Local Dictionary Research Articles

A Reconstruction Algorithm for Compression Perception Based on Local Learning Dictionary and Sparsity Iterative Update

The global sparse representation method based on compressed sensing fails to capture the local texture and detail structure of an image. To address this, a local dictionary learning method based on the wavelet domain is proposed. The wavelet high-frequency subband sub-block classification and local dictionary learning are implemented using the FCM clustering method and K-L method, respectively. At the reconstruction end, a compressed sampling Matching pursuit algorithm based on iterative update of sparsity is proposed. This adaptive iteration can effectively reconstruct the original image under unknown sparsity conditions. Simulation experiments show that compared to existing reconstruction methods, this approach has the advantages of simple computation, strong adaptive sparse representation ability for the original image, and superior reconstructed image performance.

A Federated Dictionary Learning Method for Process Monitoring With Industrial Applications

Edge computing is an indispensable technology in the Industry 4.0 era. Data is stored at the edge and transmission is prohibited due to concerns about data privacy. Thus, the traditional centralized data-driven modeling methods will face many difficulties and can hardly be used in practice. Federated learning aims to let local nodes learn a global model cooperatively while keeping their data localized. Motivated by the pioneering framework of federated learning, a federated dictionary learning method is proposed for process monitoring. In detail, local nodes train local data by K-SVD method and learn local dictionaries. Then, local dictionaries, instead of local data, are transferred to the fusion center for calculation of the global dictionary. In order to guarantee an optimal global dictionary, a novel federated dictionary average strategy is introduced. Finally, the reconstruction errors of local data and the control limit are calculated based on the global dictionary for process monitoring. The performance of the proposed method is verified on a numerical simulation, the CSTH benchmark process, and the industrial aluminum electrolysis process.

SAR Image Target Recognition Method by Global and Local Dictionary Sparse Representation

ABSTRACT Target variant detection has been challenging for Synthetic Aperture Radar (SAR), and the performance of the target variant recognition needs to be further enhanced. SAR images are widely used in the field of target recognition and are important to support reconnaissance operations. This paper proposes a novel research approach to improve the efficiency of target recognition. First, the theory surrounding sparse representation and dictionary learning is presented. Secondly, a SAR image target recognition model is constructed according to the theory. Meanwhile, a dynamic joint sparse representation model is proposed based on multi-information and applied to SAR image target recognition when sparse representation is under consideration. Finally, experiments are set up to validate the proposed model. The results are presented as follows: 1. the recognition rate range of the sample SAR image target is 0.811–0.995 and 0.867–0.990, respectively, when the two cases, without registration processing and with registration processing, utilize the recognition method of dictionary learning and sparse representation. 2. with the increase in dictionary size, the average recognition rate of SAR images based on multi-information dynamic joint sparse representation also increases under the conditions of no logarithm transformation and median filtering and after logarithm transformation and median filtering are run. Then, the average recognition rate range is 0.63–0.9 and 0.65–0.96, respectively. Thus, the recognition rate is improved by 5%-10%. 3. the recognition approach based on the sparse representation of multi-information dynamic joint has distinct sparse degrees of SAR image recognition in the case of logarithmic transformation and median filtering when the sample image has been registered and not been registered. The relative average recognition rates were 0.950–0.970 and 0.955–0.977, respectively. The key contribution of the research is to offer a workable solution to the issues plaguing SAR target variant recognition and to improve the significant limitations in the state-of-the-art literature thoroughly.

Sparse Denoising Method for High-frequency Ultrasonic Detection Signals of Flip Chip Based on Training Local Dictionary

Sparse Denoising Method for High-frequency Ultrasonic Detection Signals of Flip Chip Based on Training Local Dictionary

Correlation-based local detection for deceptive interference mitigation in multi-parameter modulated radar

To improve the ability of resisting the deceptive interference for radar, a multi-parameter modulated radar signal is investigated and its compressed sensing model is constructed in this paper. Unlike the traditional frequency modulated continuous wave (FMCW) signal model, the established model has natural advantages in mitigating the deceptive interference due to the agility of multiple signal parameters. Simultaneously, to reduce the impact of the deceptive interference on the target echo, the compressed recovery algorithm combined with the proposed correlation-based local detection is introduced. Before recovering the target echo, the local dictionary matrix is inspected by using the correlation to ensure that the expected echo signal is obtained merely. Simulation results show that the proposed method significantly improves the performance in mitigating the deceptive interference. By coherently integrating the signals over multiple periods, the relative pure range profile or the range-Doppler plane could be acquired. It helps radar system to extract the distance or velocity information in the interference environment.

Local-Dictionary Sparsity Discriminant Preserving Projections for Rotating Machinery Fault Diagnosis Based on Pre-Selected Multi-Domain Features

In this paper, a novel fault diagnosis framework for rotating machinery is put forward, centering on a firstly proposed dimensionality reduction algorithm named local-dictionary sparsity discriminant preserving projections (LDSDPP). To involve abundant fault-related information for model construction, multi-domain features are extracted directly from vibration signals as along as their sub-band signals decomposed via time-frequency domain analysis. To reduce computational complexity and improve modelling accuracy, features are pre-processed and those highly related to faults are selected based on filtering models. For feature reduction, sparsity and local structures of data points are introduced in LDSDPP by constructing graphs using sparse representation based on a local dictionary containing only neighbors for each sample. Moreover, the global information of data samples is also integrated into LDSDPP for better discriminant power. Experiments based on two datasets have demonstrated the effectiveness of the proposed framework and the superiority of LDSDPP to other comparable algorithms in classification performance.

Linguistique du corpus pour les variétés à faible densité. Langues minoritaires et enquêtes morphologiques basées sur corpus

Corpus linguistics grew up in the domain of written (and literary) varieties, while its recent methodological revolution is due to the computer-assisted capacity of elaborating massive amounts of text data. On the other hand, the so-called ‘low-density varieties’, including spoken varieties as well as varieties spoken in minority communities, have been confined to a rather marginal role. Among others, this is due to the technical problems connected to the scarce degree of normalization in linguistic –including graphemic– terms, as well as to the scarcity of language resources for automatic processing. In this paper, we will exploit the possibilities opened by corpus linguistics for acquiring and analyzing the textual patrimony of the Walser German communities of Piedmont and Aosta Valley. The varieties of Highest Alemannic spoken there, dramatically exposed to language decay, provide a limited but significant amount of data, which is accompanied by a substantial lexical documentation due to the active collaboration of the speakers’ communities in collecting and compiling local dictionaries. After briefly introducing our archive and discussing the peculiar solutions adopted for the construction of the platform, we will also present corpus-based morphological investigations regarding the representation of verbal prefixes, of the clitic group, as well as of the inflectional behaviour of verb classes.

Incremental Dictionary Learning for Multiframe Satellite Image Representation via Gradual Optimization

Dictionary learning has been widely used in image representation under the framework of sparse theory. But most of the current dictionary learning strategies can only be used for single-frame image separately, which are insufficient from the perspective of incremental information acquisition and global optimization for the sequential or multiframe satellite images. To this end, this paper proposes an incremental dictionary learning method for multiframe satellite images representation in the spectral domain. The incremental dictionary learning is formulated analytically in the framework of sparse representation with low-rank constraint, as a frame-by-frame gradual optimization process of global and local dictionaries, and their corresponding sparse coefficients with the sequence. Specifically, the global dictionary representing the common spectral information of the sequential frames, is optimized by two adjacent frames gradually. Meanwhile, the local dictionary representing the specific spectral information of each frame, is optimized by the newly added frame itself. In addition, an activity ratio for separating the global dictionary from the local dictionaries, an outlier detection method for initializing the local dictionary are also given, and the alternating direction methods of multipliers (ADMM) is employed to implement the above optimization. Comparison results with the related state-of-the-art methods on different datasets demonstrate that, our proposed method achieves the best representation performance in both spatial and spectral domains, and also helps to improve the performance of dictionary-based tasks using sequential satellite images, such as sea surface anomaly detection.

Clutter Suppression Method for Off-Grid Effects Mitigation in Airborne Passive Radars with Contaminated Reference Signals.

For an airborne passive radar with contaminated reference signals, the clutter caused by multipath (MP) signals involved in the reference channel (MP clutter) corrupts the covariance estimation in space-time adaptive processing (STAP). In order to overcome the severe STAP performance degradation caused by impure reference signals and off-grid effects, a novel MP clutter suppression method based on local search is proposed for airborne passive radar. In the proposed method, the global dictionary is constructed based on the sparse measurement model of MP clutter, and the global atoms that are most relevant to the residual are selected. Then, the local dictionary is designed iteratively, and local searches are performed to match real MP clutter points. Finally, the off-grid effects are mitigated, and the MP clutter is suppressed from all matched atoms. A range of simulations is conducted in order to demonstrate the effectiveness of the proposed method.

SAR Target Recognition Using Improved Sparse Representation with Local Reconstruction

In order to handle the problem of synthetic aperture radar (SAR) target recognition, an improved sparse representation-based classification (SRC) is proposed. According to the sparse coefficient vector resulting from the global dictionary, the largest coefficient in each class is taken as the reference. Then, the surrounding neighborhoods of the sample with the largest coefficient are selected to construct the optimal local dictionary in each training class. Afterwards, the samples in the local dictionary are used to reconstruct the test sample to be identified. Finally, the decision is made according to the comparison of the reconstruction errors from different classes. In the experiments, the proposed method is verified based on the moving and stationary target acquisition and recognition (MSTAR) dataset. The results show that the proposed method has performance advantages over existing methods, which demonstrates its effectiveness and robustness.

Ambit-SLN: an Open Source Software Library for Processing of Chemical Objects via SLN Linear Notation.

SLN (SYBYL Line Notation) is the most comprehensive and rich linear notation for representation of chemical objects of various kinds facilitating a wide range of cheminformatics algorithms. Though, it is not the most popular linear notation nowadays, SLN has capabilities for supporting the most challenging tasks of the present day cheminformatics research. We present Ambit-SLN, a new software library for cheminformatics processing of chemical objects via linear notation SLN. Ambit-SLN is developed as a part of the cheminformatics platform AMBIT. It is an open-source tool, distributed under LGPL license, written in Java and based on the Chemistry Development Kit. Ambit-SLN includes a parser for the full SLN syntax of chemical structures and substructure search queries including support for macro and Markush atoms, global and local dictionaries and user defined properties which can be stored and used by the Ambit data model. The Ambit-SLN library includes functionalities for substructure matching based on SLN query strings and utilities for conversion of SLN objects to other chemical formats such as SMILES and SMARTS. The functionality for Markush atom expansion can be used for generation of combinatorial structure sets.

Pairwise Elastic Net Representation-Based Classification for Hyperspectral Image Classification

The representation-based algorithm has raised a great interest in hyperspectral image (HSI) classification. -minimization-based sparse representation (SR) attempts to select a few atoms and cannot fully reflect within-class information, while -minimization-based collaborative representation (CR) tries to use all of the atoms leading to mixed-class information. Considering the above problems, we propose the pairwise elastic net representation-based classification (PENRC) method. PENRC combines the -norm and -norm penalties and introduces a new penalty term, including a similar matrix between dictionary atoms. This similar matrix enables the automatic grouping selection of highly correlated data to estimate more robust weight coefficients for better classification performance. To reduce computation cost and further improve classification accuracy, we use part of the atoms as a local adaptive dictionary rather than the entire training atoms. Furthermore, we consider the neighbor information of each pixel and propose a joint pairwise elastic net representation-based classification (J-PENRC) method. Experimental results on chosen hyperspectral data sets confirm that our proposed algorithms outperform the other state-of-the-art algorithms.

Single image super resolution based on multi-scale structure and non-local smoothing

In this paper, we propose a hybrid super-resolution method by combining global and local dictionary training in the sparse domain. In order to present and differentiate the feature mapping in different scales, a global dictionary set is trained in multiple structure scales, and a non-linear function is used to choose the appropriate dictionary to initially reconstruct the HR image. In addition, we introduce the Gaussian blur to the LR images to eliminate a widely used but inappropriate assumption that the low resolution (LR) images are generated by bicubic interpolation from high-resolution (HR) images. In order to deal with Gaussian blur, a local dictionary is generated and iteratively updated by K-means principal component analysis (K-PCA) and gradient decent (GD) to model the blur effect during the down-sampling. Compared with the state-of-the-art SR algorithms, the experimental results reveal that the proposed method can produce sharper boundaries and suppress undesired artifacts with the present of Gaussian blur. It implies that our method could be more effect in real applications and that the HR-LR mapping relation is more complicated than bicubic interpolation.

Target Recognition of SAR Images Based on Azimuthal Constraint Reconstruction

A synthetic aperture radar (SAR) target classification method has been developed, in the study, based on dynamic target reconstruction. According to SAR azimuthal sensitivity, the truly useful training samples for the reconstructing the test sample are those with approaching azimuths and same labels. Hence, the proposed method performs linear presentation of the test sample on the local dictionary established by several training samples selected from each class under the azimuthal correlation. By properly adjusting the azimuthal correlation constraint, the test sample can be reconstructed at different levels by different scales of training samples. During the classification phase, the reconstruction error vectors from different levels are combined by linear fusion and the label of the test sample is determined based on the fused errors. Experimental conditions are setup on the moving and stationary target acquisition and recognition (MSTAR) dataset to evaluate the proposed method. The results confirm the effectiveness of the proposed method.

WITHDRAWN: Sar Image Target Recognition Method Based On Sparse Representation Of Local Dictionary

Abstract Target segments of the Synthetic Aperture Radar (SAR) images are often of SAR images used as one of the challenge the procedure of SAR target recognition interpretation. Segmentation of the object, and then be separated from the target in order to eliminate the background noise or background clutter. However, segmentation may be discarded, and the shadow of the object where the target characteristic further comprises a portion of the difference between object recognition information. Thus, the proposed automatic detection and identification of target SAR image-based approach Sparse Representation Local Dictionary. (SRLD) Using the Model to target detection and recognition experiments to get moving and stationary targets, and identify baseline data set, verify the SAR image recognition field goal deep neural networks' effectiveness.

Combination of multiple decision principles based on sparse representation-based classification for target recognition of SAR image

A synthetic aperture radar (SAR) target recognition method using multiple decision principles in sparse representation-based classification (SRC) was proposed. The traditional SRC generally reconstructed the test sample on the global dictionary and calculated the reconstruction errors of individual training classes. And the decision was reached based on the minimum reconstruction error. However, because of the complexity of SAR target recognition, a single decision principle probably had low adaptivity to the extended operating conditions (EOC). Therefore, this paper employed the global minimum reconstruction, maximum coefficient energy, and local minimum reconstruction error principles to make decisions based on the solved coefficient vector from sparse representation. The global minimum reconstruction error principle directly adopted the traditional one. The maximum coefficient energy principle calculated the coefficient energies of different classes and made decision based on the maximum one. The local minimum reconstruction error principle represented and analyzed the test sample on the local dictionary so the azimuthal sensitivity of SAR imaging could be exploited. For the decision values from the three principles, they were transformed to the same type of probability vectors. Finally, the linear fusion was performed to combine their decisions. Experiments were conducted on the MSTAR dataset under situations including the standard operating condition (SOC), depression angle variance, noise corruption, and target occlusion. The results validate that the combination of multiple decision principles could effectively improve SAR target recognition performance.

Rapid annotation of seizures and interictal-ictal-injury continuum EEG patterns

BackgroundManual annotation of seizures and interictal-ictal-injury continuum (IIIC) patterns in continuous EEG (cEEG) recorded from critically ill patients is a time-intensive process for clinicians and researchers. In this study, we evaluated the accuracy and efficiency of an automated clustering method to accelerate expert annotation of cEEG. New methodWe learned a local dictionary from 97 ICU patients by applying k-medoids clustering to 592 features in the time and frequency domains. We utilized changepoint detection (CPD) to segment the cEEG recordings. We then computed a bag-of-words (BoW) representation for each segment. We further clustered the segments by affinity propagation. EEG experts scored the resulting clusters for each patient by labeling only the cluster medoids. We trained a random forest classifier to assess validity of the clusters. ResultsMean pairwise agreement of 62.6% using this automated method was not significantly different from interrater agreements using manual labeling (63.8%), demonstrating the validity of the method. We also found that it takes experts using our method 5.31 ± 4.44 min to label the 30.19 ± 3.84 h of cEEG data, more than 45 times faster than unaided manual review, demonstrating efficiency. Comparison with existing methodsPrevious studies of EEG data labeling have generally yielded similar human expert interrater agreements, and lower agreements with automated methods. ConclusionsOur results suggest that long EEG recordings can be rapidly annotated by experts many times faster than unaided manual review through the use of an advanced clustering method.

Block Sparse Bayesian Learning over Local Dictionary for Robust SAR Target Recognition

This paper applied block sparse Bayesian learning (BSBL) to synthetic aperture radar (SAR) target recognition. The traditional sparse representation-based classification (SRC) operates on the global dictionary collaborated by different classes. Afterwards, the similarities between the test sample and various classes are evaluated by the reconstruction errors. This paper reconstructs the test sample based on local dictionaries formed by individual classes. Considering the azimuthal sensitivity of SAR images, the linear coefficients on the local dictionary are sparse ones with block structure. Therefore, to solve the sparse coefficients, the BSBL is employed. The proposed method can better exploit the representation capability of each class, thus benefiting the recognition performance. Based on the experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset, the effectiveness and robustness of the proposed method is confirmed.

Polarity Identification for Handwritten Text in Multilingual Documents Using Open Source Optical Character Recognition Tools

Sentimental analysis is a prerequisite for many applications. We propose a model which scans handwritten text in English and Kannada languages by a CamScanner and then translated into editable text by using various Open Source Optical Character Recognition tools. The performances of different OCRs are analyzed and tabulated. Sentimental analysis is performed on the statements written in both English and Kannada languages using Wordnet, Algorithmia Rest API and local dictionaries and we have obtained the satisfied results. The same sentimental analysis module is also applied on customer reviews for the mobile product and reviews are taken from Amazon Web Services. The opinion of the customer about the product can be identified correctly.

Lexicon-Based Indonesian Local Language Abusive Words Dictionary to Detect Hate Speech in Social Media

Background: Hate speech is an expression to someone or a group of people that contain feelings of hate and/or anger at people or groups. On social media users are free to express themselves by writing harsh words and share them with a group of people so that it triggers separations and conflicts between groups. Currently, research has been conducted by several experts to detect hate speech in social media namely machine learning-based and lexicon-based, but the machine learning approach has a weakness namely the manual labelling process by an annotator in separating positive, negative or neutral opinions takes time long and tiringObjective: This study aims to produce a dictionary containing abusive words from local languages in Indonesia. Lexicon-base is very dependent on the language contained in dictionary words. Indonesia has thousands of tribes with 2500 local languages, and 80% of the population of Indonesia use local languages in communication, with the result that a significant challenge to detect hate speech of social media.Methods: Abusive words surveys are conducted by using proportionate stratified random sampling techniques in 4 major tribes on the island of Java, namely Betawi, Sundanese, Javanese, MadureseResults: The experimental results produce 250 abusive words dictionary from 4 major Indonesian tribes to detect hate speech in Indonesian social media by using the lexicon-based approach. Conclusion: A stratified random sampling technique has been conducted in 4 major Indonesian tribes to produce 250 abusive words for hate speech detection using the lexicon-based approach.