A real-time computer vision assessment and control of thermal comfort for group-housed pigs

2008 ◽  
Vol 62 (1) ◽  
pp. 15-21 ◽  
Author(s):  
Bin Shao ◽  
Hongwei Xin
Keyword(s):  
Computer Vision ◽  
Thermal Comfort ◽  
Real Time ◽  
Vision Assessment ◽  
And Control

scholarly journals Supervisory System For Monitoring, Control And Estimating Thermal Comfort For Broiler and Laying Hens Production Sheds

2020 ◽  
Vol 8 (3) ◽  
pp. 316-331
Author(s):  
Mario Mollo Neto ◽  
Mariana Matulovic ◽  
Paulo Sérgio Barbosa dos Santos
Keyword(s):  
Thermal Comfort ◽  
Real Time ◽  
Laying Hens ◽  
Production Environment ◽  
Supervisory System ◽  
Temperature And Humidity ◽  
Humidity Index ◽  
Automatic Activation ◽  
And Control ◽  
Productivity And Competitiveness

This research aims to promote the project for the construction of software, firmware, and micro-controlled hardware, which together allow the automatic control of the temperature and humidity index (THI) in real-time the intensive production environment in poultry and laying hens production houses, thus avoiding productive losses due to the stress to which the birds are subjected. This system aims, from the information from a set of sensors, connected to a microcontroller acquisition and control board, to infer the temperature and humidity index from the measured climatic variables, to enable the corresponding activation of electronic interfaces with electric actuators, for the automatic activation of ventilation devices, humidification and curtain actuation and the lighting of production houses, keeping the environmental conditions of the house within a convenient range of temperature and relative humidity to ensure thermal comfort and thus avoiding bird stress. Thus, helping to mitigate production losses and facilitating managers real-time monitoring of the shed to achieve greater productivity and competitiveness.

scholarly journals PENILAIAN KELAYAKAN OBJEK PUPIL DARI FRAME CITRA MATA PADA APLIKASI PEMERIKSA MYOPIA MENGGUNAKAN STANDAR DEVIASI

2020 ◽  
Vol 3 (2) ◽  
pp. 201
Author(s):  
Teady Matius Surya Mulyana ◽  
Herlina Herlina
Keyword(s):  
Computer Vision ◽  
Standard Deviation ◽  
Real Time ◽  
Size Range ◽  
Threshold Value ◽  
Small Samples ◽  
Time Interval ◽  
Vision Assessment ◽  
Standard Deviations ◽  
Confidence Value

The process of determining vision in patients with myopia based on computer vision requires image frames containing pupil objects that are deemed to meet the requirements before entering the vision determination stage. Image frame requirements that are considered to meet the requirements are image frames that contain objects that are not too small or too large. The extraction of eye image frames in real-time results in eye image frames being captured by the camera when the eyes are blinking, so that they contain too small pupil objects. The process of determining the threshold value in binaryization of an eye image that is too large also results in pupillary objects blending with the shadows around the eye, resulting in objects that are too large. Small samples in eye image taking require treatment for statistical assessment with small samples. The small number of samples can be used to determine the feasibility of the image to enter the next process can be implemented using standard deviations. Standard deviation values can accommodate the need to limit the size range of pupil objects in the eye image that is considered feasible. The final result of this study is the implementation of the method of determining an image considered to have a pupil object of a size that is suitable for observation so that the interval is obtained according to the conditions of each set of eye images recorded by the camera during eye observation. The use of standard deviations in determining which images are considered feasible contributes to increasing the percentage of accuracy of eye vision assessment in the application of myopia vision determination in real time from 54% without standard deviation to 73% in the confidence value for an average one-time interval of 99% in the process of determining myopia vision . AbstrakProses penentuan visus pada penderita myopia berbasis  computer vision memerlukan frame-frame citra yang berisi objek pupil yang dianggap memenuhi persyaratan sebelum masuk ke tahap penentuan visus. Persyaratan frame-frame citra yang dianggap memenuhi persyaratan adalah frame-frame citra yang berisi objek dengan ukuran yang tidak terlalu kecil ataupun terlalu besar. Pengambalian frame-frame citra mata secara real-time mengakibatkan adanya frame citra mata yang ditangkap kamera ketika mata sedang berkedip, sehingga berisi objek pupil yang terlalu kecil. Proses penentuan nilai ambang pada binerisasi citra mata yang terlalu besar juga mengakibatkan objek pupil berbaur dengan bayangan di sekitar mata, sehingga menghasilkan objek yang terlalu besar. Sampel yang kecil pada pengambilan citra mata memerlukan perlakuan untuk penilaian statistik dengan sampel kecil. Jumlah sampel yang kecil dapat untuk menentukan kelayakan citra untuk masuk proses selanjutnya dapat diimplementasikan menggunakan standar deviasi. Nilai standar deviasi dapat mengakomodasi keperluan membatasi rentang ukuran objek pupil pada citra mata yang dianggap layak. Hasil akhir dari penelitian ini adalah implementasi metode penentuan suatu citra dianggap memiliki objek pupil dengan ukuran yang layak untuk diobservasi sehingga didapatkan interval yang sesuai kondisi masing-masing set citra mata yang direkam oleh kamera pada saat observasi mata. Penggunaan standar deviasi pada penentuan citra yang dianggap layak berkontribusi menaikkan persentase ketepatan penilaian visus mata pada aplikasi penentuan visus myopia secara real time dari 54% tanpa standar deviasi menjadi 73% pada nilai confidence untuk interval satu rata-rata sebesar 99% pada proses penentu visus myopia.

Microstructural investigation of surface roughness in MBE-grown AlAs

1995 ◽  
Vol 53 ◽  
pp. 454-455
Author(s):  
R. Rajesh ◽  
R. Droopad ◽  
C. H. Kuo ◽  
R. W. Carpenter ◽  
G. N. Maracas
Keyword(s):  
Thin Film ◽  
Real Time ◽  
Growth Control ◽  
Native Oxide ◽  
Effusion Cell ◽  
Surface Oxide Layer ◽  
Microstructural Investigation ◽  
Mbe Growth ◽  
Film Substrate ◽  
And Control

Knowledge of material pseudodielectric functions at MBE growth temperatures is essential for achieving in-situ, real time growth control. This allows us to accurately monitor and control thicknesses of the layers during growth. Undesired effusion cell temperature fluctuations during growth can thus be compensated for in real-time by spectroscopic ellipsometry. The accuracy in determining pseudodielectric functions is increased if one does not require applying a structure model to correct for the presence of an unknown surface layer such as a native oxide. Performing these measurements in an MBE reactor on as-grown material gives us this advantage. Thus, a simple three phase model (vacuum/thin film/substrate) can be used to obtain thin film data without uncertainties arising from a surface oxide layer of unknown composition and temperature dependence.In this study, we obtain the pseudodielectric functions of MBE-grown AlAs from growth temperature (650°C) to room temperature (30°C). The profile of the wavelength-dependent function from the ellipsometry data indicated a rough surface after growth of 0.5 μm of AlAs at a substrate temperature of 600°C, which is typical for MBE-growth of GaAs.

Adaptive Signal Analysis and Interpretation for Real-time Intelligent Patient Monitoring

1994 ◽  
Vol 33 (01) ◽  
pp. 60-63 ◽  
Author(s):  
E. J. Manders ◽  
D. P. Lindstrom ◽  
B. M. Dawant
Keyword(s):  
Computer Architecture ◽  
Real Time ◽  
Data Abstraction ◽  
Monitoring Strategy ◽  
Intelligent Monitoring ◽  
Patient Status ◽  
On Line ◽  
Main Components ◽  
And Control ◽  
Adaptive Signal

Abstract:On-line intelligent monitoring, diagnosis, and control of dynamic systems such as patients in intensive care units necessitates the context-dependent acquisition, processing, analysis, and interpretation of large amounts of possibly noisy and incomplete data. The dynamic nature of the process also requires a continuous evaluation and adaptation of the monitoring strategy to respond to changes both in the monitored patient and in the monitoring equipment. Moreover, real-time constraints may imply data losses, the importance of which has to be minimized. This paper presents a computer architecture designed to accomplish these tasks. Its main components are a model and a data abstraction module. The model provides the system with a monitoring context related to the patient status. The data abstraction module relies on that information to adapt the monitoring strategy and provide the model with the necessary information. This paper focuses on the data abstraction module and its interaction with the model.

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