scholarly journals Foot Print Health Monitoring System based on Open Cv

2019 ◽  
Vol 8 (6) ◽  
pp. 699-701
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Raspberry Pi ◽  
Time Base ◽  
Base System ◽  
Computing Platform ◽  
Human Footprint ◽  
Key Points ◽  
The Embedded System ◽  
Data Requirements

Human footprint is considered has the latest traits that could be used to detect an individual’s identity computes parameters. The main objective is to establish the ability of image processing algorithms on a small computing platform. We designed the embedded system which reads and recognizes a person their identity. The major aim of the paper briefs the characteristics of Patient’s data, requirements and Report behind implementing a real-time base system. The person’s foot image is segmented and its key points are located. The foot is aligned and edited, cropped as per the key points and is developed and resized. These methods are used for recognizing and subdividing. Color place a major role in multiple application for footprint detection. This project is focused on lightweight technique were mainly used due to the drawback of real time based applications and Raspberry Pi capabilities

scholarly journals Health Monitoring System Based on Foot Print using High End Processor

2020 ◽  
Vol 9 (5) ◽  
pp. 2250-2252
Keyword(s):  
Data Storage ◽  
Raspberry Pi ◽  
Time Base ◽  
Base System ◽  
Computing Platform ◽  
Human Footprint ◽  
Processing Power ◽  
Key Points ◽  
Computational Resources ◽  
The Cost

Biometrics is used for identification in this article. This paper also describes how biometrics can leverage the boundless computational resources of the cloud and the striking properties of flexibility, scalability, and cost reduction to reduce the cost of the biometric system requirements of different computational resources (i.e. processing power or data storage) and to improve the performance of the processes of the biometric systems (i.e. biometric matching). The human footprint is known to have the new characteristics that could be used to identify the criteria for determining an individual's identity. The main objective is to develop image processing algorithms capability on a limited computing platform. We created the embedded framework that recognizes and accepts a person's identity. The paper's main purpose is to update the characteristics of the details, needs, and reports of the patient behind the implementation of a real-time base system. The foot picture of the human is segmented, and its key points are placed. The foot is arranged and trimmed, clipped according to key points, created and dimensioned. Colour establishes a crucial role in numerous footprint recognition applications. Due to the drawback of realtime software and Raspberry Pi technology, this effort based on lightweight methodology was primarily used.

scholarly journals The Design of a 2D Graphics Accelerator for Embedded Systems

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 469
Author(s):  
Hyun Woo Oh ◽  
Ji Kwang Kim ◽  
Gwan Beom Hwang ◽  
Seung Eun Lee
Keyword(s):  
Embedded Systems ◽  
Embedded System ◽  
Real Time ◽  
Line Drawing ◽  
Cmos Process ◽  
Embedded Processor ◽  
Processor Core ◽  
Field Programmable ◽  
The Embedded System ◽  
Graphics Processing

Recently, advances in technology have enabled embedded systems to be adopted for a variety of applications. Some of these applications require real-time 2D graphics processing running on limited design specifications such as low power consumption and a small area. In order to satisfy such conditions, including a specific 2D graphics accelerator in the embedded system is an effective method. This method reduces the workload of the processor in the embedded system by exploiting the accelerator. The accelerator assists the system to perform 2D graphics processing in real-time. Therefore, a variety of applications that require 2D graphics processing can be implemented with an embedded processor. In this paper, we present a 2D graphics accelerator for tiny embedded systems. The accelerator includes an optimized line-drawing operation based on Bresenham’s algorithm. The optimized operation enables the accelerator to deal with various kinds of 2D graphics processing and to perform the line-drawing instead of the system processor. Moreover, the accelerator also distributes the workload of the processor core by removing the need for the core to access the frame buffer memory. We measure the performance of the accelerator by implementing the processor, including the accelerator, on a field-programmable gate array (FPGA), and ascertaining the possibility of realization by synthesizing using the 180 nm CMOS process.

Portable Wireless Yield Monitoring System on Conventional Rice Combine

2021 ◽  
Vol 37 (1) ◽  
pp. 193-203
Author(s):  
Renny Eka Purti ◽  
Azmi Yahya ◽  
Oh Yun Ju ◽  
Maryam Mohd Isa ◽  
Samsuzana Abdul Aziz
Keyword(s):  
Moisture Content ◽  
Embedded System ◽  
Real Time ◽  
Crop Yield ◽  
Field Capacity ◽  
Base Station ◽  
Grain Moisture ◽  
The Embedded System ◽  
Yield Monitoring ◽  
Grain Elevator

Abstract. A simple, portable, and rugged instrumentation system has been successfully developed and field demonstrated to monitor, measure, and record the harvested crop yield and selected machine field performance parameters from the typical rice combines in Malaysia. The complete system comprises of two ultrasonic sensors located at the combine header to measure the cutting width, microwave solid flow, and microwave moisture sensors at the combine clean grain auger to measure the flow rate and moisture content of the cleaned grains going into the grain tank, electromagnetic detector on the combine grain elevator drive shaft to monitor the grain elevator rotational speed, and lastly a DGPS receiver on the combine console roof to indicate the travel speed and geo-position in the field. All these measured parameters were made to display in-real time on the touch panel screen of the embedded system on-board the combine for the interest of the combine operator and also made to display in-real time on the monitor of the toughbook at the on-ground base station for the interest of the system controller. Static calibrations on the individual sensors showed excellent measurement linearity having R2 values within 0.8760 to 1.000 ranges. The wireless communication between the embedded system on-board the combine and the toughbook at the on-ground base station could be sustained to a maximum distance of 185 m apart. Site specific variability maps of crop yield, harvested grain moisture content, combine cutting width, combine traveling speed, combine field capacity, and combine field efficiency within the harvested area could be produced from the data obtained with the instrumentation system using a GIS software. Keywords: Grain harvesting, Paddy mechanization, Precision farming, Wireless data transmission, Yield monitoring.

scholarly journals Artificial vision system for object classification in real time using Raspberry Pi and a Web camera

2021 ◽  
pp. 20-25
Author(s):  
Tomás Serrano-Ramírez ◽  
Ninfa del Carmen Lozano-Rincón ◽  
Arturo Mandujano-Nava ◽  
Yosafat Jetsemaní Sámano-Flores
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Vision System ◽  
Low Cost ◽  
Artificial Vision ◽  
Maintenance Cost ◽  
Raspberry Pi ◽  
Smart Cameras ◽  
Suggested Technique ◽  
Web Camera

Computer vision systems are an essential part in industrial automation tasks such as: identification, selection, measurement, defect detection and quality control in parts and components. There are smart cameras used to perform tasks, however, their high acquisition and maintenance cost is restrictive. In this work, a novel low-cost artificial vision system is proposed for classifying objects in real time, using the Raspberry Pi 3B + embedded system, a Web camera and the Open CV artificial vision library. The suggested technique comprises the training of a supervised classification system of the Haar Cascade type, with image banks of the object to be recognized, subsequently generating a predictive model which is put to the test with real-time detection, as well as the calculation for the prediction error. This seeks to build a powerful vision system, affordable and also developed using free software.

The Realization of Multiple Degree of Freedom Prosthetic Method of Random Target Location in Embeded System

2012 ◽  
Vol 220-223 ◽  
pp. 1977-1981
Author(s):  
Bing Hui Fan ◽  
Peng Ji ◽  
Jian Gong Li
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Inverse Kinematics ◽  
Target Location ◽  
Three Dimensional ◽  
Degree Of Freedom ◽  
Embeded System ◽  
Inverse Kinematics Problem ◽  
The Embedded System ◽  
Object Relative

In order to make prosthetic work in unstructured environments in real time to solve the inverse kinematics problem, the coordinates of the location of the end of the workspace of the manipulator rod needed to know. The spatial orientation value of random object relative to prosthesis basic coordinates be calculated in real time is realized in the embedded system by means of two three-dimensional attitude sensors and one laser ranging sensor.This method can provide the necessary raw information for the multiple degree of freedom prosthesis which works in an unstructured environment to complete the operational tasks assigned.

scholarly journals A Low-cost Artificial Neural Network Model for Raspberry Pi

2020 ◽  
Vol 10 (2) ◽  
pp. 5466-5469 ◽  
Author(s):  
S. N. Truong
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Embedded System ◽  
Real Time ◽  
Image Recognition ◽  
Low Cost ◽  
Recognition Rate ◽  
Raspberry Pi ◽  
The Neural Network ◽  
Binary Array

In this paper, a ternary neural network with complementary binary arrays is proposed for representing the signed synaptic weights. The proposed ternary neural network is deployed on a low-cost Raspberry Pi board embedded system for the application of speech and image recognition. In conventional neural networks, the signed synaptic weights of –1, 0, and 1 are represented by 8-bit integers. To reduce the amount of required memory for signed synaptic weights, the signed values were represented by a complementary binary array. For the binary inputs, the multiplication of two binary numbers is replaced by the bit-wise AND operation to speed up the performance of the neural network. Regarding image recognition, the MINST dataset was used for training and testing of the proposed neural network. The recognition rate was as high as 94%. The proposed ternary neural network was applied to real-time object recognition. The recognition rate for recognizing 10 simple objects captured from the camera was 89%. The proposed ternary neural network with the complementary binary array for representing the signed synaptic weights can reduce the required memory for storing the model’s parameters and internal parameters by 75%. The proposed ternary neural network is 4.2, 2.7, and 2.4 times faster than the conventional ternary neural network for MNIST image recognition, speech commands recognition, and real-time object recognition respectively.

scholarly journals A METHOD OF PREDICTING THE MAINTENANCE PERIOD OF EMBEDDED SYSTEMS FOR PREVENTING BREACH OF THEIR TIME REQUIREMENTS

2018 ◽  
pp. 94-101
Author(s):  
Dmytro Fedasyuk ◽  
Tetyana Marusenkova ◽  
Ratybor Chopey
Keyword(s):  
Embedded Systems ◽  
Response Time ◽  
Embedded System ◽  
Real Time ◽  
Execution Time ◽  
Significant Problem ◽  
Maintenance Period ◽  
Time Requirements ◽  
The Embedded System

The work deals with a significant problem of ensuring that the execution time of a firmware running inside a microcontroller-based real-time embedded system never goes out of its expected range, no matter for how long the embedded system has been used. Once having been tested before the first usage, a newly created embedded system is gradually getting slower in its response, due to the fact that its hardware components get worn-out with aging. A possible solution is a replacement of the hardware components that most contribute to such a change in the response time of the embedded system. If such a replacement takes place too far in advance, long before hardware components actually start showing any decline in their response time, the above-mentioned solution is cost-ineffective and impractical, as it leads to a waste of equipment and efforts. We introduce a method for predicting the appropriate maintenance period of a real-time embedded system on the basis of the characteristics of its hardware components.

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