Basic Components of Neuronetworks with Parallel Vertical Group Data Real-Time Processing

Micro SAR outperforms other Technologies in radar system study since it is small, light and concealable. Airborne dual-antenna micro synthetic aperture radar (SAR) system collects ground digital elevation information (DEM) from two different SAR images. Across-track interferometer mode based on UAV SAR reduces the mounted platform’s requirement, thus making it more convenient to perform flight experiments due to its flexible deployment. This type of advanced technology is highly demanding, and it is a hit trend of study field among most researchers. Real-time interferometric measurement is required to deal with a massive amount of complex data and process it within a short period. This feature also plays a significant role in multiple fields such as battlefield surveillance, unmanned fighter navigation, and blind landing of helicopters. However, the traditional way of serial processing can no longer satisfy the real-time processing requirements due to its massive amount of algorithm steps and complex computations. Therefore, this paper proposes a real-time InSAR processing algorithm and applied multi-core and multi-thread acceleration to boost the three most time-consuming modules: Flat-earth Phase Removal Module, Phase Filtering Module, and Phase to Height Conversion and Geocoding Module. In addition, using data block and parallel processing methods while doing calculations can significantly increase signal processing efficiency. Finally, this paper validates the effectiveness of the algorithm by testing it on the whole system. The results show that using an 8-thread computer, the parallel processing module’s time is only 15% compared to the original module. Moreover, the overall system operation speed is accelerated two times more than the previous traditional technique, the relative error of elevation is 0.3911 m. In conclusion, this paper validates that parallel processing is an effective way for real-time processing.

Визначено операцiйний базис нейромереж, обгрунтовано доцiльнiсть розроблення апаратних нейромереж паралельноертикального типу, розроблено орiєнтований на НВIС-реалiзацiю паралельно-вертикальний метод опрацювання даних у нейроелементах (нейромережах), який забезпечує зменшення кiлькостi виводiв iнтерфейсу, розрядностi мiжнейронних зв’язкiв i затрат обладнання та запропоновано принципи НВIС-реалiзацiї нейромереж.

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.

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

Parallel processing is an execution of processes that make computation and calculation on many things simultaneously. In addition, parallel processing methods are applied extensively to the examination of MR imaging in treatment. As parallel computer systems become larger and faster at the present time; scientists, researchers and engineers are eventually able to find solutions to the problems in medicine, which had been taken too long to run before. Therefore, various fields including medicine and bioinformatics have already taken the advantages of parallel processing. In this review study, we deal with analyzing key concepts and eminent parallel processing methods that have been used to analyze the brain MRI images. In addition to this, we indicate great number of examples from the current literature in a comprehensive literature matrix. Based on the literature matrix that is created according to the Web of Science analysis, information graphics are presented in a comprehensive manner. As a result, parallel processing methods in brain magnetic resonance imaging offer powerful replacements to computer clusters in order to run large, disseminated solicitations.KeywordsParallel processingMagnetic resonance imagingBrainReview

Machine learning for Big Data analytics in plants.

Large-scale data analysis applications require processing and analyzing of Terabytes or even Petabytes of data, particularly in the areas of web analysis or scientific data management. This trend has been discussed as "web-scale data management" in a panel at VLDB 2009. Formerly, parallel data processing was the domain of parallel database systems. Today, novel requirements like scaling out to thousands of machines, improved fault-tolerance, and schema free processing have made a case for new approaches.

Strains of recombinant bioluminescent bacteria (RBB) which respond to toxic environments using various stress promoters are practical means of assessing toxicity. In previous research, RBB has proven useful for highthroughput screening in the drug development process. The goal of this research is to demonstrate that RBB can also be used for the toxicity screening of natural products. The RBB strains used were DPD2511, BBTSbmC, TV1061, and GC2, which were selected to respond to oxidative stress, DNA damage, protein damage, and cellular toxicity, respectively. The test drugs (paclitaxel, etoposide, and pentostatin) were carefully selected because these drugs needed to be natural products or their derivatives whose cellular toxicity had previously been reported from human cell line assays. After treating the RBB strains with various doses of the chosen drugs, their bioluminescent signals were measured over time. The effectiveness of the RBB method was proven by comparing its results to existing toxicity data for the selected drugs. In addition, a similar test using podophyllotoxin, a precursor of etoposide, and a derivative of podophyllotoxin, teniposide, was conducted to prove that the RBB method is suitable for a comparative analysis of toxicity among chemicals with similar molecular structures. As a detection method, RBB bacteria provide a much easier and more rapid culturing process compared to conventional human cell line assays. Because the implementation of the RBB method in the drug discovery process would enable efficient prescreening, a significant reduction in time, effort, and development costs are expected.

The objective was to compare case ascertainment, agreement, validity, and missing values for clinical research data obtained, processed, and linked electronically from electronic health records (EHR) compared to "manual" data processing and record abstraction in a cohort of out-of-hospital trauma patients. This was a secondary analysis of two sets of data collected for a prospective, population-based, out-of-hospital trauma cohort evaluated by 10 emergency medical services (EMS) agencies transporting to 16 hospitals, from January 1, 2006, through October 2, 2007. Eighteen clinical, operational, procedural, and outcome variables were collected and processed separately and independently using two parallel data processing strategies by personnel blinded to patients in the other group. The electronic approach included EHR data exports from EMS agencies, reformatting, and probabilistic linkage to outcomes from local trauma registries and state discharge databases. The manual data processing approach included chart matching, data abstraction, and data entry by a trained abstractor. Descriptive statistics, measures of agreement, and validity were used to compare the two approaches to data processing. During the 21-month period, 418 patients underwent both data processing methods and formed the primary cohort. Agreement was good to excellent (kappa = 0.76 to 0.97; intraclass correlation coefficient [ICC] = 0.49 to 0.97), with exact agreement in 67% to 99% of cases and a median difference of zero for all continuous and ordinal variables. The proportions of missing out-of-hospital values were similar between the two approaches, although electronic processing generated more missing outcomes (87 of 418, 21%, 95% confidence interval [CI] = 17% to 25%) than the manual approach (11 of 418, 3%, 95% CI = 1% to 5%). Case ascertainment of eligible injured patients was greater using electronic methods (n = 3,008) compared to manual methods (n = 629). In this sample of out-of-hospital trauma patients, an all-electronic data processing strategy identified more patients and generated values with good agreement and validity compared to traditional data collection and processing methods.

MapReduce has been widely used in Hadoop for parallel processing larger-scale data for the last decade. However, remote-sensing (RS) algorithms based on the programming model are trapped in dense disk I/O operations and unconstrained network communication, and thus inappropriate for timely processing and analyzing massive, heterogeneous RS data. In this paper, a novel in-memory computing framework called Apache Spark (Spark) is introduced. Through its merits of transferring transformation to in-memory datasets of Spark, the shortages are eliminated. To facilitate implementation and assure high performance of Spark-based algorithms in a complex cloud computing environment, a strip-oriented parallel programming model is proposed. By incorporating strips of RS data with resilient distributed datasets (RDDs) of Spark, all-level parallel RS algorithms can be easily expressed with coarse-grained transformation primitives and BitTorrent-enabled broadcast variables. Additionally, a generic image partition method for Spark-based RS algorithms to efficiently generate differentiable key/value strips from a Hadoop distributed file system (HDFS) is implemented for concealing the heterogeneousness of RS data. Data-intensive multitasking algorithms and iteration-intensive algorithms were evaluated on a Hadoop yet another resource negotiator (YARN) platform. Experiments indicated that our Spark-based parallel algorithms are of great efficiency, a multitasking algorithm took less than 4 h to process more than half a terabyte of RS data on a small YARN cluster, and 9*9 convolution operations against a 909-MB image took less than 260 s. Further, the efficiency of iteration-intensive algorithms is insensitive to image size.

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>

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.