High Resolution Image Processing Research Articles

High-Resolution Real-Time Imaging Processing for Spaceborne Spotlight SAR With Curved Orbit via Subaperture Coherent Superposition in Image Domain

With the increasing of application requirements, the high-resolution real-time imaging processing of the spaceborne spotlight synthetic aperture radar (SAR) has been developed. Since the traditional real-time imaging algorithms have the problems that the range model has errors and the two-dimensional (2-D) space-variance of the equivalent velocity caused by the curved orbit cannot be effectively eliminated. Thus, this article proposes a high-resolution real-time imaging algorithm for spaceborne spotlight SAR with curved orbit via subaperture coherent superposition in image domain. In this article, the echo data are first divided into subapertures to avoid the azimuth spectrum aliasing. After that, the 2-D space-variance of the equivalent velocity caused by the curved orbit can be eliminated by the method of azimuth time scale transformation, higher order phase compensation, and introducing phase transition function. Then, the dechirp function is applied for the subaperture signals to obtain the partial-resolution subaperture images. Finally, these partial-resolution subaperture images are coherently superposed in the image domain to obtain the final full-resolution image of the whole echo data. Moreover, the proposed algorithm improves the real-time performance by adopting the idea that the subaperture data recording and subaperture real-time imaging processing are synchronized, which greatly accelerates the acquisition of the final full-resolution imaging result. At the end of this article, the simulations and the real-time performance analysis are performed to validate the proposed algorithm.

BASNet: Burned Area Segmentation Network for Real-Time Detection of Damage Maps in Remote Sensing Images

Since remote sensing images of post-fire vegetation are characterized by high resolution, multiple interferences, and high similarities between the background and the target area, it is difficult for existing methods to detect and segment the burned area in these images with sufficient speed and accuracy. In this paper, we apply Salient Object Detection (SOD) to burned area segmentation, the first time this has been done, and propose an efficient burned area segmentation network (BASNet) to improve the performance of unmanned aerial vehicle (UAV) high-resolution image segmentation. BASNet comprises positioning module and refinement module. The positioning module efficiently extracts high-level semantic features and general contextual information via global average pooling layer and convolutional block to determine the coarse location of the salient region. The refinement module adopts the convolutional block attention module to effectively discriminate the spatial location of objects. In addition, to effectively combine edge information with spatial location information in the lower layer of the network and the high-level semantic information in the deeper layer, we design the residual fusion module to perform feature fusion by level to obtain the prediction results of the network. Extensive experiments on two UAV datasets collected from Chongli in China and Andong in South Korea, demonstrate that our proposed BASNet significantly outperforms state-of-the-art SOD methods quantitatively and qualitatively. BASNet also achieves a promising prediction speed for processing high-resolution UAV images, thus providing wide-ranging applicability in post-disaster monitoring and management.

Farmland Aerial Images Fast-Stitching Method and Application Based on Improved SIFT Algorithm

The main black land conservation measure in China is the straw return to the fields. The processing of high-resolution images collected by aerial photography of UAVs through image stitching technology can provide image information for achieving fast and accurate detection of straw cover over large areas. The classical SIFT algorithm has many drawbacks, such as high dimensionality of feature descriptors, high computational effort, and low matching efficiency. To solve the problems above, this study proposes an improved algorithm. First, the method down sampled the high-resolution images before detecting the features to reduce the number of feature points and improve the efficiency of feature detection. Then, matching among feature points is achieved by gradient normalization-based feature descriptors to improve the matching accuracy. Next, the Progressive Sample Consistency algorithm eliminates the mismatch points and optimizes the transformation model. Finally, the images are fused with optimal stitching combined with fade-in and fade-out to achieve high-quality stitching. The comparative experimental results show that compared with the traditional SIFT and the speed-up robust feature algorithms, the algorithm has the advantage of the speed and good robustness to angle rotation, and makes full use of the texture information and the detail information, so it has higher accuracy. Compared with the traditional methods, the panoramic stitching image quality herein is excellent and can be applied to subsequent straw cover detection, the straw cover error is ≤3%, meeting the demand for large-area straw cover detection. Overall, the method proposed herein achieves an ideal balance between accuracy and efficiency; and outperforms other widely used and superior methods.

Directional Analytic Discrete Cosine Frames

Block frames called <i>directional analytic discrete cosine frames</i> (DADCFs) are proposed for sparse image representation. In contrast to conventional overlapped frames, the proposed DADCFs require a reduced amount of 1) computational complexity, 2) memory usage, and 3) global memory access. These characteristics are strongly required for current high-resolution image processing. Specifically, we propose two DADCFs based on discrete cosine transform (DCT) and discrete sine transform (DST). The first DADCF is constructed from parallel separable transforms of DCT and DST, where the DST is permuted by row. The second DADCF is also designed based on DCT and DST, while the DST is customized to have no DC leakage property which is a desirable property for image processing. Both DADCFs have rich directional selectivity with slightly different characteristics each other and they can be implemented as non-overlapping block-based transforms, especially they form Parseval block frames with low transform redundancy. We perform experiments to evaluate our DADCFs and compare them with conventional directional block transforms in image recovery.

Correcting remote-sensed shaded image with urban surface radiative transfer model

Shaded areas reduce the quality of digital images, bring many difficulties to image interpretation, especially in the high-resolution remote-sensed images. In order to improve the urban application capability, shadow correction should be performed. In this article, a new methodology for shadow correction was proposed applying the Urban Solar Radiative Transfer (USRT)11USRT: Urban Solar Radiative Transfer. model, which was a shadow correction scheme based on physical model. The sky view factor (SVF)22SVF: Sky view factor. was selected as one factor to quantify the morphological and structural characteristics of urban underlying surface, and then each radiation component was expressed quantitatively at pixel-level scale. Finally, the shadow correction was performed by calculating the actual reflectance of the urban materials in shadow areas. To validate the proposed scheme, three different spatial-resolution remote-sensed images were applied to perform shadow correction, including samples of Landsat-8, Sentinel-2, and Worldview-3 satellite image data. The results showed the following: (1) The shadow correction method can better improve the reflectance of the shadow area to the level of the illuminated area. There was a positive effect on the spectral reconstruction of different materials in the shadow areas after correction, and the spectral curves of corrected results coincided with the illuminated areas. (2) With the increase of spatial resolution, there was an increasing trend in the accuracy of shadow correction. In fact, the difference in the reflectance between the shadow corrected region and the illumination region decreased with the spatial resolution increasing. For example, after USRT shading correction, the rRMSE values of vegetation in the Landsat-8, Sentinel-2, and Worldview-3 images was 20.4%, 13.2%, and 9.8%, respectively. Generally, this proposed scheme can restore the spectral characteristics in the shadow areas and improve image quality, and there is a good application prospect in the field of high-resolution remote-sensed image processing.

Retraction Note: Evolution of dissolved total solids in groundwater based on high resolution image processing and evaluation of urban English translation

In recent years, learning based super-high resolution image reconstruction technology has been rapidly applied. It also has good development and application effect in related fields. So in this paper, by combining the characteristics of SAR image, the learning based super-high resolution image reconstruction technology is added to the field of super-high resolution image reconstruction, and has been applied in reality. Image classification and ultra-high resolution technology are closely integrated. However, because the hardware facilities of some equipment are relatively expensive, and will inevitably be affected by other factors in the imaging process, in order to better study the influence of the increase of dissolved total solids in groundwater on the water level in the core of the funnel in the plain area, and the main components of the increase of dissolved total solids in groundwater. Every country in the world is carrying on a kind of propaganda to its national image, which is also a main manifestation of the overall level of the country. This paper studies the process of City English translation, makes corresponding analysis and preparation before city English translation, and pays attention to relevant reply and report after city English translation. This paper focuses on the construction system of City English translation and the improvement of the ways and methods of using English to publicize the city. Through the construction of City English translation system, this paper expounds the influence of Public English translation in City publicity construction, and changes the corresponding strategies for high-resolution image processing, so as to make English translation more accurate for the change process of total dissolved solids in groundwater, and improve the ability and level of City English translation.

HRTEM observation of morphological and structural evolution of aromatic fringes during the transition from coal to graphite

The maximum reflectance of vitrinite or graphite microcrystals in Yongan Coalfield, Fujian Province, China, ranges from 5.33% to 10.21%, comprising meta-anthracite, semi-graphite, and graphite. The morphological characteristics of aromatic fringes during different evolution stages were quantitatively analyzed using high-resolution transmission electron microscopy and image processing techniques. The results show that a nonlinear evolution of aromatic fringe morphology in the process of coal graphitization is manifested in four aspects, involving the rapid connection and growth, enhancement of ordering alignment, increase of stacking and development of straightening. In meta-anthracite samples, 3 × 3 aromatic rings representing short aromatic fringes are dominated, a majority of aromatic fringes were curved, disjoint or separate, and show a turbostratic layers, with poorly aligned. The spacing between adjacent aromatic fringes exceeded the range of graphitized carbon, and structural defects, such as nanopores were generally visible. In semi-graphite samples, in addition to 3 × 3 aromatic rings, the proportion of >8 × 8 aromatic rings represented longer aromatic fringes increasing significantly, along with ordering alignment and straightening. Stacking with two more layers increased and the spacing between adjacent aromatic fringes decreased to the range of graphitized carbon, and the structural defects decreased considerably. In graphite sample, the proportion of >8 × 8 aromatic rings increase significantly, the length, stacking, and ordering alignment of aromatic fringes increased abruptly, the spacing between adjacent aromatic fringes remained within the range of natural graphite, and a rapid graphite-like structure with flat aromatic fringes formed. A stage evolution of aromatic fringe morphology, whereas remaining almost constant of mainly organic elements, which imply the evolution of aromatic fringes in meta-anthracite originated from ring condensation dominated by late coalification, graphitization starts at semi-graphite stage and enhanced during graphite stage. The making up, originating from linkup and pileup, considered as a special mechanism for the graphitization of aromatic fringes.

Retraction Note: Simulation of air environment and sports development in coastline cities based on high-resolution image processing system

At present, China is in the historical stage of accelerating the transition from tradition to modernity. The continuous economic growth and the deepening of market reforms have also led to the increase in the development of natural resources and the sharp increase in pollutant emissions. China is facing severe air pollution challenges. Therefore, studying regional air environmental management, preventing and controlling air pollution, and continuously improving air environmental quality have become an important part of regional sustainable development. Taking a new urban area as an example, this article focuses on the modern method of air pollution control in coastal cities’ overall capacity control, pollution permit system, and emission trading system, which perfectly combine these three methods to develop a set of suitable methods and specific measures. The regional air management model provides some management ideas and methods to manage the future regional air in order to better solve the regional air pollution problem. After all, through actual research and analysis, coastal cities have unique natural environments and obvious geographical advantages. Extensive transportation provides a developed transportation network and a solid economic foundation, as well as available funds as a guarantee, providing a very good basic condition for the development of coastal sports. Coastal cities must take advantage of the rare historical opportunities, make full use of their advantages, overcome disadvantages, and carry out more activities to develop coastal city sports with regional characteristics.

High-resolution imaging as a tool for identifying quantitative trait loci that regulate photomorphogenesis in Arabidopsis thaliana.

A primary component of seedling establishment is the photomorphogenic response as seedlings emerge from the soil. This process is characterized by a reduced growth rate in the hypocotyl, increased root growth, opening of the apical hook and expansion of the cotyledons as photosynthetic organs. While fundamental to plant success, the photomorphogenic response can be highly variable. Additionally, studies of Arabidopsis thaliana are made difficult by subtle differences in growth rate between individuals. High-resolution imaging and computational processing have emerged as useful tools for quantification of such phenotypes. This study sought to: (i) develop an imaging methodology which could capture changes in growth rate as seedlings transition from darkness to blue light in real time, and (ii) apply this methodology to single-quantitative trait locus (QTL) analysis using the Cvi × Ler recombinant inbred line (RIL) mapping population. Significant differences in the photomorphogenic response were observed between the parent lines and analysis of 158 RILs revealed a wide range of growth rate phenotypes. Quantitative trait locus analysis detected significant loci associated with dark growth rate on chromosome 5 and significant loci associated with light growth rate on chromosome 2. Candidate genes associated with these loci, such as the previously characterized ER locus, highlight the application of this approach for QTL analysis. Genetic analysis of Landsberg lines without the erecta mutation also supports a role for ER in modulating the photomorphogenic response, consistent with previous QTL analyses of this population. Strengths and limitations of this methodology are presented, as well as means of improvement.

Deep Integration of Physical Health Education Based on Intelligent Communication Technology.

In recent years, intelligent medical communication technology has developed rapidly, and the advancement of hardware technology has accelerated the development of software technology. Physical exercise breaks the limits of all aspects of the human body and requires scientific and reliable information. Industrial cameras realize collection and high-resolution image processing functions and use artificial intelligence to create conditions for collecting scientific data. With the powerful driving force of artificial intelligence, it is believed that physical education will develop to a new height. The intellectualization of physical education makes people expect that it will also bring some inevitable problems, but as long as we continue to overcome and improve, we believe that artificial intelligence will better serve physical education and benefit mankind. Artificial intelligence is the current trend of social development. This paper takes physical education as a breakthrough point to discuss the application of artificial intelligence in physical education. This paper uses the grey GM prediction model and questionnaire survey method to build an online education platform and develop an effective education model, which can integrate the country's best educational resources and reduce time and space constraints. Providing a relaxing learning environment for education, one can learn anytime, anywhere. In a typical physical education system, the average self-efficacy score is converted from 20.48 points to 68.37 points, and the average self-score of physical education in the integrated artificial intelligence environment is converted from 30.25 points to 75.54 points. The data show that the quality of education has greatly improved.

RETRACTED ARTICLE: Evolution of dissolved total solids in groundwater based on high resolution image processing and evaluation of urban English translation

In recent years, learning based super-high resolution image reconstruction technology has been rapidly applied. It also has good development and application effect in related fields. So in this paper, by combining the characteristics of SAR image, the learning based super-high resolution image reconstruction technology is added to the field of super-high resolution image reconstruction, and has been applied in reality. Image classification and ultra-high resolution technology are closely integrated. However, because the hardware facilities of some equipment are relatively expensive, and will inevitably be affected by other factors in the imaging process, in order to better study the influence of the increase of dissolved total solids in groundwater on the water level in the core of the funnel in the plain area, and the main components of the increase of dissolved total solids in groundwater. Every country in the world is carrying on a kind of propaganda to its national image, which is also a main manifestation of the overall level of the country. This paper studies the process of City English translation, makes corresponding analysis and preparation before city English translation, and pays attention to relevant reply and report after city English translation. This paper focuses on the construction system of City English translation and the improvement of the ways and methods of using English to publicize the city. Through the construction of City English translation system, this paper expounds the influence of Public English translation in City publicity construction, and changes the corresponding strategies for high-resolution image processing, so as to make English translation more accurate for the change process of total dissolved solids in groundwater, and improve the ability and level of City English translation.

Characterization of Surface Runoff Pathways and Erosion Using Hydrological Attributes Under Simulated Rainfall

Conceptualisation of geo-hydrological characteristic of erosive runoff are of particular importance and has been required in recent soil erosion control. This study aimed to explore the feasibility of applying hydrological attributes to characterize surface runoff pathways in the process of hillslope soil erosion due to rainfall. Combined with sub-millimeter high-resolution laser scanning and computer digital image processing method, three hydrological indicators (i.e., sinuosity, gradient and orientation) were used to investigate the changes of the surface runoff pathways on the slope of three typical southern red soils (i.e., shale (HS), and Quaternary red clay soils (HQ1 and HQ2) under simulated rainfall conditions). The results indicated no significant changes of sinuosity with a mean value of 1.19. After the rainfall with the intensity of 1 mm/min and 2 mm/min, the orientation and gradient changed dramatically. The greatest changes appeared at the first rainfall, which showed that the biggest increase of gradient was 26.78% and it tended to be close to the original slope of the test plot, while the orientation dropped by 5.60–31.44%. Compared with HS and HQ1, the runoff pathway characteristics of HQ2 changed more consistent. The rainfall intensities had a significant impact on the correlation between indicators. The determination coefficients sorting with surface roughness were orientation &amp;gt; graient &amp;gt; sinuosity. And they were significantly linearly related to runoff under 1 mm/min rainfall intensity, while had positive correlation with sediment under 2 mm/min rainfall intensity (p &amp;lt; 0.05). In conclusion, there were more remarkable relationships between orientation, gradient and slope erosion under 1 mm/min rainfall intensity. This provided an innovative idea, that is applying the orientation and gradient to the simulation and prediction model of the rainfall erosion process in the sloping farmland in the southern red soil area.

Dilated Adversarial U-Net Network for automatic gross tumor volume segmentation of nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) is a malignant tumor in the nasopharyngeal epithelium and is mainly treated by radiotherapy. The accurate delineation of the target tumor can greatly improve the radiotherapy effectiveness. However, due to the small size of the NPC imaging volume, the scarcity of labeled samples, the low signal-to-noise ratio in small target areas and the lack of detailed features, automatic gross tumor volume (GTV) delineation inspired by advances in domain adaption for high-resolution image processing has become a great challenge. In addition, since computed tomography (CT) images have the low resolution of soft tissues, it is difficult to identify small volume tumors, and segmentation accuracy of this kind of small GTV is very low. In this paper, we propose an automatic segmentation model based on adversarial network and U-Net for NPC delineation. Specifically, we embed adversarial classification learning into a segmentation network to balance the distribution differences between the small targets in the sample and the large target categories. To reduce the loss weight of large target categories with large samples, and simultaneously increase the weight of small target categories, we design a new U-Net based on focal loss as a GTV segmentation model for adjusting the effect of different categories on the final loss. This method can effectively solve the feature bias caused by the imbalance of the target volume distribution. Furthermore, we conduct a pre-processing of images using an algorithm based on distribution histograms to ensure that the same or approximate CT value represents the same organization. In order to evaluate our proposed method, we perform experiments on the open datasets from StructSeg2019 and the datasets provided by Sichuan Provincial Cancer Hospital. The results of the comparison with some typical up-to-date methods demonstrate that our model can significantly enhance detection accuracy and sensitivity for NPC segmentation.

AUTOMATED PANSHARPENING INFORMATION TECHNOLOGY OF SATELLITE IMAGES

Context. Nowadays, information technologies are widely used in digital image processing. The task of joint processing of satellite image obtained by different space systems that have different spatial differences is important. The already known pansharpening methods to improve the quality of the resulting image, there are new scientific problems associated with increasing the requirements for high-resolution image processing and the development of automated technology for processing the satellite data for further thematic analysis. Most spatial resolution techniques result in artifacts. Our work explores the major remote sensing data fusion techniques at pixel level and reviews the concept, principals, limitations and advantages for each technique with the program implementation of research. Objective. The goal of the work is analyze the effectiveness of the traditional pan-sharpening methods like the Brovey, the wavelet-transform, the GIHS, the HCT and the combined pansharpening method for satellite images of high-spatial resolution. Method. In this paper we propose the information technology for pansharpening high spatial resolution images with automation of choosing the best method based on the analysis of quantitative and qualitative evolutions. The method involves the scaling multispectral image to the size of the panchromatic image; using histogram equalization to adjust the primary images by aligning the integral areas of the sections with different brightness; conversion of primary images after the spectral correction on traditional pansharpening methods; analyze the effectiveness of the results obtained for conducts their detailed comparative visual and quantitative evaluation. The technology allows determining the best method of pansharpening by analyzing quantitative metrics: the NDVI index, the RMSE and the ERGAS. The NDVI index for the methods Brovey and HPF indicate color distortion in comparison with the reference data. This is due to the fact that the Brovey and HPF methods are based on the fusion of three channel images and do not include the information contained in the near infrared range. The RMSE and the ERGAS show the superiority of the combined HSVHCT-Wavelet method over the conventional and state-of-art image resolution enhancement techniques of high resolution satellite images. This is achieved, in particular, by preliminary processing of primary images, data processing localized spectral bases, optimized performance information, and the information contained in the infrared image. Results. The software implementing proposed method is developed. The experiments to study the properties of the proposed algorithm are conducted. Experimental evaluation performed on eight-primary satellite images of high spatial resolution obtained WorldView-2 satellite. The experimental results show that a synthesized high spatial resolution image with high information content is achieved with the complex use of fusion methods, which makes it possible to increase the spatial resolution of the original multichannel image without color distortions. Conclusions. The experiments confirmed the effectiveness of the proposed automated information technology for pansharpening high-resolution satellite images with the development of a graphical interface to obtain a new synthesized image. In addition, the proposed technology will effectively carry out further recognition and real-time monitoring infrastructure.

A Portable Smartphone-Based Laryngoscope System for High-Speed Vocal Cord Imaging of Patients With Throat Disorders: Instrument Validation Study.

BackgroundCurrently, high-speed digital imaging (HSDI), especially endoscopic HSDI, is routinely used for the diagnosis of vocal cord disorders. However, endoscopic HSDI devices are usually large and costly, which limits access to patients in underdeveloped countries and in regions with inadequate medical infrastructure. Modern smartphones have sufficient functionality to process the complex calculations that are required for processing high-resolution images and videos with a high frame rate. Recently, several attempts have been made to integrate medical endoscopes with smartphones to make them more accessible to people in underdeveloped countries.ObjectiveThis study aims to develop a smartphone adaptor for endoscopes, which enables smartphone-based vocal cord imaging, to demonstrate the feasibility of performing high-speed vocal cord imaging via the high-speed imaging functions of a high-performance smartphone camera, and to determine the acceptability of the smartphone-based high-speed vocal cord imaging system for clinical applications in developing countries.MethodsA customized smartphone adaptor optical relay was designed for clinical endoscopy using selective laser melting–based 3D printing. A standard laryngoscope was attached to the smartphone adaptor to acquire high-speed vocal cord endoscopic images. Only existing basic functions of the smartphone camera were used for HSDI of the vocal cords. Extracted still frames were observed for qualitative glottal volume and shape. For image processing, segmented glottal and vocal cord areas were calculated from whole HSDI frames to characterize the amplitude of the vibrations on each side of the glottis, including the frequency, edge length, glottal areas, base cord, and lateral phase differences over the acquisition time. The device was incorporated into a preclinical videokymography diagnosis routine to compare functionality.ResultsSmartphone-based HSDI with the smartphone-endoscope adaptor could achieve 940 frames per second and a resolution of 1280 by 720 frames, which corresponds to the detection of 3 to 8 frames per vocal cycle at double the spatial resolution of existing devices. The device was used to image the vocal cords of 4 volunteers: 1 healthy individual and 3 patients with vocal cord paralysis, chronic laryngitis, or vocal cord polyps. The resultant image stacks were sufficient for most diagnostic purposes. The cost of the device including the smartphone was lower than that of existing HSDI devices. The image processing and analytics demonstrated the successful calculation of relevant diagnostic variables from the acquired images. Patients with vocal pathologies were easily differentiable in the quantitative data.ConclusionsA smartphone-based HSDI endoscope system can function as a point-of-care clinical diagnostic device. The resulting analysis is of higher quality than that accessible by videostroboscopy and promises comparable quality and greater accessibility than HSDI. In particular, this system is suitable for use as an accessible diagnostic tool in underdeveloped areas with inadequate medical service infrastructure.

Fully Parallel Implementation of Otsu Automatic Image Thresholding Algorithm on FPGA

This work proposes a high-throughput implementation of the Otsu automatic image thresholding algorithm on Field Programmable Gate Array (FPGA), aiming to process high-resolution images in real-time. The Otsu method is a widely used global thresholding algorithm to define an optimal threshold between two classes. However, this technique has a high computational cost, making it difficult to use in real-time applications. Thus, this paper proposes a hardware design exploiting parallelization to optimize the system’s processing time. The implementation details and an analysis of the synthesis results concerning the hardware area occupation, throughput, and dynamic power consumption, are presented. Results have shown that the proposed hardware achieved a high speedup compared to similar works in the literature.

A convolutional neural network for common coordinate registration of high-resolution histology images.

MotivationRegistration of histology images from multiple sources is a pressing problem in large-scale studies of spatial -omics data. Researchers often perform ‘common coordinate registration’, akin to segmentation, in which samples are partitioned based on tissue type to allow for quantitative comparison of similar regions across samples. Accuracy in such registration requires both high image resolution and global awareness, which mark a difficult balancing act for contemporary deep learning architectures.ResultsWe present a novel convolutional neural network (CNN) architecture that combines (i) a local classification CNN that extracts features from image patches sampled sparsely across the tissue surface and (ii) a global segmentation CNN that operates on these extracted features. This hybrid network can be trained in an end-to-end manner, and we demonstrate its relative merits over competing approaches on a reference histology dataset as well as two published spatial transcriptomics datasets. We believe that this paradigm will greatly enhance our ability to process spatial -omics data, and has general purpose applications for the processing of high-resolution histology images on commercially available GPUs.Availability and implementationAll code is publicly available at https://github.com/flatironinstitute/st_gridnet.Supplementary information Supplementary data are available at Bioinformatics online.

RETRACTED ARTICLE: Simulation of air environment and sports development in coastline cities based on high-resolution image processing system

RETRACTED ARTICLE: Simulation of air environment and sports development in coastline cities based on high-resolution image processing system

Automatic Balancing Control of Air-bearing Simulator Based on Firefly Algorithm Improved Neural Network

In order to accurately reproduce the multi-satellite networking technology and simulate the multi-mode high-resolution imaging process on the ground, the three-axis air-bearing test bed was the key device of simulation. In this paper, a fast automatic balance adjustment control algorithm was proposed based on firefly algorithm improved BP (Back Propagation) neural network PID (Proportion Integration Differentiation) control. Aiming at the problem of long adjustment time and easy to obtain a non-optimal solution, the firefly algorithm was introduced to optimize the initial weight and threshold value of the BP neural network, and improve the algorithm performance in convergence rate and stability. Based on the kinematics and dynamics model of the three-axis air-bearing simulator platform, the simulation results show that the optimized algorithm reduces the <italic>x</italic>-axis centroid offset to 2.3×10^-7 m in 3.1 s. The algorithm has faster and higher stability on air-bearing simulator automatic balancing control, and satisfies the demand for multi-satellite imaging process simulation.

An IoT-enabled real-time overhead view person detection system based on Cascade-RCNN and transfer learning

Internet of things (IoT) is transforming technological evolution in several practical applications. These applications range from smart cities, smart healthcare to intelligent video surveillance, where the primary interest is person monitoring and detection. The amalgamation of Artificial Intelligence (AI) and IoT-based techniques maintain a balance between computational cost and efficiency that is essential for next-generation IoT networks. In this context, a real-time IoT-enabled people detection system is introduced. The developed system performs image processing task over the cloud using an internet connection, thus reduces the computational cost by processing high-resolution images over the cloud. For person detection, a pre-trained Cascade RCNN, a deep learning approach is used. It is an object detection architecture, seeks to address discrediting performance with increased Intersection over Union (IoU) thresholds. As the architecture is pre-trained with COCO data set and the person body’s appearance in overhead perspective is significantly different; thus, additional training is performed to enhance the detection results. Taking advantage of transfer learning architecture is trained for overhead person images, and the newly trained feature layer is added to the existing architecture. Experimental outcomes reveal that additional training increases the detection architecture’s performance with an accuracy rate of 0.96.