scholarly journals An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6281
Author(s):  
Ander Zuazo ◽  
Jordi Grinyó ◽  
Vanesa López-Vázquez ◽  
Erik Rodríguez ◽  
Corrado Costa ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Sea ◽  
Chlorophyll Concentration ◽  
Biological Information ◽  
Test Accuracy ◽  
Times Series ◽  
Transient Activity ◽  
Paragorgia Arborea ◽  
Artificial Neural Network Ann

Imaging technologies are being deployed on cabled observatory networks worldwide. They allow for the monitoring of the biological activity of deep-sea organisms on temporal scales that were never attained before. In this paper, we customized Convolutional Neural Network image processing to track behavioral activities in an iconic conservation deep-sea species—the bubblegum coral Paragorgia arborea—in response to ambient oceanographic conditions at the Lofoten-Vesterålen observatory. Images and concomitant oceanographic data were taken hourly from February to June 2018. We considered coral activity in terms of bloated, semi-bloated and non-bloated surfaces, as proxy for polyp filtering, retraction and transient activity, respectively. A test accuracy of 90.47% was obtained. Chronobiology-oriented statistics and advanced Artificial Neural Network (ANN) multivariate regression modeling proved that a daily coral filtering rhythm occurs within one major dusk phase, being independent from tides. Polyp activity, in particular extrusion, increased from March to June, and was able to cope with an increase in chlorophyll concentration, indicating the existence of seasonality. Our study shows that it is possible to establish a model for the development of automated pipelines that are able to extract biological information from times series of images. These are helpful to obtain multidisciplinary information from cabled observatory infrastructures.

Basic Cellular Neural Networks Image Processing

2011 ◽  
pp. 218-222
Author(s):  
J. Álvaro Fernández
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Continuous Time ◽  
Engineering Application ◽  
Cellular Neural Network ◽  
Vlsi Circuits ◽  
Ann Model ◽  
Anatomy And Physiology ◽  
Artificial Neural Network Ann ◽  
Cellular Computer

Since its seminal publication in 1988, the Cellular Neural Network (CNN) (Chua & Yang, 1988) paradigm have attracted research community’s attention, mainly because of its ability for integrating complex computing processes into compact, real-time programmable analogic VLSI circuits (Rodríguez et al., 2004). Unlike cellular automata, the CNN model hosts nonlinear processors which, from analogic array inputs, in continuous time, generate analogic array outputs using a simple, repetitive scheme controlled by just a few real-valued parameters. CNN is the core of the revolutionary Analogic Cellular Computer, a programmable system whose structure is the so-called CNN Universal Machine (CNN-UM) (Roska & Chua, 1993). Analogic CNN computers mimic the anatomy and physiology of many sensory and processing organs with the additional capability of data and program storing (Chua & Roska, 2002). This article reviews the main features of this Artificial Neural Network (ANN) model and focuses on its outstanding and more exploited engineering application: Digital Image Processing (DIP).

scholarly journals PENERAPAN ARTIFICIAL NEURAL NETWORK UNTUK KLASIFIKASI FERTILITAS TELUR ITIK MENGGUNAKAN RASPBERRY PI

BUANA ILMU ◽  
2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Jamaludin Indra
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Raspberry Pi ◽  
Detection Accuracy ◽  
Neural Network Method ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

ABSTRAK Artificial Neural Network (ANN) telah banyak diterapkan pada berbagai bidang, salah satunya penerapan pada bidang peternakan. Penetasan menggunakan mesin penetas telur, proses pengklasifikasian embrio telur menjadi sangat penting dalam proses penetasan untuk membedakan antara yang layak, berdasarkan adanya perkembangan embrio yang dapat dilanjutkan dalam proses inkubasi atau tidak layak (fertile atau infertile), dalam penelitian ini menyajikan klasifikasi menggunakan teknik pengolahan citra digital menggunakan metode artificial neural network yang diaplikasikan pada Raspberry Pi sebagai pemroses gambar dan menampilkan hasil klasifikasi. Dengan metode artificial neural network dan penggunaan Raspberry Pi mampu mencapai akurasi pendeteksian 95%. Kata kunci: Artificial Neural Network, Pengolahan Citra Digital, Embrio , Klasifikasi, Telur . ABSTRACT Artificial Neural Network (ANN) has been widely applied in various fields, one of which is the application in the field of animal husbandry. Hatching using an egg incubator machine, the classification process of egg embryos is very important in the hatching process to distinguish between the appropriate, based on the embryonic development that can be continued in the process of incubation or inadequate (fertile or infertile), in this study presents classification using image processing techniques digital uses the artificial neural network method that is applied to the Raspberry Pi as an image processor and displays the classification results. With the artificial neural network method and the use of Raspberry Pi it is expected to be able to achieve 90% detection accuracy. Key word : Artificial Neural Network, Digital Image Processing, Embriyo, Calssification, Egg.

Advanced Cellular Neural Networks Image Processing

2011 ◽  
pp. 45-50
Author(s):  
J. Álvaro Fernández
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Cellular Neural Network ◽  
Research Community ◽  
Vlsi Circuits ◽  
Anatomy And Physiology ◽  
The Core ◽  
Scientific Papers ◽  
Artificial Neural Network Ann ◽  
Cellular Computer

Since its introduction to the research community in 1988, the Cellular Neural Network (CNN) (Chua & Yang, 1988) paradigm has become a fruitful soil for engineers and physicists, producing over 1,000 published scientific papers and books in less than 20 years (Chua & Roska, 2002), mostly related to Digital Image Processing (DIP). This Artificial Neural Network (ANN) offers a remarkable ability of integrating complex computing processes into compact, real-time programmable analogic VLSI circuits as the ACE16k (Rodríguez et al., 2004) and, more recently, into FPGA devices (Perko et al., 2000). CNN is the core of the revolutionary Analogic Cellular Computer (Roska et al., 1999), a programmable system based on the so-called CNN Universal Machine (CNN-UM) (Roska & Chua, 1993). Analogic CNN computers mimic the anatomy and physiology of many sensory and processing biological organs (Chua & Roska, 2002). This article continues the review started in this Encyclopaedia under the title Basic Cellular Neural Network Image Processing.

Research on Maize Varieties Recognition System Based on Image Processing

2013 ◽  
Vol 397-400 ◽  
pp. 2335-2339
Author(s):  
Li Miao Deng ◽  
Tao Luan ◽  
Wen Jie Ma
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Support Vector Machine ◽  
Support Vector Machine Model ◽  
Recognition System ◽  
Support Vector ◽  
Test Accuracy ◽  
Machine Model ◽  
Maize Varieties ◽  
Texture Characteristics

In order to realize highly intelligent and automatic species identification and recognition, we obtained the images of 11 varieties and each variety includes 50 seeds. For each image, we acquired 33 characteristics including shape, color and texture characteristics. And then we constructed the Artificial Neural Network and Support Vector Machine model to train and identify different varieties. We built the recognition system based on Visual C++ 6.0 and OpenCV library.Results shows that the SVM method has higher recognition effect than neural network overall and the recognition effect is more stability, the overall self-_recognition performance can reach 100% and test accuracy can reach 85%. The recoginition System base on Visual C++ runs faster than that of Matlab, which is more suitable for real-time varieties identification.

scholarly journals Screening of Tuberculosis Using Artificial Neural Network

2020 ◽  
Vol 3 (11) ◽  
pp. 145-149
Author(s):  
A. Anand Kumar ◽  
T. Mani ◽  
S. Gokulnath ◽  
S. K. Kabilesh ◽  
K. Dinakaran ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Bacterial Disease ◽  
Image Processing Techniques ◽  
Neural Organization ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Processing Techniques

Tuberculosis is an infectious bacterial disease that most commonly affects the lungs. This paper reviews, screening of tuberculosis in chest radiograph images using an artificial neural network (ANN). Implementing image processing techniques having segmentation, feature extraction from chest radiographs, at that point building up a fake neural organization for programmed characterization dependent on back proliferation calculation to group tuberculosis accurately. The performance was evaluated using SVM and ANN classifiers regarding exactness, review, and precision. The trial results Confirm the effectiveness of the proposed strategy that gives great Classification proficiency.

scholarly journals Deteksi Level Kolesterol melalui Citra Mata Berbasis HOG dan ANN

2019 ◽  
Vol 7 (2) ◽  
pp. 284 ◽  
Author(s):  
MUHAMMAD ARSYAD SIDDIK ◽  
LEDYA NOVAMIZANTI ◽  
I NYOMAN APRAZ RAMATRYANA
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
High Risk ◽  
Clinical Laboratory ◽  
Computing Time ◽  
Histogram Of Oriented Gradients ◽  
Cholesterol Levels ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

ABSTRAKKolesterol merupakan lemak yang berada di dalam darah yang dibutuhkan untuk pembentukan hormon dan sel baru. Kadar kolesterol normal harus kurang dari 200 mg/dL, namun jika di atas 240 mg/dL akan berisiko tinggi terkena penyakit stroke dan jantung koroner. Penelitian ini menghasilkan suatu sistem yang dapat mendeteksi kadar kolesterol seseorang melalui citra mata menggunakan metode iridologi dan image processing. Citra mata diperoleh dari pasien laboratorium klinik sebanyak 120 citra mata. Proses sistem diawali dengan mengolah citra mata dengan metode cropping, resize, dan segmentasi. Metode ekstaksi ciri menggunakan Histogram of Oriented Gradients (HOG), dan klasifikasi menggunakan Artificial Neural Network (ANN). Sistem dapat mendeteksi kadar kolesterol dengan tiga level klasifikasi, yaitu normal, berisiko kolesterol tinggi, dan kolesterol tinggi dengan tingkat akurasi sebesar 93% dan waktu komputasi 0,0862 detik.Kata kunci: citra mata, kadar kolesterol, Histogram of Oriented Gradients, Artificial Neural Network ABSTRACTCholesterol is fat in the blood that is needed for the formation of hormones and new cells. Normal cholesterol levels should be less than 200 mg / dL, but if above 240 mg / dL will be at high risk of stroke and coronary heart disease. This study produced a system that can detect a person's cholesterol levels through eye images using iridology and image processing methods. Eye images obtained from clinical laboratory patients were 120 eye images. The system process begins with processing eye images using the method of cropping, resizing, and segmentation. Feature extraction method uses Histogram of Oriented Gradients (HOG), and classification using Artificial Neural Network (ANN). The system can detect cholesterol levels with three levels of classification, namely normal, at high risk of cholesterol, and high cholesterol with an accuracy rate of 93% and computing time of 0.0862 seconds.Keywords: eye image, cholesterol level, Histogram of Oriented Gradients, Artificial Neural Network

Identifikasi permukaan aluminium dengan image processing dan artificial neural network

2010 ◽  
Vol 8 (1) ◽  
pp. 717
Author(s):  
Irwin Syahri
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Roughness Surface ◽  
Surface Image ◽  
Pixel Image ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Penelitian ini bertujuan untuk mengidentifikasi permukaan suatu logam, khususnya Aluminium berdasarkan image processing yang ditampilkan logam dengan pendekatan komputasi menggunakan Artificial Neural Network (ANN). Specimen dikerjakan dengan menggunakan beberapa mesin dan tingkat kecepatan putaran spindle dan kecepatan pemotongan yang berbeda sehingga didapatkan kekasaran permukaan yang berbeda. Specimen diambil image-nya menggunakan kamera digital 4 mega piksel dengan sumber pencahayaan, jarak dan jumlah pixel image yang sama. Image Alumunium selanjutnya di proses untuk dapat dikenali dengan ANN. Hasil penelitian menunjukkan model ANN 11 input 5S hidden dan 1 output: (11-5-1) menunjukkan hasil terbaik untukmengidentifikasi bentuk permukaan Alumunium dengan RMSE yang terkecil: 0.0038 untuk training dan testing.Kata kunci : Roughness surface, Image Processing, ANN

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann
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