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Artykuły w czasopismach na temat „Compressed Row Storage”

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Data publikacji: 6 września 2023

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Bani-Ismail, Basel, and Ghassan Kanaan. "Comparing Different Sparse Matrix Storage Structures as Index Structure for Arabic Text Collection." International Journal of Information Retrieval Research 2, no. 2 (2012): 52–67. http://dx.doi.org/10.4018/ijirr.2012040105.

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In the authors’ study they evaluate and compare the storage efficiency of different sparse matrix storage structures as index structure for Arabic text collection and their corresponding sparse matrix-vector multiplication algorithms to perform query processing in any Information Retrieval (IR) system. The study covers six sparse matrix storage structures including the Coordinate Storage (COO), Compressed Sparse Row (CSR), Compressed Sparse Column (CSC), Block Coordinate (BCO), Block Sparse Row (BSR), and Block Sparse Column (BSC). Evaluation depends on the storage space requirements for each

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Mohammed, Saira Banu Jamal, M. Rajasekhara Babu, and Sumithra Sriram. "GPU Implementation of Image Convolution Using Sparse Model with Efficient Storage Format." International Journal of Grid and High Performance Computing 10, no. 1 (2018): 54–70. http://dx.doi.org/10.4018/ijghpc.2018010104.

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With the growth of data parallel computing, role of GPU computing in non-graphic applications such as image processing becomes a focus in research fields. Convolution is an integral operation in filtering, smoothing and edge detection. In this article, the process of convolution is realized as a sparse linear system and is solved using Sparse Matrix Vector Multiplication (SpMV). The Compressed Sparse Row (CSR) format of SPMV shows better CPU performance compared to normal convolution. To overcome the stalling of threads for short rows in the GPU implementation of CSR SpMV, a more efficient mod

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Yan, Kaizhuang, Yongxian Wang, and Wenbin Xiao. "A New Compression and Storage Method for High-Resolution SSP Data Based-on Dictionary Learning." Journal of Marine Science and Engineering 10, no. 8 (2022): 1095. http://dx.doi.org/10.3390/jmse10081095.

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The sound speed profile data of seawater provide an important basis for carrying out underwater acoustic modeling and analysis, sonar performance evaluation, and underwater acoustic assistant decision-making. The data volume of the high-resolution sound speed profile is vast, and the demand for data storage space is high, which severely limits the analysis and application of the high-resolution sound speed profile data in the field of marine acoustics. This paper uses the dictionary learning method to achieve sparse coding of the high-resolution sound speed profile and uses a compressed sparse

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Knopp, T., and A. Weber. "Local System Matrix Compression for Efficient Reconstruction in Magnetic Particle Imaging." Advances in Mathematical Physics 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/472818.

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Magnetic particle imaging (MPI) is a quantitative method for determining the spatial distribution of magnetic nanoparticles, which can be used as tracers for cardiovascular imaging. For reconstructing a spatial map of the particle distribution, the system matrix describing the magnetic particle imaging equation has to be known. Due to the complex dynamic behavior of the magnetic particles, the system matrix is commonly measured in a calibration procedure. In order to speed up the reconstruction process, recently, a matrix compression technique has been proposed that makes use of a basis transf

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Christnatalis, Christnatalis, Bachtiar Bachtiar, and Rony Rony. "Comparative Compression of Wavelet Haar Transformation with Discrete Wavelet Transform on Colored Image Compression." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 3, no. 2 (2020): 202–9. http://dx.doi.org/10.31289/jite.v3i2.3154.

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In this research, the algorithm used to compress images is using the haar wavelet transformation method and the discrete wavelet transform algorithm. The image compression based on Wavelet Wavelet transform uses a calculation system with decomposition with row direction and decomposition with column direction. While discrete wavelet transform-based image compression, the size of the compressed image produced will be more optimal because some information that is not so useful, not so felt, and not so seen by humans will be eliminated so that humans still assume that the data can still be used e

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Tanaka, Teruo, Ryo Otsuka, Akihiro Fujii, Takahiro Katagiri, and Toshiyuki Imamura. "Implementation of D-Spline-Based Incremental Performance Parameter Estimation Method with ppOpen-AT." Scientific Programming 22, no. 4 (2014): 299–307. http://dx.doi.org/10.1155/2014/310879.

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In automatic performance tuning (AT), a primary aim is to optimize performance parameters that are suitable for certain computational environments in ordinary mathematical libraries. For AT, an important issue is to reduce the estimation time required for optimizing performance parameters. To reduce the estimation time, we previously proposed the Incremental Performance Parameter Estimation method (IPPE method). This method estimates optimal performance parameters by inserting suitable sampling points that are based on computational results for a fitting function. As the fitting function, we i

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AlAhmadi, Sarah, Thaha Mohammed, Aiiad Albeshri, Iyad Katib, and Rashid Mehmood. "Performance Analysis of Sparse Matrix-Vector Multiplication (SpMV) on Graphics Processing Units (GPUs)." Electronics 9, no. 10 (2020): 1675. http://dx.doi.org/10.3390/electronics9101675.

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Graphics processing units (GPUs) have delivered a remarkable performance for a variety of high performance computing (HPC) applications through massive parallelism. One such application is sparse matrix-vector (SpMV) computations, which is central to many scientific, engineering, and other applications including machine learning. No single SpMV storage or computation scheme provides consistent and sufficiently high performance for all matrices due to their varying sparsity patterns. An extensive literature review reveals that the performance of SpMV techniques on GPUs has not been studied in s

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Zhang, Xi Xi, Yu Jing Jia, and Guang Zhen Cheng. "The Water Sump Cleaning Machine by Vacuum Suction." Applied Mechanics and Materials 201-202 (October 2012): 785–88. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.785.

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This article describes a vacuum water sump cleaning machine which is used to clean up coal mine water sump. Cleaning machine is composed of mechanical structure and electrical control devices. The parts of machine are made up of Walk the flatbed, storage mud tank, vacuum pumps, suction pipe, mud tubes, swing devices, control valves, suction pipe and pressure tracheal. When working, under the function of vacuum pumping, cleaning machine pulls out the vacuum from storage mud tank through the vacuum air feeder. As the vacuum level in the tank is increasing, under the function of atmospheric press

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Ji, Guo Liang, Yang De Feng, Wen Kai Cui, and Liang Gang Lu. "Implementation Procedures of Parallel Preconditioning with Sparse Matrix Based on FEM." Applied Mechanics and Materials 166-169 (May 2012): 3166–73. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.3166.

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A technique to assemble global stiffness matrix stored in sparse storage format and two parallel solvers for sparse linear systems based on FEM are presented. The assembly method uses a data structure named associated node at intermediate stages to finally arrive at the Compressed Sparse Row (CSR) format. The associated nodes record the information about the connection of nodes in the mesh. The technique can reduce large memory because it only stores the nonzero elements of the global stiffness matrix. This method is simple and effective. The solvers are Restarted GMRES iterative solvers with

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Mahmoud, Mohammed, Mark Hoffmann, and Hassan Reza. "Developing a New Storage Format and a Warp-Based SpMV Kernel for Configuration Interaction Sparse Matrices on the GPU." Computation 6, no. 3 (2018): 45. http://dx.doi.org/10.3390/computation6030045.

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Sparse matrix-vector multiplication (SpMV) can be used to solve diverse-scaled linear systems and eigenvalue problems that exist in numerous, and varying scientific applications. One of the scientific applications that SpMV is involved in is known as Configuration Interaction (CI). CI is a linear method for solving the nonrelativistic Schrödinger equation for quantum chemical multi-electron systems, and it can deal with the ground state as well as multiple excited states. In this paper, we have developed a hybrid approach in order to deal with CI sparse matrices. The proposed model includes a

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Saad, Aouatif, Adil Echchelh, Mohammed Hattabi, and Ganaoui El. "An improved computational method for non isothermal resin transfer moulding simulation." Thermal Science 15, suppl. 2 (2011): 275–89. http://dx.doi.org/10.2298/tsci100928016s.

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The optimization in the simulation time of non-isothermal filling process without losing effectiveness remains a challenge in the resin transfer moulding process simulation. We are interested in this work on developing an improved computational approach based on finite element method coupled with control volume approach. Simulations can predict the position of the front of resin flow, pressure and temperature distribution at each time step. Our optimization approach is first based on the modification of conventional control volume/finite element method, then on the adaptation of the iterative

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Sadineni, Praveen Kumar. "Compression Methodologies for Columnar Database Optimization." Journal of Computational Science and Intelligent Technologies 3, no. 1 (2022): 24–32. http://dx.doi.org/10.53409/mnaa/jcsit/e202203012432.

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Today’s life is completely dependent on data. Conventional relational databases take longer to respond to queries because they are built for row-wise data storage and retrieval. Due to their efficient read and write operations to and from hard discs, which reduce the time it takes for queries to produce results, columnar databases have recently overtaken traditional databases. To execute Business Intelligence and create decision-making systems, vast amounts of data gathered from various sources are required in data warehouses, where columnar databases are primarily created. Since the data are

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Ahmed, Muhammad, Sardar Usman, Nehad Ali Shah, et al. "AAQAL: A Machine Learning-Based Tool for Performance Optimization of Parallel SPMV Computations Using Block CSR." Applied Sciences 12, no. 14 (2022): 7073. http://dx.doi.org/10.3390/app12147073.

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The sparse matrix–vector product (SpMV), considered one of the seven dwarfs (numerical methods of significance), is essential in high-performance real-world scientific and analytical applications requiring solution of large sparse linear equation systems, where SpMV is a key computing operation. As the sparsity patterns of sparse matrices are unknown before runtime, we used machine learning-based performance optimization of the SpMV kernel by exploiting the structure of the sparse matrices using the Block Compressed Sparse Row (BCSR) storage format. As the structure of sparse matrices varies a

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Zeng, Guangsen, and Yi Zou. "Leveraging Memory Copy Overlap for Efficient Sparse Matrix-Vector Multiplication on GPUs." Electronics 12, no. 17 (2023): 3687. http://dx.doi.org/10.3390/electronics12173687.

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Sparse matrix-vector multiplication (SpMV) is central to many scientific, engineering, and other applications, including machine learning. Compressed Sparse Row (CSR) is a widely used sparse matrix storage format. SpMV using the CSR format on GPU computing platforms is widely studied, where the access behavior of GPU is often the performance bottleneck. The Ampere GPU architecture recently from NVIDIA provides a new asynchronous memory copy instruction, memcpy_async, for more efficient data movement in shared memory. Leveraging the capability of this new memcpy_async instruction, we first prop

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Wang, Zhi, and Sinan Fang. "Three-Dimensional Inversion of Borehole-Surface Resistivity Method Based on the Unstructured Finite Element." International Journal of Antennas and Propagation 2021 (September 24, 2021): 1–13. http://dx.doi.org/10.1155/2021/5154985.

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The electromagnetic wave signal from the electromagnetic field source generates induction signals after reaching the target geological body through the underground medium. The time and spatial distribution rules of the artificial or the natural electromagnetic fields are obtained for the exploration of mineral resources of the subsurface and determining the geological structure of the subsurface to solve the geological problems. The goal of electromagnetic data processing is to suppress the noise and improve the signal-to-noise ratio and the inversion of resistivity data. Inversion has always

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Sreenivasulu, P., and S. Varadarajan. "An Efficient Lossless ROI Image Compression Using Wavelet-Based Modified Region Growing Algorithm." Journal of Intelligent Systems 29, no. 1 (2018): 1063–78. http://dx.doi.org/10.1515/jisys-2018-0180.

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Abstract Nowadays, medical imaging and telemedicine are increasingly being utilized on a huge scale. The expanding interest in storing and sending medical images brings a lack of adequate memory spaces and transmission bandwidth. To resolve these issues, compression was introduced. The main aim of lossless image compression is to improve accuracy, reduce the bit rate, and improve the compression efficiency for the storage and transmission of medical images while maintaining an acceptable image quality for diagnosis purposes. In this paper, we propose lossless medical image compression using wa

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Mysior, Marek, Paweł Stępień, and Sebastian Koziołek. "Modeling and Experimental Validation of Compression and Storage of Raw Biogas." Processes 8, no. 12 (2020): 1556. http://dx.doi.org/10.3390/pr8121556.

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A significant challenge in sustainability and development of energy systems is connected with limited diversity and availability of fuels, especially in rural areas. A potential solution to this problem is compression, transport, and storage of raw biogas, that would increase diversity and availability of energy sources in remote areas. The aim of this study was to perform experimental research on raw biogas compression concerning biogas volume that can be stored in a cylinder under the pressure of 20 MPa and to compare obtained results with numerical models used to describe the state of gas a

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Mostert, Clemens, Berit Ostrander, Stefan Bringezu, and Tanja Kneiske. "Comparing Electrical Energy Storage Technologies Regarding Their Material and Carbon Footprint." Energies 11, no. 12 (2018): 3386. http://dx.doi.org/10.3390/en11123386.

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The need for electrical energy storage technologies (EEST) in a future energy system, based on volatile renewable energy sources is widely accepted. The still open question is which technology should be used, in particular in such applications where the implementation of different storage technologies would be possible. In this study, eight different EEST were analysed. The comparative life cycle assessment focused on the storage of electrical excess energy from a renewable energy power plant. The considered EEST were lead-acid, lithium-ion, sodium-sulphur, vanadium redox flow and stationary s

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Hasugian, Paska Marto, and Rizki Manullang. "File Compression Application Design Using Half Byte Algorithm." Jurnal Info Sains : Informatika dan Sains 11, no. 2 (2021): 18–21. http://dx.doi.org/10.54209/infosains.v11i2.43.

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The need for large data storage capacity seems increasingly important. This need is caused by the data that must be stored more and more. The storage is not allocated in one place only. But they will also store data or files elsewhere as data backups. How much storage capacity must be provided to hold all the data. Half Byte algorithm is one of the data compression algorithms. The Half Byte algorithm utilizes four left bits that are often the same sequentially especially text files. When the first four bits of the character are received in a row seven or more times, the algorithm compresses th

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Du, Jiang, Bin Lang Chen, and Zeng Qin. "The Application of Compound Document Storage Technology in Forensic Analysis System." Applied Mechanics and Materials 610 (August 2014): 756–59. http://dx.doi.org/10.4028/www.scientific.net/amm.610.756.

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Due to the special nature of electronic data, we need to create a complete copy of the raw disks before computer forensics. There is two ways to create a copy. One is copy from Disk to disk, another is disk mirroring. The former capacity of the copy disk has fixed while image file of the latter is highly compressed already. Neither any of them can add the forensic analysis evidence; it will be handed over to the court as a whole set of evidence. This will affect the completeness and admissibility of the evidence. In this thesis, we will take the disk mirroring and storage technology into consi

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Chlopkowski, Marek, Maciej Antczak, Michal Slusarczyk, Aleksander Wdowinski, Michal Zajaczkowski, and Marta Kasprzak. "High-order statistical compressor for long-term storage of DNA sequencing data." RAIRO - Operations Research 50, no. 2 (2016): 351–61. http://dx.doi.org/10.1051/ro/2015039.

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Shin, Hyun Kyu, and Sung Kyu Ha. "A Review on the Cost Analysis of Hydrogen Gas Storage Tanks for Fuel Cell Vehicles." Energies 16, no. 13 (2023): 5233. http://dx.doi.org/10.3390/en16135233.

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The most practical way of storing hydrogen gas for fuel cell vehicles is to use a composite overwrapped pressure vessel. Depending on the driving distance range and power requirement of the vehicles, there can be various operational pressure and volume capacity of the tanks, ranging from passenger vehicles to heavy-duty trucks. The current commercial hydrogen storage method for vehicles involves storing compressed hydrogen gas in high-pressure tanks at pressures of 700 bar for passenger vehicles and 350 bar to 700 bar for heavy-duty trucks. In particular, hydrogen is stored in rapidly refillab

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Pope, Gunnar C., and Ryan J. Halter. "Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications ‡." Sensors 19, no. 11 (2019): 2450. http://dx.doi.org/10.3390/s19112450.

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While modern low-power microcontrollers are a cornerstone of wearable physiological sensors, their limited on-chip storage typically makes peripheral storage devices a requirement for long-term physiological sensing—significantly increasing both size and power consumption. Here, a wearable biosensor system capable of long-term recording of physiological signals using a single, 64 kB microcontroller to minimize sensor size and improve energy performance is described. Electrodermal (EDA) signals were sampled and compressed using a multiresolution wavelet transformation to achieve long-term stora

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Li, Daoyuan, Tegawende F. Bissyande, Jacques Klein, and Yves Le Traon. "Time Series Classification with Discrete Wavelet Transformed Data." International Journal of Software Engineering and Knowledge Engineering 26, no. 09n10 (2016): 1361–77. http://dx.doi.org/10.1142/s0218194016400088.

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Time series mining has become essential for extracting knowledge from the abundant data that flows out from many application domains. To overcome storage and processing challenges in time series mining, compression techniques are being used. In this paper, we investigate the loss/gain of performance of time series classification approaches when fed with lossy-compressed data. This extended empirical study is essential for reassuring practitioners, but also for providing more insights on how compression techniques can even be effective in smoothing and reducing noise in time series data. From a

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Liu, Jing, Xiaoqing Tian, Jiayuan Jiang, and Kaiyu Huang. "Distributed Compressed Sensing Based Ground Moving Target Indication for Dual-Channel SAR System." Sensors 18, no. 7 (2018): 2377. http://dx.doi.org/10.3390/s18072377.

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The dual-channel synthetic aperture radar (SAR) system is widely applied in the field of ground moving-target indication (GMTI). With the increase of the imaging resolution, the resulting substantial raw data samples increase the transmission and storage burden. We tackle the problem by adopting the joint sparsity model 1 (JSM-1) in distributed compressed sensing (DCS) to exploit the correlation between the two channels of the dual-channel SAR system. We propose a novel algorithm, namely the hierarchical variational Bayesian based distributed compressed sensing (HVB-DCS) algorithm for the JSM-

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Rohr, David. "Usage of GPUs in ALICE Online and Offline processing during LHC Run 3." EPJ Web of Conferences 251 (2021): 04026. http://dx.doi.org/10.1051/epjconf/202125104026.

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ALICE will significantly increase its Pb–Pb data taking rate from the 1 kHz of triggered readout in Run 2 to 50 kHz of continuous readout for LHC Run 3. Updated tracking detectors are installed for Run 3 and a new twophase computing strategy is employed. In the first synchronous phase during the data taking, the raw data is compressed for storage to an on-site disk buffer and the required data for the detector calibration is collected. In the second asynchronous phase the compressed raw data is reprocessed using the final calibration to produce the final reconstruction output. Traditional CPUs

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Et. al., G. Megala,. "State-Of-The-Art In Video Processing: Compression, Optimization And Retrieval." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (2021): 1256–72. http://dx.doi.org/10.17762/turcomat.v12i5.1793.

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Video compression plays a vital role in the modern social media networking with plethora of multimedia applications. It empowers transmission medium to competently transfer videos and enable resources to store the video efficiently. Nowadays high-resolution video data are transferred through the communication channel having high bit rate in order to send multiple compressed videos. There are many advances in transmission ability, efficient storage ways of these compressed video where compression is the primary task involved in multimedia services. This paper summarizes the compression standard

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Vijaykumar, Padmaja, and Jeevan K. Mani. "Face Recognition with Frame size reduction and DCT compression using PCA algorithm." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (2021): 168. http://dx.doi.org/10.11591/ijeecs.v22.i1.pp168-178.

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<p><span>Face recognition has become a very important study of research because it has a variety of applications in research field such as human computer interaction, pattern recognition (PR). A successful face recognition procedure, be it mathematical or numerical, depends on the particular choice of the features used by the classifier. Feature selection in pattern recognition consists of the derivation of salient features present in the raw input data in order to reduce the amount of data used for classification. For the successful face recognition, the database images must have

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Crosa, G., F. Pittaluga, A. Trucco, F. Beltrami, A. Torelli, and F. Traverso. "Heavy-Duty Gas Turbine Plant Aerothermodynamic Simulation Using Simulink." Journal of Engineering for Gas Turbines and Power 120, no. 3 (1998): 550–56. http://dx.doi.org/10.1115/1.2818182.

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This paper presents a physical simulator for predicting the off-design and dynamic behavior of a single shaft heavy-duty gas turbine plant, suitable for gas-steam combined cycles. The mathematical model, which is nonlinear and based on the lumped parameter approach, is described by a set of first-order differential and algebraic equations. The plant components are described adding to their steady-state characteristics the dynamic equations of mass, momentum, and energy balances. The state variables are mass flow rates, static pressures, static temperatures of the fluid, wall temperatures, and

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Pankiraj, Jeya Bright, Vishnuvarthanan Govindaraj, Yudong Zhang, Pallikonda Rajasekaran Murugan, and Anisha Milton. "Development of Scalable Coding of Encrypted Images Using Enhanced Block Truncation Code." Webology 19, no. 1 (2022): 1620–39. http://dx.doi.org/10.14704/web/v19i1/web19109.

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Only few researchers are reported on scalable coding of encrypted images, and it is an important area of research. In this paper, a novel method of scalable coding of encrypted images using Enhanced Block Truncation Code (EBTC) has been proposed. The raw image is compressed using EBTC and then encrypted using the pseudo-random number (PSRN) at the transmitter and the Key is disseminated to the receiver. The transmitted image is decrypted at the receiver by using the PSRN key. Finally, the output image is constructed using EBTC, scaled by scaling factor 2 and Bilinear Interpolation Technique. T

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Huang, Y. S., G. Q. Zhou, T. Yue, et al. "VECTOR AND RASTER DATA LAYERED FUSION AND 3D VISUALIZATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 8, 2020): 1127–34. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-1127-2020.

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Abstract. Although contemporary geospatial science has made great progress, spatial data fusion of vector and raster data is still a problem in the geoinformation science environment. In order to solve the problem, this paper proposes a method which merges vector and raster data. Firstly, the row and column numbers of the raster data, and the X, Y values of the vector data are represented by Morton code in the C++ environment, respectively. Secondly, we establish the the raster data table and the vector data table in the Oracle database to store the vector data and the raster data. Third, this

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Hussain, D. Mansoor, D. Surendran, and A. Benazir Begum. "Feature Extraction in JPEG domain along with SVM for Content Based Image Retrieval." International Journal of Engineering & Technology 7, no. 2.19 (2018): 1. http://dx.doi.org/10.14419/ijet.v7i2.19.11656.

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Content Based Image Retrieval (CBIR) applies computer vision methods for image retreival purposes from the databases. It is majorly based on the user query, which is in visual form rather than the traditional text form. CBIR is applied in different fields extending from surveillance to remote sensing, E-purchase, medical image processing, security systems to historical research and many others. JPEG, a very commonly used method of lossy compression is used to reduce the size of the image before being stored or transmitted. Almost every digital camera in the market are storing the captured imag

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Huang, Lan, Jia Zeng, Shiqi Sun, Wencong Wang, Yan Wang, and Kangping Wang. "Coarse-Grained Pruning of Neural Network Models Based on Blocky Sparse Structure." Entropy 23, no. 8 (2021): 1042. http://dx.doi.org/10.3390/e23081042.

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Deep neural networks may achieve excellent performance in many research fields. However, many deep neural network models are over-parameterized. The computation of weight matrices often consumes a lot of time, which requires plenty of computing resources. In order to solve these problems, a novel block-based division method and a special coarse-grained block pruning strategy are proposed in this paper to simplify and compress the fully connected structure, and the pruned weight matrices with a blocky structure are then stored in the format of Block Sparse Row (BSR) to accelerate the calculatio

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Weihe, Thomas, Robert Wagner, Uta Schnabel, et al. "Microbial Control of Raw and Cold-Smoked Atlantic Salmon (Salmo salar) through a Microwave Plasma Treatment." Foods 11, no. 21 (2022): 3356. http://dx.doi.org/10.3390/foods11213356.

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The control of the pathogenic load on foodstuffs is a key element in food safety. Particularly, seafood such as cold-smoked salmon is threatened by pathogens such as Salmonella sp. or Listeria monocytogenes. Despite strict existing hygiene procedures, the production industry constantly demands novel, reliable methods for microbial decontamination. Against that background, a microwave plasma-based decontamination technique via plasma-processed air (PPA) is presented. Thereby, the samples undergo two treatment steps, a pre-treatment step where PPA is produced when compressed air flows over a pla

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Shan, Nanliang, Xinghua Xu, Xianqiang Bao, and Shaohua Qiu. "Fast Fault Diagnosis in Industrial Embedded Systems Based on Compressed Sensing and Deep Kernel Extreme Learning Machines." Sensors 22, no. 11 (2022): 3997. http://dx.doi.org/10.3390/s22113997.

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With the complexity and refinement of industrial systems, fast fault diagnosis is crucial to ensuring the stable operation of industrial equipment. The main limitation of the current fault diagnosis methods is the lack of real-time performance in resource-constrained industrial embedded systems. Rapid online detection can help deal with equipment failures in time to prevent equipment damage. Inspired by the ideas of compressed sensing (CS) and deep extreme learning machines (DELM), a data-driven general method is proposed for fast fault diagnosis. The method contains two modules: data sampling

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Miquel, Jonathan, Laurent Latorre, and Simon Chamaillé-Jammes. "Energy-Efficient Audio Processing at the Edge for Biologging Applications." Journal of Low Power Electronics and Applications 13, no. 2 (2023): 30. http://dx.doi.org/10.3390/jlpea13020030.

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Biologging refers to the use of animal-borne recording devices to study wildlife behavior. In the case of audio recording, such devices generate large amounts of data over several months, and thus require some level of processing automation for the raw data collected. Academics have widely adopted offline deep-learning-classification algorithms to extract meaningful information from large datasets, mainly using time-frequency signal representations such as spectrograms. Because of the high deployment costs of animal-borne devices, the autonomy/weight ratio remains by far the fundamental concer

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Gudodagi, Raveendra, and R. Venkata Siva Reddy. "Security Provisioning and Compression of Diverse Genomic Data based on Advanced Encryption Standard (AES) Algorithm." International Journal of Biology and Biomedical Engineering 15 (May 14, 2021): 104–12. http://dx.doi.org/10.46300/91011.2021.15.14.

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Compression of genomic data has gained enormous momentum in recent years because of advances in technology, exponentially growing health concerns, and government funding for research. Such advances have driven us to personalize public health and medical care. These pose a considerable challenge for ubiquitous computing in data storage. One of the main issues faced by genomic laboratories is the 'cost of storage' due to the large data file of the human genome (ranging from 30 GB to 200 GB). Data preservation is a set of actions meant to protect data from unauthorized access or changes. There ar

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Pathak, Sudipta, and Sanguthevar Rajasekaran. "RETRACTED: LFQC: a lossless compression algorithm for FASTQ files." Bioinformatics 35, no. 9 (2014): e1-e7. http://dx.doi.org/10.1093/bioinformatics/btu701.

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Abstract Motivation Next-generation sequencing (NGS) technologies have revolutionized genomic research by reducing the cost of whole-genome sequencing. One of the biggest challenges posed by modern sequencing technology is economic storage of NGS data. Storing raw data is infeasible because of its enormous size and high redundancy. In this article, we address the problem of storage and transmission of large Fastq files using innovative compression techniques. Results We introduce a new lossless non-reference-based fastq compression algorithm named lossless FastQ compressor. We have compared ou

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Wang, Rongjie, Junyi Li, Yang Bai, Tianyi Zang, and Yadong Wang. "BdBG: a bucket-based method for compressing genome sequencing data with dynamic de Bruijn graphs." PeerJ 6 (October 19, 2018): e5611. http://dx.doi.org/10.7717/peerj.5611.

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Dramatic increases in data produced by next-generation sequencing (NGS) technologies demand data compression tools for saving storage space. However, effective and efficient data compression for genome sequencing data has remained an unresolved challenge in NGS data studies. In this paper, we propose a novel alignment-free and reference-free compression method, BdBG, which is the first to compress genome sequencing data with dynamic de Bruijn graphs based on the data after bucketing. Compared with existing de Bruijn graph methods, BdBG only stored a list of bucket indexes and bifurcations for

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Kullaa, Jyrki. "Damage Detection and Localization under Variable Environmental Conditions Using Compressed and Reconstructed Bayesian Virtual Sensor Data." Sensors 22, no. 1 (2021): 306. http://dx.doi.org/10.3390/s22010306.

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Structural health monitoring (SHM) with a dense sensor network and repeated vibration measurements produces lots of data that have to be stored. If the sensor network is redundant, data compression is possible by storing the signals of selected Bayesian virtual sensors only, from which the omitted signals can be reconstructed with higher accuracy than the actual measurement. The selection of the virtual sensors for storage is done individually for each measurement based on the reconstruction accuracy. Data compression and reconstruction for SHM is the main novelty of this paper. The stored and

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Giada, Giuffrida, Rosa Caponetto, and Francesco Nocera. "Hygrothermal Properties of Raw Earth Materials: A Literature Review." Sustainability 11, no. 19 (2019): 5342. http://dx.doi.org/10.3390/su11195342.

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Raw earth historic and contemporary architectures are renowned for their good environmental properties of recyclability and low embodied energy along the production process. Earth massive walls are universally known to be able to regulate indoor thermal and hygroscopic conditions containing energy consumptions, creating comfortable interior spaces with a low carbon footprint. Therefore, earth buildings are de facto green buildings. As a result of this, some earthen technologies have been rediscovered and implemented to be adapted to the contemporary building production sector. Nevertheless, th

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Wilke, Daniel N., Paul W. Cleary, and Nicolin Govender. "From discrete element simulation data to process insights." EPJ Web of Conferences 249 (2021): 15001. http://dx.doi.org/10.1051/epjconf/202124915001.

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Industrial-scale discrete element simulations typically generate Gigabytes of data per time step, which implies that even opening a single file may require 5 - 15 minutes on conventional magnetic storage devices. Data science’s inherent multi-disciplinary nature makes the extraction of useful information challenging, often leading to undiscovered details or new insights. This study explores the potential of statistical learning to identify potential regions of interest for large scale discrete element simulations. We demonstrate that our in-house knowledge discovery and data mining system (KDS

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Smith, H. J., S. J. Bakke, B. Smevik, et al. "Comparison of 12-Bit and 8-Bit Gray Scale Resolution in Mr Imaging of the CNS." Acta Radiologica 33, no. 6 (1992): 505–11. http://dx.doi.org/10.1177/028418519203300601.

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A reduction in gray scale resolution of digital images from 12 to 8 bits per pixel usually means halving the storage space needed for the images. Theoretically, important diagnostic information may be lost in the process. We compared the sensitivity and specificity achieved by 4 radiologists in reading laser-printed films of original 12-bit MR images and cathode ray tube displays of the same images which had been compressed to 8 bits per pixel using a specially developed computer program. Receiver operating characteristic (ROC) curves showed no significant differences between film reading and

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Santoyo-Garcia, Hector, Eduardo Fragoso-Navarro, Rogelio Reyes-Reyes, Clara Cruz-Ramos, and Mariko Nakano-Miyatake. "Visible Watermarking Technique Based on Human Visual System for Single Sensor Digital Cameras." Security and Communication Networks 2017 (2017): 1–18. http://dx.doi.org/10.1155/2017/7903198.

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In this paper we propose a visible watermarking algorithm, in which a visible watermark is embedded into the Bayer Colour Filter Array (CFA) domain. The Bayer CFA is the most common raw image representation for images captured by single sensor digital cameras equipped in almost all mobile devices. In proposed scheme, the captured image is watermarked before it is compressed and stored in the storage system. Then this method enforces the rightful ownership of the watermarked image, since there is no other version of the image rather than the watermarked one. We also take into consideration the

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Silitonga, Parasian D. P., and Irene Sri Morina. "Compression and Decompression of Audio Files Using the Arithmetic Coding Method." Scientific Journal of Informatics 6, no. 1 (2019): 73–81. http://dx.doi.org/10.15294/sji.v6i1.17839.

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Audio file size is relatively larger when compared to files with text format. Large files can cause various obstacles in the form of large space requirements for storage and a long enough time in the shipping process. File compression is one solution that can be done to overcome the problem of large file sizes. Arithmetic coding is one algorithm that can be used to compress audio files. The arithmetic coding algorithm encodes the audio file and changes one row of input symbols with a floating point number and obtains the output of the encoding in the form of a number of values greater than 0 a

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Akoguz, A., S. Bozkurt, A. A. Gozutok, et al. "COMPARISON OF OPEN SOURCE COMPRESSION ALGORITHMS ON VHR REMOTE SENSING IMAGES FOR EFFICIENT STORAGE HIERARCHY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4 (June 10, 2016): 3–9. http://dx.doi.org/10.5194/isprs-archives-xli-b4-3-2016.

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High resolution level in satellite imagery came with its fundamental problem as big amount of telemetry data which is to be stored after the downlink operation. Moreover, later the post-processing and image enhancement steps after the image is acquired, the file sizes increase even more and then it gets a lot harder to store and consume much more time to transmit the data from one source to another; hence, it should be taken into account that to save even more space with file compression of the raw and various levels of processed data is a necessity for archiving stations to save more space. L

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Zhang, Yang, Yuandong Liu, and Lee D. Han. "Real-Time Piecewise Regression." Transportation Research Record: Journal of the Transportation Research Board 2643, no. 1 (2017): 9–18. http://dx.doi.org/10.3141/2643-02.

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Ubiquitous sensing technologies make big data a trendy topic and a favored approach in transportation studies and applications, but the increasing volumes of data sets present remarkable challenges to data collection, storage, transfer, visualization, and processing. Fundamental aspects of big data in transportation are discussed, including how many data to collect and how to collect data effectively and economically. The focus is GPS trajectory data, which are used widely in this domain. An incremental piecewise regression algorithm is used to evaluate and compress GPS locations as they are p

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Dritsas, Elias, Andreas Kanavos, Maria Trigka, Spyros Sioutas, and Athanasios Tsakalidis. "Storage Efficient Trajectory Clustering and k-NN for Robust Privacy Preserving Spatio-Temporal Databases." Algorithms 12, no. 12 (2019): 266. http://dx.doi.org/10.3390/a12120266.

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The need to store massive volumes of spatio-temporal data has become a difficult task as GPS capabilities and wireless communication technologies have become prevalent to modern mobile devices. As a result, massive trajectory data are produced, incurring expensive costs for storage, transmission, as well as query processing. A number of algorithms for compressing trajectory data have been proposed in order to overcome these difficulties. These algorithms try to reduce the size of trajectory data, while preserving the quality of the information. In the context of this research work, we focus on

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Lee, Dohyeon, and Giltae Song. "FastqCLS: a FASTQ compressor for long-read sequencing via read reordering using a novel scoring model." Bioinformatics 38, no. 2 (2021): 351–56. http://dx.doi.org/10.1093/bioinformatics/btab696.

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Abstract Motivation Over the past decades, vast amounts of genome sequencing data have been produced, requiring an enormous level of storage capacity. The time and resources needed to store and transfer such data cause bottlenecks in genome sequencing analysis. To resolve this issue, various compression techniques have been proposed to reduce the size of original FASTQ raw sequencing data, but these remain suboptimal. Long-read sequencing has become dominant in genomics, whereas most existing compression methods focus on short-read sequencing only. Results We designed a compression algorithm b

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Rekha, Bhanu, and Ravi Kumar AV. "High Definition Video Compression Using Saliency Features." Indonesian Journal of Electrical Engineering and Computer Science 7, no. 3 (2017): 708. http://dx.doi.org/10.11591/ijeecs.v7.i3.pp708-717.

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<p>High Definition (HD) devices requires HD-videos for the effective uses of HD devices. However, it consists of some issues such as high storage capacity, limited battery power of high definition devices, long encoding time, and high computational complexity when it comes to the transmission, broadcasting and internet traffic. Many existing techniques consists these above-mentioned issues. Therefore, there is a need of an efficient technique, which reduces unnecessary amount of space, provides high compression rate and requires low bandwidth spectrum. Therefore, in the paper we have int

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