Smart System Applications Research Articles

YOLOV7-BASED MOVING OBJECT DETECTION IN DENSE FOG CONDITIONS

Detecting moving objects in dense foggy conditions is a challenging problem in computer vision, with critical applications in smart transportation systems, surveillance, and autonomous driving. Fog particles scatter and absorb light, significantly reducing visibility and making it difficult for traditional computer vision algorithms to accurately detect moving objects. To address this challenge, researchers have proposed learning-based approaches that leverage deep neural networks to recognize moving objects and adapt to the unique characteristics of foggy environments. In this study, we present a learning-based method utilizing the YOLOv7 framework to effectively detect moving objects in dense fog conditions. The proposed approach involves four key stages: feature extraction, feature fusion, object detection, and non-maximum suppression. The results achieved are highly promising when compared to state-of-the-art techniques.

MQTT Protocol in Smart Home Environments: Principles of Operation and Application

Internet of Things as a revolutionary concept in technology of modern days has led to the popularization of “smart” things. It introduces low-cost and reliable solutions to provide independent operations of devices and machines. Smart home systems have become one of the most important implementations as they significantly improve the quality of people’s lives. Since communication plays a key role in these systems, this article provides insights into Message Queuing Telemetry Transport protocol application in smart home systems. Although it is not the only protocol used, MQTT stands out in terms of reliability, efficiency, and simplicity. The article provides major MQTT characteristics, advantages and its application in a particular smart home system for ambient parameters monitoring and control. The system integrates NodeMCU based sensor node with mobile and web applications via MQTT protocol. Results of the performance analysis of MQTT use in this system are given. Tests have shown great connection establishment speed, a high percentage of successfully sent messages, and a large number of messages sent per minute. Thanks to the flexibility of the MQTT protocol, the system is easy to upgrade and expand.

Self-powered asphalt-based sensors for smart roads

Monitoring traffic conditions and pavement structures is essential for intelligent transportation systems. However, conventional sensors have limitations, including poor compatibility with pavement and high maintenance costs. Here, we present the concept of transforming asphalt from a pavement structural component to a sensing component and demonstrate its application in smart road systems. The functional asphalt was customized by adding piezoelectric materials into the asphalt matrix. We optimized its piezoelectric properties by improving the fabrication process and measured the electrical performance of the asphalt-based sensors. In the traffic monitoring experiment, we developed a system incorporating data acquisition, signal processing, and wireless transmission functions to capture tire-ground contact information. The details, such as speed and wheelbase, are decoded by a feature extraction algorithm and input into a support vector machine (SVM) classification model for training and testing. The model reaches a test accuracy of 97 % in training a small-sample dataset. In addition, the self-powered asphalt-based sensor showed its feasibility and potential in the verification experiments of acoustic source localization (error = 2.4 %) and energy harvesting. Our work proposes a low-cost, environmentally friendly, multifunctional material suitable for road sensors, potentially facilitating the large-scale implementation of smart road networks.

Digital Twins in the Sustainable Construction Industry

Digital Twin (DT) technology, as the evolution of Building Information Modeling (BIM), has emerged to address global concerns regarding the environmental impacts of the construction industry and to meet sustainability indicators. Despite numerous studies targeting the integration of DT and sustainability, there is a noticeable gap in creating a comprehensive overview of the efforts and future directions in this field. Therefore, this research aims to provide both a scientometric analysis and a thematic review of 235 papers extracted from the Scopus database. These papers, all published between 2017 and 2024, focus on previous efforts, current trends, and future directions of using the Digital Twin for construction sustainability. In addition, 34 papers that were cited more than 20 times were classified by the application into four categories: simulation, technology integration, smart systems, and literature review. Furthermore, regarding the application of smart systems in sustainability, the authors discussed applications of BIM-DT in smart construction, smart buildings, smart infrastructures, and smart cities based on the most-cited papers. Subsequently, five research gaps were identified and suggested for future investigation. The research gives a holistic insight into the current trend of DT among researchers, previous achievements, and future directions.

Empowering Education Through Teacher-Technology Collaboration

The emergence of information systems integrated with artificial intelligence (AI) technology has led to great changes in education. It provides strong support and guarantees for the education field in adapting to the transformation of the digital environment. As end users and managers of information system and technology, teachers' innovative leadership contributes to this rapid digitalization transformation in education. The current study highlights the leading role of teachers' innovative leadership in the teaching process involving the application of smart learning systems. In the smart learning environment, teachers who practice more innovative leadership improve the engagement of end user learners in collaborative activities and further improve campus organizational culture. This study further presents feasible measures for practicing innovative teacher leadership in class and conducting blended management for teacher–AI collaboration in a smart environment.

Thermally robust hierarchical nanofiber triboelectric yarns for efficient energy harvesting in firefighting E-textiles

Textile-based triboelectric nanogenerators (TENGs) face challenges in achieving high performance and fulfilling functional requirement for special applications. This paper presents the development of a high-performance, thermally robust hierarchical polyimide (PI) nanofiber triboelectric yarns with a charge-trapping interlayer for application in firefighting E-textiles. The hierarchical nanofiber yarns, comprising a stainless-steel core electrode, a PI/MXene interlayer and PI outer tribo-layer, exhibit enhanced triboelectric performance due to the charge-trapping capabilities of MXene, which preventing the recombination of triboelectric and induced charges. When the MXene content in the charge-trapping interlayer reached 1 wt%, the corresponding TENG achieved its optimal output performance, with an output voltage of 153 V and a current of 13.13 μA and a peak power density of 0.84 W/m2 under optimal conditions. It also demonstrates exceptional thermal stability, maintaining stable output at temperatures up to 400 °C. The integration of the TENG into E-textiles enables real-time monitoring of firefighters’ physical activities and location tracking, significantly enhancing their safety and operational efficiency in fire scenarios. Additionally, the TENG powers electroluminescent yarns, improving visibility in dense smoke and dust environments. This research opens new avenues for the application of smart wearable systems in high-temperature environments, providing effective strategies for ensuring the safety of firefighters in fire rescue operations.

Thermally drawn multi-material fibers: from fundamental research to industrial applications

Thermally drawn fiber devices, with their complex micro- to nanoscale architectures, hold great promises not only for scientific research but also for scalable industrial applications in soft smart systems.

Three-dimensional triboelectric nanogenerator manufacturing using water transfer printing

Triboelectric nanogenerators (TENGs) have been widely studied due to their efficient energy harvesting capabilities and fast electrical response signals. In this study, a processing method based on water transfer printing (WTP) is developed for preparing TENG devices with three-dimensional (3D) structures. With this method, electrodes and ink layers can be integrated onto a variety of 3D objects to form a one-piece structure, which offers advantages including application to 3D surfaces, abrasion resistance, handling ease, adaptability, and decorativeness. The process istaken to prepare a 5 × 5 cm2 single-electrode TENG (IF-TENG), with FEP serving as the negative triboelectric layer and ink serving as the positive triboelectric layer. The TENG’s open-circuit voltage and short-circuit current are 184 V and 1.53 μA, respectively. Additionally, a door handle TENG (DH-TENG) device has been built for practical application demonstrations. The DH-TENG device is used in wireless alarm systems that prevent children from accidentally ingesting medication. It is also used in self-powered guidance systems that illuminate LED arrow lights to help the elderly or children at night. Seamless integration of 3D surface TENG devices using WTP is expected to have applications in smart home systems, smart transportation and security, wearable electronics, and energy harvesting.

Security and privacy challenges, issues, and enhancing techniques for Internet of Medical Things: A systematic review

AbstractThe Internet of Things (IoT) is a rapidly expanding network of interconnected things that use embedded sensors to gather and share data in real‐time. IoT technologies have given rise to many networking applications in our everyday life such as smart homes, smart cities, smart transport, and so forth. Smart healthcare is one such application that has been revolutionized by the IoT, introducing a new branch of IoT known as the Internet of Medical Things (IoMT). IoMT encompasses an entire ecosystem consisting of smart wearable, implantable sensing equipment's or devices, transmitters that are critical for monitoring the patients remotely and continuing the real‐time and has opened the door to new innovative smart healthcare approaches while improving patient care outcomes. IoMT wearable and embedded sensing devices are commonly utilized in smart healthcare to capture medical data and transmit the medical data in a communication network stored in the cloud. The large volume of data generated and transmitted by these IoMT devices is rising at an exponential rate, resulting in an increase in security and privacy vulnerabilities of healthcare data. To ensure the Confidentiality and integrity of the IoMT devices and the sensitive medical data, there should be proper security and privacy measures such as access control, passwords, multifactor authentication, and encryption of data generated, transmitted, or processed in the IoMT framework. In this paper, we identified the internet of things and its applications in smart healthcare systems. Additionally, the paper focuses on the architecture of IoMT, and several challenges, including the IoMT security and privacy requirements, and attack taxonomy. Furthermore, the paper thoroughly investigates both cryptographic and non‐cryptographic based security and privacy‐enhancing techniques for IoMT or healthcare systems with particular emphasis on advancements in key areas such as Homomorphic Encryption, Differential Privacy, and Federated Learning.

A novel double-layer liquid crystal electro-optic switch and its infrared shielding properties investigation

ABSTRACT In this study, a novel light switch was developed by integrating polymer dispersion liquid crystals (PDLC) and polymer-stabilised cholesteric liquid crystals (PSCLC), leveraging the infrared shielding properties of these two liquid crystal films. The optimal formulation for each layer of the liquid crystal structure was investigated. Subsequently, the effects of light intensity, high temperature, and humidity on the performance of the light switch were examined. This new light switch can be selectively driven layer by layer at room temperature, achieving three states: the off state, PDLC layer-driven state, and full-driven (on state). This functionality serves the purpose of electrogenerated temperature control. Samples under optimal conditions exhibited a 31% enhancement in infrared shielding compared to a single-layer PDLC film. Moreover, the IR shielding rate could be adjusted according to the voltage applied, realising an intelligent temperature-control effect. The results of this study suggest that such bilayer liquid crystal structures hold significant potential for application in electro-optical devices and smart curtain systems.

Self-powered piezoelectric sensor based on BaTiO3/MWCNTs/PVDF electrospun nanofibers for wireless alarm system

Abstract The piezoelectric constant of polyvinylidene fluoride (PVDF) is inferior to that of piezoelectric ceramics, which will impede the efficient application in smart systems. In this work, we modulated the content of BaTiO3/multi-wall carbon nanotubes (MWCNTs) in the BaTiO3/MWCNTs/PVDF electrospun nanofibers to facilitate the β phase formation in the PVDF to enhance the piezoelectric properties of BaTiO3/MWCNTs/PVDF films. The BaTiO3 combined with MWCNTs through chemical bands can enhance the electrostatic interaction at the vicinity of BaTiO3-PVDF interface to induce augmentation of the local conformational disorder and result in the enhanced nucleation and stabilization of β phase in the BaTiO3/MWCNTs/PVDF films. When the ratio between BaTiO3 and MWCNTs is about 3:1, the PVDF-BM-3 can deliver the output voltage of 39.5 V under 250 kPa with a frequency of 10 Hz. The PVDF-BM-3 electrospun nanofibers as triggers used in the wireless alarm system can achieve comparable sensitivity under different external stresses. This work paves a new promising pathway for self-powered piezoelectric sensors in the Internet of Things.

Study of the technology of smart cleaning of solar power modules of an autonomous power station

The advancement of solar technology has facilitated the development of autonomous power stations that primarily rely on solar energy. This article delves into the crucial role that smart cleaning technologies play in enhancing the efficiency of these solar power modules. Solar panels, pivotal in harnessing solar energy, require regular and effective cleaning to maintain their efficiency. Traditional cleaning methods, although somewhat effective, are increasingly becoming insufficient due to the evolving demands of autonomous systems. The focus of this research is on the innovative integration of the Internet of Things (IoT) technologies to optimize the cleaning process. IoT not only enables remote monitoring and control but also automates the cleaning process through advanced algorithms that assess environmental data and panel conditions. This optimization ensures that the panels operate at peak efficiency by scheduling cleaning sessions based on actual needs rather than fixed intervals. Specifically, the study explores various smart devices such as autonomous robots and drones equipped with sensors and cleaning systems. These devices perform tasks ranging from dust detection to thorough cleaning without manual intervention, significantly reducing the labor cost and time involved in maintaining large solar farms. The use of embedded sensors and cameras facilitates real-time monitoring of the panels’ conditions, detecting any inefficiencies or malfunctions early. Furthermore, this article highlights the potential of mobile applications in smart cleaning systems. These applications provide operators with real-time data analysis, system status updates, and the flexibility to adjust operational parameters remotely. This capability not only enhances the responsiveness of the maintenance teams but also contributes to a proactive maintenance culture, preventing potential downtimes. In conclusion, smart cleaning technologies, powered by IoT and supported by innovative hardware and software solutions, represent a significant advancement in the field of renewable energy. They ensure that solar power modules operate at their highest potential, thereby supporting the broader goal of sustainable and efficient energy use in autonomous power stations. This research underscores the transformative impact of integrating smart technologies in the renewable energy sector, suggesting that continued innovation in this area is essential for the future of sustainable energy infrastructure.

Functional fibers/textiles for smart sensing devices and applications in personal healthcare systems.

Personalized medical diagnostics and monitoring have become increasingly important due to inefficient and delayed medical services of traditional centralized healthcare systems. To enhance the comfort and portability, flexible health monitoring systems have been developed in recent years. In particular, smart fiber/textile-based sensing devices show superiority for continuously monitoring personal health and vital physiological parameters owing to their light weight, good flexibility and inherent miniaturization. This review focuses on the recent advances in smart fiber/textile-based sensing devices for wearable electronic applications. First, fabrication strategies of smart sensing fibers/textiles are introduced in detail. In addition, sensing performances, working principles and applications of smart sensing fibers/textiles such as pressure sensing fibers/textiles, stretchable strain sensing fibers/textiles, temperature sensing fibers/textiles, and biofluid, gas and humidity sensing fibers/textiles in health monitoring are also reviewed systematically. Finally, we propose current challenges and future prospects in the area of fiber/textile-based sensors for wearable healthcare monitoring and diagnosis systems. In general, this review aims to give an overall perspective of the promising field by reviewing various fiber/textile-based sensing devices and highlighting the importance for researchers to keep up with the sequential exploration of soft sensing fibers/textiles for applications in wearable smart systems.

A stimulus responsive microneedle-based drug delivery system for cancer therapy.

The intricate nature of the tumor microenvironment (TME) results in the inefficient delivery of anticancer drugs within tumor tissues, significantly compromising the therapeutic effect of cancer treatment. To address this issue, transdermal drug delivery microneedles (MNs) with high mechanical strength have emerged. Such MNs penetrate the skin barrier, enabling efficient drug delivery to tumor tissues. This approach enhances drug bioavailability, while also mitigating concerns such as liver and kidney toxicity associated with intravenous and oral drug administration. Notably, stimulus responsive MNs designed for drug delivery have the capacity to respond to various biological signals and pathological changes. This adaptability enables them to exert therapeutic effects within the TME, exploiting biochemical variations and tailoring treatment strategies to suit tumor characteristics. The present review surveys recent advancements in responsive MN systems. This comprehensive analysis serves as a valuable reference for the prospective application of smart MN drug delivery systems in cancer therapy.

Design of an Iot Based Automatic Lawn Vacuum & Leaf Bag Using Usability and Work Posture Method

Smart Garden is a system design created to make work in plantations easier. Currently, most Indonesians use sling type lawn mowers because they are cheap but can causing noise and air pollution. It can also cause CTDS in users due to bad posture while using the tool. The researchers innovated an IoT-based automatic cutting machine. Where in the machine application, the machine can be moved using a smartphone connected using bluetooth. Which consists of two options, namely machine and vacuum. The REBA method are used to analyzing ergonomics for all parts of the body and a usability method which will determine the extent to which the product can achieve effectiveness, efficiency and comfort. This data collection was carried out by distributing the SUS questionnaire. The REBA results showed that the average body posture at moderate risk had a score of 7.8 and 9 which required further analysis of the bent posture even though it was still classified as moderate. Meanwhile, the results of the usability approach use a questionnaire as a reference for processing data. From this data, users can accept the presence of the Control Smart System Application and are quite satisfied with the interface used.

A Smart System for Future Generation based on the Internet of Things Employing Machine Learning, Deep Learning, and Artificial Intelligence : Comprehensive Survey

The Internet of Things (IoT) is a networked system including interconnected things, devices, and networks that utilize the internet for communication and data exchange. The entity engages in interactions with both its internal and external surroundings. The IoT is capable of seeing the surrounding environment and responding in a way that is appropriate and adaptive. The utilization of advanced technology in this context enhances the environment and thus enhances the overall well-being of humanity. The IoT facilitates inter-device communication, whether through physical or virtual means. The IoT facilitates the enhancement of environmental intelligence, enabling seamless connectivity across many devices at any given moment. The concepts centred on the IoT, such as augmented reality, high-resolution video streaming, autonomous vehicles, intelligent environments, and electronic healthcare, have become pervasive in contemporary society. These applications have requirements for faster data rates, larger bandwidths, enhanced capacities, decreased latencies, and increased throughputs. IoT and Machine learning (ML) are among the fields of research that have shown significant potential for advancement. ML and IoT are used to build intelligent systems. Those networks will modify the ways in which worldwide entities exchange information. This article gives a comprehensive survey of the upcoming 5G-IoT situation, as well as a study of IoT smart system applications and usages. In addition to covering the latest developments in ML and deep learning (DL) and their impact on 5G-IoT, this article describes a comprehensive study of these important enabling technologies and the developing use cases of 5G-IoT.

Application of Smart and Early Fire Detection Systems in Hospitals: Insights from Iraq

Application of Smart and Early Fire Detection Systems in Hospitals: Insights from Iraq

Development of Optimal Hyperparameter Tuning-Cycle GAN for Photo-realistic Face Age Progression Model

Face age progression aims to change an individual’s face from a provided face image to forecast how that image will look in the future. Face aging is gaining much attention in today’s environment, which needs better security and a touchless unique identification mechanism. Researchers are focused on creating face processing algorithms to address the difficulty of producing realistic aged faces for smart system applications over the earlier decades. In the literature, the two basic needs of face age progression, aging accuracy, and identity preservation are not thoroughly addressed. According to the extraordinary gains in image synthesis made by deep generative methods and their significant influence on a wide variety of practical applications such as identifying missing persons using entertainment, childhood images, and so on, face age progression/regression has reawakened attention. The majority of present techniques concentrate on face age progression and is beneficial and productive in learning the transition across age groups utilizing paired data, i.e., face images of the similar individual at various ages. Through the motivation of the important success attained by Generative Adversarial Networks (GANs), this paper uses tactics to implement the improved Cycle GAN-based intelligent face age progression model. Initially, the standard datasets for the face progression are gathered, and the face is detected using the Viola-Jones object detection algorithm. Then, the pre-processing of the facial image is performed by median filtering and contrast enhancement techniques. Once the image is pre-processed, the Hyperparameter Tuning-Cycle GAN (HT-Cycle GAN) is adopted for face age progression. As an improvement, the hyperparameters of the Cycle GAN are optimized or tuned by the modified Galactic Swarm Optimization (GSO), known as Best Fitness-based Galactic Swarm Optimization (BF-GSO). From the evaluation of statistical analysis, the similarity score of BF-GSO-HT-CycleGAN is 0.80%, 3.33%, and 2.86% higher than cGAN, CycleGAN, and Dubbed FaceGAN, respectively. Here, the Dubbed FaceGAN is the 2nd greatest network. Furthermore, compared to traditional models utilizing distinct standard datasets, the experimental findings show that the suggested technique attains efficiency, accuracy, and flexibility.

Building Energy Savings by Developing Complex Smart Windows and Their Controllers

The interest in zero-energy buildings has increased in Korea recently. Following the significant increases in cooling and lighting energy consumption in offices, various studies have been conducted to implement energy-saving measures. The purpose of this study is to reduce lighting and cooling energy consumption in the summer through the dimming control of a complex smart window system. To achieve this, the optimal dimming control algorithm has been derived and applied in simulations to analyze the energy consumption for lighting and cooling. A smart window incorporates suspended particle display glass that actively responds to changes in indoor and outdoor environments and controls light transmittance. It also includes a light-guiding glass that can actively control solar reflectance. Simulations of office buildings were conducted to develop optimal control algorithms and controllers based on solar radiation. Subsequently, we installed this complex smart window in a test room along with the developed control algorithm and controller, which responded to the amount of insolation and time. To ensure the accuracy of the experiment, we constructed separate test and reference rooms. The experimental results obtained under the same conditions showed a reduction of approximately 36.9% in cooling energy consumption in the test room compared with the reference room and a 54.5% reduction in lighting energy consumption. Furthermore, based on additional simulations and experiments, we confirmed that the application of complex smart window systems in office buildings could reduce considerably the energy consumption for cooling and lighting.

An Empirical Study of the Technoparks in Turkey in Investigating the Challenges and Potential of Designing Intelligent Spaces

The use of innovative technologies in workspaces, such as the Internet of Things (IoT) and smart systems, has been increasing, yet it remains in the minority of the total number of smart system applications. However, universities and technopoles are part of open innovation that can encourage experimental IoT and smart system projects in places. This research considers the challenges and advantages of developing intelligent environments with smart systems in the Technology Development Zones (TDZs) of Turkey. The growth of Silicon Valley has inspired many technopoles in different countries. Thus, the article includes first a comprehensive survey of the story of Silicon Valley and the emerging technological potential of open and responsible innovation for intelligent spaces and technoparks with rising innovative interest. The study then conducts empirical research in inspecting the performance of TDZs in Turkey. In the research, machine learning and Artificial Intelligence (AI) models are applied in the analyses of critical performance indicators for encouraging incentives and investments in innovative attempts and productivity in TDZs; the challenges, potential, and need for intelligent spaces are evaluated accordingly. This article also reports on the minority of the design staff and the lack of innovation in developing intelligent spaces in the organization of the creative class in Turkey. Consequently, the research proposes a set of implementations for deploying intelligent spaces to be practiced in new and existing TDZs by considering their potential for sustainable and responsible innovation.