Data Frame Research Articles

Design and Implementation of Manhole Cover Safety Monitoring System based on Smart Light Pole

Aiming at the current problems in the safety monitoring of urban manhole covers, this paper proposes a safety monitoring system for manhole covers based on smart light poles. The system uses STM32F103C8T6 as the microcontroller, and processes and controls the movement, loss, tilt, flooding, and positioning data of the manhole cover. Then, the data frame is sent to the LoRa gateway of the nearby smart light pole through the LoRa communication protocol, and the LoRa gateway will transmit the abnormal state of the manhole cover to the large screen display of the smart light pole through the bus. At the same time, a voice broadcast is carried out to remind localized road vehicles and pedestrians. Besides, it is sent to the IoT cloud platform KitLink, and the cloud platform will push the abnormal status data to the subscribed management user, and the user will quickly process it according to the positioning data. Among them, the terminal sensing control device of the manhole cover adopts an integrated package, and at the same time, it is installed at the center point under the manhole cover by using a waterproof material. It can not only carry out reliable and accurate installation without changing the original components but also can effectively monitor the safety. Through the design and implementation test of the system, it can be seen that the system can efficiently and accurately realize all-round and multi-modal safety linkage monitoring of urban manhole covers.

A Novel Theory-Based Virtual Reality Training to Improve Patient Safety Culture in the Department of Surgery of a Large Academic Medical Center: Protocol for a Mixed Methods Study.

BackgroundPreventable surgical errors of varying degrees of physical, emotional, and financial harm account for a significant number of adverse events. These errors are frequently tied to systemic problems within a health care system, including the absence of necessary policies/procedures, obstructive cultural hierarchy, and communication breakdown between staff. We developed an innovative, theory-based virtual reality (VR) training to promote understanding and sensemaking toward the holistic view of the culture of patient safety and high reliability.ObjectiveWe aim to assess the effect of VR training on health care workers’ (HCWs’) understanding of contributing factors to patient safety events, sensemaking of patient safety culture, and high reliability organization principles in the laboratory environment. Further, we aim to assess the effect of VR training on patient safety culture, TeamSTEPPS behavior scores, and reporting of patient safety events in the surgery department of an academic medical center in the clinical environment.MethodsThis mixed methods study uses a pre-VR versus post-VR training study design involving attending faculty, residents, nurses, technicians of the department of surgery, and frontline HCWs in the operation rooms at an academic medical center. HCWs’ understanding of contributing factors to patient safety events will be assessed using a scale based on the Human Factors Analysis and Classification System. We will use the data frame theory framework, supported by a semistructured interview guide to capture the sensemaking process of patient safety culture and principles of high reliability organizations. Changes in the culture of patient safety will be quantified using the Agency for Healthcare Research and Quality surveys on patient safety culture. TeamSTEPPS behavior scores based on observation will be measured using the Teamwork Evaluation of Non-Technical Skills tool. Patient safety events reported in the voluntary institutional reporting system will be compared before the training versus those after the training. We will compare the Agency for Healthcare Research and Quality patient safety culture scores and patient safety events reporting before the training versus those after the training by using descriptive statistics and a within-subject 2-tailed, 2-sample t test with the significance level set at .05.ResultsEthics approval was obtained in May 2021 from the institutional review board of the University of North Carolina at Chapel Hill (22-1150). The enrollment of participants for this study will start in fall 2022 and is expected to be completed by early spring 2023. The data analysis is expected to be completed by July 2023.ConclusionsOur findings will help assess the effectiveness of VR training in improving HCWs’ understanding of contributing factors of patient safety events, sensemaking of patient safety culture, and principles and behaviors of high reliability organizations. These findings will contribute to developing VR training to improve patient safety culture in other specialties.

Predicting subscription renewal using binary classification in World of Warcraft

Multiplayer online video games in the past decade have become one of the world's most popular gaming formats. Analyzing player data can provide insights into player behavior, such as how long players continue to play or where they stop playing. This information can help developers to come up with solutions to retain their players. We have compared binary classification algorithms on different time frames of player history data along with level and guild from World of Warcraft – a previously popular massively multiplayer online role-playing game (MMORPG), to assess the probability of subscription renewal. Our findings suggest that random forest and decision tree algorithms give the highest accuracy in all cases (more than 90 %). This was achieved using level and guild as parameters along with a combination of active days and total playing time taken from different time frames in the past month along.

An intelligent spatial stream processing framework for digital forensics amid the COVID-19 outbreak.

In recent times, several strategies to minimize the spread of 2019 novel coronavirus disease (COVID-19) have been adopted. Some recent technological breakthroughs like the drone-based tracking systems have focused on devising specific dynamical approaches for administering public mobility and providing early detection of symptomatic patients. In this paper, a smart real-time image processing framework converged with a non-contact thermal temperature screening module was implemented. The proposed framework comprised of three modules , smart temperature screening system, tracking infection footprint, and monitoring social distancing policies. This was accomplished by employing Histogram of Oriented Gradients (HOG) transformation to identify infection hotspots. Further, Haar Cascade and local binary pattern histogram (LBPH) algorithms were used for real-time facial recognition and enforcing social distancing policies. In order to manage the redundant video frames generated at the local computing device, two holistic models, namely, event-triggered video framing (ETVF) and real-time video framing (RTVF) have been deduced, and their respective processing costs were studied for different arrival rates of the video frame. It was observed that the proposed ETVF approach outperforms the performance of RTVF by providing optimal processing costs resulting from the elimination of redundant data frames. Results corresponding to autocorrelation analysis have been presented for the case study of India pertaining to the number of confirmed COVID-19 cases, recovered cases, and deaths to obtain an understanding of epidemiological spread of the virus. Further, choropleth analysis was performed for indicating the magnitude of COVID-19 spread corresponding to different regions in India.

Experimental demonstration of hidden node problem in visible light communication networks

In this paper, hardware investigations show the effects of the hidden and exposed node problems in visible light communication (VLC) networks. Furthermore, the request to send/clear to send (RTS/CTS) mechanism, as a physical layer-independent solution, is used for solving the hidden node problem in VLC networks. A VLC hardware system, called VLCIoT, which was designed and implemented in our laboratory based on PHY I of the IEEE 802.15.7 standard, is used for the physical layer. The medium access control layer of the IEEE 802.15.7 is considered for the network. We implement the multiple access protocol of the IEEE 802.15.7 and the RTS/CTS mechanism on the microcontroller of each device using the C programming language. Goodput, message loss ratio, fairness, average delay, energy efficiency, network load, frame size, number of hidden nodes, and number of contending nodes are the evaluated parameters. The results show that for the IEEE 802.15.7 increase of the hidden nodes decreases the goodput and energy efficiency and increases the message loss ratio and the average delay. But, using the RTS/CTS mechanism, the hidden nodes do not affect the goodput, the message loss ratio, the energy efficiency, and the average delay because this mechanism solves the hidden node problem. The results show that the RTS/CTS mechanism increases the saturation goodput by 300%, decreases the message loss ratio by 94%, decreases the average delay by 50%, and increases the energy efficiency by 300%. The results show that at higher network load, higher data frame size, and more contending nodes, the performance of the RTS/CTS mechanism is better. Also, in symmetric networks, in which all nodes are hidden, or no hidden node exists in the network, fairness is better than the asymmetric networks, in which some nodes are hidden while others are not.

FPS: Fast Path Planner Algorithm Based on Sparse Visibility Graph and Bidirectional Breadth-First Search

The majority of planning algorithms used are based on the occupancy grid maps, but in complicated situations, the occupancy grid maps have a significant search overhead. This paper proposed a path planner based on the visibility graph (v-graph) for the mobile robot that uses sparse methods to speed up and simplify the construction of the v-graph. Firstly, the complementary grid framework is designed to reduce graph updating iteration costs during the data collection process in each data frame. Secondly, a filter approach based on the edge length and the number of vertices of the obstacle contour is proposed to reduce redundant nodes and edges in the v-graph. Thirdly, a bidirectional breadth-first search is combined into the path searching process in the proposed fast path planner algorithm in order to reduce the waste of exploring space. Finally, the simulation results indicate that the proposed sparse v-graph planner can significantly improve the efficiency of building the v-graph and reduce the time of path search. In highly convoluted unknown or partially known environments, our method is 40% faster than the FAR Planner and produces paths 25% shorter than it. Moreover, the physical experiment shows that the proposed path planner is faster than the FAR Planner in both the v-graph update process and laser process. The method proposed in this paper performs faster when seeking paths than the conventional method based on the occupancy grid.

3D real-time human reconstruction with a single RGBD camera.

3D human reconstruction is an important technology connecting the real world and the virtual world, but most of previous work needs expensive computing resources, making it difficult in real-time scenarios. We propose a lightweight human body reconstruction system based on parametric model, which employs only one RGBD camera as input. To generate a human model end to end, we build a fast and lightweight deep-learning network named Fast Body Net (FBN). The network pays more attention on the face and hands to enrich the local details. Additionally, we train a denoising auto-encoder to reduce unreasonable states of human model. Due to the lack of human dataset based on RGBD images, we propose an Indoor-Human dataset to train the network, which contains a total of 2500 frames of action data of five actors collected by Azure Kinect camera. Depth images avoid using RGB to extract depth features, which makes FBN lightweight and high-speed in reconstructing parametric human model. Qualitative and quantitative analysis on experimental results show that our method can improve at least 57% in efficiency with similar accuracy, as compared to state-of-the-art methods. Through our study, it is also demonstrated that consumer-grade RGBD cameras can provide great applications in real-time display and interaction for virtual reality.

Research on motion distortion optimization algorithm of laser SLAM

Aiming at the problems of inaccurate laser point cloud matching, large reference pose error and insufficient utilization of sensor observation data during 2D laser SLAM positioning and mapping, which lead to the motion distortion of lidar, a piecewise linear interpolation based method is proposed. Fusion of radar motion distortion optimization strategy and PL-ICP algorithm. The optimization algorithm first uses the piecewise linear interpolation method to fuse the data of the wheel odometer and the lidar, solve the robot pose corresponding to each laser point in the current frame of laser data, then convert all the laser points to the same coordinate system according to the solved pose, and finally the laser data scan matching is performed by the PL-ICP algorithm. In the experiment, the ICP and PL-ICP algorithms were compared for the radar data, and the SLAM algorithm equipped with the motion distortion optimization module was used for localization and mapping. The experimental results show that the motion distortion optimization algorithm effectively removes the motion distortion of low-precision lidar and improves the accuracy of mobile robot positioning and mapping.

A moving target trajectory prediction method based on neighborhood fitting

In order to solve the influence of time delay and data frame loss on the tracking accuracy of high mobility target, a moving target trajectory prediction method based on neighborhood fitting is proposed. The influence of neighborhood width, polynomial order and prediction steps on prediction accuracy are analyzed, and suggestions on parameter selection are given. Four typical moving target trajectories, such as parabola, climb line, spiral line and gyratory line are predicted. The experimental results show that the trajectory prediction method has high accuracy and good stability, and can meet the requirements of moving target tracking.

Efficient addressing schemes for internet of things

The internet of things (IoT) defines the connectivity of physical devices to provide the machine to machine communication. This communication is achieved through various wireless standards for sensor node connectivity. The IoT calls from the formation of various wireless sensor nodes (WSNs) in a network. The existing neighborhood discovery method had the disadvantage of time complexity to calculate the cluster distance. Our proposed method rectifies this issue and gives accurate execution time. This paper proposed mobility management system based on proxy mobile IPv6 as distributed PMIPv6 with constrained application protocol (CoAP-DPMIP) and PMIPv6 with constrained application protocol (CoAP-PMIP). It also provides the optimized transmission path to reduce the delay handover in IoT network. The PMIPv6 described the IPv6 address of mobile sensor device for efficient mobility management. The network architecture explains three protocol layers of open systems interconnection model (OSI model). The OSI layers are data link layer, network layer and transport layer. We have proposed the distance estimation algorithm for efficient data frames transmission. This paper mainly focuses the secure data transmission with minimum loss of error. The evaluation result proved that proposed technique performance with delay, energy, throughput and packet delivery ratio (PDR). Also, it measures the computational time very effectively.

PAPR Reduction and Bit Error Rate Evaluation in OFDM System using Hybrid Techniques

Orthogonal frequency division multiplexing (OFDM) has become an important component of waveform generation in wideband transmission. it's a superior technology for the high-speed rate of wired and wireless communication systems. Currently, multiple-input multiple-output orthogonal frequency division multiplexing (MIMO OFDM) systems are crucial wireless communication systems like 4G and 5G networks & tactical communication. The OFDM has many advantages over other techniques like its high capacity and immunity against multipath fading channels. However, one amongst the foremost drawbacks of the OFDM system is that the high-peak-to-average power ratio (PAPR) that leads the system to provide in-band distortion and out-of-band radiation and reduces its efficiency. This problem increases with a rise within the number of transmit antennas. Therefore, it's highly desirable to cut back the PAPR of an OFDM signal. For this, numerous techniques are proposed to beat the PAPR problem like i) Selective mapping (SLM) ii) Partial transmit sequence (PTS), iii) Clipping, iv) Clipping and filtering. All of those are reduced the PAPR by generating alternative subcarrier vector that are statistically independent OFDM symbols for a given data frame and transmitting the symbol with rock bottom peak power. During this paper we also proposed, some hybrid techniques. The hybrid techniques are the technique of clipping is employed in conjunction with SLM and PTS to cut back computational complexity. And also the combination of SLMPTS to scale back PAPR. Simulations are acquainted with analyze the efficiency of the techniques used which signifies OFDM to be providing much better PAPR reduction and a way better Bit Error Rate (BER). it's shown in simulation results that the proposed scheme performs well reducing PAPR. But the proposed scheme is more complex than the prevailing techniques.

Deep Reinforcement Learning-Based Diving/Pull-out Control for Bioinspired Morphing UAVs

Next-generation unmanned aerial vehicles (UAVs) should possess the capability to autonomously perform multiple tasks with agility and efficiency in complex obstructive environments and over open terrains. Morphing UAVs can autonomously transform in response to changes in flight environments and tasks, always maintaining an optimal aerodynamic profile. The falcon-inspired morphing UAV folds/twists its wings/tail simultaneously to accomplish diving/pull-out flight during the predation process. In the diving/pull-out flight, falcon-inspired morphing UAVs are able to balance maneuverability and stability, which is hard to realize by current control methods. This paper proposes a deep reinforcement learning (DRL)-based diving/pull-out cooperative control strategy. Considering the continuity of the state space and the action space of morphing UAVs, the deep deterministic policy gradient (DDPG) algorithm based on the actor-critic (AC) network is adopted and refined. With the aim of ensuring the smoothness of the flight action, the proposed DRL-based strategy is tasked with controlling multiple data frames of airspeed, altitude and pitch angle to desired reference values. Numerical experiments have been conducted on fixed-speed ascent/descent flight and diving/pull-out maneuvers flight missions. The results demonstrate the superiority of the proposed DRL-based control strategy compared with a classical proportional-integral-derivative (PID) control strategy. Furthermore, the proposed DRL controller is shown to generalize well to the random white noises which are added to gyroscope measurements and wind disturbance in flight.

Research on dual-terminal synchronization and ranging for laser communication link

With the development of technologies such as spatial information networks, precision time synchronization and distance measurement have played a paramount role in space laser links. Time synchronization is the basis of bidirectional laser ranging, and its accuracy significantly affects the quality of laser communication and ranging. However, the only way for the current integrated communication and ranging system to improve the system’s time synchronization accuracy is to increase the communication rate. It is impossible to achieve higher-precision time synchronization. In this study, we propose a coarse and fine composite time synchronization algorithm for laser communication link. Based on a high-precision time synchronization protocol, a small amount of ranging timestamps are added to the communication data frame for dual terminals time exchange as a coarse measurement ruler. And use the precise mixing phase comparison of the communication data recovery clock as a fine measuring ruler. The exact phase difference between the dual-terminal of the laser link is obtained, and the experimental work of the synchronization algorithm is performed in an equivalent space link. The experiment shows that the double-ended time synchronization accuracy is 90ps(3σ), which can be well applied to laser communication and ranging links.

Tidyqpcr

Software is intended to be a tool used to complete tasks efficiently. However, the intent is not always matched by the execution as users may become frustrated with design idiosyncrasies or suboptimal implementations. In order to support reproducibility and the user experience, research software needs to be rewarded on its usability and documentation as well as its functionality. Quality documentation needs to cover design decisions, functionality, and how to contribute to the software. Here we describe our work to tackle the reproducibility crisis in qPCR analysis by creating the open-source software package tidyqpcr. We also introduced the extensive infrastructure available for creating software documentation in the R programming language. 
 Quantitative polymerase chain reaction (qPCR) is a fundamental technique in molecular biology to detect and quantify DNA and RNA. However, the ubiquitous use of qPCR across research disciplines has led to inconsistencies in implementation and reporting, leading to a reproducibility crisis and the publication of the Minimum Information in a Quantitative PCR experiment (MIQE) guidelines. In addition, each stage of a qPCR experiment can be customised to extract a wide variety of information from numerous biological processes. Developing versatile and reliable software built with best-practices and thorough documentation would promote reproducible qPCR analysis across diverse disciplines. tidyqpcr is an open source software package for user-friendly qPCR analysis using the tidyverse suite of R packages. tidyqpcr offers a consistent user interface and structure for qPCR analysis, within the tidyverse paradigm of spreadsheet-like rectangular data frames and generic functions that build up complex analyses in a series of simple steps. tidyqpcr focuses on experimental design in microwell plates, and relative quantification using changes in quantification cycle (∆Cq). tidyqpcr has been improved in response to software review from the rOpenSci non-profit initiative, which co-ordinates with the Journal of Open Source Software. Overall, tidyqpcr empowers scientists to conduct reproducible, flexible, and best-practice compliant quantitative PCR analysis.

A Video Key Frame Extraction Method Based on Multiview Fusion

A massive amount of video data is stored in the real-time road monitoring system, especially in high-speed scenes. Traditional methods of video key frame extraction have the problems of large computation and long-time consumption. Thus, it is imperative to decrease the massive video data generated by monitoring and help researchers to study key frames. Aiming at the above problems, we propose an efficient key frame extraction method based on multiview fusion, where the autoencoder is used to compress the video data. Specifically, all the video frames of the video data are subjected to feature dimensionality reduction, and the features after dimensionality reduction are subjected to multiview fusion. Finally, dynamic programming and clustering are used to extract key frames. The experimental results show that the proposed method has lower computational complexity in extracting key frames, while the mutual information in the extracted key frames is large. It illustrates the reliability and efficiency of the proposed method, which provides technical support for subsequent video research.

Estimation of the interaction force between human and passive lower limb exoskeleton device during level ground walking

To develop sophisticated and efficient control strategies for exoskeleton devices, acquiring the information of interaction forces between the wearer and the wearable device is essential. However, obtaining the interaction force via conventional methods, such as direct measurement using force sensors, is problematic. This paper proposes a kinematic data-based estimation method to evaluate the interaction force between human lower limbs and passive exoskeleton links during level ground walking. Unlike conventional methods, the proposed method requires no force sensors and is computationally cheaper to obtain the calculation results. To obtain more accurate kinematic data, a marker refinement algorithm based on bilevel optimization framework is adopted. The interaction force is evaluated by a spring model, which is used to imitate the binding behavior between human limbs and the exoskeleton links. The deflection of the spring model is calculated based on the assumption that the phase delay between human limb and exoskeleton link can be presented by the sequence of frames of kinematic data. Experimental results of six subjects indicate that our proposed method can estimate the interaction forces during level ground walking. Moreover, a case study of bandage location optimization is conducted to demonstrate the usefulness of obtaining the interaction information.

Digitization and data frames for card index records

We develop a methodology for converting card index archival records into usable data frames for statistical and textual analyses. Leveraging machine learning and natural-language processing tools from Amazon Web Services (AWS), we overcome hurdles associated with character recognition, inconsistent data reporting, column misalignment, and irregular naming. In this article, we detail the step-by-step conversion process and discuss remedies for common problems and edge cases, using historical records from the Reconstruction Finance Corporation.

Unity Human Eye Model for Gaze Tracking with a Query-Driven Dynamic Vision Sensor.

Objective measurement of gaze pattern and eye movement during untethered activity has important applications for neuroscience research and neurological disease detection. Current commercial eye-tracking tools rely on desk-top devices with infrared emitters and conventional frame-based cameras. Although wearable options do exist, the large power-consumption from their conventional cameras limit true long-term mobile usage. The query-driven Dynamic Vision Sensor (qDVS) is a neuromorphic camera which dramatically reduces power consumption by outputting only intensity-change threshold events, as opposed to full frames of intensity data. However, such hardware has not yet been implemented for on-body eye-tracking, but the feasibility can be demonstrated using a mathematical simulator to evaluate the eye-tracking ca-pabilities of the qDVS under controlled conditions. Specifically, a framework utilizing a realistic human eye model in the 3D graphics engine, Unity, is presented to enable the controlled and direct comparison of image-based gaze tracking methods. Eye-tracking based on qDVS frames was compared against two different conventional frame eye-tracking methods - the traditional ellipse pupil-fitting algorithm and a deep learning neural network inference model. Gaze accuracy from qDVS frames achieved an average of 93.2% for movement along the primary horizontal axis (pitch angle) and 93.1 % for movement along the primary vertical axis (yaw angle) under 4 different illumination conditions, demonstrating the feasibility for using qDVS hardware cameras for such applications. The quantitative framework for the direct comparison of eye tracking algorithms presented here is made open-source and can be extended to include other eye parameters, such as pupil dilation, reflection, motion artifact, and more.

Semantic Depth Data Transmission Reduction Techniques Based on Interpolated 3D Plane Reconstruction for Light-Weighted LiDAR Signal Processing Platform

In vehicles for autonomous driving, light detection and ranging (LiDAR) is one of the most used sensors, along with cameras. LiDAR sensors that produce a large amount of data in one scan make it difficult to transmit and calculate data in real-time in the vehicle’s embedded system. In this paper, we propose a platform based on semantic depth data-based data reduction and reconstruction algorithms that reduce the amount of data transmission and minimize the errors between original and restored data in a vehicle system using four LiDAR sensors. The proposed platform consists of four LiDAR sensors, an integrated processing unit (IPU) that reduces the data of the LiDAR sensors, and the main processor that reconstructs the reduced data and processes the image. In the proposed platform, the 58,000 bytes of data constituting one frame detected by the VL-AS16 LiDAR sensor were reduced by an average of 87.4% to 7295 bytes by the data reduction algorithm. In the IPU placed near the LiDAR sensor, the memory usage increased by the data reduction algorithm, but the data transmission time decreased by an average of 40.3%. The transmission time where the vehicle’s processor received one frame of data decreased from an average of 1.79 to 0.28 ms. Compared with the original LiDAR sensor data, the reconstructed data showed an average error of 4.39% in the region of interest (ROI). The proposed platform increased the time required for image processing in the vehicle’s main processor by an average of 6.73% but reduced the amount of data by 87.4% with a decrease in data accuracy of 4.39%.

Hyperspectral image super-resolution based on attention ConvBiLSTM network

ABSTRACT In this paper, a hyperspectral (HS) image super-resolution (SR) approach based on attention convolutional bi-long short-term memory (ConvBiLSTM) network is proposed, aiming to explore the collaborative spatial and spectral attention characteristics, thereby enhancing the spatial resolution of HS image. ConvBiLSTM network combines the spatial feature mining and sequential predicting abilities of convolutional neural network and recurrent neural network, respectively. We adapt the ConvBiLSTM network for our super-resolution purpose by regarding each band as a single frame of sequential data, and propose a band-sharing spatial-channel attention-combined ConvBiLSTM SR method to intensify the saliency features. Moreover, a spatial-regularized loss function is presented to further promote the fidelity of the super-resolved HS image. Experiments on four HS data sets show that the proposed approach outperforms some state-of-the-art HS image SR techniques, from the aspect of spectral fidelity.