scholarly journals Function Extension Based Real-Time Wavelet De-Noising Method for Projectile Attitude Measurement

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 200 ◽  
Author(s):  
Zhihong Deng ◽  
Jinwen Wang ◽  
Xinyu Liang ◽  
Ning Liu
Keyword(s):  
Real Time ◽  
Signal To Noise Ratio ◽  
Absolute Error ◽  
Attitude Measurement ◽  
Extension Method ◽  
The Real ◽  
Projectile Motion ◽  
Function Extension ◽  
Process Guidance ◽  
The Right

The real-time measurement of the projectile attitude is the key to realize the whole process guidance of the projectile. Due to the high dynamic characteristics of the projectile motion, the attitude measurement is affected by the real-time and accuracy of the gyro signal de-noising. For the nonlinear discontinuity of the conventional extension method in real-time wavelet de-noising, a function extension real-time wavelet de-noising method is proposed in this paper. In this method, a prediction model of gyro signal is established based on the Roggla formula. According to the model, the signal is fitted in the sliding window, and the signal of the same length is predicted to realize the right boundary extension. The simulation and experiment results show that compared with the traditional extension method, the proposed method can in-crease the signal-to-noise ratio (SNR) and the smoothness, and can decrease the attitude mean absolute error (AMAE) and the attitude root mean square error (ARMSE). Moreover, the time delay caused by signal de-noising can be effectively solved. The real-time performance of the attitude measurement can be ensured.

scholarly journals Incremental and predictive interpretation: Experimental evidence and possible accounts

2015 ◽  
Vol 25 ◽  
pp. 57 ◽  
Author(s):  
Adrian Brasoveanu ◽  
Jakub Dotlacil
Keyword(s):  
Experimental Evidence ◽  
Real Time ◽  
Formal Semantics ◽  
Main Question ◽  
The Real ◽  
To Come ◽  
The Right ◽  
Present Experimental Evidence

The main question we investigate is whether meaning representations of the kind that are pervasive in formal semantics are built up incrementally and predictively when language is used in real time, in much the same way that the real-time construction of syntactic representations has been argued to be. The interaction of presupposition resolution with conjunctions vs. conditionals with a sentence-final antecedent promises to provide us with the right kind of evidence. Consider the following 'cataphoric' example and the contrast between "and" and "if": "Tina will have coffee with Alex again and / if she had coffee with him at the local cafe". We expect the second clause to be more difficult after "and" than after "if": the conjunction "and" signals that an antecedent that could resolve the "again" presupposition is unlikely to come after this point (the second conjunct is interpreted relative to the context provided by the first conjunct), while the conditional "if" leaves open the possibility that a suitable resolution for the "again" presupposition is forthcoming (the first clause is interpreted relative to the context provided by the second clause). We present experimental evidence supporting these predictions and discuss two approaches to analyze this kind of data.

scholarly journals Real Time Product Feedback Review and Analysis Using Apache Technologies and NOSQL Database

2017 ◽  
Vol 6 (10) ◽  
pp. 22551-22558
Author(s):  
BiswaRanjan Samal ◽  
Mrutyunjaya Panda
Keyword(s):  
Real Time ◽  
Complex Analysis ◽  
Feedback System ◽  
Time Data ◽  
The Real ◽  
Real Time Analysis ◽  
Nosql Database ◽  
Data Ingestion ◽  
The Right ◽  
User Friendly

Whenever a feedback system comes into mind, it’s always a demand of the e-commerce organizations to get the customer feedbacks in real time and to build some strong dashboards on top of these feedbacks/ratings. So that they can easily know the performance of any product at any point of time as well as they could able to take a decision, on what to do with the products those are getting very poor feedbacks. Which will result in a minimum impact on the tangible and intangible assets of the organizations. For achieving the above goal it is very necessary for these organizations to adopt the right tool and implement the required environment which can deal with the real time big data ingestion, enrichment, indexing and have the power to perform simple as well as complex analysis algorithm on the stored data. In this paper, we have collected Amazon Product Ratings for doing analysis and used Apache NiFi for ingesting real-time data into Apache Solr and have taken help of Banana Dashboard to show the real time analysis results in the form of attractive and user-friendly dashboards.

scholarly journals Real-Time Detection and Location of Potted Flowers Based on a ZED Camera and a YOLO V4-Tiny Deep Learning Algorithm

Horticulturae ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 21
Author(s):  
Jizhang Wang ◽  
Zhiheng Gao ◽  
Yun Zhang ◽  
Jing Zhou ◽  
Jianzhi Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Point Cloud ◽  
Learning Algorithm ◽  
Absolute Error ◽  
Frame Rate ◽  
3D Point Cloud ◽  
The Real ◽  
Deep Learning Algorithm ◽  
The Mean

In order to realize the real-time and accurate detection of potted flowers on benches, in this paper we propose a method based on the ZED 2 stereo camera and the YOLO V4-Tiny deep learning algorithm for potted flower detection and location. First, an automatic detection model of flowers was established based on the YOLO V4-Tiny convolutional neural network (CNN) model, and the center points on the pixel plane of the flowers were obtained according to the prediction box. Then, the real-time 3D point cloud information obtained by the ZED 2 camera was used to calculate the actual position of the flowers. The test results showed that the mean average precision (MAP) and recall rate of the training model was 89.72% and 80%, respectively, and the real-time average detection frame rate of the model deployed under Jetson TX2 was 16 FPS. The results of the occlusion experiment showed that when the canopy overlap ratio between the two flowers is more than 10%, the recognition accuracy will be affected. The mean absolute error of the flower center location based on 3D point cloud information of the ZED 2 camera was 18.1 mm, and the maximum locating error of the flower center was 25.8 mm under different light radiation conditions. The method in this paper establishes the relationship between the detection target of flowers and the actual spatial location, which has reference significance for the machinery and automatic management of potted flowers on benches.

Container Elasticity: Based on Response Time using Docker

2020 ◽  
Vol 13 ◽  
Author(s):  
Mahendra Pratap Yadav ◽  
Harishchandra A. Akarte ◽  
Dharmendra Kumar Yadav
Keyword(s):  
Response Time ◽  
Real Time ◽  
Scale Up ◽  
Dynamic Environment ◽  
Resource Provisioning ◽  
Allocation Of Resources ◽  
The Real ◽  
Cloud Providers ◽  
The Right ◽  
Auto Scaling

Objective: Cloud computing is an approach to provide the computing resources (machine) to end-users for running their application over the Internet. The computing resources consist of various things (e.g. RAM, Memory, CORE, etc.). These resources are allocated to an application without human intervention for managing the fluctuating workload. To manage the real-time fluctuating workload, cloud providers use VM based or Container-based virtualization to host the client services. Adding/removing resources dynamically as per the demand of application through cloud is known as elasticity. Cloud providers use the auto-scaling mechanism to implement elasticity. A machine that hosts an application can be either overloaded or under-loaded due to the real-time fluctuating workload. The cloud providers use an auto-scaling mechanism to automatically scale up or down the computing resources at the right moment for managing the real-time fluctuating workload. The failure of allocation/de-allocation of resources at right time leads to SLA violation, service unavailability, customers lost, more power consumption, minimum throughput and maximum response time. Hence, the allocation/de-allocation of resources at right moment becomes critical for successful completion of task in dynamic environment efficiently. Methods: Resource provisioning for managing dynamic and fluctuating workload has been achieved through an algorithm (PID with dynamic HAProxy) which is based on decision-making approach that depends on response time of container using mechanism of control theory. Results: The proposed work has improved performance of the system in terms of resource utilization and response time to manage the fluctuating workload. Conclusion: The addition/removal of containers dynamically to manage fluctuating workload can be achieved more efficiently.

scholarly journals The Manipulation of Real-Time Kinect-Based Robotic Arm Using Double-Hand Gestures

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Chern-Sheng Lin ◽  
Pei-Chi Chen ◽  
Yu-Ching Pan ◽  
Che-Ming Chang ◽  
Kuo-Liang Huang
Keyword(s):  
Real Time ◽  
Human Body ◽  
Relevant Information ◽  
Robotic Arm ◽  
Depth Sensor ◽  
Hand Gestures ◽  
The Real ◽  
Left Hand ◽  
Right Hand ◽  
The Right

This study focused on utilizing the Kinect depth sensor to track double-hand gestures and control a real-time robotic arm. The control system is mainly composed of the microprocessor, a color camera, the depth sensor, and the robotic arm. The Kinect depth sensor was used to take photos of the human body to analyze the skeleton of a human body and obtain the relevant information. Such information was used to identify the gestures of the left hand and the left palm of the user. The gesture of left hand was used as an input command device. The gesture of the right hand was used for imitation movement teaching of robotic arm. From the depth sensor, the real-time images of the human body and the deep information of each joint were collected and converted to the relative positions of the robotic arm. Combining forward kinematics and inverse kinematics and D-H link, the gesture information of the right hand was calculated, which was converted via coordinates into each angle of the motor of the robotic arm. From the color camera, when the left palm was not detected, the user could simply use the right hand to control the action and movement of the real-time robotic arm. When the left palm was detected and 5 fingertips were identified, it meant the start of recording the real-time imitation movement of the robotic arm by the right hand. When 0 fingertip was identified, it meant the stoppage of the above recording. When 2 fingertips were identified, the user could not only control the real-time robotic arm but also repeat the recorded actions.

scholarly journals Improved Real-time Denoising Method Based on Lifting Wavelet Transform

2014 ◽  
Vol 14 (3) ◽  
pp. 152-159 ◽  
Author(s):  
Zhaohua Liu ◽  
Yang Mi ◽  
Yuliang Mao
Keyword(s):  
Wavelet Transform ◽  
Real Time ◽  
Signal To Noise Ratio ◽  
Threshold Function ◽  
Time Signal ◽  
Signal Denoising ◽  
Lifting Wavelet Transform ◽  
Denoising Method ◽  
The Real ◽  
Lifting Wavelet

Abstract Signal denoising can not only enhance the signal to noise ratio (SNR) but also reduce the effect of noise. In order to satisfy the requirements of real-time signal denoising, an improved semisoft shrinkage real-time denoising method based on lifting wavelet transform was proposed. The moving data window technology realizes the real-time wavelet denoising, which employs wavelet transform based on lifting scheme to reduce computational complexity. Also hyperbolic threshold function and recursive threshold computing can ensure the dynamic characteristics of the system, in addition, it can improve the real-time calculating efficiency as well. The simulation results show that the semisoft shrinkage real-time denoising method has quite a good performance in comparison to the traditional methods, namely soft-thresholding and hard-thresholding. Therefore, this method can solve more practical engineering problems.

Application of Real-Time Wavelet Threshold Filtering in FOG Attitude Measurement System

2012 ◽  
Vol 571 ◽  
pp. 421-426
Author(s):  
Xia Qing Tang ◽  
Xu Wei Cheng ◽  
Huan Zhang ◽  
Xiang Yuan Huang
Keyword(s):  
Real Time ◽  
Measurement System ◽  
Threshold Function ◽  
Attitude Measurement ◽  
Time Data ◽  
Threshold Method ◽  
The Real ◽  
Stochastic Errors ◽  
Recursive Computation ◽  
Data Update

A wavelet method is introduced to compensate stochastic errors of FOG attitude measurement system, analysing and comparing threshold and threshold function principle, a new threshold function combining with soft and hard threshold was designed. Adopting data moving window realizes recursive computation and real-time data update and solves filtering signal’s boundary problem with symmetric periodic extension method, and achieves real-time wavelet threshold filter. Experimental results show the feasibility of the real time wavelet threshold method in FOG signal de-noising, the real-time wavelet de-noising algorithm with new threshold function has the advantages of single algorithm. which improves the precision and stability of the FOG.

Photonic Reservoir Computing for Real-Time Respiratory Motion Prediction

2021 ◽  
Author(s):  
Zhizhuo Liang ◽  
Meng Zhang ◽  
Chengyu Shi ◽  
Z. Rena Huang
Keyword(s):  
Real Time ◽  
Respiratory Motion ◽  
Absolute Error ◽  
Motion Prediction ◽  
Reservoir Computing ◽  
Data Set ◽  
Real Time Processing ◽  
The Real ◽  
Look Ahead ◽  
Clinical Radiotherapy

Abstract The application of reservoir computing (RC) is for the first time studied in a class of forecasting tasks in which signals are under random physical perturbations, meaning that the data-baring waveform distortions are versatile, and the process is not repeatable. Tumor movement caused by respiratory motion is such a problem and real-time prediction of tumor motion is required by the clinical radiotherapy. In this work, a true-time delay (TTD) respiration monitor based on photonic RC with adjustable nodes connection is developed specifically for this task. A breathing data set from a total of 76 patients with breathing speeds ranging from 3 to 20 breath per minute (BPM) are studied. A double-sliding window technology is demonstrated to enable the real-time establishment of an individually trained model for each patient and the real-time processing of live-streamed tumor position data. Motion prediction of look-ahead times of 66.6 ms, 166.6 ms and 333 ms are investigated. With a 333 ms look-ahead time, the real-time RC model achieves an average normalized mean square error (NMSE) of 0.0246, an average mean absolute error (MAE) of 0.338 mm, an average therapeutic beam exposure efficiency of 94.14% for an absolute error (AE) < 1mm and 99.89% for AE < 3mm. This study demonstrates that real-time RC is an efficient computing framework for high precision respiratory motion prediction.

The Real-Time Neural Genesis of a Punchline

2014 ◽  
Author(s):  
Irving Biederman ◽  
Ori Amir
Keyword(s):  
Real Time ◽  
The Real
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