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Named data networking (NDN) aims to change the traditional content delivery method and caching by router nodes caching and participating in forwarding. NDN-caching can reduce the expected flood of global data traffic by providing cache storage at intermediate nodes for transmitted content objects, making data broadcasting in an efficient way. In this paper, a novel caching strategy based on entropy weighting method and TOPSIS is proposed for efficient content dissemination to improve NDN’s in-network caching performance. Firstly, the consumer’s request process is modeled by the entropy weighting method and TOPSIS to obtain the best cache node for the cache object according to the real-time status of the node. Secondly, two cache replacement algorithms (composed of an active cache replacement algorithm and a passive cache replacement algorithm) are proposed to reduce the cache redundancy on the delivery path and improve the utilization of data packets in the nodes. Finally, an effective cache mechanism and data packet migration scheme are proposed to further improve the cache performance according to the different types of cache nodes. The performance evaluation shows that the proposed scheme performs better in terms of cache hit rate, latency, and link load compared with some existing strategies.

Energy consumption has risen to be a bottleneck in wireless sensor networks. This is caused by the challenges faced by these networks due to their tiny sensor nodes that have limited memory storage, small battery capacity, limited processing capability, and bandwidth. Data compression has been used to reduce energy consumption and improve network lifetime, as it reduces data size before it can be forwarded from the sensing node to the sink node in the network. In this paper, a survey and comparison of currently available data compression techniques in wireless sensor networks are conducted. Suitable sets of criteria are defined to classify existing data compression algorithms. An adaptive lossless data compression algorithm (ALDC) is analyzed through MATLAB coding and simulation from the reviewed data compression techniques. The analysis aims to discover strategies that can be used to reduce the amount of data further before it is transmitted. From this analysis, it was discovered that encoding residue samples, rather than raw data samples, reduced the bitstream from 112 bits to a range of 30 to 36 bits depending on the sample block sizes. The average length of data samples to be passed to the encoder was minimized from the original 14 bits per symbol to 1.125 bits per symbol. This demonstrated a 0.875 code efficiency or redundancy. It resulted in an energy saving of 67.8% to 73.2%. This work further proposes a data compression algorithm that encodes the residue samples with fewer bits than the ALDC algorithm. The algorithm reduced the bitstream to 26 bits. The average length of the code is equal to the entropy of the data samples, demonstrating zero redundancy and an improved energy saving of 76.8% compared to ALDC. The proposed algorithm, therefore, shows improved energy efficiency through data compression.

In this article, we propose a depth data coding method by plane modeling. The plane modeling is a prediction method based on a plane estimation from depth pixels in a block. The plane modeling improves the intra-picture prediction for depth data comparing with intra modes of conventional coding standards. The plane modeling coefficients, which are the information about the estimated plane, are required to be provided during the depth data coding. The plane modeling coefficients are predicted from neighboring depth pixels. A prediction error of the plane modeling coefficients is calculated through the plane modeling for the selected pixels. If the prediction error is below a certain threshold, then the plane modeling coefficients are applied to the plane modeling for the block. From the simulation results, we verify that the proposed method achieves up to 6.76% bit rate saving in the same coding distortion condition compared to VVC test model.

This paper is about the estimation of the cyber-resilience of CPS. We define two new resilience estimation metrics: $k$ -steerability and $\ell $ -monitorability. They aim at assisting designers to evaluate and increase the cyber-resilience of CPS when facing stealthy attacks. The $k$ -steerability metric reflects the ability of a controller to act on individual plant state variables when, at least, $k$ different groups of functionally diverse input signals may be processed. The $\ell $ -monitorability metric indicates the ability of a controller to monitor individual plant state variables with $\ell $ different groups of functionally diverse outputs. Paired together, the metrics lead to CPS reaching $(k,\ell)$ -resilience. When $k$ and $\ell $ are both greater than one, a CPS can absorb and adapt to control-theoretic attacks manipulating input and output signals. We also relate the parameters $k$ and $\ell $ to the recoverability of a system. We define recoverability strategies to mitigate the impact of perpetrated attacks. We show that the values of $k$ and $\ell $ can be augmented by combining redundancy and diversity in hardware and software, in order to apply the moving target paradigm. We validate the approach via simulation and numeric results.

Considering that large quantities of soil hyperspectral data include the redundancy and overlap of spectral information, the technology of selecting feature wavelength can effectively solve these problems, and improve the accuracy and stability of the soil organic matter (SOM) content retrieval model. Traditional methods of wavelength selection mainly attempt to establish the empirical relationship between reflectance and SOM contents, and the performance is directly related to the quality and representativeness of the “training data”. This study first distinguished the sensitive wavelength interval of SOM through the sensitivity analysis (SA) of the SOM to soil reflectance in radiative transfer model. Then sensitive wavelength points of SOM were ascertained using the successive projection algorithm (SPA): 468, 476, 496, 599, 775 and 900nm. Results show that SOM content can be estimated with high accuracy (root-mean-square error of prediction (RMSEP) 2) > 82.9%) by adopting the selected six wavelengths. Especially at 599nm, the accuracy of SOM content estimation is the highest (RMSEP: 0.176%, R2: 90.4%). Compared with traditional empirical wavelength selection methods, the wavelength selection based on the SA-SPA with the SOM radiative transfer model improves the generalizability and accuracy of the result. The research provides theoretical basis and technical support for the remote sensing retrieval of SOM, the development of rapid spectral instruments, and the bands setting of sensor instrument.

This article focuses on the estimation of the direction of Arrival based on the interpolation approaches of the sparse array. Such as the minimum redundancy array, co-prime array, and the nested array have attracted much attention, especially for a non-linear array antenna. We analyze the effect of antennas locations errors on overall estimation performance for deterministic and stochastic error models and to improve the estimator performance in the case of uncorrelated sources of signals in spatially-smoothed MUSIC scenarios. In two scenarios: we determine the Cramér-Rao bound in the case of the antennas locations are perfect, and in other cases, we take into account antennas locations errors. For the first scenario, the Cramér-Rao bound converges as the SNR increase. For the second scenario, the Signal-to-noise ratio increases to infinity, to achieve the same Cramér-Rao bound as in the first scenarios. There is a significant gap between the Cramér-Rao bound values in a situation where antennas locations errors are considered, and when other locations errors are not considered. This disparity shows that unknown antenna locations errors have a significant impact on the estimated achievable performance of the direction of Arriva of scattered linear matrices.

Since great redundancy of telemetry data of spacecraft, telemetry data compression is a good solution for the limited bandwidth and contact wireless links. It is important to obtain accurate data characteristic firstly. State-of-the-art machine learning methods work well on data mining and pattern recognition under conditions of the given test data set, which could be used as the available tools for post-event data processing and analysis, such as trend forecasting and outlier detection, but they have not provided the proper solution from the source on-board. In this paper, four base classes of the telemetry data are suggested and studied through the time series feature and information entropy analysis, then a new on-board lightweight self-learning algorithm named Classification Probability calculation - Window Step optimization (CP-WS) is proposed to obtain the class features and make the decision of each single parameter from the continuous discrete telemetry time series. Simulation results show that, our algorithm correctly classifies the simulation and real mission data into the appropriate base class with advantages of high classification accuracy as 100% and adaptive computational complexity from $O(L^{2})$ to $O(L)$ , which could be used in satellite on-board data compression for space-to-ground transmission, especially for the deep space explorers to save important status with less on-board storage space.

Reversible data hiding in encrypted image (RDHEI) is a technique that can provide the security and invisibility of their own information during the acquisition and sharing of them among multi-users. Among which, parametric binary tree labelling (PBTL) is a novel technique designed to serve for high-capacity RDHEI. However, considering the local smoothness of the image, its potential redundancy room has not been fully explored. In this paper, we propose an improved PBTL-RDHEI scheme (IPBTL-RDHEI in short). In IPBTL-RDHEI, an adjusting pixel modulation strategy is considered to reduce the probability of the overflow of pixels, making more embeddable pixels usage to carry secret data. Moreover, a block-wise rearrangement and compression (BRC) mechanism is introduced to decrease the length of the required auxiliary information. Experimental results confirm that the proposed scheme is able to achieve an average embedding rate as large as 1.827 bpp when the block size is set to $3\times 3$ .

The C4.5 algorithm has three shortcomings: the wide range of candidate segmentation threshold sequences for continuous attributes, the comprehensive influence of different attributes and local subsets under the same attribute, and the inter-attribute redundancy. When dealing with continuous attributes, sampling and threshold supplement processing near the transition boundary of the attribute interval corresponding to the adjacent different categories are performed for narrowing the range of candate segmentation threshold sequences. By adding standardizing Euclidean distance of the attribute global and local factors to represent attribute weight, the calculation of C4.5 information gain is otpimized. And averaging Gini index of other attributes and adding correction factor, the influence of redundancy between attributes is greatly decreased. The overall average improvement range of the base classifier and the bagging integration classifier is 0.6%~2.1% and 0.7% ~ 2.7%, respectively, which shows that this integration model can improve the classification accuracy and also validate its feasibility and reliability.

L-shaped array (LA) is traditionally used as two one-dimensional uniform linear arrays (ULAs) for two-dimensional direction of arrival (DOA) estimations. With elevation and azimuth angles estimated separately, the detectable source number is strictly limited by the number of elements in the ULAs. To increase the detection ability, in this paper, we propose to exploit the diff-sum co-array (DSCA) concept on the L-shaped array. As such, a virtual element in the quadrants of DSCA can be generated by only one pair of sensors in the LA. Therefore, it can acquire much higher number of degrees of freedom for DOA estimation. To further reduce the redundancy of elements on the horizontal and vertical axes of DSCA, an element moving strategy is proposed. It is proved that the DSCA can remain intact with any one or more physical sensors relocated to the centrosymmetric positions. Based on this strategy, a generalized L-shape array concept and a new configuration referred to as generalized L-shaped array with odd-even locations (GLA-OEL) are developed. For GLA-OEL, the number of sensor pairs with small spacing is as small as one, which dramatically reduces the mutual coupling of adjacent elements. Besides, with the same number of sensors, the GLA-OEL can generate much larger virtual uniform rectangular array in the DSCA. Simulations verify the superiorities of the proposed array in terms of detection performance and estimation accuracy.

Filters are the fastest among the different types of feature selection methods. They employ metrics from information theory, such as mutual information (MI), Joint-MI (JMI), and minimal redundancy and maximal relevance (mRMR). The determination of the optimal feature selection set is an NP-hard problem. This work proposes the engineering of the Genetic Algorithm (GA) in which the fitness of solutions consists of two terms. The first is a feature selection metric such as MI, JMI, and mRMR, and the second term is the overlapping-coefficient that accounts for the diversity in the GA population. Experimental results show that the proposed algorithm can return multiple good quality solutions that also have minimal overlap with each other. Numerous solutions provide significant benefits when the test data contains none or missing values. Experiments were conducted using two publicly available time-series datasets. The feature sets are also applied to perform forecasting using a simple Long Short-Term Memory (LSTM) model, and the solution quality of the forecasting using different feature sets is analyzed. The proposed algorithm was compared with a popular optimization tool `Basic Open-source Nonlinear Mixed INteger programming' (BONMIN), and a recent feature selection algorithm `Conditional Mutual Information Considering Feature Interaction' (CMFSI). The experiments show that the multiple solutions found by the proposed method have good quality and minimal overlap.

The class of maximum rank distance generalized Gabidulin codes, suggested by Kshevetskiy and Gabidulin in 2005, extends the class of Gabidulin codes. For generalized Gabidulin codes we propose an efficient decoding algorithm based on the skew transform and the skew shift register synthesis.

Currently, statistical tests for random number generators (RNGs) are widely used in practice, and some of them are even included in information security standards. But despite the popularity of RNGs, consistent tests are known only for stationary ergodic deviations of randomness (a test is consistent if it detects any deviations from a given class when the sample size goes to infinity). However, the model of a stationary ergodic source is too narrow for some RNGs, in particular, for generators based on physical effects. In this article, we propose computable consistent tests for some classes of deviations more general than stationary ergodic and describe some general properties of statistical tests. The proposed approach and the resulting test are based on the ideas and methods of information theory.

In this paper an interpolation-based decoding algorithm to decode Gabidulin codes, transmitted through a finely restricted channel, is proposed. The algorithm is able to decode rank errors beyond half the minimum distance by one unit. Also the existing decoding algorithms for generalized twisted Gabidulin codes and additive generalized twisted Gabidulin codes are improved., Comment: arXiv admin note: text overlap with arXiv:2105.03115

We derive simplified sphere-packing and Gilbert--Varshamov bounds for codes in the sum-rank metric, which can be computed more efficiently than previous ones. They give rise to asymptotic bounds that cover the asymptotic setting that has not yet been considered in the literature: families of sum-rank-metric codes whose block size grows in the code length. We also provide two genericity results: we show that random linear codes achieve almost the sum-rank-metric Gilbert--Varshamov bound with high probability. Furthermore, we derive bounds on the probability that a random linear code attains the sum-rank-metric Singleton bound, showing that for large enough extension fields, almost all linear codes achieve it.

This paper considers the problem of multi-server Private Linear Computation, under the joint and individual privacy guarantees. In this problem, identical copies of a dataset comprised of K messages are stored on N non-colluding servers, and a user wishes to obtain one linear combination of a D-subset of messages belonging to the dataset. The goal is to design a scheme for performing the computation such that the total amount of information downloaded from the servers is minimized, while the privacy of the D messages required for the computation is protected. When joint privacy is required, the index set of these D messages must be kept private, and when individual privacy is required, the identity of each individual message required for the computation must be kept private. In this work, we characterize the capacity, which is defined as the maximum achievable download rate, under both joint and individual privacy requirements. Our converse proofs are based on reduction from two variants of the multi-server Private Information Retrieval problem with side information. Our achievability schemes build up on our recently proposed schemes for single-server Private Linear Transformation and the multi-server private computation scheme proposed by Sun and Jafar. Using similar techniques, we also establish bounds on the capacity for the cases in which the user wants to compute multiple (more than one) linear combinations of a D-subset of messages.

Technological advancements have led to easy and rapid generation and collection of huge volumes of varieties of data from of wide ranges of rich data sources. These big data may be of different levels of veracity, including precise data and imprecise or uncertain data. Embedded in the data are valuable information and useful knowledge that can be discovered by data mining. Discovered information and knowledge may help to build a smart world. In this paper, we present a solution for scalable mining of big data. In particular, we focus on scalable mining of huge volumes of temporal coronavirus disease 2019 (COVID-19) data at different granularity levels. Since its outbreak, there have been millions of COVID-19 cases worldwide. These are huge volumes of data, and new cases have been reported every day. Embedded in these COVID-19 data is implicit, previously unknown and potentially useful information and knowledge, which can be discovered by data mining for social good. Analyzing and mining these data helps users (e.g., researchers, civilian) to get better understanding of the disease, and thus take an active role in fighting, controlling, and/or combating the disease. Evaluation results on real-life COVID-19 data show the benefits of our solution in scalable mining of big data.

The traditional fault detection method only establishes a global model and does not consider the local information of the process. At the same time, the data in the industrial process has time-varying and non-linear characteristics, limiting the prediction accuracy of fault monitoring. Therefore, fault detection and diagnosis of the method is proposed based on multi-block just-in-time-learning slow feature analysis (JITL-MBSFA). Firstly, mutual information (MI) is segmented into two sub-blocks based on normal observation data sets. Then, through just-in-time-learning (JITL) to screen the optimal data set, and based on slow feature analysis (SFA) to build a sub-model, calculate the corresponding monitoring statistics, the support vector machine (SVDD) to monitor the results of fusion. Finally, the comparative simulation experiment in the Tennessee Eastman (TE) process verified the effectiveness and superiority of the proposed method.

This paper presents the revealed patterns of constructing polynomial codes with different detecting characteristics. The dependence of the number of the code check groups over which the data vectors are distributed on the form of the generating polynomial is established. It is proved that only in the presence of a free term in the generating polynomial, the maximum possible number of check groups of the code is used for a given length of the check vector. A formula is obtained for calculating the number of undetectable errors by polynomial codes constructed using various generators of polynomials. A theorem is proved about the presence among polynomial codes of codes with a theoretical indicator of the minimum number of undetectable errors for given values of the lengths of data and check vectors. As the length of the check vector increases, the number of such codes also increases. A theorem is proved on the existence of parity codes with better detecting characteristics in comparison with classical parity codes. The useful properties of the considered codes for the synthesis tasks of systems with error detection are presented. In the example of benchmarks, experiments were carried out to evaluate the detecting characteristics of special polynomial codes. For some polynomials and benchmarks, complete error-detection at their outputs has been achieved. Besides, the occupied areas of concurrent error-detection systems based on the considered codes and the duplication method were calculated and compared, according to the results of which, even with such a large value of the check vector length, systems based on polynomial codes have a significant advantage in hardware costs.

The authors of the paper consider the features of the weight-based sum code in the residue ring by arbitrary modulus. For the first time, the error detection characteristics by this codes class and some patterns of using specific modules and weight coefficient values have been established. It is shown that the number of different weight-based codes is limited, but there are a large number of ways to construct them for each value of the bits number in data vectors. An algorithm for obtaining the bits of the check vectors of the considered codes is presented. Examples of their construction are given, and calculating the number of undetectable errors in data vectors is described. The code properties under study are determined, the account of which is useful in solving problems of technical diagnostics. An example of such a task is the synthesis of self-checking digital devices and devices with controllable architecture. The analysis results of the considered code detecting abilities for the built-in control circuits of combinational benchmarks are presented. The results obtained in this work are universal and are not focused on using only one element base of the implemented devices.

In view of the defects of tooth profile defect and end face defect in the production process of injection gears due to the influence of technology, equipment and environment, there is an urgent need for rapid and accurate detection methods of gear surface integrity. In this paper, digital image processing technology is used to propose a rapid and accurate target matching detection method for the gear surface integrity based on normalized cross-correlation coefficients: First, the first derivative of the Gaussian function is used to construct a sub-pixel edge detector optimized based on the Canny operator to accurately extract the edge information of the gear to determine the target area; then, calculate the normalized correlation coefficient between the target area image and the template image, and realize the judgment of the surface integrity of the injection gear according to the matching coefficient threshold. The experimental results verify the feasibility and reliability of the algorithm. Its accuracy rate reaches 99.33%, and the detection time does not exceed 0.2s, which has a good industrial detection application value.

The raw image data occupies a large storage space and communication bandwidth. Compression of the image data saves storage space and transmission bandwidth by removing the redundancy. In many image compression algorithms, wavelet transform can decompose the image with multi-level resolution, and can perform embedded coding. The author studies the set partitioning in hierarchical trees (SPIHT) algorithm based on wavelet transform and proposed an improved SPIHT algorithm (NE-SPIHT). First, the initialization of LIS in the SPIHT coding scheme is improved, The LIP and LIS are initialized to the nodes with no descendants in the lowest frequency subband. Secondly, when the nodes in the LIS chain table are judged as significant, its four child nodes are regarded as one whole for judgment, reducing the output bits. Experimental results demonstrate that the NE-SPIHT algorithm outperform SPIHT in terms of PSNR and SSIM, so applying NE-SPIHT method to the practical image compression can achieve a high compression efficiency.

In the investigation reports of vehicle fire accidents, sensitive information such as human faces, license plates and texts need to be blurred in the accident scene photos. In this paper, AI-based sensitive information detection and desensitization methods for vehicle fire scene images are investigated. In the proposed algorithm, RetinaFace is used to detect faces as well as license plates, while PSENet is used in text detection, and pixel unification operations are performed on each detected target separately. The experimental results show that the F1-score of face detection, license plate detection and text detection in our proposed algorithm reached 84.6%, 96.9% and 85.5% respectively, which has achieved the expected goal and could be further used for other sensitive information detection and desensitization tasks.

The non-linear analysis of redundancy and relevance is made by a filter based on information theory. The Renyi entropy is integrated with a Parzen window and a Cauchy-Schwartz equation, in order to directly estimates the mutual information of the data that is used to choose the most relevant variables and the less relevant yet not redundant ones. The proposed modified filter can be applied to determine the most promising variables that do not have previous knowledge available. Finally, an Artificial Neural Network is used to estimate the Brazilian exchange rate, showing positive and more stable results.

In the multi-input multi-output (MIMO) signal detection based on deep learning, there is a wireless channel overtraining problem, that is, the signal detection network generally only remembers a single wireless channel model. An artificial neural network (ANN)-assisted receiver trained for a specific channel model is often not suitable for another channel model. In mobile communication, the wireless channel is greatly randomly affected by the wireless propagation environment. Variations in channel parameters will change the channel model, and this makes the signal detector based on deep learning use limited and poor robustness. In this paper, a continual learning method based on elastic weight consolidation(EWCCL) is proposed to solve the issue. The basic idea of this paper is to calculate the importance of each parameter to the task through the EWCCL method after the completion of one training. Then, in the next training, regular terms are added into the loss function to avoid the result of the last training being completely covered due to excessive deviation of these parameters. This method can make the neural network memorize the training results of multiple wireless channels, so it has stronger robustness to the transformation of wireless channels, and also reduces the cost of retraining. Finally, we verified this hypothesis through simulation experiments. The simulation experiment results show that the EWCCL method proposed in this paper has better detection performance and better robustness than the fine-tune method.

The problem of correcting deletions has recently received significantly increased attention, partly because of the prevalence of these errors in DNA data storage. In this paper, we study the problem of correcting a burst of at most two deletions in non-binary sequences. The problem was first studied for binary sequences by Levenshtein, who presented a construction with optimal redundancy. We propose a non-binary code correcting a burst of at most 2 deletions for q-ary alphabets with redundancy $\log n+O$ (log $q$ log log n) bits, for even $q$ . Further, we construct codes with lower redundancy to correct a burst of exactly 2 deletions caused by a single deletion in alternating sequences that arise in terminator-free enzymatic DNA synthesis.

Sequencing a DNA strand, as part of the read process in DNA storage, produces multiple noisy copies which can be combined to produce better estimates of the original strand; this is called trace reconstruction. One can reduce the error rate further by introducing redundancy in the write sequence and this is called coded trace reconstruction. In this paper, we model the DNA storage channel as an insertion-deletion-substitution (IDS) channel and design both encoding schemes and low-complexity decoding algorithms for coded trace reconstruction. We introduce Trellis BMA, a new reconstruction algorithm whose complexity is linear in the number of traces, and compare its performance to previous algorithms. Our results show that it reduces the error rate on both simulated and experimental data. The performance comparisons in this paper are based on a new dataset of traces that will be publicly released with the paper. Our hope is that this dataset will enable research progress by allowing objective comparisons between candidate algorithms., Extended version of paper presented at ISIT 2021

Two dimensional (2D) error correcting codes have been investigated for a long time owing to their numerous applications. Recently, 2D codes correcting row-deletions and column-deletions, also known as criss-cross deletion correcting codes, have been studied as a generalisation of one dimensional deletion correcting codes. In this work, we show that 2D deletion correcting codes are useful to correct errors in racetrack memories. With motivation from both theoretical and practical point of view, we study these 2D codes and aim to improve the previous known results. Our first main result is a construction of an optimal (1,1)-criss-cross deletion correcting code with the redundancy is at most $2n+2\log n+o(\log n)$ bits. Then, we also present a construction of an asymptotic optimal $(t_{r},\ t_{c})$ -criss-cross deletion correcting code with less redundancy than the best known results. Furthermore, since a 2D binary code correcting multiple row-deletions is equivalent to a I1D q-ary code correcting multiple deletions with large $q$ , we also improve some previous known results on 1D q-ary code correcting multiple deletions.

We consider the problem of constructing codes that can correct deletions that are localized within a certain part of the codeword that is unknown a priori. Namely, the model that we study is when at most $k$ deletions occur in a window of size $k$, where the positions of the deletions within this window are not necessarily consecutive. Localized deletions are thus a generalization of burst deletions that occur in consecutive positions. We present novel explicit codes that are efficiently encodable and decodable and can correct up to $k$ localized deletions. Furthermore, these codes have $\log n+\mathcal{O}(k \log^2 (k\log n))$ redundancy, where $n$ is the length of the information message, which is asymptotically optimal in $n$ for $k=o(\log n/(\log \log n)^2)$., 10 pages, a full version of the paper accepted to 2021 IEEE ISIT

This paper investigates the problem of correcting multiple criss-cross deletions in arrays. More precisely, we study the unique recovery of $n\times n$ arrays affected by any combination of $t_{\mathrm{r}}$ row and $t_{\mathrm{c}}$ column deletions such that $t_{\mathrm{r}}+t_{\mathrm{c}}=t$ for a given $t$ . We refer to these type of deletions as t-criss-cross deletions. We show that the asymptotic redundancy of a code correcting t-criss-cross deletions is at least $tn+t\log n-\log(t!)$ . Then, we present an existential construction of a code capable of correcting t-criss-cross deletions where its redundancy is bounded from above by $tn+\mathcal{O}(t^{2}\log^{2}n)$ . The main ingredients of the presented code are systematic binary t-deletion-correcting codes and Gabidulin codes. The first ingredient helps locating the indices of the deleted rows and columns, thus transforming the deletion-correction problem into an erasure-correction problem which is then solved using the second ingredient.

In this paper, by applying the precoding technique in conjunction with the syndrome compression approach, we construct systematic $t$ -deletion $s$ -substitution correcting codes, where $t$ and $s$ are fixed positive integers. The redundancy of our construction is $(4t+3s)\log n+o(\log n)$ for the binary case and $(4t+4s-1- \mathrm{L}\frac{2s-1}{q}\rfloor)\bar{\mathrm{l}}\text{og}_{q}n+o(\bar{\mathrm{l}}\text{og}_{q}n)$ for the $q$ -ary case, where $n$ is the length of the codes and $q> 2$ is a fixed prime power.11If $x$ is a positive real number, then $\log_{q}\alpha$ is the logarithm of $x$ with base $q$ ; if $q=2$ , we simply denote $\log x=\text{lo}\bar{\mathrm{g}}_{q}x$ . We also construct binary t-deletion correcting codes (i.e., $s=0$ ) with redundancy $(4t-1)\log n+o(\log n)$ . The encoding/decoding complexities of all constructions are polynomial in $n$ .

To solve the problem of the unstable feature point extraction and the difficult feature matching in SAR image processing, we proposed a detection algorithm based on hybrid feature extraction. First, the repeated gray information is removed to reduce the complexity and import the real-time performance of the algorithm. Then MSER is utilized to extract regional extremes as candidates. And Harris-Laplace algorithm, which ensures uniform and stable feature-point-distribution, is employed to detect the feature points. Next, an improved SAR-SIFT algorithm is used for the feature matching. Finally, FSC and VFC are devised to refine the feature matching results. The dataset we used is the GaoFen-3 series image as it retains fine edge and structural texture information. The experiments have demonstrated the effectiveness of the proposed algorithm with insert performance metric here.

Small vessels in synthetic aperture radar (SAR) images usually have weak scattering intensity and occupy only a few numbers of image pixels, resulting in a high miss detection rate during the detection process. Regarding the problem, two solutions were presented in this paper. Firstly, dual-polarimetric SAR data were used and dual-polarimetric features were adaptively fused. Comparing to single-polarization and conventional non-adaptive fusion method, it optimally enhanced the characteristics of small vessels. Secondly, the conventional feature pyramid network (FPN) was enhanced by reducing the downsampling factor, adding spatial attention, and channel attention. The added spatial attention enhanced the significant features of small vessels on the large-scale feature map; the added channel attention filtered out the spliced features maps that were benefiting small vessel detection and reduced feature redundancy. Experimental results on the small vessel data set of Sentinel-1 verified that it not only reduced the miss detection rate but also improved calculation efficiency.

The fusion of hyperspectral image (HSI) and light detection and ranging (LiDAR) data can provide complementary information and improve the accuracy of land cover classification. In this paper, a novel fusion method is proposed to fuse the HSI and LiDAR dataset based on multi-scale feature extraction and total variation. In the method, the extended multi-attribute profile (EMAP) is utilized to automatically extract structural information from HSI and LiDAR elements. The extracted features are then estimated in a lower-dimensional space by multi-scale total variation (MSTV). Finally, the classification map is generated by applying random forest classifiers on the fused data. In the experiment, the performance of the proposed method is evaluated on an urban dataset of Houston. The results demonstrate that classification accuracy could be significantly improved by the proposed method compared with other methods.

The aim of hyperspectral image super-resolution (HSI-SR) is to produce high spatial resolution hyperspectral image (HR - HSI) by exploiting the available high spatial resolution multispectral image (HR-MSI) and low spatial resolution hyperspectral image (LR-HSI). In this work, we develop a novel matrix factorization (MF)-based HSI -SR way, which formulates the HSI -SR problem as estimating the spectral dictionary from the observed LR - HSI and the coefficient matrix from both the observed HR-MSI and LR-HSI. Specifically, we first estimate the spectral dictionary from the observed LR - HSI by the dictionary learning algorithm with redundancy assumption. Moreover, based on the superpixel segmentation technology used in the observed HR-MSI, the coefficient vectors are grouped. By concatenating the joint-sparse, nonlocallow-rank, and nonnegative priors of the grouped coefficient vectors, we develop a novel coefficient matrix estimation variational model, which fully explores the nonlocal self-similarity of the desired HR-HSI. The proposed coefficient matrix estimation model is solved under the alternating direction method of multipliers (ADMM) framework. Experimental results prove the superiority of the proposed way from the quantitative and qualitative analysis.

The new HEVC standard generation has enhanced the compression performance and has resulted in considerable code complexity. The current trend of video development is reducing the video's perceived redundancy in achieving a better compression impact. In most video applications, compression is a vital step towards efficiency. High Density Encoding (HDE) is an entirely lossless encoding technology for Bayer pattern images. The HDE-encoded images of ARRIRAW are about 60% of their original size, which is a considerable cost saving. This is based on a technique known as Zig Zag encoding. CODEX HDE is a reordering system utilizing ZigZag encoding process that repacks Bayer pattern files efficiently. It is not an Image compression approach. Designed to achieve high encoding and decoding performance. In this paper, we compare the standard video compression method with HDE of ARRI in objective quality. Furthermore, this study delves into ARRIRAW's high-density encoding system as an advanced technique generating a possibility of compressing the video images with nuanced features enabling time and cost-effectiveness.

Light detection and ranging (LiDAR) plays a vital role in autonomous driving applications. Due to the enormous volume of point cloud data from LiDAR acquisition, LiDAR point cloud compression schemes are desirable under limited storage and transmission bandwidth. This paper proposes an improved coarse-to-fine motion estimation scheme to significantly reduce the spatial redundancy among LiDAR point clouds, combining registration-based global motion estimation and block-based local motion estimation. First, in the global motion estimation process, an efficient scheme is introduced to segment ground level and object level from original point clouds. Then we use an ICP-based registration method for extracted object point cloud to estimate global motion transformation. Second, an irregular prediction unit (PU) partition method is introduced in the block-based local motion estimation part, which provides more flexibility to estimate complex motions. Experimental results have shown significant gains of our proposed model over the state-of-the-art test model (TMC13) of MPEG Geometry-based PCC (GPCC) standard. For the dynamically acquired point clouds, average coding gains of 8.0% and 1.2% are obtained respectively in lossy and lossless geometric coding.

With the continuous improvement of information and communication technology, the popularity of mobile terminals and endless new technologies, the application of mobile Internet is increasingly extensive. This paper mainly studies the multi-source data analysis method based on mobile Internet. Through the connection of multi-source database, the data is associated and the foundation of data fusion is constructed. It is stored in the specified storage database or data medium according to certain rules. On this basis, first of all, preprocess the data, sort out and correct those disordered storage formats, disordered data records and redundant fields to form standard data. Secondly, these data are cleaned to remove data redundancy and noise. Through the combination of multi-source data, new data records are obtained, and these data are sorted and improved. According to the actual situation and problems of the project, the multi-source data is fused to build an effective data set. Data show that the accuracy of the model can reach more than 85%. The results show that mobile Internet technology can provide security and risk information for the participants of the exhibition.

With the technological development, it has become easy to use digital data, and thus medical institutions have turned to analyzing images and video content. Medical video is recorded in order to collect experimental information about the use of technologies and integrated data that consists of a series of doctors with video recording, and this leads to user interaction with computers. Using the coding methodology, the video is analyzed for a medical diagnosis that contains important information about the patient’s condition with the help of computers, as well as performing surgeries in a medical environment to conduct clinical trials and diagnostics. This leads to software development, reduction in medical effort real-time quality measurement and utilization of available treatments for patient medical diagnosis Medical video has become one of the leading and most common fields, in addition to medical images for diagnosing heart and lung diseases, brain and digestive diseases, and this diagnosis is made by using ultrasound and magnetic resonance imaging in addition to a CT scan. All this helped to provide health care through new methods of research and experiments clinical. Lately, Compression technologies developed video and images are widely used, and by using these effective compression techniques, little or no damage is achieved, the file size is reduced and the visual quality is not affected, major target of compression is to enable data to get saved or transmit in the better form types of compression in various manners like the lossless and the lossy. The aim of this study is to compress video film to lower the irrelevance and the redundancy of data, principle component analysis (PCA) is used in this research, the main component analysis of medical video compression based on self-matrix analysis that all readers will be able to understand the PCA method. This paper presents a suitable technique for medical video compression using Principle Component Analysis PCA, compressed the frames of medical video with eigenvalue and eigenvector techniques. Experimental results shown the efficient and quick technique for medical video frames.

Recent works on learned image coding using autoencoder models have achieved promising results in rate-distortion performance. Typically, an autoencoder is used to transform an image into a latent tensor, which is then quantized and entropy coded. Based on a work by Balle et al., we adapted the autoencoder with a hyperprior model to code images in a block-based approach. When the autoencoder model is directly applied to code small image blocks, spatial redundancy in the larger image cannot be fully utilized, resulting in a decrease in ratedistortion performance. We propose a method to utilize border information in the entropy coding of latent and hyper-latent tensors, which has achieved promising results. We show that using intra-prediction to help entropy coding is more effective than applying a convolutional autoencoder with hyper priors to intra-prediction residual blocks.

Polar codes have been shown to provide an effective mechanism for achieving physical-layer security over various wiretap channels. A majority of these schemes require channel state information (CSI) at the encoder for both intended receivers and eavesdroppers. In this paper, we consider a polar coding scheme for secrecy over a Gaussian wiretap channel when no CSI is available. We show that the availability of a shared keystream between friendly parties allows polar codes to be used for both secure and reliable communications, even when the eavesdropper knows a large fraction of the keystream. The scheme relies on a predetermined strategy for partitioning the bits to be encoded into a set of frozen bits and a set of information bits. The frozen bits are filled with bits from the keystream, and we evaluate the security gap when the cyclic redundancy check-aided successive cancellation list decoder is used at both receivers in the wiretap channel model.

Learning-based video compression has achieved substantial progress during recent years. The most influential approaches adopt deep neural networks (DNNs) to remove spatial and temporal redundancies by finding the appropriate lower-dimensional representations of frames in the video. We propose a novel DNN based framework that predicts and compresses video sequences in the latent vector space. The proposed method first learns the efficient lower-dimensional latent space representation of each video frame and then performs inter-frame prediction in that latent domain. The proposed latent domain compression of individual frames is obtained by a deep autoencoder trained with a generative adversarial network (GAN). To exploit the temporal correlation within the video frame sequence, we employ a convolutional long short-term memory (ConvLSTM) network to predict the latent vector representation of the future frame. We demonstrate our method with two applications; video compression and abnormal event detection that share the identical latent frame prediction network. The proposed method exhibits superior or competitive performance compared to the state-of-the-art algorithms specifically designed for either video compression or anomaly detection.1

The ON/OFF model is considered, known as the packet series model, in which the source states alternate between ON and OFF states, where the time spent in at least one of these states is described by distributions with heavy tails - the Pareto distribution. It is shown that the correct implementation of the Pareto distribution is associated with specific computational difficulties caused by the slow decay of the heavy power-law tail of the distribution.

Currently, many information processing and control systems are based on computer vision technologies. Among the many problems that need to be solved in such systems, it is important to reduce the redundancy of information in the image. To solve this problem, various methods of image compression are used, including those based on the wavelet transform. This paper is devoted to one of these methods. The proposed method is based on the calculation and analysis of energy features, which allow us to assess the significance of the detailing coefficients for the representation of the image in a compressed form. As a result of its application, a matrix of approximating coefficients of the initial level, significance maps and sets of significant detailing coefficients of various levels of the wavelet transform are formed. To improve the compression quality, significant detailing coefficients are normalized. In addition, you can apply entropy coding for each level of data. The described method is simple to implement and provides flexible parameter settings to obtain the required image compression quality indicators.

Scientific simulations on high-performance computing systems produce vast amounts of data that need to be stored and analyzed efficiently. Lossy compression significantly reduces the data volume by trading accuracy for performance. Despite the recent success of lossy compression, such as ZFP and SZ, the compression performance is still far from being able to keep up with the exponential growth of data. This paper aims to further take advantage of application characteristics, an area that is often under-explored, to improve the compression ratios of adaptive mesh refinement (AMR) - a widely used numerical solver that allows for an improved resolution in limited regions. We propose a level reordering technique zMesh to reduce the storage footprint of AMR applications. In particular, we group the data points that are mapped to the same or adjacent geometric coordinates such that the dataset is smoother and more compressible. Unlike the prior work where the compression performance is affected by the overhead of metadata, this work re-generates restore recipe using a chained tree structure, thus involving no extra storage overhead for compressed data, which substantially improves the compression ratios. The results demonstrate that zMesh can improve the smoothness of data by 67.9% and 71.3% for Z-ordering and Hilbert, respectively. Overall, zMesh improves the compression ratios by up to 16.5% and 133.7% for ZFP and SZ, respectively. Despite that zMesh involves additional compute overhead for tree and restore recipe construction, we show that the cost can be amortized as the number of quantities to be compressed increases.

Association classification (AC) has p-roven to be a promising approach in data mining by integrating association mining and classification. The evidential association rule-based classification (EARC), as an extension of AC in belief function framework, is an effective classification model for addressing multiple uncertainties in real-world applications, but it encounters difficulties in dealing with high-dimensional data. To adapt the EARC to high-dimensional data, an improved evidential association classification method, called EARC-HD, is developed based on four stages: entropy-based fuzzy partition, evidential class association rule mining, rule prescreening and genetic rule selection. Comparing with EARC, an entropy-based fuzzy partition strategy is designed for deriving a series of fuzzy regions, with which some irrelevant attributes can be deleted. Moreover, the number of antecedent attributes is limited for effectively reducing the computational complexity in rule generation. To improve the efficiency of the classifier, the techniques of redundancy reduction and subgroup discovery are used for prescreening the mined rule set before a genetic rule selection process. Experimental results based on real-world datasets demonstrate the effectiveness of the proposed method in dealing with high-dimensional problems.

Lifted Reed-Solomon codes and multiplicity codes are two classes of evaluation codes that allow for the design of high-rate codes that can recover every codeword or information symbol from many disjoint sets. Recently, the underlying approaches have been combined to construct lifted bi-variate multiplicity codes, that can further improve on the rate. We continue the study of these codes by providing lower bounds on the rate and distance for lifted multiplicity codes obtained from polynomials in an arbitrary number of variables. Specifically, we investigate a subcode of a lifted multiplicity code formed by the linear span of m-variate monomials whose restriction to an arbitrary line in $\mathbb{F}_q^m$ is equivalent to a low-degree uni-variate polynomial. We find the tight asymptotic behavior of the fraction of such monomials when the number of variables m is fixed and the alphabet size q = 2l is large. For some parameter regimes, lifted multiplicity codes are then shown to have a better tradeoff between redundancy and the number of disjoint recovering sets for every codeword or information symbol than previously known constructions.

A fault risk prediction method for distribution network based on improved RelieF-Softmax algorithm is proposed. Firstly, the fault data of distribution network is obtained and 14 fault characteristics are extracted through data investigation. The improved relief filtering feature extraction method is adopted to screen the initial fault feature set and select key fault features. Finally, a distribution network fault warning method based on the cross entropy loss function of focal loss is proposed, and the results of calculation examples demonstrate the effectiveness of the method proposed in this paper.

Multiple data reduction techniques have been investigated to lower storage costs for a wide variety of customers. In this work, we focus on similarity-based delta compression, which calculates and stores the difference of very similar, but non-duplicate, chunks in storage systems. Delta compression is often implemented along with deduplication and has been shown to achieve a much higher compression ratio.Currently, the N-Transform method is the most popular and widely-used approach to generate features for data content (e.g. chunks) to detect similar candidates (and then apply delta compression). For delta compression systems, though, the throughput of N-Transform is often the bottleneck. Finesse is a high throughput variant of N-Transform, but it suffers from lower detection accuracy and compression ratio. The computation overhead of N-Transform consists of two parts: calculating the rolling hash across data and applying time-consuming transforms on each hash. In this work, we propose Odess, a fast resemblance detection approach, that uses a novel Content-Defined Sampling method to generate a much smaller proxy hash set and then applies transforms on this small hash set. This reduces the calculations in the transform step from being the bottleneck. Meanwhile, Odess also leverages the faster Gear hash to generate rolling hashes. Thus, Odess greatly reduces the computational overhead for resemblance detection while achieving high detection accuracy and high compression ratio.Our evaluation results show that Odess is ~ 5.4× (Finesse) and ~ 26.9× (N-Transform) faster (on average) at generating features for resemblance detection. When considering an end-to-end data reduction storage system, Odess increases throughput by ~ 1.36× (Finesse) and ~ 2.76× (N-Transform) while maintaining the compression ratio of N-Transform and increasing the compression ratio ~ 1.22× over Finesse.

Lossy video compression is widely applied in the process of video transmission and storage. With the pursuit of higher compression ratio, the demand for compressed video-enhancement is derived from the annoying degradation. Recently some deep-neural-network-based approaches have achieved impressive performance in this task. Unfortunately, most of them are plainly designed for images without exploiting the high temporal redundancy for video restoration. To this end, we present a two-stage Video compression Artifacts Removal Neural Network (VARNN) with deformable convolutional kernels and modified non-local blocks. Conventional non-local block is incapable of capturing dependencies among channels and suffers from high computation and memory cost. We thus introduce a Divided Non-Local Block (DNLB), in which global dependencies can be captured in a fast and low space-cost way. Finally, experiments show that the proposed VARNN outperforms some state-of-the-art methods.