With the advent of the Internet of Things technology, the smart home has been redefined by the concept of "digital home". Especially under the circumstances of global epidemic, the Internet of Things technology can monitor the temperature and humidity of the living environment at any time through sensors, which allows people to know the temperature and humidity in their homes in time, so as to predict the potential danger in advance. The system, The Internet of Things temperature and humidity detection, is mainly designed to collect the data of temperature and humidity in the home and access to achieve communication with Mobile phone. The system, by using the Micro Python language, collects the data of temperature and humidity through the DHT11 temperature and humidity sensor, and achieve communication function with upper monitor with the help of WIFI.
In view of the fusion of SAR image and RGB image, different methods based on wavelet transform (WT) were studied. Based on WT, SAR images are fused with different channel of RGB images respectively. In order to suppress the speckle in SAR images, Speckle Reducing Anisotropic Diffusion (SRAD) and nonlocal mean filter (NLM) were introduced respectively. The image fusion effect was comprehensively measured by using several image quality indexes. The results show that WT and NLM are more helpful to improve the quality of image fusion.
Aiming at the problem that SAR image segmentation is seriously interfered by the speckle, this paper studies the methods of SAR image segmentation based on different gradients under nonlocal mean (NLM) filter. We focus on four gradient operators, contained ratio of average(ROA), ratio of an exponentially weighted averages (ROEWA), Unsampled Wavelet Transform (UWT) and instantaneous coefficient of variation (ICOV). Firstly, the NLM filter is used to suppress the speckle and protect the structure in the SAR image. After that, the gradient of SAR image is calculated based on the above different gradient operators. Finally, the watershed technique is used to segment the image based on the gradient of SAR image. Experimental results show that the segmentation based on ICOV and UWT obtain more accurate boundary location and fewer segmentation regions than the methods based on ROA and ROEWA.
Aiming at the shortcomings of traditional wired charging methods for electric vehicles, a wireless charging device based on supercapacitor is designed to realize the full automation of wireless charging of cars. The system uses the electromagnetic induction to transfer energy through coil induction, and uses the quick charge and bigger energy storage of supercapacitor to avoid poor contact, low charging speed, interface heating and damage caused by multiple plugging and unplugging because of oxidation and passivation of the metal surfaces. This system has very important practical significance to increase the service life of the whole machine, and improve the environmental adaptability of the entire system.
There is serious coherent speckle noise in the synthetic aperture radar (SAR) image. Several gradient operators’ performance was studied without speckle reducing in SAR image, included the Canny, Ratio operator and instantaneous coefficient of variation (ICOV). We mainly studied the performance of the above operators on edge detection capability and positioning accuracy for SAR image. Simulation test of one-dimensional synthesis signal showed that only ICOV can get the optimal single-value width response. In order to further validation, synthetic image and real SAR image would also be respectively applied in segmentation experiments. According to the segmentation results, it can be easily known that the segmentation results based on ICOV operator always get the fastest efficiency and the highest accuracy.
The article introduced several common filters used to reduce speckle in SAR image, including Lee filter, Frost filter, AD-homomorph filter, Speckle Reduction Anisotropic Diffusion (SRAD) and nonlocal means filter (NL means). The basic principles and characteristics of the above mentioned filters would be researched and compared in detailed. According to the filtering experimental results of synthetic image and real SAR image, it is obviously shown that SRAD is more suitable for image homogenous region filtering and speckle reduction, while NL means is more suitable to preserve the texture structures and weak characteristics in SAR image.
Aiming at the shortcomings of endoscopic image processing and detection technology in aero-engine fault diagnosis and maintenance, this paper proposes an endoscopic image processing and diagnosis method based on BP neural network learning algorithm. In this method, the feature extraction technology of endoscopic image in aero-engine fault diagnosis is studied, and the effective feature parameters are extracted from the internal damage region of aero-engine. The BP neural network model is established to improve the endoscopic image processing effect and improve the level of fault diagnosis. Finally, the BP neural network endoscopic image processing and diagnosis mechanism is simulated and experimentally studied for a Boeing 787 engine, and the expected diagnosis effect is achieved.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.