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It is hard to construct an optimal classifier for high-dimensional imbalanced data, on which the performance of classifiers is seriously affected and becomes poor. Although many approaches, such as resampling, cost-sensitive, and ensemble learning methods, have been proposed to deal with the skewed data, they are constrained by high-dimensional data with noise and redundancy. In this study, we propose an adaptive subspace optimization ensemble method (ASOEM) for high-dimensional imbalanced data classification to overcome the above limitations. To construct accurate and diverse base classifiers, a novel adaptive subspace optimization (ASO) method based on adaptive subspace generation (ASG) process and rotated subspace optimization (RSO) process is designed to generate multiple robust and discriminative subspaces. Then a resampling scheme is applied on the optimized subspace to build a class-balanced data for each base classifier. To verify the effectiveness, our ASOEM is implemented based on different resampling strategies on 24 real-world high-dimensional imbalanced datasets. Experimental results demonstrate that our proposed methods outperform other mainstream imbalance learning approaches and classifier ensemble methods.

Recent works on learned image compression perform encoding and decoding processes in a full-resolution manner, resulting in two problems when deployed for practical applications. First, parallel acceleration of the autoregressive entropy model cannot be achieved due to serial decoding. Second, full-resolution inference often causes the out-of-memory(OOM) problem with limited GPU resources, especially for high-resolution images. Block partition is a good design choice to handle the above issues, but it brings about new challenges in reducing the redundancy between blocks and eliminating block effects. To tackle the above challenges, this paper provides a learned block-based hybrid image compression (LBHIC) framework. Specifically, we introduce explicit intra prediction into a learned image compression framework to utilize the relation among adjacent blocks. Superior to context modeling by linear weighting of neighbor pixels in traditional codecs, we propose a contextual prediction module (CPM) to better capture long-range correlations by utilizing the strip pooling to extract the most relevant information in neighboring latent space, thus achieving effective information prediction. Moreover, to alleviate blocking artifacts, we further propose a boundary-aware postprocessing module (BPM) with the edge importance taken into account. Extensive experiments demonstrate that the proposed LBHIC codec outperforms the VVC, with a bit-rate conservation of 4.1%, and reduces the decoding time by approximately 86.7% compared with that of state-of-the-art learned image compression methods., 13 pages, 13 figures, accepted by IEEE Trans. on Circuits and Systems for Video Technology

We propose a new model of asynchronous batch codes that allow for parallel recovery of information symbols from a coded database in an asynchronous manner, i.e. when queries arrive at random times and they take varying time to process. We show that the graph-based batch codes studied by et al. are asynchronous. Further, we demonstrate that hypergraphs of Berge girth larger or equal to 4, respectively larger or equal to 3, yield graph-based asynchronous batch codes, respectively private information retrieval (PIR) codes. We prove the hypergraph-theoretic proposition that the maximum number of hyperedges in a hypergraph of a fixed Berge girth equals the quantity in a certain generalization of the hypergraph-theoretic (6,3)-problem, first posed by Brown, Erd\H{o}s and S\'{o}s. We then apply the constructions and bounds by Erd\H{o}s, Frankl and R\"{o}dl about this generalization of the (6,3)-problem, known as the (3$\varrho$-3,$\varrho$)-problem, to obtain batch code constructions and bounds on the redundancy of the graph-based asynchronous batch and PIR codes. We derive bounds on the optimal redundancy of several families of asynchronous batch codes with the query size $t=2$. In particular, we show that the optimal redundancy $\rho(k)$ of graph-based asynchronous batch codes of dimension $k$ for $t=2$ is $2\sqrt{k}$. Moreover, for graph-based asynchronous batch codes with $t \ge 3$, $\rho(k) = O\left({k}^{1/(2-\epsilon)}\right)$ for any small $\epsilon>0$., Comment: 18 pages

Vacating room after encryption (VRAE) is a popular framework of reversible data hiding for encrypted images (RDHEI). Most VRAE based RDHEI methods do not make a desirable payload. To address this issue, this paper proposes a novel data hiding technique using adaptive difference recovery (ADR) and exploits this novel technique to design an efficient VRAE based RDHEI method. Specifically, the proposed ADR-based data hiding technique adaptively determines the range of original difference between a cover pixel and a reference pixel with correlation according to the marked difference generated by bit substitution technique. The cover pixel can be recovered losslessly with the range of original difference and the reference pixel. In the proposed RDHEI method, the encrypted image is generated by block permutation and block-based modulation so that spatial redundancy within encrypted image blocks is preserved. Then the ADR-based data hiding technique is applied to the encrypted blocks. Experiment results show that the proposed RDHEI method outperforms some state-of-the-art RDHEI methods in payload. The average embedding rates of the proposed RDHEI method on the BOSSbase, BOWS-2 and UCID datasets are 3.2045, 3.1145 and 2.4633 bpp, respectively.

An $(n,k,\ell)$ -vector MDS code over a field $\mathbb {F}$ is a $\mathbb {F}$ -linear subspace of $(\mathbb {F}^\ell)^{n}$ of dimension $k\ell $ , such that any $k$ (vector) symbols of the codeword suffice to determine the remaining $r=n-k$ (vector) symbols. The length $\ell $ of each codeword symbol is called the sub-packetization of the code. Such a code is called minimum storage regenerating (MSR), if any single symbol of a codeword can be recovered by downloading $\ell /r$ field elements (which is known to be the minimum possible) from each of the other symbols. MSR codes are attractive for use in distributed storage systems, and by now a variety of ingenious constructions of MSR codes are available. However, they all suffer from exponentially large sub-packetization $\ell \gtrsim r^{k/r}$ . Our main result is an almost tight lower bound showing that for an MSR code, one must have $\ell \geqslant \exp (\Omega (k/r))$ . Previously, a lower bound of $\approx \exp (\sqrt {k/r})$ , and a tight lower bound for a restricted class of “optimal access” MSR codes, were known.

This study investigates the fundamental limits of variable-length compression in which prefix-free constraints are not imposed (i.e., one-to-one codes are studied) and non-vanishing error probabilities are permitted. Due in part to a crucial relation between the variable-length and fixed-length compression problems, our analysis requires a careful and refined analysis of the fundamental limits of fixed-length compression in the setting where the error probabilities are allowed to approach either zero or one polynomially in the blocklength. To obtain the refinements, we employ tools from moderate deviations and strong large deviations. Finally, we provide the third-order asymptotics for the problem of variable-length compression with non-vanishing error probabilities. We show that unlike several other information-theoretic problems in which the third-order asymptotics are known, for the problem of interest here, the third-order term depends on the permissible error probability., Comment: 14 pages, Accepted by the IEEE Transactions on Information Theory

Point cloud geometry (PCG) is used to precisely represent arbitrary-shaped 3D objects and scenes, is of great interest to vast applications which puts forward the pressing desire of high-efficiency PCG compression for transmission and storage. Existing PCG coding mostly relies on the octree model by which point-wise processing is applied without exploring nonlocal regional geometry similarity across the entire 3D surface. This work, instead, suggests the region-wise processing to leverage the region similarity to exploit inter-region redundancy for efficient lossy point cloud geometry compression. Towards this goal, a given PCG is first segmented into numerous local regions each of which comprises a portion of point cloud surface, and can be represented by a surface vector that describes the geometry shape numerically in a projected principal space. Subsequently, these regions are grouped into several discriminative clusters, assuring that inter-cluster similarity is minimized and intra-cluster similarity is maximized simultaneously, where the similarity is calculated using the regional surface vectors. In each cluster, we set a reference region having the largest similarity score to the others, which enables the non-reference region prediction from the reference one using alignment transform. In the end, we encode the reference regions directly using the lossless mode of the Geometry-based Point Cloud Compression (G-PCC), while corresponding non-reference regions are signaled using associated transform parameters. Compared with the state-of-the-art G-PCC using octree model, our region-wise approach can offer remarkable coding efficiency improvement, e.g., 32.4% and 22.0% Bjontegaard-delta rate (BD-Rate) gains for respective point-to-point (D1) and point-to-plane (D2) distortion evaluations, across a variety of common test sequences used in standard committee.

In this paper, an improved Quadrature Multi-carrier Differential Chaos Shift Keying (QMC-DCSK) based on Chaotic Shape-forming Filter (CSF) and Cross Correction (CC) is proposed. The chaotic signal generated by CSF is used as reference spreading sequence in QMC-DCSK. Quadrature multi-carriers are used to carry the reference and multiple information bearing signals simultaneously in order to improve the Bit Transmission Rate (BTR). By this way, the proposed system is able to transmit 2M 1 bits Traditional Data Stream (TDS) during one traditional DCSK bit duration, where M is the number of carrier frequencies. Meanwhile, the reference signal transmits Additional Data Stream (ADS) encoded by CSF together with 2M 1 identical or inverse data stream carried by the rest 2M 1 channels (information bearing signals). By this system configuration, the channel redundancy can be used to improved bit error rate (BER) performance of ADS, after decoding TDS. In turn, the TDS reliability can also be improved by checking most ADS polarity of the corresponding carrier. This process is referred to as CC. The contribution of this work lies in the CC operation, by which the reliability of both TDS and ADS is improved significantly. It is the CSF and multi-carrier that make CC possible. The analytical BER is derived for the proposed method. The merits of the proposed system include, first, it improves the BTR as compared to that of QMC-DCSK, because ADS conveys more information bits; second, the ADS reliability is improved dramatically because the 2M duplication can be used to improve the signal to noise ratio; third, the TDS reliability is improved as well because the information bits conveyed by each carrier frequency is enhanced and corrected by the 2M repetitions. By this way, not only TDS and ADS are transmitted (i.e., the higher BTR is achieved), but also both reliability are improved (i.e. the lower BER is achieved). In order to testify the feasibility and superiority of the proposed method, simulation and wireless open-access research platform (WARP) experiment are conducted. The simulation and experiment results show that the proposed method is superior to other comparison methods.

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 $\mathsf {OpenMPL}$ .

This paper introduced an algorithm for lossless image compression to compress natural and medical images. It is based on utilizing various casual fixed predictors of one or two dimension to get rid of the correlation or spatial redundancy embedded between image pixel values then a recursive polynomial model of a linear base is used. The experimental results of the proposed compression method are promising in terms of preserving the details and the quality of the reconstructed images as well improving the compression ratio as compared with the extracted results of a traditional linear predicting coding system.

Open Food Facts provides a database of food products such as product names, compositions, and additives, where everyone can contribute to add the data or reuse the existing data. The open food facts data are dirty and needs to be processed before storing the data to our system. To reduce redundancy in food ingredients data, we measure the similarity of ingredient food using two similarities: the conceptual similarity and textual similarity. The conceptual similarity measures the similarity between the two datasets by its word meaning (synonym), while the textual similarity is based on fuzzy string matching, namely Levenshtein distance, Jaro-Winkler distance, and Jaccard distance. Based on our evaluation, the combination of similarity measurements using textual and Wordnet similarity (conceptual) was the most optimal similarity method in food ingredients.

The subject of research in the article are the processes of video image processing using an orthogonal transformation for data transmission in information and telecommunication networks. The aim is to build a method of compression of video images while maintaining the efficiency of its delivery at a given informative probability. That will allow to provide a gain in the time of delivery of compressed video images, a necessary level of availability and authenticity at transfer of video data with preservation of strictly statistical regulations and the controlled loss of quality . Task: to study the known algorithms for selective processing of static video at the stage of approximation and statistical coding of the data based on JPEG-platform. The methods used are algorithm based on JPEG-platform, methods of approximation by orthogonal transformation of information blocks, arithmetic coding. It is a solution of scientific task-developed methods for reducing the computational complexity of transformations (compression and decompression) of static video images in the equipment for processing visual information signals, which will increase the efficiency of information delivery. The following results were obtained. The method of video image compression with preservation of the efficiency of its delivery at the set informative probability is developed. That will allow to fulfill the set requirements at the preservation of structural-statistical economy, providing a gain in time to bring compressed images based on the developed method, relative to known methods, on average up to 2 times. This gain is because with a slight difference in the compression ratio of highly saturated images compared to the JPEG-2000 method, for the developed method, the processing time will be less by at least 34%. Moreover, with the increase in the volume of transmitted images and the data transmission speed in the communication channel - the gain in the time of delivery for the developed method will increase. Here, the loss of quality of the compressed/restored image does not exceed 2% by RMS, or not worse than 45 dB by PSNR. What is unnoticeable to the human eye. Conclusions. The scientific novelty of the obtained results is as follows: for the first time the method of classification (separate) coding (compression) of high-frequency and low-frequency components of Walsh transformants of video images is offered and investigated, which allows to consider their different dynamic range and statistical redundancy reduced using arithmetic coding. This method will allow to ensure the necessary level of availability and authenticity when transmitting video data, while maintaining strict statistical statistics. Note that the proposed method fulfills the set tasks to increase the efficiency of information delivery. Simultaneously, the method for reducing the time complexity of the conversion of highly saturated video images using their representation by the transformants of the discrete Walsh transformation was further developed. It is substantiated that the perspective direction of improvement of methods of image compression is the application of orthogonal transformations on the basis of integer piecewise-constant functions, and methods of integer arithmetic coding of values of transformant transformations. It is substantiated that the joint use of Walsh transformation and arithmetic coding, which reduces the time of compression and recovery of images; reduces additional statistical redundancy. To further increase the degree of compression, a classification coding of low-frequency and high-frequency components of Walsh transformants is developed. It is shown that an additional reduction in statistical redundancy in the arrays of low-frequency components of Walsh transformants is achieved due to their difference in representation. Recommendations for the parameters of the compression method for which the lowest value of the total time of information delivery is provided are substantiated.

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.

Cross-project defect prediction (CPDP) refers to identifying defect-prone software modules in one project (target) using historical data collected from other projects (source), which can help developers find bugs and prioritize their testing efforts. Recently, CPDP has attracted great research interest. However, the source and target data usually exist redundancy and nonlinearity characteristics. Besides, most CPDP methods do not exploit source label information to uncover the underlying knowledge for label propagation. These factors usually lead to unsatisfactory CPDP performance. To address the above limitations, we propose a landmark selection-based kernelized discriminant subspace alignment (LSKDSA) approach for CPDP. LSKDSA not only reduces the discrepancy of the data distributions between the source and target projects, but also characterizes the complex data structures and increases the probability of linear separability of the data. Moreover, LSKDSA encodes label information of the source data into domain adaptation learning process and makes itself with good discriminant ability. Extensive experiments on 13 public projects from three benchmark datasets demonstrate that LSKDSA performs better than a range of competing CPDP methods. The improvement is $ 3.44\%-11.23\%$ in g-measure , $ 5.75\%-11.76\%$ in AUC , and $ 9.34\%-33.63\%$ in MCC , respectively.

Feature selection is an important preprocessing process in machine learning. It selects the crucial features by removing irrelevant features or redundant features from the original feature set. Most of feature selection algorithms focus on maximizing relevant information and minimizing redundant information. In order to remove more redundant information in the evaluation criteria, we propose a feature selection based on mutual information with correlation coefficient (CCMI) in this paper. We introduce the correlation coefficient in the paper, and combine the correlation coefficient and mutual information to measure the relationship between different features. We use the absolute value of the correlation coefficient between two different features as the weight of the redundant item denoted by the mutual information in the evaluation standard. In order to select low redundancy features effectively, we also use the principle of minimization in the evaluation criteria. By comparing with 7 popular contrast algorithms in 12 data sets, CCMI has achieved the highest average classification accuracy for two classifiers of SVM and KNN. Experimental results show that our proposed CCMI has better feature classification capability.

One of the important tasks in the operating room is monitoring the depth of anesthesia (DoA) during surgery, and noninvasive techniques are very popular. Hence, we propose a new scheme for DoA monitoring considering the time-frequency analysis of electroencephalography (EEG) signals and GLCM features extracted from them. To this end, at first, the time-frequency map (TFM) of each channel of each EEG is computed by smoothed pseudo-Wigner–Ville distribution (SPWVD), where the EEG signal used in this paper is recorded in 15 channels. After that, we consider the gray-level co-occurrence matrix (GLCM) to obtain the content of TFM, and after that, four features such as homogeneity, correlation, energy, and contrast are obtained for each GLCM. Finally, after the selection of efficient features using the minimum redundancy maximum relevance (MRMR) method, the K-nearest neighbor (KNN) classifier is utilized to determine the DoA. Here, we consider the three states, namely, deep hypnotic, surgical anesthesia, and sedation and awake states according to bispectral index (BIS), and each EEG epoch is classified to these states. We also employ data augmentation to enhance the training phase and increase accuracy. We obtain the accuracy and confusion matrix of the proposed method. We also analyze the effects of a number of gray levels of GLCM, distance measure in KNN classifier, and parameters of data augmentation on the performance of the proposed method. Results indicate the efficiency of the proposed method to determine the DoA during surgery.

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.

The number of greenhouse gas (GHG) observing satellites has greatly expanded in recent years, and these new datasets provide an unprecedented constraint on global GHG sources and sinks. However, a continuing challenge for inverse models that are used to estimate these sources and sinks is the sheer number of satellite observations, sometimes in the millions per day. These massive datasets often make it prohibitive to implement inverse modeling calculations and/or assimilate the observations using many types of atmospheric models. Although these satellite datasets are very large, the information content of any single observation is often modest and non-exclusive due to redundancy with neighboring observations and due to measurement noise. In this study, we develop an adaptive approach to reduce the size of satellite datasets using geostatistics. A guiding principle is to reduce the data more in regions with little variability in the observations and less in regions with high variability. We subsequently tune and evaluate the approach using synthetic and real data case studies for North America from NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite. The proposed approach to data reduction yields more accurate CO2 flux estimates than the commonly used method of binning and averaging the satellite data. We further develop a metric for choosing a level of data reduction; we can reduce the satellite dataset to an average of one observation per ∼ 80–140 km for the specific case studies here without substantially compromising the flux estimate, but we find that reducing the data further quickly degrades the accuracy of the estimated fluxes. Overall, the approach developed here could be applied to a range of inverse problems that use very large trace gas datasets.

A sequence $\left \{g_k\right \}_{k=1}^{\infty }$ in a Hilbert space ${\cal H}$ has the expansion property if each $f\in \overline {\text {span}} \left \{g_k\right \}_{k=1}^{\infty }$ has a representation $f=\sum _{k=1}^{\infty } c_k g_k$ for some scalar coefficients $c_k.$ In this paper, we analyze the question whether there exist small norm-perturbations of $\left \{g_k\right \}_{k=1}^{\infty }$ which allow to represent all $f\in {\cal H};$ the answer turns out to be yes for frame sequences and Riesz sequences, but no for general basic sequences. The insight gained from the analysis is used to address a somewhat dual question, namely, whether it is possible to remove redundancy from a sequence with the expansion property via small norm-perturbations; we prove that the answer is yes for frames $\left \{g_k\right \}_{k=1}^{\infty }$ such that $g_k\to 0$ as $k\to \infty ,$ as well as for frames with finite excess. This particular question is motivated by recent progress in dynamical sampling.

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.

Due to significant inter-band correlation resulting from the use of hundreds of contiguous spectral bands, band selection (BS) is commonly used to reduce data dimensionality for band redundancy removal. A challenge for BS is how to design an effective criterion which can select bands with crucial self-retained spectral information, while also avoiding highly correlated bands to be selected. This article presents a novel approach, referred to as self-mutual information-based band selection (SMI-BS) for hyperspectral image classification (HSIC) to address these two issues. It first constructs a hyperspectral band channel from a hyperspectral image and then takes advantage of such a band channel to coin a new concept of SMI, which is defined as the mutual information (MI) between a selected band, b, and the set of full bands, ${\boldsymbol{\Omega}} $ , $I$ (b; ${\boldsymbol{\Omega}}$ ). As a result, a curve plotted as a function of $I$ (b; ${\boldsymbol{\Omega}}$ ) versus individual band b, called SMI curve, can be used as a BS criterion which selects those bands with large $I$ (b; ${\boldsymbol{\Omega}}$ ) values as desired bands. Since such selected bands may be highly correlated, another new concept, called prominent band (PB), which is defined as a band corresponding to a prominent peak of an SMI curve, is further introduced to avoid selecting highly inter-correlated spectral bands. To validate the utility of SMI-BS in HSIC, experiments are conducted to compare existing state-of-the-art BS methods for performance evaluation. The results demonstrate that SMI-BS is indeed a very effective BS method and also performs better than other test BS methods.

In magnetic resonance imaging (MRI), noise is a limiting factor for higher spatial resolution and a major cause of prolonged scan time, owing to the need for repeated scans. Improving the signal-to-noise ratio is therefore key to faster and higher-resolution MRI. Here we propose a method for mapping and reducing noise in MRI by leveraging the inherent redundancy in complex-valued multi-channel MRI data. Our method leverages a provably optimal strategy for shrinking the singular values of a data matrix, allowing it to outperform state-of-the-art methods such as Marchenko-Pastur PCA in noise reduction. Our method reduces the noise floor in brain diffusion MRI by 5-fold and remarkably improves the contrast of spiral lung \(^{19}\)F MRI. Our framework is fast and does not require training and hyper-parameter tuning, therefore providing a convenient means for improving SNR in MRI.

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.

Protein structure networks (PSNs) have long been used to provide a coarse yet meaningful representation of protein structure, dynamics, and internal communication pathways. An important question is what criteria should be applied to construct the network so that to include relevant interresidue contacts while avoiding unnecessary connections. To address this issue, we systematically considered varying residue distance cutoff length and the probability threshold for contact formation to construct PSNs based on atomistic molecular dynamics in order to assess the amount of mutual information within the resulting representations. We found that the minimum in mutual information is universally achieved at the cutoff length of 5 Å, irrespective of the applied contact formation probability threshold in all considered, distinct proteins. Assuming that the optimal PSNs should be characterized by the least amount of redundancy, which corresponds to the minimum in mutual information, this finding suggests an objective criterion for cutoff distance and supports the existing preference towards its customary selection around 5 Å length, typically based to date on heuristic criteria.

Hyperspectral images can provide dozens to hundreds of continuous spectral bands, so the richness of information has been greatly improved. However, these bands lead to increasing complexity of data processing, and the redundancy of adjacent bands is large. Recently, although many band selection methods have been proposed, this task is rarely handled through the context information of the whole spectral bands. Moreover, the scholars mainly focus on the different numbers of selected bands to explain the influence by accuracy measures, neglecting how many bands to choose is appropriate. To tackle these issues, we propose a fast neighborhood grouping method for hyperspectral band selection (FNGBS). The hyperspectral image cube in space is partitioned into several groups using coarse-fine strategy. By doing so, it effectively mines the context information in a large spectrum range. Compared with most algorithms, the proposed method can obtain the most relevant and informative bands simultaneously as subset in accordance with two factors, such as local density and information entropy. In addition, our method can also automatically determine the minimum number of recommended bands by determinantal point process. Extensive experimental results on benchmark data sets demonstrate the proposed FNGBS achieves satisfactory performance against state-of-the-art algorithms.

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})$ .

A graphGis said to bek-vertex rigid in$$\mathbb {R}^d$$Rdif$$G-X$$G-Xis rigid in$$\mathbb {R}^d$$Rdfor all subsetsXof the vertex set ofGwith cardinality less thank. We determine the smallest number of edges in ak-vertex rigid graph onnvertices in$$\mathbb {R}^2$$R2, for all$$k\ge 4$$k≥4. We also considerk-edge-rigid graphs, defined by removing edges, as well ask-vertex globally rigid andk-edge globally rigid graphs in$$\mathbb {R}^d$$Rd. For$$d=2$$d=2we determine the corresponding tight bounds for each of these versions, for all$$k\ge 3$$k≥3. Our results complete the solutions of these extremal problems in the plane. The result onk-vertex rigidity verifies a conjecture of Kaszanitzky and Király (Graphs Combin, 32:225–240, 2016). We also determine the degree of vertex redundancy of powers of cycles, with respect to rigidity in the plane, answering a question of Yu and Anderson (Int J Robust Nonlinear Control, 19(13):1427–1446, 2009).

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.

The quantification of myelin water content in the brain can be obtained by the multi-echo $\text{T}2^\ast $ weighted images ( $\text{T}2^\ast $ WIs). To accelerate the long acquisition, a novel tensor dictionary learning algorithm with low-rank and sparse regularization (TDLLS) is proposed to reconstruct the $\text{T}2^\ast $ WIs from the undersampled data. The proposed algorithm explores the local and nonlocal similarity and the global temporal redundancy in the real and imaginary parts of the complex relaxation signals. The joint application of the low-rank constraints on the dictionaries and the sparse constraints on the core coefficient tensors improves the performance of the tensor-based recovery. Parallel imaging is incorporated into the TDLLS algorithm (pTDLLS) for further acceleration. A pulse sequence is proposed to prospectively undersample the Ky-t space to obtain the whole brain high-quality myelin water fraction (MWF) maps within 1 minute at an undersampling rate (R) of 6.

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

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.

Documents clustering based on frequent itemsets can be regarded a new method of documents clustering which is aimed to overcome curse of dimensionality of items produced by documents being clustered. The Maximum Capturing (MC) technique is an algorithm of documents clustering based on frequent itemsets that is capable of producing a better clustering quality in compared to other similar algorithms. However, since the maximum capturing technique employed frequent itemsets, it still suffers from such several weaknesses as the emergence of items redundancy that may still cause curse of dimensionality, difficult to determine the minimum support value from a set of documents to be clustered, and no weighting on items incurred to the resulting frequent itemsets. To cope with those various weaknesses, in this research, an algorithm of documents clustering based on weighted top-k closed frequent itemsets, which is called as Weighted Maximum Capturing (WMC) algorithm, is developed. The proposed algorithm involves the frequent pattern tree algorithm to mine closed frequent itemsets from a set of documents without specifying the minimum support value of items to be generated. Experimental results showed that improvement on the resulting clustering accuracy was produced. The resulting average values of F-measure of 0.713 and purity of 0.721 with improvement ratio of 1.4% for F-measure and 2% for purity. Nevertheless, results of the scalability test showed very significant improvement. The WMC algorithm only requires the average computing time of 623.77 minutes, 518.05 minutes faster than the average computing time required by the MC algorithm.

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.

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.

The paper compares SARIMA and adjusted SARIMA(ASARIMA) in a regular stationary series where the underlying variable is seasonally nonstationary. Adopting empirical rainfall data and Box-Jenkins iterative algorithm that calculates least squares estimates, Out of 11 sub-classes of SARIMA and 7 sub-classes of ASARIMA models, AIC chose ASARIMA(2,1,1)12 over all sub-classes of SARIMA(p,0,q)x(P,1,Q)12 identified. Diagnostic test indicates absence of autocorrelation up to the 48th lag. The forecast values generated by the fitted model are closely related to the actual values. Hence, ASARIMA can be recommended for regular stationary time series with seasonal characteristics and where parameter redundancy and large sum of square errors are penalized.

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.

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.

Continuous point cloud data is being used more and more widely in practical applications such as mapping, localization and object detection in autonomous driving systems, but due to the huge volume of data involved, sharing and storing this data is currently expensive and difficult. One possible solution is the development of more efficient methods of compressing the data. Other researchers have proposed converting 3D point cloud data into 2D images, or using tree structures to store the data. In a previous study targeting streaming point cloud data, we proposed an MPEG-like compression method which utilizes simultaneous localization and mapping (SLAM) results to simulate LiDAR's operating process. In this paper, instead of imitating MPEG, we propose new strategy for more efficient reference frame distribution and more natural frame prediction, and use a different algorithm to encode the residual, greatly improving the algorithm's performance and its stability in different scenarios. We also discuss how various parameters affect compression performance. Using our proposed method, streaming point cloud data collected by LiDAR sensors can be compressed to 1/50th of its original size, with only 2 cm of Root Mean Square Error for each detected point. We evaluate our proposed method by comparing its performance with several other existing point cloud compression methods in three different driving scenarios, demonstrating that our proposed method outperforms them.

Hyperspectral target detection is widely used in both military and civilian fields. In practical applications, how to select a low-correlation and representative band subset to reduce redundancy is worth discussing. However, most of the existing band selection (BS) methods usually select bands according to the statistics or correlation, which neglect the spectral characteristics of the desired target and are not specially designed for target detection. Therefore, this article proposed a novel BS method, called constrained-target BS with subspace partition (CTSPBS), to select an optimal subset with low internal correlation and strong target representability for the target detection task. By using a specially designed subspace partition method based on correlation distance (CDSP), CTSPBS divides the hyperspectral bands into several unrelated subspaces. Then, according to certain constrained-target band prioritization (BP) criteria, the band with the highest priority in each subset is selected to form the optimal subset for a specific target. Correspondingly, two versions of the proposed method, minimum variance BS with CDSP (CDSP_MinV) and minimum variance BS with CDSP (CDSP_MaxV), are derived to implement CTSPBS. Extensive experiments on three public hyperspectral datasets demonstrate that the proposed method exhibit more robust and effective performance than several state-of-the-art methods. Finally, this article focuses on the difficulty of marine benthos detection in mariculture application and proves the feasibility of the proposed method.

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

In this 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.

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.

Blood components such as hemoglobin, glucose, creatinine measuring are essential for monitoring one’s health condition. The current blood component measurement approaches still depend on invasive techniques that are painful, and uncomfortable for the patients. To facilitate measurement at home, we proposed a novel non-invasive technique to measure blood hemoglobin, glucose, and creatinine level based on PPG signal using Deep Neural Networks (DNN). Fingertip videos from 93 subjects have been collected using a smartphone. The PPG signal is generated from each video, and 46 characteristic features are then extracted from the PPG signal, its derivatives ( $1^{ \text {st}}$ and $2^{ \text {nd}}$ ) and from Fourier analysis. Additionally, age and gender are also included to feature because of the significant effects on hemoglobin, glucose, and creatinine. A correlation-based feature selection (CFS) using genetic algorithms (GA) has been used to select the optimal features to avoid redundancy and over-fitting. Finally, DNN based models have been developed to estimate the blood Hemoglobin (Hb), Glucose (Gl), and Creatinine (Cr) levels from the selected features. The approach provides the best-estimated accuracy of $R^{2} = 0.922$ for Hb, $R^{2} = 0.902$ for Gl, and $R^{2} = 0.969$ for Cr. Experimental aftermaths show that the proposed method is a suitable technique to be used clinically to measure human blood component levels without taking blood samples. This paper also reveals that smartphone-based PPG signal has a great potential to measure the different blood components.

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.