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The widespread development of monitoring devices in power system has indeed led to the generation of large amounts of power consumption data. Storing and transmitting this enormous volume of data has become a significant challenge for power system operators and researchers. Although high data rates are available for transmission, data compression is still necessary for power system applications to lessen the load of data transmission and storage. The wavelet transform plays an important role in data compression for power system applications. This paper presents an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. In general, some wavelet coefficients are neglected to produce DWT-based data compression by using hard or soft thresholds. But the volume of compressed data differs for various threshold values. In this context, selecting an optimal threshold is a challenging task in power system data compression. This paper proposes a multi-objective particle swarm optimisation (MO-PSO) for optimal threshold selection. The proposed approach is tested using IEEE power quality wave data sets and Dutch Residential Energy Datasets (DRED). The proposed MO-PSO algorithm finds the global optimum threshold, and it outperforms the other existing algorithms. The proposed algorithm efficiently compresses the test dataset with a maximum compression ratio (CR) of 2.07. • The development of an adaptive algorithm for data compression is applied in power system data. • MO-PSO algorithm is proposed to find the optimal threshold in WT based data compression. • Results are compared with recent research works. • The efficiency of data compression algorithm is evaluated by using compression ratio, mean square error and signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

To augment situational awareness capabilities for high-frequency disturbances, the modern power system necessitates the implementation of high-sampling rates for real-time Synchro-Waveform (SW) measurements. Nevertheless, the nonlinear nature of data collected from SW measurement devices poses significant challenges to the communication and storage capacities of data centers. To effectively tackle this issue, this paper proposes a Multi-stage Hybrid Coding (MHC) method for the lossless compression of SW data. The MHC method utilizes a well-structured two-stage approach to achieve efficient compression. In the first stage, a variable frame-based method is designed to reduce redundant information and achieve initial compression using multiple periodic recursive compression. Subsequently, the dictionary-based method, specifically the Lempel–Ziv–Markov chain algorithm, is seamlessly integrated to further compress the SW data. Experimental and numerical analyses are conducted using both simulated and real-world source data, with real-time performance validation in SW measurement units. The obtained results convincingly demonstrate that the proposed method enables online compression of both event and non-event SW data, achieving an impressive reduction of over 73.4% in data space, while maintaining a data loss ratio lower than 0.4% for high-density SW data. • Multiple periodic recursive method is proposed to eliminate repetitive information. • A multi-stage hybrid coding method is implemented for the lossless compression. • A series of synchro-waveform events are collected to evaluate the performance. [ABSTRACT FROM AUTHOR]

Self-organizing feature mapping neural network is a typical unsupervised neural network algorithm, which is often used for clustering analysis and data compression. As the amount of data increases, the time consumption required by the algorithm becomes increasingly large, which becomes a new challenge. To address this issue, a quantum self-organizing feature mapping neural network is proposed in this paper. This algorithm provides a method to obtain the similarity between samples and neurons based on quantum phase estimation and demonstrates the scheme to obtain winning neurons by Grover algorithm. By utilizing the superposition of quantum, the algorithm achieves parallel computing. The time complexity analysis indicates that the proposed algorithm is exponentially faster than the classical counterpart. The quantum circuit has been devised, while numerical simulation and experiment on a heart disease dataset have been conducted programming within the Qiskit framework. Both have verified the feasibility of the algorithm. Moreover, an application of classification has been developed based on the trained self-organizing feature mapping neural network, which demonstrates the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• An effective compression algorithm was proposed for real-time transmission data. • A mixed prediction model of LSTM and XGBoost was established for time series data. • High performance of the proposed algorithm was veried by comparative experiments. • Almost 50% compression rate can be reached under one in ten thousand distortions. In this paper, a compression and decompression algorithm is proposed for real-time transmission data by combining discrete wavelet decomposition and reconstruction, hybrid model prediction of long short-term memory (LSTM) and extreme gradient boosting (XGBoost), and the development of quantization sequences. The forward compression and reverse decompression are achieved by continuously acquiring the prediction error and data series with quantization error through the iterative shifting approach. To accurately forecast data series with quantization errors, three classes of data with specific distribution characteristics collected from the measurement-while-drilling (MWD) operation field are decomposed into approximate data and detailed data, which are then predicted by autoregressive integrated moving average (ARIMA), support vector regression (SVR), XGBoost, backpropagation neural network (BPNN), radial basis function neural network (RBFNN), and LSTM models. Particularly, the LSTM model and the XGBoost model are proven to be suitable strategies for working with approximate data and detailed data separately by comparing with conventional approaches in terms of the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2) predictive indexes. Besides, experiment studies show that almost 50% compression performance can be reached under one in ten thousand distortions. It is verified that the proposed algorithm is appropriate and efficient for real-time data transmission applications with high performance. [ABSTRACT FROM AUTHOR]

This paper brings an idea of establishing a downstream bit channel based on reusing of bit sequences and bit compression technique in point-to-multipoint passive optical networks for increasing their summary throughput. The downstream communication of these networks is based on broadcasting of multi-frames towards all active end-point nodes, therefore these frames cold be potentially reused multiple times to carry additional data in a form of segmented bit sequences spread within the entire bitstream. In order to achieve maximum effectiveness of a proposed method, bit addressing technique using relative sequence positions is proposed. Moreover, the presented bit compression technique might further increase the resulting summary throughput, while the application of existing bit compression algorithms cannot result in better performance. The illustrate the potential of a proposed algorithm together with the bit compression technique, the simulations and evaluations were performed and are presented within this paper as well. [ABSTRACT FROM AUTHOR]

• Collection of heart sound database and Features extraction from each PCG signal. • Performing two types of analysis with and without normalization. • Data compression using principal component analysis (PCA). • Classification using SVM and k-NN. • The best obtained accuracies were between 98% and 100% A novel approach for separation among normal and heart murmurs sounds based on Phonocardiogram (PCG) analysis is introduced in this paper. The purpose of this work is to find the appropriate algorithm able to detect heart failures. Different features have been extracted from time and frequency domains. After the normalization step, the Principal Component Analysis algorithm is used for data reduction and compression. Support Vectors Machine (SVM), and k-Nearest Neighbors (kNN) classifiers were used with different kernels and number of neighbors in the classification step. Simulation results obtained from different databases are compared. The developed system gave good results when applied to different datasets. The accuracy of 96%, and 100% for the first, and the second dataset respectively were obtained. The algorithm shows its effectiveness in separation between normal and pathological cases. [ABSTRACT FROM AUTHOR]

In this paper, we suggest an efficient conversion for Turkish character's string, from UTF-8 to ANSI character's coding for space-preserving. Likewise, we present a decoding method that transforms the encoded ANSI string back to its original format. Unlike the one-byte ANSI characters, some of the Turkish alphabets are being stored in 2 bytes size. All that space comes at a price. The developed sequential encoding technique will reduce the size of the text file. Moreover, the Turkish encoded text will retain its original form after decoding. According to our proposal, it is considered as a lossless text compression, where it's a common concern today. Thus, many parties have become interested in Unicode compression. Basically, our algorithm is mapping Unicode Turkish characters into ANSI, by using the available 8-bit legacy. [ABSTRACT FROM AUTHOR]

Federated Learning (FL), which enables multiple clients to cooperatively train global models without revealing private data, has gained significant attention from researchers in recent years. However, the data samples on each participating device in FL are often not independent and identically distributed (IID), leading to significant statistical heterogeneity challenges. In this paper, we propose FL-Enhance, a novel framework to address the non-IID-ness data issue in FL by leveraging established solutions such as data selection, data compression, and data augmentation. FL-Enhance, specifically, utilizes cGANs that are trained locally on the server level, which represents a relatively novel approach within the FL framework. Also, data compression techniques are applied to preserve privacy during data sharing between clients and servers. We compare our framework with the commonly used SMOTE data augmentation technique and test it with different FL algorithms, including FedAvg, FedNova, and FedOpt. We conducted experiments using both image and tabular data to evaluate the effectiveness of our proposed framework. The experimental findings show that FL-Enhance can substantially enhance the performance of the trained models in situations of severe pathological clients while still preserving privacy, which is the fundamental requirement in the FL context. • We introduce FL-Enhance, a new data-selection-based method to handle pathological non-IID-ness in FL. • We experiment on various datasets and compare FedAvg, FedNova, and FedOpt algorithms. • We are among the first to integrate cGAN into FL especially on tabular datasets. • FL-Enhance provides better privacy protection compared to baseline FL methods. [ABSTRACT FROM AUTHOR]

Abstract: Software implementation costs of most algorithms, designed for image compression in wireless sensor networks, do not justify their use to reduce the energy consumption and delay transmission of images. Even though the hardware solution looks to be very attractive for this problem, a specific care should be paid when designing a low power algorithm for image compression and transmission over these systems. The aim of this paper is to present and evaluate a hardware implementation for user-driven image compression scheme designed to respect the energy constraints of image transmission over wireless sensor networks (WSNs). The proposed encoder will be considered as a co-processor for tasks related with image compression and data packetization. In this paper, we discuss both of the hardware architecture and the features of this encoder circuit when prototyped on FPGA (field-programmable gate array) and ASIC (application-specific integrated circuit) circuits. [Copyright &y& Elsevier]

Abstract: The Longest Common Subsequence Problem is the problem of finding a longest string that is a subsequence of every member of a given set of strings. It has applications in FPGA circuit minimization, data compression, and bioinformatics, among others. The problem is NP-hard in its general form, which implies that no exact polynomial-time algorithm currently exists for the problem. Consequently, inexact algorithms have been proposed to obtain good, but not necessarily optimal, solutions in an affordable time. In this paper, a hyper-heuristic algorithm incorporated within a constructive beam search is proposed for the problem. The proposed hyper-heuristic is based on two basic heuristic functions, one of which is new in this paper, and determines dynamically which one to use for a given problem instance. The proposed algorithm is compared with state-of-the-art algorithms on simulated and real biological sequences. Extensive experimental reveals that the proposed hyper-heuristic is superior to the state-of-the-art methods with respect to the solution quality and the running-time. [Copyright &y& Elsevier]

• The reconstruction model is established for vibration signals. • The index vector is proposed to obtain all the feature coefficients. • The improve FISTA is proposed for a better performance in reconstruction. • The simulated signals and acquired signals are used to verify the effectiveness. We consider the compressive sensing reconstruction for vibration signals, which are complex due to the harsh working environment. The recent fast iterative shrinkage-thresholding algorithm (FISTA) has paved the way for the signal reconstruction with a low complexity and high efficiency. Unfortunately, when extending to the vibration signals, the current algorithm still has some drawbacks such as the bad reconstruction effect. In this paper, we propose the improved fast iterative shrinkage-thresholding algorithm (IFISTA) to improve the reconstruction effect. Under the new scheme, the reconstruction is promoted by extracting information from the unstable signals in the process of iteration. Then the feature coefficients will be protected from shrinkage during iteration. The effectiveness of the IFISTA is verified by simulated signals and acquired signals. It is showed that the proposed scheme has superior performance in reconstruction and feature protection for vibration signals. [ABSTRACT FROM AUTHOR]

Abstract Binary relations are commonly used to represent relationships between real-world objects. Classical representations for binary relations can be very space-consuming when the set of elements is large. In these cases, compressed representations, such as the k 2 -tree, have proven to be a competitive solution, as they are efficient in time while consuming very little space. Moreover, k 2 -trees can successfully represent both sparse and dense binary relations, using different variants of the technique. In this paper, we propose and evaluate algorithms to efficiently perform set operations over binary relations represented using k 2 -trees. More specifically, we present algorithms for computing the union, intersection, difference, symmetric difference, and complement of binary relations. Thus, this work extends the functionality of the different variants of the k 2 -tree representation for binary relations. Our algorithms are computed directly over the compressed representation, without requiring previous decompression, and generate the result in compressed form. The experimental evaluation shows that they are efficient in terms of space and time, compared with different baselines where the binary relations are represented in plain form or require a previous decompression to perform the set operation. Highlights • We present algorithms for computing set operations over compressed binary relations. • These operations extend the functionality of k 2 -trees. • We evaluate the performance over real datasets of different nature. • Set operations are computed efficiently and in compressed space. [ABSTRACT FROM AUTHOR]

Abstract In the recent years, wireless sensor networks (WSN) has been deployed in different real time applications. Energy efficiency is the critical issue in the design and deployment of WSN since the sensor nodes are powered by batteries with limited capacity. As data transmission is the main power consuming process in WSN, several energy efficient techniques have been proposed. Data compression is a popular energy efficient technique which helps to reduce the amount of data to be transmitted in the network resulting in significant power saving. This paper proposes a new algorithm called neighborhood indexing sequence (NIS) for data compression in WSN. The proposed NIS algorithm dynamically assigns shorter length codewords to each character in the input sequence by exploiting the occurrence of neighboring bits. Using the real world WSN dataset, it is shown that the compression performance of the NIS algorithm is superior to existing compression algorithms. Compared with existing methods, the proposed compression algorithm is not only efficient but also highly robust for different WSN dataset. The proposed algorithm attains a compression ratio of 89.13 with the bit rate of 1.74 per sample. Moreover, it achieved power savings up to 87.57% for the applied WSN dataset. [ABSTRACT FROM AUTHOR]

Abstract: Most of the current manual paper or electronic equipment production, but it will cause pollution of the environment .In this paper, the electronic multimedia device instructions are embedded into the processor module Flash to generate E-Manual which can be browsed in the display devices by the remote control. As Flash space provided by most of the multimedia processor module is very small, there is a high demanding for data compression. In this paper, compression problems of black and white pictures and other data embedding Flash are the main, we also do a detailed study to BI_RLE8, LZMA algorithm, and apply them to the set-top box platform that has good results. [Copyright &y& Elsevier]

Abstract: Keystream reuse, also known as the two time pad problem, is a well known weakness in stream ciphers. The implementers of the cryptographic algorithms are still underestimating this threat. The keystream reuse exploitation techniques presented so far assume the underlying plaintext to be textual data and all the heuristics presented previously are based on the language characteristics of the underlying text based data, which fail when compression is applied on the plaintext before encryption. This paper presents exploitation techniques for two time pads in case of stream ciphered digitized and compressed speech signals. In this paper we show that how an adversary can automatically recover the digitized speech signals encrypted under the same keystream provided the language (e.g. English) and digital encoding/compression scheme details of the underlying speech signals are known. Our technique of cryptanalysis is based on the modeling of the speech parameters by exploiting the inter frame correlations between each components of the speech vectors in different frames and then using these models to decode the two speech signals in the keystream reuse scenario. The technique is flexible enough to incorporate all modern speech coding schemes based on parameter or hybrid encoding and compression techniques. The simulation experiments have showed promising results for most of the present day speech digitization and compression techniques. [Copyright &y& Elsevier]

Abstract: Compressed sensing (CS) is considered as a promising signal processing technique, and successful applications of the CS theory depend mainly on the accuracy and speed of the reconstruction algorithms. In this paper, a generalized objective functional, which has been developed using the combinational estimation and an extended stabilizing functional, is proposed. An efficient iterative scheme, which integrates the beneficial advantages of the homotopy method, the shuffled frog-leaping (SFL) algorithm and the harmony search (HS) algorithm, is designed for searching a possible global optimal solution. Numerical simulations are implemented to evaluate the numerical performances and effectiveness of the proposed algorithm. Excellent numerical performances and encouraging results are observed. For the cases considered in this paper, a dramatic improvement in the reconstruction accuracy is achieved, which indicates that the proposed algorithm is a promising candidate for solving CS inverse problem. [Copyright &y& Elsevier]

Abstract: Compression algorithms based on Burrows–Wheeler transform (BWT) take advantage of the fact that the word output of BWT shows a local similarity and then turns out to be highly compressible. The aim of the present paper is to study such “clustering effect” by using notions and methods from Combinatorics on Words. The notion of balance of a word plays a central role in our investigation. Empirical observations suggest that balance is actually the combinatorial property of input word that ensure optimal BWT compression. Moreover, it is reasonable to assume that the more balanced the input word is, the more local similarity we have after BWT (and therefore the better the compression is). This hypothesis is here corroborated by experiments on “real” text, by using local entropy as a measure of the degree of balance of a word. In the setting of Combinatorics on Words, a sound confirmation of previous hypothesis is given by a result of Mantaci et al. (2003) , which states that, in the case of a binary alphabet, there is an equivalence between circularly balanced words, words having a clusterized BWT, and the conjugates of standard words. In the case of alphabets of size greater than two, there is no more equivalence. The last section of the present paper is devoted to investigate the relationships between these notions, and other related ones (as, for instance, palindromic richness) in the case of a general alphabet. [Copyright &y& Elsevier]

In this paper, we present a new technique for worst-case analysis of compression algorithms which are based on the Burrows–Wheeler Transform. We mainly deal with the algorithm proposed by Burrows and Wheeler in their first paper on the subject [M. Burrows, D.J. Wheeler, A block sorting lossless data compression algorithm, Technical Report 124, Digital Equipment Corporation, Palo Alto, California, 1994], called bw0. This algorithm consists of the following three essential steps: (1) Obtain the Burrows–Wheeler Transform of the text, (2) Convert the transform into a sequence of integers using the move-to-front algorithm, (3) Encode the integers using Arithmetic code or any order-0 encoding (possibly with run-length encoding). We achieve a strong upper bound on the worst-case compression ratio of this algorithm. This bound is significantly better than bounds known to date and is obtained via simple analytical techniques. Specifically, we show that for any input string , and , the length of the compressed string is bounded by where is the th order empirical entropy, is a constant depending only on and on the size of the alphabet, and is the standard zeta function. As part of the analysis, we prove a result on the compressibility of integer sequences, which is of independent interest. Finally, we apply our techniques to prove a worst-case bound on the compression ratio of a compression algorithm based on the Burrows–Wheeler Transform followed by distance coding, for which worst-case guarantees have never been given. We prove that the length of the compressed string is bounded by . This bound is better than the bound we give for bw0. [Copyright &y& Elsevier]

Abstract: Many wireless sensor networks (WSNs) employ battery-powered sensor nodes. Communication in such networks is very taxing on its scarce energy resources. Convergecast – process of routing data from many sources to a sink – is commonly performed operation in WSNs. Data aggregation is a frequently used energy-conversing technique in WSNs. The rationale is to reduce volume of communicated data by using in-network processing capability at sensor nodes. In this paper, we address the problem of performing the operation of data aggregation enhanced convergecast (DAC) in an energy and latency efficient manner. We assume that all the nodes in the network have a data item and there is an a priori known application dependent data compression factor (or compression factor), γ, that approximates the useful fraction of the total data collected. The paper first presents two DAC tree construction algorithms. One is a variant of the Minimum Spanning Tree (MST) algorithm and the other is a variant of the Single Source Shortest Path Spanning Tree (SPT) algorithm. These two algorithms serve as a motivation for our Combined algorithm (COM) which generalized the SPT and MST based algorithm. The COM algorithm tries to construct an energy optimal DAC tree for any fixed value of α (=1− γ), the data growth factor. The nodes of these trees are scheduled for collision-free communication using a channel allocation algorithm. To achieve low latency, these algorithms use the β-constraint, which puts a soft limit on the maximum number of children a node can have in a DAC tree. The DAC tree obtained from energy minimizing phase of tree construction algorithms is re-structured using the β-constraint (in the latency minimizing phase) to reduce latency (at the expense of increasing energy cost). The effectiveness of these algorithms is evaluated by using energy efficiency, latency and network lifetime as metrics. With these metrics, the algorithms’ performance is compared with an existing data aggregation technique. From the experimental results, for a given network density and data compression factor γ at intermediate nodes, one can choose an appropriate algorithm depending upon whether the primary goal is to minimize the latency or the energy consumption. [Copyright &y& Elsevier]

Abstract: A robust multi bit and high quality audio watermarking technique in time domain is proposed in this paper. Watermarking is a technique used to label digital media by hiding copyright or other information into the underlying data. The watermark must be imperceptible and undetectable by the user and should be robust to various types of distortion. To enhance the robustness and taper-resistance of the embedded watermark, in this paper a multi bit technique is employed. Instead of embedding one bit into an audio frame, multiple bits can be embedded into each audio sub-frame. For attackers, since they do not know the parameters, this significantly reduces the possibility of unauthorized bit detection and removal of watermark. In real-time watermarking applications, robustness is not the only factor that plays an important role. The other factor that plays a very an important role is computational complexity. In general, audio file is transmitted in compressed form. Real-time watermark embedding must take into account this compressed form, because first decompressing the data, adding a watermark and then recompressing the data is computationally too demanding. In this paper, we propose robust and low complexity audio watermarking algorithm. To evaluate the performance of the proposed audio watermarking algorithm, subjective and objective quality tests including bit error rate (BER) and signal-to-noise ratio (SNR) were conducted. Compared to traditional one bit embedding algorithm, the proposed algorithm gives highly recovery rate after attack by commonly used audio data manipulations, such as low pass filtering, requantization, resampling, and MP3 compression. [Copyright &y& Elsevier]

Abstract: This paper presents modifications to our recently introduced pre-processing method, orthogonal WAVElet correction (OWAVEC), based on the combination of wavelet analysis and an orthogonal correction algorithm, described in detail in a former paper [I. Esteban-Díez, J.M. González-Sáiz, C. Pizarro, OWAVEC: a combination of wavelet analysis and an orthogonalization algorithm as a pre-processing step in multivariate calibration, Anal. Chim. Acta 515 (2004) 31–41], aimed at extending its applicability and at improving its performance; thanks to an additional use of OWAVEC as an effective data compression tool. The OWAVEC method uses the discrete wavelet transform (DWT) to decompose each individual signal into the wavelet domain, and then an orthogonalization algorithm is applied to the obtained wavelet coefficients matrix to remove the information not related to a considered response variable. Later, the corrected wavelet coefficients are ranked by their variance or by their correlation coefficient with the response variable, and the subset providing the most stable and reliable calibration model is finally selected (data compression). The new version of OWAVEC has been applied to two NIR data sets to test its performance. For both regression problems studied, high quality calibration models with very high compression ratios were obtained, providing improved predictive results and a considerably lower overfitting than other orthogonal signal correction methods. The generalized OWAVEC method presented here may be used as a global tool for simultaneous noise suppression, data compression and orthogonal correction of signals. [Copyright &y& Elsevier]

The paper suggests a novel method for implementing recursive algorithms in hardware. The required support for recursion has been provided through a modular and a hierarchical specification of a control unit that can be translated to an implementation of the respective hardware circuit on the basis of a recursive hierarchical finite state machine and through a mechanism that permits the contents of an execution unit to be stored/restored between hierarchical calls/returns. The paper describes all the details that are required to implement recursive algorithms in hardware. It begins with software (C++) models and finishes with synthesizable VHDL codes. Two practical applications of recursive algorithms in the data sorting and compression area have been studied in detail. [Copyright &y& Elsevier]

• To accommodate high-bit-width infrared images, we propose the pre-processing and augmentation algorithm without data compression. • A multi-scale feature extraction network based on ConvNext was built to adapt the infrared images of low-resolution. • The coordinate attention module is introduced in the ConvNeXt block to focus on targets and suppress the background, and a split attention module is introduced in the neck to enhance feature fusion ability. • The algorithm offers significant performance improvements over prevalent object detection algorithms, comparative experiment on the FLIR dataset shows that the algorithm outperforms the YOLOv5 by 3% and Faster R-CNN by 5.6% in map50 and has significant advantages in parameters and FLOPs. Transfer learning is widely used in infrared object detection algorithms, but these algorithms developed from visible usually ignored the characteristic of infrared images. In this paper, we propose a new object detection algorithm YOLO-CIR based on YOLO and ConvNext for infrared (IR) images. Specifically, to accommodate high-bit-width infrared images, we propose the augmentation algorithm for infrared images and improve the pre-processing algorithm without bit-width compression. In addition, a multi-scale feature extraction network based on ConvNext was built to adapt the infrared images of low-resolution. Moreover, the coordinate attention module is introduced in the ConvNeXt block to focus on targets and suppress the background, and a split attention module is introduced in the neck to enhance feature fusion ability. The algorithm offers significant performance improvements over prevalent object detection algorithms, comparative experiment on the FLIR dataset shows that the algorithm outperforms the YOLOv5 by 3% and Faster R-CNN by 5.6% in map50 and has significant advantages in parameters and FLOPs. [ABSTRACT FROM AUTHOR]

Process Aware Information Systems manage processes within organisations on the basis of business process models. These models can be created either from scratch or by reusing exiting reference process models. Particular types of reference models are configurable process models that are created by merging multiple models into a single one that can be customized to the needs of the business experts. Using those models presents two main challenges: their creation and their configuration. In this paper, we focus on the first challenge and propose a novel algorithm for merging process models into a configurable process model. The difference in our work is the pre-annotated process models with their business capabilities that report on what actions each process element achieves. Our algorithm generates configurable models that are also annotated with their capabilities that can be used to face the second challenge of these models: the configuration phase. We tested our algorithm using real-world process models to evaluate the required creation time and resulting compression rate after merging the input models. The results show that the models can be created in few milliseconds and achieving a compression rate of 50%. We further carried out interviews with domain experts to assess the usefulness and the level of maturity of this work. The results show the importance of the automation of process merging using a tool support that we proposed. However, further adaptation efforts are required to integrate this work in the working environments of the interviewed experts. [ABSTRACT FROM AUTHOR]

Proper orthogonal decomposition (POD) is a useful technique for feature extraction, model order reduction and data compression and has been widely used in different science and engineering disciplines. Numerous papers have been published on the application of offline POD, i.e., batch POD (BPOD) in civil and mechanical engineering encompassing Karhunen–Loève decomposition (KLD), principal component analysis (PCA), and singular value decomposition (SVD). Nevertheless, online POD which is more suited for online feature extraction and monitoring has been scarcely addressed when dealing with civil and mechanical systems, particularly in structural dynamics. In this paper, a number of recursive POD (RPOD) methods in form of recursive PCA (RPCA) are overviewed with their application to structural dynamics. RPCA with numerical eigenvalue decomposition (EVD), incremental principal component analysis (IPCA), matrix perturbation method, and Kalman filter RPCA (KFRPCA) are presented; their performance is probed in terms of initialization, structural parameter modification, noisy observation, and alteration of loading statistics. The novel KFRPCA algorithm developed in this paper is reformulated to resolve the unobservability issue of higher modes which was present in its previous version in the published literature. Online stochastic output-only system identification is presented by synergizing RPCA with nonlinear Bayesian filter. Augmented extended Kalman filter (AEKF) is employed to perform unknown-input dual estimation. • Shortcomings of matrix perturbation method are underlined. • IPCA is shown to be superior to matrix perturbation method. • Higher modes observability issue of KFRPCA is resolved. • RPCAs robustness to initialization, noise, input, and parameter variation are assessed. • Unknown input system identification is dealt with using AEKF and RPCA. [ABSTRACT FROM AUTHOR]

In this paper, a parameter estimation algorithm for wideband multiple FH (multi-FH) signals based on compressed sensing (CS) is proposed. Our proposed algorithm is aiming at the condition of existing synchronous and asynchronous frequency-hopping (FH) signals, and meanwhile considering the frequency switching time. Firstly, by segmenting the received signals into several compressed measurement signals, the sparsity is estimated through the compressed spectrum sensing algorithm without reconstructing signals. Secondly, the improved orthogonal matching pursuit (OMP) algorithm is used to the roughly estimate the frequency of multi-FH signals, and the frequency is accurately estimated by frequency clustering algorithm, then the hopping time and hopping speed are roughly estimated through time–frequency analysis. Finally, inspired by the traditional sliding window algorithm, the adaptive sliding window (ASW) algorithm is proposed, which can adaptively adjust the window length and step size so as to increase the accuracy of the hopping time and hopping speed estimation. Numerical simulations demonstrate that the algorithm proposed in this paper can precisely estimate the parameters of multi-FH signals at low signal-to-noise ratio (SNR) and can significantly reduce the algorithm complexity. [ABSTRACT FROM AUTHOR]

Abstract: Wireless sensor networks (WSNs) are data-centric networks. In recent years, with the continuous development of WSNs technology, the characteristics of node storage capacity-constrained and node energy resource-constrained are prominent in WSNs technology. How to effectively compress data is an important issue in WSNs. Especially, the issue becomes a more challenging problem when the data monitored by nodes have no spatial and temporal correlation or stable correlation. To settle the above problem, this paper proposes a new data processing algorithm based on B-spline fitting model, called BFM algorithm. The BFM algorithm can compress data effectively, and thus reduce the amount of data transmitted. This can save the energy consumption of WSNs and prolong the lifetime of WSNs. Through experiments, the BFM algorithm is compared with the Tiny DB model. The results of the experiments show the BFM algorithm outperforms the Tiny DB model in terms of data transfer ratio. [Copyright &y& Elsevier]

Abstract: Automated 3-dimensional modeling pipelines include 3D scanning, registration, data abstraction, and visualization. All steps in such a pipeline require the processing of a massive amount of 3D data, due to the ability of current 3D scanners to sample environments with a high density. The increasing sampling rates make it easy to acquire Billions of spatial data points. This paper presents algorithms and data structures for handling these data. We propose an efficient octree to store and compress 3D data without loss of precision. We demonstrate its usage for an exchange file format, fast point cloud visualization, sped-up 3D scan matching, and shape detection algorithms. We evaluate our approach using typical terrestrial laser scans. [Copyright &y& Elsevier]

Abstract: Data compression via the Huffman algorithm, which is a data compression technique, is the most efficient technique between single symbol data compression techniques. This algorithm is counted among statistical data compression techniques. Many efforts have been made for optimizing this technique and some algorithms have been presented, too. One of these algorithms is the ACW algorithm which is presented by Hussein Al-Bahadili et al. At first, they used the Huffman algorithm and then used the results of the Huffman algorithm as the input of their algorithm to calculate the optimum character wordlength. So, they decreased the stored characters via increasing the character wordlength for doing the compression. In this paper, we examined and criticized the ACW algorithm and presented some of its weaknesses via suitable counterexamples. At the end, it was concluded that the optimum character wordlength can never be calculated by extant techniques. [Copyright &y& Elsevier]

Abstract: The paper presents a novel three-stage algorithm for online compression of magnetic flux leakage (MFL) signals that are acquired in inspection of oil and gas pipelines. In the first stage, blocks of MFL signal are screened for useful information using a semi-robust statistical measure, Mean Absolute Deviation (). The study presents guidelines for selecting a block size to deliver robust screening and efficient compression ratios. In the second stage, a multivariate approach is used to compress the data across sensors using Principal Component Analysis (PCA). The second stage is invoked only when an anomaly is detected by sufficiently large number of sensors. In the third stage, the signal is further compressed within each sensor (univariate approach) using Discrete Wavelet Transform (DWT). Implementation on real-time MFL signals demonstrates the algorithm''s ability to achieve high compression ratios with low Normalized Mean Square Error (NMSE) while being fairly robust to baseline shifts. [Copyright &y& Elsevier]

Abstract: This paper presents a new algorithm for the progressive compression of manifold polygon meshes. The input surface is decimated by several traversals that generate successive levels of detail through a specific patch decimation operator which combines vertex removal and local remeshing. The mesh connectivity is encoded by two lists of Boolean error predictions based on the mesh geometry: one for the inserted edges and the other for the faces with a removed center vertex. The mesh geometry is encoded with a barycentric error prediction of the removed vertex coordinates and a local curvature prediction. We also include two methods that improve the rate-distortion performance: a wavelet formulation with a lifting scheme and an adaptive quantization technique. Experimental results demonstrate the effectiveness of our approach in terms of compression rates and rate-distortion performance. [Copyright &y& Elsevier]

Abstract: The high data throughput of spaceborne Synthetic Aperture Radar (SAR) instruments introduces stringent requirements on the end-to-end system, from the on-board storage capacity and data downlink to processing power and archiving capacity in the ground segment. An efficient raw data compression scheme on-board the satellite is therefore required to maximise the return on investment for SAR missions. The next generation of European SAR satellites, the Sentinel-1 mission, will employ the Flexible Dynamic Block Adaptive Quantization (FDBAQ) compression scheme. In order to validate the implementation of the FDBAQ, expected radar reflectivity levels for the global land surface were needed. The paper presents the development of the Global Backscatter Model (GBM) which has been used to simulate realistic radar reflectivity acquisitions in support of performance validation of the Sentinel-1 FDBAQ implementation. The GBM consists of a parameter database and SAR image simulation software. The model parameter database characterises C-band radar reflectivity as a function of local incidence angle with a resolution of 1km for 85% of the global land surface. The parameter database was derived from a global multi-temporal coverage of ENVISAT Advanced Synthetic Aperture Radar (ASAR) data. The SAR image simulation software uses the parameter database together with orbit propagation and swath determination algorithms in order to account for the instrument acquisition geometry and the location specific backscattering characteristics. The model has the potential to simulate acquisitions for other SAR instruments. Furthermore, the parameter database could be a valuable source of information for global land cover studies. [Copyright &y& Elsevier]

Abstract: In this paper, a hierarchical video summarization representation algorithm was proposed for video analysis in compressed domain. In particular, Rough Sets(RS) theory is introduced for video analysis to inrease the efficiency of algorithm. Firstly, DCT coefficients and DC coefficients are extracted from video image sequences, so an Information System can construct with DC coefficient. Then, Information System is reduced by ruduction theory of RS, the representation of the video frame is obtained by reduced DC coefficients. Finaly, we can obtain the reduced Information System, i.e. the Core of Information System. Since the Core contained all the information in video sequences, and at the same time it banished redundant video frame, so it can be viewed as the efficient summarization representation. As our experimental results indicate that the algorithm can efficiently generate a set of video summarization representative of compressed domain videos sequences. Compared to conventional algorithm, the algorithm enjoys following advantages. (1)only a subset of video frames considered during video analysis, so it can avoid the computational complexity. (2) the video summarization representation becomes more scientific and efficient than previous methods. [Copyright &y& Elsevier]

Abstract: Many applications in computer vision, biomedical informatics, and graphics deal with data in the matrix or tensor form. Non-negative matrix and tensor factorization, which extract data-dependent non-negative basis functions, have been commonly applied for the analysis of such data for data compression, visualization, and detection of hidden information (factors). In this paper, we present a fast and flexible algorithm for sparse non-negative tensor factorization (SNTF) based on columnwise coordinate descent (CCD). Different from the traditional coordinate descent which updates one element at a time, CCD updates one column vector simultaneously. Our empirical results on higher-mode images, such as brain MRI images, gene expression images, and hyperspectral images show that the proposed algorithm is 1–2 orders of magnitude faster than several state-of-the-art algorithms. [Copyright &y& Elsevier]

Abstract: The paper proposes a new test data compression scheme for testing embedded cores with multiple scan chains. The new compression scheme allows broadcasting identical test data to several scan chains whenever the cells in the same depth are compatible for the current application test pattern. Thus, it efficiently utilizes the compatibility of the scan cells among the scan chain segments, increases test data run in broadcast mode and reduces test data volume and test application time effectively. It does not need complex compressing algorithm and costly hardware. Experimental results demonstrate the efficiency and versatility of the proposed method. [Copyright &y& Elsevier]

Abstract: In this paper, we propose two secret sharing approaches for 3D models using Blakely and Thien and Lin schemes. We show that encoding 3D models using lossless data compression algorithms prior to secret sharing helps reduce share sizes and remove redundancies and patterns that possibly ease cryptanalysis. The proposed approaches provide a higher tolerance against data corruption/loss than existing 3D protection mechanisms, such as encryption. Experimental results are provided to demonstrate the secrecy and safety of the proposed schemes. The feasibility of the proposed algorithms is demonstrated on various 3D models. [Copyright &y& Elsevier]

Abstract: Desynchronization attack is known as one of the most difficult attacks to resist, for it can desynchronize the location of the watermark and hence causes incorrect watermark detection. It is a challenging work to design a robust audio watermarking scheme against desynchronization attacks. Based on pseudo-Zernike moment and synchronization code, we propose a new digital audio watermarking algorithm with good auditory quality and reasonable resistance toward desynchronization attacks in this paper. Firstly, the origin digital audio is segmented and then each segment is cut into two parts. Secondly, with the spatial watermarking technique, synchronization code is embedded into the statistics average value of audio samples in the first part. And then, map 1D digital audio signal in the second part into 2D form, and calculate its pseudo-Zernike moments. Finally, the watermark bit is embedded into the average value of modulus of the low-order pseudo-Zernike moments. Meanwhile combining the two adjacent synchronization code searching technology, the algorithm can extract the watermark without the help from the origin digital audio signal. Simulation results show that the proposed watermarking scheme is not only inaudible and robust against common signals processing such as MP3 compression, noise addition, resampling, re-quantization, etc., but also robust against the desynchronization attacks such as random cropping, amplitude variation, pitch shifting, jittering, etc. [Copyright &y& Elsevier]

Abstract: A novel algorithm named NB+ which is an extended version of the traditional Naïve Bayesian algorithm has been presented in this paper. An exception occurs when there is an equal probability for the class label value in the Naïve Bayesian algorithm. The approach aims to suggest a solution with the help of a partial matching method. Consequently, the classification accuracy has drastically improved. Experimental evaluation has been done on various databases to show that NB+ algorithm outperforms the traditional Naïve Bayesian algorithm. [Copyright &y& Elsevier]

Abstract: Suffix arrays are a key data structure for solving a run of problems on texts and sequences, from data compression and information retrieval to biological sequence analysis and pattern discovery. In their simplest version, they can just be seen as a permutation of the elements in , encoding the sorted sequence of suffixes from a given text of length , under the lexicographic order. Yet, they are on a par with ubiquitous and sophisticated suffix trees. Over the years, many interesting combinatorial properties have been devised for this special class of permutations: for instance, they can implicitly encode extra information, and they are a well characterized subset of the permutations. This paper gives a short tutorial on suffix arrays and their compressed version to explore and review some of their algorithmic features, discussing the space issues related to their usage in text indexing, combinatorial pattern matching, and data compression. [Copyright &y& Elsevier]

Abstract: In this paper, an efficient macroblock-based diverse and flexible prediction modes selection algorithm is proposed for coding hyperspectral images, which is inspired by the prediction scheme of H264/AVC. Here, different modes are specified for the corresponding macroblocks (16×16 pixel regions of a band) of hyperspectral images other than the whole band image using only one reference band image for prediction. Only the 4×4 mode is employed for the intra-band prediction in view of the fact that correlation coefficients of pixels separated by not more than four pixels in the spatial domain are greater than 0.65 at most cases. The optimal reference band is determined by the fast reference band selection algorithm; thereafter, the best partition of the candidate macroblock in the optimal reference band is further selected for inter-band prediction of the current macroblock. Thus, the stronger correlation in the spectral direction or in the spatial domain is utilized for the prediction of the given macroblock. With a comparably low memory requirement, the prediction coding scheme is proposed to speed up the implemental process using the fast reference band selection algorithm, the integer DCT and the quantization, which just needs the multiplication and bit-shifts operations. Several AVIRIS images are used to evaluate the performance of the algorithm. The proposed scheme outperforms the state-of-the-art 3D-based compression algorithms at lower rates. Moreover, compared with the method by using all the prediction modes of H.264/AVC, about 80% encoding time can be saved by our method under the same experimental condition. [ABSTRACT FROM AUTHOR]

Abstract: An innovative event-based lossy compression model for effective and efficient OLAP over data streams, called ECM-DS, is presented and experimentally assessed in this paper. The main novelty of our compression approach with respect to traditional data stream compression techniques relies on exploiting the semantics of the reference application scenario in order to drive the compression process by means of the “degree of interestingness” of events occurring in the target stream. This finally improves the quality of retrieved approximate answers to OLAP queries over data streams, and, in turn, the quality of complex knowledge discovery tasks over data streams developed on top of ECM-DS, and implemented via ad-hoc data stream mining algorithms. Overall, the compression strategy we propose in this research puts the basis for a novel class of intelligent applications over data streams where the knowledge on actual streams is integrated-with and correlated-to the knowledge related to expired events that are considered critical for the target OLAP analysis scenario. Finally, a comprehensive experimental evaluation over several classes of data stream sets clearly confirms the benefits deriving from the event-based data stream compression approach proposed in ECM-DS. [Copyright &y& Elsevier]

Abstract: Multiresolution representations of data are powerful tools in data compression. For a proper adaptation to the edges, a good strategy is to consider a nonlinear approach. Thus, one needs to control the stability of these representations. In this paper, 2D multiresolution processing algorithms that ensure this stability are introduced. A prescribed accuracy is ensured by these strategies. [Copyright &y& Elsevier]

Abstract: In this paper, a supervised feature selection approach is presented, which is based on metric applied on continuous and discrete data representations. This method builds a dissimilarity space using information theoretic measures, in particular conditional mutual information between features with respect to a relevant variable that represents the class labels. Applying a hierarchical clustering, the algorithm searches for a compression of the information contained in the original set of features. The proposed technique is compared with other state of art methods also based on information measures. Eventually, several experiments are presented to show the effectiveness of the features selected from the point of view of classification accuracy. [Copyright &y& Elsevier]

Abstract: This paper proposes a novel scheme for 3D model compression based on mesh segmentation using multiple principal plane analysis. This algorithm first performs a mesh segmentation scheme, based on fusion of the well-known k-means clustering and the proposed principal plane analysis to separate the input 3D mesh into a set of disjointed polygonal regions. The boundary indexing scheme for the whole object is created by assembling local regions. Finally, the current work proposes a triangle traversal scheme to encode the connectivity and geometry information simultaneously for every patch under the guidance of the boundary indexing scheme. Simulation results demonstrate that the proposed algorithm obtains good performance in terms of compression rate and reconstruction quality. [Copyright &y& Elsevier]

Abstract: A trajectory is a sequence of locations, each associated with a timestamp, describing the movement of a point. Trajectory data is becoming increasingly available and the size of recorded trajectories is getting larger. In this paper we study the problem of compressing planar trajectories such that the most common spatio-temporal queries can still be answered approximately after the compression has taken place. In the process, we develop an implementation of the Douglas–Peucker path-simplification algorithm which works efficiently even in the case where the polygonal path given as input is allowed to self-intersect. For a polygonal path of size n, the processing time is for or depending on the type of simplification. [Copyright &y& Elsevier]

Abstract: In order to improve the performance of block adaptive quantization (BAQ) when the output of the analog to digital converter (ADC) is saturated, this paper proposes an anti-saturation BAQ algorithm. First, the concept of the standard deviation of the output signal (SDOS) of the ADC is proposed. Also, unlike traditional normalization processing, SDOS is used and the mapping between SDOS and the average signal magnitude is deduced. Second, the saturation term is introduced to the Lloyd–Max quantizer and an optimal non-uniform scalar quantizer for saturated SAR raw data quantization is proposed. After this, the implementation scheme for the proposed algorithm using an FPGA is analyzed in detail. Third, the relationships among the saturation degree of the signal, the peak-to-peak value of the ADC, standard deviation of the input and output signal of the ADC and the average signal magnitude are deduced. Based on these relationships, a power compensation decoder is designed for encoding. Numerical experiment results based on ERS-1 and the simulated data show that the performance of the proposed algorithm is better than that of BAQ. [Copyright &y& Elsevier]

Abstract: This paper studies two problems on compressed strings described in terms of straight line programs (SLPs). One is to compute the length of the longest common substring of two given SLP-compressed strings, and the other is to compute all palindromes of a given SLP-compressed string. In order to solve these problems efficiently (in polynomial time w.r.t. the compressed size) decompression is never feasible, since the decompressed size can be exponentially large. We develop combinatorial algorithms that solve these problems in time with space, and in time with space, respectively, where is the size of the input SLP-compressed strings. [Copyright &y& Elsevier]

Abstract: This paper suggests a novel compression scheme for small text files. The proposed scheme depends on Boolean minimization of binary data accompanied with the adoption of Burrows-Wheeler transformation (BWT) algorithm. Compression of small text files must fulfil special requirements since they have small context. The use of Boolean minimization and Burrows-Wheeler transformation generate better context information for compression with standard algorithms. We tested the suggested scheme on collections of small and medium-sized files. The testing results showed that proposed scheme improve the compression ratio over other existing methods. [Copyright &y& Elsevier]

Abstract: Position-specific scoring matrices are a popular choice for modelling signals or motifs in biological sequences, both in DNA and protein contexts. A lot of effort has been dedicated to the definition of suitable scores and thresholds for increasing the specificity of the model and the sensitivity of the search. It is quite surprising that, until very recently, little attention has been paid to the actual process of finding the matches of the matrices in a set of sequences, once the score and the threshold have been fixed. In fact, most profile matching tools still rely on a simple sliding window approach to scan the input sequences. This can be a very time expensive routine when searching for hits of a large set of scoring matrices in a sequence database. In this paper we will give a survey of proposed approaches to speed up profile matching based on statistical significance, multipattern matching, filtering, indexing data structures, matrix partitioning, Fast Fourier Transform and data compression. These approaches improve the expected searching time of profile matching, thus leading to implementation of faster tools in practice. [Copyright &y& Elsevier]

Abstract: This paper presents a new and efficient data compression algorithm, namely, the adaptive character wordlength (ACW) algorithm, which can be used as complementary algorithm to statistical compression techniques. In such techniques, the characters in the source file are converted to a binary code, where the most common characters in the file have the shortest binary codes, and the least common have the longest; the binary codes are generated based on the estimated probability of the character within the file. Then, the binary coded file is compressed using 8 bits character wordlength. In this new algorithm, an optimum character wordlength, , is calculated, where , so that the compression ratio is increased by a factor of . In order to validate this algorithm, it is used as a complement algorithm to Huffman code to compress a source file having 10 characters with different probabilities, and these characters are randomly distributed within the source file. The results obtained and the factors that affect the optimum value of are discussed, and, finally, conclusions are presented. [Copyright &y& Elsevier]