Search

Pelton wheel works on Newton's law which converts the kinetic energy of fluid into mechanical energy. Bearing, nozzle, servomotor and buckets are the main components of the Pelton wheel that are prone to defects. Corrosion by reactive materials, degradation by strong slurry particles, the involvement of some metallurgical defects, cavitation, and poor bearing lubrication are some of the causes which induce defects in the Pelton wheel. These failures result in significant turbine disruption, costly disassembly, and, in some cases, full Pelton wheel shutdown. Hence, it becomes a necessity to monitor the Pelton wheel through some suitable methods. A novel artificial intelligence-based method has been investigated to describe the health condition of a Pelton wheel. Traditionally, extracted features from stationary wavelet transform (SWT) decomposed signal to increase the complexity and affect the classification accuracy. This issue is resolved by developing a new fault diagnosis scheme using improved Shannon entropy based on expectation maximization principal component analysis (EM-PCA) and extreme learning machine (ELM). In the proposed scheme, F-score is initially applied to select features and construct the feature matrix. At the same time, EM-PCA is used to reduce the dimension of the constructed feature matrix, which reduces the correlation between data and eliminate the redundancy to retain the essential features for the ELM classification model. The effectiveness of the proposed scheme is compared with other reduction techniques used for the purpose. A comparison has also been made with other classification methods. The results show that EM-PCA with improved Shannon entropy can effectively eliminate correlation and redundancy of data. Further, the use of the ELM can take on better adaptability, faster computation speed and higher classification rate. The proposed method is fast as it takes 0.0020 s of computation time for both training and testing with 89.14% and 96.33% training and testing accuracies, respectively.

The quantity of Telemetry Data (TD) is rapidly augmenting because of a large extent of involved parameters in addition to the use of higher sampling frequencies. Therefore, to enhance the transmission efficiency as well as lessen the load on spacecraft resources, effectual Data Compression approaches in space TD are required. Further, the TD is suffered as of some faults. Therefore, to identify that fault data and render secure Data Transmission, the fault-analysis and data security techniques are needed onboard. It is challenging to attain higher Compression Ratios together with higher accuracy in lossless compression together with fault analysis. This paper designs an effective lossless TD compression as well as fault analysis scheme to trounce all these challenges. This scheme comprises:— (i) compression, (ii) encryption and (iii) classification. In the compression stage, the 2SMLZ compress the redundancy removed TD. In the encryption stage, the EECC algorithm encrypts the compressed data, so that the encrypted data can well be transmitted securely to the Ground Station (GS). In the GS, for recovering the original data, the decryption, as well as the decompression process, are carried out. Conversely, the CMOW-DLNN classifier identifies the fault data on the fault analysis stage. The proposed techniques effectively compress the data, as well as securely transmit the data and accurately recognize the fault data, which are experimentally proved by contrasting it with existing techniques.

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.

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.

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).

A large composition of image, sound, motion, video and other signal sequences is always a rich source of useful and reliable information. With the proliferating cost and complexities of mass storage media, the need for redundancy and complex transmission-free signals is on the rise. Furthermore, the growing increase in the number of video-specific applications has heightened the need for digital multimedia technologies that emphasized bit compression. This paper presents a Discrete Transform Technique (DTT) that leveraged the integration of Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) for higher scale video compression. The experimental study of the new technique was based on some online videos with varying sizes, sources and formats. The analysis of the experimental values obtained for standard performance metrics such as maximum difference, mean square error and peak signal to noise ratio for the pre- and post-compression videos portrayed a very significant and encouraging impact of the new technique. Comparative analysis based on compression ratios derived using the new technique and some recent video compression techniques also established a very impressive performance of the new technique.

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.

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.

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).

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.

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.

Surveillance videos which record crowd behaviors have dramatically increased due to the wide applications. A quick view of such crowd surveillance video in a constrained time is an increasing demand because it always contain a huge number of redundancy frames. In this paper, we focus on summarization of crowd surveillance videos. But it is not easy due to two reasons. First, how to make the decision to keep or discard a subshot from the input surveillance video stream so that the summary can outline the main behaviors of the crowd over a limited frames sequence. Second, how to maintain performance of summarization model for long surveillance videos. To tackle these challenges, we formulate surveillance video summarization as a sequential decision-making process and train the summarization network with reinforcement learning-based framework. A novel crowd location-density reward is proposed to teach summarization network to produce high-quality summaries. In addition, a summarization network with three layers LSTM is designed to maintain performance across longer time spans. Extensive experiments on three public crowd surveillance videos datasets show that the proposed method achieves state-of-the-art performance.

We apply the method of moments to prove a recent conjecture of Haikin, Zamir and Gavish concerning the distribution of the singular values of random subensembles of Paley equiangular tight frames. Our analysis applies more generally to real equiangular tight frames of redundancy 2, and we suspect similar ideas will eventually produce more general results for arbitrary choices of redundancy.

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.

To make full use of the key information in frame-level features, a DNN-based model for speech enhancement is proposed using self-attention on the feature dimension. Two improvement strategies are adopted to strengthen the attention of the fully connected layers to the effective information in the features. First, the model introduces the fusion of feature domains on the input features, using a 136-dimensional combination of features including the MFCC, AMS, RASTA-PLP, cochleagram, and PNCC. The fusion of features complements information from different domains, including the mel domain and gammatone domain, thus providing more effective information for self-attention. Second, a feature-level self-attention mechanism is applied to the output of the fully connected layer to obtain the information related to the task. The feature-level attention enables the fully connected layers to capture the internal correlations between different features and to reduce the redundancy brought by multiple features. The experimental results show that under the matched noisy condition, compared to the noisy signals, the proposed algorithm increased the PESQ, fwsegSNR and STOI by 40.92%, 60.2% and 8.31%, respectively, and by 23.64%, 32.55% and 3.4%, respectively, under the mismatched noisy condition. Comparisons between different neural networks indicate that the proposed algorithm is superior to the compared algorithms in both the matched and mismatched situations using fewer context frames. Therefore, the proposed model can effectively utilize the key information of the features to suppress the noise, thereby improving the speech quality and generalizing the mismatched samples.

Due to the low compression performance of traditional compression models, we have developed a new HOA based Fractional KCA with 2D-DWT for improving the multispectral image quality. In this paper, we present a novel multispectral image compression method for improving the complexity by maintaining quality reconstruction and also reducing the size of the storage of multispectral images. Initially, Karhunen–Loeve transform (KLT) is used to remove the spatial redundancies. In the second stage, 2D DWT is used to eliminate the intraband spatial redundancies. In the third stage, Fractional KCA (FKCA) is applied to improve the post-transformation process. FKCA is connected to the band of all wavelet sub-bands to minimize the spatial redundancy between intra sub-bands. Finally, the Hybrid Optimal algorithm (HOA) based FKCA is used to eliminate the residual and information redundancy among the neighboring bands. The experimental analysis of proposed 2D-DWT based Fractional KCA shows that the model improves the performance of compression data in terms of PSNR, MSSI, and VIF. Also, the multispectral image dataset shows the proposed compression model outperforms the existing compression models such as FKLT + PCA, ADWT + OADL, and DWT + DCT

In this paper, a novel compression framework based on 3D point cloud data is proposed for telepresence, which consists of two parts. One is implemented to remove the spatial redundancy, i.e., a robust Bayesian framework is designed to track the human motion and the 3D point cloud data of the human body is acquired by using the tracking 2D box. The other part is applied to remove the temporal redundancy of the 3D point cloud data. The temporal redundancy between point clouds is removed by using the motion vector, i.e., the most similar cluster in the previous frame is found for the cluster in the current frame by comparing the cluster feature and the cluster in the current frame is replaced by the motion vector for compressing the current frame. The first, the B-SHOT (binary signatures of histograms orientation) descriptor is applied to represent the point feature for matching the corresponding point between two frames. The second, the K-mean algorithm is used to generate the cluster because there are a lot of unsuccessfully matched points in the current frame. The matching operation is exploited to find the corresponding clusters between the point cloud data of two frames. Finally, the cluster information in the current frame is replaced by the motion vector for compressing the current frame and the unsuccessfully matched clusters in the current and the motion vectors are transmitted into the remote end. In order to reduce calculation time of the B-SHOT descriptor, we introduce an octree structure into the B-SHOT descriptor. In particular, in order to improve the robustness of the matching operation, we design the cluster feature to estimate the similarity between two clusters. Experimental results have shown the better performance of the proposed method due to the lower calculation time and the higher compression ratio. The proposed method achieves the compression ratio of 8.42 and the delay time of 1228 ms compared with the compression ratio of 5.99 and the delay time of 2163 ms in the octree-based compression method under conditions of similar distortion rate.

Unsupervised feature selection is an indispensable pre-processing step in many data mining and pattern recognition tasks where the unlabeled high dimensional data are ubiquitous. Most of existing methods fail to explore the local geometric structure consistency (preservation) of the input data and minimize redundancy of selected features simultaneously. In this paper we propose a novel unsupervised feature selection method which jointly integrates the local geometric structure consistency and redundancy minimization (JLSPRM) into an unified framework. JLSPRM utilizes nonnegative spectral analysis to learn the cluster labels of the input data, then the local geometric structure consistency is developed to make the learned cluster labels more accurate, during which the feature selection operation is performed. To minimize the redundancy rate among selected features, the maximal information coefficient (MIC) is utilized to evaluate the correlation of the pairwise features. Besides, the l2,1-norm is exerted on feature selection matrix which makes the framework decent for selecting features. An efficient iterative optimization algorithm is designed to obtain the solution of the unsupervised feature selection model. The superiority and effectiveness of our proposed approach over the state-of-the-art feature selection methods have also been validated through the extensive experiments on nine benchmark datasets.

Feature selection is an important data preprocessing method before classification. Multi-objective optimization algorithms have been proved an effective way to solve feature selection problems. However, there are few studies on multi-objective optimization feature selection methods for multi-label data. In this paper, a multi-objective multi-label filter feature selection algorithm based on two particle swarms (MOMFS) is proposed. We use mutual information to measure the relevance between features and label sets, and the redundancy between features, which are taken as two objectives. In order to avoid Particle Swarm Optimization (PSO) from falling into the local optimum and obtaining a false Pareto front, we employ two swarms to optimize the two objectives separately and propose an improved hybrid topology based on particle’s fitness value. Furthermore, an archive maintenance strategy is introduced to maintain the distribution of archive. In order to study the effectiveness of the proposed algorithm, we select five multi-label evaluation criteria and perform experiments on seven multi-label data sets. MOMFS is compared with classic single-objective multi-label feature selection algorithms, multi-objective filter and wrapper feature selection algorithms. The experimental results show that MOMFS can effectively reduce the multi-label data dimension and perform better than other approaches on five evaluation criteria.

Feature selection plays a fundamental role in many data mining and machine learning tasks. In this paper, we proposed a novel feature selection method, namely, Dynamic Feature Selection Method with Minimum Redundancy Information (MRIDFS). In MRIDFS, the conditional mutual information is used to calculate the relevance and the redundancy among multiple features, and a new concept, the feature-dependent redundancy ratio, was introduced. Such ratio can represent redundancy more accurately. To evaluate our method, MRIDFS is tested and compared with seven popular methods on 16 benchmark data sets. Experimental results show that MRIDFS outperforms in terms of average classification accuracy.

Sum-rank Hamming codes are introduced in this work. They are essentially defined as the longest codes (thus of highest information rate) with minimum sum-rank distance at least $ 3 $ (thus one-error-correcting) for a fixed redundancy $ r $, base-field size $ q $ and field-extension degree $ m $ (i.e., number of matrix rows). General upper bounds on their code length, number of shots or sublengths and average sublength are obtained based on such parameters. When the field-extension degree is $ 1 $, it is shown that sum-rank isometry classes of sum-rank Hamming codes are in bijective correspondence with maximal-size partial spreads. In that case, it is also shown that sum-rank Hamming codes are perfect codes for the sum-rank metric. Also in that case, estimates on the parameters (lengths and number of shots) of sum-rank Hamming codes are given, together with an efficient syndrome decoding algorithm. Duals of sum-rank Hamming codes, called sum-rank simplex codes, are then introduced. Bounds on the minimum sum-rank distance of sum-rank simplex codes are given based on known bounds on the size of partial spreads. As applications, sum-rank Hamming codes are proposed for error correction in multishot matrix-multiplicative channels and to construct locally repairable codes over small fields, including binary.

In this paper, we focus on various compression and classification algorithms for three different paralinguistic signal classification tasks. These tasks are quite difficult for humans because the sound information from such signals is difficult to distinguish. Therefore, when machine learning techniques are applied to analyze paralinguistic signals, several different aspects of speech-related information, such as prosody, energy, and cepstral information, are usually considered for feature extraction. However, when the size of the training corpus is not sufficiently large, it is extremely difficult to directly apply machine learning to classify such signals due to their high feature dimensions; this problem is also known as the curse of dimensionality. This paper proposes to address this limitation by means of feature compression. First, we present experimental results obtained by using various compression algorithms to compress signals to eliminate redundancy of the signal features. We observe that compared with the original features, the compressed signal features still provide a comparable ability to distinguish the signals, especially when using a fully connected neural network classifier. Second, we calculate the output distribution of the F1-score for each emotion in the speech emotion recognition problem and show that the fully connected neural network classifier performs more stably than other classical methods.

In this paper, a computer based system has been proposed as a support to gastrointestinal polyp detection. It can detect and classify gastrointestinal polyps from endoscopic video. Color wavelet (CW) features and convolutional neural network (CNN) features of endoscopic video frames are extracted. Mutual information based feature selection technique-Minimum redundancy maximum relevance (mRMR) is used to scale down feature vector. Instead of using a single classifier, Bootstrap Aggregrating (Bagging)- an ensemble classifier is used. Proposed system has been assessed against different public databases and our own datasets. Evaluation shows that, the system outperforms the existing methods.

The wireless visual sensor network (WVSN) as a new emerged intelligent visual system, has been applied in many video monitoring sites. However, there is still great challenge because of the limited wireless network bandwidth. To resolve the problem, we propose a real-time dynamic texture approach which can detect and reduce the temporal redundancy during many successive image frames. Firstly, an adaptively learning background model is improved to discover successive similar image frames from the inputting video sequence. Then, the dynamic texture model based on the singular value decomposition is adopted to distinguish foreground and background element dynamics. Furthermore, a background discarding strategy based on visual motion coherence is proposed to determine whether each image frame is streamed or not. To evaluate the trade-off performance of the proposed method, it is tested on the CDW-2014 dataset, which can accurately detect the first foreground frame when the moving objects of interest appear in the field of view in the most tested dynamic scenes, and the misdetection rate of the undetected foreground frames is near to zero. Compared to the original stream, it can reduce the occupied bandwidth a lot and its computational cost is relatively lower than the state-of-the-art methods.

The forensics of the median filtering is a challenging task due to its content preserving nature. Several methods have been proposed for median filtering forensics in digital images. However the performance of these methods deteriorates for compressed images, small resolutions of images and for anti-forensic operations. Moreover large feature set dimensions of these methods also pose a computational challenge. This paper proposes, a 8-dimensional feature set, derived from two state-of-the-art techniques by employing maximum relevancy and minimum redundancy (mRMR) feature selection approach. Features are selected by mRMR on the basis of distance correlation as an association measure. Extensive experiments are performed to evaluate the efficacy of proposed method through six different databases. The proposed method outperforms state-of-the-art techniques for uncompressed images, compressed images at low quality factors, low resolutions images and for an anti-forensic operation. The performance of the proposed method is also compared with convolutional neural network (CNN) based features for the detection of median filtering at low resolutions and for compressed images. Also, experimental results support the performance of proposed method over other manipulations (average and Gaussian filtering).

High dimensionality and sample imbalance of gene expression data promote the development of effective algorithms for classifying gene expression data. To improve the ability to distinguish different subtypes of gene expression data, we devise a hypervolume-based discrete evolutionary optimization algorithm (HYBDEOA) in this paper. Four objectives, namely the number of genes, the accuracy, the relevance, and the redundancy, are optimized simultaneously to guide the evolution. Firstly, binary encoding is used to choose some features, projecting data onto different subspaces. After that, a discrete neighborhood operation is conducted to generate a new binary-mapped population. Combining the new population with the current population, we employ the hypervolume-based mechanism to select the Pareto solutions. Finally, a discrete mutation method is proposed to find promising solutions in the binary search space. To demonstrate the performance of HYBDEOA, we apply HYBDEOA to 55 synthetic datasets and 35 cancer gene expression datasets. Extensive experiments are also conducted to reveal the effectiveness and efficiency of HYBDEOA. The experimental results demonstrate that our proposed method is a parameter-less and robust algorithm, which can group gene expression data with a finer and more informative classification.

A method to evaluate the mechanical performance of the corroded steel cable based on the strength condition is proposed. The corrosion safety factor (nα) was introduced, and the evaluation model was established. The eccentric effect as a result of the local corrosion was also considered. Then, the axial tensile experiment on steel-strand specimens was carried out. Furthermore, the mechanical properties, including the ultimate bearing capacity and limited strain, were analyzed. The results show that the properties of locally corroded specimens deteriorate more than uniformly corroded specimens. It is verified that the experimental data are in good agreement with the proposed theoretical model when the corrosion degree α is less than 0.35. Based on the experimental data and the related specification of the stayed cable, the relationship between nα and α was precisely determined. Besides, the level of nα or the safety redundancy can be characterized by the evaluation score δ. The lower the score δ, the less safe is the cable structure. This method also provides significant application prospects for safety evaluation research in civil engineering.

In the face of rapid urbanization, monitoring urban expansion has gained importance to sustainably manage the land resources and minimize the impact on the environment. Monitoring urban growth using satellite data involves computing the state of land use–land cover and their change over time. A number of computing methods have been developed to process and interpret the satellite results for an urban environment. However, due to a large number of parametric and nonparametric algorithms used for land-use–land-cover classification, there is uncertainty regarding choosing the best algorithm to measure the urban processes. In this study, several parametric (maximum likelihood) and nonparametric (support vector machine, spectral angle mapper, artificial neural network and decision tree) algorithms were used. The study was aimed at finding out the best available classification technique for land-use–land-cover classification and change detection. Landsat 8, the latest in Landsat series, and Landsat 7 and 5 freely available satellite data were used. Due to the redundancy reported for the traditional kappa-based indices, we applied modern disagreement indices to assess the accuracy of the classification process. Artificial neural network for Landsat 8 image had the highest kappa coefficient, while spectral angle mapper had the highest overall agreement (97%) and least quantity allocation error (1%). Spectral Angle Mapper gave the highest accuracy, while maximum likelihood classification gave the least for allocation and spatial disagreement indices. We found that spectral angle mapper gave the best results for land-use–land-cover change analysis in terms of least omission and commission errors (2.5% each) and highest overall agreement, whereas artificial neural network performed better in land-use–land-cover classification studies.

This paper introduces a new compression scheme that exploits temporal redundancies in video segments utilizing the pixogram concept recently introduced in the literature. By taking into consideration the correlation between video pixels along the time domain, a pixogram has the ability to transform uncorrelated spatial areas of separate video-frames into highly correlated temporal vectors, thus increasing redundancy in the transform domain. This strategy allows for higher compression ratios in video streams. The proposed compression scheme is the first compression technique that utilizes the pixogram concept in video compression, and aims to challenge the traditional trade-off associated with high compression ratios leading to reduced visual quality. Comparisons with popular compression standards demonstrate the advantage this new approach has for highly correlated video segments. Moreover, the proposed technique is more suitable to parallelization, and thus outperforms other compression techniques in terms of execution time.

Lossy /lossless image compression technique results will either improve the quality of image with reduced compression ratio or degrades the image with better compression ratio. But on dealing with the combined lossy/lossless techniques, they provide the output with better image quality and high compression rate, however, the techniques failed to obtain better efficiency due to less predictive nature. Hence, to acquire an image having better compression rate with improved predictive nature, a novel framework is introduced in our proposed work. It uses a Deep Residual Network Bi-Layer Enfolded Compressor (DRN-BLEC) to enhance the image quality. In order to obtain better compression rate, the Mexican Meyer Hat Wavelet Transform (MMHWT) is used. Similarly, the well knowledge quantization is achieved by utilizing DRN for learning; it is then followed by the Lloyds quantization technique that groups the feature values by quantizing it, which in turn boost-up the resolution of the image. Consequently with DRN, the process of encoding and decoding doesn’t requires to be done separately since two residual layer of the DRN are trained to encode and decode the images, which thereby reduces the time required for compression process. Thus a compressed image with high resolution is obtained without redundancy, moreover with high compression ratio. The architecture of the DRN-BLEC is implemented in MATLAB and the respective result structure is validated.

Feature selection plays a critical role in many applications that are relevant to machine learning, image processing and gene expression analysis. Traditional feature selection methods intend to maximize feature dependency while minimizing feature redundancy. In previous information-theoretical-based feature selection methods, feature redundancy term is measured by the mutual information between a candidate feature and each already-selected feature or the interaction information among a candidate feature, each already-selected feature and the class. However, the larger values of the traditional feature redundancy term do not indicate the worse a candidate feature because a candidate feature can obtain large redundant information, meanwhile offering large new classification information. To address this issue, we design a new feature redundancy term that considers the relevancy between a candidate feature and the class given each already-selected feature, and a novel feature selection method named min-redundancy and max-dependency (MRMD) is proposed. To verify the effectiveness of our method, MRMD is compared to eight competitive methods on an artificial example and fifteen real-world data sets respectively. The experimental results show that our method achieves the best classification performance with respect to multiple evaluation criteria.

Speckle noise reduction is an important issue in synthetic aperture radar (SAR) imaging. Because SAR images are distinct in being complex valued and susceptible to corruption owing to multiplicative fluctuations, specialized methods for speckle reduction are needed. Techniques based on nonlocal means perform denoising by exploiting the natural redundancy of patterns within an image. They calculate a weighted average of pixels whose neighborhoods are close to one another, where this significantly reduces noise while preserving most image content. While this method performs well on flat areas and textures, its results are excessively smooth in low-contrast areas, and leave residual noise around edges and singular structures. Another variational denoising method uses total variation (TV) minimization to restore regular images but is prone to excessively smooth textures, the staircasing effect, and contrast losses. Our proposed model is intended for the logarithmic domain of SAR data, and combines the above two methods by minimizing an adaptive TV using a nonlocal data fidelity term. In the variational functionals developed here, weighted parameters of nonlocal regularization are adaptively tuned based on local heterogeneity information and noise in the images. A fast iterative shrinkage/thresholding algorithm (FISTA) is then used to solve the optimization problem. The results of experiments on real SAR images verify the effectiveness of the proposed method in terms of speckle reduction.

The main aim of this paper is to study tensor factorization for low-rank tensor completion in imaging data. Due to the underlying redundancy of real-world imaging data, the low-tubal-rank tensor factorization (the tensor–tensor product of two factor tensors) can be used to approximate such tensor very well. Motivated by the spatial/temporal smoothness of factor tensors in real-world imaging data, we propose to incorporate a hybrid regularization combining total variation and Tikhonov regularization into low-tubal-rank tensor factorization model for low-rank tensor completion problem. We also develop an efficient proximal alternating minimization (PAM) algorithm to tackle the corresponding minimization problem and establish a global convergence of the PAM algorithm. Numerical results on color images, color videos, and multispectral images are reported to illustrate the superiority of the proposed method over competing methods.

Subband coding is a popular technique to achieve multichannel data compression and efficient data transmission in image and video communications. In this paper, we focus on designing a data-dependent image subband coder which divides the image into strongly (total) decorrelated and spectrally majorized subbands, which seeks to reduce the redundancy to achieve better compression and efficient transmission. To achieve strong decorrelation and spectral majorization between the subbands, we adopt a set of new iterative polynomial eigenvalue decomposition (PEVD) algorithms: sequential matrix diagonalization (SMD) and maximum element sequential matrix diagonalization (ME-SMD). Using this SMD-based PEVD approach, we design the data dependent subband coder (DDSC) for image subband coding. We compare the performance of the proposed SMD/ME-SMD algorithms with the existing DDSC methods like SBR2, SBR2C, K–L transform (KLT) coder and data independent subband coder (DISC)-based discrete wavelet transform (DWT) technique. To measure the performance, we use the parameters like coding gain, correlation coefficient, MSE (mean square error) and peak signal-to-noise ratio (PSNR). The presented simulation results for standard images in the absence of quantization show that the proposed SMD-based PEVD technique performs far better than the existing techniques.

Minimal-redundancy–maximal-relevance (mRMR) algorithm is a typical feature selection algorithm. To select the feature which has minimal redundancy with the selected features and maximal relevance with the class label, the objective function of mRMR subtracts the average value of mutual information between features from mutual information between features and the class label, and selects the feature with the maximum difference. However, the problem is that the feature with the maximum difference is not always the feature with minimal redundancy maximal relevance. To solve the problem, the objective function of mRMR is first analyzed and a constraint condition that determines whether the objective function can guarantee the effectiveness of the selected features is achieved. Then, for the case where the objective function is not accurate, an idea of equal interval division is proposed and combined with ranking to process the interval of mutual information between features and the class label, and that of the average value of mutual information between features. Finally, a feature selection algorithm based on equal interval division and minimal-redundancy–maximal-relevance (EID–mRMR) is proposed. To validate the performance of EID–mRMR, we compare it with several incremental feature selection algorithms based on mutual information and other feature selection algorithms. Experimental results demonstrate that the EID–mRMR algorithm can achieve better feature selection performance.

In this paper, we proposed an improvisation to Li et al.’s pixel value ordering (PVO) method and called it as generalized PVO (GePVO), which provides a suitable expansion strategy using the image redundancy more abundantly. GePVO makes use of a reference pixel and calculates prediction errors using both sides of the block in different modes (one-way GePVO, bidirectional GePVO, round-trip GePVO). The experimental results demonstrate that GePVO had better performance in terms of an increased embedding capacity compared to the other data hiding methods. When compared to Li et al.’s method, the bidirectional GePVO led to an increase in the embedding capacity by 1.8 times and the round-trip GePVO by three times. We contend that the GePVO method made the PVO method more flexible and unbound in terms of embedding capacity while keeping the image quality intact. Performance comparison with other reversible data hiding schemes is presented to demonstrate the validity of the proposed method.

This paper investigates the uncertain stochastic resource allocation problem in which the results of a given allocation of resources are described as probabilities and these probabilities are considered to be uncertain from practical aspects. Here uncertainties are introduced by assuming that these probabilities depend on random parameters which are impacted by various factors. The redundancy allocation problem (RAP) and the multi-stage weapon-target assignment (MWTA) problem are special cases of stochastic resource allocation problems. Bi-objective models for the uncertain RAP and MWTA problem in which the conditional value-at-risk measure is used to control the risk brought by uncertainties are presented in this paper. The bi-objective formulation covers the objectives of minimizing the risk of failure of completing activities and the resulting cost of resources. With the aim of determining referenced Pareto fronts, a linearized formulation and an approximated linear formulation are put forward for RAPs and MWTA problems based on problem-specific characteristics, respectively. Two state-of-the-art decomposition-based multi-objective evolutionary algorithms (i.e., MOEA/D-AWA and DMOEA- $$\varepsilon \hbox {C}$$ ) are used to solve the formulated bi-objective problem. In view of differences between MOEA/D-AWA and DMOEA- $$\varepsilon \hbox {C}$$ , two matching schemes inspired by DMOEA- $$\varepsilon \hbox {C}$$ are proposed and embedded in MOEA/D-AWA. Numerical experiments have been performed on a set of uncertain RAP and MWTA instances. Experimental results demonstrate that DMOEA- $$\varepsilon \hbox {C}$$ outperforms MOEA/D-AWA on the majority of test instances and the superiority of DMOEA- $$\varepsilon \hbox {C}$$ can be ascribed to the $$\varepsilon $$ -constraint framework.

In hyperspectral image (HSI) classification, a big challenge is the limited sample size with a relatively high feature dimension. Therefore, effective feature extraction of data is essential, which is desired to remove the redundancy as well as improve the discrimination. A huge number of methods have been proposed for HSI feature extraction. In recent years, deep learning-based feature extraction algorithms have shown their superiorities in various classification problems. Within them, deep PCA (DPCA) is a simple but efficient algorithm, which runs fast due to the absence of back-propagation. However, DPCA fails to provide satisfactory classification accuracies on HSI datasets. In this paper, we try to combine DPCA with residual-based multi-scale feature extraction and propose a residual deep PCA (RDPCA) feature extraction algorithm for HSI classification. It is a hierarchical approach consisting of multiple layers. Within each layer, PCA is utilized for layer-wise feature extraction, and the reconstruction residual is fed into the next layer. When the feature is passed deeper into the RDPCA network, finer details are mined. The layer-wise features are concatenated to form the final output feature. Furthermore, to enhance the ability of nonlinear feature extraction, we add activation functions between adjacent layers. Experimental results on real-world HSI datasets have shown the superiority of the proposed RDPCA over DPCA and PCA.

An exact phase-retrievable frame $\{f_{i}\}_{i}^{N}$ for an $n$-dimensional Hilbert space is a phase-retrievable frame that fails to be phase-retrievable if any one element is removed from the frame. Such a frame could have different lengths. We shall prove that for the real Hilbert space case, exact phase-retrievable frame of length $N$ exists for every $2n-1\leq N\leq n(n+1)/2$. For arbitrary frames we introduce the concept of redundancy with respect to its phase-retrievability and the concept of frames with exact PR-redundancy. We investigate the phase-retrievability by studying its maximal phase-retrievable subspaces with respect to a given frame which is not necessarily phase-retrievable. These maximal PR-subspaces could have different dimensions. We are able to identify the one with the largest dimension, which can be considered as a generalization of the characterization for phase-retrievable frames. In the basis case, we prove that if $M$ is a $k$-dimensional PR-subspace, then $|supp(x)| \geq k$ for every nonzero vector $x\in M$. Moreover, if $1\leq k< [(n+1)/2]$, then a $k$-dimensional PR-subspace is maximal if and only if there exists a vector $x\in M$ such that $|supp(x) | = k$., 21 pages

The density and long-term stability of DNA make it an appealing storage medium, particularly for long-term data archiving. Existing DNA storage technologies involve the synthesis and sequencing of multiple nominally identical molecules in parallel, resulting in information redundancy. We report the development of encoding and decoding methods that exploit this redundancy using composite DNA letters. A composite DNA letter is a representation of a position in a sequence that consists of a mixture of all four DNA nucleotides in a predetermined ratio. Our methods encode data using fewer synthesis cycles. We encode 6.4 MB into composite DNA, with distinguishable composition medians, using 20% fewer synthesis cycles per unit of data, as compared to previous reports. We also simulate encoding with larger composite alphabets, with distinguishable composition deciles, to show that 75% fewer synthesis cycles are potentially sufficient. We describe applicable error-correcting codes and inference methods, and investigate error patterns in the context of composite DNA letters. Toward more storage for less synthesis using a six-letter composite DNA alphabet.

Hidden Markov models are a flexible class of models that can be used to describe time series data which depends on an unobservable Markov process. As with any complex model, it is not always obvious whether all the parameters are identifiable, or if the model is parameter redundant; that is, the model can be reparameterised in terms of a smaller number of parameters. This paper considers different methods for detecting parameter redundancy and identifiability in hidden Markov models. We examine both numerical methods and methods that involve symbolic algebra. These symbolic methods require a unique representation of a model, known as an exhaustive summary. We provide an exhaustive summary for hidden Markov models and show how it can be used to investigate identifiability.

We investigate the $$L_1$$ geodesic farthest neighbors in a simple polygon P, and address several fundamental problems related to farthest neighbors. Given a subset $$S \subseteq P$$ , an $$L_1$$ geodesic farthest neighbor of $$p \in P$$ from S is one that maximizes the length of $$L_1$$ shortest path from p in P. Our list of problems include: computing the diameter, radius, center, farthest-neighbor Voronoi diagram, and two-center of S under the $$L_1$$ geodesic distance. We show that all these problems can be solved in linear or near-linear time based on our new observations on farthest neighbors and extreme points. Among them, the key observation shows that there are at most four extreme points of any compact subset $$S \subseteq P$$ with respect to the $$L_1$$ geodesic distance after removing redundancy.

Hyperspectral image sensors are resource constrained and have limited on-board memory. Processing of high volume hyperspectral images pose a challenge to the memory and resources of the sensor. Contemporary wavelet based image compression schemes have intensive memory requirement of which 3D-WBTC have superior coding performance due to through the exploitation of the inter sub-band & intra sub-band redundancy. This paper presents a low memory implementation of 3D-WBTC which is a listless scheme by using the fixed size state memory to keep track of block set partitioning and significance testing of wavelet coefficients of transformed hyperspectral images. Memory access time is significantly reduced due to the elimination of lists that leads to reduced complexity. Simulation results of the proposed scheme shows that proposed coder is fast and have very low memory requirement thereby making it a suitable candidate for implementation in resource constrained hyper spectral image sensor.

Information theoretic feature selection methods quantify the importanceof each feature by estimating mutual information terms to capture: therelevancy, the redundancy and the complementarity. These terms are commonlyestimated by maximum likelihood, while an under-explored area of research is how to use shrinkage methods instead. Our work suggests a novel shrinkage method for data-efficient estimation of information theoretic terms. The small sample behaviour makes it particularly suitable for estimation of discrete distributions with large number of categories (bins). Using our novel estimators we derive a framework for generating feature selection criteria that capture any high-order feature interaction for redundancy and complementarity. We perform a thorough empirical study across datasets from diverse sources and using various evaluation measures. Our first finding is that our shrinkage based methods achieve better results, while they keep the same computational cost as the simple maximum likelihood based methods. Furthermore, under our framework we derive efficient novel high-order criteria that outperform state-of-the-art methods in various tasks.

In multi-label learning, feature selection is a non-ignorable preprocessing step which can alleviate the negative effect of high-dimensionality. To address this problem, a number of effective information theory based feature selection algorithms for multi-label learning are proposed. However, these existing algorithms assume that the label space of multi-label training data is complete. In practice, the standpoint does not always hold true, due to the ambiguity among class labels or the cost effort to fully annotate instances. In this paper, we first define the new concepts of multi-label information entropy and multi-label mutual information. Then, feature redundancy, feature independence, and feature interaction are defined, respectively. In which, feature interaction is used to select more valuable features which may be ignored due to the incomplete label space. Moreover, a multi-label feature selection method with missing labels is proposed. Finally, extensive experiments conducted on eight publicly available data sets verify the effectiveness of the proposed algorithm via comparing it with state-of-the-art methods.

A remote sensing multispectral image compressor must be of low-complexity, high-robustness, and high-performance because it is usually located on a satellite platform where resources, such as power, memory, and processing capacity, are limited. Multispectral images having multiple bands are mainly compressed using compression algorithms based on three dimensional (3D) transforms, such as the 3D discrete wavelet transform, which exhibits satisfactory compression performance. However, the principal compression algorithm used for multispectral images having relatively a few bands is to encode each band independently, without considering the spectral redundancy between bands, which results in low compression performance. In this paper, an efficient compression method for multispectral images having a few bands is proposed, which is based on a distributed, improved post-transform in conjunction with a low-complexity, fast spectral decorrelator. First, a fast spectral transform and an improved post-transform having only a fast principal component analysis basis are used to generate the spectral and spatial sparse representation. Second, a distributed, improved bit plane encoding is integrated into the post-transform to remove the remaining spectral and spatial redundancy. Experimental results show that the proposed approach improves compression performance for test data in different performance measures: peak signal-to-noise ratio, mean structural similarity index, and visual information fidelity. Compared with current state-of-the-art compression techniques, the proposed method exhibits a performance improvement of 0.3–1.7 dB PSNR.

Data storage, especially big data storage, is a research hot spot in Internet of Things (IoT) system today. In traditional data storage methods, the fault-tolerant algorithm for data copies is adjusted with whole data file, which causes huge redundancy because there are less utilization and more cost of data storage when only a part of data blocks in the file are accessed. Therefore, an optimized cluster storage method for big data in IoT is proposed in this paper. First, weights of data blocks in each historical accessing period are calculated by temporal locality of data access, and the access frequencies of the data block in next period are predicted by the weights. Second, the hot spot of a data block is determined with a threshold which is calculated by previous data access. Meantime, in order to improve the data access efficiency and resource utilization, as well as to reduce the copy storage costs, copy of data block is dynamically adjusted and stored in different groups with high-performance and low-load nodes for data balance. Finally, experimental results show that the storage cost of proposed method is 70% less than that of traditional methods, which means that the proposed method effectively improves the data access speed, reduces storage space, and balances the storage load.

Manipulating the volumes of data and recognizing significant information was the challenging task in organizing and managing with uncertain data. In data management, evidence theory and fuzzy measure evaluated the uncertain information for decision execution from an arranged decision table. Despite, improvement in the decision performance and validity, an optimized rule generation has to be studied. Through this paper, a Dominance Principle-based Reduct with Dominance Lagrange Optimized Rule Generation (DLO-RG) model is proposed for the analysis of obtained decision rule set. In DLO-RG, the degree of coherence to the dominance factor is defined. Then, the coherence degrees of all objects are combined, called combined valued preference relation dominance factor to generate the reducts of an input decision table. To the generated reducts, a Deterministic Global Optimization model using Lagrange is designed to optimize the rule generation. Tight lower bounds and upper bounds are generated using Lagrange formulation. The Deterministic Global Optimization model contributes optimum lower bounds, eliminating uncertainty while extracting rules and therefore improving rule extraction accuracy. Theoretical analysis conducted using the DLO-RG proves that the extracted reducts have lower redundancy, therefore reducing the rule generation time and are more diverse when compared with those obtained by existing evidence theory. Experimental results prove the rules framed by the DLO-RG are more significant when compared with that of rules from existing methods.

One of the most important challenges in the preparation and maintenance of databases such as face images is the presence of a large number of variables in the raw data. Reducing the size of each observation so that its features do not disappear and the co-signer uniquely provides dependence on the original data is a necessary requirement of functioning with databases. Principal component analysis and its improved methods are the common techniques for reducing the dimensionality of such datasets and simultaneously increasing the interpretability and minimizing information loss. In this paper, our purpose is to define one preprocessing step as nonuniform sampling to preserve raw data appearance and to increase the performance of the dimensional reduction methods. We use the properties of sparse principal component analysis to identify the the location of less important values of the raw data that do not interfere with data features. By using sparse eigenvectors, two algorithms are presented to remove redundancy from the raw data in the one-dimensional and two-dimensional cases. After removing raw data redundancy, newly obtained data in other applications such as database recognition and compression can be used. Simulation results show that using this preprocessing step reduces the memory amount and also provides the higher recognition rate.

The present study aims to investigate the possible influence of solar/geomagnetic forcing on climate variables, such as the drought index, Danube discharge and large-scale atmospheric indices. Our analysis was performed separately for each season for two time periods, 1901–2000 and 1948–2000. The relationship between terrestrial variables and external indices was established based on the application of (1) information theory elements, namely, synergy, redundancy, total correlation, transfer entropy and (2) wavelet coherence analysis. Bandpass filtering has also been applied. The most significant signature of the solar/geomagnetic forcing in the climate variables was obtained for the data smoothed by the bandpass filter. According to our results, significant solar/geomagnetic forcing appears in the terrestrial variables with a delay of 2–3 years.