A model of range discrimination of multiple objects by using the linear period modulation signal

2008 ◽  
Vol 123 (5) ◽  
pp. 3206-3206
Author(s):  
Ikuo Matsuo
Keyword(s):  
Multiple Objects ◽  
Modulation Signal ◽  
Linear Period

Stereo modeling of HVEM specimens by serial tilting and computer graphics

1986 ◽  
Vol 44 ◽  
pp. 34-35
Author(s):  
J.R. McIntosh ◽  
D.L. Stemple ◽  
William Bishop ◽  
G.W. Hannaway
Keyword(s):  
Computer Graphics ◽  
Analytical Methods ◽  
Photographic Film ◽  
Complex Structures ◽  
Multiple Objects ◽  
Multiple Images ◽  
3 Dimensional

EM specimens often contain 3-dimensional information that is lost during micrography on a single photographic film. Two images of one specimen at appropriate orientations give a stereo view, but complex structures composed of multiple objects of graded density that superimpose in each projection are often difficult to decipher in stereo. Several analytical methods for 3-D reconstruction from multiple images of a serially tilted specimen are available, but they are all time-consuming and computationally intense.

The interpretation of singular nouns in the scope of an event-distributive quantifier

2019 ◽  
Vol 5 (1) ◽  
pp. 1-9
Author(s):  
Piotr Gulgowski
Keyword(s):  
Present Experiment ◽  
Multiple Objects ◽  
Multiple Events ◽  
Singular Noun ◽  
Distributive Operator

Abstract Singular nouns in the scope of a distributive operator have been shown to be treated as conceptually plural (Patson and Warren, 2010). The source of this conceptual plurality is not fully clear. In particular, it is not known whether the concept of plurality associated with a singular noun originates from distributing over multiple objects or multiple events. In the present experiment, iterative expressions (distribution over events) were contrasted with collective and distributive sentences using a Stroop-like interference technique (Berent, Pinker, Tzelgov, Bibi, and Goldfarb, 2005; Patson and Warren, 2010). A trend in the data suggests that event distributivity does not elicit a plural interpretation of a grammatically singular noun, however the results were not statistically significant. Possible causes of the non-significant results are discussed.

Improvement of Carrier Power to Third-Order Intermodulation Distortion Power Ratio for a Power Amplifier at 5.25 GHz using LINC Method

2018 ◽  
Vol 12 (1) ◽  
pp. 34-40
Author(s):  
Said Elkhaldi ◽  
Naima Amar Touhami ◽  
Mohamed Aghoutane ◽  
Taj-eddin Elhamadi
Keyword(s):  
Power Amplifier ◽  
Artificial Neural Network Model ◽  
Power Ratio ◽  
Linear Amplification ◽  
Third Order ◽  
Gaas Mesfet ◽  
Constant Envelope ◽  
Modulation Signal ◽  
Low Efficiency ◽  
Nonlinear Components

Introduction:This paper focuses on improving the power amplifier linearity for wireless communications. The use of a single branch of a power amplifier can produce high distortion with low efficiency.Method:In this paper, the Linear Amplification with Nonlinear Components (LINC) technique is used to improve the linearity and efficiency of the power amplifier. The LINC technique is based on converting the envelope modulation signal into two constant envelope phase-modulated baseband signals. After amplification and combining the resulting signals, the required linear output signal is obtained. To validate the proposed approach, LINC technique is used for linearizing an amplifier based on a GaAs MESFET (described by an artificial neural network Model).Conclusion:Good results have been achieved, and an improvement of about 40.80 dBc and 47.50 dBc respectively is obtained for the Δlower C/I and Δupper C/I at 5.25 GHz.

scholarly journals Fault detection for planetary gearbox based on an enhanced average filter and modulation signal bispectrum analysis

2020 ◽  
Vol 101 ◽  
pp. 408-420 ◽  
Author(s):  
Junchao Guo ◽  
Dong Zhen ◽  
Haiyang Li ◽  
Zhanqun Shi ◽  
Fengshou Gu ◽  
...  
Keyword(s):  
Fault Detection ◽  
Planetary Gearbox ◽  
Modulation Signal ◽  
Average Filter

scholarly journals Multiple objects tracking in the UAV system based on hierarchical deep high-resolution network

2021 ◽  
Author(s):  
Wei Huang ◽  
Xiaoshu Zhou ◽  
Mingchao Dong ◽  
Huaiyu Xu
Keyword(s):  
High Resolution ◽  
Object Tracking ◽  
High Performance ◽  
State Of The Art ◽  
Class Imbalance ◽  
Unified Framework ◽  
Multiple Objects ◽  
Tracking Process ◽  
Objects Tracking ◽  
Different Types

AbstractRobust and high-performance visual multi-object tracking is a big challenge in computer vision, especially in a drone scenario. In this paper, an online Multi-Object Tracking (MOT) approach in the UAV system is proposed to handle small target detections and class imbalance challenges, which integrates the merits of deep high-resolution representation network and data association method in a unified framework. Specifically, while applying tracking-by-detection architecture to our tracking framework, a Hierarchical Deep High-resolution network (HDHNet) is proposed, which encourages the model to handle different types and scales of targets, and extract more effective and comprehensive features during online learning. After that, the extracted features are fed into different prediction networks for interesting targets recognition. Besides, an adjustable fusion loss function is proposed by combining focal loss and GIoU loss to solve the problems of class imbalance and hard samples. During the tracking process, these detection results are applied to an improved DeepSORT MOT algorithm in each frame, which is available to make full use of the target appearance features to match one by one on a practical basis. The experimental results on the VisDrone2019 MOT benchmark show that the proposed UAV MOT system achieves the highest accuracy and the best robustness compared with state-of-the-art methods.

scholarly journals ClusterSLAM: A SLAM backend for simultaneous rigid body clustering and motion estimation

2021 ◽  
Author(s):  
Jiahui Huang ◽  
Sheng Yang ◽  
Zishuo Zhao ◽  
Yu-Kun Lai ◽  
Shi-Min Hu
Keyword(s):  
Motion Estimation ◽  
Rigid Body ◽  
Iterative Scheme ◽  
Dynamic Environments ◽  
Rigid Bodies ◽  
Factor Graph ◽  
Agglomerative Clustering ◽  
Multiple Objects ◽  
Graph Optimization ◽  
Dynamic Objects

AbstractWe present a practical backend for stereo visual SLAM which can simultaneously discover individual rigid bodies and compute their motions in dynamic environments. While recent factor graph based state optimization algorithms have shown their ability to robustly solve SLAM problems by treating dynamic objects as outliers, their dynamic motions are rarely considered. In this paper, we exploit the consensus of 3D motions for landmarks extracted from the same rigid body for clustering, and to identify static and dynamic objects in a unified manner. Specifically, our algorithm builds a noise-aware motion affinity matrix from landmarks, and uses agglomerative clustering to distinguish rigid bodies. Using decoupled factor graph optimization to revise their shapes and trajectories, we obtain an iterative scheme to update both cluster assignments and motion estimation reciprocally. Evaluations on both synthetic scenes and KITTI demonstrate the capability of our approach, and further experiments considering online efficiency also show the effectiveness of our method for simultaneously tracking ego-motion and multiple objects.

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