Frame Conversion Research Articles

Polarization Meridian Plane-Based 3-D Attitude Measurements: A Bio-Inspired Approach

As a bionic sensor inspired by the behaviors of insects, a polarized light compass determines the attitude information from the polarization distribution of the sunlight with its biomimetic optical structure. In this article, focusing on the problem that the existing attitude determination method can only obtain the 1-D heading angle from polarization mode, a polarization meridian plane (PMP)-based 3-D attitude determination method is presented. In our work, the solar polarization meridional plane is modeled as introduced information independent of the solar vector. It connects more polarization vectors (solar vector, zenith direction, and neutral point) in the measurement system and contributes to an additional relational model, that is, the PMP frame conversion model. Then, by combining with the solar vector conversion model, the proposed method obtains the real-time three-axis attitude angles by the image-based polarized light compass itself. Finally, numerical simulations and outdoor experiments are conducted to verify the effectiveness of the proposed method. The proposed PMP-based method makes it possible for the polarized light compass to have the ability of 3-D attitude determination, that is, pitch, roll, and yaw angles.

Moving Multi-Object Detection and Tracking Using MRNN and PS-KM Models

On grounds of the advent of real-time applications, like autonomous driving, visual surveillance, and sports analysis, there is an augmenting focus of attention towards Multiple-Object Tracking (MOT). The tracking-by-detection paradigm, a commonly utilized approach, connects the existing recognition hypotheses to the formerly assessed object trajectories by comparing the similarities of the appearance or the motion between them. For an efficient detection and tracking of the numerous objects in a complex environment, a Pearson Similarity-centred Kuhn-Munkres (PS-KM) algorithm was proposed in the present study. In this light, the input videos were, initially, gathered from the MOT dataset and converted into frames. The background subtraction occurred which filtered the inappropriate data concerning the frames after the frame conversion stage. Then, the extraction of features from the frames was executed. Afterwards, the higher dimensional features were transformed into lower-dimensional features, and feature reduction process was performed with the aid of Information Gain-centred Singular Value Decomposition (IG-SVD). Next, using the Modified Recurrent Neural Network (MRNN) method, classification was executed which identified the categories of the objects additionally. The PS-KM algorithm identified that the recognized objects were tracked. Finally, the experimental outcomes exhibited that numerous targets were precisely tracked by the proposed system with 97% accuracy with a low false positive rate (FPR) of 2.3%. It was also proved that the present techniques viz. RNN, CNN, and KNN, were effective with regard to the existing models.

Squirrel Search Optimization with Deep Convolutional Neural Network for Human Pose Estimation

Human pose estimation (HPE) is a procedure for determining the structure of the body pose and it is considered a challenging issue in the computer vision (CV) communities. HPE finds its applications in several fields namely activity recognition and human-computer interface. Despite the benefits of HPE, it is still a challenging process due to the variations in visual appearances, lighting, occlusions, dimensionality, etc. To resolve these issues, this paper presents a squirrel search optimization with a deep convolutional neural network for HPE (SSDCNN-HPE) technique. The major intention of the SSDCNN-HPE technique is to identify the human pose accurately and efficiently. Primarily, the video frame conversion process is performed and pre-processing takes place via bilateral filtering-based noise removal process. Then, the EfficientNet model is applied to identify the body points of a person with no problem constraints. Besides, the hyperparameter tuning of the EfficientNet model takes place by the use of the squirrel search algorithm (SSA). In the final stage, the multiclass support vector machine (M-SVM) technique was utilized for the identification and classification of human poses. The design of bilateral filtering followed by SSA based EfficientNet model for HPE depicts the novelty of the work. To demonstrate the enhanced outcomes of the SSDCNN-HPE approach, a series of simulations are executed. The experimental results reported the betterment of the SSDCNN-HPE system over the recent existing techniques in terms of different measures.

Internet of Things with Deep Learning Enabled Fraud Detection in Surveillance Video Processing

Background: In the present digital era, fraud detection using surveillance video has become a mandatory tool to determine the occurrence of abnormal events in an automated way. Since the traditional visual inspection of surveillance videos for fraud detection is time-consuming and labour-intensive, intelligent fraud detection approaches based on Deep Learning (DL) concepts have been presented in the literature. Methods: This paper designs a DL with optimal classification based on fraud detection in a video surveillance system, named the DLOC-FDVS technique. The proposed DLOC-FDVS technique aims to examine the surveillance videos for the existence of frauds (i.e., robbery) in the IoT environment. At the initial stage, IoT enables the data acquisition and frame conversion process to be carried out. For fraud detection, densely connected networks (DenseNet-169) feature extractor and optimal Long Short Term Memory (LSTM) classifiers are applied. Finally, the Grasshopper Optimization Algorithm (GOA) is utilised to alter the LSTM model’s hyperparameters. It is frequently employed in a number of industrial settings and achieves suitable answers due to its ease of deployment and excellent precision. Findings: The DLOC-FDVS model is experimentally validated using a benchmark anomaly detection dataset from the Kaggle repository, which comprises 211 frames of normal video and 100 frames of fraud video. The experimental results show that the suggested model is an excellent fraud detection tool in the IoT context, achieving maximum precision, recall, F1score, and AUC of 96.56%, 96.56%, 96.56%, and 96.56% respectively. Novelty: The use of GOA for hyperparameter adjustment of the LSTM model for fraud detection demonstrates the work’s uniqueness. As a result, the DLOC-FDVS model may be used to identify fraud in real-time surveillance recordings. Keywords: Video surveillance; Deep learning; Video processing; Internet of Things; Fraud detection; Hyperparameter tuning

Deep Learning driven automated person detection and tracking model on surveillance videos

Recently, person detection and tracking in a video scene of a surveillance system were grabbing higher interest because of its extensive range of applications in gender classification, abnormal event recognition, person identification, human gait characterization, individual counting in a dense crowd, and fall detection for older people, etc. Several methodologies were implied for person detection for surveillance applications. This paper presents a new DL driven automated person detection and tracking (DLD-APDT) model on surveillance videos. The major goal of the proposed DLD-APDT model is to identify people and track them in the videos. To accomplish this, the proposed DLD-APDT model initially performs frame conversion process where the input video is converted into a set of frames. In addition, the proposed DLD-APDT model employs EfficientDet model as object detector, i.e., persons and track them. EfficientDet is a recently developed object detector that makes use of different optimization and backbone tweaks, like bi-directional feature pyramid network (BiFPN) and a compound scaling approach. Moreover, root means square propagation (RMSProp) optimizer is applied to optimally choose the hyperparameters related to the EfficientNet model which helps in accomplishing enhanced performance. The experimental validation of the DLD-APDT model takes place using two training datasets namely PascalVOC and PenFudan datasets. The simulation results reported the enhanced detection and tracking performance of the DLD-APDT technique over recent approaches.

Complex tools and motor-to-mechanical transformations

The ability to use complex tools is thought to depend on multifaceted motor-to-mechanical transformations within the left inferior parietal lobule (IPL), linked to cognitive control over compound actions. Here we show using neuroimaging that demanding transformations of finger movements into proper mechanical movements of functional parts of complex tools invoke significantly the right rather than left rostral IPL, and bilateral posterior-to-mid and left anterior intraparietal sulci. These findings emerged during the functional grasp and tool-use programming phase. The expected engagement of left IPL was partly revealed by traditional region-of-interest analyses, and further modeling/estimations at the hand-independent level. Thus, our results point to a special role of right IPL in supporting sensory-motor spatial mechanisms which enable an effective control of fingers in skillful handling of complex tools. The resulting motor-to-mechanical transformations involve dynamic hand-centered to target-centered reference frame conversions indispensable for efficient interactions with the environment.

Opening up, closing down, or leaving ajar? How applications are used in engaging with publics about gene drive

ABSTRACT Public engagement and responsible innovation are strongly emphasised in gene drive research, together with the goal of addressing societal challenges, notably, malaria and environmental conservation. We aim to explore whether public engagement is used to ‘open up' or ‘close down' opportunities to shape gene drive research. Drawing on interviews with gene drive developers and stakeholders, we investigate how the public communication of gene drive is conceived. We find that traditional closing-down tendencies remain, but that there are new and encouraging opening-up approaches. Consistent with responsible innovation thinking, these frame gene drive as multifaceted, context-dependent and requiring deeper deliberation. We also identify a third ‘leaving ajar’ approach that seeks to engage with and respond to local communities and modify technological applications to be more acceptable. Innovation system constraints may well temper current aspirations to open up; framing public conversations around understandings of public good could offer a way forward.

Meta‐heuristics algorithms for optimization of gains for dynamic voltage restorers to improve power quality and dynamics

AbstractThis article discusses metaheuristic algorithms for optimizing controller gains for dynamic voltage restorers (DVRs) that use an impedance control strategy to compensate for unbalance in source voltages, voltage harmonics, and sag/swell in source voltages. The gains of the proportional‐integral (PI) controllers become critical for proper DVR load voltage extraction. Various techniques for optimization, such as whale optimization technique, gray wolf optimization technique, particle swarm optimization technique, and ant lion optimization technique, are used to obtain DC and AC PI controller gains for DVR. The impedance control strategy employs simple calculations to determine the resistance and reactance of a polluted source voltage, without the use of frame conversions as in synchronous reference theory, instantaneous reference power theory, and so on. The quick calculations of the impedance control scheme improve the power quality and dynamics. The Metaheuristic algorithms are used to calculate the number of iterations required to achieve the best possible controller gains, which further helps to improve power quality and dynamics. Among these optimization techniques, the antlion optimization technique provides fast convergence and the best possible controller gain values to improve the dynamics of the dc‐link voltage of voltage source converter and terminal voltage, thereby improving power quality. The proposed antlion optimization technique‐based DVR model is simulated in MATLAB R2019, and the results are validated with RT‐LAB.

A Vision-Based Detection and Spatial Localization Scheme for Forest Fire Inspection from UAV

Forest fires have the characteristics of strong unpredictability and extreme destruction. Hence, it is difficult to carry out effective prevention and control. Once the fire spreads, devastating damage will be caused to natural resources and the ecological environment. In order to detect early forest fires in real-time and provide firefighting assistance, we propose a vision-based detection and spatial localization scheme and develop a system carried on the unmanned aerial vehicle (UAV) with an OAK-D camera. During the high incidence of forest fires, UAVs equipped with our system are deployed to patrol the forest. Our scheme includes two key aspects. First, the lightweight model, NanoDet, is applied as a detector to identify and locate fires in the vision field. Techniques such as the cosine learning rate strategy and data augmentations are employed to further enhance mean average precision (mAP). After capturing 2D images with fires from the detector, the binocular stereo vision is applied to calculate the depth map, where the HSV-Mask filter and non-zero mean method are proposed to eliminate the interference values when calculating the depth of the fire area. Second, to get the latitude, longitude, and altitude (LLA) coordinates of the fire area, coordinate frame conversion is used along with data from the GPS module and inertial measurement unit (IMU) module. As a result, we experiment with simulated fire in a forest area to test the effectiveness of this system. The results show that 89.34% of the suspicious frames with flame targets are detected and the localization error of latitude and longitude is in the order of 10−5 degrees; this demonstrates that the system meets our precision requirements and is sufficient for forest fire inspection.

The covariance matrix transformation method in all-earth integrated navigation considering coordinate frame conversion

In the exploration of polar regions, navigation is one of the most important issues to be resolved. To avoid the limitations of a single navigation coordinate frame, navigation systems usually use different navigation coordinate frames in polar and nonpolar regions, such as the north-oriented geographic frame and the grid frame. However, the error states and covariance matrix are related to the definition of the navigation coordinate frame, since coordinate frame conversion will cause overshoot of the integrated navigation Kalman filter and error discontinuity. To solve this problem, the transformation relationship of error states defined in different frames is deduced, whereby the covariance matrix transformation relationship is also analyzed. On this basis, covariance transformation-based open-loop and closed-loop Kalman filter integrated navigation algorithms are proposed. The effectiveness of the algorithms is verified by flight tests with a rotational strapdown inertial navigation system/global navigation satellite system integrated navigation system.

Means and Methods of Efficiency Estimation of Video Stream Transmission Based on GigE Vision Technology Using Application Processor

The paper investigates the possibility of efficient implementation of a GigE Vision compatible video stream source on a computing platform based on a system-on-a-chip with general-purpose ARM processor cores. In particular, to implement the aforementioned video source, a proprietary prototype of a GigE Vision compatible camera was developed based on the Raspberry Pi 4 single-board computer. This computing platform was chosen due to its widespread use and wide community support. The software part of the camera is implemented using the Video4Linux and Aravis libraries. The first library is used for the primary image capturing from a video sensor connected to a single board computer. The second library is intended for forming and transmission of video stream frames compatible with GigE Vision technology over the network. To estimate the delays in the transmission of a video stream over an Ethernet channel, a methodology based on the Precise Time Protocol (PTP) has been proposed and applied. During the experiments, it was found that the software implementation of a GigE Vision compatible camera on single-board computers with general-purpose processor cores is quite promising. Without additional optimization, such an implementation can be successfully used to transmit small frames (with a resolution of up to 640 × 480 pixels), giving a delay less than 10 ms. At the same time, some additional optimizations may be required to transmit larger frames. Namely, a MTU (maximum transmission unit) size value plays the crucial role in latency formation. Thus, to implement a faster camera, it is necessary to select a platform that supports the largest possible MTU (unfortunately, it turned out that it is not possible with Raspberry Pi 4, as it supports relatively small MTU size of up to 2000 bytes). In addition, the image format conversion procedure can noticeably affect the delay. Therefore, it is highly desirable to avoid any frame processing on the transmitter side and, if it is possible, to broadcast raw images. If the conversion of the frame format is necessary, the platform should be chosen so that there are free computing cores on it, which will permit to distribute all necessary frame conversions between these cores using parallelization techniques.

Confidence in Drinking Water Remains Strong

In summer 2020, AWWA issued the results of its first survey of the public on perceptions of tap water, showing strong confidence in the safety, quality, and satisfaction of public drinking water. The survey also showed room for improvement—specifically, the need to build confidence with Black and Hispanic customers. To follow up, AWWA sponsored a survey in summer 2021, asking the same top-line questions while looking into other important topics. While results are still strong numbers for public opinion polling (Table 1), they are marginally lower (6%–8%) than in the 2020 survey. Perception of Safety % Satisfaction with Water % Perception of Quality % There is no way to be certain of the cause for this slight decrease, but it's undeniable that the COVID-19 pandemic has had massive impacts on every sector of the economy, which could have played a role. Still, the overall public's opinion of safety, quality, and satisfaction in tap water remains high. Also, the 2021 survey allows us to observe some new key findings. For example, we asked respondents whether they felt their water quality had improved or declined in the past five years, and considerably more (20%) said it had improved rather than declined (9%), with most (57%) saying it had stayed the same and the other 14% indicating they did not know or had no opinion. More than half (53%) of adults did not recall any recent communication (within the last year) from their water utility other than a bill. Those who did recall a recent communication within the past year had mostly positive perceptions about their water, including trust in their utility (5% more strongly agreed), perception of quality (11% higher), satisfaction with water (10% higher), and perception of safety (8% higher). Condo and apartment residents were considerably more likely to report no memory of a recent communication (14% higher) than those in single or multi-family homes. While it's hard to prove a direct relationship from this survey, clear and consistent communication with customers may be an essential opportunity to improve customers’ perceptions of their tap water. Another important topic in the survey involved affordability and willingness to pay. About 24% of respondents indicated they struggled to pay their water bills and 39% struggled to pay their utility bills, generally. We asked whether customers were willing to pay more for service improvements in their community, and about 58% reported they were either “very willing” or “somewhat willing” to pay more, with higher values for younger (ages 18–34, 68%) and Hispanic (65%) respondents. Of those willing to pay more, most (66%) were willing to pay somewhere between US$1 and $20 more per month. Second, we asked whether customers were willing to pay to help those who may not be able to afford their water bills. About half of surveyed individuals were asked whether they'd be willing to pay more and the other half whether they'd be willing to donate. Although the total support was similar, it was slightly higher for paying more (46%) compared with donating (43%). More than half of those who were willing (53%) indicated a willingness to pay of $1–$10 more per month. This represents a potential challenge for both ratepayer funded and voluntary contribution customer assistance programs, with less than half of the public expressing willingness to help fund them. When asked whether they supported federal assistance for water bills, the support was much higher at 71%, with especially high support from Black respondents (84%) and those living in urban areas (80%). This public polling information helps to frame conversations around perceptions of drinking water, and gives policy makers, utilities, and others useful data to advance conversations on these issues. I encourage readers to visit AWWA's Value of Water and Communications and Outreach pages to learn more. Adam T. Carpenter is the energy and environmental policy manager at the AWWA Government Affairs Office in Washington, D.C. He can be reached at acarpenter@awwa.org.

Gadolinium retention: should pediatric radiologists be concerned, and how to frame conversations with families.

Gadolinium retention in the brain and other organs has recently been identified by imaging and confirmed histologically. No direct clinical effects of gadolinium retention, which occurs after gadolinium-based contrast agent (GBCA) administration for MRI, have been scientifically accepted at this time. However, there is understandable concern among medical professionals and the public about the potential effects of gadolinium retention, particularly in the brain. Part of this concern might stem from the identification of nephrogenic systemic fibrosis caused by GBCAs in people with severe renal failure in 2006. This article briefly describes the characteristics of GBCAs; reviews and differentiates gadolinium retention, nephrogenic systemic fibrosis, and "gadolinium deposition disease" or "gadolinium toxicity"; and discusses societal guidelines and current usage in children. With the belief that GBCAs should not be withheld for appropriate indications in the absence of evidence of its potential risks, we offer a framework for determining when GBCA use is appropriate and suggestions for discussing its risks and benefits with children and their families.

Read Textual features in Images and convert to Editable form by extended use of Artificial Neural Networks, Deep learning and Maximally Stable Extremal Region techniques

Nowadays most of the information is stored in images and there is a need to convert this information into an editable format. The objective of this paper includes the development of a user-friendly tool to extract text from the video or to recognize well-written handwriting in a scanned document to an editable form. The proposed methodology is robust and can provide high-grade performance with layout distortion. The explication utilizes modalities like Optical Character Recognition (OCR), Artificial Neural Networks (ANN), Deep Learning and Maximally Stable Extremal Regions (MSER) techniques for text detection. The video extraction can be attained by using video to frame conversion and then to use the Structural Similarity Index Measure (SSIM). The prototype is developed using MATLAB and provided with a GUI, which is deployable on a workstation. The generated GUI is employed to define Region of Interest (ROI), specify the required text layout, highlighting the specific portions of the image, select the appropriate language and also export the text to a word document or notepad applications where it can be edited.

Dashboards and report cards: Using staff performance to drive outcomes.

Dashboards and report cards: Using staff performance to drive outcomes.

Ontology based multiobject segmentation and classification in sports videos

The primary objective is to identify and segments the multiple, partly occluded objects in the image. The subsequent stage carry out our approach, primarily start with frame conversion. Next in the preprocessing stage, the Gaussian filter is employed for image smoothening. Then from the preprocessed image, Multi objects are segmented through modified ontology-based segmentation, and the edge is detected from the segmented images. After that, from the edge detected frames area is extracted, which results in object detected frames. In the feature extraction stage, attributes such as area, contrast, correlation, energy, homogeneity, color, perimeter, circularity are extorted from the detected objects. The objects are categorized as human or other objects (bat/ball) through the feed-forward back propagation neural network classifier (FFBNN) based upon the extracted attributes.

Variation in the design of Do Not Resuscitate orders and other code status options: a multi-institutional qualitative study

BackgroundUS hospitals typically provide a set of code status options that includes Full Code and Do Not Resuscitate (DNR) but often includes additional options. Although US hospitals differ in the...

Beaches and Muslim belonging in France: liberty, equality, but not the burkini!

Drawing on Marco Antonsich’s framework for analyzing belonging while also engaging critical legal geographies, this article applies a legal lens to Muslim belonging. It does so through discourse analysis of court, legislative, and political pronouncements regarding burkini bans that surfaced in France shortly after a terror attack by a self-identified Muslim, and with ‘burkini’ serving as a proxy for Islam in their rhetoric. The article focuses on the language used by France’s highest administrative court in overturning one city’s ban, as well as language used by French political leaders in supporting the bans, to examine the message of belonging conveyed to Muslims in France. Legal language is the focus because of its ability to frame conversations regarding societal norms, with the language explored here framing the broader debate on Muslim belonging in French public space and arguably what the French population believes is necessary to belong in French society. This exploration provides a foundation for further research by scholars exploring issues of identity, migration, belonging, race, and religion, using methodologies such as feminism and critical race theory that can readily connect with the critical legal approach applied here.

A NONLINEAR CONVERSION MODEL FORM ITRFYY TO CGCS2000

Abstract. At present, ITRS series reference frameworks are widely used in the world. The results of GNSS are mostly based on the ITRF framework. Transform from ITRF to CGCS2000 is not easy, which restricts the promotion and use of CGCS2000. The conversion relationship between CGCS2000 and ITRF framework has imminent practical significance. This paper constructs the epoch reduction and frame conversion two-steps model which estimated the nonlinear model to solve the appeal problem. Effective test show that the nonlinear model accesses an improvement in not only precession but also accuracy relative to the tradition model.

Segmentation of An Indian Classical Dance Videos using Different Segmentation Methods.

Video to frame conversion features are retrieved to categorize the actions in an Indian classical dance video dataset. The goal is to design an automatic machine learning model that identifies the moves of a dancer in a video. A video is a collection of images of specific movements, hence, features representing shapes and color can be used to interpret the dance steps. Image segmentation based features are capable of representing the shape in varying background conditions. Segmentation has become an important objective in image analysis and computer vision. To segment the images, edge detection, thresholding and region of interest are taken for this study. The proposed system performance is analyzed for total number of 50 different movements taken from Indian classicaldances.Bharatanatyam,Kathak,Kuchipudi,Manipuri,Mohiniytam Odissi,Kathakali and Satrriya in different background conditions.