The Extrinsic/Intrinsic Classification of Motion Signals is a High-Level Process

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 104-104 ◽  
Author(s):  
E Castet ◽  
V Charton ◽  
A Dufour
Keyword(s):  
Initial Study ◽  
Unexpected Result ◽  
Experimental Procedure ◽  
Main Determinant ◽  
Long Duration ◽  
Uncrossed Disparity ◽  
High Level ◽  
Level Process ◽  
Intrinsic Classification

It has been proposed that the 2-D motion signals elicited by the bar endings of a barber-pole stimulus disambiguate 1-D motion signals with a variable strength which depends on depth (Shimojo et al, 1989 Vision Research29 619 – 626): these signals would be ‘abolished’ when they are extrinsic (ie the moving grating is behind the plane of the background containing the aperture), whereas they would be given full strength when they are intrinsic (ie the plane of the grating is in front of the background). These authors have suggested that the intrinsic/extrinsic classification is an early process. However, the very long duration (2300 ms) used in their study suggests other interpretations. Therefore, we tried to test whether the barber-pole illusion could be abolished with a shorter duration when the grating had an uncrossed disparity relative to the aperture plane, as initially described in the above-mentioned study (our 30 observers had to adjust an arrow to indicate the perceived direction of the grating). In accordance with our prediction, we could not replicate their finding with a duration of 400 ms. Surprisingly, increasing the duration up to 2300 ms was not sufficient to obtain a large bias towards 1-D signals. To understand this unexpected result, we tried to isolate the relevant difference between the initial study of Shimojo et al and our. We found that the main determinant of the suppression of the barber-pole illusion was the experimental procedure: when our observers had to assess the perceived direction of the barber-pole by choosing between horizontal and vertical, as in the initial study, the results did show a much larger bias towards 1-D signals. We suggest therefore that the extrinsic/intrinsic classification is a high-level process which can be influenced by the observer's expectations.

scholarly journals Semi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yersultan Mirasbekov ◽  
Adina Zhumakhanova ◽  
Almira Zhantuyakova ◽  
Kuanysh Sarkytbayev ◽  
Dmitry V. Malashenkov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Spatial Resolution ◽  
Mesocosm Experiment ◽  
Imaging Flow Cytometry ◽  
Leibler Divergence ◽  
Temporal And Spatial ◽  
High Level ◽  
Training Sets

AbstractA machine learning approach was employed to detect and quantify Microcystis colonial morphospecies using FlowCAM-based imaging flow cytometry. The system was trained and tested using samples from a long-term mesocosm experiment (LMWE, Central Jutland, Denmark). The statistical validation of the classification approaches was performed using Hellinger distances, Bray–Curtis dissimilarity, and Kullback–Leibler divergence. The semi-automatic classification based on well-balanced training sets from Microcystis seasonal bloom provided a high level of intergeneric accuracy (96–100%) but relatively low intrageneric accuracy (67–78%). Our results provide a proof-of-concept of how machine learning approaches can be applied to analyze the colonial microalgae. This approach allowed to evaluate Microcystis seasonal bloom in individual mesocosms with high level of temporal and spatial resolution. The observation that some Microcystis morphotypes completely disappeared and re-appeared along the mesocosm experiment timeline supports the hypothesis of the main transition pathways of colonial Microcystis morphoforms. We demonstrated that significant changes in the training sets with colonial images required for accurate classification of Microcystis spp. from time points differed by only two weeks due to Microcystis high phenotypic heterogeneity during the bloom. We conclude that automatic methods not only allow a performance level of human taxonomist, and thus be a valuable time-saving tool in the routine-like identification of colonial phytoplankton taxa, but also can be applied to increase temporal and spatial resolution of the study.

scholarly journals Human Hand Anatomy-Based Prosthetic Hand

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 137
Author(s):  
Larisa Dunai ◽  
Martin Novak ◽  
Carmen García Espert
Keyword(s):  
3D Printing ◽  
Degrees Of Freedom ◽  
Use Of Force ◽  
Hand Movements ◽  
Human Hand ◽  
Prosthetic Hand ◽  
Object Surface ◽  
Hand Phalanges ◽  
High Level

The present paper describes the development of a prosthetic hand based on human hand anatomy. The hand phalanges are printed with 3D printing with Polylactic Acid material. One of the main contributions is the investigation on the prosthetic hand joins; the proposed design enables one to create personalized joins that provide the prosthetic hand a high level of movement by increasing the degrees of freedom of the fingers. Moreover, the driven wire tendons show a progressive grasping movement, being the friction of the tendons with the phalanges very low. Another important point is the use of force sensitive resistors (FSR) for simulating the hand touch pressure. These are used for the grasping stop simulating touch pressure of the fingers. Surface Electromyogram (EMG) sensors allow the user to control the prosthetic hand-grasping start. Their use may provide the prosthetic hand the possibility of the classification of the hand movements. The practical results included in the paper prove the importance of the soft joins for the object manipulation and to get adapted to the object surface. Finally, the force sensitive sensors allow the prosthesis to actuate more naturally by adding conditions and classifications to the Electromyogram sensor.

scholarly journals Testing Todd: family types and development

2021 ◽  
pp. 1-18
Author(s):  
Jerg Gutmann ◽  
Stefan Voigt
Keyword(s):  
Rule Of Law ◽  
Civil Society Organizations ◽  
The Rule Of Law ◽  
State Fragility ◽  
Family Types ◽  
Low Levels ◽  
Parsimonious Model ◽  
The One ◽  
High Level

Abstract Many years ago, Emmanuel Todd came up with a classification of family types and argued that the historically prevalent family types in a society have important consequences for its economic, political, and social development. Here, we evaluate Todd's most important predictions empirically. Relying on a parsimonious model with exogenous covariates, we find mixed results. On the one hand, authoritarian family types are, in stark contrast to Todd's predictions, associated with increased levels of the rule of law and innovation. On the other hand, and in line with Todd's expectations, communitarian family types are linked to racism, low levels of the rule of law, and late industrialization. Countries in which endogamy is frequently practiced also display an expectedly high level of state fragility and weak civil society organizations.

scholarly journals Space Precession Target Classification Based on Radar High-Resolution Range Profiles

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Yizhe Wang ◽  
Cunqian Feng ◽  
Yongshun Zhang ◽  
Sisan He
Keyword(s):  
Parameter Extraction ◽  
Classification Performance ◽  
Support Vector ◽  
Electromagnetic Data ◽  
Feature Extractor ◽  
Different Types ◽  
Radar Echo ◽  
High Level ◽  
Cone Target

Precession is a common micromotion form of space targets, introducing additional micro-Doppler (m-D) modulation into the radar echo. Effective classification of space targets is of great significance for further micromotion parameter extraction and identification. Feature extraction is a key step during the classification process, largely influencing the final classification performance. This paper presents two methods for classifying different types of space precession targets from the HRRPs. We first establish the precession model of space targets and analyze the scattering characteristics and then compute electromagnetic data of the cone target, cone-cylinder target, and cone-cylinder-flare target. Experimental results demonstrate that the support vector machine (SVM) using histograms of oriented gradient (HOG) features achieves a good result, whereas the deep convolutional neural network (DCNN) obtains a higher classification accuracy. DCNN combines the feature extractor and the classifier itself to automatically mine the high-level signatures of HRRPs through a training process. Besides, the efficiency of the two classification processes are compared using the same dataset.

Classification of a Granular Product using High-Level Fusion of Vision Features

1997 ◽  
Vol 68 (2) ◽  
pp. 115-124 ◽  
Author(s):  
F. Ros ◽  
S. Guillaume ◽  
V. Bellon-Maurel
Keyword(s):  
High Level ◽  
Level Fusion

scholarly journals Implementation of Six Sigma in Supply Chain Management in Industries

2009 ◽  
pp. 141-144
Author(s):  
Himanshu S. Moharana ◽  
J.S. Murty ◽  
S. K. Senapati ◽  
K. Khuntia
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Six Sigma ◽  
Strategic Analysis ◽  
Process Mapping ◽  
Chain Management ◽  
Low Level ◽  
Implementation Model ◽  
High Level ◽  
Level Process

There is increasing concern about implementation failures in six sigma concept in supply chain management. The reason for many Six Sigma programmes to fail is due to an implementation model. Using a successful Six Sigma concept in an industry we have to perform strategic analysis driven by the market and the customer. It is necessary to establish a high- level, cross-functional team to drive the improvement initiative and to identify overall improvement tools. We can perform high-level process mapping and prioritize improvement opportunities. We have to develop a detailed plan for low-level improvement teams, and then to implement, document, and revise as necessary. This is important for both practitioners and academicians.

scholarly journals Co-occurrence of Hyperacusis Accelerates With Tinnitus Burden Over Time and Requires Medical Care

2021 ◽  
Vol 12 ◽  
Author(s):  
Fatma Refat ◽  
Jakob Wertz ◽  
Pauline Hinrichs ◽  
Uwe Klose ◽  
Hesham Samy ◽  
...  
Keyword(s):  
Medical Care ◽  
Global Burden ◽  
Neural Correlate ◽  
Hearing Thresholds ◽  
Causal Therapy ◽  
Advanced Therapies ◽  
High Level ◽  
Neural Gain ◽  
Over Time

Although tinnitus represents a major global burden, no causal therapy has yet been established. Ongoing controversies about the neuronal pathophysiology of tinnitus hamper efforts in developing advanced therapies. Hypothesizing that the unnoticed co-occurrence of hyperacusis and differences in the duration of tinnitus may possibly differentially influence the neural correlate of tinnitus, we analyzed 33 tinnitus patients without (T-group) and 20 tinnitus patients with hyperacusis (TH-group). We found crucial differences between the T-group and the TH-group in the increase of annoyance, complaints, tinnitus loudness, and central neural gain as a function of tinnitus duration. Hearing thresholds did not differ between T-group and TH-group. In the TH-group, the tinnitus complaints (total tinnitus score) were significantly greater from early on and the tinnitus intensity distinctly increased over time from ca. 12 to 17 dB when tinnitus persisted more than 5 years, while annoyance responses to normal sound remained nearly constant. In contrast, in the T-group tinnitus complaints remained constant, although the tinnitus intensity declined over time from ca. 27 down to 15 dB beyond 5 years of tinnitus persistence. This was explained through a gradually increased annoyance to normal sound over time, shown by a hyperacusis questionnaire. Parallel a shift from a mainly unilateral (only 17% bilateral) to a completely bilateral (100%) tinnitus percept occurred in the T-group, while bilateral tinnitus dominated in the TH-group from the start (75%). Over time in the T-group, ABR wave V amplitudes (and V/I ratios) remained reduced and delayed. By contrast, in the TH-group especially the ABR wave III and V (and III/I ratio) continued to be enhanced and shortened in response to high-level sound stimuli. Interestingly, in line with signs of an increased co-occurrence of hyperacusis in the T-group over time, ABR wave III also slightly increased in the T-group. The findings disclose an undiagnosed co-occurrence of hyperacusis in tinnitus patients as a main cause of distress and the cause of complaints about tinnitus over time. To achieve urgently needed and personalized therapies, possibly using the objective tools offered here, a systematic sub-classification of tinnitus and the co-occurrence of hyperacusis is recommended.

scholarly journals ASSESSMENT OF AIR POLLUTION LEVEL OF NITROGEN DIOXIDE AND TRENDS OF IT CHANGES IN THE CITIES OF UKRAINE

2018 ◽  
pp. 87-95 ◽  
Author(s):  
Y. Yatsenko ◽  
O. Shevchenko ◽  
S. Snizhko
Keyword(s):  
Air Pollution ◽  
Nitrogen Dioxide ◽  
Pollution Level ◽  
Method Of Least Squares ◽  
Statistical Parameters ◽  
Group 3 ◽  
Environmental Measures ◽  
High Level

The purpose of the work is to study the current level and the main trends of atmospheric air pollution of the cities of Ukraine with nitrogen dioxide to identify the most polluted cities, their ranking to determine the list of cities for the priority implementation of environmental measures. For the purpose of the study, the information of the Central Geophysical Observatory on the average annual concentrations of nitrogen dioxide in the air of 51 cities of Ukraine for the period 1998-2015 was used. The study used the classical methods of applied mathematical statistics (estimation of statistical parameters of distribution of concentrations, construction of time trends on the method of least squares, graphical methods of visualization of levels of air pollution), which were implemented using the available programs "MS-Excell" and "Statistica-8.0". The classification of cities according to the level of MPC exceeds average annual concentrations of nitrogen dioxide. 3 groups of cities were allocated: 1 group (21 cities) permissible level of pollution (<1 MPC); 2 group (27 cities) – increased level of pollution (1-2 MPC); group 3 (3 cities) – high level of pollution (2-3 MPC). It has been established that in the air of 21 cities (41% of all cities where nitrogen dioxide is monitored in the atmosphere) of 51 cities, there is an acceptable level of air pollution. In the remaining cities (59%) – there is a stable excess of MPC. In 23 cities, even the minimum concentrations of NO2 exceed the permissible standards. The study of long-term dynamics of nitrogen dioxide in air has shown that the increase of concentrations of this pollutant for 1998-2015 is observed in 28 cities (55%) of 51. The most significant increase in concentrations in the air occurred in Kherson, Lutsk, Donetsk and Gorishni Plavni. In 13 cities reduction of concentrations was recorded, and in 10 cities the content of this pollutant in the air practically does not change.

scholarly journals Fast Drivable Areas Estimation with Multi-Task Learning for Real-Time Autonomous Driving Assistant

2021 ◽  
Vol 11 (22) ◽  
pp. 10713
Author(s):  
Dong-Gyu Lee
Keyword(s):  
Real Time ◽  
Computational Efficiency ◽  
Autonomous Driving ◽  
Learning Approaches ◽  
Practical Applications ◽  
Task Learning ◽  
Lane Line ◽  
High Level ◽  
Time Autonomous

Autonomous driving is a safety-critical application that requires a high-level understanding of computer vision with real-time inference. In this study, we focus on the computational efficiency of an important factor by improving the running time and performing multiple tasks simultaneously for practical applications. We propose a fast and accurate multi-task learning-based architecture for joint segmentation of drivable area, lane line, and classification of the scene. An encoder-decoder architecture efficiently handles input frames through shared representation. A comprehensive understanding of the driving environment is improved by generalization and regularization from different tasks. The proposed method learns end-to-end through multi-task learning on a very challenging Berkeley Deep Drive dataset and shows its robustness for three tasks in autonomous driving. Experimental results show that the proposed method outperforms other multi-task learning approaches in both speed and accuracy. The computational efficiency of the method was over 93.81 fps at inference, enabling execution in real-time.

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