scholarly journals Pattern recognition-based decoding method for the negative pulsed downlink signal with a narrow pulse width

2021 ◽  
Author(s):  
Jiafeng Wu ◽  
Shuxing Zhao ◽  
Jianming Jiang ◽  
Botao Zhou ◽  
Ning Han ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Pulse Width ◽  
Transmission Rate ◽  
Signal Transmission ◽  
Distance Matrix ◽  
Transmission Efficiency ◽  
Signal Pulse ◽  
Threshold Method ◽  
Euclidean Distance Matrix ◽  
Downlink Signal

AbstractThe negative pulsed downlink communication system is used to send surface control commands to the downhole rotary steering tool at thousands of meters, which is a significant part of the current rotary steering technology. At present, the transmission efficiency of negative pulsed downlink communication is very low, and only simple control commands can be transmitted in a few minutes, which limits the development of rotary steering technology with complex control functions. To improve the transmission rate of the downlink system, the downlink pulse width needs to be shortened. However, due to the influence of signal transmission characteristics, the waveform of a narrow pulse width signal will be severely distorted, which increases the difficulty of decoding the downlink signal. Therefore, a decoding method based on pattern recognition for negative pulsed downlink signal with narrow pulse width is proposed in this paper, which establishes a Euclidean distance matrix model between similar characteristic signal segments on the rising or falling edge of the downlink signal, the pulse coding timing of among the signal segment with each rising or falling edge is analyzed, the decoding and recognition of the downlink instruction are achieved, which solves the problem of large timing deviation in decoding the downlink signal with a current threshold method. The experimental results show that the method proposed in this paper can achieve accurate decoding of the 6 s pulse width downlink signal. Compared with the threshold method, it can be seen that the decoding accuracy of the method proposed in this paper can be greatly improved, and the smaller the signal pulse width, the more significant the advantage.

scholarly journals Wireless Power and Signal Transmission Based on Time-sharing Multiplexing for WPT System

2021 ◽  
Vol 252 ◽  
pp. 01023
Author(s):  
Xiaorui Wu ◽  
Wenlan Gong ◽  
Jing Xiao ◽  
Shaonan Chen ◽  
Ning Wu
Keyword(s):  
Power Transmission ◽  
Transmission Rate ◽  
Signal Transmission ◽  
Transmission Efficiency ◽  
Wireless Transmission ◽  
Transmission Time ◽  
Time Sharing ◽  
Wireless Power ◽  
Practical Applications ◽  
The Relationship

In wireless power transfer (WPT) systems, wireless transmission of signals is particularly important for some practical applications. An effective approach of signal and energy synchronous transmission based on time-sharing multiplexing of the same coupled coil is proposed in this paper. Using the same coupled coil, the signal transmission is carried on when interruption of power transmission happens. To realize the proposed method, the relationship between power transmission time and signal transmission time is provided to obtain the optimal transmission process of the power and signal. Besides, the power transmission efficiency (PTE) and the signal transmission rate (STR) are analyzed. Finally, the effectiveness of the proposed method is verified by the experiment.

Methods for Studying Morphological Integration and Modularity

2010 ◽  
Vol 16 ◽  
pp. 213-243 ◽  
Author(s):  
Anjali Goswami ◽  
P. David Polly
Keyword(s):  
Distance Matrix ◽  
Japanese Macaques ◽  
Morphological Integration ◽  
Matrix Analysis ◽  
Euclidean Distance Matrix ◽  
Phenotypic Data ◽  
Graphical Modelling ◽  
Landmark Data ◽  
Competing Models ◽  
Matrix Correlation

Morphological integration and modularity are closely related concepts about how different traits of an organism are correlated. Integration is the overall pattern of intercorrelation; modularity is the partitioning of integration into evolutionarily or developmentally independent blocks of traits. Modularity and integration are usually studied using quantitative phenotypic data, which can be obtained either from extant or fossil organisms. Many methods are now available to study integration and modularity, all of which involve the analysis of patterns found in trait correlation or covariance matrices. We review matrix correlation, random skewers, fluctuating asymmetry, cluster analysis, Euclidean distance matrix analysis (EDMA), graphical modelling, two-block partial least squares, RV coefficients, and theoretical matrix modelling and discuss their similarities and differences. We also review different coefficients that are used to measure correlations. We apply all the methods to cranial landmark data from and ontogenetic series of Japanese macaques,Macaca fuscatato illustrate the methods and their individual strengths and weaknesses. We conclude that the exploratory approaches (cluster analyses of various sorts) were less informative and less consistent with one another than were the results of model testing or comparative approaches. Nevertheless, we found that competing models of modularity and integration are often similar enough that they are not statistically distinguishable; we expect, therefore, that several models will often be significantly correlated with observed data.

scholarly journals Ad hoc microphone array calibration: Euclidean distance matrix completion algorithm and theoretical guarantees

2015 ◽  
Vol 107 ◽  
pp. 123-140 ◽  
Author(s):  
Mohammad J. Taghizadeh ◽  
Reza Parhizkar ◽  
Philip N. Garner ◽  
Hervé Bourlard ◽  
Afsaneh Asaei
Keyword(s):  
Euclidean Distance ◽  
Ad Hoc ◽  
Microphone Array ◽  
Matrix Completion ◽  
Distance Matrix ◽  
Euclidean Distance Matrix ◽  
Array Calibration ◽  
Euclidean Distance Matrix Completion

scholarly journals A Graft-Based Chemotherapy Method for Screening Effective Molecules and Rescuing Huanglongbing-Affected Citrus Plants

2012 ◽  
Vol 102 (6) ◽  
pp. 567-574 ◽  
Author(s):  
Muqing Zhang ◽  
Charles A. Powell ◽  
Ying Guo ◽  
Melissa S. Doud ◽  
Yongping Duan
Keyword(s):  
Survival Rate ◽  
Transmission Rate ◽  
High Titer ◽  
Critical Factor ◽  
Transmission Efficiency ◽  
Pathogen Transmission ◽  
Citrus Industry ◽  
Chemical Treatments ◽  
Bacterial Titer ◽  
Transmission Studies

Huanglongbing (HLB) is the most devastating disease of citrus. The global citrus industry is in urgent need of effective chemical treatments for HLB control because of its rapid spreading worldwide. Due to the fastidious nature of the pathogens, and the poor permissibility of citrus leaf surfaces, effective screening of chemicals for the HLB control can be challenging. In this study, we developed a graft-based chemotherapy method to rapidly screen potential HLB-controlling chemical compounds. In addition, we improved transmission efficiency by using the best HLB-affected scion–rootstock combination, and demonstrated the HLB bacterial titer was the critical factor in transmission. The HLB-affected lemon scions had a high titer of HLB bacterium, survival rate (83.3%), and pathogen transmission rate (59.9%). Trifoliate, a widely used commercial rootstock, had the highest survival rate (>70.0%) compared with grapefruit (52.6%) and sour orange (50.4%). Using this method, we confirmed a mixture of penicillin and streptomycin was the most effective compounds in eliminating the HLB bacterium from the HLB-affected scions, and in successfully rescuing severely HLB-affected citrus germplasms. These findings are useful not only for chemical treatments but also for graft-based transmission studies in HLB and other Liberibacter diseases.

scholarly journals P-Path Minimum Distance Connectivity from Head Quarter to the Cities

2013 ◽  
Vol 4 (2) ◽  
pp. 280-294
Author(s):  
Revathi P ◽  
Suresh Babu C ◽  
Purusotham S ◽  
Sundara Murthy M
Keyword(s):  
Pattern Recognition ◽  
Minimum Distance ◽  
Search Algorithm ◽  
Distance Matrix ◽  
Pattern Recognition Technique ◽  
Individual Factor ◽  
Linear Polynomial ◽  
Higher Dimensional ◽  
Combinatorial Programming ◽  
Recognition Technique

Many Combinatorial programming problems are NP-hard (Non Linear Polynomial), and we consider one of them called P path minimum distance connectivity from head quarter to the cities. Let there be n cities and the distance matrix D(i, j, k) is given from ithcity to jthcity using kthfacility. There can be an individual factor which influences the distances/cost and that factor is represented as a facility k. We consider m<n cities are in cluster and to connect all the cities in subgroup (cluster) from others by using same facility k. The problem is to find minimum distance to connect all the cities from head quarter (say 1) threw p-paths subject to the above considerations. For this problem we developed a Pattern Recognition Technique based Lexi Search Algorithm, we programmed the proposed algorithm using C. we compared with the existed models and conclude that it suggested for solving the higher dimensional problems.

scholarly journals Hebbian Learning of Recurrent Connections: A Geometrical Perspective

2012 ◽  
Vol 24 (9) ◽  
pp. 2346-2383 ◽  
Author(s):  
Mathieu N. Galtier ◽  
Olivier D. Faugeras ◽  
Paul C. Bressloff
Keyword(s):  
Hebbian Learning ◽  
Dimensional Space ◽  
Equilibrium Points ◽  
Ocular Dominance ◽  
Distance Matrix ◽  
Hopfield Network ◽  
Connectivity Matrix ◽  
Euclidean Distance Matrix ◽  
Fast Analysis ◽  
Recurrent Connections

We show how a Hopfield network with modifiable recurrent connections undergoing slow Hebbian learning can extract the underlying geometry of an input space. First, we use a slow and fast analysis to derive an averaged system whose dynamics derives from an energy function and therefore always converges to equilibrium points. The equilibria reflect the correlation structure of the inputs, a global object extracted through local recurrent interactions only. Second, we use numerical methods to illustrate how learning extracts the hidden geometrical structure of the inputs. Indeed, multidimensional scaling methods make it possible to project the final connectivity matrix onto a Euclidean distance matrix in a high-dimensional space, with the neurons labeled by spatial position within this space. The resulting network structure turns out to be roughly convolutional. The residual of the projection defines the nonconvolutional part of the connectivity, which is minimized in the process. Finally, we show how restricting the dimension of the space where the neurons live gives rise to patterns similar to cortical maps. We motivate this using an energy efficiency argument based on wire length minimization. Finally, we show how this approach leads to the emergence of ocular dominance or orientation columns in primary visual cortex via the self-organization of recurrent rather than feedforward connections. In addition, we establish that the nonconvolutional (or long-range) connectivity is patchy and is co-aligned in the case of orientation learning.

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