Design of Resistance Touch Screen Based on S3C6410 Embedded System

2014 ◽  
Vol 556-562 ◽  
pp. 1491-1494 ◽  
Author(s):  
Ying Wang ◽  
Ri Bo Ge ◽  
Mei Hua Li
Keyword(s):  
Embedded System ◽  
Touch Screen ◽  
Sampled Data ◽  
Algorithm Optimization ◽  
Filtration Method ◽  
Point Mapping ◽  
Practical Applications ◽  
Calibration Algorithm ◽  
Point To Point ◽  
Selection Of

This design uses Samsung ARM11 S3C6410 microprocessor and 4 wires resistive touch screen as the hardware foundation. Based on the hardware structure, the touch screen application is developed. System performance has been improved by means of the algorithm optimization of the sampled data and software filtration method, and lead to a strong practicability. In the end, three-point calibration algorithm is introduced for the offset problem of the screen coordinates, then the calibration matrix is decided by the selection of three-point, and then using software implementation to realize the calibration of point-to-point mapping relation, and finally make it more accurate in practical applications.

A touch screen interface design with tactile feedback for practical applications

2013 ◽  
Vol 3 (1) ◽  
pp. 8 ◽  
Author(s):  
Hiroaki Nishino ◽  
Ryotaro Goto ◽  
Yuki Fukakusa ◽  
Jiaqing Lin ◽  
Tsuneo Kagawa ◽  
...  
Keyword(s):  
Interface Design ◽  
Touch Screen ◽  
Tactile Feedback ◽  
Practical Applications

scholarly journals A Novel Rail-Network Hardware Simulator for Embedded System Programming

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Balaji M ◽  
Chandrasekaran M ◽  
Vaithiyanathan Dhandapani
Keyword(s):  
Embedded Systems ◽  
Embedded System ◽  
Real Time ◽  
Learning Outcomes ◽  
Real World ◽  
Time Constraints ◽  
Network Simulator ◽  
Practical Applications ◽  
Rail Network ◽  
Knowledge Enhancement

A Novel Rail-Network Hardware with simulation facilities is presented in this paper. The hardware is designed to facilitate the learning of application-oriented, logical, real-time programming in an embedded system environment. The platform enables the creation of multiple unique programming scenarios with variability in complexity without any hardware changes. Prior experimental hardware comes with static programming facilities that focus the students’ learning on hardware features and programming basics, leaving them ill-equipped to take up practical applications with more real-time constraints. This hardware complements and completes their learning to help them program real-world embedded systems. The hardware uses LEDs to simulate the movement of trains in a network. The network has train stations, intersections and parking slots where the train movements can be controlled by using a 16-bit Renesas RL78/G13 microcontroller. Additionally, simulating facilities are provided to enable the students to navigate the trains by manual controls using switches and indicators. This helps them get an easy understanding of train navigation functions before taking up programming. The students start with simple tasks and gradually progress to more complicated ones with real-time constraints, on their own. During training, students’ learning outcomes are evaluated by obtaining their feedback and conducting a test at the end to measure their knowledge acquisition during the training. Students’ Knowledge Enhancement Index is originated to measure the knowledge acquired by the students. It is observed that 87% of students have successfully enhanced their knowledge undergoing training with this rail-network simulator.

scholarly journals Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

2018 ◽  
Vol 10 (8) ◽  
pp. 1285 ◽  
Author(s):  
Reza Attarzadeh ◽  
Jalal Amini ◽  
Claudia Notarnicola ◽  
Felix Greifeneder
Keyword(s):  
Soil Moisture ◽  
Optical Data ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Practical Applications ◽  
Initial Stage ◽  
Object Based ◽  
Single Polarization ◽  
Selection Of

This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation cover located in Kenya. In the initial stage of the process, different features are extracted from single polarization mode (VV polarization) SAR and optical data. Subsequently, proper selection of the relevant features is conducted on the extracted features. An advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture. This paper takes a new look at soil moisture retrieval in vegetated areas considering the needs of practical applications. In this context, we tried to work at the object level instead of the pixel level. Accordingly, a group of pixels (an image object) represents the reality of the land cover at the plot scale. Three approaches, a pixel-based approach, an object-based approach, and a combination of pixel- and object-based approaches, were used to estimate soil moisture. The results show that the combined approach outperforms the other approaches in terms of estimation accuracy (4.94% and 0.89 compared to 6.41% and 0.62 in terms of root mean square error (RMSE) and R2), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map.

scholarly journals Development and Application of an Atmospheric Pollutant Monitoring System Based on LoRa—Part I: Design and Reliability Tests

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3891 ◽  
Author(s):  
Yushuang Ma ◽  
Long Zhao ◽  
Rongjin Yang ◽  
Xiuhong Li ◽  
Qiao Song ◽  
...  
Keyword(s):  
Embedded System ◽  
Communication Technology ◽  
Atmospheric Pollutant ◽  
Practical Applications ◽  
Actual Application ◽  
Pollutant Concentrations ◽  
Application Data ◽  
Software And Hardware ◽  
The Internet Of Things ◽  
Monitoring Platform

At present, as growing importance continues to be attached to atmospheric environmental problems, the demand for real-time monitoring of these problems is constantly increasing. This article describes the development and application of an embedded system for monitoring of atmospheric pollutant concentrations based on LoRa (Long Range) wireless communication technology, which is widely used in the Internet of Things (IoT). The proposed system is realized using a combination of software and hardware and is designed using the concept of modularization. Separation of each function into independent modules allows the system to be developed more quickly and to be applied more stably. In addition, by combining the requirements of the remote atmospheric pollutant concentration monitoring platform with the specific requirements for the intended application environment, the system demonstrates its significance for practical applications. In addition, the actual application data also verifies the sound application prospects of the proposed system.

Discrete-Time Robust Control of Linear Periodically Time-Varying Systems

2007 ◽  
Author(s):  
S. Kalender ◽  
H. Flashner
Keyword(s):  
Robust Control ◽  
Discrete Time ◽  
Robustness Analysis ◽  
Value Theory ◽  
Time Varying ◽  
Sampled Data ◽  
Point Mapping ◽  
Time Dynamic ◽  
Time Representation ◽  
Time Varying Systems

An approach for robust control of periodically time-varying systems is proposed. The approach combines the point-mapping formulation and a parameterization of the control vector to formulate an equivalent time-invariant discrete-time representation of the system. The discrete-time representation of the dynamic system allows for the application of known sampled-data control design methodologies. A perturbed, discrete-time dynamic model is formulated and plant parametric uncertainty are obtained using a truncated point-mapping algorithm. The error bounds due to point-mapping approximation are computed and a robustness analysis problem of the system due to parametric uncertainties is formulated using structured singular value theory. The proposed approach is illustrated by two design examples. Simulation studies show good performance robustness of the control system to parameter perturbations and system nonlinearities.

Improvements of Infrared Touch Screen Calibration Algorithm Based on Affine Transformation

2014 ◽  
Vol 716-717 ◽  
pp. 1341-1345
Author(s):  
Wen Ming Guo ◽  
Gang Wang
Keyword(s):  
Affine Transformation ◽  
Touch Screen ◽  
The Other ◽  
Pass Rate ◽  
Sample Point ◽  
Affine Transform ◽  
Calibration Algorithm ◽  
Windows 8

Because the affine transformation can realize the coordinate translation, rotation, scaling, it is applied to calibrate the touch screen. But,requirements for touch errors of Windows 8 is ±0.5mm,and the pass rate of sample point must reach 4%.Therefore, simply use the method of draw-point to collect the sample point has been unable to meet the requirement of accuracy. This paper tries to propose two improved calibration algorithm based on affine transform, one method use drawing lines instead of marking point, the other devides the screen into a plurality of areas to use multiple calibration. This two kind of methods can effectively improve the calibration precision.

scholarly journals Prioritization and Selection of High Biomass Energy Producing Plant Species at Boset District, Central Ethiopia: An Ethnobotanical Approach 

2021 ◽  
Author(s):  
Tinsae Bahru Yifru ◽  
Berhane Kidane ◽  
Amsalu Tolessa
Keyword(s):  
Plant Species ◽  
Sustainable Use ◽  
Biomass Energy ◽  
Calotropis Procera ◽  
Sampled Data ◽  
High Biomass ◽  
Structured Interviews ◽  
Acacia Senegal ◽  
Selection Of Species ◽  
Selection Of

Abstract Background: In Ethiopia, about 92.3% of all the biomass energy is consumed by domestic households and the demand is growing from 10-14%. However, there are little/no practical experiences or documented indigenous knowledge on how traditional people identify and select high biomass producing plant species with short rotation periods at Boset District. Therefore, the present study was aimed at: (1) selecting and documenting high biomass energy producing plant species at Boset District; (2) identifying major predictor variables that influence the prioritization and selection of species; and (3) develop a Generalized Linear Model (GLM) to predict the selection of species. Methods: A total of 96 informants comprising 59 men and 37 women between the ages of 18 and 81 were sampled. Data were collected using structured interviews, guided field walk, discussions and field observations. Results: Collected data indicated that 88.5% of the informants involved in firewood collection, while 90% practiced charcoal making. A total of 1533.60 Birr per household on average was earned annually from this activity. A total of 25 firewood and/or charcoal plant species were identified and documented at Boset District. Of these, Acacia senegal, Acacia tortilis and Acacia robusta were the three best prioritized and selected indigenous high biomass producing species. Prosopis juliflora, Parthenium hysterophorus, Azadirachta indica, Calotropis procera, Cryptostegia grandiflora, Lantana camara and Senna occidentalis further grouped under introduced fuelwood species. Prediction of GLM assured sampled Kebeles and source of income generated from fuelwood species positively and significantly (p<0.001) related to selection of species. Higher efficiency to provide energy and heat; little or no smoke or soot; easier to cut and split the wood and easier availability were some of the main selection criteria. Conclusions: This study provides valuable information in selection and documenting of high biomass producing plant species for proper management and sustainable use at Boset District. The three most selected species (A. senegal, A. tortilis and A. robusta) should be further evaluated at laboratory to determine their energy values.

scholarly journals Understanding Mental Health and Cognitive Restructuring With Ecological Neuroscience

2021 ◽  
Vol 12 ◽  
Author(s):  
James Crum
Keyword(s):  
Mental Health ◽  
Near Infrared ◽  
Ecological Validity ◽  
Experimental Designs ◽  
Functional Near Infrared Spectroscopy ◽  
Practical Applications ◽  
Recent Developments ◽  
The Brain ◽  
Neuroimaging Techniques ◽  
Selection Of

Neuroimaging and neuropsychological methods have contributed much toward an understanding of the information processing systems of the human brain in the last few decades, but to what extent do cognitive neuroscientific findings represent and generalize to the inter- and intra-brain dynamics engaged in adapting to naturalistic situations? If it is not marked, and experimental designs lack ecological validity, then this stands to potentially impact the practical applications of a paradigm. In no other domain is this more important to acknowledge than in human clinical neuroimaging research, wherein reduced ecological validity could mean a loss in clinical utility. One way to improve the generalizability and representativeness of findings is to adopt a more “real-world” approach to the development and selection of experimental designs and neuroimaging techniques to investigate the clinically-relevant phenomena of interest. For example, some relatively recent developments to neuroimaging techniques such as functional near-infrared spectroscopy (fNIRS) make it possible to create experimental designs using naturalistic tasks that would otherwise not be possible within the confines of a conventional laboratory. Mental health, cognitive interventions, and the present challenges to investigating the brain during treatment are discussed, as well as how the ecological use of fNIRS might be helpful in bridging the explanatory gaps to understanding the cultivation of mental health.

scholarly journals Research on Robustness of Related Images for Power Equipment Inspection

2021 ◽  
Vol 2113 (1) ◽  
pp. 012045
Author(s):  
Chunlei Zhou ◽  
Xiangzhou Chen ◽  
Wenli Liu ◽  
Tianyu Dong ◽  
Huang Yun
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Feature Fusion ◽  
Power Equipment ◽  
Actual Measurement ◽  
Practical Applications ◽  
Detection Algorithms ◽  
Traction Substations ◽  
Deep Learning Model ◽  
Selection Of

Abstract With the increase in the number of traction substations year by year, manual inspections are gradually being replaced by unattended inspections. Target detection algorithms based on deep learning are more widely used in intelligent inspections of power equipment. However, in practical applications, it is found that due to the small target to be detected, the accuracy of the deep learning model will decrease when the shooting angle is inclined and the light conditions are poor. This is because the algorithm’s robustness is low, and the detection ability of the model will be seriously affected when the angle or illumination difference with the sample is large. Based on this, the feature fusion part of the YOLOv3 algorithm and the selection of the loss function and the size of the anchor frame are improved, and the improved ASFF fusion method is used to classify various images in the power equipment. Actual measurement and repeated experiments show that the proposed method can be effectively applied to image recognition of various power equipment, optimize robustness, and greatly improve the image recognition efficiency of power equipment.

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