scholarly journals Development of Scientific Fishery Biomass Estimator: System Design and Prototyping

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6095
Author(s):  
Pranesh Sthapit ◽  
MinSeok Kim ◽  
Donhyug Kang ◽  
Kiseon Kim
Keyword(s):  
System Design ◽  
Real Time ◽  
Fish Abundance ◽  
Experimental Results ◽  
Strength Calculation ◽  
Target Strength ◽  
Abundance Estimation ◽  
Processing Unit ◽  
Single Beam ◽  
Display Unit

This paper presents a new compact single beam advanced echosounder system designed to estimate fish count in real time. The proposed device is a standalone system, which consists of a transducer, a processing unit, a keypad, and a display unit to show output. A fish counting algorithm was developed and implemented in the device. The device is capable of performing all the functions required for fish abundance estimation including target strength calculation, simultaneous echo integration, and echogram generation. During operation, the device analyzes ping data continuously and calculates various parameters in real time while simultaneously displaying the echogram and results on the screen. The device has been evaluated by technical verification in a lab and on-site experiments. The experimental results demonstrate that the proposed device is on par with a commercial echosounder and is capable of accurately estimating the fish abundance. The proposed device is beneficial for fish management.

Design of Underground Displacement Monitoring Circuit Based on STC89C58

2014 ◽  
Vol 602-605 ◽  
pp. 1916-1919
Author(s):  
Ke Gang Hu ◽  
Yi Lian Zhou ◽  
Qing Li
Keyword(s):  
System Design ◽  
Real Time ◽  
Humidity Sensor ◽  
Experimental Results ◽  
Stable Operation ◽  
Displacement Monitoring ◽  
Acceleration Sensor ◽  
The Core ◽  
Hardware System ◽  
Working Principle

Using STC89C58 MCU as the core, through the acceleration sensor and a humidity sensor for the monitoring the displacement of underground conditions, to simply realize real-time monitoring function of the displacement of underground. Introduces the working principle and software/hardware system design. The experimental results show that, the system has stable operation, achieved the expected goals.

Design of Video Motion Detect System Based on SOPC

2012 ◽  
Vol 241-244 ◽  
pp. 2263-2267
Author(s):  
Jiang Chun Xu ◽  
Shi Bo Hao ◽  
Xi Liu
Keyword(s):  
System Design ◽  
Real Time ◽  
Video Surveillance ◽  
Motion Detection ◽  
Processing Speed ◽  
Video Data ◽  
Experimental Results ◽  
Design Scheme ◽  
System A

It is very necessary to apply motion detection to video surveillance in order to enhance the performance of system. A new design scheme of video motion detect system based on System On a Programmable Chip is proposed. Two cameras will capture the video data in real-time, and transmitted to DE2_70 development board. After the system which is on a programmable chip based on FPGA processing, the cameras survey whether an illegal personnel is in the range of cameras. The system achieves that two-way video to switch through the switch button. The experimental results show that the SOPC realization of the system design improves the processing speed, and the system also has good flexibility.

A Laboratory Equipment Management System Design Based on RFID

2014 ◽  
Vol 602-605 ◽  
pp. 2526-2530
Author(s):  
Peng Guo ◽  
Hai Yan Zhang ◽  
Zhen Yong Liu ◽  
Yong Wei Li
Keyword(s):  
System Design ◽  
Real Time ◽  
Management System ◽  
Experimental Results ◽  
Equipment Management ◽  
Uhf Rfid ◽  
Laboratory Equipment ◽  
Rfid Reader ◽  
Rfid System

For the needs of laboratory equipment management, combined with development of RFID, a laboratory equipment management system is designed based on RFID. System design is briefly described. UHF RFID reader design is described in detail. The anti-collision algorithm of ISO 18000-6C protocol is partially improved, so that the reader is able to make real-time adaptations of the anti-collision algorithm using IAP according to the quantity of laboratory equipments, thus raising the efficiency of algorithm recognition. Experimental results prove that the system design has reliability, stability and certain practical value.

LOW COST REAL TIME ROCKET TELEMETRY SYSTEM DESIGN

2019 ◽  
Author(s):  
Gabriel de Almeida Souza ◽  
Larissa Barbosa ◽  
Glênio Ramalho ◽  
Alexandre Zuquete Guarato
Keyword(s):  
System Design ◽  
Real Time ◽  
Low Cost ◽  
Telemetry System

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

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.

Real-time, High-resolution Depth Upsampling on Embedded Accelerators

2021 ◽  
Vol 20 (3) ◽  
pp. 1-22
Author(s):  
David Langerman ◽  
Alan George
Keyword(s):  
High Resolution ◽  
Low Power ◽  
Real Time ◽  
Mixed Reality ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Reconfigurable Logic ◽  
Depth Sensors ◽  
Time Requirements ◽  
Graphics Processing

High-resolution, low-latency apps in computer vision are ubiquitous in today’s world of mixed-reality devices. These innovations provide a platform that can leverage the improving technology of depth sensors and embedded accelerators to enable higher-resolution, lower-latency processing for 3D scenes using depth-upsampling algorithms. This research demonstrates that filter-based upsampling algorithms are feasible for mixed-reality apps using low-power hardware accelerators. The authors parallelized and evaluated a depth-upsampling algorithm on two different devices: a reconfigurable-logic FPGA embedded within a low-power SoC; and a fixed-logic embedded graphics processing unit. We demonstrate that both accelerators can meet the real-time requirements of 11 ms latency for mixed-reality apps. 1

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