242,474 research outputs found
Intention Tremor and Deficits of Sensory Feedback Control in Multiple Sclerosis: a Pilot Study
Background
Intention tremor and dysmetria are leading causes of upper extremity disability in Multiple Sclerosis (MS). The development of effective therapies to reduce tremor and dysmetria is hampered by insufficient understanding of how the distributed, multi-focal lesions associated with MS impact sensorimotor control in the brain. Here we describe a systems-level approach to characterizing sensorimotor control and use this approach to examine how sensory and motor processes are differentially impacted by MS. Methods
Eight subjects with MS and eight age- and gender-matched healthy control subjects performed visually-guided flexion/extension tasks about the elbow to characterize a sensory feedback control model that includes three sensory feedback pathways (one for vision, another for proprioception and a third providing an internal prediction of the sensory consequences of action). The model allows us to characterize impairments in sensory feedback control that contributed to each MS subject’s tremor. Results
Models derived from MS subject performance differed from those obtained for control subjects in two ways. First, subjects with MS exhibited markedly increased visual feedback delays, which were uncompensated by internal adaptive mechanisms; stabilization performance in individuals with the longest delays differed most from control subject performance. Second, subjects with MS exhibited misestimates of arm dynamics in a way that was correlated with tremor power. Subject-specific models accurately predicted kinematic performance in a reach and hold task for neurologically-intact control subjects while simulated performance of MS patients had shorter movement intervals and larger endpoint errors than actual subject responses. This difference between simulated and actual performance is consistent with a strategic compensatory trade-off of movement speed for endpoint accuracy. Conclusions
Our results suggest that tremor and dysmetria may be caused by limitations in the brain’s ability to adapt sensory feedback mechanisms to compensate for increases in visual information processing time, as well as by errors in compensatory adaptations of internal estimates of arm dynamics
Tightening the Bounds on Cache-Related Preemption Delay in Fixed Preemption Point Scheduling
Limited Preemptive Fixed Preemption Point scheduling (LP-FPP) has the ability to decrease and control the preemption-related overheads in the real-time task systems, compared to other limited or fully preemptive scheduling approaches. However, existing methods for computing the preemption overheads in LP-FPP systems rely on over-approximation of the evicting cache blocks (ECB) calculations, potentially leading to pessimistic schedulability analysis.
In this paper, we propose a novel method for preemption cost calculation that exploits the benefits of the LP-FPP task model both at the scheduling and cache analysis level. The method identifies certain infeasible preemption combinations, based on analysis on the scheduling level, and combines it with cache analysis information into a constraint problem from which less pessimistic upper bounds on cache-related preemption delays (CRPD) can be derived.
The evaluation results indicate that our proposed method has the potential to significantly reduce the upper bound on CRPD, by up to 50% in our experiments, compared to the existing over-approximating calculations of the eviction scenarios
PALS-Based Analysis of an Airplane Multirate Control System in Real-Time Maude
Distributed cyber-physical systems (DCPS) are pervasive in areas such as
aeronautics and ground transportation systems, including the case of
distributed hybrid systems. DCPS design and verification is quite challenging
because of asynchronous communication, network delays, and clock skews.
Furthermore, their model checking verification typically becomes unfeasible due
to the huge state space explosion caused by the system's concurrency. The PALS
("physically asynchronous, logically synchronous") methodology has been
proposed to reduce the design and verification of a DCPS to the much simpler
task of designing and verifying its underlying synchronous version. The
original PALS methodology assumes a single logical period, but Multirate PALS
extends it to deal with multirate DCPS in which components may operate with
different logical periods. This paper shows how Multirate PALS can be applied
to formally verify a nontrivial multirate DCPS. We use Real-Time Maude to
formally specify a multirate distributed hybrid system consisting of an
airplane maneuvered by a pilot who turns the airplane according to a specified
angle through a distributed control system. Our formal analysis revealed that
the original design was ineffective in achieving a smooth turning maneuver, and
led to a redesign of the system that satisfies the desired correctness
properties. This shows that the Multirate PALS methodology is not only
effective for formal DCPS verification, but can also be used effectively in the
DCPS design process, even before properties are verified.Comment: In Proceedings FTSCS 2012, arXiv:1212.657
Congestion Control for Network-Aware Telehaptic Communication
Telehaptic applications involve delay-sensitive multimedia communication
between remote locations with distinct Quality of Service (QoS) requirements
for different media components. These QoS constraints pose a variety of
challenges, especially when the communication occurs over a shared network,
with unknown and time-varying cross-traffic. In this work, we propose a
transport layer congestion control protocol for telehaptic applications
operating over shared networks, termed as dynamic packetization module (DPM).
DPM is a lossless, network-aware protocol which tunes the telehaptic
packetization rate based on the level of congestion in the network. To monitor
the network congestion, we devise a novel network feedback module, which
communicates the end-to-end delays encountered by the telehaptic packets to the
respective transmitters with negligible overhead. Via extensive simulations, we
show that DPM meets the QoS requirements of telehaptic applications over a wide
range of network cross-traffic conditions. We also report qualitative results
of a real-time telepottery experiment with several human subjects, which reveal
that DPM preserves the quality of telehaptic activity even under heavily
congested network scenarios. Finally, we compare the performance of DPM with
several previously proposed telehaptic communication protocols and demonstrate
that DPM outperforms these protocols.Comment: 25 pages, 19 figure
TOFEC: Achieving Optimal Throughput-Delay Trade-off of Cloud Storage Using Erasure Codes
Our paper presents solutions using erasure coding, parallel connections to
storage cloud and limited chunking (i.e., dividing the object into a few
smaller segments) together to significantly improve the delay performance of
uploading and downloading data in and out of cloud storage.
TOFEC is a strategy that helps front-end proxy adapt to level of workload by
treating scalable cloud storage (e.g. Amazon S3) as a shared resource requiring
admission control. Under light workloads, TOFEC creates more smaller chunks and
uses more parallel connections per file, minimizing service delay. Under heavy
workloads, TOFEC automatically reduces the level of chunking (fewer chunks with
increased size) and uses fewer parallel connections to reduce overhead,
resulting in higher throughput and preventing queueing delay. Our trace-driven
simulation results show that TOFEC's adaptation mechanism converges to an
appropriate code that provides the optimal delay-throughput trade-off without
reducing system capacity. Compared to a non-adaptive strategy optimized for
throughput, TOFEC delivers 2.5x lower latency under light workloads; compared
to a non-adaptive strategy optimized for latency, TOFEC can scale to support
over 3x as many requests
Implementation of Model Based Networked Predictive Control System
Networked control systems are made up of several computer nodes
communicating over a communication channel, cooperating to control a
plant. The stability of the plant depends on the end to end delay from
sensor to the actuator. Although computational delays within the
computer nodes can be made bounded, delays through the
communication network are generally unpredictable. A method which
aims to protect the stability of the plant under communication delays
and data loss, Model Based Predictive Networked Control System
(MBPNCS), has previously been proposed by the authors. This paper aims
to demonstrate the implementation of this type of networked control
system on a non-real-time communication network; Ethernet.
In this paper, we first briefly describe the MBPNCS method, then
discuss the implementation, detailing the properties of the operating
system, communications and hardware, and later give the results on the
performance of the Model Based Predictive Networked Control System
implementation controlling a DC motor.
This work was supported in part by the Scientific and Technological Re
search Council of Turkey, project code 106E155
Improving Brain–Machine Interface Performance by Decoding Intended Future Movements
Objective. A brain–machine interface (BMI) records neural signals in real time from a subject\u27s brain, interprets them as motor commands, and reroutes them to a device such as a robotic arm, so as to restore lost motor function. Our objective here is to improve BMI performance by minimizing the deleterious effects of delay in the BMI control loop. We mitigate the effects of delay by decoding the subject\u27s intended movements a short time lead in the future. Approach. We use the decoded, intended future movements of the subject as the control signal that drives the movement of our BMI. This should allow the user\u27s intended trajectory to be implemented more quickly by the BMI, reducing the amount of delay in the system. In our experiment, a monkey (Macaca mulatta) uses a future prediction BMI to control a simulated arm to hit targets on a screen. Main Results. Results from experiments with BMIs possessing different system delays (100, 200 and 300 ms) show that the monkey can make significantly straighter, faster and smoother movements when the decoder predicts the user\u27s future intent. We also characterize how BMI performance changes as a function of delay, and explore offline how the accuracy of future prediction decoders varies at different time leads. Significance. This study is the first to characterize the effects of control delays in a BMI and to show that decoding the user\u27s future intent can compensate for the negative effect of control delay on BMI performance
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