Auditory/visual distance estimation: accuracy and variability

Broadband acoustic Doppler current profiler (ADCP) is widely used in agricultural water resource explorations, such as river discharge monitoring and flood warning. Improving the velocity estimation accuracy of broadband ADCP by adjusting the waveform parameters of a phase-encoded signal will reduce the velocity measurement range and water stratification accuracy, while the promotion of stratification accuracy will degrade the velocity estimation accuracy. In order to minimize the impact of these two problems on the measurement results, the ADCP waveform optimization problem that satisfies the environment constraints while keeping high velocity estimation accuracy or stratification accuracy is studied. Firstly, the relationship between velocity or distance estimation accuracy and signal waveform parameters is studied by using an ambiguity function. Secondly, the constraints of current velocity range, velocity distribution and other environmental characteristics on the waveform parameters are studied. For two common measurement applications, two dynamic configuration methods of waveform parameters with environmental adaptability and optimal velocity estimation accuracy or stratification accuracy are proposed based on the nonlinear programming principle. Experimental results show that compared with the existing methods, the velocity estimation accuracy of the proposed method is improved by more than 50%, and the stratification accuracy is improved by more than 22%.

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Visual Distance Estimation in Static Compared to Moving Virtual Scenes

The Spanish Journal of Psychology ◽

10.1017/s1138741600006223 ◽

2006 ◽

Vol 9 (2) ◽

pp. 321-331 ◽

Cited By ~ 4

Author(s):

Harald Frenz ◽

Markus Lappe

Keyword(s):

Virtual Environment ◽

Visual Motion ◽

Human Subjects ◽

Distance Estimation ◽

Visual Space ◽

Depth Interval ◽

Natural Scenes ◽

Virtual Scene ◽

Visual Distance ◽

Control Direction

Visual motion is used to control direction and speed of self-motion and time-to-contact with an obstacle. In earlier work, we found that human subjects can discriminate between the distances of different visually simulated self-motions in a virtual scene. Distance indication in terms of an exocentric interval adjustment task, however, revealed linear correlation between perceived and indicated distances but with a profound distance underestimation. One possible explanation for this underestimation is the perception of visual space in virtual environments. Humans perceive visual space in natural scenes as curved, and distances are increasingly underestimated with increasing distance from the observer. Such spatial compression may also exist in our virtual environment. We therefore surveyed perceived visual space in a static virtual scene. We asked observers to compare two horizontal depth intervals, similar to experiments performed in natural space. Subjects had to indicate the size of one depth interval relative to a second interval. Our observers perceived visual space in the virtual environment as compressed, similar to the perception found in natural scenes. However, the nonlinear depth function we found can not explain the observed distance underestimation of visual simulated self-motions in the same environment.

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Embedded Real-Time Visual Search with Visual Distance Estimation

Image Analysis and Processing - ICIAP 2017 - Lecture Notes in Computer Science ◽

10.1007/978-3-319-68548-9_6 ◽

2017 ◽

pp. 59-69

Author(s):

Marco Paracchini ◽

Emanuele Plebani ◽

Mehdi Ben Iche ◽

Danilo Pietro Pau ◽

Marco Marcon

Keyword(s):

Visual Search ◽

Real Time ◽

Distance Estimation ◽

Visual Distance

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Multimode Pedestrian Dead Reckoning Gait Detection Algorithm Based on Identification of Pedestrian Phone Carrying Position

Mobile Information Systems ◽

10.1155/2019/4709501 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Ying Guo ◽

Qinghua Liu ◽

Xianlei Ji ◽

Shengli Wang ◽

Mingyang Feng ◽

...

Keyword(s):

Inertial Sensors ◽

Distance Estimation ◽

Dead Reckoning ◽

Principal Component ◽

Detection Algorithm ◽

Estimation Accuracy ◽

Indoor Environments ◽

Using Data ◽

The Time Domain ◽

Pedestrian Dead Reckoning

Pedestrian dead reckoning (PDR) is an essential technology for positioning and navigation in complex indoor environments. In the process of PDR positioning and navigation using mobile phones, gait information acquired by inertial sensors under various carrying positions differs from noise contained in the heading information, resulting in excessive gait detection deviation and greatly reducing the positioning accuracy of PDR. Using data from mobile phone accelerometer and gyroscope signals, this paper examined various phone carrying positions and switching positions as the research objective and analysed the time domain characteristics of the three-axis accelerometer and gyroscope signals. A principal component analysis algorithm was used to reduce the dimension of the extracted multidimensional gait feature, and the extracted features were random forest modelled to distinguish the phone carrying positions. The results show that the step detection and distance estimation accuracy in the gait detection process greatly improved after recognition of the phone carrying position, which enhanced the robustness of the PDR algorithm.

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A screen for constituents of motor control and decision making in Drosophila reveals visual distance-estimation neurons

Scientific Reports ◽

10.1038/srep27000 ◽

2016 ◽

Vol 6 (1) ◽

Cited By ~ 9

Author(s):

Tilman Triphan ◽

Aljoscha Nern ◽

Sonia F. Roberts ◽

Wyatt Korff ◽

Daniel Q. Naiman ◽

...

Keyword(s):

Decision Making ◽

Motor Control ◽

Distance Estimation ◽

Visual Distance

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Auditory and visual distance estimation

10.1121/1.3656353 ◽

2011 ◽

Cited By ~ 1

Author(s):

Paul W. Anderson ◽

Pavel Zahorik

Keyword(s):

Distance Estimation ◽

Visual Distance

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A simple faulted phase-based fault distance estimation algorithm for a loop distribution system

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v25.i1.pp14-24 ◽

2022 ◽

Vol 25 (1) ◽

pp. 14

Author(s):

Shwe Myint ◽

Warit Wichakool

Keyword(s):

Traveling Wave ◽

Distribution System ◽

Signal To Noise Ratio ◽

Distance Estimation ◽

Estimation Algorithm ◽

Estimation Accuracy ◽

Discrete Wavelet ◽

Band Pass Filter ◽

Localization Algorithm ◽

Pass Filter

This paper presents a single ended faulted phase-based traveling wave fault localization algorithm for loop distribution grids which is that the sensor can get many reflected signals from the fault point to face the complexity of localization. This localization algorithm uses a band pass filter to remove noise from the corrupted signal. The arriving times of the faulted phase-based filtered signals can be obtained by using phase-modal and discrete wavelet transformations. The estimated fault distance can be calculated using the traveling wave method. The proposed algorithm presents detail level analysis using three detail levels coefficients. The proposed algorithm is tested with MATLAB simulation single line to ground fault in a 10 kV grounded loop distribution system. The simulation result shows that the faulted phase time delay can give better accuracy than using conventional time delays. The proposed algorithm can give fault distance estimation accuracy up to 99.7% with 30 dB contaminated signal-to-noise ratio (SNR) for the nearest lines from the measured terminal.

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Point of Interest Detection and Visual Distance Estimation for Sensor-Rich Video

IEEE Transactions on Multimedia ◽

10.1109/tmm.2014.2330802 ◽

2014 ◽

Vol 16 (7) ◽

pp. 1929-1941 ◽

Cited By ~ 9

Author(s):

Jia Hao ◽

Guanfeng Wang ◽

Beomjoo Seo ◽

Roger Zimmermann

Keyword(s):

Distance Estimation ◽

Point Of Interest ◽

Visual Distance ◽

Interest Detection

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Landmark Direction and Distance Estimation in Large Scale Virtual Environments

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181397041002109 ◽

1997 ◽

Vol 41 (2) ◽

pp. 1213-1217 ◽

Cited By ~ 1

Author(s):

Robert C. Allen ◽

Daniel P. McDonald ◽

Michael J. Singer

Keyword(s):

Virtual Environments ◽

Virtual Environment ◽

Large Scale ◽

Distance Estimation ◽

Visual Display ◽

Task Demands ◽

Current Paper ◽

Estimation Accuracy ◽

Display Control

The current paper describes our classification of errors participants made when estimating direction and distances in a large scale (2000 m × 2000 m) Virtual Environment (VE). Two VE configuration groups (Low or High Interactivity) traversed a 400 m route through one of two Virtual Terrain's (Distinctive or Non-Distinctive or Terrain 1 and 2, respectively) in 100 m increments. The High VE group used a treadmill to move through the VE with head tracked visual displays; the Low VE group used a joystick for movement and visual display control. Results indicate that as experience within either terrain increased, participants demonstrated an improved ability to directionally locate landmarks. Experience in the environment did not affect distance estimation accuracy. Terrain 1 participants were more accurate in locating proximal, as opposed to distal, landmarks. They also overestimated distances to near landmarks and underestimated distances to far landmarks. In Terrain 2, the Low VE group gave more accurate distance estimations. We believe this result can be explained in terms of increased task demands placed on the High VE Group.

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