Parallel Architecture Prototype for 60 GHz High Data Rate Wireless Single Carrier Receiver

Nowadays a huge attention of the academia and research teams is attracted to the potential of the usage of the 60 GHz frequency band in the wireless communications. The use of the 60GHz frequency band offers great possibilities for wide variety of applications that are yet to be implemented. These applications also imply huge implementation challenges. Such example is building a high data rate transceiver which at the same time would have very low power consumption. In this paper we present a prototype of Single Carrier -SC transceiver system, illustrating a brief overview of the baseband design, emphasizing the most important decisions that need to be done. A brief overview of the possible approaches when implementing the equalizer, as the most complex module in the SC transceiver, is also presented. The main focus of this paper is to suggest a parallel architecture for the receiver in a Single Carrier communication system. This would provide higher data rates that the communication system canachieve, for a price of higher power consumption. The suggested architecture of such receiver is illustrated in this paper,giving the results of its implementation in comparison with its corresponding serial implementation.

Efficiency of parallel transmission methods at 7T magnetic resonance

Magdeburg, Univ., Fak. für Naturwiss., Diss., 2014

Software for Explicitly Parallel Memory-Centric Processor Architecture

Advances in computer memory technology justify research towards new and different views on computer organization. This paper proposes a novel memory-centric computing architecture with the goal to merge memory and processing elements in order to provide better conditions for parallelization and performance. The paper introduces the architectural concepts and afterwards shows the design and implementation of a corresponding assembler and simulator.

An illustrated flora of the northern United States, Canada and the British possessions, from Newfoundland to the parallel of the southern boundary of Virginia, and from the Atlantic Ocean westward to the 102d meridian, by Nathaniel Lord Britton and Addison Brown. The descriptive text chiefly prepared by Britton, with the assistance of specialists in several groups; the figures also drawn under his supervision.

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An illustrated flora of the northern United States, Canada and the British possessions, from Newfoundland to the parallel of the southern boundary of Virginia, and from the Atlantic Ocean westward to the 102d meridian, by Nathaniel Lord Britton and Addison Brown. The descriptive text chiefly prepared by Britton, with the assistance of specialists in several groups; the figures also drawn under his supervision.

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Creating and using a non-dedicated HPC cluster with ParallelKnoppix

This note describes ParallelKnoppix, a bootable CD that allows econometricians with average knowledge of computers to create and begin using a high performance computing cluster for parallel computing in very little time. The computers used may be heterogeneous machines, and clusters of up to 200 nodes are supported. When the cluster is shut down, all machines are in their original state, so their temporary use in the cluster does not interfere with their normal uses. An example shows how a Monte Carlo study of a bootstrap test procedure may be done in parallel. Using a cluster of 20 nodes, the example runs approximately 20 times faster than it does on a single computer.

User-friendly parallel computations with econometric examples

This paper shows how a high level matrix programming language may be used to perform Monte Carlo simulation, bootstrapping, estimation by maximum likelihood and GMM, and kernel regression in parallel on symmetric multiprocessor computers or clusters of workstations. The implementation of parallelization is done in a way such that an investigator may use the programs without any knowledge of parallel programming. A bootable CD that allows rapid creation of a cluster for parallel computing is introduced. Examples show that parallelization can lead to important reductions in computational time. Detailed discussion of how the Monte Carlo problem was parallelized is included as an example for learning to write parallel programs for Octave.

An atlas of human gene expression from massively parallel signature sequencing (MPSS).

We have used massively parallel signature sequencing (MPSS) to sample the transcriptomes of 32 normal human tissues to an unprecedented depth, thus documenting the patterns of expression of almost 20,000 genes with high sensitivity and specificity. The data confirm the widely held belief that differences in gene expression between cell and tissue types are largely determined by transcripts derived from a limited number of tissue-specific genes, rather than by combinations of more promiscuously expressed genes. Expression of a little more than half of all known human genes seems to account for both the common requirements and the specific functions of the tissues sampled. A classification of tissues based on patterns of gene expression largely reproduces classifications based on anatomical and biochemical properties. The unbiased sampling of the human transcriptome achieved by MPSS supports the idea that most human genes have been mapped, if not functionally characterized. This data set should prove useful for the identification of tissue-specific genes, for the study of global changes induced by pathological conditions, and for the definition of a minimal set of genes necessary for basic cell maintenance. The data are available on the Web at http://mpss.licr.org and http://sgb.lynxgen.com.

Parallel spinors on pseudo-riemannian spin(q) manifolds

We study simply-connected irreducible non-locally symmetric pseudo-Riemannian Spin(q) manifolds admitting parallel quaternionic spinors.

V-region-related and -unrelated immunosupression accompanying infections

This paper discusses current evidence for the relationship between polyclonal lymphocyte activation, specific immunossupression with decreased resistance, and autoimmune pathology, that are all often found associated with infections by a variety of virus, bacteria and parasites . The central question of class determination of immune effector activities is considered in the context of the cellular targets for nonspecific mitogenic activities associated with infection. A model is presented to integrate these findings: mitogenens produced by the microorganism or the infected cells are preferentially active on CD5 B cells, the resulting over-production of IL-10 will tend to bias all immune activities in to a Th-2mode of effector functions, with high titers of polyclonal antibodies and litle or no production of gamma IFN and other "inflamatory"lymphokines that often mediate resistance. In turn these conditions allow for parasite persistence and the corresponding long-term disregulation of self-directed immune reactivities, resulting in autoimmunity in the chronic phase. This model would predict that selective immunization with the mitogenic principles involved in desregulation, could stand better chances than strategies of vaccination based on immunopotentiation against othere, functionally neutral antigenic epitopes. It is argued, however, that the complexity of immune responses and their regulation together with our ignorance on the genetic controls of class-determination, offer poor prospects for a scientifically-based, rational development of vaccines in the near future. It is suggested that empirically-based and technologically developed vaccines might suceed, while basic scientific approaches are reinforced and given the time provide a better understanding of those process.

Textural classification of land cover using support vector machines: an empirical comparison with parametric, non parametric and hybrid classifiers in the Bolivian Amazon

Land cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims to establish an efficient classification approach to accurately map all broad land cover classes in a large, heterogeneous tropical area of Bolivia, as a basis for further studies (e.g., land cover-land use change). Specifically, we compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbour and four different support vector machines - SVM), and hybrid classifiers, using both hard and soft (fuzzy) accuracy assessments. In addition, we test whether the inclusion of a textural index (homogeneity) in the classifications improves their performance. We classified Landsat imagery for two dates corresponding to dry and wet seasons and found that non-parametric, and particularly SVM classifiers, outperformed both parametric and hybrid classifiers. We also found that the use of the homogeneity index along with reflectance bands significantly increased the overall accuracy of all the classifications, but particularly of SVM algorithms. We observed that improvements in producer’s and user’s accuracies through the inclusion of the homogeneity index were different depending on land cover classes. Earlygrowth/degraded forests, pastures, grasslands and savanna were the classes most improved, especially with the SVM radial basis function and SVM sigmoid classifiers, though with both classifiers all land cover classes were mapped with producer’s and user’s accuracies of around 90%. Our approach seems very well suited to accurately map land cover in tropical regions, thus having the potential to contribute to conservation initiatives, climate change mitigation schemes such as REDD+, and rural development policies.

Including the workload effect in the parallel program signature

Performance prediction and application behavior modeling have been the subject of exten- sive research that aim to estimate applications performance with an acceptable precision. A novel approach to predict the performance of parallel applications is based in the con- cept of Parallel Application Signatures that consists in extract an application most relevant parts (phases) and the number of times they repeat (weights). Executing these phases in a target machine and multiplying its exeuction time by its weight an estimation of the application total execution time can be made. One of the problems is that the performance of an application depends on the program workload. Every type of workload affects differently how an application performs in a given system and so affects the signature execution time. Since the workloads used in most scientific parallel applications have dimensions and data ranges well known and the behavior of these applications are mostly deterministic, a model of how the programs workload affect its performance can be obtained. We create a new methodology to model how a program’s workload affect the parallel application signature. Using regression analysis we are able to generalize each phase time execution and weight function to predict an application performance in a target system for any type of workload within predefined range. We validate our methodology using a synthetic program, benchmarks applications and well known real scientific applications.