Haptic Rendering of Data on Irregular Grids

2000 ◽  
Author(s):  
Roman Y. Novoselov ◽  
Dale A. Lawrence ◽  
Lucy Y. Pao
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Additional Data ◽  
Computation Time ◽  
Haptic Rendering ◽  
General Purpose ◽  
Data Sets ◽  
Irregular Grids ◽  
Visualization Of Data ◽  
Dsp Processors

Abstract Haptic rendering of data on irregular grids requires additional data storage and real time computation compared to the same size data sets on regular grids. At the same time, it is important to keep computation time small to avoid noticeable artifacts in haptic rendering. When the rendering algorithm is implemented on DSP processors, memory is often much smaller than on contemporary general purpose computers. By appropriate partitioning of algorithms between preprocessing and real time computation, and by quantizing and packing data, we show how scientific visualization of data sets on the order of 1 million elements by real time haptic rendering can be accomplished.

scholarly journals Assessing the Best Gap-Filling Technique for River Stage Data Suitable for Low Capacity Processors and Real-Time Application Using IoT

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6354
Author(s):  
Antonio Madueño Luna ◽  
Miriam López Lineros ◽  
Javier Estévez Gualda ◽  
Juan Vicente Giráldez Cervera ◽  
José Miguel Madueño Luna
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Raspberry Pi ◽  
Data Sets ◽  
Analysis Method ◽  
Gap Filling ◽  
Remote Communication ◽  
Catchment Basin ◽  
River Stage ◽  
Real Time Application

Hydrometeorological data sets are usually incomplete due to different reasons (malfunctioning sensors, collected data storage problems, etc.). Missing data do not only affect the resulting decision-making process, but also the choice of a particular analysis method. Given the increase of extreme events due to climate change, it is necessary to improve the management of water resources. Due to the solution of this problem requires the development of accurate estimations and its application in real time, this work present two contributions. Firstly, different gap-filling techniques have been evaluated in order to select the most adequate one for river stage series: (i) cubic splines (CS), (ii) radial basis function (RBF) and (iii) multilayer perceptron (MLP) suitable for small processors like Arduino or Raspberry Pi. The results obtained confirmed that splines and monolayer perceptrons had the best performances. Secondly, a pre-validating Internet of Things (IoT) device was developed using a dynamic seed non-linear autoregressive neural network (NARNN). This automatic pre-validation in real time was tested satisfactorily, sending the data to the catchment basin process center (CPC) by using remote communication based on 4G technology.

Design of Portable Electrocardiograph Monitoring System

2020 ◽  
Author(s):  
Jia Hua-Ping ◽  
Zhao Jun-Long ◽  
Liu Jun
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Data Storage ◽  
P Wave ◽  
Ecg Monitoring ◽  
The Public ◽  
Ecg Signals ◽  
Early Warning Mechanism ◽  
Timely Treatment ◽  
Ecg Data

Cardiovascular disease is one of the major diseases that threaten the human health. But the existing electrocardiograph (ECG) monitoring system has many limitations in practical application. In order to monitor ECG in real time, a portable ECG monitoring system based on the Android platform is developed to meet the needs of the public. The system uses BMD101 ECG chip to collect and process ECG signals in the Android system, where data storage and waveform display of ECG data can be realized. The Bluetooth HC-07 module is used for ECG data transmission. The abnormal ECG can be judged by P wave, QRS bandwidth, and RR interval. If abnormal ECG is found, an early warning mechanism will be activated to locate the user’s location in real time and send preset short messages, so that the user can get timely treatment, avoiding dangerous occurrence. The monitoring system is convenient and portable, which brings great convenie to the life of ordinary cardiovascular users.

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

Real-time Approximation of Photometric Polygonal Lights

2020 ◽  
Vol 3 (1) ◽  
pp. 1-18
Author(s):  
Christian Luksch ◽  
Lukas Prost ◽  
Michael Wimmer
Keyword(s):  
Real Time ◽  
Specular Reflection ◽  
Near Field ◽  
Measurement Data ◽  
Data Sets ◽  
Photometric Measurement ◽  
Integration Technique ◽  
Time Approximation ◽  
Light Emitter ◽  
Selection Of

We present a real-time rendering technique for photometric polygonal lights. Our method uses a numerical integration technique based on a triangulation to calculate noise-free diffuse shading. We include a dynamic point in the triangulation that provides a continuous near-field illumination resembling the shape of the light emitter and its characteristics. We evaluate the accuracy of our approach with a diverse selection of photometric measurement data sets in a comprehensive benchmark framework. Furthermore, we provide an extension for specular reflection on surfaces with arbitrary roughness that facilitates the use of existing real-time shading techniques. Our technique is easy to integrate into real-time rendering systems and extends the range of possible applications with photometric area lights.

Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network

2021 ◽  
pp. 1-10
Author(s):  
Lipeng Si ◽  
Baolong Liu ◽  
Yanfang Fu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Image Recognition ◽  
Error Detection ◽  
Convolution Neural Network ◽  
Data Sets ◽  
Small Target ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Real Time Tracking

The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.

Facial Nerve EMG: Low-Tech Monitoring with a Stopwatch

2021 ◽  
Author(s):  
Julian Prell ◽  
Christian Scheller ◽  
Sebastian Simmermacher ◽  
Christian Strauss ◽  
Stefan Rampp
Keyword(s):  
Facial Nerve ◽  
Real Time ◽  
Random Order ◽  
Automated Analysis ◽  
Data Sets ◽  
Time Analysis ◽  
Frequency Pattern ◽  
Real Time Analysis ◽  
Offline Analysis ◽  
Automated Algorithms

Abstract Objective The quantity of A-trains, a high-frequency pattern of free-running facial nerve electromyography, is correlated with the risk for postoperative high-grade facial nerve paresis. This correlation has been confirmed by automated analysis with dedicated algorithms and by visual offline analysis but not by audiovisual real-time analysis. Methods An investigator was presented with 29 complete data sets measured during actual surgeries in real time and without breaks in a random order. Data were presented either strictly via loudspeaker (audio) or simultaneously by loudspeaker and computer screen (audiovisual). Visible and/or audible A-train activity was then quantified by the investigator with the computerized equivalent of a stopwatch. The same data were also analyzed with quantification of A-trains by automated algorithms. Results Automated (auto) traintime (TT), known to be a small, yet highly representative fraction of overall A-train activity, ranged from 0.01 to 10.86 s (median: 0.58 s). In contrast, audio-TT ranged from 0 to 1,357.44 s (median: 29.69 s), and audiovisual-TT ranged from 0 to 786.57 s (median: 46.19 s). All three modalities were correlated to each other in a highly significant way. Likewise, all three modalities correlated significantly with the extent of postoperative facial paresis. As a rule of thumb, patients with visible/audible A-train activity < 1 minute presented with a more favorable clinical outcome than patients with > 1 minute of A-train activity. Conclusion Detection and even quantification of A-trains is technically possible not only with intraoperative automated real-time calculation or postoperative visual offline analysis, but also with very basic monitoring equipment and real-time good quality audiovisual analysis. However, the investigator found audiovisual real-time-analysis to be very demanding; thus tools for automated quantification can be very helpful in this respect.

Design and implementation of a virtual ARINC 653 simulation platform

SIMULATION ◽  
2021 ◽  
pp. 003754972199601
Author(s):  
Jinchao Chen ◽  
Keke Chen ◽  
Chenglie Du ◽  
Yifan Liu
Keyword(s):  
Real Time ◽  
System Development ◽  
General Purpose ◽  
Simulation Platform ◽  
Communication Management ◽  
Management Communication ◽  
Real Time Simulation ◽  
Testing And Debugging ◽  
Time Simulation ◽  
Efficiency And Effectiveness

The ARINC 653 operation system is currently widely adopted in the avionics industry, and has become the mainstream architecture in avionics applications because of its strong agility and reliability. Although ARINC 653 can efficiently reduce the weight and energy consumption, it results in a serious development and verification problem for avionics systems. As ARINC 653 is non-open source software and lacks effective support for software testing and debugging, it is of great significance to build a real-time simulation platform for ARINC 653 on general-purpose operating systems, improving the efficiency and effectiveness of system development and implementation. In this paper, a virtual ARINC 653 platform is designed and realized by using real-time simulation technology. The proposed platform is composed of partition management, communication management, and health monitoring management, provides the same operation interfaces as the ARINC 653 system, and allows dynamic debugging of avionics applications without requiring the actual presence of real devices. Experimental results show that the platform not only simulates the functionalities of ARINC 653, but also meets the real-time requirements of avionics applications.

scholarly journals Real-Time Neural Signals Decoding onto Off-the-Shelf DSP Processors for Neuroprosthetic Applications

2016 ◽  
Vol 24 (9) ◽  
pp. 993-1002 ◽  
Author(s):  
Danilo Pani ◽  
Gianluca Barabino ◽  
Luca Citi ◽  
Paolo Meloni ◽  
Stanisa Raspopovic ◽  
...  
Keyword(s):  
Real Time ◽  
Neural Signals ◽  
Dsp Processors
Sign in / Sign up

Export Citation Format

Share Document