scholarly journals Spectrum of myelinated pulmonary afferents (III) cracking intermediate adapting receptors

2020 ◽  
Vol 319 (6) ◽  
pp. R724-R732 ◽  
Author(s):  
Jerry Yu
Keyword(s):  
Afferent Fiber ◽  
Heterogeneous Sensors ◽  
Sensory Unit ◽  
Multiple Sensor ◽  
Single Sensor ◽  
Mechanical Change

Conventional one-sensor theory (one afferent fiber connects to a single sensor) categorizes the bronchopulmonary mechanosensors into the rapidly adapting receptors (RARs), slowly adapting receptors (SARs), or intermediate adapting receptors (IARs). RARs and SARs are known to sense the rate and magnitude of mechanical change, respectively; however, there is no agreement on what IARs sense. Some investigators believe that the three types of sensors are actually one group with similar but different properties and IARs operate within that group. Other investigators (majority) believe IARs overlap with the RARs and SARs and can be classified within them according to their characteristics. Clearly, there is no consensus on IARs function. Recently, a multiple-sensor theory has been advanced in which a sensory unit may contain many heterogeneous sensors, such as both RARs and SARs. There are no IARs. Intermediate adapting unit behavior results from coexistence of RARs and SARs. Therefore, the unit can sense both rate and magnitude of changes. The purpose of this review is to provide evidence that the multiple-sensor theory better explains sensory unit behavior.

scholarly journals Collaborative Navigation as a Solution for PNT Applications in GNSS Challenged Environments – Report on Field Trials of a Joint FIG / IAG Working Group

2015 ◽  
Vol 9 (4) ◽  
Author(s):  
Allison Kealy ◽  
Guenther Retscher ◽  
Charles Toth ◽  
Azmir Hasnur-Rabiain ◽  
Vassilis Gikas ◽  
...  
Keyword(s):  
Working Group ◽  
International Association ◽  
Essential Element ◽  
Field Trials ◽  
Positioning Systems ◽  
The Past ◽  
Viable Solution ◽  
Multiple Sensor ◽  
Single Sensor ◽  
Signals Of Opportunity

AbstractPNT stands for Positioning, Navigation, and Timing. Space-based PNT refers to the capabilities enabled by GNSS, and enhanced by Ground and Space-based Augmentation Systems (GBAS and SBAS), which provide position, velocity, and timing information to an unlimited number of users around the world, allowing every user to operate in the same reference system and timing standard. Such information has become increasingly critical to the security, safety, prosperity, and overall qualityof-life of many citizens. As a result, space-based PNT is now widely recognized as an essential element of the global information infrastructure. This paper discusses the importance of the availability and continuity of PNT information, whose application, scope and significance have exploded in the past 10–15 years. A paradigm shift in the navigation solution has been observed in recent years. It has been manifested by an evolution from traditional single sensor-based solutions, to multiple sensor-based solutions and ultimately to collaborative navigation and layered sensing, using non-traditional sensors and techniques – so called signals of opportunity. A joint working group under the auspices of the International Federation of Surveyors (FIG) and the International Association of Geodesy (IAG), entitled ‘Ubiquitous Positioning Systems’ investigated the use of Collaborative Positioning (CP) through several field trials over the past four years. In this paper, the concept of CP is discussed in detail and selected results of these experiments are presented. It is demonstrated here, that CP is a viable solution if a ‘network’ or ‘neighbourhood’ of users is to be positioned / navigated together, as it increases the accuracy, integrity, availability, and continuity of the PNT information for all users.

DYNAMIC MAINTENANCE OF SUPPORT COVERAGE IN SENSOR NETWORKS

2010 ◽  
Vol 20 (02) ◽  
pp. 155-172 ◽  
Author(s):  
DINESH DASH ◽  
AROBINDA GUPTA ◽  
ARIJIT BISHNU
Keyword(s):  
Constant Factor ◽  
Worst Case ◽  
Sensor Applications ◽  
Sensor Failures ◽  
Detailed Simulation ◽  
Multiple Sensor ◽  
Dynamic Maintenance ◽  
Single Sensor ◽  
Initial Support ◽  
North And South

Ensuring different types of coverage is an important problem in many wireless sensor applications. In this paper, we address the problem of maintaining support coverage in the presence of sensor failures. Given a placement of n sensors in an area A, and any two points i and f in A, the support value of any path between i and f is the maximum distance of any point on the path from its closest sensor. The path with the minimum support value is called the maximal support path. The support value of a path may increase if a sensor fails. Given a maximal support path with a support value ψ, we first present two centralized approximation algorithms that, on failure of a single sensor, compute a new path with a support value close to ψ by moving exactly one nearby sensor. The first algorithm assumes that the sensors are allowed to move in any direction, and the second one assumes that the sensors are constrained to move in any of the four directions east, west, north, and south. Both the support value for the new path computed and the movement necessary are shown to be within a constant-factor of the initial support value. We then show that even in case of multiple sensor failures, a new path with a bounded support value can be computed. Detailed simulation results are provided to show that the algorithms result in significant improvement in many cases in practice, and the improvements obtained are significantly better than the worst case bounds given by the analysis. We also discuss distributed implementations of the algorithms.

scholarly journals Deflation-activated receptors, not classical inflation-activated receptors, mediate the Hering-Breuer deflation reflex

2016 ◽  
Vol 121 (5) ◽  
pp. 1041-1046 ◽  
Author(s):  
Jerry Yu
Keyword(s):  
Respiratory Rate ◽  
Small Animals ◽  
Time Increase ◽  
Lung Inflation ◽  
Sensory Unit ◽  
Airway Secretion ◽  
Multiple Sensor ◽  
Lung Deflation ◽  
Mechanical Sensors ◽  
Large Animals

Many airway sensory units respond to both lung inflation and deflation. Whether those responses to opposite stimuli come from one sensor (one-sensor theory) or more than one sensor (multiple-sensor theory) is debatable. One-sensor theory is commonly presumed in the literature. This article proposes a multiple-sensor theory in which a sensory unit contains different sensors for sensing different forces. Two major types of mechanical sensors operate in the lung: inflation- and deflation-activated receptors (DARs). Inflation-activated sensors can be further divided into slowly adapting receptors (SARs) and rapidly adapting receptors (RARs). Many SAR and RAR units also respond to lung deflation because they contain DARs. Pure DARs, which respond to lung deflation only, are rare in large animals but are easily identified in small animals. Lung deflation-induced reflex effects previously attributed to RARs should be assigned to DARs (including pure DARs and DARs associated with SARs and RARs) if the multiple-sensor theory is accepted. Thus, based on the information, it is proposed that activation of DARs can attenuate lung deflation, shorten expiratory time, increase respiratory rate, evoke inspiration, and cause airway secretion and dyspnea.

scholarly journals A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 244
Author(s):  
Duy Tang Hoang ◽  
Xuan Toa Tran ◽  
Mien Van ◽  
Hee Jun Kang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Deep Neural Network ◽  
Feature Fusion ◽  
Superior Performance ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Sensor Signals ◽  
Multiple Sensor ◽  
Single Sensor

This paper presents a novel method for fusing information from multiple sensor systems for bearing fault diagnosis. In the proposed method, a convolutional neural network is exploited to handle multiple signal sources simultaneously. The most important finding of this paper is that a deep neural network with wide structure can extract automatically and efficiently discriminant features from multiple sensor signals simultaneously. The feature fusion process is integrated into the deep neural network as a layer of that network. Compared to single sensor cases and other fusion techniques, the proposed method achieves superior performance in experiments with actual bearing data.

scholarly journals Alarm method of communication intelligent manhole cover based on multiple event fusion

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chengfan Li ◽  
Shanming Gu ◽  
Gang Guo ◽  
Xuefeng Liu ◽  
Lan Liu ◽  
...  
Keyword(s):  
False Alarm ◽  
Random Error ◽  
Working Environment ◽  
Experimental Result ◽  
Monitoring Method ◽  
Sensor Signals ◽  
Multiple Sensor ◽  
Single Sensor ◽  
Status Signals ◽  
Monitoring Service

AbstractAs a comprehensive utilization of pipeline resources, communication intelligent manhole cover (CIMC) can effectively real-time monitor communication manhole cover and protect the safety of communication pipeline. Due to the complex working environment of manhole cover and the random error of sensor, the traditional monitoring method usual leads to frequent false alarm in actual applications. In order to ensure the monitoring service quality and improve the service efficiency, a new alarm method of CIMC with multiple event fusion in this paper via jointing analysis of multi-sensor status signals is proposed based on the equipment status signals generated by the CIMC terminal and abnormal alarm events definition. The experimental result shows that the proposed CIMC alarm method by means of multiple sensor signals in this paper can not only make up for the defect of a single sensor, but also reduces the false alarm rate caused by the random error of sensor and CIMC system. It can promote the intelligent monitoring efficiency of the manhole cover and be conducive to the construction of intelligent transportation and smart city.

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

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