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Brain machine interfaces (BMIs) can substantially improve the quality of life of elderly or disabled people. However, performing complex action sequences with a BMI system is onerous because it requires issuing commands sequentially. Fundamentally different from this, we have designed a BMI system that reads out mental planning activity and issues commands in a proactive manner. To demonstrate this, we recorded brain activity from freely-moving monkeys performing an instructed task and decoded it with an energy-efficient, small and mobile field-programmable gate array hardware decoder triggering real-time action execution on smart devices. Core of this is an adaptive decoding algorithm that can compensate for the day-by-day neuronal signal fluctuations with minimal re-calibration effort. We show that open-loop planning-ahead control is possible using signals from primary and pre-motor areas leading to significant time-gain in the execution of action sequences. This novel approach provides, thus, a stepping stone towards improved and more humane control of different smart environments with mobile brain machine interfaces. [ABSTRACT FROM AUTHOR]

Brain machine interfaces (BMIs) can substantially improve the quality of life of elderly or disabled people. However, performing complex action sequences with a BMI system is onerous because it requires issuing commands sequentially. Fundamentally different from this, we have designed a BMI system that reads out mental planning activity and issues commands in a proactive manner. To demonstrate this, we recorded brain activity from freely-moving monkeys performing an instructed task and decoded it with an energy-efficient, small and mobile field-programmable gate array hardware decoder triggering real-time action execution on smart devices. Core of this is an adaptive decoding algorithm that can compensate for the day-by-day neuronal signal fluctuations with minimal re-calibration effort. We show that open-loop planning-ahead control is possible using signals from primary and pre-motor areas leading to significant time-gain in the execution of action sequences. This novel approach provides, thus, a stepping stone towards improved and more humane control of different smart environments with mobile brain machine interfaces. [ABSTRACT FROM AUTHOR]

Remote sensing is one of the major domains witnessing the increasingly significant interest in Hyperspectral image (HSI) classification. One recent approach achieving great success in HSI classification is deep learning. However, most current HSI classification methods are performed on small datasets, using overlapped sub-regions both for training and testing, causing information leakage. Data leaks lead to improper ideal classification results. Thus, more research work is still needed to boost classification performance for non-leaking methods. This paper proposes an Encoder-Decoder deep learning-based method that can further improve the accuracy of HSI classification. The method contains two main parts: an encoder and a decoder. The encoder consists of depthwise separable convolution, squeeze, and excitation blocks based on the popular MobileNetV3 deep learning network, and the decoder is inherited from the U-Net deep learning model. This encoder-decoder model aims to take into account the relationships between neighboring pixels when performing HSI pixel classification. Since the pixel distribution in an HSI dataset is usually imbalanced, the focal loss function is employed during the model's training process to counterbalance the weight difference between the pixel classes and avoid overfitting the classes with few pixels. For performance assessment, we carried out experiments on the AeroRIT dataset, consisting of non-overlapping training, validation, and test sets. We achieved an overall accuracy of 95.54% and a mean intersection over union score of 83.53%, setting state-of-the-art results on this dataset. [ABSTRACT FROM AUTHOR]

This paper studies binary reverse multi-delimiter (RMD) data compression codes. RMD codes have a range of useful properties, such as unique decodability, completeness, universality, synchronizability, recognition using a finite automaton, and the ability for rapid data retrieval within an encoded file. The authors have constructed a simple monotonic mapping from the set of non-negative integers to the codeword set. Based on this mapping, they have developed a fast byte-aligned decoding algorithm. Computer experiments demonstrate that we can decode RMD codes nearly as quickly as the SCDC code and several times faster than the Fibonacci code. Compared to known codes of a similar type, RMD codes exhibit a better compression ratio for natural language texts (more than four times closer to the entropy bound than SCDC). Additionally, the paper describes a technology for preprocessing natural language texts, which, combined with encoding using RMD codes, enhances the efficiency of powerful modern archivers. [ABSTRACT FROM AUTHOR]

Automatically describing the detail and content of the image is a meaningful but difficult task. In this paper, we propose a variety of optimization improvements to enhance the encoder and decoder for image captioning, called multi-channel weighted fusion. In the presented model, we propose multi-channel encoder which is able to extract different features of the same image by combining various models and algorithms. In order to avoid dimensional explosion caused by multi-channel encoder, we employ the reducing multilayer perceptron to reduce the dimension and discuss how to train the reducing multilayer perceptron. For the decoder part, we discuss how the decoder receives features from different channels and propose a technique for fusing independent and identically typed decoders. To get a better description generated by the decoder, we exploit the voting weight strategy for decoder fusion and explore the entropy function to choose the best distribution. The experiment on datasets Flickr-8k, Flickr-30k and MS COCO demonstrates that the proposed model is compatible with most features with low error rate. For instance, our model is specifically outstanding on METEOR score. [ABSTRACT FROM AUTHOR]

A key component of many graph neural networks (GNNs) is the pooling operation, which seeks to reduce the size of a graph while preserving important structural information. However, most existing graph pooling strategies rely on an assignment matrix obtained by employing a GNN layer, which is characterized by trainable parameters, often leading to significant computational complexity and a lack of interpretability in the pooling process. In this paper, we propose an unsupervised graph encoder–decoder model to detect abnormal nodes from graphs by learning an anomaly scoring function to rank nodes based on their degree of abnormality. In the encoding stage, we design a novel pooling mechanism, named LCPool, which leverages locality-constrained linear coding for feature encoding to find a cluster assignment matrix by solving a least-square optimization problem with a locality regularization term. By enforcing locality constraints during the coding process, LCPool is designed to be free from learnable parameters, capable of efficiently handling large graphs, and can effectively generate a coarser graph representation while retaining the most significant structural characteristics of the graph. In the decoding stage, we propose an unpooling operation, called LCUnpool, to reconstruct both the structure and nodal features of the original graph. We conduct empirical evaluations of our method on six benchmark datasets using several evaluation metrics, and the results demonstrate its superiority over state-of-the-art anomaly detection approaches. [ABSTRACT FROM AUTHOR]

The study uses phase triangulation methods to examine the development of image processing algorithms for measuring a three-dimensional (3D) surface profile. An algorithm is proposed for decoding images of an object in structured light based on an iterative search for the minimum deviation of the model function using measurement results and compensating for the nonlinearity of the transceiver path of the measuring complex. A distinctive feature of the proposed method is that the search for the model function is desired in the form of a second-degree polynomial, ensuring the stability of the method against nonlinear distortions caused by power-law transformations of the transceiver path. The interval search method is used to reduce algorithmic complexity qualitatively. The proposed algorithm provides a stable search for the value of the initial phase shift in object images, is resistant to noise and nonlinear distortions, and allows image processing in 3D scanning systems based on triangulation methods using structured illumination and phase triangulation. Thus, the algorithm will be useful for data processing during the operation of measuring systems with a nonlinear transceiver path and measurement time constraints. [ABSTRACT FROM AUTHOR]

Phase retrieval in holographic data storage by expanded spectrum combined with dynamic sampling method is proposed, which serves to both reduce media consumption and to shorten the iterative number of phase code retrieval. Generally, high-fidelity phase retrieval requires twice Nyquist frequency in phase-modulated holographic data storage. To increase storage density, we only recorded and captured the signal with Nyquist size and used the frequency expanded method to realize high-fidelity phase retrieval. In the decoding process, the iterative Fourier transform algorithm is used to retrieve the phase information of the reconstructed beam. The expanded spectrum is dynamically sampled, which can provide a faster convergence path for the phase retrieval. We aimed to demonstrate the possibility of integrating various methods on the Fourier domain and providing a potential way to improve the performance of holographic data storage systems. The simulation and experimental results proved the combination of processing methods in frequency spectrum was benefit. [ABSTRACT FROM AUTHOR]

Recently, graph-based neural networks have been investigated in hyperspectral image (HSI) classification to address the limited global feature representation capability issue of HSI classification methods based on convolutional neural networks (CNN). However, most of the existing graph-based neural networks for HSI classification methods either characterize the relation information by the pair-wise modeling or rely on the CNNs to extract the local spectral–spatial features. To solve this problem, in this paper, a concise hypergraph convolutional network (HGCN) is proposed for semi-supervised HSI classification. To effectively and efficiently capture the global and local features of HSI, the hypergraph model is established on superpixel level which characterizes the spectral affinities rather than the spatial distance. The designed hypergraph model not only incorporates the local homogeneity and complex correlations of HSI but also consumes little computation. Two hypergraph convolution layers are designed to propagate and update the features of nodes. To construct an end-to-end architecture, a mapping matrix is defined for pixels encoding and superpixels decoding. The proposed method is hinged on the goodness the clustering algorithm used in superpixel segmentation and the experiments has shown that the clustering algorithm affects the effectiveness of proposed method. Thus, we give a strategy for selecting the segmentation parameter. The comparison experiments conducted on four real-world benchmark HSI data sets demonstrate that the proposed method provides more stable and effective classification performance than some state-of-the-art deep approaches with very limited training samples. The overall accuracies are 95.42% on Indian Pines, 98.48% on Kennedy Space center, 98.23% on Salinas Valley and 96.91% on Pavia University. [ABSTRACT FROM AUTHOR]

3D point set reconstruction is an important and challenging 3D shape analysis task. Current state-of-the-art algorithms for 3D point set reconstruction employ a deep neural network (DNN) having an encoder–decoder architecture. Recently, the decoder DNNs that transform multiple 2D planar patches to reconstruct a 3D shape have seen some success. These "patch-folding" decoders are adept at approximating smooth surfaces in 3D objects. However, 3D point sets generated by these decoders often lack local geometrical details, as 2D planar patches tend to overly constrain the patch folding process. In this paper, we propose a novel decoder DNN for 3D point sets called Hyperplane Mixing and Folding Net (HMF-Net). HMF-Net uses less constrained hyperplane, not 2D plane, patches as its input to the folding process. HMF-Net has, as its core building block, a stack of token-mixing layers to effectively learn global consistency among the hyperplane patches. In addition to HMF-Net, we also propose a novel loss for 3D point set reconstruction called Weighted Chamfer Distance (WCD). WCD tries to weight, or amplify, loss from parts of shape that are highly variable across training samples by emphasizing higher point-pair distance values between a generated point set and a groundtruth point set. This helps the decoder DNN learn shape details better. We comprehensively evaluate our algorithm under three 3D point set reconstruction scenarios, that are, shape completion, shape upsampling, and shape reconstruction from 2D images. Experimental results demonstrate that our algorithm yields accuracies higher than the existing algorithms for 3D point set reconstruction. [ABSTRACT FROM AUTHOR]

Generating the decision map with accurate boundaries is the key to fusing multi-focus images. In this paper, we introduce edge-preservation (EP) techniques into neural networks to improve the quality of decision maps, supported by an interesting phenomenon we found: the maps generated by traditional EP techniques are similar to the feature maps in the trained network with excellent performance. Based on the manifold theory in the field of edge-preservation, we propose a novel edge-aware layer derived from isometric domain transformation and a recursive filter, which effectively eliminates burrs and pseudo-edges in the decision map by highlighting the edge discrepancy between the focused and defocused regions. This edge-aware layer is incorporated to a Siamese-style encoder and a decoder to form a complete segmentation architecture, termed Y-Net, which can contrastively learn and capture the feature differences of the sourced images with a relatively small number of training data (i.e., 10,000 image pairs). In addition, a new strategy based on randomization is devised to generate masks and simulate multi-focus images with natural images, which alleviates the absence of ground-truth and the lack of training sets in multi-focus image fusion (MFIF) task. The experimental results on four publicly available datasets demonstrate that Y-Net with the edge-aware layers is superior to other state-of-the-art fusion networks in terms of qualitative and quantitative comparison. [ABSTRACT FROM AUTHOR]

Various approaches have used neural networks as probabilistic models for the design of protein sequences. These "inverse folding" models employ different objective functions, which come with trade-offs that have not been assessed in detail before. This study introduces probabilistic definitions of protein stability and conformational specificity and demonstrates the relationship between these chemical properties and the p (structure | seq) Boltzmann probability objective. This links the Boltzmann probability objective function to experimentally verifiable outcomes. We propose a novel sequence decoding algorithm, referred to as "BayesDesign", that leverages Bayes' Rule to maximize the p (structure | seq) objective instead of the p (seq | structure) objective common in inverse folding models. The efficacy of BayesDesign is evaluated in the context of two protein model systems, the NanoLuc enzyme and the WW structural motif. Both BayesDesign and the baseline ProteinMPNN algorithm increase the thermostability of NanoLuc and increase the conformational specificity of WW. The possible sources of error in the model are analyzed. [ABSTRACT FROM AUTHOR]

Pathological diagnosis is the gold standard for the diagnosis of cholangiocarcinoma. The manual segmentation of pathology sections is time-consuming. Automatic segmentation has become a clinical requirement. Recently, the UNet network has been widely used in automatic segmentation; however, due to the complex structure and diverse shapes of pathological slices of cholangiocarcinoma, the segmentation ability of UNet is insufficient. In addition, traditional RGB images cannot reflect the spectral characteristics of cancerous tissue. Therefore, in this paper, a high-level feature channel attention UNet (HLCA-UNet) network is proposed to segment cholangiocarcinoma using microscopy hyperspectral images. Compared with the original UNet, HLCA-UNet has the following improvements: (1) a new path consisting of hierarchical feature extraction and channel attention mechanism designed to deliver encoder features to decoder, in which the hierarchical feature extraction module is able to extract high-resolution high-level features and reduce semantic gaps while channel attention mechanism establishes a deep correlation between the low-level and high-level features, (2) we introduce bilinear interpolation technology to replace the transpose convolution so as to achieve a smooth segmentation boundary. We have evaluated the performance of the proposed HLCA-UNet model and compared it with other excellent models using cholangiocarcinoma microscopy hyperspectral images. Experiment results show that HLCA-UNet gains a superior performance, with accuracy, precision, and recall of 82.84%, 69.60%, and 77.99%, respectively. In general, our study provides new ideas for future pathological diagnosis of cholangiocarcinoma and makes recommendations for future researchers. [ABSTRACT FROM AUTHOR]

In recent years, much research has been devoted to free-space optical communication (FSO). The unregulated spectrum, low implementation costs, and robust security of FSO systems all are of great importance which lead to a wide range of applications for FSO links, from terrestrial communications to satellite communications. However, the fundamental limitation with FSO links is atmospheric turbulence (AT) caused by fading, significantly reducing link performance. Random phenomena are the best characteristic of atmospheric turbulence caused by changes in the air's refractive index over time. Numerous probability density functions of the AT models were presented to model the randomness in AT channels. The Log-Normal (LN) channel model is for weak atmospheric turbulence, while the Gamma–Gamma (G–G) channel is selected for moderate and strong atmospheric turbulence. The impacts of geometric losses, attenuation due to weather, and errors due to misalignment are addressed using LN and G–G channels. Channel coding is one of the possible solutions for mitigating such FSO channel impairments as the low-density parity check (LDPC) codes. In this article, the Weighted Bit Flipping (Algorithm (1)), Implementation Efficient Reliability Ratio Weighted Bit Flipping (Algorithm (2)), and Min-Sum (Algorithm (3)) algorithms are compared and evaluated against FSO atmospheric turbulence channels. In addition, two novel algorithms are proposed to enhance the complexity or Bit Error Rate performance of LDPC decoding over FSO channels. The results showed an impressive improvement of the coded FSO system by employing the proposed algorithms compared to the existing LDPC decoders for FSO communications from the point of all comparison parameters. [ABSTRACT FROM AUTHOR]

It is well known that some harmful objects in the Tanner graph of low-density parity-check (LDPC) codes have a negative impact on their error correction performance under iterative message-passing decoding. Depending on the channel and the decoding algorithm, these harmful objects are different in nature and can be stopping sets, trapping sets, absorbing sets, or pseudocodewords. Differently from LDPC block codes, the design of spatially coupled LDPC codes must take into account the semi-infinite nature of the code, while still reducing the number of harmful objects as much as possible. We propose a general procedure, based on edge spreading, enabling the design of good quasi-cyclic spatially coupled LDPC (QC-SC-LDPC) codes. These codes are derived from quasi-cyclic LDPC (QC-LDPC) block codes and contain a considerably reduced number of harmful objects with respect to the original QC-LDPC block codes. We use an efficient way of enumerating harmful objects in QC-SC-LDPCCs to obtain a fast algorithm that spans the search space of potential candidates to select those minimizing the multiplicity of the target harmful objects. We validate the effectiveness of our method via numerical simulations, showing that the newly designed codes achieve better error rate performance than codes presented in previous literature. [ABSTRACT FROM AUTHOR]

We define the bidirectional distance profile (BDP) of a convolutional code as the minimum of the distance profiles of the code and its corresponding "reverse" code. We present tables of codes with the optimum BDP (OBDP), which minimize the average complexity of bidirectional sequential decoding algorithms. The computer search is accelerated by the facts that optimum distance profile (ODP) codes of larger memory must have ODP codes of smaller memory as their "prefixes", and that OBDP codes can be obtained by "concatenating" ODP and reverse ODP codes of smaller memory. We compare the performance of OBDP codes and other codes by simulation. [ABSTRACT FROM AUTHOR]

Pixel-level crack extraction (PCE) is challenging due to topology complexity, irregular edges, low contrast ratio, and complex background. Recently, Transformer architectures have shown great potential on many vision tasks and even outperform convolutional neural networks (CNNs). Benefiting from the self-attention mechanism, Transformers can invariably capture the global context information to establish long-range dependencies on the detected objects. However, there was little work on the Transformer architectures for PCE. In this paper, a systematic analysis of three well-designed Transformer architectures for PCE task in terms of network structures and parameters, feature fusion modes, training data and strategy, and generalization ability was developed for the first time. We proposed a Crack extraction network with Vision Transformer (CrackViT) that jointly captures the detailed structures and long-distance dependencies with a novel hybrid encoder with CNN and Transformer to keep the corresponding topologies. In order to be more suitable for PCE task, we explored three feature fusion modes between CNN and Transformer. In addition, a novel feature aggregation block was proposed to sharpen the edges of the decoder upsampling and reduce the noise effect of shallow features. Moreover, a multi-task supervised training strategy was adopted to further improve the details of crack edges. Results on four challenging datasets, including CrackForest, DeepCrack, CRKWH100, and CRACK500, show that CrackViT outperforms state-of-the-art CNN-based methods and the other two novel Transformer architectures. Our codes are available at: https://github.com/SmilQe/CrackViT. [ABSTRACT FROM AUTHOR]

In this paper we review existing hard-decision decoding algorithms for product codes along with different post-processing techniques used in conjunction with the iterative decoder for product codes. We improve the decoder by Reddy and Robinson and use it to create a new post-processing technique. The performance of this new post-processing technique is evaluated through simulations, and these suggest that our new post-processing technique outperforms previously known post-processing techniques which are not tailored for specific codes. The cost of using the new post-processing technique is that the algorithm becomes more complex. However, the post-processing is applied very rarely unless the channel is very noisy, and hence the increase in computational complexity is negligible for most choices of parameters. Finally, we propose a new algorithm that combines existing techniques in a way that avoids the error floor with short relatively high rate codes. The algorithm should also avoid the error floor with long high rate codes, but further work is needed to confirm this. [ABSTRACT FROM AUTHOR]

The decoding of error syndromes of surface codes with classical algorithms may slow down quantum computation. To overcome this problem it is possible to implement decoding algorithms based on artificial neural networks. This work reports a study of decoders based on convolutional neural networks, tested on different code distances and noise models. The results show that decoders based on convolutional neural networks have good performance and can adapt to different noise models. Moreover, explainable machine learning techniques have been applied to the neural network of the decoder to better understand the behaviour and errors of the algorithm, in order to produce a more robust and performing algorithm. [ABSTRACT FROM AUTHOR]

The geometric distortion of the panoramic image makes the saliency detection method based on traditional 2D convolution invalid. "Mapped Convolution" can effectively solve this problem, which accepts a task- or domain-specific mapping function in the form of an adjacency list that dictates where the convolutional filters sample the input. However, when applied to panorama saliency detection, the method results in additional computational overhead due to repeatedly sampling overlapping regions of adjacent convolution positions along the longitude. In order to solve this problem, we improved the calculation process of "Mapped Convolution". Rather than accessing adjacency list during the convolution, we first sample the panorama based on the adjacency list for only once and obtain a sampled map. This sampling process is called the decoupled sampling of "Mapped Convolution". And then the map is convoluted in traditional 2D way, thus avoiding repeatedly sampling. In this paper, an interpolation method based on the Softmax function is also proposed and applied to the interpolation calculation of decoupled sampling. Compared with common interpolation methods such as linear interpolation, this interpolation method makes our network more efficient during training. We additionally introduce a new adaptive equator bias algorithm allowing for different attention distributions at different longitudes, which is more consistent with viewer's visual behavior. Combining the U-Autoencoder network containing the decoupled sampling with the adaptive equator bias algorithm, we construct a 360-degree visual saliency detection model. We map the original panorama into a cube, and then use the the cube isometric mapping method to remap it into a panorama and input it into the network for training. Then, the crude saliency map output by the decoder is combined with the equator bias map to obtain the final saliency map. The results show that the model proposed is superior to recent state-of-the-art models in terms of computational speed and saliency-map prediction. [ABSTRACT FROM AUTHOR]

Linear codes with complementary duals, shortly named LCD codes, are linear codes whose intersection with their duals is trivial. In this paper, we outline a construction for LCD codes over finite fields from the adjacency matrices of two-class association schemes. These schemes consist of either strongly regular graphs (SRGs) or doubly regular tournaments (DRTs). Under certain conditions, the method yields formally self-dual codes. Further, we propose a decoding algorithm that can be feasible for the LCD codes obtained using one of the given methods. [ABSTRACT FROM AUTHOR]

Relating behavior to brain activity in animals is a fundamental goal in neuroscience, with practical applications in building robust brain-machine interfaces. However, the domain gap between individuals is a major issue that prevents the training of general models that work on unlabeled subjects. Since 3D pose data can now be reliably extracted from multi-view video sequences without manual intervention, we propose to use it to guide the encoding of neural action representations together with a set of neural and behavioral augmentations exploiting the properties of microscopy imaging. To test our method, we collect a large dataset that features flies and their neural activity. To reduce the domain gap, during training, we mix features of neural and behavioral data across flies that seem to be performing similar actions. To show our method can generalize further neural modalities and other downstream tasks, we test our method on a human neural Electrocorticography dataset, and another RGB video data of human activities from different viewpoints. We believe our work will enable more robust neural decoding algorithms to be used in future brain-machine interfaces. [ABSTRACT FROM AUTHOR]

In recent years, human pose estimation has been widely used in human-computer interaction, augmented reality, video surveillance, and many other fields, but the task of pose estimation still faces many challenges. To address the large number of parameters and complicated calculation in the current mainstream human pose estimation network, this paper proposes a lightweight pose estimation network (Lightweight Polarized Network, referred to as LPNet) based on a polarized self-attention mechanism. First, ghost convolution is used to reduce the number of parameters of the feature extraction network; second, by introducing the polarized self-attention module, the pixel-level regression task can be better solved, the lack of extracted features due to the decrease in the number of parameters can be reduced, and the accuracy of the regression of human keypoints can be improved; finally, a new coordinate decoding method is designed to reduce the error in the heatmap decoding process and improve the accuracy of keypoint regression. The method proposed in this paper was evaluated on the human keypoint detection datasets COCO and MPII, and compared with the current mainstream methods. The experimental results show that the proposed method greatly reduces the number of parameters of the model while ensuring a small loss in accuracy. [ABSTRACT FROM AUTHOR]

The NOMA (non-orthogonal multiple access) techniques developed in the context of 5G networks allows several users to share the same frequency band simultaneously. The main problem posed by this approach is the existence of interferences between the signals from different users. This article proposes a novel decoder model based on two successive interference cancellation operations in parallel. The first one cancels interferences successively in increasing direction of the powers while the second one cancels in decreasing direction. The simulation of network performances with the proposed decoding algorithm allows us to observe a clear improvement of the performances with regards to the standard approach of cancellation in descending order of powers. The simulation results indicate a significant improvement in network performances of more than 35% in terms of throughput, energy efficiency and spectral efficiency compared to the standard approach. Finally, we established by calculations and confirmed by simulations the existence of an optimum transmission power of the base station which allows maximization of the energy efficiency. Newton's method has been applied to find this optimal transmission power. [ABSTRACT FROM AUTHOR]

The wide application of the Internet of Things (IoT), large-scale satellite clusters and drone clusters requires multiple access capabilities for massive devices. In order to meet the high-capacity and low-latency access requirements in application scenarios such as the IoT, this paper proposes to apply sparse code multiple access (SCMA) technology to the massive terminal access of the IoT, focusing on the study of SCMA decoding algorithm. Aiming at the high complexity of its traditional decoding algorithm, a decoding algorithm based on codeword reliability is proposed. The algorithm reduces the number of codeword iterations with higher reliability by setting a threshold to reduce the algorithm complexity. The simulation results show that the algorithm reduces the complexity of the algorithm while the loss of bit error rate performance is very small. Then, aiming at the poor bit error rate performance of SCMA technology under the condition of low signal-to-noise ratio, a joint coding and decoding simulation link based on soft input and soft output Turbo code and SCMA technology is proposed. The simulation results prove that Turbo codes can speed up the convergence speed of the decoding algorithm. When the SNR is 4 dB, the bit error rate of the joint system has been reduced to below 10e-3, which has obvious advantages compared with the SCMA system without Turbo coding. Therefore, the two methods proposed in this paper can be used for the high-capacity demand of the IoT communication system. [ABSTRACT FROM AUTHOR]

This paper proposes the distributed Reed–Solomon (RS)-coded spatial modulation (DRSC-SM) scheme over quasi-static Rayleigh fading channel. Two different RS codes are used, one having a smaller minimum distance at the source and the other having a larger minimum distance at the relay. In order to obtain an optimized code at the destination, a proper encoding strategy must be adapted at the relay. Thus, an efficient design approach is proposed that suitably selects the symbols at the relay. Using this optimized approach as a benchmark, another design is proposed to achieve reduced complexity. To jointly decode the source and relay RS codes, three different decoding algorithms known as the naive, smart and improved smart algorithms are presented. Monte Carlo simulated results reveal that the proposed DRSC-SM scheme utilizing the improved smart decoding algorithm outperforms its counterpart DRSC-SM scheme employing the naive and smart decoding algorithms under identical conditions. The simulation results also validate the superiority of the proposed DRSC-SM scheme over its corresponding coded noncooperative scheme under the same decoding algorithm. [ABSTRACT FROM AUTHOR]

We present techniques for the construction of polar codes with large kernels and their decoding. A crucial problem in the implementation of the successive cancellation decoding algorithm and its derivatives is kernel processing, i.e., fast evaluation of the log-likelihood ratios for kernel input symbols. We discuss window and recursive trellis processing methods. We consider techniques for evaluation of the reliability of bit subchannels and for obtaining codes with improved distance properties. [ABSTRACT FROM AUTHOR]

We construct codes that allow to exactly recover the support of an unknown sparse vector with almost equal absolute values of all nonzero coordinates given results of linear measurements in the presence of noise with -norm bounded from above. We propose a decoding algorithm with asymptotically minimum complexity. [ABSTRACT FROM AUTHOR]

For m ≥ 4 even, the duals of p-ary codes, for any prime p, from adjacency matrices for the m-ary 2-cube Q 2 m are shown to have subcodes with parameters [ m 2 , 2 m - 2 , m ] for which minimal PD-sets of size m 2 are constructed, hence attaining the full error-correction capabilities of the code, and, as such, the most efficient sets for full permutation decoding. [ABSTRACT FROM AUTHOR]

As a subclass of linear codes, cyclic codes have efficient encoding and decoding algorithms, so they are widely used in many areas such as consumer electronics, data storage systems and communication systems. In this paper, we give a general construction of optimal p-ary cyclic codes which leads to three explicit constructions. In addition, another class of p-ary optimal cyclic codes are presented. [ABSTRACT FROM AUTHOR]

Due to efficient encoding and decoding algorithms, BCH codes have many applications in data storage systems and satellite communications. Meanwhile, one advantage of BCH codes is to construct quantum codes. In this paper, we assume that q ≡ 1 mod m , where m is a positive integer. We first study a class of q - ary quantum codes of length r (q m - 1) q - 1 by CSS construction, where r ∣ q - 1 . Then, we construct two families of q - ary quantum codes of length r (q 2 m - 1) q 2 - 1 by Hermitian construction. These constructions produce new quantum codes with larger dimensions. [ABSTRACT FROM AUTHOR]

Fractal image compression is one of the efficient structure-based methods in applications where images are compressed only once but decoded several times due to its resolution-independent feature and fast reconstruction time. However, it has high computational complexity restricting practical use most of the time. Although several methods have been developed to speed up the compression process, these do not satisfy the compression time or the decoded image quality requirements. The affine transforms of image blocks used in fractal coding require a huge number of multiplications and additions and are very expensive in computation that may also slow down the compression process. This paper presents a novel fractal image compression using a fast affine transform and hierarchical classification scheme. The applied affine transform computation algorithm of image blocks uses relationships among neighboring pixels of transformed image block that significantly reduces the number of multiplication and addition operations. Then, this strategy with hierarchical classification and class-wise domain sorting is applied in fractal coding with quad-tree and horizontal vertical partitioning schemes to reduce compression time. Experimental results show that the quad-tree-based fractal coding with the proposed scheme can significantly speed up the compression process keeping image quality and compression ratio almost unchanged. [ABSTRACT FROM AUTHOR]

With the increase of layers in deep capsule networks, the overfitting problem also becomes more serious. Capsule-based regularization methods are important to solve this problem. However, little attention has been paid to this field. To fill this gap, we propose five regularization methods from the following aspects. In capsules represented by vectors, two methods are proposed to modify the existence and properties of their activation vectors by disturbing the length and orientation of the vectors. In capsules represented by tensors, capsule-based layer normalization is proposed to improve dynamic routing. In the training strategy, a warm restart learning rate with probability is used to improve the efficiency of training. In reconstruction, a novel image decoder provides a better regularization effect by using multiscale information of images. These regularization methods are investigated on CIFAR10, CIFAR100, and SVHN. Experiments show that using these regularization methods can effectively improve the generalization performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel multiuser generalized spatial modulation (GSM) detection scheme for uplink massive MIMO systems. In GSM, the indices of the active antennas of each user terminal are used to convey information in addition to the transmitted symbols. Hence, a two-stage detection structure is developed consisting of preprocessing (identification) and postprocessing (detection). At the receiver front end, we propose an eigenvalue decomposition-based active antennas identification algorithm to decode the first part of data. Based on the acquired channel state information, a least-square-based decorrelating detector is developed to extract the second part of data symbol. The system capacity (transmitted bits per channel use) is extensively increased compared to the conventional spatial modulation scheme, and the complexity is significantly reduced than existing algorithms. Computer simulations demonstrate that the proposed algorithm is near-far resistant and works reliably for small window size, which is essential for the low-latency requirement in 5G and future networks. [ABSTRACT FROM AUTHOR]

We consider the problem of decoding a two-valued monotone function defined on a -valued -dimensional cube. The traditional approach to solving this problem is to construct a Shannon-optimal algorithm. The Shannon-optimal decoding algorithm has minimal "worst-case" complexity (efficient for the most difficult case). The authors propose and study an approach to the decoding problem based on the application of an asymptotically optimal dualization algorithm over the product of -valued chains. The asymptotically optimal decoding of the function is aimed at the "typical case" (typical version of the problem). The conditions for the applicability of the traditional and new approaches are experimentally revealed. [ABSTRACT FROM AUTHOR]

In the production of personalized, non-standard and trial products, orders are often single-piece or small-batch, and the processing parameters and BOM structure vary greatly between products. In such cases, it is necessary to consider processing and assembly in an integrated manner, and directly formulate flexible scheduling solutions according to the precedence constraints of BOM (PCB). To design such integrated scheduling method for personalized products with no-wait constraints (ISMPNC), firstly, a mathematical model considering both no-wait constraints and flexible machines is established, then, the event-based rescheduling rule (ERR) is used to deal with dynamic production tasks; finally, to solve the mathematical model, the artificial bee colony (ABC) algorithm with enhancements is proposed. Hence, a virtual grouping-based encoding method is designed to handle the precedence constraints when the number of no-wait predecessors is greater than one; a first-time-fit decoding method based on overlapping time is proposed to guarantee that no-wait operations always meet the precedence and no-wait constraints after decoding; a discrete differential equation based on the Hemming distance is proposed so that the ABC is capable of handling discrete encoding spaces; a neighborhood action based on key product moving is proposed and embedded into the ABC to enhance the local search capability. The operation research solver OR-Tools is used to verify the correctness of the mathematical model. The proposed ISMPNC considers both no-wait constraints and flexible machines, which is more realistic than the control method and could obtain a better scheduling scheme. [ABSTRACT FROM AUTHOR]

The article presents the application of optical methods for measuring the parameters of three-dimensional objects. It also discusses strategies for decoding phase images in the presence of additive interference and a limited dynamic range of a photodetector. The existing methods for decoding phase images introduce nonlinear distortions and systematic error into the measurement results under such conditions. A phase triangulation method with statistical data filtering is proposed for measuring the three-dimensional profile of an object under random additive interference with a limited dynamic range of a photodetector, which avoids systematic distortions of the measurement results. The method is based on adaptive filtering and statistical analysis of the intensity distribution in the recorded phase images. The error of the method of decoding phase images was analyzed analytically using statistical data filtering and threshold filtering. The proposed method can decode data in systems for measuring three-dimensional geometry that implements the phase triangulation method. [ABSTRACT FROM AUTHOR]

Cyclic codes are a subclass of linear codes and have applications in consumer electronics, data storage systems and communication systems as they have efficient encoding and decoding algorithms. In this paper, by investigating the solutions of certain equations over finite fields, we make progress towards three conjectures about optimal ternary cyclic codes which were proposed by Ding and Helleseth. [ABSTRACT FROM AUTHOR]

Combined with one-time pad encryption scheme, quantum key distribution guarantees the unconditional security of communication in theory. However, error correction and privacy amplification in the post-processing phase of quantum key distribution result in high time delay, which limits the final secret key generation rate and the practicability of quantum key distribution systems. To alleviate this limitation, this paper proposes an efficient post-processing algorithm based on polar codes for quantum key distribution. In this algorithm, by analyzing the channel capacity of the main channel and the wiretap channel respectively under the Wyner's wiretap channel model, we design a codeword structure of polar codes, so that the error correction and privacy amplification could be completed synchronously in a single step. Through combining error correction and privacy amplification into one single step, this efficient post-processing algorithm reduces complexity of the system and lower the post-processing delay. Besides, the reliable and secure communicaiton conditions for this algorithm has been given in this paper. Simulation results show that this post-processing algorithm satisfies the reliable and secure communication conditions well. [ABSTRACT FROM AUTHOR]

In this paper, we propose a rateless coded transmission scheme for the multi-user downlink of Cloud Radio Access Network (C-RAN). In the network, multiple users are served by a cluster of remote radio heads (RRH), which are connected to the building baseband units (BBU) pool through the fronthaul links with limited capacity. In the proposed transmission scheme, the precoder and compressor at the BBU pool, and the decoding algorithm at the users are designed. To further improve the performance of the proposed transmission scheme, we investigate the joint optimization of the precoder and the degree profiles of the rateless codes implemented at the BBU pool to maximize the sum throughput of the network. Explicitly, the optimization problem is formulated according to the extrinsic information transfer (EXIT) function analysis on the decoding process at the users. We give simulation results on the BER and throughput performance achieved by the proposed rateless coded transmission scheme, which verify the effectiveness of the joint optimization on the degree profiles and the precoder. [ABSTRACT FROM AUTHOR]

Context-aware network logging is becoming more prevalent for enterprise networks, data centers, and forensics. Monitoring agents are strategically placed to generate log files from the activity of interests from various network points. In a distributed architecture, these agents are scattered across multiple nodes, and they have limited network visibility. Consequently, the resulting logs become fragmented and less perceptible without a unified network context. Besides, aggregating useful information from a diverse management protocol with various languages, syntax styles, and notations requires complex semantic understanding to synthesize these log files. Currently, general-purpose logs like SNMP's logs only provide parametric values at connection levels but lacks incident-specific information. Meanwhile, proprietary services like AWS CloudTrail identify more contexts at the incident-level, but they only work on selected products and infrastructure. This paper proposed a platform-agnostic log decoding and generation algorithm (SAG) for network logging that is semantic aware using context aggregation. Firstly, a protocol-agnostic controller acts as a master to collect logs from agents running in routers, firewall, IDS/IPS, load balancers, managed switches, and servers. From these logs, three traffic models, namely (1) service-activity model (SaM), (2) general-activity model (GaM), and (3) device-activity model (DaM), are trained using artificial neural network (ANN). The log generator then uses the context-filling technique to resolve and construct log entries using a generic sentence template while inferring from these machine-learning models. A sentence smoothing technique is designed to restructure entities in the logs based on traffic directionality for semantic correctness. The experimental result shows that SAG's logs have 1.8 times more contexts resolved for improved log's perceptibility. [ABSTRACT FROM AUTHOR]

When data service is not available, it is almost impossible for smartphones to receive data files. In this paper, we propose a system, called RoV, which can send data files to a smartphone via voice calls instead of data service. In this system, data files are encoded into acoustic signals by the sender, transmitted to the receiver through the normal voice channel, and decoded by the receiver at the end. There are two main challenges for the system: (1) the speaker of a smartphone is usually not accessible during a phone call, making it difficult to directly use a smartphone as the sender; and (2) the voice channel is frequency sensitive, and simple modulation methods do not work well. For the first challenge, RoV simulates a Bluetooth headset to inject voice signals to the voice channel without rooting the smartphone. For the second challenge, RoV utilizes the dual-tone multi-frequency (DTMF) method for encoding and decoding in a suitable frequency range. In addition, we introduce new break symbols in DTMF, which can avoid the synchronization problem and also lead to a simple decoding algorithm. We implement RoV and evaluate its performance with experiments. [ABSTRACT FROM AUTHOR]

In this study, an energy-efficient unrelated parallel machine scheduling problem is discussed. The speed scaling mechanism has been taken into account as an energy-efficient strategy. Unrelated parallel machine scheduling with speed scaling is generalized by considering machine-sequence dependent setup times and learning effect features. A multiobjective mixed-integer linear programming (MILP) model has been proposed for the problem. Due to the NP-hard nature of the problem, a multiobjective evolutionary algorithm, the NSGA-II-based memetic algorithm, is proposed. An encoding scheme, decoding algorithm, and local search algorithms are proposed for the problem. Speed tuning heuristic and job-machine switch heuristic algorithms are proposed as local search algorithms. A restarting strategy has been applied to ensure the diversification of the algorithm. The classical NSGA-II algorithm and the proposed memetic algorithm were compared over the generated test problems. As a result, the proposed memetic algorithm is more successful according to performance metrics. [ABSTRACT FROM AUTHOR]

Reversible logic is a nowadays promising choice for circuit design technologies since it is having diversified applications in the fields of digital signal processing, cryptography, quantum computing, and low power CMOS design. Since every two qubits in the binary logic are equivalent to one qudit in the quaternary logic, the optimum designed primary binary building blocks such as adders and subtractors easily can be used in quaternary logic design. In this paper, we first proposed a novel quaternary to a binary decoder and also binary to quaternary encoder circuits. Secondly, we have used these converters to the synthesis of the quaternary quantum reversible full adder, half subtractor, and full subtractor circuits. The proposed converter circuits have promised accomplishment compared to the existing designs; these circuits are built on the elementary quantum gates, which are realizable using the liquid ion trap in the quantum technology. The designed strategy (using the converters to realize computational circuits) is easily applicable in the large-scale quaternary quantum circuit designs. [ABSTRACT FROM AUTHOR]

Dual-view snapshot compressive imaging (SCI) aims to capture videos from two field-of-views (FoVs) using a 2D sensor (detector) in a single snapshot, achieving joint FoV and temporal compressive sensing, and thus enjoying the advantages of low-bandwidth, low-power and low-cost. However, it is challenging for existing model-based decoding algorithms to reconstruct each individual scene, which usually require exhaustive parameter tuning with extremely long running time for large scale data. In this paper, we propose an optical flow-aided recurrent neural network for dual video SCI systems, which provides high-quality decoding in seconds. Firstly, we develop a diversity amplification method to enlarge the differences between scenes of two FoVs, and design a deep convolutional neural network with dual branches to separate different scenes from the single measurement. Secondly, we integrate the bidirectional optical flow extracted from adjacent frames with the recurrent neural network to jointly reconstruct each video in a sequential manner. Extensive results on both simulation and real data demonstrate the superior performance of our proposed model in short inference time. The code and data are available at https://github.com/RuiyingLu/OFaNet-for-Dual-view-SCI. [ABSTRACT FROM AUTHOR]

Layered decoding (LD) facilitates a partially parallel architecture for performing belief propagation (BP) algorithm for decoding low-density parity-check (LDPC) codes. Such a schedule for LDPC codes has, in general, reduced implementation complexity compared to a fully parallel architecture and higher convergence rate compared to both serial and parallel architectures, regardless of the codeword length or code-rate. In this paper, we introduce a modified shuffling method which shuffles the rows of the parity-check matrix (PCM) of a quasi-cyclic LDPC (QC-LDPC) code, yielding a PCM in which each layer can be produced by the circulation of its above layer one symbol to the right. The proposed shuffling scheme additionally guarantees the columns of a layer of the shuffled PCM to be either zero weight or single weight. This condition has a key role in further decreasing LD complexity. We show that due to these two properties, the number of occupied look-up tables (LUTs) on a field programmable gate array (FPGA) reduces by about 93% and consumed on-chip power by nearly 80%, while the bit error rate (BER) performance is maintained. The only drawback of the shuffling is the degradation of decoding throughput, which is negligible for low values of E b / N 0 until the BER of 1e−6. [ABSTRACT FROM AUTHOR]

In this paper, we present a one-way relaying scheme in which two wireless nodes create an information flow to each other via a single decode-and-forward (DF) relay. We consider an additional secrecy constraint for protection against an honest-but-curious relay. Indeed, while the relay should decode the source message, it should be fully ignorant about the message content. We provide a secure lattice coding strategy based on random difference families (RDF) lattice codes for unidirectional Gaussian relay channels. RDF lattice codes are carved from infinite RDF lattices using a shaping algorithm. By RDF lattice we mean a Construction A lattice with a QC-LDPC code, which is obtained from random difference families, as underlying code. Due to the existence of low-overhead encoding and decoding algorithms, these lattice codes can be implemented practically in high dimensions. Our proposed scheme combines a new Rao–Nam-like encryption with a new DF relaying scheme for RDF lattice codes. Security analysis of the scheme against chosen-plaintext attacks like differential attack and other recent attacks on the Rao–Nam-like schemes are provided. We show that the proposed scheme resists against all variants of differential cryptanalysis. To the best of our knowledge, the proposed scheme is the first in its type and according to our simulation results, it improves the error performance, efficiency and security at the same time, compared to its counterparts. [ABSTRACT FROM AUTHOR]

We present a list decoding algorithm for -linear codes that generalize the Reed–Solomon -codes. [ABSTRACT FROM AUTHOR]

It is well known that there is a correspondence between convolutional codes and discrete-time linear systems over finite fields. In this paper, we employ the linear systems representation of a convolutional code to develop a decoding algorithm for convolutional codes over the erasure channel. In this kind of channel, which is important due to its use for data transmission over the Internet, the receiver knows if a received symbol is correct. We study the decoding problem using the state space description of a convolutional code, and this provides in a natural way additional information. With respect to previously known decoding algorithms, our new algorithm has the advantage that it is able to reduce the decoding delay as well as the computational effort in the erasure recovery process. We describe which properties a convolutional code should have in order to obtain a good decoding performance and illustrate it with an example. [ABSTRACT FROM AUTHOR]

In this research, we study soft-output decoding of polar codes. Two representative soft-output decoding algorithms are belief propagation (BP) and soft cancellation (SCAN). The BP algorithm has low latency but suffers from high computational complexity. On the other hand, the SCAN algorithm, which is proposed for reduced complexity of soft-output decoding, achieves good decoding performance but suffers from long latency. These two algorithms are suitable only for two extreme cases that need very low latency (but with high complexity) or very low complexity (but with high latency). However, many practical systems may need to work for the moderate cases (i.e., not too high latency and not too high complexity) rather than two extremes. To adapt to the various needs of the systems, we propose a very flexible soft-output decoding framework of polar codes. Depending on which system requirement is most crucial, the proposed scheme can adapt to the systems by controlling the level of parallelism. Numerical results demonstrate that the proposed scheme can effectively adapt to various system requirements by changing the level of parallelism. [ABSTRACT FROM AUTHOR]