29,891 research outputs found
Forward and backward simulations II. Timing-based systems
AbstractA general automaton model for timing-based systems is presented and is used as the context for developing a variety of simulation proof techniques for such systems. These techniques include (1) refinements, (2) forward and backward simulations, (3) hybrid forward–backward and backward–forward simulations, and (4) history and prophecy relations. Relationships between the different types of simulations, as well as soundness and completeness results, are stated and proved. These results are (with one exception) analogous to the results for untimed systems in Part I of this paper. In fact, many of the results for the timed case are obtained as consequences of the analogous results for the untimed case
The STRESS Method for Boundary-point Performance Analysis of End-to-end Multicast Timer-Suppression Mechanisms
Evaluation of Internet protocols usually uses random scenarios or scenarios
based on designers' intuition. Such approach may be useful for average-case
analysis but does not cover boundary-point (worst or best-case) scenarios. To
synthesize boundary-point scenarios a more systematic approach is needed.In
this paper, we present a method for automatic synthesis of worst and best case
scenarios for protocol boundary-point evaluation.
Our method uses a fault-oriented test generation (FOTG) algorithm for
searching the protocol and system state space to synthesize these scenarios.
The algorithm is based on a global finite state machine (FSM) model. We extend
the algorithm with timing semantics to handle end-to-end delays and address
performance criteria. We introduce the notion of a virtual LAN to represent
delays of the underlying multicast distribution tree. The algorithms used in
our method utilize implicit backward search using branch and bound techniques
and start from given target events. This aims to reduce the search complexity
drastically. As a case study, we use our method to evaluate variants of the
timer suppression mechanism, used in various multicast protocols, with respect
to two performance criteria: overhead of response messages and response time.
Simulation results for reliable multicast protocols show that our method
provides a scalable way for synthesizing worst-case scenarios automatically.
Results obtained using stress scenarios differ dramatically from those obtained
through average-case analyses. We hope for our method to serve as a model for
applying systematic scenario generation to other multicast protocols.Comment: 24 pages, 10 figures, IEEE/ACM Transactions on Networking (ToN) [To
appear
Soma-Axon Coupling Configurations That Enhance Neuronal Coincidence Detection
Coincidence detector neurons transmit timing information by responding preferentially to concurrent synaptic inputs. Principal cells of the medial superior olive (MSO) in the mammalian auditory brainstem are superb coincidence detectors. They encode sound source location with high temporal precision, distinguishing submillisecond timing differences among inputs. We investigate computationally how dynamic coupling between the input region (soma and dendrite) and the spike-generating output region (axon and axon initial segment) can enhance coincidence detection in MSO neurons. To do this, we formulate a two-compartment neuron model and characterize extensively coincidence detection sensitivity throughout a parameter space of coupling configurations. We focus on the interaction between coupling configuration and two currents that provide dynamic, voltage-gated, negative feedback in subthreshold voltage range: sodium current with rapid inactivation and low-threshold potassium current, IKLT. These currents reduce synaptic summation and can prevent spike generation unless inputs arrive with near simultaneity. We show that strong soma-to-axon coupling promotes the negative feedback effects of sodium inactivation and is, therefore, advantageous for coincidence detection. Furthermore, the feedforward combination of strong soma-to-axon coupling and weak axon-to-soma coupling enables spikes to be generated efficiently (few sodium channels needed) and with rapid recovery that enhances high-frequency coincidence detection. These observations detail the functional benefit of the strongly feedforward configuration that has been observed in physiological studies of MSO neurons. We find that IKLT further enhances coincidence detection sensitivity, but with effects that depend on coupling configuration. For instance, in models with weak soma-to-axon and weak axon-to-soma coupling, IKLT in the axon enhances coincidence detection more effectively than IKLT in the soma. By using a minimal model of soma-to-axon coupling, we connect structure, dynamics, and computation. Although we consider the particular case of MSO coincidence detectors, our method for creating and exploring a parameter space of two-compartment models can be applied to other neurons
Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition
A neuromorphic chip that combines CMOS analog spiking neurons and memristive
synapses offers a promising solution to brain-inspired computing, as it can
provide massive neural network parallelism and density. Previous hybrid analog
CMOS-memristor approaches required extensive CMOS circuitry for training, and
thus eliminated most of the density advantages gained by the adoption of
memristor synapses. Further, they used different waveforms for pre and
post-synaptic spikes that added undesirable circuit overhead. Here we describe
a hardware architecture that can feature a large number of memristor synapses
to learn real-world patterns. We present a versatile CMOS neuron that combines
integrate-and-fire behavior, drives passive memristors and implements
competitive learning in a compact circuit module, and enables in-situ
plasticity in the memristor synapses. We demonstrate handwritten-digits
recognition using the proposed architecture using transistor-level circuit
simulations. As the described neuromorphic architecture is homogeneous, it
realizes a fundamental building block for large-scale energy-efficient
brain-inspired silicon chips that could lead to next-generation cognitive
computing.Comment: This is a preprint of an article accepted for publication in IEEE
Journal on Emerging and Selected Topics in Circuits and Systems, vol 5, no.
2, June 201
The time-of-propagation counter for Belle II
The Belle II detector operating at the future upgrade to the KEKB accelerator
will perform high-statistics precision investigations into the flavor sector of
the Standard Model. As charged hadron identification is a vital element of the
experiment's success, the time-of-propagation (TOP) counter has been chosen as
the primary particle identification device in the barrel region of Belle II.
The TOP counter is a compact variant of the detection of internally reflected
Cherenkov light (DIRC) technique and relies heavily on exquisite single photon
timing resolution with micro-channel plate photomultiplier tubes. We discuss
the general principles of TOP operation and optimization of the Belle II TOP
configuration, which is expected to provide 4 sigma or better separation of
kaons and pions up to momenta of approximately 4 GeV/c.Comment: 5 pages, 5 figures, 2 tables; Submitted to special edition of NIMA,
Proceedings of RICH201
Imprecise dynamic walking with time-projection control
We present a new walking foot-placement controller based on 3LP, a 3D model
of bipedal walking that is composed of three pendulums to simulate falling,
swing and torso dynamics. Taking advantage of linear equations and closed-form
solutions of the 3LP model, our proposed controller projects intermediate
states of the biped back to the beginning of the phase for which a discrete LQR
controller is designed. After the projection, a proper control policy is
generated by this LQR controller and used at the intermediate time. This
control paradigm reacts to disturbances immediately and includes rules to
account for swing dynamics and leg-retraction. We apply it to a simulated Atlas
robot in position-control, always commanded to perform in-place walking. The
stance hip joint in our robot keeps the torso upright to let the robot
naturally fall, and the swing hip joint tracks the desired footstep location.
Combined with simple Center of Pressure (CoP) damping rules in the low-level
controller, our foot-placement enables the robot to recover from strong pushes
and produce periodic walking gaits when subject to persistent sources of
disturbance, externally or internally. These gaits are imprecise, i.e.,
emergent from asymmetry sources rather than precisely imposing a desired
velocity to the robot. Also in extreme conditions, restricting linearity
assumptions of the 3LP model are often violated, but the system remains robust
in our simulations. An extensive analysis of closed-loop eigenvalues, viable
regions and sensitivity to push timings further demonstrate the strengths of
our simple controller
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