Link failure detection in software defined networks: an active feedback mechanism

2017 ◽  
Vol 53 (11) ◽  
pp. 722-724 ◽  
Author(s):  
Han Xu ◽  
Lianshan Yan ◽  
Huanlai Xing ◽  
Yunhe Cui ◽  
Saifei Li
Keyword(s):  
Failure Detection ◽  
Feedback Mechanism ◽  
Link Failure ◽  
Software Defined Networks ◽  
Active Feedback

scholarly journals Link Failure Detection and Classification in Wireless Sensor Networks using Classification Method

2019 ◽  
Vol 8 (12) ◽  
pp. 1132-1135
Keyword(s):  
System Performance ◽  
Detection Rate ◽  
Sensor Node ◽  
Weak Link ◽  
Failure Detection ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Link Failure ◽  
Packet Delivery

This paper develops a method to detect the failures of wireless links between one sensor nodes to another sensor node in WSN environment. Every node in WSN has certain properties which may vary time to time based on its ability to transfer or receive the packets on it. This property or features are obtained from every node and they are classified using Neural Networks (NN) classifier with predetermined feature set which are belonging to both weak link and good link between nodes in wireless networks. The proposed system performance is analyzed by computing Packet Delivery Ratio (PDR), Link Failure Detection Rate (LFDR) and latency report.

scholarly journals Sensory reweighting dynamics in human postural control

2014 ◽  
Vol 111 (9) ◽  
pp. 1852-1864 ◽  
Author(s):  
Lorenz Assländer ◽  
Robert J. Peterka
Keyword(s):  
Balance Control ◽  
High Amplitude ◽  
Feedback Mechanism ◽  
Sensory Reweighting ◽  
Healthy Humans ◽  
Active Feedback ◽  
Orientation Cues ◽  
Young Subjects ◽  
Low Amplitude ◽  
Control Balance

Healthy humans control balance during stance by using an active feedback mechanism that generates corrective torque based on a combination of movement and orientation cues from visual, vestibular, and proprioceptive systems. Previous studies found that the contribution of each of these sensory systems changes depending on perturbations applied during stance and on environmental conditions. The process of adjusting the sensory contributions to balance control is referred to as sensory reweighting. To investigate the dynamics of reweighting for the sensory modalities of vision and proprioception, 14 healthy young subjects were exposed to six different combinations of continuous visual scene and platform tilt stimuli while sway responses were recorded. Stimuli consisted of two components: 1) a pseudorandom component whose amplitude periodically switched between low and high amplitudes and 2) a low-amplitude sinusoidal component whose amplitude remained constant throughout a trial. These two stimuli were mathematically independent of one another and, thus, permitted separate analyses of sway responses to the two components. For all six stimulus combinations, the sway responses to the constant-amplitude sine were influenced by the changing amplitude of the pseudorandom component in a manner consistent with sensory reweighting. Results show clear evidence of intra- and intermodality reweighting. Reweighting dynamics were asymmetric, with slower reweighting dynamics following a high-to-low transition in the pseudorandom stimulus amplitude compared with low-to-high amplitude shifts, and were also slower for inter- compared with intramodality reweighting.

scholarly journals Cyclic neutropenia (CN): a clue to the control of granulopoiesis

Blood ◽  
1982 ◽  
Vol 59 (1) ◽  
pp. 27-37
Author(s):  
GK von Schulthess ◽  
NA Mazer
Keyword(s):  
Bone Marrow ◽  
Production Rate ◽  
Negative Feedback ◽  
Normal State ◽  
Oscillation Period ◽  
Feedback Mechanism ◽  
Sustained Oscillation ◽  
Feedback Signal ◽  
Cyclic Neutropenia ◽  
Active Feedback

A simple quantitative feedback model of granulopoiesis is presented and discussed within the framework of existing data on granulopoiesis in both normals and patients with cyclic neutropenia (CN). The model assumes that the controlled compartment is the bone marrow pool of mature neutrophils (PMNs), which sends a negative feedback signal to the mitotic pool of early granulocyte precursors (i.e., CFU-C, myeloblasts, etc.) thus controlling the granulocyte production rate. Three parameters are found to play important roles in determining the response of the system to perturbations. These are: TM, the granulocyte maturation time; a, a parameter reflecting the strength of the negative feedback exerted by mature PMNs on the granulocyte production rate; and b, a parameter describing the leakiness of the bone marrow for PMN egress. It is shown that depending on the relative magnitudes of a and b, the system will either respond to perturbations with a damped oscillation (a less than b: the normal state) or with a sustained oscillation (a greater than b: the CN state). In both cases, the oscillation period is found to approximately equal 2TM. Deductions of the values of a, b, and TM from experimental data are consistent with the predictions of the model and show an increased value of a in CN relative to the normal state. This suggests an overly active feedback mechanism as the pathophysiologic basis of CN. In addition, the model can explain how various therapeutic agent correct CN and also provides insight into why other hematologic cell lines and CSA oscillate in CN.

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