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In this paper, we propose a protocol for quantum secure multiparty summation and privacy sorting based on inverse quantum Fourier transform. The protocol allows multiple participants to obtain the summation and sorting of their secrets without revealing their private inputs. Each participant in the protocol encodes his/her own secret input into the phase of the d-level entangled state of n particles by means of a phase transformation operator and an inverse quantum Fourier transform. Finally, all participants perform measurements and jointly calculate the sum of all the secret data, meanwhile deriving their own rankings of the private inputs based on the final results. Compared to the existing similar quantum summation and sorting protocols, this protocol requires only a one-time particle transmission and does not require private key sequences to encrypt secret information, resulting in higher quantum efficiency. The participants can further obtain the ranking of their secret inputs by themselves. The credibility of the protocol is demonstrated in security analysis and simulation. [ABSTRACT FROM AUTHOR]

Digital twins have attracted more and more attention in the past few years. To put digital twins into practice, a large number of modeling approaches have been proposed, vast amounts of data have been collected, and their accuracy has been improving. However, current research has paid insufficient attention to the multi-scale features of the shop floor, which hinders the effective application of the digital twin shop floor. To address the problem of how to achieve effective multi-level and multi-dimensional fusion of digital twin models with production process data, this paper first proposes a structured data modeling framework for sorting out all the production process data collected in real-time; and then proposes a multi-level fusion framework for supporting the fusion of real-time data and twin models from the unit level to the system level. The method judges the parsed received data streams through the full-factor semanticization framework, and at the same time fuses the parsed data streams with the constructed full-factor twin model from multiple dimensions and layers, forming a twin model fusion method with real-time data streams as the blood and twin model as the skeleton. Finally, the micro-assembly-based production shop environment is selected as a case study to verify the correctness and feasibility of the proposed data grooming framework, data, and model fusion method. [ABSTRACT FROM AUTHOR]

Designing an efficient data sorting algorithm that requires less time and space complexity is essential for computer science, different engineering disciplines, data mining systems, wireless networks, and the Internet of things. This paper proposes a general low-complex data sorting framework that distinguishes the sorted or similar data, makes independent subarrays approximately in equal length, and sorts the subarrays’ data using one of the popular comparison-based sorting algorithms. Two frameworks, one for serial realization and another for parallel realization, are proposed. The time complexity analyses of the proposed framework demonstrate an improvement compared to the conventional Merge and Quick sorting algorithms. Following complexity analysis, the simulation results indicate slight improvements in the elapsed time and the number of swaps of the proposed serial Merge-based and Quick-based frameworks compared to the conventional ones for low/high variance integer/non-integer data sets, in different data sizes and the number of divisions. It is about $(1-1.6\%)$ to $(3.5-4\%)$ and $(0.3-1.8\%)$ to $(2-4\%)$ improvements in the elapsed times for 1, 2, 3, and 4 divisions, respectively for small and very large data sets in Merge-based and Quick-based scenarios. Although these improvements in serial realization are minor, making independent low-variance subarrays allows the sorted components to be extracted sequentially and gradually before the end of the sorting process. Also, it proposes a general framework for parallelizing conventional sorting algorithms using non-connected (independent) or connected (dependent) multi-core structures. As the second experiment, the numerical analyses that compare the results of the parallel realization of the proposed framework to the serial one in 1, 2, 3, and 4 divisions, show a speedup factor of $(2-4)$ for small to $(2-16)$ for very large data sets. The third experiment shows the effectiveness of the proposed parallel framework to the parallel sorting based on the random-access machine model. Finally, we prove that the mean-based pivot is as efficient as the median-based and much better than the random pivot for making subarrays of approximately equal length.

This paper presents a novel SRAM-based architecture of a data structure that represents a set of multiple priority queues that can be implemented in FPGA or ASIC. The proposed architecture is based on shift registers, systolic arrays and SRAM memories. Such architecture, called MultiQueue, is optimized for minimum chip area costs, which leads to lower energy consumption too. The MultiQueue architecture has constant time complexity, constant critical path length and constant latency. Therefore, it is highly predictable and very suitable for real-time systems too. The proposed architecture was verified using a simplified version of UVM and applying millions of instructions with randomly generated input values. Achieved FPGA synthesis results are presented and discussed. These results show significant savings in FPGA Look-Up Tables consumption in comparison to existing solutions. More than 63% of Look-Up Tables can be saved using the MultiQueue architecture instead of the existing priority queues. [ABSTRACT FROM AUTHOR]

This paper presents a novel SRAM-based architecture of a data structure that represents a set of multiple priority queues that can be implemented in FPGA or ASIC. The proposed architecture is based on shift registers, systolic arrays and SRAM memories. Such architecture, called MultiQueue, is optimized for minimum chip area costs, which leads to lower energy consumption too. The MultiQueue architecture has constant time complexity, constant critical path length and constant latency. Therefore, it is highly predictable and very suitable for real-time systems too. The proposed architecture was verified using a simplified version of UVM and applying millions of instructions with randomly generated input values. Achieved FPGA synthesis results are presented and discussed. These results show significant savings in FPGA Look-Up Tables consumption in comparison to existing solutions. More than 63% of Look-Up Tables can be saved using the MultiQueue architecture instead of the existing priority queues.

The paper presents a comparison of the two languages Python and R related to the classification tools and demonstrates the differences in their syntax and graphical output. It indicates the functionality of R and Python packages {dendextend} and scipy.cluster as effective tools for the dendrogram modelling by the algorithms of sorting and ranking datasets. R and Python programming languages have been tested on a sample dataset including marine geological measurements. The work aims to detect how bathymetric data change along the 25 bathymetric profiles digitized across the Mariana Trench. The methodology includes performed hierarchical cluster analysis with dendrograms and plotted clustermap with marginal dendrograms. The statistical libraries include Matplotlib, SciPy, NumPy, Pandas by Python and {dendextend}, {pvclust}, {magrittr} by R. The dendrograms were compared by the model-simulated clusters of the bathymetric ranges. The results show three distinct groups of the profiles sorted by the elevation ranges with maximal depths detected in a group of profiles 19-21. The dendrogram visualization in a cluster analysis demonstrates the effective representation of the data sorting, grouping and classifying by the machine learning algorithms. The programming codes presented in this study enable to sort a dataset in a similar research aimed to group data based on the similarity of attributes. Effective visualization by dendrograms is a useful modelling tool for the geospatial management where data ranking is required. Plotting dendrograms by R, comparing to Python, presented functional and sophisticated algorithms, refined design control and fine graphical data output. The interdisciplinary nature of this work consists in application of the coding algorithms for spatial data analysis. [ABSTRACT FROM AUTHOR]

Acoustic telemetry data often contain erroneous detections, which need to be addressed before further analysis. It is important that this process is systematic, standardised and reproducible.The r package actel provides a systematic conditional pipeline to filter and analyse acoustic telemetry data in a reproducible fashion, for animals moving between receiver arrays.actel outputs a standardised report containing the main results and the steps taken by the user, in addition to summary information for each animal and other content such as efficiency estimations for each receiver array.actel improves the reproducibility of the acoustic data filtering and analysis, and simplifies the integration of acoustic data sets originating from different studies (in time and/or in space). [ABSTRACT FROM AUTHOR]

In order to optimize the game experience, the game platform often provides decision-making AI hosting functions, which causes the platform to face low data quality when presenting images to users. The log mixes the AI and the decision-making behavior made by the player, and sometimes there is a lack of behavior. This paper proposed a behavior extraction and analysis method for decision-oriented hosting applications. Through the behavior feature extraction method based on heuristic rules, this method extracted the player' s autonomic behavior from the log. Finally, it used a variety of portrait methods to analyze and compare the results on the original data set and the reduced data set respectively. It is concluded that the method can greatly reduce the amount of data while retaining the player's behavior characteristics. [ABSTRACT FROM AUTHOR]

The paper presents a comparison of the two languages Python and R related to the classification tools and demonstrates the differences in their syntax and graphical output. It indicates the functionality of R and Python packages {dendextend} and scipy.cluster as effective tools for the dendrogram modelling by the algorithms of sorting and ranking datasets. R and Python programming languages have been tested on a sample dataset including marine geological measurements. The work aims to detect how bathymetric data change along the 25 bathymetric profiles digitized across the Mariana Trench. The methodology includes performed hierarchical cluster analysis with dendrograms and plotted clustermap with marginal dendrograms. The statistical libraries include Matplotlib, SciPy, NumPy, Pandas by Python and {dendextend}, {pvclust}, {magrittr} by R. The dendrograms were compared by the model-simulated clusters of the bathymetric ranges. The results show three distinct groups of the profiles sorted by the elevation ranges with maximal depths detected in a group of profiles 19-21. The dendrogram visualization in a cluster analysis demonstrates the effective representation of the data sorting, grouping and classifying by the machine learning algorithms. The programming codes presented in this study enable to sort a dataset in a similar research aimed to group data based on the similarity of attributes. Effective visualization by dendrograms is a useful modelling tool for the geospatial management where data ranking is required. Plotting dendrograms by R, comparing to Python, presented functional and sophisticated algorithms, refined design control and fine graphical data output. The interdisciplinary nature of this work consists in application of the coding algorithms for spatial data analysis. [ABSTRACT FROM AUTHOR]

The issue of productivity and energy is an important objective of the optimization of parallel applications. The size of the problem for a large number of data on multiprocessor platforms forces the use of parallel algorithms. Efficient management of large memories using modern processors in Big data processing requires innovative techniques and efficient algorithms. For years have found the results of tests conducted on methods for use in various computing environments and improvements. This article shows the energy consumption analysis by parallel sorting algorithms. Sort algorithms are used in information systems and databases, to select and organize the information. The subject of this article is research into energy consumption and computational complexity for parallel sorting methods by merging compared to classic methods. The tests carried out confirm the reduction of energy consumption by using parallel sorting algorithms. The presented parallel fast sort and parallel modified merge sort for large task dimensions have less power consumption than classic methods and can be used successfully in NoSQL databases. [ABSTRACT FROM AUTHOR]

How to reduce the number of transmissions or prolong the lifetime of wireless sensor networks significantly has become a great challenge. Based on the spatio-temporal correlations of sensory data, in this paper, we propose a hierarchical adaptive spatio-temporal data compression (HASDC) scheme to address this issue. The proposed compression scheme explores the temporal correlation of original sensory data by employing the discrete cosine transform and adaptive threshold compression algorithm (ATCA). And then, the cluster head node explores the spatial correlation among the compressed temporal readings by utilizing discrete wavelet transform (DWT) and ATCA. The HASDC scheme obtains better recovery quality and compression ratio by combining data sorting, ATCA and spatio-temporal compression concept. At the same time, according to the correlation of sensory data and the adaptive threshold value, the HASDC scheme can adjust the compression ratio adaptively, thus it's applicable to different physical scenarios. Finally, the simulation results confirm that the transformed coefficients are more concentrated than the ones without introducing DWT, and the proposed scheme outperforms other spatio-temporal schemes in terms of compression and recovery performances. [ABSTRACT FROM AUTHOR]

Given a set of numerical values and a threshold, finding the minimum subset from the set such that its sum is not less than the threshold value, is termed as SelectSum problem which is widely applicable in data compression and image processing. In this article, we provide two new linear-time algorithms for the problem. Different from the current approaches that directly calling the Selection algorithms for partitioning on median elements, our proposed algorithms embed the Selection process and use cheap pivot elements for partitioning. The experimental tests indicate these two new algorithms are significant faster than the current existing algorithms on larger data sets. [ABSTRACT FROM AUTHOR]

Modern architectures make possible development in new algorithms for large data sets and distributed computing. The newly proposed versions can benefit both from faster computing on the multi core architectures, and intelligent programming techniques that use efficient procedures available in the latest programming studios. Frequently used algorithms to sort arrays of data in NoSQL databases is merge sort, where as NoSQL we understand any database without typical SQL programming interpreter. The author describes how to use the parallelization of the sorting processes for the modified method of sorting by merging for large data sets. The subject of this research is the claim that the parallelization of the sorting method is faster and beneficial for multi-core systems. Presented results show how the number of processors influences the sorting performance. The results are presented in theoretical assumptions and confirmed in practical benchmark tests. The method is compared to other sorting methods like quick sort, heap sort, and merge sort to show potential efficiency. [ABSTRACT FROM AUTHOR]

Graphics processing units (GPUs) have been increasingly used for general-purpose computation in recent years. The GPU accelerated applications are found in both scientific and commercial domains. Sorting is considered as one of the very important operations in many applications, so its efficient implementation is essential for the overall application performance. This paper represents an effort to analyze and evaluate the implementations of the representative sorting algorithms on the graphics processing units. Three sorting algorithms (Quicksort, Merge sort, and Radix sort) were evaluated on the Compute Unified Device Architecture (CUDA) platform that is used to execute applications on NVIDIA graphics processing units. Algorithms were tested and evaluated using an automated test environment with input datasets of different characteristics. Finally, the results of this analysis are briefly discussed.

Wireless sensor networks (WSNs) provide a promising approach to monitor the physical environments, to prolong the network lifetime by exploiting the mutual correlation among sensor readings has become a research focus. In this paper, we design a hierarchical network framework which guarantees layered-compression. Meanwhile, a data sorting-based adaptive spatial compression scheme (DS-ASCS) is proposed to explore the spatial correlation among signals. The proposed scheme reduces the amount of data transmissions and alleviates the network congestion. It also obtains high compression performance by sorting original sensor readings and selectively discarding the small coefficients in transformed matrix. Moreover, the compression ratio of this scheme varies according to the correlation among signals and the value of adaptive threshold, so the proposed scheme is adaptive to various deploying environments. Finally, the simulation results show that the energy of sorted data is more concentrated than the unsorted data, and the proposed scheme achieves higher reconstruction precision and compression ratio as compared with other spatial compression schemes. [ABSTRACT FROM AUTHOR]

The primary goal of the presented experiment was to judge the usefulness of FPGA technology in the sorting operation performed by computer systems. We were interested to see if it was possible to achieve better system performance and lower energy consumption when the CPU is supported by FPGA chips. The method of custom processing was applied. We proposed dedicated sorting hardware to increase performance and save energy. Our concept addresses High Throughput Computing (HTC) systems. The custom hardware approach was proposed because this technique is available in supercomputing infrastructures today. Another important issue of the work is that the hardware was programmed using High Level Language (HLL). As a semiconductor platform for hardware implementation the FPGA was chosen. We evaluated the efficiency of an FPGA based sorting processor that was programmed in Mitrion-C HLL. The FPGA approach was compared to the CPU approach in terms of efficiency and power consumption. [ABSTRACT FROM AUTHOR]

Two improved compressed sensing (CS)-based image reconstruction methods for MRI are proposed: prior estimate-based compressed sensing (PECS) and sensitivity encoding-based compressed sensing (SENSECS). PECS allows prior knowledge of the underlying image to be intrinsically incorporated in the image recovery process, extending the use of data sorting as first proposed by Adluru and DiBella (Int J Biomed Imaging 2008: 341648). It does so by rearranging the elements in the underlying image based on the magnitude information gathered from a prior image estimate, so that the underlying image can be recovered in a new form that exhibits a higher level of sparsity. SENSECS is an application of PECS in parallel imaging. In SENSECS, image reconstruction is carried out in two stages: SENSE and PECS, with the SENSE reconstruction being used as a image prior estimate in the following PECS reconstruction. SENSECS bypasses the conflict of sampling pattern design in directly applying CS recovery in multicoil data sets and exploits the complementary characteristics of SENSE-type and CS-type reconstructions, hence achieving better image reconstructions than using SENSE or CS alone. The characteristics of PECS and SENSECS are investigated using experimental data. Magn Reson Med, 2010. © 2010 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

This paper presents the VLSI architecture design of pipeline sorter which is suitable for the fast sorting of the continuous serial input data stream. By decomposing the Batcher’s merge-sort process into a network of compare-and-swap (C&S) operations, two different styles of pipeline architectures based on the feedback and feed-forward data shuffling modules can be first achieved. However, both architectures suffer the low hardware utilization due to the discrepancy of input sample rate and internal processing rate. Therefore, this paper further proposes a novel digit-serial pipeline sorter architecture by dividing the data into two sub-words. In addition, the most-significant half-word data are processed first in order to reduce the internal register overhead incurred in the C&S unit. Our experimental results show that about 50% saving of gate counts can be achieved by the digit-serial approach. [ABSTRACT FROM AUTHOR]

Circuit architecture for parallel data processing directly carried out on the hardware have been developed based on a high-functionality transistor, neuron MOSFET (neuMOS or νMOS for short). In the νMOS data sorting circuit, multiple analog input data are numbered in binary codes according to the order of their magnitudes after a single ramp voltage scan. A νMOS motion-vector detector has been developed for on-chip moving image processing based on x- and y-projection data. The circuit can find the movement of an image in two successive frames within a few 100 nsec. The projection-data-based motion detection algorithm has been tested by computer simulation. Test circuits were fabricated by a double-polysilicon CMOS process and basic operation of the circuits has been demonstrated. [ABSTRACT FROM AUTHOR]