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Recently, multilabel classification has generated considerable research interest. However, the high dimensionality of multilabel data incurs high costs; moreover, in many real applications, a number of labels of training samples are randomly missed. Thus, multilabel classification can have great complexity and ambiguity, which means some feature selection methods exhibit poor robustness and yield low prediction accuracy. To solve these issues, this paper presents a novel feature selection method based on multilabel fuzzy neighborhood rough sets (MFNRS) and maximum relevance minimum redundancy (MRMR) that can be used on multilabel data with missing labels. First, to handle multilabel data with missing labels, a relation coefficient of samples, label complement matrix, and label-specific feature matrix are constructed and implemented in a linear regression model to recover missing labels. Second, the margin-based fuzzy neighborhood radius, fuzzy neighborhood similarity relationship, and fuzzy neighborhood information granule are developed. The MFNRS model is built based on multilabel neighborhood rough sets combined with fuzzy neighborhood rough sets. Based on algebra and information views, certain fuzzy neighborhood entropy-based uncertainty measures are proposed for MFNRS. The fuzzy neighborhood mutual information-based MRMR model with label correlation is improved to evaluate the performance of candidate features. Finally, a feature selection algorithm is designed to improve the performance for multilabel data with missing labels. Experiments on twenty datasets verify that our method is effective not only for recovering missing labels but also for selecting significant features with better classification performance.

Multiple patterning lithography has been widely adopted in advanced technology nodes of VLSI manufacturing. As a key step in the design flow, multiple patterning layout decomposition (MPLD) is critical to design closure. Due to the $\mathcal {N} \mathcal {P} $ -hardness of the general decomposition problem, various efficient algorithms have been proposed with high-quality solutions. However, with increasingly complicated design flow and peripheral processing steps, developing a high-quality layout decomposer becomes more and more difficult, slowing down further advancement in this field. This article presents $\mathsf {OpenMPL}$ (2020), an open-source layout decomposition framework, with well-separated peripheral processing and core solving steps. Besides, previous algorithms or techniques are inspected and several issues are discovered. We then propose corresponding new algorithms to resolve these issues. The experiments demonstrate the effectiveness of our proposed algorithms and the efficiency of $\mathsf {OpenMPL}$ .

We propose new repair schemes for Reed-Solomon codes that use subspace polynomials and hence generalize previous works in the literature that employ trace polynomials. The Reed-Solomon codes are over $\mathbb {F}_{{q}^{\ell }}$ and have redundancy ${{r}} = {{n}}-{{k}} \geq {{q}}^{{m}}$ , $1\leq {{m}}\leq \ell $ , where ${{n}}$ and ${{k}}$ are the code length and dimension, respectively. In particular, for one erasure, we show that our schemes can achieve optimal repair bandwidths whenever ${{n}}={{q}}^\ell $ and ${{r}} = {{q}}^{{m}}$ , for all $1 \leq {{m}} \leq \ell $ . For two erasures, our schemes use the same bandwidth per erasure as the single erasure schemes, for $\ell /{{m}}$ is a power of ${{q}}$ , and for $\ell ={{q}}^{{a}}$ , ${{m}}={{q}}^{{b}}-1>1$ ( ${{a}} \geq {{b}} \geq 1$ ), and for ${{m}}\geq \ell /2$ when $\ell $ is even and ${{q}}$ is a power of two.

Feature selection based on entropy structure can be roughly divided into two types according to whether they are related to kernel density estimation (KDE). The first type is feature selection based on non-KDE entropy. The second type is feature selection based on KDE entropy. Compared with the first type, the second type avoids discretization and obtains more accurate mutual information when handling continuous data. However, existing feature selection methods based on KDE entropy neglect the fact that samples with noise have negative impacts on KDE. Besides, the feature evaluation functions don’t effectively assess relevance and redundancy of features. Thus, a feature selection method via maximizing independent and minimizing redundant classification information ratio is constructed based on adaptive weighted kernel density estimation. Firstly, an adaptive weighted kernel density estimation model is designed. Secondly, the entropy structure is defined by the adaptive weighted kernel density estimation model, and their theoretical properties are explored. Thirdly, a feature selection algorithm via maximizing independent and minimizing redundant classification information ratio is designed from the viewpoint of adaptive weighted kernel density estimation. Finally, comprehensive experiments are performed. The results illustrate that our approach has certain robustness, validity and superiority compared with other representative feature selection approaches.

To reduce the redundancy among different color channels, e.g., YUV, previous methods usually adopt a linear model that tends to be oversimple for complex image content. We propose a neural-network-based method for cross-channel prediction in intra frame coding. The proposed network utilizes twofold cues, i.e., the neighboring reconstructed samples with all channels, and the co-located reconstructed samples with partial channels. Specifically, for YUV video coding, the neighboring samples with YUV are processed by several fully connected layers; the co-located samples with Y are processed by convolutional layers; and the proposed network fuses the twofold cues. We observe that the integration of twofold information is crucial to the performance of intra prediction of the chroma components. We have designed the network architecture to achieve a good balance between compression performance and computational efficiency. Moreover, we propose a transform domain loss for the training of the network. The transform domain loss helps obtain more compact representations of residues in the transform domain, leading to higher compression efficiency. The proposed method is plugged into HEVC and VVC test models to evaluate its effectiveness. Experimental results show that our method provides more accurate cross-channel intra prediction compared with previous methods. On top of HEVC, our method achieves on average 1.3%, 5.4%, and 3.8% BD-rate reductions for Y, Cb, and Cr on common test sequences, and on average 3.8%, 11.3%, and 9.0% BD-rate reductions for Y, Cb, and Cr on ultra-high-definition test sequences. On top of VVC, our method achieves on average 0.5%, 1.7%, and 1.3% BD-rate reductions for Y, Cb, and Cr on common test sequences.

In our recent work on iterative computation in hardware, we showed that arbitrary-precision solvers can perform more favourably than their traditional arithmetic equivalents when the latter’s precisions are either under- or over-budgeted for the solution of the problem at hand. Significant proportions of these performance improvements stem from the ability to infer the existence of identical most-significant digits between iterations. This technique uses properties of algorithms operating on redundantly represented numbers to allow the generation of those digits to be skipped, increasing efficiency. It is unable, however, to guarantee that digits will stabilise, i.e. never change in any future iteration. In this article, we address this shortcoming, using interval and forward error analyses to prove that digits of high significance will become stable when computing the approximants of systems of linear equations using stationary iterative methods. We formalise the relationship between matrix conditioning and the rate of growth in most-significant digit stability, using this information to converge to our desired results more quickly. Versus our previous work, an exemplary hardware implementation of this new technique achieves an up-to 2.2x speedup in the solution of a set of variously conditioned systems using the Jacobi method.

The Reversible data hiding (RDH) approach can retrieve the original image from the marked image without any distortion. RDH in encrypted images is an approach that hides extra information into the ciphertext using a skill of recovering the actual data losslessly. To guarantee reversibility for addressing the information redundancy drawback, the cover image pixels are copied into two images. This paper presents a high capacity RDH scheme in encrypted images using fuzzy-based encryption. Initially, the texture classification is processed by a convolutional neural network (CNN) to classify the dense and transparent region. It automatically identifies the significant features without any individual supervision. Then, the plain text encryption is activated by the fuzzy group teaching with infinite elliptic curve (FGTIE) method. To overcome the demerit of FCM, the GTA is hybrid with FCM approach and the encryption is processed by the IE method. Next, a new embedding approach is used to enhance the embedding capacity, namely quotient multi-pixel value differencing (QMPVD). In order to obtain the higher PSNR and payload, the multi-pixel differencing is hybrid with the quotient value differencing. Finally, the original data is extracted and recovered with good quality and high capacity. The performances are evaluated using several performance metrics such as PSNR, SSIM, BER, MSE, embedding capacity/payload, sensitivity, specificity, tampering ratio, correlation coefficient, number of pixel change rate and unified average changing intensity. The performance of PSNR and capacity is compared with existing approaches named Encrypted image-based RDH with Paillier cryptosystem (EIRDH-PC), EIRDH with Redundancy Transfer (EIRDH-RT) and EIRDH with pixel value ordering (EIRDH-PVO). The performance is calculated for three groups of images such as the brain, lungs and abdomen. The implementation results show that the introduced model attained better performance compared to existing approaches in terms of PSNR and capacity. Besides, the proposed approach achieved the merits of no pixel expansion, lossless and alternative order recovery.

This paper details the examination of a particular case of data compression, where the compression algorithm removes the redundancy from data, which occurs when edge-based compression algorithms compress (previously compressed) pixelated images. The newly created redundancy can be removed using another round of compression. This work utilized the JPEG-LS as an example of an edge-based compression algorithm for compressing pixelated images. The output of this process was subjected to another round of compression using a more robust but slower compressor (PAQ8f). The compression ratio of the second compression was, on average, 18%, which is high for random data. The results of the second compression were superior to the lossy JPEG. Under the used data set, lossy JPEG needs to sacrifice 10% on average to realize nearly total lossless compression ratios of the two-successive compressions. To generalize the results, fast general-purpose compression algorithms (7z, bz2, and Gzip) were used too.

An indel refers to a single insertion or deletion, while an edit refers to a single insertion, deletion or substitution. In this article, we investigate codes that correct either a single indel or a single edit and provide linear-time algorithms that encode binary messages into these codes of length n. Over the quaternary alphabet, we provide two linear-time encoders. One corrects a single edit with $\lceil {\log \text {n}}\rceil+\text {O}(\log \log \text {n})$ redundancy bits, while the other corrects a single indel with $\lceil {\log \text {n}}\rceil+2$ redundant bits. These two encoders are order-optimal . The former encoder is the first known order-optimal encoder that corrects a single edit, while the latter encoder (that corrects a single indel) reduces the redundancy of the best known encoder of Tenengolts (1984) by at least four bits. Over the DNA alphabet, we impose an additional constraint: the $\mathtt {GC}$ -balanced constraint and require that exactly half of the symbols of any DNA codeword to be either $\mathtt {C}$ or $\mathtt {G}$ . In particular, via a modification of Knuth’s balancing technique, we provide a linear-time map that translates binary messages into $\mathtt {GC}$ -balanced codewords and the resulting codebook is able to correct a single indel or a single edit. These are the first known constructions of $\mathtt {GC}$ -balanced codes that correct a single indel or a single edit.

The paper presents techniques for analyzing the expected download time in distributed storage systems that employ systematic availability codes. These codes provide access to hot data through the systematic server containing the object and multiple recovery groups. When a request for an object is received, it can be replicated (forked) to the systematic server and all recovery groups. We first consider the low-traffic regime and present the close-form expression for the download time. By comparison across systems with availability, maximum distance separable (MDS), and replication codes, we demonstrate that availability codes can reduce download time in some settings but are not always optimal. In the high-traffic regime, the system consists of multiple inter-dependent Fork-Join queues, making exact analysis intractable. Accordingly, we present upper and lower bounds on the download time, and an M/G/1 queue approximation for several cases of interest. Via extensive numerical simulations, we evaluate our bounds and demonstrate that the M/G/1 queue approximation has a high degree of accuracy., Comment: Accepted for a publication in IEEE Transactions on Communications

Levenshtein introduced the problem of constructing k -deletion correcting codes in 1966, proved that the optimal redundancy of those codes is $ {O}(k~\log ~{N})$ for constant k, and proposed an optimal redundancy single-deletion correcting code (using the so-called VT construction). However, the problem of constructing optimal redundancy k -deletion correcting codes remained open. Our key contribution is a major step towards a complete solution to this longstanding open problem for constant k . We present a k -deletion correcting code that has redundancy $8 {k}\log~{N} + {o}(\log~{N})$ when $ {k}= {o}(\sqrt {\log \log~{N}})$ and encoding/decoding algorithms of complexity $ {O}({N}^{2 {k}+1})$ .

Fractional interpolation has been extensively utilized in a series of video coding standards to generate fractional precision prediction to remove the temporal redundancy in consecutive frames. In the traditional interpolation filter based methods, the fractional samples are interpolated through a linear combination of the neighboring integer samples. This method is simple yet unable to accurately characterize the nonstationary video signals. Recently, convolutional neural network has been utilized in the fractional interpolation and shows superior performance compared with the traditional methods. However, only the reconstruction of the reference frame is used as the infer information source. All the other information contained in the bitstream or generated during the encoding/decoding procedure denoted as the compression prior is not utilized at all. In this paper, we give the first trial to involve some compression priors into the CNN to improve the performance of a in-loop coding tool. Specifically, we propose a Compression Priors assisted Convolutional Neural Network (CPCNN) to further improve the fractional interpolation efficiency. In addition to the reconstructed component, we additionally utilize two other compression priors – the corresponding residual component and col-located high quality component to boost the performance. Specifically, the residual component that indicates the prediction efficiency and contains effective texture information is utilized as a complementary input to the reconstructed one. While the col-located component provides more useful high quality information to help the reconstruction get rid of the quality fluctuation. Furthermore, a special network structure is designed to learn powerful representations of these triple input components. Comprehensive experiments have been conducted to demonstrate the effectiveness of our proposed CPCNN. The experimental results show that compared to HEVC, our proposed CPCNN achieves on average of 5.3%, 2.8% and 1.9% BD-Rate savings under LDP, LDB and RA configurations, respectively.

We investigate a new approach for designing spectral shaping block codes with a target spectrum, $H_{t}(f)$ , that has been specified at a plurality of frequencies. We analyze the probability density function of the spectral power density function of uncoded $n$ -symbol bipolar codewords. We present estimates of the redundancy and the spectrum of spectral shaping codes with specified target spectral densities $H_{t}(f_{i})$ at frequencies $f_{i}$ . Constructions of low-redundancy codes with suppressed low-frequency content are presented that compare favorably with conventional dc-balanced codes currently used in data transmission and data storage devices with applications in consumer electronics.

This letter proposes novel methods for designing two-dimensional (2-D) weight-constrained codes for reducing the parasitic currents in the crossbar resistive memory array. In particular, we present efficient encoding/decoding algorithms for capacity-approaching 2-D weight-constrained codes of size $m \times n$ , where each row has a weight $pn$ with $p ; and each column has a weight $qm$ with $q \leq 1/2$ . We show that the designed codes provide higher code rates compared to the prior art codes for $p \le 1/2$ and $q \le 1/2$ .

Timely reception of information is of utmost importance in many cyber-physical systems. We study timely status update schemes for a single source. We consider a scheme which exploits the temporal correlation in source messages to send differential information to the receiver, over an unreliable channel. In this scheme, the source sends the true state information, interleaved with differential messages. We observe that the differential encoding improves the timeliness performance even in the absence of feedback, if the codeword lengths for both differential and true messages are chosen judiciously.

We consider the problem of constructing binary codes for correcting deletions that are localized within certain parts of the codeword that are unknown a priori. The model that we study is when $\delta \leq w$ deletions are localized in a window of size $w$ bits. These $\delta$ deletions do not necessarily occur in consecutive positions, but are restricted to the window of size $w$. The localized deletions model is a generalization of the bursty model, in which all the deleted bits are consecutive. In this paper, we construct new explicit codes for the localized model, based on the family of Guess & Check codes which was previously introduced by the authors. The codes that we construct can correct, with high probability, $\delta \leq w$ deletions that are localized in a single window of size $w$, where $w$ grows with the block length. Moreover, these codes are systematic; have low redundancy; and have efficient deterministic encoding and decoding algorithms. We also generalize these codes to deletions that are localized within multiple windows in the codeword., Comment: Accepted for publication in IEEE Transactions on Information Theory

Using a closed-form inverse kinematics solution for motion planning has many advantages compared to traditional numerical approaches, most notably much faster computation times and better suitability for real-time applications. Steady progress has been made to develop an analytic inverse kinematics solution for seven degrees of freedom (DoF) manipulators without joint offsets. Redundancy resolutions have been proposed to push the individual joint angles away from their limits. However, no further objectives are considered in these closed-form solutions. We propose a multi-objective real-time optimization approach that is able to minimize joint velocities and accelerations, while still avoiding joint limits. A closed-form solution to the optimization is derived, which only requires a single intuitive tuning parameter. We provide a free and open-source software implementation of the full kinematics algorithm that covers the complete solution space. The null-space motion resulting from our method is evaluated, achieving faster motion times and smoother arm motions along Cartesian paths in comparison to the state-of-the-art approach. Finally, experimental results with a KUKA iiwa robot are reported.

We study the consideration of fairness in redundant assignment for multi-agent task allocation. It has recently been shown that redundant assignment of agents to tasks provides robustness to uncertainty in task performance. However, the question of how to fairly assign these redundant resources across tasks remains unaddressed. In this paper, we present a novel problem formulation for fair redundant task allocation, which we cast as the optimization of worst-case task costs under a cardinality constraint. Solving this problem optimally is NP-hard. We exploit properties of supermodularity to propose a polynomial-time, near-optimal solution. In supermodular redundant assignment, the use of additional agents always improves task costs. Therefore, we provide a solution set that is $\alpha$ times larger than the cardinality constraint. This constraint relaxation enables our approach to achieve a super-optimal cost by using a sub-optimal assignment size. We derive the sub-optimality bound on this cardinality relaxation, $\alpha$. Additionally, we demonstrate that our algorithm performs near-optimally without the cardinality relaxation. We show simulations of redundant assignments of robots to goal nodes on transport networks with uncertain travel times. Empirically, our algorithm outperforms benchmarks, scales to large problems, and provides improvements in both fairness and average utility., Comment: (c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

To achieve various tradeoffs between storage and repair bandwidth, this article proposes to construct exact repair codes by grafting two codes ${\mathcal{ C}}_{1}$ and ${\mathcal{ C}}_{2}$ . By replacing certain nonzero entries in the generator matrix of ${\mathcal{ C}}_{1}$ by zero, the repair bandwidth of the resulting grafting part decreases. However, it may no longer keep the maximum-distance-separable (MDS) property. As a result, the grafted code ${\mathcal{ C}}_{2}$ takes these nonzero entries into account such that the entire graftage code can keep the MDS property. The relationship between the bandwidth reduction of ${\mathcal{ C}}_{1}$ and the file size of ${\mathcal{ C}}_{2}$ is derived to optimize graftage codes. Our analysis indicates that these graftage codes may provide better tradeoffs than space-sharing.

In this letter, the problem of distributed Secure Batch Matrix Multiplication (SBMM) is studied, where a user wishes to compute the pairwise products of two batches of massive matrices $\mathbf {A}$ and $\mathbf {B}$ generated by two external source nodes, with the aid of $N$ distributed servers. The security for data matrices $\mathbf {A}$ (resp. $\mathbf {B}$ ) is guaranteed against any group of up to $X_{\mathbf {A}}$ (resp. $X_{\mathbf {B}}$ ) colluding servers. As a result, a computation strategy is presented to characterize the trade-off between recovery threshold, system cost and system complexity, based on grouping Lagrange encoding, which unifies and improves the previous strategies for SBMM.

A correlation-based digital background calibration technique to correct the capacitor mismatch error in successive approximation register (SAR) analogue-to-digital converters (ADCs) is proposed. To facilitate the calibration technique, an energy-efficient detect-and-skip (DAS) algorithm is exploited to perform the pseudorandom noise (PN) injection without adding extra hardware overhead. By randomly injecting the error weights and correlating the ADC output with the PN sequence in the digital backend, the real bit weights are identified. In addition, only the error weights are injected into the residue. Therefore, the residue increment caused by the injection is negligible, and the redundancy overhead is minimized. The proposed calibration technique is verified in a 12-bit SAR ADC. Behavioral simulations show that, with calibration, the signal-to-noise and distortion ratio (SNDR) and the spurious-free dynamic range (SFDR) are increased from 55.6 to 71.2 dB and 64.2 to 90.3 dB, respectively.

Redundancy is an extremely significant challenge in data integration and decision making based on the model of soft set which is able to process data under uncertainty. There are four available methods which are designed to reduce the redundant parameters of the soft set. But there is a very low success rate on a large number of data sets obtained from practices by these methods. In order to overcome the inherent weakness, we propose a parameter reduction method based on chi square distribution for the model of soft set. Experimental results on two real-life application cases and thirty randomly generated data sets demonstrate that our algorithm largely improves the success rate of parameter reduction, redundant degree of parameter, and has higher practicability in comparison with the four existing normal parameter reduction algorithms.

International audience; Recent years have witnessed a trend of monitoring human respiration using Channel State Information (CSI) retrieved from commodity WiFi devices. Existing approaches essentially leverage signal propagation in a Line-of-Sight (LoS) setting to achieve good performance. However, in real-life environments, LoS can be easily blocked by furniture, home appliances and walls. This paper presents a novel smartphone-based system named WiPhone, aiming to robustly monitor human respiration in NLoS settings. Since a smartphone is usually carried around by one subject, leveraging directly-reflected CSI signals in LoS becomes infeasible. WiPhone exploits ambient reflected CSI signals in a Non-Line-of-Sight (NLoS) setting to quantify the relationship between CSI signals reflected from the environment and a subject's chest displacement. In this way, WiPhone successfully turns ambient reflected signals which have been previously considered "destructive" into beneficial sensing capability. CSI signals obtained from smartphone are usually very noisy and may scatter over different sub-carriers. We propose a density-based preprocessing method to extract useful CSI amplitude patterns for effective respiration monitoring. We conduct extensive experiments with 8 subjects in a real home environment. WiPhone achieves a respiration rate error of 0.31 bpm (breaths per minute) on average in a range of NLoS settings.

We propose a neural network-based feature selection (FS) scheme that can control the level of redundancy in the selected features by integrating two penalties into a single objective function. The Group Lasso penalty aims to produce sparsity in features in a grouped manner. The redundancy-control penalty, which is defined based on a measure of dependence among features, is utilized to control the level of redundancy among the selected features. Both the penalty terms involve the $L_{2,1}$ -norm of weight matrix between the input and hidden layers. These penalty terms are nonsmooth at the origin, and hence, one simple but efficient smoothing technique is employed to overcome this issue. The monotonicity and convergence of the proposed algorithm are specified and proved under suitable assumptions. Then, extensive experiments are conducted on both artificial and real data sets. Empirical results explicitly demonstrate the ability of the proposed FS scheme and its effectiveness in controlling redundancy. The empirical simulations are observed to be consistent with the theoretical results.

In this letter, we propose soft compression, an lossless compression approach to shape coding for images using location index and codebook of designed shapes with various sizes. This method is different from traditional image compression methods, as it aims at finding the basic shape blocks of pictures to improve the compression ratio from the perspective of information theory and frequency. In particular, the triplets including location and codeword are stored when encoding an image instead of the entire array. It is shown that soft compression can greatly reduce the bandwidth and storage space needed in the process of transmitting and storing the same kind of images.

A novel sparsity adaptive compressed sensing (CS) scheme based on Reed-Solomon (RS) codes is proposed to reduce the amount of sampled data, which can adjust the sampling rate adaptively to achieve data sampling and reconstruct the original data accurately by any decoding algorithm on the finite field. Moreover, the proposed sparsity adaptive reconstruction architecture can greatly improve the data reconstruction efficiency of the proposed CS scheme. The simulation results show that the sparsity adaptive scheme can reduce 83.6% of the sampled data. The results illustrate that the proposed architecture needs about 95k gates and operates at 500 MHz to achieve the throughout of 4.0 Gb/s, which can greatly improve the efficiency of the communication system. Meanwhile, it is at least 21.4% more area-efficient compared with previously reported fixed sampling rate architecture based on RS codes.

In the digital era, massive growth in Wireless Multimedia Sensor Networks (WMSNs) can be used in various applications such as environmental and industrial monitoring, disaster management. WMSN comprises many low-power camera sensor nodes that may have overlapping field of view for enhancing the accuracy of the monitored environment. Hence, huge volume of redundant data which make it inefficient in terms of network lifetime and energy. It needs to establish a tremendous suitable approach to communicate images over WMSN that handle critical factors such as maximize QoS and minimize energy consumption efficiently. There is a need for energy efficient image processing and transmission over WMSN. So aggregating multi-view images using cluster based distributed routing approaches with QoS constraints have received significant research interest. The Distributed Two-Layer Cluster framework is used in this paper for transmitting aggregated data by local cluster head (LCH) and master cluster head from various clusters to base station in multi-hop basis. This aggregated data are generated from multi-view images captured by correlated camera sensor nodes of local cluster (LC) and master cluster (MC) using first level data aggregation (FLDA) and second level data aggregation (SLDA) algorithms respectively. In FLDA, less complex novel protein sequence alignment based patch matching algorithm is used to reduce inter-view redundancy of multi-view images. Then, this first level aggregated data transmission of LC is reduced using Varying Bit Encoding based on Arithmetic Operations algorithm by combining multi-view images together and achieves its compression ratio of about 2.54. In SLDA, Higher-Order SVD is used to aggregate FLDA data from LCH for reducing MC transmission rate and achieves its compression ratio of about 9.1. The comparison of network performance results with existing cluster based routing algorithms exhibit the improvement of the proposed system in terms of performance metrices such as energy consumption, packet delivery ratio, end-to-end delay and network lifetime. This results show that PSA based TLDA performs better and average energy consumption of processing and transmitting multi-view images per round is considerably minimized to 0.320 J. Thus the proposed system prolongs the network lifetime by 35%, 6% when compared with DFRP and GRAD-CORR based TLDA in terms of number of rounds.

Light field (LF) imaging has generated considerable interest owing to its ability to capture both spatial and angular information of light rays simultaneously. However, the extremely large volume of data associated with LF imaging poses challenges to both data storage and transmission. This study addresses this issue by proposing a view synthesis-based LF image compression method using a generative adversarial network (GAN). The primary basis of compression relies on the fact that adjacent sub-aperture images (SAIs) are highly correlated. Accordingly, only sparsely sampled SAIs are transmitted and the others are reconstructed at the decoder side. The proposed sparse SAI sampling method enhances the quality of reconstructed SAIs by considering a fair trade-off between the number of SAIs available for use as priors in the synthesis process and SAI redundancy. The quality of reconstructed SAIs is further enhanced by a GAN-based SAI synthesis method, where the synthesis procedure is broken into disparity estimation and un-sampled SAI estimation components, and the adversarial nature of the jointly trained generative and discriminative networks results in a more accurate generative model. Furthermore, more texture details can be preserved in the synthesized SAIs by adopting a loss function in the GAN model based on perceptual quality. Extensive experimental results demonstrate the superiority of the proposed method relative to several other state-of-the-art compression methods in terms of standard quality metrics and the perceptual quality of the synthetic SAIs at the decoder side.

Recently, a reversible data hiding scheme by redundant space transfer has been proposed by Liu et al. In this scheme, the encryption process consists of bit plane disordering, sub-block position disordering and different-time Arnold transform in each sub-block. Then, the additional data is embedded into the LSB planes of the encrypted image by compressing the redundant space. However, to transfer the redundancy of the plain image into encrypted image, this scheme essentially just performs three types of disorder operations, which is vulnerable to security analysis. In this paper, we firstly demonstrate that the encryption method of this scheme can be broken by chosen-plaintext attack (CPA). Furthermore, in order to achieve the resistance against CPA, we introduce 2-D Logistic-adjusted-sine map (2D-LASM) to produce the encryption sequences with the control parameters generated from the plain image. Apart from that, we also improve the sparse matrix compression to achieve a superior embedding capacity. Extensive experiments and analyses demonstrate that the proposed scheme outperforms the original one in terms of both security and embedding capacity.

Advanced image scanning techniques produce high resolution medical images such as CT, MRI which in turn needs large storage space and bandwidth for transmitting over a network. Lossless compression is preferred for medical images to preserve important diagnostic details. However, it is only sufficient to maintain the high quality of an image in a diagnostically important region, namely Region of interest (ROI) for an accurate diagnosis. Non -ROI portion when compressed near-losslessly does not affect the image quality but reduces the file size effectively. We propose a compression technique where the prediction is done by Resolution Independent Gradient Edge Detector (RIGED) to de-correlate the image pixels and block-based arithmetic coding is used for encoding. The optimal threshold value, optimal q-level and the block-based coding removes inter-pixel, psycho-visual and coding redundancy from non-ROI part to achieve high compression whereas ROI part is compressed losslessly by removing inter-pixel and coding redundancy only. In this paper, optimal threshold-based predictive lossless compression in the ROI and optimal quantization (q) based near-lossless compression in the rest of the region is proposed. The proposed method is evaluated on volumetric 8 bit and 16 bit standard MR image data-set and validated on real patient’s 16 bit depth MR images collected from local hospitals. The performance of the proposed technique showed improvement over the existing techniques JPEG 2000, JPEG-LS, M-CALIC, JP3D, and CALIC by 40.89%, 34.50%, 32.92%, 22.36%, and 17.25% respectively in terms of Bits per Pixel (BPP).

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.

Symbolic Regression searches for a mathematical expression that fits the input data set by minimizing the approximation error. The search space explored by this technique is composed of any mathematical function representable as an expression tree. This provides more flexibility for fitting the data but it also makes the task more challenging. The search space induced by this representation becomes filled with redundancy and ruggedness, sometimes requiring a higher computational budget in order to achieve good results. Recently, a new representation for Symbolic Regression was proposed, called Interaction-Transformation, which can represent function forms as a composition of interactions between predictors and the application of a single transformation function. In this work, we show how this representation can be modeled as a multi-layer neural network with the weights adjusted following the Extreme Learning Machine procedure. The results show that this approach is capable of finding equally good or better results than the current state-of-the-art with a smaller computational cost.

In this work, we show that the incoherence alone is sufficient to establish the statistical restricted isometry property (StRIP) and statistical incoherence property (SInCoP) for unit norm tight frames (UNTFs). Further, we derive three simple properties that binary matrices need to satisfy, in order to produce incoherent UNTFs (IUNTFs) with high redundancy (ratio of the number of columns to the number of rows) via an existing embedding operation. We show that biadjacency matrices corresponding to biregular graphs satisfy the required properties. Thereby, we provide a connection between graph theory and the construction of IUNTFs. We also provide a bouquet of constructions of IUNTFs from finite fields and combinatorial designs. Another important aspect of our construction is that the sparse recovery guarantees for the embedded IUNTFs can in fact be translated to the constituent binary matrix. We show that if the constituent $m\times M$ binary matrix has constant row and column weight, it can support sparse recovery through $\ell_{1}$ -minimization for all but an $\epsilon$ -fraction of $t$ -sparse signals chosen from a random signal model, provided $m=O(t(\log(\frac{M}{\epsilon}))^{3}),$ which is a significant improvement over the existing $m=O(t^{2})$ bound, where $m$ denotes the number of measurements. Also, the StRIP and SInCoP based approach results in matrices whose column size is exponential in the fourth root of the row size. To the best of our knowledge, this is the first construction of deterministic matrices satisfying StRIP and SInCoP with such high redundancy.

Existing mobile photo crowdsensing approaches focus on the participant-to-server photo pre-selection, i.e., reducing the photo redundancy from participants to a server. The server may still receive plenty of photos for a target area. Yet, another important problem is to select a proper photo subset of an area from the server to a requester. This is a challenging problem because the selected subset with a small size should attain both coverage on the PoIs - Points of Interest (i.e., photo coverage of the area) and quality on the views (i.e., view quality). In this paper, we propose a novel and generic server-to-requester photo selection approach even when there are neither photo shooting direction information nor reference photos. A utility model is designed to measure photo merits of coverage and quality by exploiting photos’ spatial distribution and visual representativeness. We present two photo selection schemes, basic and PoI number-aware, to maximize the photo selection utility with multiple levels of granularity. Experimental results on real-world datasets show that our basic scheme outperforms the baselines by an average of $33\%$ 33 % and $18.7\%$ 18 . 7 % on photo coverage and view quality, respectively. Our PoI number-aware scheme can yield an additionally 44.8 percent improvement on the photo coverage performance.

Reversible data hiding generally exploits the redundancy of the cover medium and prediction-error expansion (PEE) has become the most effective mechanism. However, although the pairwise PEE technique has been proposed to jointly modify the prediction-errors to achieve less degradation, there is still room for improvement. In this paper, a dual pairwise PEE strategy is proposed to fully exploit the potential of pairwise PEE. The key observation behind dual pairwise PEE lies in that most capacity is provided by individually expanding only one pairing error. For such separable error-pairs, we propose to recalculate and collect the rest pairing error to form an error sequence after shifting any one pairing error. Next, by considering every two neighboring errors of the sequence together, a new set of error-pairs for double pairwise PEE can be obtained. Compared with original pairwise PEE, dual pairwise PEE significantly better exploits the correlation of errors such that it leads to better capacity-distortion performance. Experimental results also demonstrate that the proposed scheme outperforms several state-of-the-art schemes.

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.

A high-quality data transmission scheme in wireless body area networks is implemented by a joint source-channel coding (JSCC) system with $M$ -ary differential chaos shift keying modulation over an on-body channel. Current DP-LDPC code pairs, which have shown good bit-error ratio performance over additive white Gaussian noise channel and Rayleigh fading channel, cannot perform well over on-body channel. In this paper, the DP-LDPC codes are redesigned for the JSCC system with new methods depending on the on-body channel characteristics. A protomatrix dimension-restrictive searching algorithm for DP-LDPC codes in the JSCC system is proposed according to the on-body channel bandwith and source statistic, which is effective to predesign the dimension of the code pair for a new JSCC system. In this way, the source-channel code rates are appropriately allocated to leave more source redundancy for improving the error floor. Moreover, the joint protomatrix is reconstructed into two kinds of compact structures by simplifying the edge linking relationships and modifying joint protograph extrinsic information transfer algorithm, which decreases the induction of searching entries to further accelerate the code searching speed. The searched code pairs show better bit-error ratio performances compared with existing DP-LDPC codes. The power consumption in the new joint coding system is lower than the separate coding system in WBAN on the physical layer.

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.

For multidimensional data, Space-Filling Curves (SFCs) have been used to improve the execution time of spatial data queries. However, their effect on compression, when used to reorder the uncompressed values, is known to a lesser extent. We investigate the impact of three SFCs on Shuttle Radar Topographic Mission (SRTM) elevation data and Square-Kilometre Array telescope (SKA) radio-astronomy data: two types of datasets to which SFCs have not been extensively applied, within a compression context. This work contributes to the understanding of how such reorderings impact compression performance and affect different compression schemes and preprocessing techniques through their use. We show empirical results from combining eight common compression schemes, the Z-Order, Gray-Code, and Hilbert space-filling curves, and the bitwise preprocessing technique BitShuffle. The Hilbert Curve consistently outperforms the other orderings for the SRTM dataset though the mapping implementation incurs a significant speed penalty. However, the Z-Order and Gray-Code Curves are best for the SKA dataset. Through an analysis of the dataset autocorrelations, file-entropies, and block-entropies; we show that the SKA dataset’s dimensional bias is not exploited as much by the Hilbert Curve compared to the Z-Order and Gray-Code Curves. However, the Hilbert Curve is the most appropriate for the SRTM dataset as it can be modelled as isotropic and has a significantly higher level of local autocorrelation. BitShuffle is necessary to practically compress the SKA data, but does contribute to the compression performance of the SRTM dataset. These curves and BitShuffle are advantageous in reducing block-entropy values for such datasets.

We discuss the robust sampled-data dynamic output-feedback controller design problem for linear time-invariant (LTI) systems subject to parametric uncertainties and $$\mathcal {L}_{\infty }$$ disturbances. The descriptor redundancy-based methods are proposed, and their main features are: (i) sampled-data syntheses are performed in a continuous-time domain; (ii) discrete-time modeling of sampled-data systems, which is adversely affected by uncertainties in LTI plant dynamics, is not required; (iii) linear matrix inequality conditions are presented for asymptotic stability and $$\mathcal {L}_{\infty }$$ – $$l_{\infty }$$ disturbance attenuation.

Granular structure is a mathematical expression of knowledge in granular computing and a direct determinant of the data processing efficiency. To improve the efficiency of data processing, many scholars have studied the reduction of granular structure. The attribute reduction and the granular reduction are two types of reduction on different layers of a granular structure, with the latter being both an essential step for granular structure reduction and the foundation of the attribute reduction. Yet compared with the attribute reduction, the granular reduction has received less attention from scholars. Therefore, a fuzzy granular reduction theory and a granular matrix based on the fuzzy $\beta$ -coverings is proposed in this article. The insufficiency of the existing granular reduction theory for fuzzy $\beta$ -coverings is pointed out, and proper sufficient and necessary conditions for two fuzzy $\beta$ -coverings generating the same upper and lower approximations are also given in this article. In addition, to reduce and evaluate a fuzzy $\beta$ -covering, a novel reduction algorithm based on a granular matrix is proposed for the first time. Also, since fuzzy covering reduction is NP-hard, a heuristic greedy algorithm is designed to obtain a reduct. Numerical experiments show that the redundancy rates of neighborhood granule sets induced by some big-scale data sets exceed 99 $\%$ , which indicates that the existing neighborhood granulation methods need to be urgently improved. Based on this, concise granular structures and much more efficient feature selection algorithms can be proposed in the future.

A primitive $k$ -batch code encodes a string $\boldsymbol {x}$ of length $n$ into a string $\boldsymbol {y}$ of length $N$ , such that each multiset of $k$ symbols from $\boldsymbol {x}$ has $k$ mutually disjoint recovering sets from $\boldsymbol {y}$ . In this paper, we discuss new constructions of binary primitive batch codes. First, we develop novel explicit and random coding constructions of linear primitive batch codes based on finite geometries. Second, a new explicit coding construction of binary primitive batch codes based on bivariate lifted multiplicity codes is provided. For any $k=n^ \varepsilon $ with $\varepsilon \in (0,0.47)\setminus \{1/5,1/4\}$ , our proposed codes have a better trade-off between the redundancy and the parameters $k,n$ than previously known batch codes.

The performance of some three-dimensional mutual information-based algorithms can be affected, since only relevance and interaction are considered. Aiming at solving the problem, a feature selection algorithm based on redundancy analysis and interaction weight is proposed in this paper. The proposed algorithm adopts three-way interaction information to measure the interaction among the class label and features, and processes features for interaction weight analysis. Then, it employs symmetric uncertainty to measure the relevance between features and the class label as well as the redundancy between features, and selects the features with greater relevance and interaction as well as smaller redundancy. To validate the performance, the proposed algorithm is compared with several feature selection algorithms. Since relevance, redundancy, and interaction analysis are all presented, the proposed algorithm can obtain better feature selection performance.

Huge earth observatory video data (EOVD) and the limited transmission bandwidth from satellites to terrestrial devices pose serious challenges to compression efficiency for satellite video. In this work, we deeply explore long-term background redundancy caused by periodical satellite revisit, and make utilization of long-term background reference (LTBR) to design a high-efficiency coding method specific for EOVD. Firstly, we use data of Google Earth as prior background knowledge and construct LTBR from it. Then, two novel prediction methods are proposed to make full use of prior information of LTBR, including color and definition correction based inter prediction with LTBR (CDIP-LTBR), structure and texture constrained intra prediction with LTBR (STIP-LTBR). Lastly, the proposed two new prediction schemes are integrated into one unified coding framework along with HEVC to achieve improved coding performance, and an improved RDO method is designed for additional prediction modes selection problem in the framework to obtain a higher prediction efficiency. Extensive experiments on real-world EOVD show that the proposed coding scheme exhibit significant improvement over HEVC and H.264, in terms of BD-Rate, BD-PSNR and rate distortion comparison.

Pole number recognition is highly important for the positioning tasks in high-speed rail catenary systems. The complicated working environment poses difficulties for number recognition algorithms, and this situation becomes even more challenging when illumination changes, image blurs and occlusions are considered. In this work, we present a high-accuracy pole number recognition framework including a high-performance cascaded CNN-based Detection and Recognition YOLO (DR-YOLO) and a temporal redundancy approach. First, the DR-YOLO utilizes global features for coarse plate detection and local features for accurate number recognition. Next, context-based combination of adjacent frames utilizes the complementarity and consistency of the same pole number recognition results and generates a unique result. The context-based amendment of adjacent poles utilizes the continuity of adjacent poles and corrects the fault or partial missing results caused by blur or occlusions. Extensive experimental testing is performed on 4 datasets representing high-speed train routine working environments. The reported experimental results validate the effectiveness and efficiency of the proposed approach.

Variable-length Quantity (VLQ) is a class of universal codes widely used in integer data compression. Git-VLQ is the removing redundancy version of big-endian VLQ with high computational complexity. Little-Endian Base 128 (LEB128) is the little-endian version of VLQ. In this letter, we propose a variant of VLQ, termed Removing-Redundancy LEB128 (RR-LEB128), to eliminate the coding redundancy in little-endian VLQ. In addition, a fast decoding algorithm of RR-LEB128 is also proposed. Simulations show that the encoding throughput is 55% higher than that of Git-VLQ, and the decoding throughput is about 20% higher than that of Git-VLQ and LEB128.

This paper presents a new framework for wireless video streaming services using the resource-constrained embedded camera sensors based on block compressive sensing. We propose adaptive encoding scheme that exploits high redundancy between successive video frames to reduce transmission cost while effectively handling occlusion effects and, at the same time, maintains the simple and energy conserving encoder design. The proposed methodology adapts the compression ratio for different blocks of the non-key frame depending on temporal correlation. We also propose compressed domain motion and measurements estimation techniques to exploit the high correlation between successive frames at the decoder. The proposed motion estimation technique makes use of restricted isometry property of the sensing matrix to seek the best matching measurement vector for motion estimation as opposed to block matching in conventional video coding. In the proposed measurement estimation technique, efficient utilization of bandwidth is achieved by skipping some measurements at the transmitter side. The skipped measurements are estimated at the receiver by exploiting the correlation between CS measurements of the non-key frame and corresponding motion predicted frame using multiple regression model. Extensive simulation results on a set of diverse video sequences are presented to demonstrate the effectiveness of the proposed video coding technique.

Spectral analysis and Information-theory are two powerful and successful frameworks for feature selection in supervised classification problems. However, most of the methods developed under these frameworks have been introduced for handling exclusively numerical or non- numerical data. In this paper, we propose a supervised filter feature selection method that combines Spectral Feature Selection and Information-theory based redundancy analysis for selecting relevant and non-redundant features in supervised mixed datasets; i.e., datasets where the objects are described simultaneously by both, numerical and non-numerical features. To demonstrate the effectiveness of our proposed supervised filter feature selection method, we conducted several experiments on 40 public real-world datasets. Additionally, we compare our method against relevant state-of-the-art supervised filter methods for numerical, non-numerical, and mixed data. From this comparison, our method, in general, obtains better results than the results obtained by the other evaluated filter feature selection methods.

This letter investigates a multiuser mobile-edge computing (MEC) system with data compression technique to reduce the redundancy of sensed data, save the energy consumption and reduce the latency for wireless transmission. In this regard, the problem of minimizing the total energy consumption by jointly optimizing the transmission bandwidth allocation and data compression ratio is firstly considered under the constraints of latency and limited computation resource. Moreover, Lagragian and iterative-based algorithms are proposed to solve the non-convex optimization problem. Finally, simulation results are shown to verify the efficiency of the proposed algorithms and demonstrate that the application of data compression technique into MEC system can effectively reduce the energy consumption and latency.

Contextual information can be used in recommender systems to make recommendation more efficient. Recent research has made progress in combining contextual information into representation models for recommendations. However, the existing approaches do not well address the problem of data sparsity, and they suffer from context redundancy. To deal with these problems, this paper proposes a context-aware recommendation approach based on embedded feature selection. It gets rid of context redundancy by generating a minimum subset of all contextual information and allocates the weight to each context appropriately. Experiments on the restaurant recommendation shows that the proposed approach has better performance.