Implementation of computationally efficient real-time voice conversion

2012 ◽  
Author(s):  
Tomoki Toda ◽  
Takashi Muramatsu ◽  
Hideki Banno
Keyword(s):  
Real Time ◽  
Voice Conversion ◽  
Computationally Efficient

RSNet: Rail semantic segmentation network for extracting aerial railroad images

2021 ◽  
pp. 1-18
Author(s):  
R.S. Rampriya ◽  
Sabarinathan ◽  
R. Suganya
Keyword(s):  
Real Time ◽  
Visual Processing ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Vital Role ◽  
Obstacle Detection ◽  
Aerial Images ◽  
Computationally Efficient ◽  
Fusion Algorithm ◽  
Uav Images

In the near future, combo of UAV (Unmanned Aerial Vehicle) and computer vision will play a vital role in monitoring the condition of the railroad periodically to ensure passenger safety. The most significant module involved in railroad visual processing is obstacle detection, in which caution is obstacle fallen near track gage inside or outside. This leads to the importance of detecting and segment the railroad as three key regions, such as gage inside, rails, and background. Traditional railroad segmentation methods depend on either manual feature selection or expensive dedicated devices such as Lidar, which is typically less reliable in railroad semantic segmentation. Also, cameras mounted on moving vehicles like a drone can produce high-resolution images, so segmenting precise pixel information from those aerial images has been challenging due to the railroad surroundings chaos. RSNet is a multi-level feature fusion algorithm for segmenting railroad aerial images captured by UAV and proposes an attention-based efficient convolutional encoder for feature extraction, which is robust and computationally efficient and modified residual decoder for segmentation which considers only essential features and produces less overhead with higher performance even in real-time railroad drone imagery. The network is trained and tested on a railroad scenic view segmentation dataset (RSSD), which we have built from real-time UAV images and achieves 0.973 dice coefficient and 0.94 jaccard on test data that exhibits better results compared to the existing approaches like a residual unit and residual squeeze net.

Real-Time Robot Capability Analysis

2005 ◽  
Author(s):  
Chalongrath Pholsiri ◽  
Chetan Kapoor ◽  
Delbert Tesar
Keyword(s):  
Decision Making ◽  
Path Planning ◽  
Real Time ◽  
Computationally Efficient ◽  
New Approach ◽  
Ellipsoid Method ◽  
Capability Analysis ◽  
Computationally Intensive ◽  
Planar Robots

Robot Capability Analysis (RCA) is a process in which force/motion capabilities of a manipulator are evaluated. It is very useful in both the design and operational phases of robotics. Traditionally, ellipsoids and polytopes are used to both graphically and numerically represent these capabilities. Ellipsoids are computationally efficient but tend to underestimate while polytopes are accurate but computationally intensive. This article proposes a new approach to RCA called the Vector Expansion (VE) method. The VE method offers accurate estimates of robot capabilities in real time and therefore is very suitable in applications like task-based decision making or online path planning. In addition, this method can provide information about the joint that is limiting a robot capability at a given time, thus giving an insight as to how to improve the performance of the robot. This method is then used to estimate capabilities of 4-DOF planar robots and the results discussed and compared with the conventional ellipsoid method. The proposed method is also successfully applied to the 7-DOF Mitsubishi PA10-7C robot.

Design and Development of a Multi-Module Deployable Manipulator

1999 ◽  
Author(s):  
Kenneth Wong ◽  
Vinod J. Modi ◽  
Clarence W. de Silva ◽  
Arun K. Misra
Keyword(s):  
Real Time ◽  
Experimental Investigations ◽  
Computationally Efficient ◽  
Real Time Control ◽  
Design And Development ◽  
Time Control ◽  
Dynamics And Control ◽  
Manipulator Design ◽  
A Chain ◽  
And Control

Abstract This paper presents the design and development of a Multi-module Deployable Manipulator System (MDMS) as well as a dynamical formulation for it. The system is designed for experimental investigations aimed at dynamics and control of this variable geometry manipulator by implementing different control algorithms to regulate its performance. The manipulator operates in a horizontal plane and is unique in that it comprises of four modules, each of which has one revolute joint and one prismatic joint, connected in a chain topology. Each module has a slewing link of approximately 20cm length and is capable of extending by 15cm. The manipulator design involves the selection and sizing of actuators, the design of mounting and connecting components, and the selection of hardware as well as software for real-time control. The dynamical model is formulated using an O(N) algorithm, based on the Lagrangian approach and velocity transformations. The O(N) character is computationally efficient permitting real-time control of the system.

scholarly journals The Current Spectrum Formation of a Non-Periodic Signal: A Differential Approach

Inventions ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 15
Author(s):  
Sergey Sokolov ◽  
Daniil Marshakov ◽  
Arthur Novikov
Keyword(s):  
Real Time ◽  
Fixed Time ◽  
Signal Spectrum ◽  
Time Interval ◽  
Periodic Signal ◽  
Periodic Functions ◽  
Computationally Efficient ◽  
Sampling Step ◽  
Current Spectrum ◽  
Original Definition

The paper deals with the problem of forming spectra of non-periodic signals in real-time. The disadvantage of the existing approaches is the dependence of the formed spectrum on time as a parameter and the possibility of obtaining the signal spectrum in its original definition only for a fixed time, as well as a high amount of computation. In this regard, a computationally efficient algorithm is proposed for forming a spectrum of non-periodic functions on a time interval that is constantly updated with a given sampling step, which ensures the invariance of the generated spectrum to time as a parameter. The algorithm is based on obtaining differential equations that are based on generalized differentiation with respect to a variable time interval of spectral components and their solving while using the fourth-order Runge–Kutta method. A numerical simulation of the developed algorithm was performed using the MATLAB mathematical modeling package using the example of a substantially non-linear function. Based on the practical results, a comparative evaluation of computational and time complexity has been performed in solving the problem. Based on the obtained experimental results, it is concluded that it is possible to effectively use the proposed algorithm to calculate the current spectrum of non-periodic functions with the requirement of small sampling steps, i.e., when calculating the spectrum in real-time.

scholarly journals Real-Time Moving Object Detection in High-Resolution Video Sensing

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3591 ◽  
Author(s):  
Haidi Zhu ◽  
Haoran Wei ◽  
Baoqing Li ◽  
Xiaobing Yuan ◽  
Nasser Kehtarnavaz
Keyword(s):  
High Resolution ◽  
Object Detection ◽  
Real Time ◽  
Moving Object Detection ◽  
Moving Object ◽  
Computationally Efficient ◽  
Real Time Processing ◽  
Time Processing ◽  
Video Frames ◽  
High Resolution Images

This paper addresses real-time moving object detection with high accuracy in high-resolution video frames. A previously developed framework for moving object detection is modified to enable real-time processing of high-resolution images. First, a computationally efficient method is employed, which detects moving regions on a resized image while maintaining moving regions on the original image with mapping coordinates. Second, a light backbone deep neural network in place of a more complex one is utilized. Third, the focal loss function is employed to alleviate the imbalance between positive and negative samples. The results of the extensive experimentations conducted indicate that the modified framework developed in this paper achieves a processing rate of 21 frames per second with 86.15% accuracy on the dataset SimitMovingDataset, which contains high-resolution images of the size 1920 × 1080.

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