Real-time, low-latency image processing with high throughput on a multi-core SoC

2016 ◽  
Author(s):  
Barath Ramesh ◽  
Alan D. George ◽  
Herman Lam
Keyword(s):  
Image Processing ◽  
Real Time ◽  
High Throughput ◽  
Low Latency

scholarly journals Plant Growth and LAI Estimation using quantized Embedded Regression models for high throughput phenotyping

2021 ◽  
Author(s):  
Dhruv Sheth
Keyword(s):  
Image Processing ◽  
Linear Regression ◽  
Plant Growth ◽  
Regression Model ◽  
Real Time ◽  
Crop Yield ◽  
High Throughput ◽  
Linear Regression Model ◽  
Growth Patterns ◽  
Biomass Estimation

Abstract Due to the influence of climate change, and due to it's unpredictable nature, majority of agricultural crops have been affected in terms of production and maintenance. Hybrid and cost-effective crops are making their way into the market, but monitoring factors which affect the increase in yield of these crops, and conditions favorable for growth have to be manually monitored and structured to yield high throughput. Farmers are showing transition from traditional means to hydroponic systems for growing annual and perennial crops. These crop arrays possess growth patterns which depend on environmental growth conditions in the hydroponic units. Semi-autonomous systems which monitor these growth may prove to be beneficial, reduce costs and maintenance efforts, and also predict future yield beforehand to get an idea on how the crop would perform. These systems are also effective in understanding crop drools and wilt/diseases from visual systems and traits of plants.Forecasting or predicting the crop yield well ahead of its harvest time would assist the strategists and farmers for taking suitable measures for selling and storage. Accurate prediction of crop development stages plays an important role in crop production management. In this article, I~propose an Embedded Machine Learning approach to predicting crop yield and biomass estimation of crops using an Image based Regression approach using EdgeImpulse that runs on Edge system, Sony Spresense, in real time. This utilizes few of the 6 Cortex M4F cores provided in the Sony Spresense board for Image processing, inferencing and predicting a regression output in real time. This system uses Image processing to analyze the plant in a semi-autonomous environment and predict the numerical serial of the biomass allocated to the plant growth. This numerical serial contains a threshold of biomass which is then predicted for the plant. The biomass output is then also processed through a linear regression model to analyze efficacy and compared with the ground truth to identify pattern of growth. The image Regression and linear regression model contribute to an algorithm which is finally used to test and predict biomass for each plant semi-autonomously.

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

Highly Sensitive Detection of Paraquat with Pillar[5]arene as an aptamer in α-Hemolysin Nanopore

2021 ◽  
Author(s):  
Xiaojia Jiang ◽  
Mingsong Zang ◽  
Fei Li ◽  
Chunxi Hou ◽  
Quan Luo ◽  
...  
Keyword(s):  
Real Time ◽  
High Throughput ◽  
Single Molecule ◽  
Single Molecule Detection ◽  
Sensitive Detection ◽  
High Throughput Analysis ◽  
Throughput Analysis ◽  
The Real ◽  
Highly Sensitive ◽  
Highly Sensitive Detection

Biological nanopore-based techniques have attracted more and more attention recently in the field of single-molecule detection, because they allow the real-time, sensitive, high-throughput analysis. Herein, we report an engineered biological...

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.

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