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Many families of binary nonlinear codes (e.g., Kerdock, Goethals, Delsarte–Goethals, Preparata) can be very simply constructed from linear codes over the Z 4 ring (ring of integers modulo 4), by applying the Gray map to the quaternary symbols. Generalized Kerdock codes represent an extension of classical Kerdock codes to the Z 2 S ring. In this paper, we develop two novel soft-input decoders, designed to exploit the unique structure of these codes. We introduce a novel soft-input ML decoding algorithm and a soft-input soft-output MAP decoding algorithm of generalized Kerdock codes, with a complexity of O (N S log 2 N) , where N is the length of the Z 2 S code, that is, the number of Z 2 S symbols in a codeword. Simulations show that our novel decoders outperform the classical lifting decoder in terms of error rate by some 5 dB. [ABSTRACT FROM AUTHOR]

How to model the effect of reflection is crucial for single image reflection removal (SIRR) task. Modern SIRR methods usually simplify the reflection formulation with the assumption of linear combination of a transmission layer and a reflection layer. However, the large variations in image content and the real-world picture-taking conditions often result in far more complex reflection. In this paper, we introduce a new screen-blur combination based on two important factors, namely the intensity and the blurriness of reflection, to better characterize the reflection formulation in SIRR. Specifically, we present Screen-blur Reflection Networks (SRNet), which executes the screen-blur formulation in its network design and adapts to the complex reflection on real scenes. Technically, SRNet consists of three components: a blended image generator, a reflection estimator and a reflection removal module. The image generator exploits the screen-blur combination to synthesize the training blended images. The reflection estimator learns the reflection layer and a blur degree that measures the level of blurriness for reflection. The reflection removal module further uses the blended image, blur degree and reflection layer to filter out the transmission layer in a cascaded manner. Superior results on three different SIRR methods are reported when generating the training data on the principle of the screen-blur combination. Moreover, extensive experiments on six datasets quantitatively and qualitatively demonstrate the efficacy of SRNet over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

An efficient hybrid optimization algorithm is introduced in this paper to optimize the lightweight design of high-aspect-ratio wings, tackling the complexities associated with the mixed optimization design of layout and size variables within these wing structures. A hybrid binary unified coding description facilitates the optimization process for layout and size variables. The study influences one-dimensional convolutional neural networks to establish an aeroelastic surrogate model, primarily chosen for their exceptional performance in handling multi-parameter aeroelastic regression problems. Additionally, the squirrel optimization algorithm is chosen over the genetic algorithm for the mixed optimization problem, leading to notable savings in computational costs. The research demonstrates that the proposed hybrid optimization method, integrating the one-dimensional convolutional neural network and the squirrel optimization algorithm, offers superior performance in optimizing high aspect ratio wings. Specifically, it results in a reduction of 4.1% in the weight of the wing structure. Moreover, the study highlights the necessity of this hybrid approach due to the observed coupling between the layout variables of the wing ribs and the size variables of the wing beams. [ABSTRACT FROM AUTHOR]

Binary duadic codes are an interesting subclass of cyclic codes since they have large dimensions and their minimum distances may have a square-root bound. In this paper, we present several families of binary duadic codes of length 2 m - 1 and develop some lower bounds on their minimum distances by using the BCH bound on cyclic codes, which partially solves one case of the open problem proposed in Liu et al (Finite Field Appl 91:102270, 2023). It is shown that the lower bounds on their minimum distances are close to the square root bound. Moreover, the parameters of the dual and extended codes of these binary duadic codes are investigated. [ABSTRACT FROM AUTHOR]

Binary codes have a special place in coding theory since they are one of the most commonly used in practice. There are classes of codes specific only to the binary case. One such class is self-complementary codes. Self-complementary linear codes are binary codes that, together with any vector, contain its complement as well. This paper is about binary linear self-complementary codes. A natural goal in coding theory is to find a linear code with a given length n and dimension k such that the minimum distance d is maximal. Codes with these properties are called optimal. Another important issue is classifying the optimal codes, i.e., finding exactly one representative of each equivalence class. In some sense, the classification problem is more general than the minimum distance bounds problem. In this work, we summarize the classification results for self-complementary codes with the maximum possible minimum distance and a length of up to 20. For the classification, we developed a new algorithm that is much more efficient compared to existing ones in some cases. [ABSTRACT FROM AUTHOR]

Pulse compression techniques enable the radar designers to transmit long duration pulse to cater the need of high power at the transmitter and achieve the range resolution of short duration pulse at the radar receiver simultaneously. Due to easy generation and processing, binary codes are widely used as pulse compression codes in radars, sonars and spread-spectrum communication systems. But binary codes have high unwanted pulse compression sidelobes at the output of pulse compression receivers. In this paper, optimum binary codes reported in the literatures are overlaid on Discrete frequency sequences to reduce unwanted peaks sidelobes of optimum binary codes. The proposed approach not only reduced peak sidelobes of optimum binary codes but also increased the pulse compression ratio. In this paper, optimum discrete frequency sequences for overlaying with binary codes are optimized using the Modified Simulated Annealing Algorithm (MSAA). A Discrete Frequency Coded binary signal has a complex signal structure which will be difficult to detect and analyze by enemy Electronics Support Measures (ESM). In this paper, a unified adaptive radar model is also proposed to cater the needs of today's radar signal design requirements. The proposed model will help to improve range resolution, the detection capability of radar, reduce the peak sidelobes significantly, and also improve the ESM capability of radar systems. [ABSTRACT FROM AUTHOR]

In this paper, we used the definition of fuzzy vector spaces and fuzzy subsets in combination in order to define fuzzy codes over a fuzzy vector space. We found that the combined definitions did not satisfy the definition of fuzzy vector spaces over a Galois field F2, and gave the conditions in which the combined definitions hold the definition of fuzzy vector spaces in order to define binary fuzzy codes. By defining binary fuzzy codes over fuzzy vector space in relation to the probability of binary symmetric channels (BSC) sending a codeword incorrectly and the weight of error pattern between codewords, we updated properties of Hamming distance of binary fuzzy codes over fuzzy vector space. From the updated properties, we found that the properties of Hamming distance stated over a vector space also satisfy in binary fuzzy codes defined over a fuzzy vector space. Furthermore, we found some interesting results on the decoding of fuzzy codewords sent over a BSC using minimum nonzero Hamming distance of binary fuzzy codes. [ABSTRACT FROM AUTHOR]

In this paper, a network consisting of an object localization module and a discriminative feature extraction module is designed for fine-grained image retrieval (FGIR). In order to reduce the interference of complex backgrounds, the object localization module is introduced into the network before feature extraction. By selecting the convolutional feature descriptors, the main object is separated from the background, and thus, most of the interference is filtered out. Further, in order to improve the overall performance of the network, a discriminative filter bank is introduced into the network as the local feature detector. Hence, the local discriminative features can be extracted directly from the original feature map. The experimental results based on the CUB-200-2011 and Cars-196 datasets demonstrate that the proposed method can improve the performance of FGIR. [ABSTRACT FROM AUTHOR]

In recent years, researchers have studied various methods for transferring data in a network-separated environment, and the most representative method is the use of inaudible frequency signals like ultrasonic waves. This method has the advantage of being able to transfer data without other people noticing, but it has the disadvantage that speakers must exist. In a laboratory or company, external speakers may not be attached to each computer. Therefore, this paper presents a new covert channel attack that transfers data using internal speakers on the computer's motherboard. The internal speaker can also produce a sound of the desired frequency, and, therefore, data can be transferred using high frequency sounds. We encode data into Morse code or binary code and transfer it. Then we record it using a smartphone. At this time, the location of the smartphone can be any distance within 1.5 m when the length per bit is longer than 50 ms, such as on the computer body or on the desk. Data are obtained by analyzing the recorded file. Our results show that data is transferred from a network-separated computer using an internal speaker with 20 bits/s in maximum. [ABSTRACT FROM AUTHOR]

Binary linear codes with few weights have wide applications in communication, secret sharing schemes, authentication codes, association schemes, strongly regular graphs, etc. Projective binary linear codes are among the most important subclasses of binary linear codes for practical applications. In this paper, motivated by the two excellent recent papers (Li et al. in IEEE Trans Inf Theory 67(7):4263–4275, 2021) and (Wang et al. in IEEE Trans Inf Theory 67(8):5133–5148, 2021), several new families of few-weight projective binary linear codes are constructed from the defining sets, and then their Hamming weight distributions are determined by employing the Walsh transform of the corresponding two-to-one functions over finite fields with the even characteristic. Our constructions can produce binary linear codes with new parameters. Some of the constructed binary linear codes are optimal or almost optimal according to the online Database of Grassl, and the duals of some of them are distance-optimal with respect to the sphere packing bound. This paper also shows once again that the two-to-one functions initially studied in (Mesnager and Qu in IEEE Trans Inf Theory 65(12):7884–7895, 2019) are also promising objects in coding theory. Although our derived codes use objects considered in the very recent literature, the analysis of our designed codes involves functions having different algebraic structures (and, therefore, other Walsh transform distribution) and requires solving new systems of equations over finite fields, which is an essential step in determining the weight distribution of our constructed codes. As applications, some of the codes presented in this paper can be used to construct association schemes and secret sharing schemes with interesting access structures. [ABSTRACT FROM AUTHOR]

The zero-error capacity of a channel is defined as the maximum rate at which it is possible to transmit information with zero probability of error. In this paper, we settle all previously unsolved cases for the zero-error capacity of binary channels with one memory. [ABSTRACT FROM AUTHOR]

Image binarization is the process of representing an image pixel in binary format by assigning a value to the pixel as either 0 or 1. Before conversion to binary, the image can be in either gray-scale having pixel value between 0 to 255 or color, i.e., a pixel having value between 0 to 255 for each of the red, green, blue (RGB) channels separately. The method through which this conversion is implemented over an image is called as the binarization method. This paper reviews the methodology, contributions, advantages, and disadvantages of the existing studies on binarization methods. Further, the paper also highlights the problems in image processing with the scope for future enhancements. [ABSTRACT FROM AUTHOR]

Firmware formats vary from vendor to vendor, making it difficult to track which vendor or device the firmware belongs to, or to identify the firmware used in an embedded device. Current firmware analysis tools mainly distinguish firmware by static signatures in the firmware binary code. However, the extraction of a signature often requires careful analysis by professionals to obtain it and requires a significant investment of time and effort. In this paper, we use Doc2Vec to extract and process the character information in firmware, combine the file size, file entropy, and the arithmetic mean of bytes as firmware features, and implement the firmware classifier by combining the Extra Trees model. The evaluation is performed on 1,190 firmware files from 5 router vendors. The accuracy of the classifier is 97.18%, which is higher than that of current approaches. The results show that the proposed approach is feasible and effective. [ABSTRACT FROM AUTHOR]

A classical method of constructing a linear code over \mathrm GF(q) with a $t$ -design is to use the incidence matrix of the $t$ -design as a generator matrix over \mathrm GF(q) of the code. This approach has been extensively investigated in the literature. In this paper, a different method of constructing linear codes using specific classes of 2-designs is studied, and linear codes with a few weights are obtained from almost difference sets, difference sets, and a type of 2-designs associated to semibent functions. Two families of the codes obtained in this paper are optimal. The linear codes presented in this paper have applications in secret sharing and authentication schemes, in addition to their applications in consumer electronics, communication and data storage systems. A coding-theory approach to the characterization of highly nonlinear Boolean functions is presented. [ABSTRACT FROM AUTHOR]

This paper introduces a new method of absolute position measurement based on the hybrid encoding principle that can precisely measure the absolute position of a two-dimensional (2D) micro displacement table. The method uses pseudo-random sequences and binary codes to generate the long-distance position encoding sequences. It combines the encoding sequences with checkerboards to form a hybrid encoding to complete the design of the 2D encoding of plane positions. Then a 2D encoding disk is designed as a tool to measure 2D absolute position. Among them, the X and Y positions are designed by the modular approach, respectively, to efficiently detect the accuracy of 2D position values during the measurement. Moreover, the designed 2D encoding disk can quickly realize image correction. In order to verify the feasibility of the designed 2D encoding disk for measuring the 2D absolute position, the corresponding measurement system is built using a 2D encoding disk, a CCD camera, and a telecentric lens. The experimental results demonstrate that the average position deviation of the 2D absolute position measurement system is less than ±1 µm. [ABSTRACT FROM AUTHOR]

In this paper we provide some properties and applications of MV-algebras. We prove that a Fibonacci stationary sequence in an MV-algebra gives us an idempotent element. Moreover, taking into account of the representation of a finite MV-algebra, by using Boolean elements of this algebra, we prove that a Fibonacci sequence in an MV-algebra is always stationary. This result is interesting comparing with the behavior of such a sequence on the group (Zn, +), where the Fibonacci sequences are periodic, with the period given by the Pisano period. We also give some examples of finite MV-algebras and the number of their idempotent elements. As an application in Coding Theory, to a Boolean algebra it is attached a binary block code and it is proved that, under some conditions, the converse is also true. [ABSTRACT FROM AUTHOR]

In this paper, we construct a self-dual complete resolution of a module defined by a pair of embedded complete intersection ideals in a local ring. Our construction is based on a gluing construction of Herzog and Martsinkovsky and exploits the structure of Koszul homology in the embedded complete intersection case. As a consequence of our construction, we produce an isomorphism between certain stable homology and cohomology modules. [ABSTRACT FROM AUTHOR]

The ever-increasing need to ensure that code is reliably, efficiently and safely constructed has fueled the evolution of popular static binary code analysis tools. In identifying potential coding flaws in binaries, tools such as IDA Pro are used to disassemble the binaries into an opcode/ assembly language format in support of manual static code analysis. Because of the highly manual and resource-intensive nature involved with analyzing large binaries, the probability of overlooking potential coding irregularities and inefficiencies is quite high. In this paper, a light-weight, unsupervised data flow methodology is described which uses highly correlated data flow graph (CDFGs) to identify coding irregularities such that analysis time and required computing resources are minimized. Such analysis accuracy and efficiency gains are achieved by using a combination of graph analysis and unsupervised machine learning techniques which allows an analyst to focus on the most statistically significant flow patterns while performing binary static code analysis. [ABSTRACT FROM AUTHOR]

Hashing is an important topic in image processing, as it can help save a considerable amount of storage and computational cost. Recently, inspired by 2D strategies employed in other areas of image processing, such as feature extraction, some 2D-based hashing methods were proposed. Related papers have shown that these methods may have better image retrieval performance in terms of both effectiveness and efficiency. However, the difference in the retrieval performances of hashing methods resulting from different forms of input (1D or 2D) has not been previously studied. Whether the widely used bilinear strategy in 2D-based hashing can truly help improve the retrieval precision has not been investigated in existing research. In this paper, we conduct a comparison study on 1D and 2D feature-based hashing methods and attempt to theoretically and experimentally analyse the differences in using 1D and 2D features in hashing. Furthermore, two new hashing methods are proposed for conducting the comparison experiments. Through a comprehensive study, we obtain three main conclusions in this paper: 1) Linear projection on 1D features and bilinear projection on 2D features are essentially the same. 2) 2D-based hashing methods are obviously more efficient than 1D-based methods for analysing high-dimensional input features. 3) 2D-based hashing methods show generally better performance for solving small sample size problems. [ABSTRACT FROM AUTHOR]

POI recommendation has gradually become an important topic in the field of service recommendation, which is always achieved by mining user behavior patterns. However, the context information of the collaborative signal is not encoded in the embedding process of traditional POI recommendation methods, which is not enough to capture the collaborative signal among different users. Therefore, a POI recommendation algorithm is presented by using social-time context graph neural network model (GNN) in location-based social networks. First, it finds similarities between different social relationships based on the users' social and temporal behavior. Then, the similarity among different users is calculated by an improved Euclidean distance. Finally, it integrates the graph neural network, the interaction bipartite graph of users and social-time information into the algorithm to generate the final recommendation list in this paper. Experiments on real datasets show that the proposed method is superior to the state-of-the-art POI recommendation methods. [ABSTRACT FROM AUTHOR]

Boolean functions with high algebraic immunity are important cryptographic primitives in some stream ciphers. In this paper, two methodologies for constructing minimal binary codes from sets, Boolean functions and vectorial Boolean functions with high algebraic immunity, are proposed. More precisely, a general construction of new minimal codes using minimal codes contained in Reed-Muller codes and sets without nonzero low degree annihilators is presented. The other construction allows us to yield minimal codes from certain subcodes of Reed-Muller codes and vectorial Boolean functions with high algebraic immunity. Via these general constructions, infinite families of minimal binary linear codes of dimension $m$ and length less than or equal to $m(m+1)/2$ are obtained. Besides, a lower bound on the minimum distance of the proposed minimal linear codes is established. Conjectures and open problems are also presented. The results of this paper show that Boolean functions with high algebraic immunity have nice applications in several fields additionally to symmetric cryptography, such as coding theory and secret sharing schemes. [ABSTRACT FROM AUTHOR]

Against the backdrop of highly developed software engineering, code reuse has been widely recognized as an effective strategy to significantly alleviate the burden of development and enhance productivity. However, improper code citation could lead to security risks and license issues. With the source codes of many pieces of software being difficult to obtain, binary code similarity analysis (BCSA) has been extensively implemented in fields such as bug search, code clone detection, and patch analysis. This research selects 39 papers on BCSA from top-tier and emerging conferences within artificial intelligence, network security, and software engineering from 2016 to 2022 for deep analysis. The central focus lies on methods utilizing deep learning technologies, detailing a thorough summary and the arrangement of the application and implementation specifics of various deep learning technologies. Furthermore, this study summarizes the research patterns and development trends in this field, thereby proposing potential directions for future research. [ABSTRACT FROM AUTHOR]

Previous studies have shown that the application of the M-coder in the H.264/AVC and H.265/HEVC video coding standards allows for highly parallel implementations without decreasing maximal frequencies. Although the primary limitation on throughput, originating from the range register update, can be eliminated, other limitations are associated with low register processing. Their negative impact is revealed at higher degrees of parallelism, leading to a gradual throughput saturation. This paper presents optimizations introduced to the generative hardware architecture to increase throughputs and hardware efficiencies. Firstly, it can process more than one bypass-mode subseries in one clock cycle. Secondly, aggregated contributions to the codestream are buffered before the low register update. Thirdly, the number of contributions used to update the low register in one clock cycle is decreased to save resources. Fourthly, the maximal one-clock-cycle renormalization shift of the low register is increased from 32 to 64 bit positions. As a result of these optimizations, the binary arithmetic coder, configured for series lengths of 27 and 2 symbols, increases the throughput from 18.37 to 37.42 symbols per clock cycle for high-quality H.265/HEVC compression. The logic consumption increases from 205.6k to 246.1k gates when synthesized on 90 nm TSMC technology. The design can operate at 570 MHz. [ABSTRACT FROM AUTHOR]

The feature graph (Q-H graph) is the best way to intuitively and quantitatively reflect all features of the ventilation network. In this paper, an optimized adaptive genetic algorithm is proposed to solve the problem that rectangular blocks are cut in the process of drawing a Q-H diagram of a three-dimensional ventilation network. The algorithm adopts binary coding based on node sorting and mixed genetic coding based on integer coding. The formulas for calculating the adaptive crossover rate and mutation rate are designed, which can effectively generate new individuals and get rid of the search for local optimal values, ensuring the global optimal solution. Matlab was used to test the optimization effect of the Q-H graph; the results show that, for a complex ventilation network, the improved adaptive genetic algorithm can make the Q-H graph significantly reduce the number of rectangular pieces, which is divided, and make the Q-H graph have better effect to draw clearly and intuitively. [ABSTRACT FROM AUTHOR]

Nested linear coding is a widely used technique in wireless communication systems for improving both security and reliability. Some parameters, such as the relative generalized Hamming weight and the relative dimension/length profile, can be used to characterize the performance of nested linear codes. In addition, the rank properties of generator and parity-check matrices can also precisely characterize their security performance. Despite this, finding optimal nested linear secrecy codes remains a challenge in the finite-blocklength regime, often requiring brute-force search methods. This paper investigates the properties of nested linear codes, introduces a new representation of the relative generalized Hamming weight, and proposes a novel method for finding the best nested linear secrecy code for the binary erasure wiretap channel by working from the worst nested linear secrecy code in the dual space. We demonstrate that our algorithm significantly outperforms the brute-force technique in terms of speed and efficiency. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) cross-modal text–image retrieval has great application value in many fields such as military and civilian. Existing methods utilize the deep network to project the images and texts into a common space and measure the similarity. However, the majority of those methods only utilize the inter-modality information between different modalities, which ignores the rich semantic information within the specific modality. In addition, due to the complexity of the RS images, there exists a lot of interference relation information within the extracted representation from the original features. In this paper, we propose a jointly guided deep network for fine-grained cross-modal RS text–image retrieval. First, we capture the fine-grained semantic information within the specific modality and then guide the learning of another modality of representation, which can make full use of the intra- and inter-modality information. Second, to filter out the interference information within the representation extracted from the two modalities of data, we propose an interference filtration module based on the gated mechanism. According to our experimental results, significant improvements in terms of retrieval tasks can be achieved compared with state-of-the-art algorithms. The source code is available at https://github.com/CQULab/JGDN. [ABSTRACT FROM AUTHOR]

The new type of multi-temporal global land use data with multiple classes is able to provide information on both the different land covers and their temporal changes; furthermore, it is able to contribute to many applications, such as those involving global climate and Earth ecosystem analyses. However, the current accuracy assessment methods have two limitations regarding multi-temporal land cover data that have multiple classes. First, multi-temporal land cover uses data from multiple phases, which is time-consuming and inefficient if evaluated one by one. Secondly, the conversion between different land cover classes increases the complexity of the sample stratification, and the assessments with different types of land cover suffer from inefficient sample stratification. In this paper, we propose a spatiotemporal stratified sampling method for stratifying the multi-temporal GlobeLand30 products for China. The changed and unchanged types of each class of data in the three periods are used to obtain a reasonable stratification. Then, the strata labels are simplified by using binary coding, i.e., a 1 or 0 representing a specified class or a nonspecified class, to improve the efficiency of the stratification. Additionally, the stratified sample size is determined by the combination of proportional allocation and empirical evaluation. The experimental results show that spatiotemporal stratified sampling is beneficial for increasing the sample size of the "change" strata for multi-temporal data and can evaluate not only the accuracy and area of the data in a single data but also the accuracy and area of the data in a multi-period change type and an unchanged type. This work also provides a good reference for the assessment of multi-temporal data with multiple classes. [ABSTRACT FROM AUTHOR]

This paper proposes a blind color image watermarking method based on Hadamard transform to deal with the infringement of color images in the complex network environment. First of all, the color watermark image is channeled and each pixel in the channel image is converted into an 8-bit binary code. And then, the color host image is also channeled and expanded into 4 × 4 non-overlapping pixel blocks based on the selected embedding position. Finally, Hadamard transform is put on the pixel block, and 1-bit watermark information is embedded by the coefficient pair of the first line of Hadamard coefficient matrix. When extracting watermark, the watermark can be extracted only by comparing the size relationship of the coefficient pairs without the help of the original image. In this paper, a voting system of the minority obeying the majority is designed and implemented, which actually enhances the robustness of the method. In addition, a high security encryption system is designed by combining symmetric key and public key, and the correlation between RGB color channels is considered. Simulation results show that the proposed method has better robustness, real-time performance and security compared with similar methods, and meets certain requirements in terms of invisibility and capacity. [ABSTRACT FROM AUTHOR]

We study the probability of an undetected error for general q-ary codes. We give upper and lower bounds on this quantity, by the Linear Programming and the Polynomial methods, as a function of the length, size, and minimum distance. Sharper bounds are obtained in the important special case of binary Hamming codes. Finally, several examples are given to illustrate the results of this paper. [ABSTRACT FROM AUTHOR]

Image representation and approximate query is always a research challenge and is affected greatly by the dimension and size of images. Since hash-based methods and binary encodings in combination with other techniques, such as kernel tricks, a longer binary code and mapping vectors rotation, can maintain a linear query time and query accuracy, they have been used in this area broadly. This paper develops principal component analysis hashing (PCAH) and unequal length of binary coding to divide images into more categories, denoted as PCA-MD, to improve accuracy of the representation and lookup of images. This paper firstly proves that the eigenvector mapping is locality sensitive, which is the basis for more classes division. For the anisotropy of the eigenvectors, PCA-MD utilizes an unequal length of binary coding and fewer eigenvectors, rather than an equal code, to divide the images mapped on every eigenvector to more categories. Moreover, L1-norm distance is applied to measure the distances of images to avoid the enormous computation of Euclidean distance. Theoretical analysis and extensive experimental results demonstrate that the PCA-MD has a higher query performance and a slight longer run time than the state-of-the-art approaches based on the Hamming distance. This in turn verifies that PCAH is a locality sensitive hash and that partitioning into more categories rather than only two categories is reasonable. [ABSTRACT FROM AUTHOR]

Two general constructions of linear codes with functions over finite fields have been extensively studied in the literature. The first one is given by C(ƒ) = {Tr(aƒ(x) + bx)x∈Fqm*: a, b ∈ Fqm}, where q is a prime power, Fqm* = Fqm \ {0}, Tr is the trace function from Fqm to Fq, and ƒ(x) is a function from Fqm to Fqm with ƒ(0) = 0. Almost bent functions, quadratic functions and some monomials on F2m were used in the first construction, and many families of binary linear codes with few weights were obtained in the literature. This paper studies some punctured codes of these binary codes. Several families of binary linear codes with few weights and new parameters are obtained in this paper. Several families of distance-optimal binary linear codes with new parameters are also produced in this paper. [ABSTRACT FROM AUTHOR]

Fingerprint retrieval aims to identify a query fingerprint image in a large database using indexing algorithms. Because of the abundant level 3 pore features within high-resolution fingerprint images, pore-based fingerprint retrieval algorithms have been rapidly developed. These retrieval algorithms, however, suffer from severe calculation-consuming problems with the pores increasing. This paper proposes a pore-based fingerprint retrieval method for high-resolution fingerprint images. The proposed method consists of two main steps. 1) In the pore indexing step, an indexing space is constructed using the binary codes of pores in enrolled images. Then, a designed graph-based searching algorithm searches the nearest neighbors of pores from the query image to construct one-to-many correspondences. 2) In the refinement step, the one-to-many correspondences are refined by a random walker-based graph comparison algorithm to remove the false correspondences. The remained nearest neighbors are used to calculate the similarities between the query image and the enrolled images. The proposed method is evaluated on two databases, showing that our method achieves better retrieval accuracies with a higher speed than the existing pore-based retrieval algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we apply two-to-one functions over F2n in two generic constructions of binary linear codes. We consider two-to-one functions in two forms: (1) generalized quadratic functions; and (2) (x2t + x)e with gcd (t, n) = gcd(e, 2n−1) = 1. Based on the study of the Walsh transforms of those functions or their variants, we present many classes of linear codes with few nonzero weights, including one weight, three weights, four weights, and five weights. The weight distributions of the proposed codes with one weight and with three weights are determined. In addition, we discuss the minimum distance of the dual of the constructed codes and show that some of them achieve the sphere packing bound. Moreover, examples show that some codes in this paper have best-known parameters. [ABSTRACT FROM AUTHOR]

Because Gray code has good spatial traversal and easy to generate binary conversion, this paper proposes an image encryption algorithm based on Gray code scrambling. Firstly, the Lorenz system is used to generate chaotic sequences for binary and Gray code transformation to achieve global scrambling. Different from the traditional row (column) XOR method, this paper uses the snake shape to diffuse the row (column) XOR. The lines of the scrambled image are first XORed from left to right in a serpentine order, and the row elements are cyclically shifted. Then, the columns of the image are XORed from right to left in a serpentine order, and the column elements are cyclically shifted. Finally, the scrambled image is further diffused. The comparative experiment shows that the algorithm has good encryption effect. Not only is it well resistant to differential cryptanalysis and exhaustive attack, but it also improves key sensitivity. [ABSTRACT FROM AUTHOR]

Time series research in academic and industrial fields has attracted wide attention. However, the frequency information contained in time series still lacks effective modeling. The studies found that time series forecasting relies on different frequency patterns: short‐term series forecasting relies more on high‐frequency components, while long‐term forecasting focuses more on low‐frequency data. To better describe the multifrequency mode, a dual‐stage multifeature adaptive frequency domain prediction model (DMAFD) is proposed in this paper. DMAFD contains two stages. First, it adopts the XGBoost algorithm to obtain a feature vector by analyzing the feature importance. Second, the frequency feature extraction of time series and the frequency aware modeling of the target sequence is integrated, for building an end‐to‐end prediction network based on the dependence of time series on frequency mode. The innovation is reflected in the fact that the prediction network can automatically focus on multifrequency components according to the dynamic evolution of the input sequence. Extensive experiments on four real data sets from different fields show that DMAFD obtains higher accuracy and smaller lags in time step analysis compared with state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

Multiple-input-multiple-output (MIMO) can provide superior performances such as system capacity, linkage, etc. But also it will bring high RF costs and system complexity, especially in large scale MIMO systems. Antenna selection (AS) is proved to be a trade-off between good performances and complexity. Specifically, from the perspective of both transmit and receive antennas, the joint transmit and receive antenna selection (JTRAS) is employed in MIMO systems. Up to now, some algorithms of JTRAS have been studied in MIMO systems. However, most of them are mainly focused on just one aspect about accuracy or complexity. Especially, compared to numerical analysis, the implementation of swarm intelligence algorithm in JTRAS needs to be studied extensively. In the paper, three intelligent algorithms, i.e. genetic algorithm, cat swarm algorithm and particle swarm algorithm are studied and compared in terms of accuracy, cost, and complexity. In addition, fractional coding is proposed in the algorithms instead of binary integer coding. The simulation results demonstrate that all three algorithms can efficiently accomplish the antenna selection. PSO has the best accuracy and stability, but the complexity of PSO is also highest. If we take overall performances in consideration, CSO is the best choice especially in practical implementation. Moreover, fractional coding will provide better performance than binary integer coding. [ABSTRACT FROM AUTHOR]

The primary goal of this paper is to study Spanier–Whitehead duality in the K(n)-local category. One of the key players in the K(n)-local category is the Lubin–Tate spectrum E n , whose homotopy groups classify deformations of a formal group law of height n, in the implicit characteristic p. It is known that E n is self-dual up to a shift; however, that does not fully take into account the action of the automorphism group G n of the formal group in question. In this paper we find that the G n -equivariant dual of E n is in fact E n twisted by a sphere with a non-trivial (when n > 1 ) action by G n . This sphere is a dualizing module for the group G n , and we construct and study such an object I G for any compact p-adic analytic group G . If we restrict the action of G on I G to certain type of small subgroups, we identify I G with a specific representation sphere coming from the Lie algebra of G . This is done by a classification of p-complete sphere spectra with an action by an elementary abelian p-group in terms of characteristic classes, and then a specific comparison of the characteristic classes in question. The setup makes the theory quite accessible for computations, as we demonstrate in the later sections of this paper, determining the K(n)-local Spanier–Whitehead duals of E n hH for select choices of p and n and finite subgroups H of G n . [ABSTRACT FROM AUTHOR]

Nonlinear optical switching is a prominent technology for high-speed data processing. All-optical switching based on phase-shifted fiber Bragg Grating provides a tremendous opportunity with advantages such as improved efficiency, sensitivity, lightweight, and cost-effective parameters as compared to traditional optical switching for designing different types of optical combinational digital circuits. The paper describes the concepts of phase-shifted FBG structure, which actually, performs the switching on the basis of cross-phase modulation (XPM) scheme and non-linear optics. The paper describes the details of the coupled-mode theory associated with the FBG technology and its effectiveness as a powerful optical switch using the concept of non-linear optics. The non-linear FBG-based switch is analyzed to implement the all-optical 4-bit binary to gray code converter that has been designed. The proposed architecture leads to Very large-scale integration optics (VLSIO), which involves perfect and high-speed switching, switch in fiber(SIF) in the field of all-optical communication systems and switching technology. The proposed scheme can be a stepping stone in the field of fast switching and signal processing and it may provide a new horizon to the switching phenomena. [ABSTRACT FROM AUTHOR]

Let f be a normalized Hecke eigenform of weight k for the full modular group S L 2 (ℤ). In this paper, we obtain the asymptotic of higher moments of general divisor functions associated to the Fourier coefficients of Rankin–Selberg L-functions R (s , f × f) , supported at the integers represented by primitive integral positive-definite binary quadratic forms (reduced forms) of a fixed discriminant D < 0. We improve previous results in the case when the reduced form is given by (x 1 , x 2) = x 1 2 + x 2 2 . [ABSTRACT FROM AUTHOR]

BCH codes are a special subclass of cyclic codes and have many important applications in data storage and communication systems. In this paper, we investigate the structure of binary linear complementary dual (LCD) BCH codes with length $ n = \frac{{{2^m} + 1}}{3} $, where $ m \geq 7 $ is an odd integer. By exploring cyclotomic cosets modulo $ n $, we determine the dimension of LCD BCH codes for designed distance $ \delta $ in the range $ 2 \le \delta \le{2^{\frac{{m + 1}}{2}}} $. Furthermore, we compute the first five largest coset leaders modulo $ n $ and construct some binary LCD BCH codes. We also present two families of optimal binary linear codes from LCD BCH codes. [ABSTRACT FROM AUTHOR]

In this paper, a genetic algorithm, one of the evolutionary algorithm optimization methods, is used for the first time for the problem of computing extremal binary self-dual codes. We present a comparison of the computational times between the genetic algorithm and a linear search for different size search spaces and show that the genetic algorithm is capable of computing binary self-dual codes significantly faster than the linear search. Moreover, by employing a known matrix construction together with the genetic algorithm, we are able to obtain new binary self-dual codes of lengths 68 and 72 in a significantly short time. In particular, we obtain 11 new binary self-dual codes of length 68 and 17 new binary self-dual codes of length 72. [ABSTRACT FROM AUTHOR]

CSS-T codes were recently introduced as quantum error-correcting codes that respect a transversal gate. A CSS-T code depends on a CSS-T pair, which is a pair of binary codes (C 1 , C 2) such that C 1 contains C 2 , C 2 is even, and the shortening of the dual of C 1 with respect to the support of each codeword of C 2 is self-dual. In this paper, we give new conditions to guarantee that a pair of binary codes (C 1 , C 2) is a CSS-T pair. We define the poset of CSS-T pairs and determine the minimal and maximal elements of the poset. We provide a propagation rule for nondegenerate CSS-T codes. We apply some main results to Reed–Muller, cyclic and extended cyclic codes. We characterize CSS-T pairs of cyclic codes in terms of the defining cyclotomic cosets. We find cyclic and extended cyclic codes to obtain quantum codes with better parameters than those in the literature. [ABSTRACT FROM AUTHOR]

In this paper, we study the relation between the linear subspace of the pseudo-noise (PN)-sequences generated by a primitive polynomial and the simplex code. This family of sequences can be also seen as an Maximum Distance Separable (MDS) 2 -linear code over 2 r . Furthermore, we see how to compute the family of generalized sequences produced by a primitive polynomial by means of a first-order Reed–Muller code. [ABSTRACT FROM AUTHOR]

Weber's law reveals the relationship between human perception and perceptual stimuli. Inspired by the theory, this paper designs a multi-level convolution correlation feature statistic method for image retrieval. Firstly, the difference between a central pixel and its neighbors is described by Weber's law through computing the differential excitation of image. Then, a multi-level saliency map is obtained by binary transformation and convolution operation. Thirdly, to exploit spatial correlation information of the image, a pixels pair-wise correlation and hierarchy statistic model is constructed. Finally, all intermediate features are concatenated into one histogram, which includes salient color and texture features. Extensive experiments demonstrate the proposed method of this paper has excellent performance. [ABSTRACT FROM AUTHOR]

In this paper, we give an explicit representation and enumeration for negacyclic codes of length 2kn over the local non-principal ideal ring R = Z4 + uZ4 (u2 = 0), where k,n are arbitrary positive integers and n is odd. In particular, we present all distinct negacyclic codes of length 2kn over R precisely. Moreover, we provide an exact mass formula for the number of negacyclic codes of length 2kn over R and correct several mistakes in some literatures. [ABSTRACT FROM AUTHOR]

With the widespread application of new technologies, fine-grained authorization requires a large number of access control policies. However, the existing policy retrieval method applied to a large-scale policy environment has the problem of low retrieval efficiency. Therefore, this paper proposes an attribute access control policy retrieval method based on the binary sequence. This method uses binary identification and binary code to express access control requests and policies. When the policy is retrieved, the appropriate group is selected through the logical operation of the access control request and the policy binary identification. Within the group, the binary code of the access control request is matched with the binary code of all rules to find suitable rules, thereby reducing the number of matching attribute-value pairs in the rule and improving the efficiency of policy retrieval. Experimental results show that the policy retrieval method proposed in this paper has higher retrieval efficiency. [ABSTRACT FROM AUTHOR]

This paper takes a fresh look at the image authentication problem and proposes an alternative framework for personalized authentication based on the hash learning technology. Conventional image authentication methods tend to provide a general authentication framework for all images with the fixed quantization strategy and fixed control parameters determined based on given limited images and attacks. However, they may suffer from more or less misjudgments in practice, not to mention performance degradation when encountering out-of-sample images. Instead of proposing a new feature extraction algorithm, a novel personalized authentication framework which incorporates the distance metric learning technology and supervised quantization strategy to the process of image authentication is proposed in this paper. The tamper detection task is reformulated as a new supervised manipulation classification problem. For each input image, various content-preserving and content-changing samples are generated automatically firstly. Then, feature representations of all samples can be obtained by existing feature extraction methods. After that, a weighted large margin for manipulation classification (WLMMC) scheme is proposed to learn an effective feature mapping space to improve the classification performance between content-changing samples and content-preserving samples. During the quantization stage, a novel supervised personalized quantization strategy (SPQ), which is motivated by the observation that different attacks have different degrees of influence on feature components, is proposed to learn more compact yet discriminative binary codes for each input image. Effectiveness of the proposed framework is qualitatively and quantitatively demonstrated on a variety of images. Extensive experiments show that the proposed framework can significantly improve the authentication performance over the state-of-the-art techniques while achieve more compact hash codes flexibly as required. [ABSTRACT FROM AUTHOR]

The endoscopy procedure has demonstrated great efficiency in detecting stomach lesions, with extensive numbers of endoscope images produced globally each day. The content‐based gastric image retrieval (CBGIR) system has demonstrated substantial potential in gastric image analysis. Gastric precancerous diseases (GPD) have higher prevalence in gastric cancer patients. Thus, effective intervention is crucial at the GPD stage. In this paper, a CBGIR method is proposed using a modified ResNet‐18 to generate binary hash codes for a rapid and accurate image retrieval process. We tested several popular models (AlexNet, VGGNet and ResNet), with ResNet‐18 determined as the optimum option. Our proposed method was valued using a GPD data set, resulting in a classification accuracy of 96.21 ± 0.66% and a mean average precision of 0.927 ± 0.006, outperforming other state‐of‐art conventional methods. Furthermore, we constructed a Gastric‐Map (GM) based on feature representations in order to visualize the retrieval results. This work has great auxiliary significance for endoscopists in terms of understanding the typical GPD characteristics and improving aided diagnosis. [ABSTRACT FROM AUTHOR]

Based on the relaxation digital‐to‐analog converter (ReDAC) architecture and the capacitor charge/discharge function under discrete time conditions, this paper proposes a new mathematical model for the output voltage of the ReDAC to implement the unconstrained relaxation DAC (Uc‐ReDAC). The system clock calibration is not required. An adaptive probabilistic binary particle swarm optimization (AP‐BPSO) algorithm is proposed for solving the binary coding configuration problem under the Uc‐ReDAC model. Simulation results show that the 8‐bit (10‐bit) Uc‐ReDAC has an average sampling rate of 7.9 MS/s (6.76 MS/s). [ABSTRACT FROM AUTHOR]