Location Of Devices Research Articles

A Set of Models for Device Positioning in Sixth Generation Networks. Part 2. Review of Algorithms and Accuracy Assessment

Relevance. This work is the second part of a series devoted to the study of a set of positioning models in sixth-generation terahertz networks and solves the problems of systematizing algorithms and assessing the accuracy of determining the location of a user device depending on the configuration and size of the antenna array at the base station.Purpose. Within the framework of the scientific problem of searching for means of achieving decimeter accuracy of coordinate estimates, outlined in the first part of the cycle, the analysis of accuracy assessment models, a review of algorithms and ways of their optimization, as well as a numerical experiment, performed in this study serve the purpose of justifying the configuration and dimensions of the antenna array used at the base station.The research method is an analytical review of the state of the problem based on current scientific publications, conceptual modeling, categorical approach, expert combination, comparative analysis, formalization, mathematical and simulation modeling.Solution / results. The paper presents models for assessing the accuracy of positioning in 6G terahertz networks, formalizes the relationship between primary measurements and coordinate estimates for multi-position and single-position positioning in the near and far zones. It provides an overview of algorithms for geometric positioning and positioning with training for cases of one-stage and two-stage processing; analyzes the specifics of implementing algorithms for simultaneous tracking and map construction. It provides an analysis of the features of optimizing algorithms in offline and online modes. Simulation modeling is used to assess the accuracy for a scenario of territorial distribution with direct visibility and ideal synchronization.Novelty. Using simulation modeling tools, the achievement of decimeter accuracy of coordinate and orientation estimates of 1° in the terahertz range for a far-field model using a 1 GHz band and a composite antenna array of more than half a thousand elements has been scientifically substantiated.The theoretical significance lies in establishing the dependence of the accuracy of coordinate and orientation estimates of the device on the configuration and dimensions of the antenna array at the base station.The practical significance of the developed simulation model lies in the numerical justification of the limits of device positioning accuracy in sixth-generation networks depending on the antenna array used at the base station for a given scenario.

GPS Tracking and Indoor Navigation System

This project is an idea of developing an application based on a navigation system formed by Graph Data Structure. An indoor positioning system (IPS) can be thought of as a global positioning system (GPS) for indoor venues. While GPS is used outdoors, IPS can detect real-time locations to accurately determine the coordinates of people or assets inside a building. These coordinates are typically represented visually by a blue dot on a digital indoor map to provide additional context to users and features such as wayfinding. The project aims to have certain objectives such as a tracking system, navigation system that is inside a small area of a building. This idea aims to track the location of any particular device within a building and show a navigation to that device in an efficient manner. Not only tracking of devices but also solving a problem of finding a particular room in the building through data of different paths that is stored. This project comes under the intelligent system design and development domain and falls under the sector of analysing and designing hardware, software and operating systems for computers. The main objective of this system design is to navigate through the area and find places to reach with ease and as fast as possible, it is helpful for people who are new at that place and even finding a user in that particular vicinity by live tracking technique.

A Genetic Optimized Federated Learning Approach for Joint Consideration of End-to-End Delay and Data Privacy in Vehicular Networks

In 5G vehicular networks, two key challenges have become apparent, including end-to-end delay minimization and data privacy. Learning-based approaches have been used to alleviate these, either by predicting delay or protecting privacy. Traditional approaches train machine learning models on local devices or cloud servers, each with their own trade-offs. While pure-federated learning protects privacy, it sacrifices delay prediction performance. In contrast, centralized training improves delay prediction but violates privacy. Existing studies in the literature overlook the effect of training location on delay prediction and data privacy. To address both issues, we propose a novel genetic algorithm optimized federated learning (GAoFL) approach in which end-to-end delay prediction and data privacy are jointly considered to obtain an optimal solution. For this purpose, we analytically define a novel end-to-end delay formula and data privacy metrics. Accordingly, a novel fitness function is formulated to optimize both the location of training model and data privacy. In conclusion, according to the evaluation results, it can be advocated that the outcomes of the study highlight that training location significantly affects privacy and performance. Moreover, it can be claimed that the proposed GAoFL improves data privacy compared to centralized learning while achieving better delay prediction than other federated methods, offering a valuable solution for 5G vehicular computing.

Investment Research of China Medical Device Industry under the Concept of Corporate Finance — Take Yuyue Medical Co., Ltd. as Example

With the optimization of China’s healthcare policy, the rise of the aging population, the increasing health awareness of society, and the impact of the global epidemic, China’s medical device industry has become a key market for investors. However, compared with the mature and well-funded medical device companies in overseas developed countries, China’s local medical device companies have apparent disadvantages and jumping battles. Yuyue Medical Co., Ltd. is a prominent player in the medical device industry in China, having sustained a significant profit margin over an extended period. Its profitability has changed over the post-epidemic period, in any case. Given Yuyue Medical Co., Ltd.’s exceptional standing in the medical equipment sector, an analysis of the company as an industry investment case makes sense. Based on Yuyue Medical Co., Ltd. as the industry investment benchmark, the study analyses the industry’s competitive development situation, financial performance, and impact on the external environment by adopting Porter’s Five Forces, ratio analysis, and PESTEL. The study found that China’s medical device industry has growth potential due to the low bargaining power of customers and suppliers, high technological barriers, and low threat of substitution by new entrants. Yuyue Medical Co., Ltd. has healthy financial performance and several advantages from the external environment, so Yuyue Medical Co., Ltd. is worthy of investment.

Enhancing data privacy in federated learning using artificial intelligence

Federated Learning (FL) is a decentralised approach to machine learning that enables model training on local devices without the need to share raw data. While FL inherently provides some level of privacy, significant challenges remain in fully protecting sensitive information. This article explores advanced artificial intelligence (AI) techniques for improving privacy in federated learning. I propose novel methods that include differential privacy, homomorphic encryption, and secure multiparty computation, enhanced by AI-driven optimizations. My empirical studies and theoretical analyses demonstrate the effectiveness and efficiency of these techniques in maintaining data protection.

Investigation of the Versatile Utilization of Three-Dimensional Vertical Resistive Random-Access Memory in Neuromorphic Computing.

The continuous advancement of computing technologies such as the Internet of Things and artificial intelligence emphasizes the need for innovative approaches to data processing. Faced with limitations in processing speed due to the exponentially increasing volume of data, conventional von-Neumann computing systems are transforming into a new paradigm called neuromorphic computing, inspired by the efficiency of the human brain. We fabricate three-dimensional vertical resistive random-access memory (VRRAM), which is highly suited for neuromorphic computing, and demonstrate its value as an artificial synapse. Beyond simulating simple synaptic functionalities such as spike-rate-dependent plasticity, spike-timing-dependent plasticity, and paired-pulse facilitation, we propose specific applications and experimentally implement them. In pattern recognition simulations based on the weight update characteristics of the fabricated VRRAM, the accuracy achieved in pattern recognition using appropriate pulse schemes reaches 90.4%. Additionally, we demonstrate adaptive learning behavior on the device by mimicking Pavlov's dog experiment with combinations of applied voltage pulses. Finally, we employ suitable write/erase pulse trains to implement binary representations for decimal numbers ranging from 0 to 15, thereby illustrating the significant potential of local devices for edge computing applications.

Towards transforming malaria vector surveillance using VectorBrain: a novel convolutional neural network for mosquito species, sex, and abdomen status identifications

Malaria is a major public health concern, causing significant morbidity and mortality globally. Monitoring the local population density and diversity of the vectors transmitting malaria is critical to implementing targeted control strategies. However, the current manual identification of mosquitoes is a time-consuming and intensive task, posing challenges in low-resource areas like sub-Saharan Africa; in addition, existing automated identification methods lack scalability, mobile deployability, and field-test validity. To address these bottlenecks, a mosquito image database with fresh wild-caught specimens using basic smartphones is introduced, and we present a novel CNN-based architecture, VectorBrain, designed for identifying the species, sex, and abdomen status of a mosquito concurrently while being efficient and lightweight in computation and size. Overall, our proposed approach achieves 94.44±2% accuracy with a macro-averaged F1 score of 94.10±2% for the species classification, 97.66±1% accuracy with a macro-averaged F1 score of 96.17±1% for the sex classification, and 82.20±3.1% accuracy with a macro-averaged F1 score of 81.17±3% for the abdominal status classification. VectorBrain running on local mobile devices, paired with a low-cost handheld imaging tool, is promising in transforming the mosquito vector surveillance programs by reducing the burden of expertise required and facilitating timely response based on accurate monitoring.

Empirical study of facade retrofits for optimizing energy efficiency and cooling in school buildings in Saudi Arabia

Educational buildings in Saudi Arabia are characterized by high energy consumption, primarily due to cooling and lighting demands. This study aims to evaluate and optimize facade retrofitting strategies to improve energy efficiency and evaluate economic performance of many scenarios in Saudi school buildings, aligning with the sustainability objectives of Saudi Vision 2030. The research employs a simulation-based optimization methodology utilizing Design Builder software to explore key facade design variables, including Window-to-Wall Ratio, glazing type, local shading devices, and external wall insulation. The methodology is structured in sequential stages: it begins with the collection of base-case data, followed by a data-driven optimization process targeting specific design variables. This is then complemented by an economic and environmental assessment of the optimal retrofit scenarios, ultimately leading to the identification of the most effective energy-saving solution presented at the conclusion of the study. Through an optimization process, the research identifies retrofitting strategies that reduce cooling loads by up to 17 %, lighting energy consumption by 49 % and annual energy costs by 18 %. The study also includes an economic analysis of the retrofits, revealing significant savings in annual electricity costs and a reduction in the building's carbon footprint. This research contributes valuable insights into sustainable building practices, offering practical solutions for improving energy efficiency in the educational sector. The novelty of this study lies in its comprehensive analysis of facade retrofits within the context of extreme climatic conditions, which provides a replicable model for retrofitting educational buildings across the region. The findings have significant implications for policymakers, architects, and sustainability advocates seeking to meet Saudi Arabia's energy conservation goals.

Determining the relevance of noise barriers along railway lines based on a multi-criteria analysis

Rail transport is one of the friendliest in environmental terms, but the noise generated by this mode of transport remains a weak point. To improve local residents' living conditions against this noise, two locations for protection devices are possible : close to the source, to be preferred in accordance with French legislation, within the limit of economical acceptability (not defined) or by façade insulation otherwise. A method has been developed to assess the relevance of noise barriers or merlons based on a cost-benefit analysis. A multi-criteria analysis is required, based on acoustic benefits provided by protection near to the source, building density, number of people protected, technical feasibility, costs directly linked to the construction conditions and landscape integration possibilities. The article details the method used during the preliminary study phase to determine the possible conditions for implementation, the associated costs and the impact of protection, and presents the analysis grids used to validate the choice of protection type, close to the source or on the facade.

Experimental studies of sensors based on fiber Bragg gratings embedded in the internal structure of composite plates

The main purpose of this article is to study the structural characteristics of carbon fiber samples printed on a 3D printer, including embedded sensors, during 3D printing, through numerical and experimental studies. There is a widespread tendency to replace metals with modern composite materials. Various methods can be used to measure the destruction of the composite structure. Fiber Bragg array sensors are known as intelligent localized and global structural condition prediction devices for all kinds of structural applications, especially those using composite materials. In this work, we created control plates by impregnating a selected number of layers of carbon fabric with epoxy resin. Fiber-optic Bragg sensors were additionally located between the layers of carbon fabric. In the course of experimental studies, a fiber Bragg grating sensor was embedded in the middle of the sample, and a second sensor was attached to the finished surface of the sample. We conducted environmental tests to examine the durability of the additively manufactured standard’s components and the impact of integrated sensors on the composite sample. We applied the concept of equations, known as conjugate modes, and used the transition matrix method to solve them numerically. The scientific novelty of the work is the development of a new method and technology for measuring spatial deformation in composite materials using fiber-optic Bragg gratings.

X-law operation for energy-efficient time-of-arrival positioning to achieve higher accuracy with lower bandwidth: A game-changer for UWB localization devices

Ultra-wideband (UWB) technology is a good candidate for low-latency high-resolution localization. This paper proposes the x-law operation (XLO) to boost the positioning accuracy of UWB localization systems. The proposed scheme significantly reduces the contribution of low-SNR samples and increases the time resolution of the pulse. Thus, the XLO achieves a time-of-arrival (ToA) positioning accuracy that existing methods require more energy and bandwidth to attain. The significant performance gain of the XLO for localization makes the XLO a good candidate for simultaneous positioning and communication with low complexity, high performance, and low power consumption suited for Internet of Things (IoT) devices.

Interest-aware joint caching, computing, and communication optimization for mobile VR delivery in MEC networks

In the upcoming B5G/6G era, Virtual Reality (VR) over wireless has become a typical application, which is an inevitable trend in the development of video. However, in immersive and interactive VR experiences, VR services typically exhibit high delay, while simultaneously posing challenges for the energy consumption of local devices. To address these issues, this paper aims to improve the performance of VR service in the edge-terminal cooperative system. Specifically, we formulate a joint Caching, Computing, and Communication (3C) VR service policy problem by optimizing the weighted sum of the total VR delivery delay and the energy consumption of local devices. To design the optimal VR service policy, the optimization problem is decoupled into three independent subproblems to be solved separately. To improve the caching efficiency within the network, a Bert-based user interest analysis method is first proposed to accurately characterize the content request behavior. Based on this, a service cost minimum-maximization problem is formulated under the consideration of performance fairness among users. Then, the joint caching and computing scheme is derived for each user with a given allocation of communication resources while a bisection-based communication scheme is acquired with the given information on the joint caching and computing policy. With alternative optimization, an optimal policy for joint 3C based on user interest can be finally obtained. Simulation results are presented to demonstrate the superiority of the proposed user interest-aware caching scheme and the effectiveness of the joint 3C optimization policy while considering user fairness. Our code is available at https://github.com/mrfuqaq1108/Interest-Aware-Joint-3C-Optimization.

Multi-Split Configuration Design for Fluid-Based Thermal Management Systems

Abstract High power density systems require efficient cooling to maintain their thermal performance. Despite this, as systems get larger and more complex, human expertise and insight may not suffice to determine the desired thermal management system designs. To this end, a framework for automatic architecture exploration is presented in this article for a class of single-phase, multi-split cooling systems. For this class of systems, heat generation devices are clustered based on their spatial information, and flow splits are added only when required and at the location of heat devices. To generate different architectures, candidate architectures are represented as graphs. From these graphs, dynamic physics models are created automatically using a graph-based thermal modeling framework. Then, an optimal fluid flow distribution problem is solved by addressing temperature constraints in the presence of exogenous heat loads to achieve optimal performance. The focus in this work is on the design of general multi-split heat management systems. The methods presented here can be used for diverse applications in the domain of configuration design. The multi-split algorithm can produce configurations where splitting can occur at any of the vertices. The results presented include three categories of problems and are discussed in detail.

Business Ecosystem Architecture Development: A Case Study of IoT Business in the Transportation Industry

The research aims to study business ecosystem architecture in the Internet of Things (IoT) scope, especially in the transport industry. This research uses a qualitative approach with the Research and Development (R&D) method with the Modeling Business Ecosystem Architecture (TEAM) research design. Informants consist of internal and external sources who provide a practical and comprehensive perspective on the IoT transportation business ecosystem at Telkom Indonesia. Data collection was conducted through in-depth interviews, observations, and literature studies. The results showed that, in mapping the IoT business ecosystem in Telkom Indonesia’s transportation sector. The findings highlighted several challenges, such as the limited readiness of human resources and the lack of local device manufacturers resulting in a lack of competitiveness of IoT solutions. There is a need for a business ecosystem architecture that focuses on examining actors, roles, and value offered and mapping interactions between actors. With a cocreation approach, actors in the ecosystem can actively collaborate in designing, developing, and implementing more innovative and adaptive solutions. Through the cocreation process, customers, service providers, application developers, and other actors can share knowledge, experiences, and resources to create solutions that better suit the needs and expectations of all parties involved. This research maps the IoT business ecosystem in the transport sector developed by Telkom Indonesia. Telkom Indonesia is a solution provider, where the importance of collaboration of each actor, including device providers, application developers, and customers, is crucial to overcoming challenges and maximizing the ecosystem’s potential.

Hyperspectral Imaging Database of Human Facial Skin.

The perceived color of human skin is the result of the interaction of environmental lighting with the skin. Only by resorting to human skin spectral reflectance, it is possible to obtain physical outcomes of this interaction. The purpose of this work was to provide a cured and validated database of hyperspectral images of human faces, useful for several applications, such as psychophysics-based research, object recognition, and material modeling. The hyperspectral imaging data from 29 human faces with different skin tones and sexes, under constant lighting and controlled movements, were described and characterized. Each hyperspectral image, which comprised spectral reflectance of the whole face from 400 to 720 nm in 10 nm steps at each pixel, was analyzed between and within nine facial positions located at different areas of the face. Simultaneously, spectral measurements at the same nine facial positions using conventional local point and/or contact devices were used to ascertain the data. It was found that the spectral reflectance profile changed between skin tones, subjects, and facial locations. Important local variations of the spectral reflectance profile showed that extra care is needed when considering average values from conventional devices at the same area of measurement.

Equitable Access for Persons with Disabilities: Assessing the Effectiveness of Assistive Device Service Provision in San Mateo, Rizal

The Philippines is home to over 1.5 million Persons with Disabilities, with an estimated 0.33% or 5,157 of the population living in San Mateo, Rizal. The country recognizes ten (10) types of disabilities, but availability of assistive devices is only specific to the needs of three (3) types: deaf or hard of hearing, visual, and physical (orthopedic). Despite efforts to break barriers through assistive devices for a livable and sustainable living condition for Persons with Disabilities, access to equal opportunities in education, employment, health services, and meaningful participation in mainstream society remains a prominent limitation, rather than the disability itself. The study aims to assess the effectiveness of the Assistive Device Service Provision (ADSP) program for Persons with Disabilities, an umbrella program under the social welfare services of the Local Government of San Mateo, Rizal. The assessment is grounded in the principles of New Public Administration, focusing on the program’s relevance, value orientation, social equity and ability to drive change in protecting, promoting, and supporting the rights and well-being of all Persons with Disabilities. A mixed-methodology approach was employed to explore the relationship between Persons with Disabilities and the effective use of Assistive Devices, using surveys and open-ended data collection to quantify responses and deepen understanding. Findings showed that the ADSP is relevant, as it largely meets the needs of Persons with Disabilities in terms of access to mobility, though some devices could be better aligned by considering the specific requirements of each disability. While the ADSP is perceived by recipients as significant in demonstrating a strong value orientation, disparities in distribution—affected by socioeconomic status and type of disability—persist, indicating the need for more inclusive distribution strategies to ensure equitable access for all Persons with Disabilities. The program's ability to drive change varies among individuals, highlighting the need for continuous monitoring and support. The study focuses on assessing the effectiveness of a localized assistive device provision program based on its relevance, value orientation, social equity, and ability to drive change in promoting the rights and welfare of Persons with Disabilities (Union of Public Service Commission, 2024).

NANOMEDICINE BREAKTHROUGH: REVOLUTIONIZING CERVICAL CANCER TREATMENT WITH PRECISION DRUG DELIVERY

Cervical cancer, a global health issue, is a major concern, particularly in regions with limited healthcare resources. To address this, targeted drug delivery strategies have emerged as promising solutions. These involve delivering therapeutic agents directly to cancerous cells, minimizing systemic side effects, and enhancing treatment efficacy. Delivery systems like nanoparticles, liposomes, polymeric micelles, hydrogels, and local drug delivery devices have been developed to overcome barriers like drug resistance and limited penetration. Emerging targeting strategies like tumor-specific ligands, immunotherapy, virotherapy, and gene therapy are also promising for improving treatment specificity and efficacy in cervical cancer. These approaches address the intricate cellular pathways involved in the disease progression, paving the way for more personalized and effective treatments. The synergy between advancements in drug delivery technologies and targeting strategies offers hope for a future where cervical cancer can be effectively managed and potentially eradicated. KEYWORDS: Targeted drug delivery, Nanoparticles, Immunotherapy, Gene therapy, Personalized treatments, Cervical cancer

US urban land-use reform: a strategy for energy sufficiency

Policy approaches to the global energy transition often focus on technology-based solutions while ignoring challenges of overall energy demand. A sufficiency-first approach aims to limit superfluous consumption while achieving wellbeing for all. This study focuses on US built environment mechanisms of sufficiency under urban land-use policy. The historical context of US exclusionary and car-oriented planning is reviewed with an order-of-magnitude assessment of the effects on greenhouse gas emissions (GHGE). Using national vehicle-miles traveled (VMT) data derived from mobile device locations (Replica) and validated here with federal data, a hypothetical scenario explores the potential for state urban land-use reforms to enable energy sufficiency. Tenth percentile-VMT (per capita) neighborhoods are defined by state: in 47 states, the typical such neighborhood has less than 33% of its housing units in structures larger than four units. Assuming each state redresses its housing shortage while matching this VMT, 31 Mt CO2e (direct GHGE) and about 38 Mt CO2e (indirect and life-cycle GHGE) would be avoided in 2033. Texas, California, and Florida have the largest absolute emissions reduction opportunity. Urban land-use reforms comprise a logical starting point for a US sufficiency agenda. Key priorities for research, data collection, and technology and policy innovation are proposed. Policy relevance International climate policy is increasingly focused on enabling people to consume less energy: not just technological ‘efficiency’ but ‘sufficiency’ is needed. However, sufficiency has seen little uptake in the US. It may be more relevant to US policymakers if related to the growing momentum for reforming land-use planning and housing policy to address the housing shortage and affordability crisis. This crisis stems in part from the US prevalence of single-family zoning and car-centric planning, rooted in a history of racial segregation; these same laws effectively mandate people to maintain more polluting lifestyles. This study estimates how much climate pollution could be avoided with state-led land-use reform. If states committed to solving the housing shortage while building new housing in neighborhoods where people can drive less, the savings could be comparable with expanding electric vehicle policies. Policymakers and practitioners can enable these reforms while supporting complementary policy goals.

Assessment of a local and low-cost passive in-line chlorination device in rural Guatemala

Assessment of a local and low-cost passive in-line chlorination device in rural Guatemala

Automatic device arrangement and wiring for fire alarm systems in metro stations: A case study

Fire alarm systems in metro stations play an important role in safeguarding lives and properties. The design process for these systems is complex and time-consuming due to the diversity of device types, wide distribution of device locations, and numerous compulsory design codes. This paper proposes an automatic design framework for fire alarm systems aimed at enhancing design efficiency. The framework includes five automated processes: information preprocessing, device arrangement, system loop generation, device wiring, and result generation. By converting the computer-aided design (CAD) results into building information models (BIM), building information and related professional device information are obtained, addressing the need for fire alarm system (FAS) design to integrate with multiple disciplines. The process can also automatically arrange FAS devices according to design specifications. Optimization algorithms, such as Ant Colony Algorithm and Prim Algorithm, are used to determine the device connection order and solve the problems of obstacle avoidance and orthogonal connections between two points. Compared to traditional design methods, the proposed automatic design framework for fire alarm systems in metro stations can not only satisfy the mandatory design requirements but also avoid the cumbersome and repetitive design process, providing new solutions for designers. Additionally, it can reduce wire length and save on construction costs. Using a real metro station as a case study, it is calculated that the automatic design can save between 8 % and 48 % of the wire length.