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In the context of the rapid development of quantum computing technology, in order to meet the security requirements of cryptographic applications, this paper proposed an ElGamal public key cryptosystem based on Polar codes. The paper adopted Polar codes as the public code in the ElGamal public key cryptosystem based on error-correcting codes, used SC decoding algorithm to decode, and analyzed the decoding failure probability and security of the scheme. The results show that the algorithm has a high transmission rate, the selected parameters meet the requirements of information set decoding complexity and decoding failure probability, and the algorithm meets IND-CPA security. [ABSTRACT FROM AUTHOR]

This algorithm considers the tap delay channel model in 5G protocol, and does not consider the influence of SNR on the log-likelihood ratio of the received sequence for the segmented cyclic redundancy bit adaptive (SCADSCL) polarization decoding algorithm. This paper proposes the post-processing (PSCAD-SCL) decoding algorithm for SCAD-SCL. The algorithm determines the number of constant and unreliable log-likelihood ratio used to improve the reliability setting through simulation, and flips and appropriately enlarges the unreliable log-likelihood ratio to improve the bit error rate performance. The simulation results show that the bit error rate of PSCAD-SCL is better than the original SCAD-SCL decoding algorithm in the signal-to-noise ratio of 1～3dB. When the bit error rate is 10-4, the performance of PSCAD-SCL algorithm is about 0.3dB higher than that of SCAD-SCL algorithm. The proposed algorithm can improve 5G transmission performance. [ABSTRACT FROM AUTHOR]

Plant-part segmentation is one of the most important steps to achieve non-destructive, high-throughput, fullyautomated phenotyping measurement in plant phenotyping. However, the existing plant-part segmentation can be usually required the empirical setting of reasonable threshold parameters. It is a high demand to simultaneously perform semantic and instance segmentation. In this study, a Multi-Task Segmentation Network was proposed for the Plant on 3D Point Cloud (MTSegNet). Multi-Value Conditional Random Field (MV-CRF) model was combined to simultaneously implement stem and leaf semantic segmentation and leaf instance segmentation. A multi-head attentive pooling module in MT-SegNet was constructed using the attention mechanism. The reason was that the neighborhood point features were aggregated with the maximum or average pooling, leading to the loss of important information. Here the important neighborhood point features were automatically learned to improve the segmentation performance of the network. Specifically, a U-net design was adopted in MT-SegNet to follow an encoder-decoder architecture with skip connections. MT-SegNet consisted of three main modules: a multi-head attentive pooling, a downsampling, and an upsampling module. Each encoding layer contained a multi-head attentive pooling and a downsampling module. The point cloud was downsampled at a rate of four times, retaining only 25% of the points after each layer. There was a gradual decrease in the number of points in the point cloud while increasing the feature dimensionality of each layer. After the encoder, the number of points in the point cloud was recovered using four decoder modules, each of which consisted of an upsampling module and a shared multilayer perceptron layer. The high-level features were taken from the corresponding scale of the decoder part, whereas, the low-level features were from the corresponding scale of the encoder. A jump connection was also fused them after that. Then, MT-SegNet was divided into two branches for the prediction of the semantic classes of points. These points were then embedded into the high-dimensional vectors, in order to cluster into the instances. Finally, the joint optimization of multiple tasks was performed using MV-CRF. The experimental results on the colored-leaved taro point-cloud dataset show that the mean values of intersection over union, precision, recall, and F1 score for the stem and leaf semantic segmentation using the improved model were 84.54%, 93.64%, 91.39%, and 92.48%, respectively, and the mean precision, mean recall, mean coverage, and mean weighted coverage for the leaf instance segmentation using the advocated method were 88.10%, 78.44%, 76.24%, and 76.93%, respectively. All these results were better than the existing deep-learning networks, such as PointNet and JSNet. Meanwhile, the MV-CRF improved the segmentation of MT-SegNet combining the multi-task learning with joint optimization. A separate ablation test was designed to verify the effectiveness of the modules in the MT-SegNet for the plant segmentation tasks, including the location coding module in downsampling, multi-head attention pooling, and residual modules. These modules improved the splitting performance of the network. The addition of a multi-head attentive pooling module can significantly improve the network segmentation performance. The average scores of semantic segmentation metrics intersection over union, precision, recall and F1 all increased by about 2 percent, respectively, and the values of instance segmentation metrics the mean precision, mean recall, mean coverage, and mean weighted coverage increased by 5-9 percent. The multi-head attentive pooling module can be used to capture the important features among the neighborhood points, according to the learned attention weights, and aggregate them to get the global features, thus effectively improving the performance of semantic and instance segmentation. This improved model can be also applicable to the point cloud segmentation tasks for similar plants to colored-leaf taro, particularly in the automatic measurement of plant phenotypic parameters. [ABSTRACT FROM AUTHOR]

Considering that the traditional simultaneous pickup and delivery mode is single and cannot cope with the complex and changeable reality needs, this paper proposed VRPD-SPD. Firstly, the method used a hybrid integer linear programming model to minimize the total cost between vehicles and drones. Secondly, the algorithm made a TSHAG algorithm. In the first stage, the greedy algorithm combined with the saving algorithm to generate the initial solution. In the second stage, in order to optimize initial solution, the solving process used improved genetic algorithm, which constructed the multivariate encoding mode to boost the decoding efficiency, improved the crossover operator to increase the search space of the neighborhood solution, and designed a new mutation operator to improve the global optimization performance of the algorithm. Finally, the experimental results show that the proposed TSHAG algorithm can solve VRPD-SPD problem effectively. [ABSTRACT FROM AUTHOR]

There are two kinds of tasks in aspect-based sentiment analysis: one is extraction task, which aims to extract aspect words and opinion words from sentences. The other is the classification task, which aims to analyze the sentimental polarity. On the basis of these two complex tasks, aiming at the problem that the coupling between aspect words and opinion words is poor, which leads to the error of classification task, this paper proposed an opinion triplet sentiment analysis model (OTPM) integrating position information. The model used bi-directional long short-term memory network to obtain the text representation, then used the self attention mechanism to enhance the correlation between aspect words and opinion words, and then extracted the opinion triplet in the multi task framework. At the same time, it conducted weighted fusion of extracted representation and position information. Finally, the experiment used biaffine scorer to analyze the sentimental dependence between the weighted aspect words and opinion words, and then used stop-on-non-I algorithm to decode the triplet and output the triplet. This paper conducted a lot of experiments on Lap14, Rest14, Rest15, Rest16 dataset. The results show that the proposed model is superior to a series of baseline models. [ABSTRACT FROM AUTHOR]

Aiming at the problems of word segmentation errors, fuzzy word boundaries and low model calculation efficiency in the process of entity recognition task, a Chinese electronic medical record named entity recognition method that combines multiple features and IDCNN is proposed. This method first constructs a CNN-based character embedding algorithm to train the char vector, then splices it with the word vector and other additional features, then sends it to the iterative expanded convolutional neural network for feature learning, and finally decodes the optimal label sequence through CRF. Experimental results show that the F1 value of this method in the CCKS 2017 Chinese electronic medical record dataset reaches 91.36%, and the training efficiency is better than the existing model, which verifies the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Most of the existing image tampering detection methods are only for a certain tampering method, and there is a problem that the detection accuracy of the tampered area boundary is not high. In this regard, this paper proposed a U-shaped network based on dual-stream encoder-decoder architecture for image tampering. Firstly, the method used the skip connection between the encoder and the decoder to fuse the low-level and high-level features in the tampered image, and used the atrous convolution and CBAM attention mechanism to fuse the features output by the encoder, so that the network had better localization performance for tampered regions of different scales; Secondly, in order to improve the network’s detection accuracy of the boundary of the tampered area, this algorithm used the image morphological method to make a tampered boundary dataset; Finally, it used multiple loss functions to optimize the performance of the network simultaneously, namely it used cross-entropy and root-mean-square loss functions to measure the tampered region loss and tampered boundary loss of the prediction graph respectively. The experimental results on 4 public datasets: CASIA, Columbia, NIST16, Coverage show that the method proposed in this paper can effectively detect the tampering area of the fake image with splicing and copy-paste tampering methods, and output pixel-level tampering region location map. Compared with other mainstream tampering detection methods, the proposed method achieves the highest AUC values on the CASIA and Columbia dataset and the highest F1 values on the Columbia dataset. [ABSTRACT FROM AUTHOR]

In order to meet the individual needs of end users and reduce the transmission delay of D2 D networks, this paper proposes an Instantly Solvable Network Coding (IDNC) cooperative retransmission scheme based on terminal differentiation. First of all, this scheme proposes a new IDNC algorithm framework for the decoding conflict and transmission conflict in PCD2 D networks, and searches for a maximal independent set (MIS) on the basis of this framework. User requirements and link packet loss rate are designed for weights, and the weights are selected to generate coded packets by combining the concurrent cooperative retransmission terminal with the smallest retransmission delay increment and data packets. At the same time, considering the future decoding opportunities provided by unnecessary data packets, optimize the data packets that the terminal does not need to further reduce the transmission delay. The simulation results show that the proposed scheme can effectively reduce the decoding delay and completion time while meeting the personalized needs of the terminal. [ABSTRACT FROM AUTHOR]

Aiming at the flexible job shop scheduling problem with dual resource constraints (DRCFJSP), this paper designs a cuckoo algorithm with an improved decoding scheme to solve it with the goal of optimizing the maximum completion time. Since DRCFJSP needs to consider the allocation of machines and the processing situation of workers, this paper improves the traditional decoding method to avoid the conflict of processing time between machines and workers, and makes use of the idle time of machines and workers as much as possible during decoding. Under the core framework of the cuckoo algorithm, this paper divides the cuckoo population into three subpopulations randomly, and each subpopulation adopts different Lévy flight methods to search for the optimum independently, and realizes the information exchange between subpopulations through the difference operator, which not only enhances the global search ability of the algorithm but also balances the local search ability of the algorithm. Through the experimental simulation analysis of the benchmark test case, the results show the effectiveness and superiority of the improved cuckoo algorithm and the improved decoding method. [ABSTRACT FROM AUTHOR]

The successive cancellation(SC) decoding method for polar codes under additive white Gaussian noise (AWGN) channels is performed in the log-likelihood ratio(LLR) domain. The calculation of the f function nodes use a sum-product algorithm based on hyperbolic tangent rules. This paper proposed a polyline approximation algorithm, which simplified the hyperbolic tangent function and the inverse hyperbolic tangent function into a 9-segment polyline function respectively. In order to obtain better FER performance under the polyline approximation algorithm, this algorithm added a 16 bit CRC to the information bits before encoding. Simulation experiments show that for the polar codes with code length N = 1 024 and information bit length K = 496, the proposed algorithm has better FER performance than the sum-product algorithm, and it reduces the decoding complexity and improves the decoding speed. [ABSTRACT FROM AUTHOR]

Focusing on the inflexibility of error value solving in the key steps of RS decoding, this paper proposed a general algorithm. The algorithm dealt with a variety of primitive elements to make it universally applicable to different parameters. Then, with regard to large computational complexity of error value polynomial involved in the algorithm, according to characteristics of the Galois field, this paper proposed an optimization method to eliminate half of the operation and save storage resources. In order to solve the problem of high iterative complexity when solving the error location polynomial of RS decoding, it presented an algorithm for fast search iterations, through the detailed analysis of compensation difference, and it reduced the complexity of search iteration from O( n² ) to O( n) . Taking RS ( 255,223) code under the satellite communication CCSDS standard as the specific object, combined with the proposed RS decoding algorithm, the paper carried out specific data simulation and BER test. Simulation experiments show that the improved error value solving algorithm and optimized fast search algorithm can be used to decode effectively and quickly. [ABSTRACT FROM AUTHOR]

McEliece public-key crypto system is a public-key cryptosystem based on coding theory. Its security can be reduced to the general linear code decoding problem and it can resist quantum attack. This paper proposed an improved McEliece variant scheme based on QC-MDPC code and QC-LDPC code. The main improvement was that it combined the parity check matrices of QC-LDPC code and QC-MDPC code as a private key, and applied the concatenated codewords generated to the McEliece variant scheme, then gave out an improved decoding algorithm. The analysis shows that under the 80 bit security parameter, it has small system key and low-implement complexity. In addition, this system can resist the recently proposed key recovery attacks on QC-MDPC and QC-LDPC respectively. [ABSTRACT FROM AUTHOR]

In order to improve the classification accuracy and calculation speed of P300 electroencephalogram(EEG)signals in the brain-computer interface(BCI)， a channel automatic selection algorithm of P300 EEG signal based on group sparsity Bayesian logistic regression was proposed. First， the algorithm established the decoding model of P300 EEG signals under the Bayesian framework， and then， a priori group automatic relevance determination(GARD)was proposed to determine the weight coefficients of P300 EEG channels under group sparse constraints. Finally， the maximum likelihood estimation was used to solve the hyperparameters and select the optimal subset of P300 EEG channels， avoiding a large number of cross-validation processes. The proposed method was verified on the BCI competition dataset and self-acquisition dataset. The experimental results showed that the proposed method can automatically select P300 related channels and may improve the accuracy of P300 feature classification. [ABSTRACT FROM AUTHOR]

On the requests of coded targets in close range photogrammetry for precision location and accurate recognition, this paper proposed a designing and decoding algorithm of circular coded target. This algorithm added three locators on the basis of traditional circular coded target to achieve precision position and increase the number of targets. Firstly, the algorithm detected the locator coordinates and their positions in the targets. Then, it used the perspective transformation of the coded target to achieve image distortion correction. Finally, this paper used the method based on ring scan to achieve the decoding of the circular coded target. Experimental results show that the proposed algorithm has good detection and recognition results for all the coded targets which rotate any angle. Vi hen the angle between the camera and the marking plane is less than 65°, the recognition accuracy rate can reach 99. 3% , the average recognition accuracy rate is 97. 4% , false recognition rate is 1.25% , and the recognition rate is 2. 15 s per picture under the complex background. [ABSTRACT FROM AUTHOR]

A fast correlation attack algorithm FCA-MLD-CS-FWT based on classification search, fast transformation and maximum likelihood decoding( MLD) is constructed. The classification search strategy is introduced for the first time in finding the checkout equation, and the fast Walsh transform is used for the check equation. In the decoding stage, the state of LFSR is segmented and MLD is used to decipher the initial state of LFSR. The experimental shows that FCAMLD-CS-FWT can shorten the decoding time of the stream cipher with the original key length of 60 on the single core computing platform for about 1 hours. Through the establishment of the static dictionary, the fast search for the calibration equation of different parameters can be realized; the time complexity of the decoding phase is reduced from O( 2~Bm) to O( 2~BB + m). [ABSTRACT FROM AUTHOR]

The polar codes proposed by Arikan caused much attention due to their simple structure. As a high-performance channel encoding, polar codes would achieve good performances once the code length N was greater than 210. On the basis of the successive cancelation (SC) decoding algorithm, the decoding latency increased with the increase of code length. By analysis of the position of frozen bits and the SC decoder architecture, this paper proposed an improved SC decoding algorithm, which effectively reduced the latency of the traditional SC decoder algorithm. The algorithm improved the decoding delay by 50%compared to the original. What’s more, it further improves the error correction capability of the decoding algorithm by using the succession cancellation single-bit-flipping. [ABSTRACT FROM AUTHOR]

Focusing on the difficulty in multi-target detection under conditions of strong sea-clutter, interference and noise, this paper proposes an algorithm for the instantaneous frequency(IF) estimation of multi-component linear frequency modulate (LFM) signals-SMVF (S-method viterbi fitting), which integrates advantages of the S-method time-frequency distribution and the viterbi algorithm. An adaptive multi-target detection scheme is proposed based on the performance of straight line fitting for the estimated IF. Data processing results demonstrate that accuracy of the proposed algorithm is increased by approximately 20% when compared with WDM and WDV under low SNR conditions, verifying the feasibility and effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

By applying direct linear transformation theory in photogrammetry and based on the marked points information in accident site, the 2D direct linear transformation coefficients of orthographic projection transformation are solved out. By frame decoding of accident surveillance video, a group of pictures are obtained, and then a series of contact points between wheel and road are marked on decoded picture group and an orthographic projection is conducted on the marked points on wheel. Based on these marked points the trajectory curve is fitted, and the length of trajectory is calculated by integration. Combined with frame rate information of video the speed of accident vehicle is finally obtained. The method is applied to a real traffic accident case with the data of driving trajectory length and vehicle speed calculated. [ABSTRACT FROM AUTHOR]