A Combination Algorithm of Multiple Lattice-Valued Concept Lattices

Today, the complex large scale computing tasks can be performed by using the various multithreading architectures. Despite the multithreading mechanisms of modern programming languages are efficient, the reliable and accuracy of the application data processing is depending on the computational architecture. Therefore, parallel data processing are more complex than the sequential ones and requires additional work hard. The main problem of multithreading mechanisms is the non-centralized usage of these mechanisms. In this case, the code becomes dependent on particular programming techniques and it is difficult to modify the functions of application. The use of multithreading technique requires the study of the application architecture. This article describes an application architecture which can improve the design requirements of software products. The paper presents a class diagram «Computing Process subsystem» for the entire computing process of the application and a detailed analysis of the entities objects. In this article, the development of automated traffic scheduling system of subway will take into account as a real world application of the proposed computational architecture.

Cloud computing is a technology in which the Cloud Service Providers (CSP) provide many virtual servers to the users to store their information in the cloud. The faults occurring on the assignment and dismission of the virtual machines, the processing cost in the allocation of resources must also be considered. The parallel processing of the information on the virtual machines must be done effectively and in an efficient manner. A variety of systems were developed to facilitate Many Task Computing (MTC). These systems aim to hide the issues of parallelism and fault tolerant and they are used in many applications. In this paper, we introduced Nephele, a data processing framework to exploit dynamic resource provisioning offered by IaaS clouds. The performance evaluation of the virtual machines has been evaluated and the allocation and deallocation of job tasks to the specific virtual machines has also been considered. A performance comparison with the well known data processing framework hadoop has been done. Thus this paper tells about the effective and efficient manner of processing the data by parallel processing and allocating the correct resources for the desired task. It also helps to reduce the cost of resource utilization by exploiting the dynamic resource utilization.

Multifrequency Spin Wave Device for Parallel Data Processing using Micro Structured Yttrium Iron Garnet Thin Films

Despite an overwhelming increase in the number of concept analyses published since the early 1970s, there are significant limitations to the impact of this work in promoting progress in nursing science. We conducted an extensive review of concept analyses published between 1972 and 2017 to identify patterns in analysis and followed this with exploration of an exemplar related to the concept of normalization to demonstrate the capabilities of analysis for promoting concept development and progress. Scoping review of peer-reviewed literature published in the Cumulative Index to Nursing and Allied Health Literature (CINAHL) in which the terms "concept analysis," "concept clarification," and "concept derivation" appeared in any part of the reference. The original search returned 3,489 articles. This initial pool was refined to a final sample of 958 articles published in 223 journals and addressing 604 concepts. A review of citations of the original analysis of the concept of normalization resulted in 75 articles selected for closer examination of the process of concept development. Review showed a clear pattern of repetition of analysis of the same concept, growth in number of published analyses, preponderance of first authors with master's degrees, and 43 distinct descriptions of methods. Review of the 75 citations to the normalization analysis identified multiple ways concept analysis can inform subsequent research and theory development. Conceptual work needs to move beyond the level of "concept analysis" involving clear linkage to the resolution of problems in the discipline. Conceptual work is an important component of progress in the knowledge base of a discipline, and more effective use of concept development activities are needed to maximize the potential of this important work. It is important to the discipline that we facilitate progress in nursing science on a theoretical and conceptual level as a part of cohesive and systematic development of the discipline. The absence of effective concepts impedes the ability to recognize, discuss, define, and conduct studies important to clinical practice and research. This article reflects the pressing need as well as the potential for concept analysis work to be approached in a way that promotes nursing science and enables conceptually sound research to improve clinical care.

Neuroalgorithms and neuronetwork structures were analyzed, basic components of neuronetworks were defined and the principles of their development were chosen. It was shown that using the method of parallel vertical group data processing for the implementation of the neuronetworks basic components provides speed increase, reduce of hardware costs and increasing of the equipment use efficiency. Parallel vertical group codes converter, which provides time alignment of data receipt processes and bit sections formation, was developed. The methods and the structures of the components with parallel vertical group data processing for definition of maximum and minimum numbers in the arrays, calculation of the sum of differences squares and scalar product, which due to parallel processing of bit sections groups, provide speed increase, were developed. It was shown that use of the developed basic components for neuronetworks synthesis will provide reduction of time and development cost.

We apply optical parallel processing to operations for multiplication modulo, which is one of the key components of a factorization algorithm. With this method, optical phase modulation provides the results of modulo operations. We construct a prototype system based on a Michelson interferometer with a photodetector array. Mirrors are set at both object and reference arms to generate interference fringes. A mirror in the object arm is tilted slightly, whereas the reference arm is set perpendicular to the optical axis. The tilt angle is determined by parameters for the target modulo operations. The presented system can achieve massive data processing in parallel with only simple implementation. We present our experimental results to verify the usefulness of our method.

Cyber-physical applications need to process a lot of sensor data, for example, to analyze traffic patterns and structural soundness of critical infrastructures. Although the amount of sensor data to process is increasing fast, system support to efficiently store and analyze an extensive amount of sensor data largely lags behind. To efficiently store, retrieve, and process massive sensor data, we are developing a sensor data center (SDC) that supports spatio-temporal sensor data structures and parallel sensor data processing using clustered computational nodes composed of commodity hardware. The SDC sharply contrasts to most existing data centers that do not support spatio-temporal sensor data storage, retrieval, and processing. In this paper, we especially focus on the problem of potential load imbalance due to data access skews that adversely affects the timeliness of parallel sensor data processing. Specifically, we present an adaptive data replication method to address access skews in a SDC. In our performance evaluation performed in a preliminary version of a SDC, our adaptive approach substantially outperforms a baseline that does not support adaptive data replication.

The authors report online parallel data processing in the GA.SP (gamma spectrometer) data acquisition system. A transputer network (TNET) has been built as a dedicated parallel machine for high-speed data processing and a nuclear experiment oriented parallel language (NEO) has been designed to support parallel programming on TNET. Event pipeline analyzing domain data structures and interactive communication with the program are characteristics of the NEO language. A multichain pipeline machine and its shell are also presented to show how to run the NEO program and to operate the TNET efficiently.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Owing to the outstanding performance of memristors in brain-like parallel computing and data processing, especially in the efficient recognition of handwritten digits and complex patterns, they are regarded as key components for next-generation artificial intelligence systems. This study developed a memristor based on the P-N heterostructure CdS/Cu2ZnSn(S,Se)4 (CdS/CZTSSe). The results indicate that the Ag/CdS/CZTSSe/Mo memristor exhibits stable non-volatile bipolar resistive switching. By investigating the conductivity mechanism of the device, a resistive switching model is established that regulates the conductive filaments of Cu ions in the heterojunction. This device not only demonstrates a concentrated Set/Reset voltage distribution, good durability (>200 cycles), and time retention characteristics (>104s) but also features continuously adjustable conductance under electrical pulse square wave stimulation. Such a behavior enables the memristor to simulate important biological synaptic functions, including excitatory postsynaptic current, excitatory and inhibitory synaptic plasticity, and short-term/long-term plasticity and paired-pulse facilitation. Furthermore, neuromorphic simulations validated that the artificial neural network model using this memristor achieved a 94.1% recognition rate for Modified National Institute of Standards and Technology handwritten digits. These results significantly advance the development of heterojunction memristors in artificial synapses and lay a foundation for future neuromorphic applications.

Algorithmically optimized AVC (MPEG-4 part 10/H.264) video encoder with parallel processing of data is presented in the paper. The paper reveals the architecture of the proposed encoder together with the description of the applied software optimization techniques. Conducted experiments show exactly the degree of parallelization of computations in the encoder and the compression performance of video encoding.

The article describes a new approach to the processing of long series of ozone monitoring data in the surface atmosphere called the Data Parallel Processing in Block Streams. The proposed method is based on splitting of a sequential series of initial data into blocks filled with surface ozone monitoring data for one day. Further, a chain is formed from such blocks, the length of which is determined by the total length of the monitoring process. Along this chain of blocks, parallel processing of the initial data is carried out, aimed at smoothing out fast fluctuations. The smoothed data is then used to determine the daily production of ozone due to photochemical reactions, its minimum nighttime levels, as well as the magnitude of nighttime maxima. The possibilities of the proposed approach are demonstrated using the analysis of surface ozone monitoring data in Moscow, Russia, in 2020. The prospects for further application of the developed method are discussed.

This study presents a new method on 3D visualization in reservoir modeling system by using the computation power of modern programmable Graphics hardware (GPU). The proposed scheme is devised to achieve parallel processing of massive reservoir logging data. By taking advantage of the GPU's parallel processing capability, moreover, the performance of our scheme is discussed in comparison with that of the implementation entirely running on CPU. Experimental results clearly show that the proposed parallel processing can remarkably accelerate the data clustering task. Especially, although data-transferring from GPU to CPU is generally costly, acceleration by GPU is significant to save the total execution time of data-clustering and also significantly alleviates the computing load on CPU.

Parallel data processing complicates the completion of string similarity joins because parallel data processing requires the use of a well designed data partitioning scheme. Moreover, efficient verification of string pairs is needed to speed up the entire string similarity join process. We propose a novel framework that addresses these requirements through the use of edit distance constraints. The Landmark-Join framework has two functions that reduce two kinds of search spaces. The first, q-bucket partitioning, reduces the number of verifications of dissimilar string pairs and lowers skewness among buckets. The second, local upper bound calculation, prunes the search space of edit distance to speed up each verification. Experimental results show that Landmark-Join has good parallel scalability and that the two proposed functions speed up the entire string similarity join process.

Large-scale visualization systems are typically designed to efficiently “push” datasets through the graphics hardware. However, exploratory visualization systems are increasingly expected to support scalable data manipulation, restructuring, and querying capabilities in addition to core visualization algorithms. We posit that new emerging abstractions for parallel data processing, in particular computing clouds, can be leveraged to support large-scale data exploration through visualization. In this paper, we take a first step in evaluating the suitability of the MapReduce framework to implement large-scale visualization techniques. MapReduce is a lightweight, scalable, general-purpose parallel data processing framework increasingly popular in the context of cloud computing. Specifically, we implement and evaluate a representative suite of visualization tasks (mesh rendering, isosurface extraction, and mesh simplification) as MapReduce programs, and report quantitative performance results applying these algorithms to realistic datasets. For example, we perform isosurface extraction of up to l6 isovalues for volumes composed of 27 billion voxels, simplification of meshes with 30GBs of data and subsequent rendering with image resolutions up to 80000 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> pixels. Our results indicate that the parallel scalability, ease of use, ease of access to computing resources, and fault-tolerance of MapReduce offer a promising foundation for a combined data manipulation and data visualization system deployed in a public cloud or a local commodity cluster.

The paper presents a modification of the structure of a biological neural network (BNN) based on spiking neuron models. The proposed modification allows to influence the level of the stimulus response of particular neurons in the BNN. We consider an extended, three-dimensional Hodgkin-Huxley model of the neural cell. A typical BNN composed of such neural cells have been expanded by addition of resistors in each branch point. The resistors can be treated as the weights in such BNN. We demonstrate that adding these elements to the BNN significantly affects the waveform of the potential on the membrane of the neuron, causing an uncontrolled excitation. This provides a better description of processes that take place in nervous cell. Such BNN enables an easy adaptation of the learning rules used in artificial or spiking neural networks. The modified BNN has been implemented on Graphics Processing Unit (GPU) in the CUDA C language. This platform enables a parallel data processing, which is an important feature in such applications.