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Forward head posture (FHP) has become a prevailing health issue in modern society as people spend more time on computers and smartphones. FHP is a posture where the head is forward and the anterior and posterior curvatures of the lower cervical and upper thoracic spines are both, respectively, exaggerated. FHP is often associated with neck pain, bad static balance, and hunched shoulders or back. To prevent this, consciously maintaining good posture is important. Therefore, in this study, we propose a system that gives users real-time, accurate information about their neck posture, and it also encourages them to maintain a good posture. This inexpensive system utilizes a single inertial measurement unit sensor and a Raspberry Pi system to detect the changes in state that can progress to an FHP. It retrieves data from the sensor attached to the user's cervical spine to indicate their real-time posture. In a real-world office environment experiment with ten male participants, the system accurately detected the transition to the FHP state for more than 10 s, with a delay of less than 0.5 s, and it also provided personalized feedback to encourage them to maintain good posture. All ten participants recognized that their average craniovertebral angle had to be increased after receiving visual alerts regarding their poor postures in real time. The results indicate that the system has potential for widespread applications. [ABSTRACT FROM AUTHOR]

The can-sized satellite, ScoreSAT satellite is a small communications satellite. ScoreSAT helps to develop a platform for finding directions and the exact spot where a lack of communications signal occurs, as well as a realtime visual feed for analysis of communication during and after landing. The project focuses on the design of ScoreSAT and provides a real-time system for capturing real-time data during descent. The objective of the realtime system is to improve the accuracy and location of ScoreSAT data collection, which can provide pressure, humidity, temperature, altitude, latitude, and longitude readings. The main components of this platform are the hardware design that comprises the flight controller, GPS module, and telemetry kit, the software design, which are Mission Planner, and the realtime system (RTS). Based on the entire research, the compact design of the ScoreSAT and ground station was developed to provide alternative meteorological parameter monitoring to complement primary meteorology ground observation such as weather station and radiosonde and to enhance the reliability of the remote sensing observation for environmental studies concerning the factors determining the environment and atmospheric. [ABSTRACT FROM AUTHOR]

Lossless image compression techniques shrink the image size to improve the transmission efficiency and reduce the occupied storage space while ensuring the quality of the image is lossless. Among them, the LOCO‐I/JPEG‐LS algorithm benefits high lossless compression ratio and low computational complexity and thus is widely used for various real‐time applications. However, due to the problems of the context dependency in the LOCO‐I, the parallelism in the algorithm is greatly constrained, which significantly limits the throughput and the real‐time performance of hardware implementations. Existing designs achieve more parallelism by using a lot of hardware costs or straightforward chunking with losing compression ratio. In order to trade off the parallelism and the compression ratio, this paper proposes a chunk‐oriented error modeling scheme for LOCO‐I, which enables parallelism in both compression and decompression and achieves a better compression ratio in chunks. Based on the optimized algorithm, a high‐throughput flexible lossless compression and decompression architecture (HFCD) is proposed, which achieves higher pixel per clock (PPC) with less hardware cost. Additionally, HFCD introduces a parameter sharing mechanism to enable random access of image chunks to improve the flexibility for decompression. Experimental results show that, compared with state‐of‐the‐art works, HFCD achieves 3.02–13.50 times improvement for the PPC of compression. For decompression, benefiting from our optimizations, HFCD achieves 22.4 times speedup compared to the software solution. [ABSTRACT FROM AUTHOR]

In today's multiprocessor systems-on-a-chip, the shared memory subsystem is a known source of temporal interference. The problem causes logically independent cores to affect each other's performance, leading to pessimistic worst-case execution time analysis. Memory regulation via throttling is one of the most practical techniques to mitigate interference. Traditional regulation schemes rely on a combination of timer and performance counter interrupts to be delivered and processed on the same cores running real-time workload. Unfortunately, to prevent excessive overhead, regulation can only be enforced at a millisecond-scale granularity. In this work, we present a novel regulation mechanism from outside the cores that monitors performance counters for the application core's activity in main memory at a microsecond scale. The approach is fully transparent to the applications on the cores, and can be implemented using widely available on-chip debug facilities. The presented mechanism also allows more complex composition of metrics to enact load-aware regulation. For instance, it allows redistributing unused bandwidth between cores while keeping the overall memory bandwidth of all cores below a given threshold. We implement our approach on a host of embedded platforms and conduct an in-depth evaluation on the Xilinx Zynq UltraScale+ ZCU102, NXP i.MX8M and NXP S32G2 platforms using the San Diego Vision Benchmark Suite. [ABSTRACT FROM AUTHOR]

Real-time systems are widely implemented in the Internet of Things (IoT) and safety-critical systems, both of which have generated enormous social value. Aiming at the classic schedulability analysis problem in real-time systems, we proposed an exact Boolean analysis based on interference (EBAI) for schedulability analysis in real-time systems. EBAI is based on worst-case interference time (WCIT), which considers both the release jitter and blocking time of the task. We improved the efficiency of the three existing tests and provided a comprehensive summary of related research results in the field. Abundant experiments were conducted to compare EBAI with other related results. Our evaluation showed that in certain cases, the runtime gain achieved using our analysis method may exceed 73% compared to the state-of-the-art schedulability test. Furthermore, the benefits obtained from our tests grew with the number of tasks, reaching a level suitable for practical application. EBAI is oriented to the five-tuple real-time task model with stronger expression ability and possesses a low runtime overhead. These characteristics make it applicable in various real-time systems such as spacecraft, autonomous vehicles, industrial robots, and traffic command systems. [ABSTRACT FROM AUTHOR]

Efficiently handling Automatic Identification System (AIS) data is vital for enhancing maritime safety and navigation, yet is hindered by the system’s high volume and error-prone datasets. This paper introduces the Automatic Identification System Database (AISdb), a novel tool designed to address the challenges of processing and analyzing AIS data. AISdb is a comprehensive, open-source platform that enables the integration of AIS data with environmental datasets, thus enriching analyses of vessel movements and their environmental impacts. By facilitating AIS data collection, cleaning, and spatio-temporal querying, AISdb significantly advances AIS data research. Utilizing AIS data from various sources, AISdb demonstrates improved handling and analysis of vessel information, contributing to enhancing maritime safety, security, and environmental sustainability efforts.

Background: Traditional pharmacological interventions are well tolerated in the management of elevated blood pressure (BP) for individuals with resistant hypertension. Although neuromodulation has been investigated as an alternative solution, its open-loop (OL) modality cannot follow the patient's physiological state. In fact, neuromodulation for controlling highly fluctuating BP necessitates a closed-loop (CL) stimulation modality based on biomarkers to monitor the patient's continuously varying physiological state. Objective: By leveraging its intuitive linkage with BP responses in ongoing efforts aimed at developing a CL system to enhance temporal BP reduction effect, this study proposes a CL neuromodulation modality that controls nucleus tractus solitarius (NTS) activity to effectively reduce BP, thus reflecting continuously varying physiological states. Method: While performing neurostimulation targeting the NTS in the rat model, the arterial BP response and neural activity of the NTS were simultaneously measured. To evaluate the temporal BP response effect of CL neurostimulation, OL (constant parameter; 20 Hz, 200 μA) and CL (Initial parameter; 11 Hz, 112 μA) stimulation protocols were performed with stimulation 180 s and rest 600 s, respectively, and examined NTS activity and BP response to the protocols. Results: In-vivo experiments for OL versus CL protocol for direct NTS stimulation in rats demonstrated an enhancement in temporal BP reduction via the CL modulation of NTS activity. Conclusion: This study proposes a CL stimulation modality that enhances the effectiveness of BP control using a feedback control algorithm based on neural signals, thereby suggesting a new approach to antihypertensive neuromodulation.

Traditional pharmacological interventions are well tolerated in the management of elevated blood pressure (BP) for individuals with resistant hypertension. Although neuromodulation has been investigated as an alternative solution, its open-loop (OL) modality cannot follow the patient's physiological state. In fact, neuromodulation for controlling highly fluctuating BP necessitates a closed-loop (CL) stimulation modality based on biomarkers to monitor the patient's continuously varying physiological state. By leveraging its intuitive linkage with BP responses in ongoing efforts aimed at developing a CL system to enhance temporal BP reduction effect, this study proposes a CL neuromodulation modality that controls nucleus tractus solitarius (NTS) activity to effectively reduce BP, thus reflecting continuously varying physiological states. While performing neurostimulation targeting the NTS in the rat model, the arterial BP response and neural activity of the NTS were simultaneously measured. To evaluate the temporal BP response effect of CL neurostimulation, OL (constant parameter; 20 Hz, 200 μA) and CL (Initial parameter; 11 Hz, 112 μA) stimulation protocols were performed with stimulation 180 s and rest 600 s, respectively, and examined NTS activity and BP response to the protocols. In-vivo experiments for OL versus CL protocol for direct NTS stimulation in rats demonstrated an enhancement in temporal BP reduction via the CL modulation of NTS activity. This study proposes a CL stimulation modality that enhances the effectiveness of BP control using a feedback control algorithm based on neural signals, thereby suggesting a new approach to antihypertensive neuromodulation. • Real-time closed-loop brainstem modulation using NTS activity feedback enhances BP reduction. • BP response and NTS activity were analyzed with open-loop and closed-loop protocols. • Closed-loop protocol temporally enhanced stimulus-driven BP reduction compared to open-loop protocol. • Regulation of NTS activity increased temporal BP reduction. [ABSTRACT FROM AUTHOR]

Multi-Object Tracking, also known as Multi-Target Tracking, is an important area of computer vision with various applications in different domains. The advent of deep learning has had a profound impact on this field, forcing researchers to explore innovative avenues. Deep learning methods have become the cornerstone of today's state-of-the-art solutions, consistently delivering exceptional tracking results. However, the significant computational demands of deep learning models require powerful hardware resources that do not always match real-time tracking requirements, limiting their practical applicability in real-world scenarios. Thus, there is an imperative to strike a balance by merging robust deep learning strategies with conventional approaches to enable more accessible, cost-effective solutions that meet real-time requirements. This paper embarks on this endeavor by presenting a hybrid strategy for real-time multi-target tracking. It effectively combines a classical optical flow algorithm with a deep learning architecture tailored for human crowd tracking systems. This hybrid approach achieves a commendable balance between tracking accuracy and computational efficiency. The proposed architecture, subjected to extensive experimentation in various settings, demonstrated notable results, achieving a Mean Object Tracking Accuracy (MOTA) of 0.608. This level of performance placed it as the highest ranking solution on the MOT15 benchmark, surpassing the state-of-the-art benchmark of 0.549, and consistently ranked among the superior models on the MOT17 and MOT20 benchmarks. Additionally, the incorporation of the optical flow phase resulted in a substantial reduction in processing time, nearly halving the duration, while simultaneously maintaining accuracy levels comparable to established techniques. [ABSTRACT FROM AUTHOR]

In order to prevent the spread of respiratory viruses like COVID-19 and influenza, an effective protection method is to wear facial masks in densely populated areas. This has led to a growing need for smart services that automatically detect facial masks and replace manual reminding. To address this challenging task and to contribute towards health safety, this paper introduces MedNetV2 system, which is an efficient deep learning-based facial mask detector with a low computational cost. In comparison with existing systems, the main specificities of the proposed system are: (1) the adoption of an effective deep learning-based framework to deal with both the large scale diversity and position variations of masked faces involved in natural scenes, (2) the interaction between face localization and facial mask detection modules to achieve the overall system goal, (3) the lightweight design and the real-time response well suited for real-world scenarios. Extensive experiments on public dataset and real-world video streaming are carried out to validate quantitatively and visually the effectiveness of the proposed system. Promising results, in terms of detection accuracy as well as time response, are achieved when compared it with other state-of-the-art systems. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the advancement of manufacturing equipment and the development of sensing technology, the measurement cycle of data is becoming very short, and the characteristics of data observed at the same time are also diversifying. A Fault Detection and Classification (FDC) system is in operation to collect and analyze measurement data in real time in the display manufacturing factory. However, the anomaly detection function provided by the FDC system is based on a threshold comparison method for data in seconds, so there is a limit to accuracy in processing measurement data in milliseconds. In particular, if the process for one panel is divided into several steps and each of them shows various characteristics, it is difficult to manage because it takes a lot of airlift to set search conditions and thresholds for anomaly detection. To overcome this, a waveform analysis system is implemented to assist the FDC system. This system extracts waveforms from milliseconds of measurement data during the processing time of one panel in the equipment, and diagnoses whether the equipment processing is normal through GAN‐based waveform pattern analysis. Generative models are used in the anomaly detection process in consideration of the manufacturing environment in which the normal data is overwhelmingly larger than the abnormal data. The DCGAN‐based model that is excellent in image processing and the TadGAN‐based model that combines Auto encoder and GAN were implemented and used. In this paper, the anomaly detection performance of models is compared and evaluated for the display photo process Coater Pressure sensor using the waveform analysis system implemented in this way. [ABSTRACT FROM AUTHOR]

This study focuses on the communication challenges among processes in mobile drone systems, specifically addressing the dynamics and decentralization of their topology. An algorithmic approach for real-time systems is proposed, emphasizing its application in drones. The Fan-shaped Real-Time Task Scheduling Algorithm (APTTRA) serves as the cornerstone, distributing processes with deadline constraints in a fan-shaped manner to ensure timely completion. It introduces a metric that evaluates not only task compliance but also when and how long, providing a comprehensive insight into the system's effectiveness. To support performance evaluation, the use of a connected acyclic graph is proposed, offering a detailed understanding of performance across various process sections. The system's adaptability is highlighted through the incorporation of variables in real-time applications, providing a complete view in dynamic situations. Along with the use of Minix as a modular operating system, allows for testing APTTRA before implementation in real drones. The importance of real-time task scheduling for drones, especially helicopters and quadcopters, is emphasized, underscoring the need to tailor control algorithms. The evaluation focuses on implementation, successes in real flights, and the application of APTTRA in a genetic algorithm for calibration within the planning ranges. [ABSTRACT FROM AUTHOR]

The proliferation of internet communication channels has increased telecom fraud, causing billions of euros in losses for customers and the industry each year. Fraudsters constantly find new ways to engage in illegal activity on the network. To reduce these losses, a new fraud detection approach is required. Telecom fraud detection involves identifying a small number of fraudulent calls from a vast amount of call traffic. Developing an effective strategy to combat fraud has become challenging. Although much effort has been made to detect fraud, most existing methods are designed for batch processing, not real-time detection. To solve this problem, we propose an online fraud detection model using a Neural Factorization Autoencoder (NFA), which analyzes customer calling patterns to detect fraudulent calls. The model employs Neural Factorization Machines (NFM) and an Autoencoder (AE) to model calling patterns and a memory module to adapt to changing customer behaviour. We evaluate our approach on a large dataset of real-world call detail records and compare it with several state-of-the-art methods. Our results show that our approach outperforms the baselines, with an AUC of 91.06%, a TPR of 91.89%, an FPR of 14.76%, and an F1-score of 95.45%. These results demonstrate the effectiveness of our approach in detecting fraud in real-time and suggest that it can be a valuable tool for preventing fraud in telecommunications networks.

Smart cities facilitate the comprehensive management and operation of urban data generated within a city, establishing the foundation for smart services and addressing diverse urban challenges. A smart system for public laundry management uses artificial intelligence-based solutions to solve the challenges of the inefficient utilization of public laundries, waiting times, overbooking or underutilization of machines, balancing of loads across machines, and implementation of energy-saving features. We propose SmartLaundry, a real-time system design for public laundry smart recommendations to better manage the loads across connected machines. Our system integrates the current status of the connected devices and data-driven forecasted usage to offer the end user connected via a mobile application a list of recommended machines that could be used. We forecast the daily usage of devices using traditional machine learning techniques and deep learning approaches, and we perform a comparative analysis of the results. As a proof of concept, we create a simulation of the interaction with our system. [ABSTRACT FROM AUTHOR]

Specific workloads are increasingly offloaded to accelerators such as a graphic processing unit (GPU) and field-programmable gate array (FPGA) for real-time processing and computing efficiency. Because accelerators are expensive and consume much power, it is desirable to increase the efficiency of accelerator utilization by sharing accelerators among multiple servers over a network. However, task offloading over a network has the problem of latency due to network processing overhead in remote offloading. This paper proposes a low-latency system for accelerator offloading over a network. To reduce the overhead of remote offloading, we propose a system composed of (1) fast recombination processing of chunked data with a simple protocol to reduce the number of memory copies, (2) polling-based packet receiving check to reduce overhead due to interrupts in interaction with a network interface card, and (3) a run-to-completion model in network processing and accelerator offloading to reduce overhead with context switching. We show that the system can improve performance by 66.40% compared with a simple implementation using kernel protocol stack and confirmed the performance improvement with a virtual radio access network use case as a low-latency application. Furthermore, we show that this performance can also be achieved in practical usage in data center networks. [ABSTRACT FROM AUTHOR]

This research presents the development of a cutting-edge real-time multilingual speech recognition and speaker diarization system that leverages OpenAI's Whisper model. The system specifically addresses the challenges of automatic speech recognition (ASR) and speaker diarization (SD) in dynamic, multispeaker environments, with a focus on accurately processing Mandarin speech with Taiwanese accents and managing frequent speaker switches. Traditional speech recognition systems often fall short in such complex multilingual and multispeaker contexts, particularly in SD. This study, therefore, integrates advanced speech recognition with speaker diarization techniques optimized for real-time applications. These optimizations include handling model outputs efficiently and incorporating speaker embedding technology. The system was evaluated using data from Taiwanese talk shows and political commentary programs, featuring 46 diverse speakers. The results showed a promising word diarization error rate (WDER) of 2.68% in two-speaker scenarios and 11.65% in three-speaker scenarios, with an overall WDER of 6.96%. This performance is comparable to that of non-real-time baseline models, highlighting the system's ability to adapt to various complex conversational dynamics, a significant advancement in the field of real-time multilingual speech processing. [ABSTRACT FROM AUTHOR]

Increasing security concerns in crowd centric topologies have raised major interests in reliable face recognition systems globally. In this context, certain deep learning frameworks have been proposed till date, for example, Haar Cascade, MTCNN, Dlib to name a few. In this communication, we propose a deep neural network for reliable face recognition in high face density images. The proposed framework is inspired by multi-task cascaded convolutional neural Networks (MTCNN) and, hence the name MTCNN++. In this framework, we have modified the layer density with increasing the neuron count. All the three internal layers of MTCNN, viz. P-Net, R-Net, and O-Net layers and observe that the modified Net-Layer MTCNN (MTCNN++) perform equally well to the MTCNN library or better. Moreover, 20% dropout has been used for tuning the framework for better recognition of the faces, both in terms of face clarity and face count. MTCNN++ exhibits better results as the preprocessing is done dynamically in contrast to the previous versions. The training of the model was done on a dataset comprising of 113,586 human faces in a bucket of 9661 images. The comprehensive dataset comprised of photographs from varied events, thereby presenting multiple human expressions. The accuracy of the model varies from 87.7% (average of 12 faces per image) to 99.7% (average of 2 images per images). The proposed framework fares better with large face count per image. MTCNN++ has further been compared to other literary proposals, and the results are appreciable. [ABSTRACT FROM AUTHOR]

Overfishing, marine pollution, and climate change are exacerbating the global depletion of fishery resources, which has become a significant problem. To manage this issue and promote sustainable fishing, many countries are implementing the Total Allowable Catch (TAC) system. To this end, numerous countries, including the United States, Canada, and the EU, have introduced the Electronic Monitoring (EM) system, which requires fishing vessels to record their operations and submit the footage to the institutions responsible for managing fishery resources. In this paper, we researched a system that measures the catch volume from videos filmed in real-time on fishing vessels by integrating a CCTV system (EM) and deep learning technology. Using the deep convolutional neural network of the video data filmed in real-time by the CCTV system, we implemented and evaluated the recognition of catch species and a counting algorithm based on Nvidia's Jetson hardware system to measure the catch volume [ABSTRACT FROM AUTHOR]

This research aims to develop a real-time mask-wearing detection system using deep convolutional neural networks (CNNs). This is crucial in the coronavirus disease 2019 (COVID-19) pandemic to alert individuals who are not wearing masks early on, thereby reducing the spread of the virus. Since COVID-19 primarily spreads through respiratory droplets and mask-wearing is recommended, our proposed study utilizes computer vision techniques, specifically image processing, to detect masked and unmasked faces. We employ a customized CNN architecture consisting of five convolutional layers, followed by max-pooling layers and fully connected (FC) layers. The final output layer utilizes softmax activation for classification. The model is updated with optimized layer configurations and parameter values. We are developing an application that uses a digital camera as an input device. The application utilizes a dataset comprising 11,792 image samples, which are used for training and testing purposes with the 80:20 ratio. Real-time testing is conducted using human subjects captured by the camera. The experimental results demonstrate that the CNN method achieves a classification accuracy of 99% on the training data and 98.83% during real-time video testing. These findings suggest that the real-time mask detection system using CNN performs effectively. [ABSTRACT FROM AUTHOR]

In real-life situations where human care robots are deployed, there are custom sound events whose acoustic characteristics change depending on the user’s choice unlike general sound events so that the human care robots cannot recognize custom sound events correctly in a conventional way. To solve this critical problem, a real-time sound event recognition system with customization process is proposed. The human care robot collects custom sound samples of a specific user and customizes a sound event recognition model. The overfitting-based customized model shows the best recognition performance by improving F-scores by 66.4% on average compared to the conventional recognition model. After the customization process, the human care robot performs a real-time sound recognition by consistently streaming robot’s real-time microphone signals into the overfitting-based customized SER model. In this process, an optimized overlap is applied on subsequent audio inputs on SER to achieve sufficiently fast response and robust performance. As a pilot test of the human care robot implemented in actual environment, the real-time sound recognition system shows the best average F-score of 0.982 with 75% overlap for sound events including custom sounds. This pilot test result confirms that the real-time sound recognition system with customization process can be successfully applied to human care robots to respond to the custom sounds.

This work explores water-to-air optical wireless communication (W2A-OWC) transmission schemes and realizes a prototype of real-time W2A-OWC system based on field programmable gate array. This prototype comprises underwater nodes, aerial nodes, and transmitter-receiver hardware circuits. The real-time system employs multiple-input multiple-output technique and the low density parity check (LDPC) code of 5G-new radio for dynamic W2A-OWC. Additionally, the impact of background radiation is mitigated through spatial optical filtering. To validate the practical feasibility of the system, experiments are conducted in both indoor water tank and outdoor deep pool under strong background radiation. The frame error rate of the real-time system is tested under different LDPC code rates and transmitter-receiver configurations. The experimental results verify the feasibility of the realized W2A-OWC system.

Forward head posture (FHP) has become a prevailing health issue in modern society as people spend more time on computers and smartphones. FHP is a posture where the head is forward and the anterior and posterior curvatures of the lower cervical and upper thoracic spines are both, respectively, exaggerated. FHP is often associated with neck pain, bad static balance, and hunched shoulders or back. To prevent this, consciously maintaining good posture is important. Therefore, in this study, we propose a system that gives users real-time, accurate information about their neck posture, and it also encourages them to maintain a good posture. This inexpensive system utilizes a single inertial measurement unit sensor and a Raspberry Pi system to detect the changes in state that can progress to an FHP. It retrieves data from the sensor attached to the user’s cervical spine to indicate their real-time posture. In a real-world office environment experiment with ten male participants, the system accurately detected the transition to the FHP state for more than 10 s, with a delay of less than 0.5 s, and it also provided personalized feedback to encourage them to maintain good posture. All ten participants recognized that their average craniovertebral angle had to be increased after receiving visual alerts regarding their poor postures in real time. The results indicate that the system has potential for widespread applications.

Real-time multisensory social interactions (RMSIs) between people are at the center of the metaverse, a new computer-mediated environment consisting of virtual 'worlds' in which people act and communicate with each other in real-time via avatars. This research investigates whether RMSIs in the metaverse, when accessed through virtual-reality headsets, can generate more value for interactants in terms of interaction outcomes (interaction performance, evaluation, and emotional responses) than those on the two-dimensional (2D) internet (e.g., Zoom meetings). We combine theoretical logic with extensive field-experimental probes (which support the value-creation potential of the virtual-reality metaverse, but contradict its general superiority) to develop and refine a framework of how RMSIs in the metaverse versus on the 2D internet affect interaction outcomes through interactants' intermediate conditions. The refined framework serves as foundation for a research roadmap on RMSIs in the metaverse, in which we highlight the critical roles of specific mediating and moderating forces along with interactional formats for future investigations of the metaverse and also name key business areas and societal challenges that deserve scholarly attention., Author(s): Thorsten Hennig-Thurau [sup.1] , Dorothea N. Aliman [sup.1] , Alina M. Herting [sup.1] , Gerrit P. Cziehso [sup.1] , Marc Linder [sup.1] , Raoul V. Kübler [sup.2] Author Affiliations: [...]

This study proposes an enhanced algorithm design and comparative performance analysis of the positioning system based on the concurrent AC magnetic fields. For this, a new approximated field representation model with respect to the circular magnetic coil is developed for achieving on-board algorithm implementation. In the existing researches, the magnetic positioning is usually implemented by the dipole model even though they employ circular coils. Despite the model simplicity, the dipole model suffers typically from the significant representation model deviation from actual magnetic measurement near the transmitter coil area. To overcome this, more complicated but computationally comparable formula is employed, which can reflect effectively the dimensional field distribution characteristics of the magnetic coil. This study also investigates a real-time implementation of the proposed method. Considering the computing performance of the microprocessor, the on-board algorithm is developed considering its calculation speed and memory usage. As a result, the real-time result achieved millimeters level difference compared with the post processing result using full computing power. In experimental validation, a reference optical positioning system providing a sub-millimeter accuracy is employed for evaluating the on-board real-time results during dynamic trajectory tests. The result presents an enhanced estimation error around full operational range compared with the other representation models, which specifically demonstrates centimeters level error in positioning and about 3 degree heading error within operation range. [ABSTRACT FROM AUTHOR]

Copyright of Problems of Mechatronics. Armament, Aviation, Safety Engineering / Problemy Mechatroniki. Uzbrojenie, lotnictwo, Inżynieria Bezpieczeństwa is the property of Index Copernicus International and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)