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In low signal-to-noise ratio (SNR) environments, the traditional radar emitter recognition (RER) method struggles to recognize multiple radar emitter signals in parallel. This paper proposes a multi-label classification and recognition method for multiple radar-emitter modulation types based on a residual network. This method can quickly perform parallel classification and recognition of multi-modulation radar time-domain aliasing signals under low SNRs. First, we perform time-frequency analysis on the received signal to extract the normalized time-frequency image through the short-time Fourier transform (STFT). The time-frequency distribution image is then denoised using a deep normalized convolutional neural network (DNCNN). Secondly, the multi-label classification and recognition model for multi-modulation radar emitter time-domain aliasing signals is established, and learning the characteristics of radar signal time-frequency distribution image dataset to achieve the purpose of training model. Finally, time-frequency image is recognized and classified through the model, thus completing the automatic classification and recognition of the time-domain aliasing signal. Simulation results show that the proposed method can classify and recognize radar emitter signals of different modulation types in parallel under low SNRs.

This article presents a preliminary suitable sizing methodology for the design process of the powertrain architecture for a hybrid-electric propulsion system for ultra-light and general aviation aircraft. The main objective of this activity is to design and realize a prototype of a hybrid-electric propulsion system for Cessna 337 aircraft with a maximum take-off power of 134 kW. At the same mission, two operating strategies have been chosen, max recharge and max efficiency. The first one consists of the engine running at wide-open throttle to quickly charge the battery, while the second runs at minimum specific consumption to reduce consumption. The primary energy assessment has been conducted in all proposed propulsion configurations with the same aircraft, mission, and maximum take-off weight. The results also indicated that parallel hybrid propulsion shows a better compromise in terms of 10% energy saving, 4% CO2 reduction, and mission duration.

Cloud computing is an on-demand availability of computing resources. The current cloud computing environment uses different algorithms for its security; RC4 is one of them. However, due to the serial implementation of RC4 over cloud computing, the process of encryption and data transmission is badly affected by the factors of poor latency. This research is proposed to cover this factor by implementing a parallel RC4 algorithm in a cloud computing environment. The parallel RC4 model has been developed in web-based architecture relevant to the cloud computing environment. The method processed data simultaneously in four parallel pipelines encrypted through the RC4 algorithm. The proposed method used RC4 algorithms’ parallel implementation to encrypt and decrypt the data. The proposed method increases the efficiency of data encryption and decryption and transmission over a cloud environment using four pipelines at once. These four pipelines receive data and encrypt them using a key and then merge those four streams into a single cipher. These four pipelines enhance the speed of communication over cloud and solve the latency issue majorly. Data sending from node devices to cloud servers is divided into four equal streams and then encrypted using RC4 distinctly but parallel. Then after the roaming, the data are again first combined and then decrypted using the RC4 algorithm. The sample data sets are evaluated on the criteria of the starting time and ending time of the encryption and decryption of data. Results show that the proposed method is able to enhance the speed of the whole process up to average 3.7% due to the implementation of parallel technique and had reduced the time notably. The future work on this research involves enhancement of security process of RC4 algorithm since it can be breached easily.

We implement two novel algorithms for sparse-matrix dense-matrix multiplication (SpMM) on the GPU. Our algorithms expect the sparse input in the popular compressed-sparse-row (CSR) format and thus do not require expensive format conversion. While previous SpMM work concentrates on thread-level parallelism, we additionally focus on latency hiding with instruction-level parallelism and load-balancing. We show, both theoretically and experimentally, that the proposed SpMM is a better fit for the GPU than previous approaches. We identify a key memory access pattern that allows efficient access into both input and output matrices that is crucial to getting excellent performance on SpMM. By combining these two ingredients—(i) merge-based load-balancing and (ii) row-major coalesced memory access—we demonstrate a 4.1 × peak speedup and a 31.7% geomean speedup over state-of-the-art SpMM implementations on real-world datasets.

We implement two novel algorithms for sparse-matrix dense-matrix multiplication (SpMM) on the GPU. Our algorithms expect the sparse input in the popular compressed-sparse-row (CSR) format and thus do not require expensive format conversion. While previous SpMM work concentrates on thread-level parallelism, we additionally focus on latency hiding with instruction-level parallelism and load-balancing. We show, both theoretically and experimentally, that the proposed SpMM is a better fit for the GPU than previous approaches. We identify a key memory access pattern that allows efficient access into both input and output matrices that is crucial to getting excellent performance on SpMM. By combining these two ingredients—(i) merge-based load-balancing and (ii) row-major coalesced memory access—we demonstrate a 4.1 × peak speedup and a 31.7% geomean speedup over state-of-the-art SpMM implementations on real-world datasets.

The objective of this paper is to develop a reliability model for a two-unit parallel system using the concept of inspection. A single server is available to inspect the failed unit before its repair. Failed unit is replaced by new one if its repair is not possible. Semi-Markov process and regenerative techniques are used to derive the expressions for various reliability and economic measures such as mean time to system failure, availability, busy period analysis of server, expected number of visits by the server and profit function. The numerical values are also assigned to various parameters and costs for studying the graphical behavior of performance measures like MTSF, availability and profit of the developed models. The findings of the present study help the system developers to identify hazardous conditions, which further help in either correct them immediately or report them for corrective action before replacement of the system. [ABSTRACT FROM AUTHOR]

Sample rate conversion is an essential scheme used in almost every radio design. Supporting sampling rates higher than the clock rates require parallel processing. In this paper, we propose an algorithm for a sample rate converter (SRC) with multiple parallel output phases so that the conversion ratio can be a fixed rational number. Due to the structure of the proposed algorithm, it is suitable for embedded platforms which are restricted by their clock frequency but require very high sample rates. A dual phase output variant of the proposed algorithm is simulated with a 400 MHz input signal to perform a 15/8 conversion. The test and verification of the SRC algorithm is presented with the aid of a design example. A VLSI architecture of the dual phase output SRC is implemented on a Virtex-7 field-programmable gate array (FPGA) and results are presented. [ABSTRACT FROM AUTHOR]

Lattice-based cryptography (LBC) stands out as one of the most viable classes of quantum-resistant schemes. This brief explores a time-sharing approach, with different parallelism levels, for a crucial operation in LBC cryptosystems, i.e., polynomial multiplication. We also employ an innovative coefficient ordering method in our time-shared schoolbook polynomial multiplication (SPM) to combine the best of two worlds: design compactness and lower processing latency. Thus, our work offers a choice of design points with performance vs. resource trade-offs. Our fastest proposed design exhibits 80% and 57% reductions in LUTs and throughput, respectively, compared to the existing fully parallel SPM architecture (on Xilinx Ultrascale+), which lead to a 53% improvement in the area-time-product efficiency. Our smallest proposed design is more than $2.2\times $ faster than the existing low-cost parallel SPM architecture (on Xilinx Kintex-7) at the expense of 85% additional area resources. [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM) has received considerable attention due to its rapid learning speed and powerful fitting capabilities. One of its important variants, the elastic-net ELM (Enet-ELM), was recently proposed to improve its sparsity and stability of simulations simultaneously. However, entering the era of big data, the explosive growth of data volume and dimensions poses a huge challenge to Enet-ELM. On the other hand, the alternating direction method of multipliers (ADMM) is a powerful iterative algorithm for solving large-scale optimization problems by splitting a large problem into a set of executable sub-problems. But its performance is highly restricted by its astringency and convergence rates. In this paper, we therefore develop a novel Enet-ELM algorithm based on the over-relaxed ADMM, termed over-relaxed Enet-ELM (OE-ELM), which accelerates model training by applying the results of the previous iteration to the next iteration. Besides, we also propose a parallel version of OE-ELM (POE-ELM) to implement parallel and distributed computation, which is trained by the consensus over relaxation ADMM algorithm. Finally, the convergence analysis conducted on the two proposed algorithms proves the effectiveness of model training, and extensive experiments on classification and regression datasets demonstrate their competitiveness in accuracy and convergence rate. [ABSTRACT FROM AUTHOR]

Some efficient top-k algorithms, i.e., Fagin's Algorithm, threshold algorithm (TA), and best position algorithm (BPA), can be used to answer k nearest neighbor (kNN) queries. However, extending the existing algorithms without further changes to the algorithms themselves would not be efficient since there are the different characteristics between the kNN queries and top-k queries. For example, the kNN queries are more distance-sensitive rather than the position of data points. Second, it is necessary to add some novel parallel heuristics and pruning policies for the kNN queries. Third, there are still many redundant random accesses among FA, TA, and BPA. In this paper, we address aforementioned these problems and take these algorithms to answer parallel kNN (PkNN) queries in spatial databases. We integrate the advantages of the B + -tree and Open MP programming and propose three efficient parallel kNN query algorithms, namely distance priority-based PkNN, optimized PkNN, and partition-based PkNN. Our performance evaluation shows that our proposed algorithms achieve significant improvement in comparison with existing algorithms, i.e., BPA and BPA2. In addition, our approaches are also capable of returning kNN results incrementally which greatly shorten the query response time and enhance user experience. [ABSTRACT FROM AUTHOR]

The RLWE encryption scheme is one of the most potential candidates in the lattice cryptosystem in the post-quantum era. In view of the problem of high latency and low throughput in RLWE cryptoprocessor, this study proposes a high-performance RLWE cryptoprocessor hardware architecture. The parallel circuit structure of two NTT modules and four butterfly modules are adopted in the proposed architecture. In the pre-calculation and post-calculation process, the multipliers in the four butterfly modules are used for parallel calculation. In the encryption process, NTT calculation and ciphertext calculation are performed in parallel. In the processing of NTT and INTT operations, the data read and write process and calculation process are ping-pong operations, thereby hiding the data read and write cycle, reducing the delay of the RLWE encryption processor, and improving the throughput of the RLWE encryption processor. A hardware architecture is designed for resource reuse, the multiplier and adder are reused in the butterfly module during the encryption and decryption process, and the circuit structure of NTT is reused by INTT, thereby reducing the hardware resource consumption of the encryption processor. The cryptoprocessor with parameters of n=256 and q=65 537 is implemented on the Spartan-6 FPGA development platform. The results indicate that the encryption time is only 12.18 μs, the throughput is 21.01 Mbit·s-1, the decryption time is only 8.65 μs, and the throughput is 29.60 Mbit·s-1. Compared with other cryptoprocessor, the proposed design has improved the delay and throughput of the cryptoprocessor. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The content-based publish/subscribe system is an effective paradigm for implementing on-demand event distribution. Each event needs to be matched against subscriptions to identify the target subscribers. To improve the matching performance, many novel data structures have been proposed. However, the predicates contained in subscriptions are handled the same way in most existing data structures, without considering their differences in matching probability. In this paper, we propose the concept of parallel ensemble matching (PEM) based on subscription partitioning. The basic idea is that we have the right algorithm handling the right subscriptions at the right time. First of all, we design a PEM framework by classifying subscriptions according to their matching probabilities and use the proper algorithms to process each subscription category. Furthermore, to deal with high-dimensional subscriptions, we propose a fine-grained PEM (fgPEM) that exploits matching algorithms with complementary behaviors by partitioning subscriptions into sub-subscriptions. We implement the prototype of PEM and fgPEM based on two existing algorithms. The experiment results show that PEM improves the matching performance by 43%. On the basis of PEM, fgPEM further improves the performance by 31%. [ABSTRACT FROM AUTHOR]

Large-scale graphs are widely used to model the entities and their complex relations. Uncertain graphs are adopted when the relations between entities contain some uncertainty. However, the inherent uncertainties, which are embedded underlying the data and structure of the graphs derived from various sources introduce difficulties on data analysis. To understand the underlying characteristics of large graphs, graph aggregation techniques are critical. However, the existing graph aggregation techniques are designed for deterministic graphs therefore are not applicable on uncertain graphs. In this paper, we provide the first attempt on addressing the aggregation problem on uncertain graphs. To deal with the computation complexity of the aggregation problem, we propose a heuristic-based aggregation algorithm for uncertain graphs and some optimization methods to improve its efficiency in real world implementation. Besides the optimization, to further speed up the process, we design a parallel aggregation implementation approach. The intensive evaluations on the two datasets, DBLP and Flickr, demonstrate that our proposed algorithms are able to produce high quality aggregation results within reasonable operation time and the parallel implementation accelerates the aggregation by up to 82 times compared with the baseline algorithm. [ABSTRACT FROM AUTHOR]

During the last years, object-based image segmentation (OBIA) has seen a considerable increase in the image segmentation. OBIA is generally based on superpixel methods, in which the clustering-based method plays an increasingly important role. Most clustering methods for generating superpixels suffer from inaccurate classification points with inappropriate cluster centers. To solve the problem, we propose a competitive mechanism-based superpixel generation (CMSuG) method, which both accelerates convergence and promotes robustness for noise sensitivity. Then, image segmentation results will be obtained by a region adjacent graph (RAG)-based merging algorithm after constructing an RAG. However, high segmentation accuracy is customarily accompanied by expensive time-consuming costs. To improve computational efficiency, we address a parallel CMSuG algorithm, the time of which is much less than the CMSuG method. In addition, we present a parallel RAG method to decrease the expensive time-consuming cost in serial RAG construction. By leveraging parallel techniques, the running time of the whole image segmentation method decline with the time complexity from O (N) + O (K2) to O (N/K) or O (K2), in which N is the size of an input image and K is the given number of the superpixel. In the experiments, both nature image and remote sensing image segmentation results demonstrate that our CMSuG method outperforms the state-of-the-art superpixel generation methods, and then performs well for image segmentation in turn. Compared with the serial segmentation method, our parallel techniques gain more than four times acceleration in both remote sensing image dataset and nature image dataset. [ABSTRACT FROM AUTHOR]

Affected by solar radiation, atmospheric windows, radiation aberrations, and other air and sky environmental factors, remote sensing images usually contain a large amount of noise and suffer from problems such as non-uniform image feature density. These problems bring great difficulties to the segmentation of high-precision remote sensing image. To improve the segmentation effect of remote sensing images, this study adopted an improved metaheuristic algorithm to optimize the parameter settings of pulse-coupled neural networks (PCNNs). Using the Taguchi method, the optimal parallelism scheme of the algorithm was effectively tailored for a specific target problem. The blindness in the design of the algorithm parallel structure was effectively avoided. The superiority of the customized parallel SCA based on the Taguchi method (TPSCA) was demonstrated in tests with different types of benchmark functions. In this study, simulations were performed using IKONOS, GeoEye-1, and WorldView-2 satellite remote sensing images. The results showed that the accuracy of the proposed remote sensing image segmentation model was significantly improved. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article explores two independent Old Bulgarian translations of an excerpt from De Spiritu sancto by Basil the Great that are part of Simeon’s Florilegium under article [422] Τοῦ ἁγίου Βασιλείου ἐκ τοῦ Περὶ τοῦ ἁγίου πνεύματος Svyatago vasiliya ot togo yezhe svyatym dus and of Efremovskaya Kormchaya as Τοῦ αὐτοῦ ἐκ τοῦ κζ΄ λόγου τῶν Περὶ τοῦ ἁγίου πνεύματος πρὸς Ἀμφιλόχιον γεγράμενων; Τοῦ αὐτοῦ ἐκ τοῦ κϑ΄ κεφαλαίου τῆς αὐτῆς πραγματείας togo zhe dvadesetisemaago slova o svyatym dse k amfilohiyu napisanyh; togo zhe ot dvadeseti devetyya glavy togo zhe stroyeniya. The structure and contents of the Greek article used for the two Old Bulgarian translations is examined and evidence is found that it was the same but in Simeon’s Florilegium it was slightly abridged. The identical places and the differences in the two translations are identified through the isolation of the lexical parallels against one Greek word. Based on the result of the analysis the hypothesis is made that the second translator probably had an earlier translation of the article at his disposal (as evidenced by several common places in the two translations). The conclusion is that the second translation is an independent one, which is proven by the translation differences at all lexical levels as well as the isolated 104 lexical parallels against Greek words). Both Old Bulgarian translations have been made during the rule of king Simeon in Eastern Bulgaria but by two different translators. [ABSTRACT FROM AUTHOR]