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In this paper, we define a window code to be the portion of a Spatially-coupled low-density parity check (SC-LDPC) code seen by a single iteration of a windowed decoder. We consider the design of SC-LDPC codes for windowed decoding via optimization of the window code. In particular, because iterative decoding is optimal on codes with cycle-free graph representations, we ask fundamental questions about the construction and parameters of cycle-free window codes. We show that it is possible to have an SC-LDPC code with cycles and with cycle-free window codes. We consider the relationship between the distance of the window code and the distance of the SC-LDPC code. Further, we show that SC-LDPC codes with MDS window codes exist, and all such codes are asymptotically bad. This work gives insight into the tradeoffs between window code parameters and performance of the SC-LDPC code. [ABSTRACT FROM AUTHOR]

Computational fluid dynamics (CFD) plays a critical role in many scientific and engineering applications, with aerodynamic design optimization being a primary area of interest. Recently, there has been much interest in using artificial intelligence approaches to accelerate this process. One promising method is the graph convolutional neural network (GCN), a deep learning method based on artificial neural networks (ANNs). In this paper, we propose a novel GCN-based aerodynamic design optimization acceleration framework, GCN-based aerodynamic design optimization acceleration framework. The framework significantly improves processing efficiency by optimizing data flow and data representation. We also introduce a network model called GCN4CFD that uses the GCF framework to create a compact data representation of the flow field and an encoder–decoder structure to extract features. This approach enables the model to learn underlying physical laws in a space-time efficient manner. We then evaluate the proposed method on an airfoil aerodynamic design optimization task and show that GCN4CFD provides a significant speedup compared to traditional CFD solvers while maintaining accuracy. Our experimental results demonstrate the robustness of the proposed framework and network model, achieving a speedup average of 3. 0 ×. [ABSTRACT FROM AUTHOR]

This paper focuses on the matrix sequences that represent negative indices Pell and Pell–Lucas number. It will specifically provide the important relationships between negative indices Pell and Pell–Lucas number and Pell and Pell–Lucas matrix sequences. The research of new error correcting codes has received a lot of attention in recent years, specifically due to its potential use in quantum-resistant cryptosystems. These matrices are then applied in cryptology and have a great opportunity to report some cases that might be a mistake between the sender and the receiver by using Lucas blocking algorithm. [ABSTRACT FROM AUTHOR]

At present, there are many semantic segmentation algorithms with excellent performance for intelligent driving vehicles, but most of them only work well on scenes with good illumination. In order to solve the problem of scene segmentation under low illumination, this paper proposes a novel semantic segmentation algorithm that combines visible and infrared images. In this algorithm, two parallel encoders are designed as the input of the images, and the decoder divides the fused images output from the encoder. The model is based on ResNet algorithm, and the residual attention module is used in each branch to mine and enhance the spatial features of multilevel channels to extract images information. Experiments are carried out on publicly available thermal infrared and visible datasets. The results show that the algorithm proposed in this paper is superior to the algorithm using only visible images in semantic segmentation of traffic environment. [ABSTRACT FROM AUTHOR]

When a top-down method is taken to the task of human pose estimation, the accuracy of joint point localization is often limited by the accuracy of human detection. In addition, conventional algorithms commonly encode the image to generate a heat map before processing, but the systematic error in decoding the heat map back to the original image has an impact on the positioning. Therefore, to address the two problems, we propose an algorithm that uses multiple angle models to generate the human boxes and then performs lightweight decoding to recover the image. The new boxes can better fit humans and the recovery error can be reduced. First, we split the backbone network into three sub-networks, the first sub-network is responsible for generating the original human box, the second sub-network is responsible for generating a coarse pose estimation in the boxes, and the third sub-network is responsible for a high-precision pose estimation. In order to make the human box fit the human body better, with only a small number of interfering pixels inside the box, models of the human boxes with multiple rotation angles are generated. The results from the second sub-network are used to select the best human box. Using this human box as input to the third sub-network can significantly improve the accuracy of the pose estimation. Then to reduce the errors arising from image decoding, we propose a lightweight unbiased decoding strategy that differs from traditional methods by combining multiple possible offsets to select the direction and size of the final offset. On the MPII dataset and the COCO dataset, we compare the proposed algorithm with 11 state-of-the-art algorithms. The experimental results show that the algorithm achieves a large improvement in accuracy for a wide range of image sizes and different metrics. [ABSTRACT FROM AUTHOR]

This study presents two reconfigurable turbo/low-density parity-check (LDPC) decoding kernels for the two powerline communication standards, HomePlug and G.hn. Two architectures are presented, both of which use the radix-4 double-binary enhanced max-log maximum a posteriori probability algorithm with next-iteration initialization in turbo decoding. In LDPC decoding, the two architectures employ the normalized min-sum and the layered radix-4 forward and backward algorithms. The two algorithms cause differences in the architecture and throughput rate. Consequently, the proposed decoding kernels have different architectures when combined with the turbo decoding algorithm, and the two proposed decoding kernels each have their own advantages and disadvantages in terms of throughput and area cost. To make the features of two kernels more evident, we have implemented the proposed decoding kernels that lead to significant throughput gains and better area efficiency compared with other studies. The proposed decoding kernels can be operated in all modes specified in the HomePlug and G.hn standards using a 40-nm complementary metal-oxide-semiconductor (CMOS) process. Moreover, the proposed decoding kernels provide different solutions to achieve the expected throughput rates of the G.hn and HomePlug standards. [ABSTRACT FROM AUTHOR]

Identification of potential drug–target interactions (DTIs) plays a pivotal role in the development of drug and target discovery in the public healthcare sector. However, biological experiments for predicting interactions between drugs and targets are still expensive, complicated, and time-consuming. Thus, computational methods are widely applied for aiding drug–target interaction prediction. In this paper, we propose a novel model, named GCMCDTI, for DTIs prediction which adopts a graph convolutional network based on matrix completion. We regard the association prediction between drugs and targets as link prediction and treat the process as matrix completion, and then a graph convolutional auto-encoder framework is employed to construct the drug and target embeddings. Then, a bilinear decoder is applied to reconstruct the DTI matrix. We conduct our experiments on four benchmark datasets consisting of enzymes, G protein-coupled receptors (GPCRs), ion channels, and nuclear receptors. The five-fold cross-validation results achieve the high average AUC values of 95.78%, 95.31%, 93.90%, and 91.77%, respectively. To further evaluate our method, we compare our proposed method with other state-of-the-art approaches. The comparison results illustrate that our proposed method obtains improvement in performance on DTI prediction. The proposed method will be a good choice in the field of DTI prediction. [ABSTRACT FROM AUTHOR]

Hippocampal pyramidal cells and interneurons play a key role in spatial navigation. In goal-directed behavior associated with rewards, the spatial firing pattern of pyramidal cells is modulated by the animal's moving direction toward a reward, with a dependence on auditory, olfactory, and somatosensory stimuli for head orientation. Additionally, interneurons in the CA1 region of the hippocampus monosynaptically connected to CA1 pyramidal cells are modulated by a complex set of interacting brain regions related to reward and recall. The computational method of reinforcement learning (RL) has been widely used to investigate spatial navigation, which in turn has been increasingly used to study rodent learning associated with the reward. The rewards in RL are used for discovering a desired behavior through the integration of two streams of neural activity: trial-and-error interactions with the external environment to achieve a goal, and the intrinsic motivation primarily driven by brain reward system to accelerate learning. Recognizing the potential benefit of the neural representation of this reward design for novel RL architectures, we propose a RL algorithm based on Q -learning with a perspective on biomimetics (neuro-inspired RL) to decode rodent movement trajectories. The reward function, inspired by the neuronal information processing uncovered in the hippocampus, combines the preferred direction of pyramidal cell firing as the extrinsic reward signal with the coupling between pyramidal cell–interneuron pairs as the intrinsic reward signal. Our experimental results demonstrate that the neuro-inspired RL, with a combined use of extrinsic and intrinsic rewards, outperforms other spatial decoding algorithms, including RL methods that use a single reward function. The new RL algorithm could help accelerate learning convergence rates and improve the prediction accuracy for moving trajectories. [ABSTRACT FROM AUTHOR]

A perfect t -code in a graph Γ is a subset of V (Γ) such that every vertex of Γ is at a distance not more than t , to exactly one vertex of . In this paper, we present a new family of perfect t -codes in Cayley graphs of groups. We proposed the role of the subgroups of a group to create perfect t -codes by restricting the elements of the left transversal of the subgroups in the given group. Also, we introduce a new decoding algorithm for the all of perfect t -codes in Cayley graphs. These codes are able to correct every t -error pattern. [ABSTRACT FROM AUTHOR]

Reed–Solomon codes are widely used to establish a reliable channel to transmit information in digital communication which has a strong error correction capability and a variety of efficient decoding algorithm. Usually we use the maximum likelihood decoding (MLD) algorithm in the decoding process of Reed–Solomon codes. MLD algorithm relies on determining the error distance of received word. Dür, Guruswami, Wan, Li, Hong, Wu, Yue and Zhu et al. got some results on the error distance. For the Reed–Solomon code C , the received word u is called an ordinary word of C if the error distance d (u , C) = n − deg ⁡ u (x) with u (x) being the Lagrange interpolation polynomial of u. We introduce a new method of studying the ordinary words. In fact, we make use of the result obtained by Y.C. Xu and S.F. Hong on the decomposition of certain polynomials over the finite field to determine all the ordinary words of the standard Reed–Solomon codes over the finite field of q elements. This completely answers an open problem raised by Li and Wan in [On the subset sum problem over finite fields, Finite Fields Appl.14 (2008) 911–929]. [ABSTRACT FROM AUTHOR]

The annotation-free word spotting method that is proposed in this paper makes document images searchable without requiring any labeled training data. Thus, our method supports the exploration of a document collection directly without demanding any manual efforts from the users for the preparation of a training dataset. Our method works in the query-by-example scenario where the user selects an exemplary occurrence of the query word. Afterwards, the entire collection of document images is searched according to visual similarity to the query. The proposed method requires only minimal assumptions about the visual appearance of text. This is achieved by processing document images as a whole without requiring a given segmentation of the images on word level or on line level. Therefore, the method is also segmentation-free. Word size variabilities can be handled by representing the sequential structure of text with a statistical sequence model. In order to make the computationally costly application of the sequence model feasible in practice, regions are retrieved according to approximate similarity with an efficient model decoding algorithm. Re-ranking these regions according to the visual similarity obtained with the sequence model leads to highly accurate word spotting results. The method is evaluated on five benchmark datasets. In the segmentation-free query-by-example scenario where no annotated training data is available, the method outperforms all other methods that have been evaluated on any of these five benchmarks. [ABSTRACT FROM AUTHOR]

Recently, technologies based on neural networks (NNs) and deep learning have improved in different areas of Science such as wireless communications. This study demonstrates the applicability of NN-based receivers for detecting and decoding sparse code multiple access (SCMA) codewords. The simulation results reveal that the proposed receiver provides highly accurate predictions based on new data. Moreover, the performance analysis results of the primary optimization algorithms used in machine learning are presented in this study. [ABSTRACT FROM AUTHOR]

In the nonbinary low-density parity check (NB-LDPC) codes decoding algorithms, the iterative hard reliability based on majority logic decoding (IHRB-MLGD) algorithm has poor error correction performance. The essential reason is that the hard information is used in the initialization and iterative processes. For the problem of partial loss of information, when the reliability is assigned during initialization, the error correction performance is improved by modifying the assignment of reliability at initialization. The initialization process is determined by the probability of occurrence of the number of erroneous bits in the symbol and the Hamming distance. In addition, the IHRB-MLGD decoding algorithm uses the hard decision in the iterative decoding process. The improved algorithm adds soft decision information in the iterative process, which improves the error correction performance while only slightly increasing the decoding complexity, and improves the reliability accumulation process which makes the algorithm more stable. The simulation results indicate that the proposed algorithm has a better decoding performance than IHRB algorithm. [ABSTRACT FROM AUTHOR]

Constant dimension subspace codes are subsets of the finite Grassmann Variety. Orbit codes are constant dimension subspace codes that arise as the orbit of subgroup of general linear group acting on subspaces in an ambient space. In particular, orbit codes of symplectic subgroup of the general linear group have been investigated recently. In this paper, we determine product of symplectic groups and its orbit code, and decoding algorithm of this code is considered. [ABSTRACT FROM AUTHOR]

Maximum distance profile (MDP) convolutional codes have the property that their column distances are as large as possible. It has been shown that, transmitting over an erasure channel, these codes have optimal recovery rate for windows of a certain length. Reverse MDP convolutional codes have the additional advantage that they are suitable for forward and backward decoding algorithms. Beyond that the subclass of complete MDP convolutional codes has the ability to reduce the waiting time during decoding. The first main result of this paper is to show the existence and genericity of (n , k , δ) complete MDP convolutional codes for all code parameters with (n − k) | δ as well as that complete MDP convolutional codes cannot exist if (n − k) ∤ δ. The second main contribution is the presentation of two concrete construction techniques to obtain complete MDP convolutional codes. These constructions work for all code parameters with (n − k) | δ but require that the size of the underlying base field is (sufficiently) large. [ABSTRACT FROM AUTHOR]

In securities trading, low latency helps investors take the leading position in the market. Conventionally, market data is decoded with software running on general computers. However, the serial structure of software and complex operating system scheduling cause high latency. This paper designs an accelerator for decoding market data based on field-programmable gate array (FPGA). We propose a pipeline in the accelerator, where every part works independently and parallelly. Furthermore, we present a mechanism for encoding templates, which avoids reconstructing the accelerator and decreases the cost when the template is renewed. We evaluate this accelerator with real Financial Information eXchange (FIX) messages and FIX Adapting for Streaming (FAST) templates, attaining an average latency of 447 ns. [ABSTRACT FROM AUTHOR]

People, cars and other moving objects in videos generate time series data that can be labeled in many ways. For example, classifiers can label motion tracks according to the object type, the action being performed, or the trajectory of the motion. These labels can be generated for every frame as long as the object stays in view, so object tracks can be modeled as Markov processes with multiple noisy observation streams. A challenge in video recognition is to recover the true state of the track (i.e. its class, action and trajectory) using Markov models without (a) counter-factually assuming that the streams are independent or (b) creating a fully coupled Hidden Markov Model (FCHMM) with an infeasibly large state space. This paper introduces a new method for labeling sequences of hidden states. The method exploits external consistency constraints among streams without modeling complex joint distributions between them. For example, common sense semantics suggest that trees cannot walk. This is an example of an external constraint between an object label (“tree”) and an action label (“walk”). The key to exploiting external constraints is a new variation of the Viterbi algorithm which we call the Viterbi–Segre (VS) algorithm. VS restricts the solution spaces of factorized HMMs to marginal distributions that are compatible with joint distributions satisfying sets of external constraints. Experiments on synthetic data show that VS does a better job of estimating true states with the given observations than the traditional Viterbi algorithm applied to (a) factorized HMMs, (b) FCHMMs, or (c) partially-coupled HMMs that model pairwise dependencies. We then show that VS outperforms factorized and pairwise HMMs on real video data sets for which FCHMMs cannot feasibly be trained. [ABSTRACT FROM AUTHOR]

In this paper, we present a new method of coding/decoding algorithms using Fibonacci -matrices. This method is based on the blocked message matrices. The main advantage of our model is the encryption of each message matrix with different keys. Our approach will not only increase the security of information but also has high correct ability. [ABSTRACT FROM AUTHOR]

The neural command from motor neurons to muscles - sometimes referred to as the neural drive to muscle - can be identified by decomposition of electromyographic (EMG) signals. This approach can be used for inferring the voluntary commands in neural interfaces in patients with limb amputations. This paper proposes for the first time an innovative method for fully automatic and real-time intramuscular EMG (iEMG) decomposition. The method is based on online single-pass density-based clustering and adaptive classification of bivariate features, using the concept of potential measure. No attempt was made to resolve superimposed motor unit action potentials. The proposed algorithm was validated on sets of simulated and experimental iEMG signals. Signals were recorded from the biceps femoris long-head, vastus medialis and lateralis and tibialis anterior muscles during low-to-moderate isometric constant-force and linearly-varying force contractions. The average number of missed, duplicated and erroneous clusters for the examined signals was , , and , respectively. The average decomposition accuracy (defined similar to signal detection theory but without using True Negatives in the denominator) and coefficient of determination (variance accounted for) for the cumulative discharge rate estimation were , and , respectively. The time cost for processing each 200ms iEMG interval was (21-97)ms. However, computational time generally increases over time as a function of frames/signal epochs. Meanwhile, the incremental accuracy defined as the accuracy of real-time analysis of each signal epoch, was % for epochs recorded after initial one second. The proposed algorithm is thus a promising new tool for neural decoding in the next-generation of prosthetic control. [ABSTRACT FROM AUTHOR]

In this paper, we propose an operation-reduced low-density parity check (LDPC) decoder design and implementation by stopping reliable operation of check nodes of the iterative two-phase message passing (TPMP) min-sum algorithm (MSA). A check node stopping (CNS) scheme is used to tag reliability of check nodes by detecting the magnitudes of the check node belief messages with a threshold. The operation of reliable check nodes tagged by the CNS scheme can be stopped in the later iterations. The proposed LDPC decoder that employs the CNS scheme can significantly terminate the redundant operations of check nodes and efficiently reduce the power consumption of decoder. From the simulations under WiMAX QC LDPC decoding with high channel quality, the CNS scheme achieves up to 12% stopping rate of check nodes with a loss of coding gain less than 0.1 dB. The WiMAX QC LDPC decoder chip that employs the CNS scheme is implemented by a 90-nm CMOS process. Compared with the LDPC decoder that employs no CNS scheme, the overall power dissipation of the proposed LDPC decoder is decreased by 4.1% with 0.5% area overhead. [ABSTRACT FROM AUTHOR]

In [K. Kuhapatanakul, The Lucas -matrix, Internat. J. Math. Ed. Sci. Tech. (2015), http://dx.doi.org/10.1080/0020739X.2015.1026612], Kuhapatanakul introduced Lucas matrix, whose elements are Lucas numbers. In this paper, we developed a new coding and decoding method followed from Lucas matrix, . We established the relations among the code matrix elements, error detection and correction for this coding theory. Correction ability of this method is % for and for , the correction ability is %. In general, correction ability of this method increases as increases. [ABSTRACT FROM AUTHOR]

In this comment, we show that the efficiency of the proposed quantum dialogue scheme in the paper [Mod. Phys. Lett. B29 (2015) 1550018] cannot reach 100%, but 66.7%. [ABSTRACT FROM AUTHOR]

In this paper, a 10-bit 8-2 segmented current-steering digital-to-analog converter (DAC) is presented which uses a novel nested binary to thermometer (BT) decoder based on domino logic gates. High accuracy and high performances are achieved with this structure. The proposed decoder has a pipelining scheme and it is designed symmetrically in three stages with repeatable logic gates. Thus, power consumption, chip area and the number of control signals are reduced. The proposed DAC is simulated in 0.18-μm CMOS technology and the spurious-free dynamic range (SFDR) is 65.3 dB over a 500 MHz output bandwidth at 1 GS/s. Total power consumption of the designed DAC is only 23.4 mW while the digital and analog supply voltages are 1.2 and 1.8 V, respectively. The active area of the proposed DAC is equal to 0.3 mm2. [ABSTRACT FROM AUTHOR]

In this paper, we consider the series of Tribonacci numbers. Thereby, we introduce a new coding theory called Tribonacci coding theory based on Tribonacci numbers and show that in the simplest case, the correct ability of this method is 99.80% whereas the correct ability of the Fibonacci coding/decoding method is 93.33%. [ABSTRACT FROM AUTHOR]

We propose a decoding algorithm for the (u | u + v)-construction that decodes up to half of the minimum distance of the linear code. We extend this algorithm for a class of matrix-product codes in two different ways. In some cases, one can decode beyond the error-correction capability of the code. [ABSTRACT FROM AUTHOR]

We investigate the physical modifications caused by a linear filter in a chaotic trajectory. We show that while the filter strongly modifies the topological characteristics of the chaotic signal, it might not alter its information content. We propose a chaos-based communication system that takes advantage of this fundamental characteristic of the dynamics. We devise procedures both to recover all the symbols at the receiver end and to decode the received higher-dimensional filtered chaotic signal by using techniques from the theory of pattern recognition. Our results show that a message bearing chaotic signal can be transmitted over a low bandwidth physical channel and be decoded with a low decoding bit error rate. [ABSTRACT FROM AUTHOR]