextual information between multiple images, fusing them to enhance the extraction of global location information. AFM fuses the local detail information extracted by UAAM and the global location information extracted by CCEM with the decoding layer feature for multiple fusion and adaptive attention to enhance feature representation. Our method is extensively experimented on four public datasets and generally achieves state‐of‐the‐art performance compared to other advanced methods. [ABSTRACT FROM AUTHOR]

The effective integration of RGB and depth map features to improve the performance of RGB‐D salient object detection (SOD) has garnered significant research interest. The existing dual‐stream models can be used for high‐level feature fusion or unidirectionally transferring depth features to RGB features; however, they are unable to fully exploit the differences in modality. Furthermore, owing to the influence of image background information, the generated salient object is affected by background swallow. Herein, a three‐stream RGB‐D SOD method based on cross‐layer and cross‐modal dual‐attention (CMDA) fusion is proposed. In the encoding stage, the CMDA fusion module is used to fuse RGB and depth features layer by layer. Through this module, merged interactive features may be used to extract the richer features of salient objects, realize the commonality and complementarity of fusion features, and achieve effective cross‐modal fusion. In addition, for the decoding stage, a cross‐level feature fusion module that introduces global context features into the up‐sampling process, reduces the impact of salient objects being swallowed by the background, and helps to accurately detect salient areas is proposed. Three different branch features are used for simultaneous end‐to‐end training. The experimental results demonstrate that the proposed method outperforms other methods in terms of multiple evaluation metrics on four datasets. Furthermore, the authors visualize the precision–recall curve, F‐measure curve, and saliency map, which indicate that the detection effect of the proposed method is superior to those of other methods. During the testing stage, our model ran at 14 frames per second (FPS). [ABSTRACT FROM AUTHOR]

Polar codes have received widespread attention because of their excellent performance. However, although successive cancellation (SC)‐based decoding algorithms have achieved excellent error correction performance, they still encounter large time delays in decoding because a serial decoding structure is employed. At present, a cyclic redundancy check (CRC) is used to judge whether the decoding result of a frame is erroneous. To obtain such a result, the decoder has to wait until the end of decoding. In this work, a judgement strategy for error frames using frozen bits is proposed. With the proposed strategy, the recognition of erroneously decoded frames can be achieved based on the error state of the frozen bits in this frame. Accordingly, early termination of decoding can be realized. By using this mechanism, the strategy can also be applied in the SC List algorithm to prune the path. In addition, the SC Flip decoding algorithm can also use the proposed strategy to help find the first error message bit. The experimental results show that at least 85% of the frames decoded erroneously can be recognized by the proposed strategy so that decoding can be terminated in advance and computational resources can be saved. [ABSTRACT FROM AUTHOR]

In this paper, the decoding problem for multiple Bose–Chaudhuri–Hocquenghem (BCH)‐Goppa codes over the same finite field is investigated. A new iterative decoding algorithm is proposed based on the quotient difference (qd)‐type discrete hungry Lotka–Volterra equation over finite fields. Compared with certain existing algorithms, the proposed algorithm manifests its advantage in computational complexity. A few of examples are presented to demonstrate its efficiency. [ABSTRACT FROM AUTHOR]

The distributed Goppa coded spatial modulation (DGC‐SM) scheme is proposed in cooperative scenarios. In the proposed DGC‐SM scheme, the relay chooses the partial information from the source by using the idea of information selection, and then re‐encodes the selected information. At the destination, a distributed Goppa code is obtained corresponding to each selection. In order to get the best possible code, an optimal information selection algorithm is proposed at the relay. In the proposed optimal algorithm, all selections and source message sequences are considered, which results in high complexity. Thus, another optimized information selection algorithm with low complexity is proposed at the relay. To further enhance the capability to get the source information, two efficient joint decoding algorithms called smart decoding algorithm and improved smart decoding algorithm are proposed. Moreover, the bit error rate (BER) performance bound of the proposed DGC‐SM scheme is derived. Through the simulations, the effectiveness of the proposed two optimized information selection algorithms is confirmed. Also, it is observed that the simulation and analytical results closely match at high signal‐to‐noise ratio (SNR). [ABSTRACT FROM AUTHOR]

A latest coding scheme named polarization‐adjusted convolutional (PAC) codes is shown to approach the dispersion bound for the code (128,64) under list decoding. However, to achieve the near‐bound performance, the list size of list decoding needs to be excessively large, which leads to insufferable time complexity. In this paper, to improve the speed of list decoding, fast list decoders for PAC codes are proposed. Four types of constituent nodes are defined and fast list decoding algorithms are provided for each of them. Simulation results present that fast list decoding with three types of constituent nodes can yield exactly the same error‐correction performance as list decoding, and reduce more than 50% time steps for the code (128,64). Moreover, fast list decoding with four types of constituent nodes can further reduce decoding complexity with negligible performance degradation. [ABSTRACT FROM AUTHOR]

Frequency‐time selective fading degrades the performance of communication systems, but it also provides an opportunity to collect multipath diversity and Doppler diversity. In this paper, an oversampled grouped‐linear‐constellation‐precoding (GLCP) scheme is proposed for orthogonal frequency division multiplexing (OFDM) systems to extract the diversity inherent in doubly selective fading channels. In the proposed scheme, GLCP is employed to precode the information symbols of OFDM at the transmitter, and the precoded OFDM symbols are oversampled in the time domain at the receiver. A full‐groups maximum likelihood (ML) decoder and a successive group decoder to decode the oversampled OFDM symbols with diversity gains are developed. The successive group decoder combines the group ML decoding algorithm with a block‐decision‐feedback‐equalization (BDFE) algorithm. The proposed scheme can take advantage of both the GLCP scheme and the oversampling scheme to achieve multipath diversity gains and Doppler diversity gains in doubly selective fading channels. Simulation results show that the proposed scheme can significantly improve the performance of OFDM systems in the frequency selective fading channel and the doubly selective fading channel in terms of bit error ratio, indicating that the diversity gains can be greatly enhanced by the proposed scheme in doubly selective fading channels. [ABSTRACT FROM AUTHOR]

Colon cancer has been reported to be one of the frequently diagnosed cancers and the leading cause of cancer deaths. Early detection and removal of malicious polyps, which are precursors of colon cancer, can enormously lessen the fatality rate. The detection and segmentation of polyps in colonoscopy is a challenging task even for an experienced colonoscopist, due to divergences in the size, shape, texture, and the close resemblance of polyps with the colon lining. Machine‐assisted detection, localization, and segmentation of polyps in the screening procedure can profoundly help the clinicians. Autoencoder‐based architectures used in polyp segmentation lack the efficiency in incorporating both local and long‐range pixel dependencies. To address the challenges in the automatic segmentation of colon polyps we propose an autoencoder architecture, augmented with a feature attention module in the decoder part. The salient features from RGB colonoscopic images are extracted using the residual skip‐connected autoencoder. The decoder attention module joins spatial subspace with feature subspace extracted from the deep residual convolutional neural network and enhances the feature weight for precise segmentation of polyp regions. Extensive experiments on four publicly available polyp datasets demonstrate that the proposed architecture provides very impressive performance in terms of segmentation metrics (Dice scores and Jaccard scores) when compared with the state‐of‐the‐art polyp segmentation approaches. [ABSTRACT FROM AUTHOR]

This work studies the equalization methods for the reduced zero‐padding version of the orthogonal time frequency space modulation. Here, the focus is on applications in low data rate scenarios such as the Internet of Things, and the case where the number of subcarriers and time slots are small will be considered. First, the system equation is established to model the inter‐subcarrier and inter‐slot interference phenomena. Based on the system model, three sphere‐decoding based equalization methods are proposed. The first method is to directly apply the sphere‐decoding algorithm on the full‐scale system model; for the second method, the transmitter needs to perform the triangular precoding; and for the third method, the transmitter needs to perform the diagonal precoding. Simulation results show that the proposed sphere‐decoding based methods achieve better bit error rate performance than the state‐of‐the‐art methods like message passing, unitary approximate message passing, and variational approximate message passing, especially when the channel has severe inter‐slot interference. [ABSTRACT FROM AUTHOR]

We propose a 3D+time framework for modeling dynamic sequences of 3D facial shapes, representing realistic non‐rigid motion during a performance. Our work extends neural 3D morphable models by learning a motion manifold using a transformer architecture. More specifically, we derive a novel transformer‐based autoencoder that can model and synthesize 3D geometry sequences of arbitrary length. This transformer naturally determines frame‐to‐frame correlations required to represent the motion manifold, via the internal self‐attention mechanism. Furthermore, our method disentangles the constant facial identity from the time‐varying facial expressions in a performance, using two separate codes to represent neutral identity and the performance itself within separate latent subspaces. Thus, the model represents identity‐agnostic performances that can be paired with an arbitrary new identity code and fed through our new identity‐modulated performance decoder; the result is a sequence of 3D meshes for the performance with the desired identity and temporal length. We demonstrate how our disentangled motion model has natural applications in performance synthesis, performance retargeting, key‐frame interpolation and completion of missing data, performance denoising and retiming, and other potential applications that include full 3D body modeling. [ABSTRACT FROM AUTHOR]

The successive cancellation flip (SC Flip) decoding algorithm was recently suggested for decoding polar codes, which could improve the performance of the frame error rate (FER). The performance of the SC Flip (SCF) decoding algorithm is strong, and its computational complexity tends to be the same as that of the SC decoder at medium to high signal‐to‐noise ratios (SNRs). However, the decoding latency of the SCF decoding algorithm is large. In this paper, a new method for detecting whether or not the flipped bit is correct is proposed. The proposed method makes a decision according to the changing log likelihood ratio (LLR) value caused by the flipped bit to ensure that the decoding process can be terminated in advance and the decoding latency is reduced. The simulation results show that the proposed Latency‐Reduced SCF decoding algorithm can decrease computational complexity and decoding latency, while also achieving similar decoding performance compared to its counterpart. [ABSTRACT FROM AUTHOR]

Two constructions of lattice packings of n$ n$‐dimensional cross‐polytopes (ℓ1$ \ell _1$ balls) are described, the density of which exceeds that of any prior construction by a factor of at least 2nlnn(1+o(1))$ 2^{\frac{n}{\ln n}(1 + o(1))}$. The first family of lattices is explicit and is obtained by applying Construction A to a class of Reed–Solomon codes. The second family has subexponential construction complexity and is based on the notion of Sidon sets in finite Abelian groups. The construction based on Sidon sets also gives the highest known asymptotic density of packing discrete cross‐polytopes of fixed radius r⩾3$ r \geqslant 3$ in Zn$ \mathbb {Z}^n$. [ABSTRACT FROM AUTHOR]

The authors propose encoding and decoding network‐based normalizing flows (EaD‐Flow) for the detection and localization of anomalies in an image. Multiple networks of the EaD‐Flow estimate probability densities of tenor image. The anomaly localization model based on multiple networks shows superior anomaly detection and localization performance. [ABSTRACT FROM AUTHOR]

In optimization theory, to speed up the convergence of iterative procedures, many mathematicians often use the inertial extrapolation method. In this article, based on the three‐term derivative‐free method for solving monotone nonlinear equations with convex constraints [Calcolo, 2016;53(2):133‐145], we design an inertial algorithm for finding the solutions of nonlinear equation with monotone and Lipschitz continuous operator. The convergence analysis is established under some mild conditions. Furthermore, numerical experiments are implemented to illustrate the behavior of the new algorithm. The numerical results have shown the effectiveness and fast convergence of the proposed inertial algorithm over the existing algorithm. Moreover, as an application, we extend this method to solve the LASSO problem to decode a sparse signal in compressive sensing. Performance comparisons illustrate the effectiveness and competitiveness of the method. [ABSTRACT FROM AUTHOR]

The informed dynamic scheduling (IDS) strategies, in which the edge message with the maximum message‐residual is updated preferentially, achieve remarkable error‐correction performance when applied to low‐density parity‐check (LDPC) codes. However, the IDS strategies incur inferior convergence in iterative decoding owing to the greedy problem, which is called the update‐relayed trend here. In order to solve the greediness, two locally informed dynamic scheduling algorithms based on the law of large numbers are proposed. The proposed decoding algorithms use random select of check nodes over a predefined update range (RSPUR) which effectively suppresses the propagation of the update‐relayed trend and accordingly restrains the forming of multi‐update cycles. Moreover, the decoding algorithm is further improved based on random select of check nodes over an adjustable update range (RSCAR). The update ranges are selected based on the law of large numbers. Therefore, the computational resources can be allocated more equitably by increasing iterations. Simulation results show that both the proposed algorithms achieve excellent performance in terms of throughput and convergence with low decoding complexity over the Additive White Gaussian Noise (AWGN) and the fading channels compared to the previous IDS strategies. Hence, the proposed algorithms behave excellently over the wireless channels. [ABSTRACT FROM AUTHOR]

Summary: Future generation communication devices, particularly the Internet of Things (IoTs) demand for low bit error rate (BER) and higher spectral efficiencies. In this context, a low complexity detection algorithm plays a vital role in determining the performance of receivers. In this paper, we have evaluated the performance of low complexity sphere decoders using K best algorithm by modifying the conventional sphere decoders into new K and K1 sphere decoders. The proposed algorithm enables the sphere decoders to use smaller values of K for better performance. Numerical simulation is performed using MATLAB to support the validity of the proposed algorithm in terms of BER and spectral efficiency at higher order modulation techniques such as 1024 quadrature amplitude modulation (QAM) and offset quadrature amplitude modulation (OQAM). Further, we have also analyzed the computational complexity of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Hollmann built a theoretical framework for subspace regenerating storage code. In this framework, a storage node is equipped with a constant dimension subspace chosen from an overall message space Fqm${\bm{F}}_{\bm{q}}^{\bm{m}}$. The projection of user message on the subspace is stored on the corresponding node. A subspace code needs to fulfil decoding and repairing functionality. The framework stipulates the property for subspace storage code, but a simple implementable construction is still missing. To decrease the complexity of code design as well as encoding and decoding, this article proposed a subspace code implementation over F2 with the parameters of (m,m,2,⌈m2⌉,r,β)$({\bm{m}},{\bm{m}},2,\lceil {\frac{{\bm{m}}}{2}} \rceil ,{\bm{\ r}},{\bm{\beta }})$. It is proved theoretically the code is characterized by minimum decoding bandwidth, minimum storage, and exact repair. Still, the code is explicit, feasible and lightweight. A series of examples are given to elucidate the encoding, decoding and repairing procedures, which show in cases of m ≤ 5, the code could achieve high repair efficiency by combining with network coding. [ABSTRACT FROM AUTHOR]

This letter focuses on improving the error correction performance of the extended min‐sum (EMS) decoding algorithm. Introducing factor/offset modifications in the check node update process of the EMS algorithm can improve the performance. Based on this, the authors propose a novel threshold‐based modified EMS algorithm that introduces the factor/offset in a targeted manner by setting a threshold for the posterior probability. Simulation results show that an additional performance gain of the order of 0.08 dB is obtained with the proposed method without significantly increasing the complexity of the method. [ABSTRACT FROM AUTHOR]

Summary: Information theory coding is an impressive and most celebrated field of research that has spawned numerous extremely important solutions to the intractable problems of secure data communications. Recent advancements in error control coding methods have seen a huge surge in using low‐density parity‐check (LDPC) code‐based decoding algorithms to solve imperative issues related to reliable data transmission and reception. Till date, extensive research efforts have been consistently being made on LDPC codes which focus on algorithm‐driven and hardware‐realization‐based approaches. The main intension of this research work is to provide an extensive systematic elucidation on the recent advancements in LDPC decoding algorithms. In addition, a thorough performance evaluation and analysis of several outstanding LDPC decoding techniques is presented. Finally, conclusions are drawn by summarizing the important research findings, interesting open problems, current challenges and broader perspectives for future directions of research. [ABSTRACT FROM AUTHOR]

Magnetic nanowires (MNWs) have been shown to be promising candidates for nanobarcoding and biolabeling over decades. However, surprisingly, they have never been realized successfully for these applications due to the lack of a reliable encoding strategy that allows reliable decoding of readout signatures. Here, a novel encoding/decoding approach is reported by tailoring the MNWs remanence spectrum that makes decoding the magnetic nanobarcodes feasible even though there is no prior knowledge regarding the readout remanence spectrum. The remanence spectra of several MNWs is tuned by varying their composition, dimensions, and arrangements to generate several distinct nanobarcodes. Furthermore, a simple automated decoding algorithm is illustrated to identify and verify the type and number of MNWs in unknown remanence spectra. The experimental and analytical approaches show that the remanence spectrum is a strong probe for reliable encoding/decoding because it employs three authentication factors that significantly enhance their security performance in nanobarcoding and biolabeling. [ABSTRACT FROM AUTHOR]

Summary: Decoding of frequency‐hopping spread spectrum (FHSS) methods to intercept the symbol content in signals and brilliant measurement is presented in this paper. The switching of the frequency channels rapidly using a pseudorandom code makes interception of FHSS signals very difficult. Therefore, a proposed adaptive compressive measurement for decoding the frequency‐hopping receivers that have no prior knowledge of the hopping sequence is the main focus of this work. The proposed methods rely on the instantaneous sparsity of the spectra of FHSS signals, and the transmitted symbols can be recovered from the proposed compressive measurements. Furthermore, the contribution of this work is the design of a proposed adaptive method that is used to design measurement kernels for compressive measurement and decoding based on knowledge enhancement, which includes both prior information enhanced and adaptive methods based on the properties of the intersignals. Simulation results on Gaussian frequency‐shift keying (GFSK) FHSS signals illustrate that the proposed compressive method enhances the detection performance of the received frequencies with acceptable decoding accuracy compared with other techniques such as enhanced orthogonal matching pursuit (EOMP) and an adaptive sampling kernels method with a random compressive ratio. Also, an enhancement of the proposed algorithm is achieved due to enhancement in both the normalized mean square error (NMSE) and the probability of correction error (Pce). The comparison between these algorithms is performed using simulation based on MATLAB and receiver operating characteristic (ROC) curves. [ABSTRACT FROM AUTHOR]

Summary: The problem of model‐free adaptive control (MFAC) design for a class of nonlinear systems with data quantization is considered in this article. Consider the case that the system output signal should be quantized before being transmitted to the controller. A MFAC algorithm with uniform quantizer is first proposed. As a result, the proposed MFAC algorithm cannot obtain a zero‐tracking error because of the reduction of available information due to data quantization. To suppress the influence of data quantization, an improved MFAC algorithm with encoding and decoding mechanism is proposed. The improved design first encodes the system information and then transmits it through the network. Then, the controller receives the information and then decodes it to construct the MFAC algorithm. Theoretical result shows that the improved MFAC algorithm with encoding and decoding mechanism can obtain the aim of zero‐tracking error. Finally, there have two examples to verify the effectiveness and applicability of the quantized MFAC algorithm design. [ABSTRACT FROM AUTHOR]

White modules of quick response codes (QR codes) are degraded by dot gain, especially when printed at smaller sizes, which results in more false blacks than false whites in error analysis, and also severely affects decoding ability. To reduce the false blacks generated from printing and to improve code readability, modifying the microstructure of graphic QR codes by reducing the size of the information dots is proposed. Experimental results show that the resultant QR code reduces the number of false blacks and thus the error rate. This method applies to the physical output of both conventional and graphic QR codes. [ABSTRACT FROM AUTHOR]

Luby transform (LT) codes have received less attention in larger non-binary fields. Robust soliton distribution (RSD) was designed to provide universal optimality over the binary erasure channel in terms of minimising the overhead. In this study, the authors introduce a fundamental degree distribution (FDD), which when used with non-binary LT codes, outperforms their binary counterparts using the RSD in terms of error rate and realised code rate. For instance, over an additive white Gaussian noise (AWGN) channel at Eb/N0 of 12 dB and a rateless criteria (zero error probability), a non-systematic 4-ary LT code using the FDD can achieve a code rate of 0.75, compared to its binary counterpart which only achieves a code rate of 0.7. [ABSTRACT FROM AUTHOR]

In this study, the authors investigate the use of combined belief propagation (BP), mean field (MF) and expectation propagation (EP) message passing to achieve joint channel estimation and decoding (JCED) for orthogonal frequency division multiplexing where the channel sparsity is exploited, and a low-complexity BP-MF-EP-based JCED receiver is designed. Moreover, comparisons in message updating of state-of-the-art message passing-based JCED receivers are provided to illustrate the merits of the proposed one. In addition, message passing schedules are optimised to achieve better system performance. Simulation results verify the superiority of the proposed combined message passing receiver in terms of both bit-error-rate performance and convergence speed. [ABSTRACT FROM AUTHOR]

Sparse code multiple access (SCMA) is a promising candidate air interface of the next generation mobile networks. However, the decoding complexity of current message passing algorithm for SCMA is very high. In this study, the authors map SCMA constellation to q-order Galois field (GF(q)) and introduce a trellis representation to SCMA. Based on the trellis representation, they propose low-complexity decoding algorithms for SCMA by using truncated messages, which is referred as extended max-log (EML) algorithm. As the truncated length of EML is unitary for each user, they further propose a channel-adaptive EMLalgorithm to truncate the messages with a rule that can be adaptive to the channel state. Simulation results show that the proposed schemes obtain a low computational complexity with only a slight performance degradation when the truncated length is selected appropriately. [ABSTRACT FROM AUTHOR]

Summary: This paper tackles the problem of pilot contamination (PC) in mmWave Massive MIMO cellular systems. We propose an analog precoder based on antenna subset transmission technique to mitigate the PC. This technique ensures the interfering signal to become noise‐like signal thus helping a more efficient estimation of channel coefficients. Also, this strategy is low cost and introduces no complexity load. The result shows the effectiveness of the proposed precoder to mitigate PC issue. [ABSTRACT FROM AUTHOR]

The Luby transform (LT) code is the first practical implementation of the digital fountain code, and its utilisation over a noisy channel invokes iterative decoding with soft information. For a good decoding performance, the role of the encoder degree distribution is vital. Especially, when utilising the soft iterative decoder, the bit error rate (BER) performance of the LT code can be improved by increasing the number of the degree-1 encoding nodes and reducing the number of lower-degree variable nodes. This study proposes an efficient LT-code encoding algorithm that satisfies the conditions for improving the BER performance. The proposed scheme applies different priorities for selecting the neighbouring source nodes for given degrees of the encoding nodes. The simulation results demonstrate that the proposed scheme outperforms the existing schemes. [ABSTRACT FROM AUTHOR]

By multiplexing waveform mapped with shifting and weighted symbols, overlapped x domain multiplexing (OVXDM) encoding scheme can obtain high spectral efficiency under a low signal-to-noise ratio. Though the maximum likelihood sequence detection decoders can obtain the optimal decoding performance, their computational complexities which increase exponentially with spectral efficiency in OVXDM are unbearable for practical implementations. In this study, a low-complexity multiple-signal joint decoding (MSJD) algorithm for OVXDM signalling is proposed over additive white Gaussian noise (AWGN) channel and extended to frequency selective fading (FSF) channel. Based on a novel joint information-based metric, the proposed algorithm utilises information of each transmitted symbol contained in multiple received signals, and selects the proper number of received signals for decoding to achieve performance maximisation. Moreover, theoretical analysis is provided for the proposed algorithm in both AWGN and FSF channels, which indicates the relationship between performance and number of received signals under different OVXDM encoding parameters including multiplexing waveform and overlapping fold. Simulation results confirm the superior performance of the MSJD algorithm in terms of decoding performance and computational complexity compared with the present approaches. [ABSTRACT FROM AUTHOR]

A novel concatenated physical-layer encryption (CPLE) scheme is proposed in this study, where better security can be achieved as well as reliability advantage. In CPLE scheme, the encryption is embedded in rateless encoding. The secret key, which is generated from wireless channel, controls the random degree and random linear combination. The rateless codes are also concatenated with other conventional channel codes, which can achieve better reliability. Different from security codes, both the security and reliability in the proposed scheme can be improved. Furthermore, there is no extra computation requirement on decoding process. Security analysis is presented including time complexity comparison and attacks resistance in practical application. Compared to other PLE schemes, the proposed scheme achieves lower complexity, and higher channel adaptation and performance advantage. On the basis of National Institute of Standards and Technology statistical test suite, CPLE scheme can achieve randomness as the traditional cipher system such as advanced encryption standard. Furthermore, simulation results show that the CPLE scheme outperforms the conventional concatenated codes at the same coderate. [ABSTRACT FROM AUTHOR]

An efficient joint Viterbi detection and decoding (JVDD) algorithm for a bluetooth low energy (BLE) system is proposed. Since the convolutional coded Gaussian minimum-shift keying (GMSK) signal is specified in the BLE 5.0 standard, two Viterbi processors are needed for detection and decoding. However, the proposed JVDD scheme uses only one Viterbi processor by modifying the branch metric with inter-symbol interference information from GMSK modulation; therefore, the hardware complexity can be significantly reduced without performance degradation. [ABSTRACT FROM AUTHOR]

All test patterns are decoded in the conventional Chase-2 algorithm, but only a few candidate codewords are derived. In this work, a highly efficient Chase-2 decoding algorithm has been proposed, where only the test patterns that can be decoded into distinct candidate codewords are processed. It means that the total number of decoding attempts is reduced from that of the test patterns to that of the candidate codewords, which consequently reduces the complexity, especially in high signal-to-noise ratio (SNR) region. The criterion of the test pattern selection is based on the property of minimum Hamming distance, it can be expected that the proposed algorithm can be applied to all linear block codes without performance degradation compared to the conventional Chase-2 algorithm. [ABSTRACT FROM AUTHOR]

Unequal error protection (UEP) divides the data into different levels of importance in order to ensure that the most important parts of the source information have more protection than the less important parts. In this study, the authors investigate the application of UEP using polar codes and propose a technique that utilises the channel polarisation property of these codes to achieve UEP without any significant modification. They demonstrate the error bounds of the proposed technique particularly within the context of JPEG2000. In addition, they propose a joint source-channel decoding method that is based on iterative decoding. This method takes the advantage of the error resilience tools that already exist in the JPEG2000 decoder. It will be shown that the proposed technique offers better performance than the conventional transmission with equal error protection. [ABSTRACT FROM AUTHOR]

The authors study the problem of two-user Gaussian multiple access channel (GMAC) in the presence of an external eavesdropper, where all transmitted messages should be kept confidential against the eavesdropper. For this purpose, they propose a secure coding scheme on this channel which utilises low-density parity-check (LDPC) codes by employing random bit insertion and puncturing techniques. At each encoder, the confidential message with some random bits as a random message are systematically encoded, and then the associated bits to the confidential message are punctured. Next, the encoders send their unpunctured bits over a Gaussian multiple access wiretap channel (GMAC-WT). The puncturing distribution applied to the LDPC code is considered in random and optimised cases. They utilise a modified extrinsic information transfer chart analysis to optimise the puncturing distributions. The security gap is used as a measure of secrecy. They compared the achieved secure rate pair with an achievable secrecy rate region of GMAC-WT to show the effective performance of the proposed scheme. In this study, equal and unequal power conditions at the transmitters are investigated. For both cases, they attain a fairly small security gap which is equivalent to achieve the points near the secrecy rate region of GMAC-WT. [ABSTRACT FROM AUTHOR]

Expanding window fountain (EWF) codes use a windowing technique to achieve unequal error protection (UEP) and unequal recovery time. UEP affords significant performance improvement of the more important bits (MIB), but at the cost of performance deterioration of the less important bits (LIB), resulting in poor intermediate performance of the EWF coding. In this study, the authors propose the utilisation of intermediate feedback in EWF coding to terminate the encoding of the MIB after their recovery, with the purpose of enhancing the intermediate performance of EWF codes over binary input additive white Gaussian noise (BIAWGN) channels. The effectiveness of the proposed coding scheme is analysed and evaluated by using extrinsic information transfer functions to investigate the theoretical performance and obtain the closed-form formula of the bit error rate (BER) of the LIB after the intermediate feedback. The results of the theoretical analysis and simulation of the scheme using additive white Gaussian noises with different signal-to-noise ratios reveal an improvement in the BER performance of the LIB compared to the case of conventional EWF coding, and also show that the scheme significantly enhances the intermediate performance of the EWF codes and improves the transmission efficiency. [ABSTRACT FROM AUTHOR]

A rate adaption scheme is given at the receiver using rateless codes and log-likelihood ratio (LLR) threshold-based adaptive demodulation (ADM). The received bits with LLR absolute values higher than the preset threshold are demodulated by the receiver, otherwise, deleted. Through the theoretical analysis of the average mutual information (MI) of the demodulated bits, the method is obtained to calculate the LLR demodulation threshold under the required error performance after decoding and the constraint of codeword length for decoding. Then, the rate adaption scheme is applied in a parallel multiple relay wireless communication system. The LLR threshold is set according to the instantaneous SNR of the received signals in order to keep the average MI per demodulated bit of each relay link the same. The demodulated bits coming from all links are combined for decoding. The 256-QAM constellation and Raptor code are then considered as a special case to design a sample scheme. Simulation results prove the correctness of the theoretical analysis and the feasibility of the proposed scheme, and when it is employed by a multi-relay transmission system, the diversity gain can be obtained, and the anti-fading and the antijamming abilities of the system are promoted. [ABSTRACT FROM AUTHOR]

The physical layer security in the up-link of the wireless communication systems is often modelled as the multiple access wiretap channel (MAC-WT), and recently it has received a lot attention. In this paper, the MAC-WT has been re-visited by considering the situation that the legitimate receiver feeds his received channel output back to the transmitters via two noiseless channels, respectively. This model is called the MAC-WT with noiseless feedback. Inner and outer bounds on the secrecy capacity region of this feedback model are provided. To be specific, we first present a decode-and-forward (DF) inner bound on the secrecy capacity region of this feedback model, and this bound is constructed by allowing each transmitter to decode the other one's transmitted message from the feedback, and then each transmitter uses the decoded message to reencode his own messages, i.e. this DF inner bound allows the independent transmitters to co-operate with each other. Then, we provide a hybrid inner bound which is strictly larger than the DF inner bound, and it is constructed by using the feedback as a tool not only to allow the independent transmitters to co-operate with each other but also to generate two secret keys, respectively, shared between the legitimate receiver and the two transmitters. Finally, we give a sato-type outer bound on the secrecy capacity region of this feedback model. The results of this paper are further explained via a Gaussian example, and from this example, we see that the noiseless feedback helps to enhance the achievable secrecy rate region of the Gaussian MAC-WT without feedback. [ABSTRACT FROM AUTHOR]

A new decoding strategy of belief propagation (BP) for low-density parity-check codes is presented by serially concatenating the traditional simultaneous decoding (also known as flooding) and the informed dynamic decoding (IDD) (e.g. node-wise residual belief propagation (NW RBP) or informed variable-to-check (IVC) RBP). The frame error rates (FER) and bit error rates (BER) of the concatenated BP decoder outperform that of the non-concatenated single decoder. In addition, as the signal-to-noise ratio (SNR) increases, the average number of decoding iterations required for the concatenated BP decoder decreases, and tends to merge with that of the first sub-decoder in the high SNR region. Moreover, since the IDD strategies concerned in this paper require extra effort for the residual computation, and the sequential updating of IDD is much more time consuming than the simultaneous updating of flooding. Therefore, compared with using a single IDD, serially concatenating flooding and IDD reduces both the approximate decoding complexity and the decoding latency because the concatenated decoder is still dominated by the sub-decoder flooding. [ABSTRACT FROM AUTHOR]

In this study, the authors propose a new two-stage decoding scheme for low-density parity-check codes to lower the error-floor, which consists of the conventional belief propagation (BP) decoding algorithm as the first-stage decoding and the redecodings with manipulated log-likelihood ratios (LLRs) of variable nodes as the second-stage decoding. In the first-stage decoding, an early stopping criterion is proposed for early detection of decoding failure and the candidate set of the variable nodes are determined, which can be partly included in the small trapping sets. In the second-stage decoding, the scores of the variable nodes in the candidate set are computed by the proposed unreliable path search algorithm and the variable nodes are sorted in ascending order by their scores for the re-decoding trials. Each re-decoding trial is performed by BP decoding algorithm with manipulated LLR of a selected variable node in the candidate set one at a time with the second early stopping criterion. The parallel unreliable path search algorithm is also proposed for practical application of the proposed algorithm. Numerical results show that the proposed early stopping criteria and the proposed decoding algorithms for the second-stage decoding can correct most of the unsuccessfully decoded codewords by the first-stage decoding in the error-floor region. [ABSTRACT FROM AUTHOR]

Leader-following consensus is investigated for second-order continuous-time multi-agent systems with limited communication data rate in this study. The network communication topology is directed and static. Using the sampled position and velocity data, a corresponding discrete-time system, whose leader-following consensus is proved to be equivalent to that of the original system, is given. A distributed protocol is proposed based on dynamic encoding and decoding. A necessary and sufficient condition is given for the existence of such communication and control protocol to ensure the achievement of leaderfollowing consensus, if the upper bound of the communication data rate is predetermined. Two algorithms are also presented to achieve the leader-following consensus if the communication data rate is unfixed or limited, respectively. Finally, simulation examples are given to verify and illustrate the theoretical analysis. [ABSTRACT FROM AUTHOR]