Location Of Devices Research Articles

Collaborative Healthcare Data Management Framework using Parallel Computing and the Internet of Things

Healthcare data management has become a critical research area, primarily driven by the widespread adoption of personal health monitoring systems and applications. These systems generate an immense volume of data, necessitating efficient and reliable management solutions for lossless sharing. This article introduces a Collaborative Data Management Framework (CDMF) that leverages the combined strengths of parallel computing and federated learning. The proposed CDMF is designed to achieve two primary objectives: reducing computational complexity in data handling and ensuring high sharing accuracy, regardless of the data generation rate. The framework employs parallel computing to streamline the scheduling and processing of data acquired at various intervals. This approach minimizes processing delays by operating on a less complex scheduling algorithm, making it suitable for handling high-frequency data generation. Federated learning, on the other hand, plays a pivotal role in verifying data distribution and maintaining sharing accuracy. By enabling decentralized learning, federated learning ensures that data remains on local devices while sharing only the necessary model updates. This approach enhances privacy and security, a critical consideration in healthcare data management. It ensures that data distribution and sharing are verified based on appropriate requests while avoiding latency issues. By decentralizing the learning process, federated learning enhances privacy and security, as raw data does not leave the local systems. This cooperative interaction between parallel computing and federated learning operates in a cyclic manner, allowing the framework to adapt dynamically to increasing monitoring intervals and varying data rates. The performance of the CDMF is validated through improvements in two key metrics. First, the framework achieves a 15.08% enhancement in sharing accuracy, which is vital for maintaining data integrity and reliability during transfers. Second, it reduces computation complexity by 9.48%, even when handling maximum data rates. These results highlight the framework’s potential to revolutionize healthcare data management by addressing the dual challenges of scalability and accuracy.

Leveraging Digital Mobility Data to Estimate Visitation in National Wildlife Refuges

The US Fish and Wildlife Service (USFWS) manages over 560 National Wildlife Refuges and dozens of National Fish Hatcheries across the United States. Accurately estimating visitor numbers to these areas is essential for understanding current recreation demand, planning for future use, and ensuring the ongoing protection of the ecosystems that refuges safeguard. However, accurately estimating visitation across the entire refuge system presents significant challenges. Building on previous research conducted on other federal lands, this study evaluates methods to overcome constraints in estimating visitation levels using statistical models and digital mobility data. We develop and test a visitation modeling approach using multiple linear regression, incorporating predictors from eight mobility data sources, including four social media platforms, one community science platform, and three mobile device location datasets from two commercial vendors. We find that the total number of observed visitors to refuges correlates with the volume of data from each mobility data source. However, neither social media nor mobile device location data alone provide reliable proxies for visitation due to inconsistent relationships with observed visitation; these relationships vary by data source, refuge, and time. Our results demonstrate that a visitation model integrating multiple mobility datasets accounts for this variability and outperforms models based on individual mobility datasets. We find that a refuge-level effect is the single most important predictor, suggesting that including site characteristics in future models will make them more generalizable. We conclude that statistical models which incorporate digital mobility data have the potential to improve the accuracy of visitor estimates, standardize data collection methods, and simplify the estimation process for agency staff.

Performance Evaluation of Fault Tolerance in 6G Software Defined Network (SDN)

Traditional Operations and Maintenance (O&M) have made real-time fault location of devices challenging due to the rapid growth of network devices. Therefore, Intelligent O&M in 6G networks leveraged using Software Defined Network (SDN) are expected to optimise network operations through fault tolerance mechanisms. SDN is an emerging paradigm that offers dynamic and efficient solutions to the complex network environment. This paper provides a comprehensive overview of fault tolerance as a critical algorithm for SDN under Intelligent O&M, evaluating the performance of fault tolerance mechanisms and investigating the effectiveness of fault tolerance mechanisms towards TCP, UDP and ICMPv4 with two different scenarios. Simulation results demonstrate that the proposed fault tolerance mechanisms under Intelligent O&M within a 6G network can reduce the delay by 98.79%, ensuring more reliable network infrastructure.

Experiences with techniques and sensors for smartphone positioning

Abstract In this paper, current alternative techniques and sensors embedded in smart mobile devices for localization are reviewed. Firstly, the evolution of smartphone development since the first IBM phone in 1992 and the introduction of the term ‘smart-phone’ in 2000 until present is discussed. The main part of the paper deals then with the sensors and positioning techniques where an overview about the topologies and positioning methods and their applicability for smartphone positioning is provided. The most important sensors and techniques are then elaborated and discussed in detail. They include MEMS-based sensors and wireless positioning options. The wireless options include Bluetooth, Wi-Fi and UWB which may be employed together with MEMS-based inertial sensors. Also the use of the smartphone camera and LiDAR is briefly discussed. Moreover, the most applicable sensor fusion approaches including Kalman filtering, particle filter and map matching are also analyzed. In concluding the paper, new ways in using smartphones for science based on different application examples are showcased.

Retrieving and Identifying Remnants of Artefacts on Local Devices Using Sync.com Cloud

Most current research in cloud forensics is focused on tackling the challenges encountered by forensic investigators in identifying and recovering artifacts from cloud devices. These challenges arise from the diverse array of cloud service providers as each has its distinct rules, guidelines, and requirements. This research proposes an investigation technique for identifying and locating data remnants in two main stages: artefact collection and evidence identification. In the artefacts collection stage, the proposed technique determines the location of the artefacts in cloud storage and collects them for further investigation in the next stage. In the evidence identification stage, the collected artefacts are investigated to identify the evidence relevant to the cybercrime currently being investigated. These two stages perform an integrated process for mitigating the difficulty of locating the artefacts and reducing the time of identifying the relevant evidence. The proposed technique is implemented and tested by applying a forensics investigation algorithm on Sync.com cloud storage using the Microsoft Windows 10 operating system.

Optimization design of stress relief coalbed methane surface well in a high-gas mine

Coalbed methane drainage through surface wells has become increasingly important in high-gas mines. Surface wells are prone to different types of deformations and failures owing to mining-induced effects. In this paper, a displacement and deformation model was established for the overlying strata. Three forms of deformation and failure of the surface well casing was proposed, namely shearing, stretching, and uneven extrusion. Moreover, the mathematical model functions for the S-type shear deformation, delamination tensile deformation, and non-uniform extrusion of the surface well were established. In the Shanxi Coal Group Yue Cheng Mine, coalbed methane drainage at the surface well was conducted on the mining-affected area to achieve good effects. The YCCD-04 well without a local protective device was deformed and damaged, and the dynamic deformation and failure of the surface well was detected using a well imager. The YCCD-02 surface well with a local protective device showed good mining and gas drainage, which verified the effectiveness of the local protective device; the coalbed methane drainage time of this surface well was 35 months, and the cumulative coalbed methane drainage volume extracted was 1.3 × 107 m3. Simultaneously, the problem of gas control in the working face was solved, thereby ensuring the safety of coal mining and achieving good social as well as economic benefits.

Computer Vision-Based Gait Recognition on the Edge: A Survey on Feature Representations, Models, and Architectures.

Computer vision-based gait recognition (CVGR) is a technology that has gained considerable attention in recent years due to its non-invasive, unobtrusive, and difficult-to-conceal nature. Beyond its applications in biometrics, CVGR holds significant potential for healthcare and human-computer interaction. Current CVGR systems often transmit collected data to a cloud server for machine learning-based gait pattern recognition. While effective, this cloud-centric approach can result in increased system response times. Alternatively, the emerging paradigm of edge computing, which involves moving computational processes to local devices, offers the potential to reduce latency, enable real-time surveillance, and eliminate reliance on internet connectivity. Furthermore, recent advancements in low-cost, compact microcomputers capable of handling complex inference tasks (e.g., Jetson Nano Orin, Jetson Xavier NX, and Khadas VIM4) have created exciting opportunities for deploying CVGR systems at the edge. This paper reports the state of the art in gait data acquisition modalities, feature representations, models, and architectures for CVGR systems suitable for edge computing. Additionally, this paper addresses the general limitations and highlights new avenues for future research in the promising intersection of CVGR and edge computing.

Study on the Effect of Local Heating Devices on Human Thermal Comfort in Low-Temperature Built Environment

Localized heating systems are an effective approach to improve thermal comfort while reducing energy consumption in a cold indoor environment. Furthermore, localized heating devices have found widespread application in the hot-summer and cold-winter zones of China. This study investigates the heating characteristics of the local heating device in a low-temperature environment, as well as its effects on subjective perception and physiological responses, and develops a personalized control system for the device based on the experimental findings. We conducted experimental tests and questionnaires in a test room with air temperature set at 12 °C and 14 °C and a relative humidity of 55%. A total of six experimental conditions were designed using five types of heating equipment (heating wrist straps, heating insoles, heating leg straps, warm air blower, electric radiant heater), each employing different heat transfer methods. The results demonstrate that the head, hands, legs, and feet are susceptible to feeling cold in a low-temperature environment, and the use of a warm air blower and electric radiant heater can significantly enhance the thermal sensation of these parts, improve thermal acceptability, and raise local skin temperature. The electrocardiogram data indicate that heart rate variability can be utilized to assess thermal sensation in a cold environment with localized heating. Additionally, the relationship between thermal response and skin temperature was investigated, leading to the development of a control strategy for the local heating device in a low-temperature conditions.

Electrical properties of separated zigzag graphene nanoribbon devices with methyl chain adsorption

Abstract Zigzag graphene nanoribbon demonstrates significant capabilities in electronic transport, and the separation of graphene nanoribbons through the adsorption of atoms or groups holds considerable research value. In this work, we employed density functional theory combined with the non-equilibrium Green's function method to investigate the structure and electronic properties of graphene nanoribbons with methyl adsorption and hydrogen chain adsorption. Firstly, based on the figures of local device density of states and transmission pathways , the region where methyl is adsorbed forms a bonding state between the methyl group and carbon atoms. This bonding state restricts the migration of π-electrons within the nanoribbon, achieving a more stable separation effect. Additionally, analyses of I-V curves, band structure diagrams, and transmission pathways reveal that the edge effects of zigzag graphene nanoribbon exhibit different behaviors in two molecular separation devices. The methyl chain separator demonstrated the most favorable performance, with little difference in electron transfer between the edge and the interior. Finally, methyl adsorption is obviously superior to hydrogen chain adsorption in terms of separation and reduction of scattering effect.

Enhanced Aiot Multi‐Modal Fusion for Human Activity Recognition in Ambient Assisted Living Environment

ABSTRACTMethodologyHuman activity recognition (HAR) has emerged as a fundamental capability in various disciplines, including ambient assisted living, healthcare, human‐computer interaction, etc. This study proposes a novel approach for activity recognition by integrating IoT technologies with Artificial Intelligence and Edge Computing. This work presents a fusion HAR approach that combines data readings from wearable sensors such as accelerometer and gyroscope sensors and Images captured by vision‐based sensors such as cameras incorporating the capabilities of Long Short‐Term Memory (LSTM) and Convolutional Neural Network (CNN) models. The aim of fusing these models is to capture and extract the temporal and spatial information, improving the accuracy and resilience of activity identification systems. The work uses the CNN model to find spatial features from the images that represent the contextual information of the activities and the LSTM model for processing sequential accelerometer and gyroscope sensor data to extract the temporal dynamics from the human activities.ResultsThe performance of our fusion approach is evaluated through different experiments using varying parameters and applies the best‐suited parameters for our model. The results demonstrate that the fusion of LSTM and CNN models outperforms standalone models and traditional fusion methods, achieving an accuracy of 98%, which is almost 9% higher than standalone models.ConclusionThe fusion of LSTM and CNN models enables the integration of complementary information from both data sources, leading to improved performance. The computation tasks are performed at the local edge device resulting to enhanced privacy and reduced latency. Our approach greatly impacts real‐world applications where accurate and reliable HAR systems are essential for enhancing human‐machine interaction and monitoring human activities in various domains.

Issues in federated learning: some experiments and preliminary results

The growing need for data privacy and security in machine learning has led to exploring novel approaches like federated learning (FL) that allow collaborative training on distributed datasets, offering a decentralized alternative to traditional data collection methods. A prime benefit of FL is its emphasis on privacy, enabling data to stay on local devices by moving models instead of data. Despite its pioneering nature, FL faces issues such as diversity in data types, model complexity, privacy concerns, and the need for efficient resource distribution. This paper illustrates an empirical analysis of these challenges within specially designed scenarios, each aimed at studying a specific problem. In particular, differently from existing literature, we isolate the issues that can arise in an FL framework to observe their nature without the interference of external factors.

Comparing the improvement of occupant thermal comfort with local heating devices in cold environments

Local heating can improve the thermal comfort of occupants and save energy in buildings in cold environments; however, few studies have investigated the effects of heat-transfer modes. Here, we aimed to evaluate different local heating measurements including five-sided enclosed radiant panel, heating plate, and fan heater using climate chamber experiments with 20 participants in cold environments (14 °C). Skin temperature and thermal perception votes under two radiation types with low and high power, one condition of conduction, and one condition of convection were collected and analyzed (RL, RH, CD, and CV). Under conditions RH, CD, and CV, the investigated parameters significantly improved by three local heating devices, with foot skin temperature rising by 0.46 °C, foot thermal sensation rising by 1.25 scores, and overall thermal comfort increasing by 0.36 score; however, their energy consumption varied greatly. In contrast, no significant improvement was observed in the RL group. Additionally, direct application of heat at sites of palpable cold discomfort was not always the optimal approach, and heating other parts of the body may provide significant alleviation. We recommend that the surface temperature of local heaters based on conduction and radiation for heat transfer should not be lower than 40.38 °C and 52.15 °C, respectively. To maintain thermal comfort, air outlet temperature should not be lower than 34.56 °C for convection type heaters. Our results provide technical and experimental basis for designing energy-saving buildings.

Privacy-Enhanced Sentiment Analysis in Mental Health: Federated Learning with Data Obfuscation and Bidirectional Encoder Representations from Transformers

This research aims to find an optimal balance between privacy and performance in forecasting mental health sentiment. This paper investigates federated learning (FL) augmented with a novel data obfuscation (DO) technique, where synthetic data is used to "mask" real data points. Bidirectional Encoder Representations from Transformer (BERT) is used for sentiment analysis, forming a new framework, FL-BERT+DO, that addresses the privacy-performance trade-off. With FL, data remains decentralized, ensuring that user-sensitive information is retained on local devices rather than being shared with the FL server. The integration of BERT gives our system an enhanced feature of context sense-making from text conduct, and our model is extremely proficient in emotion categorization tasks. The experiments were performed on combined (real and replica synthetic) datasets containing emotions and showed significant enhancements compared to baseline methods. The proposed FL-BERT+DO framework shows the following metrics: prediction accuracy, 82.74%; precision, 83.30%; recall, 82.74%; F1-score, 82.80%. Further, we assessed its performance in the adversarial setup using membership inference and linkage attacks to ensure the privacy-preserved performance did not suffer deeply. It demonstrates that, even for large datasets, providing privacy-preserving prediction is possible and can significantly improve existing methods of addressing personal issues, like mental health support. Based on the results of our work, we can propose the development of secure decentralized learning systems that are capable of providing high accuracy of sentiment analysis and meeting strict privacy constraints.

Optimal sizing and placement of STATCOM, TCSC and UPFC using a novel hybrid genetic algorithm-improved particle swarm optimization

The increase in global power demand has caused most of today's power networks to become overloaded especially in Sub-Saharan Africa. The increased load demand can be met through expansion of existing generation and transmission system. However, construction of new power infrastructure is limited by financing and technical constraints. Thus, power networks have been left to operate at overload conditions with high power losses and many power quality (PQ) problems. Flexible AC Transmission System (FACTS) devices can improve the power transfer capability of the existing transmission networks without the need of constructing new power infrastructure. In this paper, a multi-objective function comprising of minimization of power loss (PL), voltage deviation (VD) and operational cost (OC) was formulated and solved using a novel algorithm. A novel Genetic Algorithm-Improved Particle Swarm Optimization (GA-IPSO) technique is proposed in this paper for optimization of size and location of FACTS devices. Static Synchronous Compensator (STATCOM), Thyristor Controlled Series Capacitor (TCSC) and Unified Power Flow Controller (UPFC) are the three FACTS devices considered. The proposed technique was validated using IEEE-33 Bus Test System, which is a popular benchmark Radial Distribution System (RDS). The three FACTS devices were optimized separately and also in a combined manner. Under the separate optimization, the size and location of individual FACTS devices were optimized. For combined optimization, the sizes and locations of more than one device were optimized in the same test system. For separate optimization, UPFC produced the best results by reducing the active power losses by 38.44 % and OC from $1.59×105 to $ 1.15×105. Under the combined optimization, combination of TCSC, STATCOM and UPFC gave better results by achieving active power loss reduction of 56.09 % and reducing OC from $1.59×105 to $ 1.03×105. Comparison of GA-IPSO technique with other algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Improved Grey Wolf Optimization (IGWO) and Differential Evolution Algorithm (DEA) showed that the proposed hybrid technique was superior and more efficient in solving the FACTS optimization problem.

Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent tradeoff between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller).

Improving thermal comfort using personalized local conditioning: A large-scale retrospective questionnaire survey conducted during a heatwave in China

The increasingly frequent heatwaves caused by global warming indicate that more energy consumption is required to maintain individuals' thermal comfort and health. Understanding how to utilize local conditioning for optimizing conditioning devices and creating a comfortable environment is crucial. Due to variations in individuals' adaptability to their surroundings, people from different climatic zones exhibit different needs for environmental conditioning. Current research findings in built environments have not adequately explained how individuals in various climatic zones use local conditioning to meet these diverse thermal needs. Therefore, it is highly important to conduct quantitative research on individuals' willingness to use local conditioning and their preferences across different climatic zones. To address this issue, a retrospective survey of 2,247 participants across five climate zones in China was conducted over a two-month period. The results revealed that many participants chose to use local conditioning to further improve thermal comfort, even if they initially felt comfortable. Participants from the severe cold and cold zones preferred water-cooled fans and floor fans, whereas floor fans were the preferred choice in other zones. Price and convenience were the main factors considered when selecting local conditioning devices (average composite score > 7). Differences in the methods, duration, parameter settings, placement of local conditioning, and regulated body parts varied among participants from the five climate zones. These results aid in improving our understanding of thermal comfort and health in high-temperature environments during summer.

Optimizing Federated Learning Efficiency: A Comparative Analysis of Model Compression Techniques for Communication Reduction

Abstract. The rapidly growing number of connected devices continuously produces massive amounts of heterogeneous data, posing challenges to data privacy and security when leveraging them to train the high-quality big model. Federated learning, which enables numerous users to train a global model without cooperatively exchanging data, has emerged as a viable alternative. Though achieving significant progress, existing federated learning methods still struggle with large communication volumes, especially when the local devices only have limited computing and communication capabilities. To alleviate the efficiency issue, this paper compares the effect of three compression methods in promoting the training of federated learning models, including pruning, quantization, and knowledge distillation. The findings reveal that these methods reduce resource consumption while maintaining high model performance. Combining the pruning, quantization, and knowledge distillation technology through sequential application and parameter aggregation helps balance the model size and performance. Our cascading lightweight strategy, which preserves each method's unique edge while promoting deeper collaboration between them, has been shown beneficial through extensive testing.

Rayleigh wave-based monitoring of mortar coating and concrete core cracking on prestressed concrete cylinder pipe under external pressure using piezoelectric lead zirconate titanate

Prestressed concrete cylinder pipe (PCCP) is a critical type of pressure pipe widely used in major water conveyance projects worldwide. As essential components of a PCCP, the mortar coating and concrete core are directly linked to its operational safety. This study employed piezoelectric lead zirconate titanate (PZT) devices as transducers to generate excitation vibrations and measure the resulting Rayleigh waves, thereby providing a means to detect cracking in and monitor the health of the mortar coating and concrete core of a prototype 1400 mm inner-diameter PCCP under external pressure. First, a theoretical analysis was conducted to define the propagation laws of Rayleigh waves. Next, a finite element analysis was undertaken to identify the tensile regions of the PCCP mortar coating and concrete core when under external pressure applied along the length of the PCCP and thereby establish the locations for the PZT devices. Experiments were subsequently conducted by incrementally increasing the external pressure load from 0 to 700 kN while monitoring the cracking in the mortar coating and concrete core using sinusoidal excitation signals with frequencies of 5, 10, and 20 kHz. The voltage amplitudes measured at each external pressure load were analysed, confirming the Rayleigh wave propagation laws and revealing that the change in the measured voltage amplitude curve aligned with the experimental observations, thereby validating the proposed measurement method. Finally, the relative percentage deviation of amplitude (RPDA) and relative percentage deviation of energy (RPDE) damage indices were established based on the voltage measurements to further evaluate the damage state of the mortar coating and concrete core. The RPDA measured at an excitation frequency of 20 kHz was best for detecting cracks in the PCCP mortar coating, whereas the RPDA and RPDE measured at excitation frequencies of 20 and 10 kHz, respectively, were best for monitoring concrete core cracking.

Intrasaccular Device Occlusion and Location Scale IDOL - a Novel Grading Scale For Cerebral Aneurysms Treated with Intrasaccular Devices

Intrasaccular Device Occlusion and Location Scale IDOL - a Novel Grading Scale For Cerebral Aneurysms Treated with Intrasaccular Devices

Fingerprinting‐Based Indoor Localization in a 3 × 3 Meter Grid Using OFDM Signals at Sub‐6 GHz

ABSTRACTAccurately determining the indoor location of mobile devices has garnered significant interest due to the complex challenges posed by non‐line‐of‐sight (NLOS) propagation and multipath effects. To address this challenge, this paper proposes a new approach to indoor positioning that utilises channel state information (CSI) and machine learning (ML) techniques to improve accuracy. The proposed method extracts the amplitude and phase differences of the subcarriers from the CSI data to create fingerprints. ML algorithms and network architecture are utilised to train the CSI data from two antennas, in the form of phase and amplitude. Experiments conducted in a standard indoor environment demonstrate the effectiveness of the proposed method.