Access Points Research Articles

Design of a low-cost force insoles to estimate ground reaction forces during human gait

This paper proposes a low-cost electronic system for estimating ground reaction forces (GRF) during human gait. The device consists of one master node and two slave nodes. The master node sends instructions to slave nodes that sample and store data from two force insoles located at the participant’s feet. These insoles are equipped with 14 piezo-resistive FlexiForce A301 sensors (FSR). The slave nodes are attached to the ankles and feet of each participant. Subsequently, the start command is transmitted through the master node, which is connected to the USB port of a personal computer (PC). Once the walking session is completed, the information obtained by the slave nodes can be downloaded by accessing the access point generated by these devices through Wi-Fi communication. The GRF estimation system was validated with force platforms (BTS Bioengineering P6000, Italy), giving on average a fit measure equal to 68.71%±4.80% in dynamic situations. Future versions of this device are expected to increase this fit by using machine learning models.

Gas adsorption analysis for quantifying the edge sites of graphite

The edge plane exposed on the surface of carbon materials is the active site that determines their properties. When graphite is used as the negative electrode in lithium-ion batteries, the edge plane acts as an access point for lithium insertion/desorption. However, because of the low specific surface area and amorphous carbon coating of graphite particles, accurately estimating the number of edge planes is challenging. Therefore, we propose a technique for determining graphite edge planes based on the stepwise adsorption of Kr onto the hexagonal carbon network surface. Graphite particles with highly modified surface structures were prepared by controlling the particle size, heat treatment temperature, and amorphous carbon coating. Electrochemical evaluation revealed significant differences in charge-transfer resistance, an indicator of edge planes. The edge plane determination results obtained using conventional methods such as X-ray diffractometry and Raman spectroscopy did not correlate well with charge-transfer resistance. Meanwhile, Kr adsorption measurements revealed that the stepwise adsorption behavior, because of the interaction of Kr with the energetically homogenous surface, varied significantly depending on the surface properties of graphite particles. The number of edge planes estimated by gas adsorption strongly correlated with charge-transfer resistance. Consequently, we determined that the graphite edge plane tied to the electrochemical index can be derived using gas adsorption analysis. This edge plane determination technique would be applicable not only to graphite but also to highly crystalline carbon materials with an exposed hexagonal carbon network surface.

Telehealth in Libraries: The Creation of a Novel Pre-Implementation Workbook

ABSTRACT Telehealth services are a growing aspect of the US healthcare delivery system. Some rural patients, however, lack proper internet speed in their home to access telehealth services and need alternative telehealth access points. Public libraries can be an alternative telehealth access point due to their access to high-speed internet services; however, these programs often experience limited success due to a lack of planning with community health providers and local health organizations. This article describes a novel workbook that guides public libraries to collaborate with community stakeholders when considering the implementation of telehealth services. Development of the workbook leveraged expertise of library and public health professionals. The workbook is currently free to download and updated versions will be available as more knowledge is gained in the field.

Abstract B075: The "let's talk about Black people and cancer" conference

Abstract In the United Kingdom, despite having a healthcare service that is free at the point of access, inequalities in cancer care persist. Recent data shows that for certain cancer groups (e.g. breast cancer), Black patients present with worse grade and stage leading to poorer outcomes. Reasons for the disparities are multifactorial but include lack of awareness in the community, structural barriers to accessing healthcare, limited research into genetic variability and/or biological differences, and hesitancy to engage with health care professionals due to historic wrongs. Often different stakeholders work in different silos to try and remediate these barriers however they are not often in one space and have the opportunity to learn from patient experiences and how they can all dovetail together to improve patient outcomes. In an attempt to bring different sectors together the senior author established the “Let’s talk about Black people and cancer” conference 2024. The aim of this event was to increase awareness and understanding of cancer in the UK Black population. Methods This event covered the major cancer groups (breast, prostate, haematological, bowel and gynaecological) focusing on genomics, symptom awareness, screening programs, and how and when to access secondary care. We sought patient feedback from this conference style intervention to see the effect on attendees in terms of increase in knowledge specifically around Black people and cancer. This study utilized a two-part survey to evaluate the effectiveness of the conference, assessing knowledge held by registered attendees pre- and post-conference. A 5-point Likert scale was used to extract degrees of opinion, providing accurate and representative feedback. There was a total of 46 questions in the survey targeting the demographics of the attendees, their connection to cancer, and knowledge about cancer including the risks/outcomes regarding Black people specifically. Results The world cancer day conference had 190 registrants. 83 responses to the questionnaire (43.7% response rate). Pre-conference questionnaires highlighted around two thirds had knowledge of the 3 major screening programs, but after the conference this increased to more than 90%. A similar trend was noticed when looking at change in knowledge regarding the five cancer groups covered. Conclusion We have demonstrated that this targeted conference style intervention was effective in raising awareness not just for the public but for those professionals involved in the delivery of cancer care for Black people. While these are snap shots in time it would be useful to see how that information translates in a longitudinal fashion in terms of positive modifications in health behaviors and delivery of services. Citation Format: Teniola Adeboye, Georgette Oni. The "let's talk about Black people and cancer" conference [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B075.

Optimizing the Utilization of Online Public Access Catalog as a Means of Information Retrieval in Libraries

This study aims to explore the optimization of the Online Public Access Catalogue (OPAC) as an information retrieval tool in the Library of SMA Negeri 3 Medan, focusing on identifying the challenges and barriers to its effective use. A qualitative descriptive approach was employed, utilizing interviews, observations, and documentation as data collection methods. Interviews were conducted with a librarian and three students, while observations focused on the practical issues related to OPAC usage. The findings reveal that the use of OPAC in the library is not optimal, with challenges including system integration difficulties, insufficient librarian training, and limited user familiarity. Efforts to enhance OPAC usage involve promoting OPAC awareness, increasing computer access points, and ongoing system maintenance. However, the effectiveness of these measures is hindered by technical and educational constraints, highlighting the need for targeted interventions to fully leverage OPAC's potential in improving library services.

Floor-Level Occupancy Estimation of a Multi-Story Building Using Coarse Wi-Fi Data

In recent years, there has been an extraordinary increase in wireless capable devices and network infrastructure, which spawned a corresponding rise in data produced from the interactions of these technologies. Mobile devices constantly roam, leading to a perpetual dialog between a mobile device and wireless access points. This dialogue generates a continuous stream of device-specific data, including but not limited to a device’s media access control address, time of access, and received signal strength. Given the knowledge of the access point’s location and received signal strength, it is possible to infer the position of user devices and estimate their mobility and occupancy. This article presents two methods for accurately measuring floor-level occupancy in a multi-story building at Texas State University using coarse Wi-Fi log data. The first method employs a static filter, while the second incorporates user-role data and user location to create a dynamic filter. Quantitative methods are used to evaluate these filters against field-collected reference data and existing internal people-counting sensors. Our results demonstrate that the dynamic filter, leveraging variable thresholds, provides a more accurate estimation of occupancy compared to the fixed 5-min static filter which consistently overestimated occupancy. This research sheds light on the potential of dynamic filters derived from user-role data for precise floor-level occupancy estimations, with implications for various applications.

Trust and Affinity Based Clustering for Deterministic Multicast Routing Using Honey Badger Algorithm

Wireless Ad hoc networks (WANETs) and mobile ad hoc networks (MANETs) are forms of wireless networks with no central controller. In contrast to traditional networks based on routers and access points, ad hoc networks do not rely on pre-existing infrastructure. Recently, multicast routing protocols in MANETs have effectively managed group communications. The multicast routing protocol can greatly increase the reliability of the MANET network. This protocol aims to minimize the energy consumption (EC) and network lifetime of the MANET to address these issues. This paper presents a novel Affinity based Clustering for Deterministic Multicast Routing (ACD-MR). The overall process is carried out in three stages: cluster head selection (CHs), trust management, and routing. In the initial phase, the cluster head (CH) is selected and utilizes affinity propagation based on node degree, distance, energy, and lifetime. In the second phase, the data are encrypted using the enhanced AES algorithm, and the trust value of each node is calculated employing direct and indirect trust. In the third phase, the Honey Badger algorithm is employed to select an optimal route. The proposed ACD-MR model’s performance is calculated utilizing various parameters, including the network lifetime, average end-to-end delay, EC, packet delivery ratio (PDR), and detection ratio. The proposed model increased the network lifetime by 17.24%, 19.45%, and 22.34% better than MLLR, CS-MAODV, and DQN, respectively.

Optimized Received Signal Strength-Based Radio Map Interpolation for Indoor Positioning Systems

The most viable strategy to establish a dependable indoor positioning system is by employing the received signal strength (RSS) based fingerprinting technique, which encompasses both the offline and online phases. The offline phase involves constructing a radio map, which can be arduous in vast indoor environments. To tackle this, radio map interpolation is often used to interpolate RSS by utilizing the RSS recorded at a coarser level of known reference points (RPs). This paper proposes a novel RSS-based radio map interpolation to enhance the existing inverse distance weighting (IDW) interpolation technique. The method divides the deployment area into zones and optimizes the density of known RPs in each zone based on the number of access points (APs) with average RSS exceeding the threshold. It allocates higher RP density for the zones with poor AP coverage and reduces it for well-covered zones. Results demonstrates that the proposed method achieves substantial improvements over the baseline IDW scheme in average positioning error of up to 6.58% at the floor level and 3.77% overall.

“The Ocean is a Part of Me”: The Importance of Coastal Place Attachment to Well-Being and Implications for Coastal Access Management

Public coastal access is a growing issue in coastal management, particularly as both environmental changes and increased coastal development threaten existing public coastal access points around the country. This has implications both economically and on non-economic factors such as individual and community well-being. Through a mixed-methods case study of coastal users in Rhode Island (n = 200 surveys and n = 29 interviews), this study explores the concept of attachment to coasts as a specific type of place attachment by identifying Coastal Place Attachment (CPA) as a concept, developing a quantitative index to measure CPA, and qualitatively describing the value that coastal attachment brings to well-being. Results show that individuals do have a unique attachment to coastal environments that is not transferable to other natural areas, although it may be generalizable to other coastal areas. We also document how this attachment provides important contributions to psychological, physical, and social well-being. Implications for coastal access management are discussed.

Learning-based visibility prediction for terahertz communications in 6G networks

Terahertz communications are envisioned as a key enabler for 6G networks. The abundant spectrum available in such ultra high frequencies has the potential to increase network capacity to huge data rates. However, they are extremely affected by blockages, to the point of disrupting ongoing communications. In this paper, we elaborate on the relevance of predicting visibility between users and access points (APs) to improve the performance of THz-based networks by minimizing blockages, that is, maximizing network availability, while at the same time keeping a low reconfiguration overhead. We propose a novel approach to address this problem, by combining a neural network (NN) for predicting future user–AP visibility probability, with a probability threshold for AP reselection to avoid unnecessary reconfigurations. Our experimental results demonstrate that current state-of-the-art handover mechanisms based on received signal strength are not adequate for THz communications, since they are ill-suited to handle hard blockages. Our proposed NN-based solution significantly outperforms them, demonstrating the interest of our strategy as a research line.

Formal estimation of wireless network services for signal strength and electromagnetic wave association in an International Green University

The United Nations focuses on 17 urgent problems to call for action in all countries. Goal 7 of the 17 urgent problems is based on affordable and clean energy. Since 2017, National Taitung University (NTTU) has dedicated more time and effort to attain the wisdom, health, sustainability and aesthetics as an international green university. To accomplish this, we adhere and construct a safe radiofrequency and electromagnetic wave environment to achieve healthy and sustainable campus objectives. According to the UI GreenMetric World University Rankings, NTTU was ranked 74th in 2021, 67th in 2022 and 58th in 2023. In this study, we propose a formal estimation of wireless network services for classrooms or smart spaces to achieve the goal of safe radiofrequency and electromagnetic waves. Inside classrooms or smart spaces, better wireless signal strength and safer electromagnetic waves are achieved. Moreover, the proposed method can be used to determine the quantity of wireless access points for a given classroom or smart space to avoid unsafe electromagnetic waves and inappropriate energy consumption. The experimental results show that all benchmarks meet the wireless exposure limits of the WHO and physician safe technologies in the NTTU.

Differences in the physiology of Pichia pastoris grown on glycerol/glucose using polyurethane foam slabs as solid support

AbstractBACKGROUNDGrowth physiology and metabolic flux of Pichia pastoris were analyzed using glass columns containing slabs of polyurethane foam as a solid support and culture medium with glycerol or glucose as a carbon source.RESULTSMedia with glycerol produced an average of 30.91% more biomass than media where glucose was used. However, the specific growth rate registered higher values for media with glucose than those with glycerol at low substrate concentrations: 0.46 versus 0.22 h−1. Media with glycerol produced a large amount of d‐arabitol, whereas ethanol and citrate were the predominant metabolites in media with glucose. Both types of culture systems generated biphasic curves in terms of the respiratory quotient value, whose inflection point coincided with the transition from aerobic metabolism to a mixed one.CONCLUSIONAdequate oxygen transfer occurred in both culture systems; therefore, the differences found in growth physiology can be explained by the transport mechanism that internalizes glycerol, the access point to primary metabolism, and the degree of glycerol oxidation. The arrangement of cells in the polyurethane matrix allowed an attenuation of the repression phenomenon due to excess substrate for both carbon sources. This is the first work to study metabolic flux comparisons using this system. © 2024 Society of Chemical Industry (SCI).

CodelessML: A Beginner's Web Application for Getting Started with Machine Learning

Building machine learning models requires intensive coding and installation of certain software. This is frequently a barrier for beginners learning about machine learning. To overcome this situation, we present CodelessML, a reproducible web-based application designed for Machine Learning beginners due to its coding-free and installation-free design, published under Code Ocean capsule. It provides a common workflow that eases the process of building Machine Learning models and using the model for predictions. Using the Agile method, CodelessML was successfully built using Python, Anaconda, and Streamlit It. By using CodelessML, users can get a walkthrough and interactive experience of building machine learning through a simplified machine learning process: exploratory data analytics (EDA), modelling, and prediction. The impact of the software was evaluated based on feedback from 79 respondents, which showed that based on a 5-scale Likert, CodelessML received average ratings of 4.4 in accessibility, 4.3 in content, and 4.4 in functionality. CodelessML serves as an accessible entry point for learning machine learning, offering online, free, and reproducible features.

A cost-efficient content distribution optimization model for fog-based content delivery networks

The massive data demand requires content distribution networks (CDNs) to use evolving techniques for efficient content distribution with guaranteed quality of service (QoS). The distributed fog-based CDN model, with optimal fog node placements, is a suggested aproach by researchers to meet this demand. While many studies have focused on improving QoS by optimizing fog node placement, they have rarely considered the impact on content distribution, affected by placement, usage changes, and delivery rates. Therefore, the practical approach to fog node placement for CDN services must examine its impact on content distribution. Further, current research on fog-based CDN lacks formal methods to address key challenges: R1) strategic placement of fog nodes to process end-user requests; R2) construction of a content distribution path with guaranteed QoS; R3) cost minimization of building a fog-based CDN model. We construct this as a joint optimization problem by considering four parameters: geographical regions, open public Wi-Fi access points (OPWAPs) locations, QoS, and cost to achieve research objectives R1–R3. As a solution, we propose a dual-step framework. First, a heuristic for optimal fog node placement based on geographic regions and OPWAP locations is proposed. Second, we propose two algorithms, Greedy Performance-based Node Selection (GPDS) and Greedy Fog Node Selection algorithm (GFNSA), for selecting fog nodes, minimizing the cost of building a fog-based CDN while achieving optimal content distribution paths. The results demonstrate that the proposed methods outperform the baseline techniques and provide near-optimal solutions to the problem.

A novel method for reducing the effect of doppler frequency shift in high-speed railway mobile communication using machine learning

Currently, high-speed railway systems are rapidly expanding worldwide, necessitating reliable and efficient mobile communication solutions. However, the Doppler frequency shift caused by the high speeds of trains presents significant challenges to communication systems, particularly those using OFDM in LTE. This paper presents a novel for Doppler frequency compensation in high-speed railway communication based on the results of estimating the train's velocity using machine learning algorithms. By leveraging advanced algorithm such as neural networks, our method dynamically predicts and compensates for Doppler shifts in real-time. In this proposed novel, the Doppler frequency value is calculated based on the actual train’s velocity and the scenario of a high-speed railway, then the Doppler frequency is used to compensate the carrier frequency offset (CFO) directly at the Access Point (AP) device on the train. We conduct simulations to evaluate the effectiveness of our proposed solution in a high-speed railway scenario. The results demonstrate a marked improvement in communication reliability and data integrity, highlighting the potential of machine learning to enhance the performance of mobile communication systems in high-speed railways. The results of the proposed model are evaluated based on the system’s bit error rate BER after the CFO compensation decreases and the ratio of average energy per bit (Eb) to noise power density (N0) (Eb/N0) increases. This shows that the proposed solution works effectively and reduces the system’s bit errors while improving the communication performance. This study opens new avenues for integrating intelligent systems in transportation networks, ensuring seamless connectivity, and improving passenger experience

Enhancing security offloading performance in NOMA heterogeneous MEC networks using access point selection and meta-heuristic algorithm

The research delves into the intricate domain of security offloading within the context of non-orthogonal multiple access (NOMA) heterogeneous mobile edge computing (het-MEC) networks operating over Rayleigh fading channels. The investigation centers on a system model comprising a single antenna-equipped edge user, denoted as U, which strategically offloads computational tasks to two distinct heterogeneous wireless access points (APs): the far AP (AP1) and the near one (AP2), employing NOMA techniques. Notably, the research accounts for a passive eavesdropper (E) intending to intercept the U−AP2 transmission. A four-phase protocol is proposed to ensure the security offloading process, namely SAPS, which leverages wireless access point selection (APS) and physical layer security (PLS) techniques. The focus extends to derive a closed-form expression for a novel critical system performance metric: the secrecy successful computation probability (SSCP). Furthermore, an algorithm based on Ant Colony Optimization (ACO) within the continuous domain is introduced, which aims to enhance the SSCP by intelligently determining system parameters. The impact of critical factors such as transmit power, power allocation coefficient, bandwidth, CPU frequency, and task division ratio under the SAPS scheme is explored and compared to the conventional approach using pure NOMA. Remarkably, the algorithm in the proposed scheme demonstrates up to a 3% performance improvement. The validity and accuracy of the study findings are verified through Monte-Carlo simulations. The work contributes significantly to advancing secure offloading strategies in NOMA-based MEC networks, offering valuable insights for practical deployment and optimization.

Multi-Fingerprints Indoor Localization for Variable Spatial Environments: A Naive Bayesian Approach.

Fingerprint-based indoor localization has been a hot research topic. However, the current fingerprint-based indoor localization approaches still rely on a single fingerprint database, where the average level of data at reference points is used as the fingerprint representation. In variable environmental conditions, the variations in signals caused by changes in the environmental states introduce significant deviations between the average level and the actual fingerprint characteristics. This deviation leads to a mismatch between the constructed fingerprint database and the real-world conditions, thereby affecting the effectiveness of fingerprint matching. Meanwhile, the sharp noise interference caused by uncertainties such as personnel movement has a significant interference on the creation of the fingerprint database and fingerprint matching in online stage. Examination of the sampling data after denoising with Robust Principal Component Analysis (RPCA) revealed distinct multi-fingerprint characteristics with clear boundaries at certain access points. Based on these observations, the concept of constructing a fingerprint database using multiple fingerprints is introduced and its feasibility is explored. Additionally, a multi-fingerprint solution based on naive Bayes classification is proposed to accurately represent fingerprint characteristics under different environmental conditions. This method is based on the online stage fingerprints. The corresponding state space is selected using the naive Bayes classifier, enabling the selection of an appropriate fingerprint database for matching. Through simulations and empirical evaluations, the proposed multi-fingerprints construction scheme consistently outperforms the traditional single-fingerprint database in terms of positioning accuracy across all tested localization algorithms.

Fragments in Cyberspace: The Ohio State University’s Digital Collection of Medieval and Renaissance Manuscript Fragments

Making pre-modern European manuscript fragment collections accessible to students and researchers virtually is essential due to the nature of book-breaking and the for-profit manuscript fragments trade, which has led many fragments to traverse large distances. However, effectively conveying information on these context-dependent materials presents various challenges. This article chronicles efforts to digitize, describe, and structure materials in Ohio State University Libraries’ (OSUL) Medieval and Renaissance Manuscript Fragments Collection to make the collection accessible to researchers virtually through OSUL’s Digital Collections platform and the Digital Public Library of America (DPLA). Records in this collection use a customized set of Dublin Core metadata elements and have a parent-child record structure to illustrate relationships between fragment leaves. Primary challenges included establishing intellectual control of the materials, implementing consistent access points for contributors, and conveying approximate information, such as the date of production. Contributing to DPLA, limitations, and next steps are also discussed.

Correction: Zhu et al. Energy Efficient Access Point Placement for Distributed Massive MIMO. Network 2022, 2, 288–310

Following publication, concerns were raised regarding the peer-review process related to the publication of this article [...]

Truthful mechanism for joint resource allocation and task offloading in mobile edge computing

In the context of mobile edge computing (MEC), the delay-sensitive tasks can achieve real-time data processing and analysis by offloading to the MEC servers. The objective is maximizing social welfare in an auction-based model. However, the distances between mobile devices and access points lead to differences in energy consumption. Unfortunately, existing works have not considered both maximizing social welfare and minimizing energy consumption. Motivated by this, we address the problem of joint resource allocation and task offloading in MEC, with heterogeneous MEC servers providing multiple types of resources for mobile devices (MDs) to perform tasks remotely. We split the problem into two sub-problems: winner determination and offloading decision. The first sub-problem determines winners granted the ability to offload tasks to maximize social welfare. The second sub-problem determines how to offload tasks among the MEC servers to minimize energy consumption. In the winner determination problem, we propose a truthful algorithm that drives the system into equilibrium. We then show the approximate ratios for single and multiple MEC servers. In the offloading decision problem, we propose an approximation algorithm. We then show it is a polynomial-time approximation scheme for a single MEC server. Experiment results show that our proposed mechanism finds high-quality solutions in changing mobile environments.