scholarly journals 565: AUTOMATED IMAGE PROCESSING WITH POINT-OF-CARE OCULAR ULTRASOUND FOR REAL-TIME ICP MONITORING

2021 ◽  
Vol 50 (1) ◽  
pp. 274-274
Author(s):  
David You ◽  
Marc LaFonte ◽  
Ilker Hacihaliloglu ◽  
Matthew Lissauer
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Point Of Care ◽  
Icp Monitoring ◽  
Ocular Ultrasound ◽  
Automated Image Processing

scholarly journals The development of modern automated image processing and transfer systems for agriculture unmanned aerial vehicles

2020 ◽  
Vol 161 ◽  
pp. 01087 ◽  
Author(s):  
Marina Vasileva ◽  
Ilyas Ismagilov ◽  
Alexander Gerasimov
Keyword(s):  
Image Processing ◽  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Crop Yields ◽  
Walsh Functions ◽  
Aerial Vehicles ◽  
Transmission Of Information ◽  
High Resolution Images ◽  
Automated Image Processing

The paper contains results of analytic research of unmanned aerial vehicles using in agriculture. The main problems arising in the creation and subsequent large volumes of high-resolution images real time transfer in unmanned aerial vehicles are highlighted. The Automated image processing and transfer system using new methods of information compression on unmanned aerial vehicles board is proposed. The paper considers the issues of consider the problems of constructing new orderings of Walsh functions and constructing fast compression algorithms in synthesized systems of discrete Walsh functions. For processing and subsequent transmission of information from UAVs recommended to use the fast DWT procedure, it allows for a hardware implementation capable of the real-time conversion performing due to its simplicity. The introduction of the proposed solutions for UAVs in agriculture allows to increase accurasy of electronic cartographic material, to keep electronic records of agricultural operations, to carry out operational monitoring of the crops state and to respond quickly for violations and deviations, to predict crop yields and plan their activities for short-term and long-term prospects.

Echtzeit-Bauteilprüfung beim Feinschneiden/Real-time quality analysis during fine blanking – Test rig for sheared part characterization using image processing and neural networks

2020 ◽  
Vol 110 (06) ◽  
pp. 382-388
Author(s):  
Herman Voigts ◽  
Rafael Hild ◽  
Andreas Feuerhack ◽  
Thomas Bergs
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Quality Assessment ◽  
Real Time ◽  
Quality Analysis ◽  
Test Rig ◽  
Fine Blanking ◽  
Real Time Analysis ◽  
Automated Image Processing

Die Schnittteilqualität beim Feinschneiden unterliegt einer Vielzahl von Einflussfaktoren. Derzeit findet die Qualitätsbewertung offline vom Prozess statt. Um eine echtzeitfähige Qualitätsbeurteilung für den Einsatz von Assistenzsystemen zu ermöglichen, wurde eine bildverarbeitende Methodik untersucht. Es wurde ein Prüfstand entwickelt zur Erforschung der Methoden für eine automatisierte bildverarbeitende und echtzeitfähige Analyse der Schnittteilqualität mittels neuronalen Netzen.   The quality of the sheared surface during fine blanking is subject to a large number of influencing factors. Currently, quality assessment is carried out offline. To enable real-time quality assessment based on assistance systems, an image-processing methodology was investigated. A test rig was developed to investigate methods for automated image processing and real-time analysis of the sheared surface quality by means   of neural networks.

Model Klasifikasi Mata Katarak dan Normal Menggunakan Histogram

2017 ◽  
Vol 9 (1) ◽  
pp. 33-36
Author(s):  
Valencia Wirawan ◽  
Yustinus Eko Soelistio
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Index Terms

Telah banyak penelitian pada citra medis telah diadopsi oleh sebagian besar ilmuwan dan dokter yang dapat membantu dalam mendeteksi gangguan pada mata terutama katarak. Namun, umumnya penelitian tersebut menggunakan citra medis atau digital yang relatif mahal dan sulit didapatkan oleh sebagian orang, dan metode yang rentan akan translasi (pergeseran), serta perubahan ukuran gambar dan bentuk objek. Penelitian ini mengembangkan sebuah metode menggunakan model histogram untuk mengklasifikasi mata katarak dari citra digital dengan (1) format yang lebih umum seperti JPEG dan (2) lebih toleranterhadap translasi dan perubahan ukuran. Metode ini juga mampu bekerja dengan baik menggunakan citra digital dalam citra mata yang tidak tegak lurus terhadap kamera. Metode ini mencapai akurasi 79,03% dalam kondisi bebas dan 88.47% dalam kondisi mata tegak lurus terhadap kamera. Metode ini mempunyai kompleksitas yang rendah sehingga dapat digunakan pada komputer dengan spesifikasi rendah dan sistem yang membutuhkan kecepatan mendekati real-time. Index Terms—Image processing, cataract, classification, histogram

Development of real time welding control system by using image processing.

1989 ◽  
Vol 7 (3) ◽  
pp. 363-367 ◽  
Author(s):  
Takaichi Koyama ◽  
Yoichi Takahashi ◽  
Masahiro Kobayashi ◽  
Junichiro Morisawa
Keyword(s):  
Image Processing ◽  
Control System ◽  
Real Time

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.

scholarly journals A miniaturized optoelectronic biosensor for real-time point-of-care total protein analysis.

MethodsX ◽  
2021 ◽  
pp. 101414
Author(s):  
Ophir Vermesh ◽  
Fariah Mahzabeen ◽  
Jelena Levi ◽  
Marilyn Tan ◽  
Israt S. Alam ◽  
...  
Keyword(s):  
Real Time ◽  
Total Protein ◽  
Point Of Care ◽  
Protein Analysis ◽  
Time Point

DFOG-Image Processing Application for Real-Time Defogging

2020 ◽  
Author(s):  
Indiketiya I.H.O.H ◽  
Kulasekara K.M.R.A ◽  
J.M. Thomas ◽  
Ishara Gamage ◽  
Thusithanjana Thilakarathna
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Processing Application ◽  
Image Processing Application
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