Programmable real-time applications with the 3D-Flow for input data rate systems of hundreds of MHz

1996 ◽  
Vol 43 (1) ◽  
pp. 178
Author(s):  
D. Crosetto
Keyword(s):  
Real Time ◽  
Input Data ◽  
Data Rate ◽  
3D Flow ◽  
Real Time Applications

Maintaining Consistency Over A Network in Real-Time Applications

1989 ◽  
Author(s):  
Insup Lee ◽  
Susan Davidson ◽  
Victor Wolfe
Keyword(s):  
Real Time ◽  
Real Time Applications

Peak-Power-Aware Energy Management for Periodic Real-Time Applications

2020 ◽  
Vol 39 (4) ◽  
pp. 779-788 ◽  
Author(s):  
Mohsen Ansari ◽  
Amir Yeganeh-Khaksar ◽  
Sepideh Safari ◽  
Alireza Ejlali
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Peak Power ◽  
Real Time Applications

scholarly journals A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 234 ◽  
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Normal Control ◽  
Input Data ◽  
Deep Neural Network ◽  
Pattern Mining ◽  
F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

Effects of multilevel security on real-time applications

2002 ◽  
Author(s):  
R.K. Clark ◽  
I.B. Greenberg ◽  
P.K. Boucher ◽  
T.F. Lunt ◽  
P.G. Neumann ◽  
...  
Keyword(s):  
Real Time ◽  
Multilevel Security ◽  
Real Time Applications

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

Efficient placement of audio data on optical disks for real-time applications

1989 ◽  
Vol 32 (7) ◽  
pp. 862-871 ◽  
Author(s):  
Clement Yu ◽  
Wei Sun ◽  
Dina Bitton ◽  
Qi Yang ◽  
Richard Bruno ◽  
...  
Keyword(s):  
Real Time ◽  
Audio Data ◽  
Optical Disks ◽  
Real Time Applications
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