99,298 research outputs found
Design and Performance of the Data Acquisition System for the NA61/SHINE Experiment at CERN
This paper describes the hardware, firmware and software systems used in data
acquisition for the NA61/SHINE experiment at the CERN SPS accelerator. Special
emphasis is given to the design parameters of the readout electronics for the
40m^3 volume Time Projection Chamber detectors, as these give the largest
contribution to event data among all the subdetectors: events consisting of
8bit ADC values from 256 timeslices of 200k electronic channels are to be read
out with ~100Hz rate. The data acquisition system is organized in "push-data
mode", i.e. local systems transmit data asynchronously. Techniques of solving
subevent synchronization are also discussed.Comment: 14 pages, 13 figure
MOSDEN: A Scalable Mobile Collaborative Platform for Opportunistic Sensing Applications
Mobile smartphones along with embedded sensors have become an efficient
enabler for various mobile applications including opportunistic sensing. The
hi-tech advances in smartphones are opening up a world of possibilities. This
paper proposes a mobile collaborative platform called MOSDEN that enables and
supports opportunistic sensing at run time. MOSDEN captures and shares sensor
data across multiple apps, smartphones and users. MOSDEN supports the emerging
trend of separating sensors from application-specific processing, storing and
sharing. MOSDEN promotes reuse and re-purposing of sensor data hence reducing
the efforts in developing novel opportunistic sensing applications. MOSDEN has
been implemented on Android-based smartphones and tablets. Experimental
evaluations validate the scalability and energy efficiency of MOSDEN and its
suitability towards real world applications. The results of evaluation and
lessons learned are presented and discussed in this paper.Comment: Accepted to be published in Transactions on Collaborative Computing,
2014. arXiv admin note: substantial text overlap with arXiv:1310.405
Goal-Directed Behavior under Variational Predictive Coding: Dynamic Organization of Visual Attention and Working Memory
Mental simulation is a critical cognitive function for goal-directed behavior
because it is essential for assessing actions and their consequences. When a
self-generated or externally specified goal is given, a sequence of actions
that is most likely to attain that goal is selected among other candidates via
mental simulation. Therefore, better mental simulation leads to better
goal-directed action planning. However, developing a mental simulation model is
challenging because it requires knowledge of self and the environment. The
current paper studies how adequate goal-directed action plans of robots can be
mentally generated by dynamically organizing top-down visual attention and
visual working memory. For this purpose, we propose a neural network model
based on variational Bayes predictive coding, where goal-directed action
planning is formulated by Bayesian inference of latent intentional space. Our
experimental results showed that cognitively meaningful competencies, such as
autonomous top-down attention to the robot end effector (its hand) as well as
dynamic organization of occlusion-free visual working memory, emerged.
Furthermore, our analysis of comparative experiments indicated that
introduction of visual working memory and the inference mechanism using
variational Bayes predictive coding significantly improve the performance in
planning adequate goal-directed actions
Data Provenance and Management in Radio Astronomy: A Stream Computing Approach
New approaches for data provenance and data management (DPDM) are required
for mega science projects like the Square Kilometer Array, characterized by
extremely large data volume and intense data rates, therefore demanding
innovative and highly efficient computational paradigms. In this context, we
explore a stream-computing approach with the emphasis on the use of
accelerators. In particular, we make use of a new generation of high
performance stream-based parallelization middleware known as InfoSphere
Streams. Its viability for managing and ensuring interoperability and integrity
of signal processing data pipelines is demonstrated in radio astronomy. IBM
InfoSphere Streams embraces the stream-computing paradigm. It is a shift from
conventional data mining techniques (involving analysis of existing data from
databases) towards real-time analytic processing. We discuss using InfoSphere
Streams for effective DPDM in radio astronomy and propose a way in which
InfoSphere Streams can be utilized for large antennae arrays. We present a
case-study: the InfoSphere Streams implementation of an autocorrelating
spectrometer, and using this example we discuss the advantages of the
stream-computing approach and the utilization of hardware accelerators
A structural analysis of the A5/1 state transition graph
We describe efficient algorithms to analyze the cycle structure of the graph
induced by the state transition function of the A5/1 stream cipher used in GSM
mobile phones and report on the results of the implementation. The analysis is
performed in five steps utilizing HPC clusters, GPGPU and external memory
computation. A great reduction of this huge state transition graph of 2^64
nodes is achieved by focusing on special nodes in the first step and removing
leaf nodes that can be detected with limited effort in the second step. This
step does not break the overall structure of the graph and keeps at least one
node on every cycle. In the third step the nodes of the reduced graph are
connected by weighted edges. Since the number of nodes is still huge an
efficient bitslice approach is presented that is implemented with NVIDIA's CUDA
framework and executed on several GPUs concurrently. An external memory
algorithm based on the STXXL library and its parallel pipelining feature
further reduces the graph in the fourth step. The result is a graph containing
only cycles that can be further analyzed in internal memory to count the number
and size of the cycles. This full analysis which previously would take months
can now be completed within a few days and allows to present structural results
for the full graph for the first time. The structure of the A5/1 graph deviates
notably from the theoretical results for random mappings.Comment: In Proceedings GRAPHITE 2012, arXiv:1210.611
- …