Partition Tree Research Articles

Motion-Aware Compression and Transmission of Mesh Animation Sequences

With the increasing demand in using 3D mesh data over networks, supporting effective compression and efficient transmission of meshes has caught lots of attention in recent years. This article introduces a novel compression method for 3D mesh animation sequences, supporting user-defined and progressive transmissions over networks. Our motion-aware approach starts with clustering animation frames based on their motion similarities, dividing a mesh animation sequence into fragments of varying lengths. This is done by a novel temporal clustering algorithm, which measures motion similarity based on the curvature and torsion of a space curve formed by corresponding vertices along a series of animation frames. We further segment each cluster based on mesh vertex coherence, representing topological proximity within an object under certain motion. To produce a compact representation, we perform intra-cluster compression based on Graph Fourier Transform (GFT) and Set Partitioning In Hierarchical Trees (SPIHT) coding. Optimized compression results can be achieved by applying GFT due to the proximity in vertex position and motion. We adapt SPIHT to support progressive transmission and design a mechanism to transmit mesh animation sequences with user-defined quality. Experimental results show that our method can obtain a high compression ratio while maintaining a low reconstruction error.

Secure and robust color image watermarking scheme using partial homomorphic cryptosystem in ASWDR compressed domain

The primary requirement of Secure Signal Processing (SSP) is to protect the identity and content of the transmitted data. To this end, various encryption techniques are employed. Homomorphic encryption in general and Paillier cryptosystem in particular is found to be one such algorithm suitable for image processing applications. It offers additive privacy homomorphism. In this paper, the Paillier encryption is employed for watermarking of six different compressed images in ASWDR (Adaptively Scanned Wavelet Difference Reduction) domain. The results are found to be encouraging both in terms of visual quality and the correlation coefficient values. The proposed watermarking scheme ensures robustness towards selected image processing attacks. The encryption parameters like NPCR (Number of Pixels Change Rate), UACI (Unified Average Changing Intensity) and Encryption Speed are evaluated. The proposed technique is compared with the one in SPIHT (Set Partitioning in Hierarchical Trees) domain. The experimental results obtained for ASWDR compression are better placed in comparison to those obtained by SPIHT compression. The proposed scheme is further analyzed in view of other recent schemes carried out by using RSA as an encryption tool in DWT domain. It is concluded that our proposed scheme is better placed in comparison to this scheme.

Notice of Retraction Effects vanishing moments of discrete wavelet transform on MRI image compression algorithms

Notice of Retraction-----------------------------------------------------------------------After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of APTIKOM's Publication Principles.We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.The presenting author of this paper has the option to appeal this decision by contacting ij.aptikom@gmail.com.-----------------------------------------------------------------------Image data usually contain considerable quantity of data that is redundant and much irrelevant, whereas an image compression technique overcomes this by compressing the amount of data required to represent the image. In this work, Discrete Wavelet Transform based image compression algorithm is implemented for decomposing the image. The various encoding schemes such as Embedded Zero wavelet, (EZW), Set Partitioning In Hierarchical Trees(SPIHT) and Spatial orientation Tree Wavelet(STW) are used and their performances in the compression is evaluated and also the effectiveness of different wavelets with various vanishing moments are analyzed based on the values of PSNR, Compression ratio, Means square error and bits per pixel. The optimum compression algorithm is also found based on the results.

A Reexamination of Breast Cancer Gene Expression Data Using (and Comparing) Advanced Predictive Modeling Techniques

Using the human breast cancer data from the MAQC‐II Project (GEO Accession GSE20194), we re‐explore the association between gene expression and Estrogen Receptor Status across advancing stages of the disease. We specifically compare various predictive modeling techniques, such as Radial Basis Machines and Partition Trees, as well as various predictor reduction techniques, including K‐Means Clustering and LASSO, and we discuss the merits of each. We use k‐fold cross‐validation to improve upon a simple single training/validation split in comparison of model performance and consistency.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Low-Complexity CTU Partition Structure Decision and Fast Intra Mode Decision for Versatile Video Coding

Quadtree with nested multi-type tree (QTMT) partition structure is an efficient improvement in versatile video coding (VVC) over the quadtree (QT) structure in the advanced high-efficiency video coding (HEVC) standard. With the exception of the recursive QT partition structure, recursive multi-type tree partition is applied to each leaf node, which generates more flexible block sizes. Besides, intra prediction modes are extended from 35 to 67 so as to satisfy various texture patterns. These newly developed techniques achieve high coding efficiency but also result in very high computational complexity. To tackle this problem, we propose a fast intra-coding algorithm consisting of low-complexity coding tree units (CTU) structure decision and fast intra mode decision in this paper. The contributions of the proposed algorithm lie in the following aspects: 1) the new block size and coding mode distribution features are first explored for a reasonable fast coding scheme; 2) a novel fast QTMT partition decision framework is developed, which can determine the partition decision on both QT and multi-type tree with a novel cascade decision structure; and 3) fast intra mode decision with gradient descent search is introduced, while the best initial search point and search step are also investigated in this paper. The simulation results show that the complexity reduction of the proposed algorithm is up to 70% compared to VVC reference software (VTM), and averagely 63% encoding time saving is achieved with 1.93% BDBR increasing. Such results demonstrate that our method yields a superior performance in terms of computational complexity and compression quality compared to the state-of-the-art methods.

Comparative Analysis of SVD and Progressive SPIHT techniques for Compression of MRI and CT Images

MRI and CT images generated by medical imaging techniques require different compression techniques with considerable image quality by reducing the storage space. This paper presents the performances two compression techniques for images i.e. Singular Value Decomposition (SVD) using singular values and wavelet transform based progressive structure of Set Partitioning In Hierarchical Trees (SPIHT). These two techniques are practiced on MRI and CT images of brain and the results of two techniques are compared. SVD with less singular value provides high Compression Ratio (CR) with less Peak Signal to Noise Ratio (PSNR), where as multi resolution based SPIHT technique provides more PSNR with better CR. These two techniques are compared with the quality metrics of PSNR, Mean Squared Error (MSE), CR and Bit Per Pixels (BPP). From the results, Wavelet based progressive SPIHT technique provides high PSNR, low MSE with better CR compared to SVD technique.

Enhanced SPIHT Algorithm with Pipelined Datapath Architecture Design

DOI : 10.26650/electrica.2018.15101 Set partitioning in hierarchical trees (SPIHT) is an efficient algorithm which is used for the image compression widely. SPIHT operates sequentially so, its parallel implementation is difficult. In this study, the SPIHT algorithm is improved for providing that it is suitable for the parallel processing applications, and the corresponding pipelined datapath is designed for the proposed enhanced SPIHT algorithm. The datapath is designed to have three stages as preprocessing, list generation and output stream. In the preprocessing stage, the flags which are supports the list generation stage are constituted. List of insignificant sets (LIS), list of insignificant pixels (LIP) and list of significant pixels (LSP) are formed in list generation stage. These lists contain the bit values which generate the output bit stream. The performance of the improved datapath design has been tested by compressing different images, and the obtained results are given. Cite this article as: Cekli S, Akman A. Enhanced SPIHT Algorithm with Pipelined Datapath Architecture Design. Electrica, 2019; 19(1): 29-36.

ORIENTED FLIP GRAPHS, NONCROSSING TREE PARTITIONS, AND REPRESENTATION THEORY OF TILING ALGEBRAS

AbstractThe purpose of this paper is to understand lattices of certain subcategories in module categories of representation-finite gentle algebras called tiling algebras, as introduced by Coelho Simões and Parsons. We present combinatorial models for torsion pairs and wide subcategories in the module category of tiling algebras. Our models use the oriented flip graphs and noncrossing tree partitions, previously introduced by the authors, and a description of the extension spaces between indecomposable modules over tiling algebras. In addition, we classify two-term simple-minded collections in bounded derived categories of tiling algebras. As a consequence, we obtain a characterization of c-matrices for any quiver mutation-equivalent to a type A Dynkin quiver.

Very large‐scale integration architecture for wavelet‐based ECG signal adaptive coder

Very large scale integration architecture for the blocks in the novel algorithm with parallel processing is proposed. It increases the performance of the proposed coder. Since the mean is removed from electrocardiogram (ECG) signal it is represented by less number of bits. The lifting scheme-based Haar wavelet transform with five levels decomposition is used to decompose the mean removed ECG signal. One sub-band of approximation coefficient ( A 5 ) and five sub-bands of detailed coefficients ( D 5 , D 4 , D 3 , D 2 and D 1 ) are obtained. Each sub-band is represented by the corresponding number of bits by using adaptive encoder. The converted bits are transmitted through the channel using a compact format. The coding efficiency shows that the proposed coder outperforms than other coders such as novel algorithm, Djohn, Alshamali, EZW, Benzid, Chan, Khaldi, Gurkan, Wang and Set Partitioning In Hierarchical Trees (SPIHT). The proposed coder is tested on the MIT-BIH arrhythmia database (48 records) and the MIT-BIH ECG compression test database (two records) and its performance is evaluated by using evaluation metrics such as compression ratio (CR) and per cent root mean square difference (PRD). For MIT-BIH arrhythmia database record 117, a CR of 9.132:1 is achieved by the proposed coder with PRD 1.2274%.

Joint medical image compression–encryption in the cloud using multiscale transform-based image compression encoding techniques

The recent years have witnessed rapid strides in the use of cloud computing and its countless applications. A cloud can contain massive volumes of multimedia data in the form of images, video and audio. Cloud computing platforms confront challenges in terms of data confidentiality, message integrity, user authentication and compression. Multimedia data needs plenty of storage capacity. Consequently, there is a need for multimedia data compression to reduce data size. Compression techniques are quite reliable, offering benefits to organizations dealing with metasized data in the cloud. Compressing large quanta of data leads to superior utilization of cloud storage. Compression techniques can compress data used for storage and transmission, yet compression alone is inadequate because multimedia data shared should, of necessity, be secure. Therefore, both multimedia compression and security are mandatory in the cloud. The chief goal of this paper is to propose a new framework, comprising multiscale transforms, public key cryptography and appropriate encoding techniques, that performs joint medical image compression and image encryption in the cloud. Multiscale transforms play a lead role in image compression, and the ones discussed in this paper include wavelet, bandelet, curvelet, ridgelet and contourlet transforms. Wavelet transforms offer robust localization both in terms of time and frequency domains. Bandelet transforms offer natural images geometric regularity to help improve the efficiency of representation. Curvelet transforms handle curve discontinuities well, with ridgelet transforms being the core idea behind curvelets. Contourlet transforms capture smooth contours and edges at any orientation. The Rivest-Shamir-Adleman (RSA) algorithm is used to encrypt images to provide maximum security when they are being transferred. Encoding techniques involved in this paper comprise the Embedded Zerotree Wavelet (EZW), Set Partitioning in Hierarchical Trees (SPIHT), Wavelet Difference Reduction (WDR), and Adaptively Scanned Wavelet Difference Reduction (ASWDR). Performance parameters such as peak signal to noise ratio (PSNR), mean square error (MSE), image quality index and structural similarity index (SSIM) are used for evaluation. It is justified that the proposed framework compresses images securely in the cloud.

Failure Localization in Power Systems via Tree Partitions

Cascading failures in power systems propagate non-locally, making the control and mitigation of outages extremely hard. In this work, we use the emerging concept of the tree partition of transmission networks to provide an analytical characterization of line failure localizability in transmission systems. Our results rigorously formalize the well-known intuition that failures cannot cross bridges, and reveal a finer-grained concept that encodes more precise information on failure propagation within tree-partition regions. Specifically, when a non-bridge line is tripped, the impact of this failure only propagates within components of the tree partition defined by the bridges. In contrast, when a bridge line is tripped, the impact of this failure propagates globally across the network, affecting the power flow on all remaining lines. This characterization suggests that it is possible to improve the system robustness by temporarily switching off certain transmission lines, so as to create more, smaller components in the tree partition; thus spatially localizing line failures and making the grid less vulnerable to large outages.

Failure Localization in Power Systems via Tree Partitions

Cascading failures in power systems propagate non-locally, making the control and mitigation of outages extremely hard. In this work, we use the emerging concept of the tree partition of transmission networks to provide an analytical characterization of line failure localizability in transmission systems. Our results rigorously establish the well perceived intuition in power community that failures cannot cross bridges, and reveal a finer-grained concept that encodes more precise information on failure propagations within tree-partition regions. Specifically, when a non-bridge line is tripped, the impact of this failure only propagates within well-defined components, which we refer to as cells, of the tree partition defined by the bridges. In contrast, when a bridge line is tripped, the impact of this failure propagates globally across the network, affecting the power flow on all remaining transmission lines. This characterization suggests that it is possible to improve the system robustness by temporarily switching off certain transmission lines, so as to create more, smaller components in the tree partition; thus spatially localizing line failures and making the grid less vulnerable to large-scale outages. We illustrate this approach using the IEEE 118-bus test system and demonstrate that switching off a negligible portion of transmission lines allows the impact of line failures to be significantly more localized without substantial changes in line congestion.

Exploration of Distributed Image Compression and Transmission Algorithms for Wireless Sensor Networks

To prolong the life cycle of wireless sensor network, the basic theory of wavelet transform and its application in image compression are described, and several classic image compression algorithms based on wavelet transform are studied in depth. A compression algorithm combining the improved wavelet transform and Set Partitioning in Hierarchical Trees (SPIHT) algorithm of hierarchical wavelet tree set segmentation is proposed to effectively balance the energy consumption of each node in the sensor network and prolong the life of the whole wireless sensor network and an improved distributed image compression and transmission algorithm is proposed based on the distributed multi-node cooperative processing algorithm based on wavelet transformation, and detailed analytical test and energy consumption simulation experiment are carried out to verify the feasibility of the algorithm. The results show that the platform effectively implements and verifies the proposed algorithm, which can effectively realize the compression and transmission of distributed images, equalize the energy consumption of each sensor node in the network, and has strong practicability.

Medical Video Coding Based on 2nd-Generation Wavelets: Performance Evaluation

The operations of digitization, transmission and storage of medical data, particularly images, require increasingly effective encoding methods not only in terms of compression ratio and flow of information but also in terms of visual quality. At first, there was DCT (discrete cosine transform) then DWT (discrete wavelet transform) and their associated standards in terms of coding and image compression. The 2nd-generation wavelets seeks to be positioned and confronted by the image and video coding methods currently used. It is in this context that we suggest a method combining bandelets and the SPIHT (set partitioning in hierarchical trees) algorithm. There are two main reasons for our approach: the first lies in the nature of the bandelet transform to take advantage of capturing the geometrical complexity of the image structure. The second reason is the suitability of encoding the bandelet coefficients by the SPIHT encoder. Quality measurements indicate that in some cases (for low bit rates) the performance of the proposed coding competes with the well-established ones (H.264 or MPEG4 AVC and H.265 or MPEG4 HEVC) and opens up new application prospects in the field of medical imaging.

Tree-based methods for clustering time series using domain-relevant attributes

ABSTRACTWe propose two methods for time-series clustering that capture temporal information (trend, seasonality, autocorrelation) and domain-relevant cross-sectional attributes. The methods are based on model-based partitioning (MOB) trees and can be used as automated yet transparent tools for clustering large collections of time series. We address the challenge of using common time-series models in MOB by instead utilising least squares regression. We propose two methods. The single-step method clusters series using trend, seasonality, lags and domain-relevant cross-sectional attributes. The two-step method first clusters by trend, seasonality and cross-sectional attributes, and then clusters the residuals by autocorrelation and domain-relevant attributes. Both methods produce clusters interpretable by domain experts. We illustrate our approach by considering one-step-ahead forecasting and compare to autoregressive integrated moving average (ARIMA) models for forecasting many Wikipedia pageviews time series. The tree-based approach produces forecasts on par with ARIMA, yet is significantly faster and more efficient, thereby suitable for large collections of time-series. The simple parametric forecasting models allow for interpretable time-series clusters.

Higher-order principal component analysis for the approximation of tensors in tree-based low-rank formats

This paper is concerned with the approximation of tensors using tree-based tensor formats, which are tensor networks whose graphs are dimension partition trees. We consider Hilbert tensor spaces of multivariate functions defined on a product set equipped with a probability measure. This includes the case of multidimensional arrays corresponding to finite product sets. We propose and analyse an algorithm for the construction of an approximation using only point evaluations of a multivariate function, or evaluations of some entries of a multidimensional array. The algorithm is a variant of higher-order singular value decomposition which constructs a hierarchy of subspaces associated with the different nodes of the tree and a corresponding hierarchy of interpolation operators. Optimal subspaces are estimated using empirical principal component analysis of interpolations of partial random evaluations of the function. The algorithm is able to provide an approximation in any tree-based format with either a prescribed rank or a prescribed relative error, with a number of evaluations of the order of the storage complexity of the approximation format. Under some assumptions on the estimation of principal components, we prove that the algorithm provides either a quasi-optimal approximation with a given rank, or an approximation satisfying the prescribed relative error, up to constants depending on the tree and the properties of interpolation operators. The analysis takes into account the discretization errors for the approximation of infinite-dimensional tensors. Several numerical examples illustrate the main results and the behavior of the algorithm for the approximation of high-dimensional functions using hierarchical Tucker or tensor train tensor formats, and the approximation of univariate functions using tensorization.

Improvement in Classification Algorithms through Model Stacking with the Consideration of their Correlation

In this research we analyzed the performance of some well-known classification algorithms in terms of their accuracy and proposed a methodology for model stacking on the basis of their correlation which improves the accuracy of these algorithms. We selected; Support Vector Machines (svm), Naive Bayes (nb), k-Nearest Neighbors (knn), Generalized Linear Model (glm), Latent Discriminant Analysis (lda), gbm, Recursive Partitioning and Regression Trees (rpart), rda, Neural Networks (nnet) and Conditional Inference Trees (ctree) in our research and preformed analyses on three textual datasets of different sizes; Scopus 50,000 instances, IMDB Movie Reviews having 10,000 instances, Amazon Products Reviews having 1000 instances and Yelp dataset having 1000 instances. We used R-Studio for performing experiments. Results show that the performance of all algorithms increased at Meta level. Neural Networks achieved the best results with more than 25% improvement at Meta-Level and outperformed the other evaluated methods with an accuracy of 95.66%, and altogether our model gives far better results than individual algorithms’ performance.

LDPart: Effective Location-Record Data Publication via Local Differential Privacy

Driven by the advance of positioning technology and the tremendous popularity of location-based services, location-record data have become unprecedentedly available. Publishing such data is of vital importance to the advancement of a wide spectrum of applications, such as marketing analysis, targeted advertising, and urban planning. However, the data collection may pose considerable threats to the individuals privacy. Local differential privacy (LDP) has recently emerged as a strong privacy standard for collecting sensitive information from users. Due to the inherent high dimensionality, it is particularly challenging to publish the location-record data under LDP. In this paper, we propose LDPart , a probabilistic top-down partitioning algorithm to effectively generate a sanitized location-record data. Our approach employs a carefully designed partition tree model to extract essential information in terms of location records. Furthermore, it also makes use of a novel adaptive user allocation scheme and a series of optimization techniques to improve the accuracy of the released data. The extensive experiments conducted on real-world datasets demonstrate that the proposed approach maintains high utility while providing privacy guarantees.

Different roles for geography, energy and environment in determining three facets of freshwater molluscan beta diversity at broad spatial scales

Current understanding of different facets of beta diversity and their underlying determinants remains limited at broad scales in the freshwater realm. We examined the geographical patterns and spatial congruence of three beta diversity facets of freshwater molluscs across all of China, and evaluated the relative importance of environmental and spatial factors underlying the observed patterns. Taxonomic (β-TD), functional (β-FD) and phylogenetic (β-PD) beta diversity were calculated for 212 drainage basins belonging to 10 hydrographic regions using compiled occurrence data of 313 molluscan species. Geographical patterns of the three diversity facets were visualized on maps and pairwise spatial congruence among them was evaluated using regression on distance matrices. Variation partitioning and multivariate regression trees were used to assess the relative importance of different factors underlying beta diversity patterns. Beta diversity maps revealed that geographical patterns of β-TD and β-PD showed strong spatial clustering and were well matched with hydrographic regions' boundaries, while β-FD showed only moderate spatial aggregation. The three facets were only moderately congruent, with over 60% of the variation in one facet remaining unexplained by any other facet. Remarkably, all diversity facets were best explained by the spatial factors with considerable unique effects. Environmental filtering associated with energy gradients also made a large contribution, while habitat availability only explained minor fractions of the variation in beta diversity. At the national scale, β-TD and β-PD were more strongly related to spatial processes, whereas β-FD was more strongly associated with energy gradients. Our results suggested that, for freshwater organisms with low dispersal capacity, dispersal processes may override environmental filtering in driving geographical diversity patterns. However, different ecological drivers were important for each diversity facet. Importantly, rather weak spatial congruence among the different diversity facets stresses the need to incorporate functional and phylogenetic facets into the development of conservation planning.

Ripening season affects tissue mineral concentration and nutrient partitioning in peach trees

AbstractThe objective of this research was to determine the influence of ripening season on nutrient concentration and nutrient partitioning of peach trees (Prunus persica L. Batsch). We selected peach trees from three different ripening seasons and measured: (1) the concentration of macronutrients in pruned wood, thinned fruitlets, harvested fruit, and leaves fallen in autumn and (2) the total amount of macronutrients at each of these removal events. Our results showed that early‐season cultivars had more K in pruned wood, more P and K in fallen leaves, and more N, P, K, and Mg in mature fruits than mid‐ and late‐season cultivars. Also, early‐season cultivars removed more dry weight from pruned wood and fallen leaves but less from fruit than mid‐ and late‐season cultivars. These results suggest that different ripening season can affect peach tree nutrient concentration and nutrient partitioning and, consequently, peach cultivars harvested at different times of the year may benefit from specific fertilization programs rather than uniform, calendar‐based fertilization programs.