Innovative Smart Research Articles

Design and Realization of a Low-Cost Smart Walking Aid for Visually Impaired and Blind People

We have developed an affordable and intelligent walking aid designed to provide visually impaired and blind individuals with alert-based support while navigating both outdoor streets and indoor environments. This innovative smart blind stick offers more than just collision avoidance capabilities; it boasts a range of advantages including cost-effectiveness, rapid responsiveness, minimal power consumption, lightweight design, and water detection features. The smart walking aid utilizes an ultrasonic sensor to detect obstacles in the user's path and promptly notifies them through a combination of buzzer alerts and mechanical vibrations, helping them avoid potential obstacles. Moreover, it can not only identify solid obstructions but can also alert the user to the presence of water using a dedicated water sensing mechanism. We rigorously evaluated the prototype's performance through extensive testing to optimize its accuracy in a variety of scenarios, revealing an impressive maximum obstacle detection range of approximately 2 meters on average.

Construction and Application of English Smart Classroom Teaching Model Integrating MOOC and Flipped Classroom

Abstract This paper first builds a mobile teaching platform based on the teaching mode of MOOC and flipped classroom and designs functional modules such as system management, course transmission, course design and user experience. Then, it analyzes the characteristics of students' learning behaviors using the xgboost algorithm and teaches English in innovative smart classrooms. The mobile teaching platform created for this paper is evaluated in terms of learners' examination and video-viewing behavior. From the learners’ examination behavior, the non-perfect score rate gradually declines from the highest value of 41.56% in Chapter 1 to the lowest value of 17.64% in Chapter 5, with a small fluctuation in the last chapters. This shows that the mobile teaching platform for MOOCs and flipped classrooms constructed in this paper can facilitate intelligent English classroom teaching innovation.

CO2 concentration forecasting in smart cities using a hybrid ARIMA–TFT model on multivariate time series IoT data

Carbon Dioxide (CO_{2}) is a significant contributor to greenhouse gas emissions and one of the main drivers behind global warming and climate change. In spite of the global economic slowdown due to the COVID-19 pandemic, the global average atmospheric CO_{2} concentration reached a new record high in 2020 with its year-on-year increase being the fifth highest annual increase in 63 years, according to the National Oceanic and Atmospheric Administration. Furthermore, the years 2020 and 2019 were respectively the second and third warmest, while the decade 2010–2019 was the warmest decade ever recorded. In an attempt to curb this climate emergency, many countries and organizations globally have adopted ambitious goals and announced plans to help dramatically reduce CO_{2} emissions. As part of these plans, various innovative smart city projects are being developed, focusing on implementing Internet of Things (IoT) technologies. By collecting sensor-based data, such technologies aim towards automating data-driven decision-making around carbon emission management and reduction. In this work, a hybrid machine learning system, aimed at forecasting CO_2 concentration levels in a smart city environment was developed using a multivariate time series dataset containing IoT sensor measurements of CO_{2}, as well as various environmental factors, taken at every second. The proposed system demonstrated superior performance to similar methods, while also maintaining a high degree of interpretability. More specifically, the approach was empirically compared against other similar approaches in several scenarios and use cases, thus also offering more insight into the predictive capabilities of such state-of-the-art systems. For this comparison, both traditional time series and deep learning approaches were employed, including the current state-of-the-art architectures, such as attention-based, transformer networks. Results demonstrated that, when measured across various settings and metrics, including three different forecasting horizons, the hybrid solution achieved the best overall results, and in some cases, the difference in performance was statistically significant. At the same time, insights from the system’s inner workings were extracted, shedding light on the reasoning behind the model’s predictions and the factors that contribute to them, thus showcasing its transparency. Lastly, throughout the experiments, deep learning approaches illustrated their ability to better handle the multivariate nature of the dataset and in general tended to outperform the traditional time series methods, especially for longer forecasting horizons.

Fostering Fuzzy Logic in Enhancing Pedestrian Safety: Harnessing Smart Pole Interaction Unit for Autonomous Vehicle-to-Pedestrian Communication and Decision Optimization

In autonomous vehicles (AVs), ensuring pedestrian safety within intricate and dynamic settings, particularly at crosswalks, has gained substantial attention. While AVs perform admirably in standard road conditions, their integration into unique environments like shared spaces devoid of traditional traffic infrastructure control presents complex challenges. These challenges involve issues of right-of-way negotiation and accessibility, particularly in “naked streets”. This research delves into an innovative smart pole interaction unit (SPIU) with an external human–machine interface (eHMI). Utilizing virtual reality (VR) technology to evaluate the SPIU efficacy, this study investigates its capacity to enhance interactions between vehicles and pedestrians at crosswalks. The SPIU is designed to communicate the vehicles’ real-time intentions well before arriving at the crosswalk. The study findings demonstrate that the SPIU significantly improves secure decision making for pedestrian passing and stops in shared spaces. Integrating an SPIU with an eHMI in vehicles leads to a substantial 21% reduction in response time, greatly enhancing the efficiency of pedestrian stops. Notable enhancements are observed in unidirectional (one-way) and bidirectional (two-way) scenarios, highlighting the positive impact of the SPIU on interaction dynamics. This work contributes to AV–pedestrian interaction and underscores the potential of fuzzy-logic-driven solutions in addressing complex and ambiguous pedestrian behaviors.

A perspective study for the assessment of field robots in agriculture: An enhanced fuzzy MADM approach

The industrial revolution led in the creation of robots capable of performing complicated tasks in a range of industries. Robotic agrifarming is an innovative smart farming technology that can improve food safety and crop yield and address the global labour crisis. Agribots are used to undertake field tasks ranging from seeding through harvesting, which improve soil quality and guarantee long-term growth. As a result, the development of a decision-support system for determining the most appropriate robot for an agricultural operation is necessary. This study develops a T2IF-based integrated decision system for evaluating the situation at hand. The performance of five field robots is assessed based on technical, economic, social, political, and environmental aspects using the integrated BWM-MULTIMOORA methodology. The advantage of the developed ranking technique is that it follows Borda’s rule for prioritizing the alternatives rather than dominance theory, where both the utility and the subordinate ranking order of methods are considered. In addition, a comparative and sensitivity investigation is carried out to examine the stability of the results.

Photothermally Triggered Nanoreactors with a Tunable Catalyst Location and Catalytic Activity.

Thermosensitive microgels based on poly(N-isopropylacrylamide) (PNIPAm) have been widely used to create nanoreactors with controlled catalytic activity through the immobilization of metal nanoparticles (NPs). However, traditional approaches with metal NPs located only in the polymer network rely on electric heating to initiate the reaction. In this study, we developed a photothermal-responsive yolk-shell nanoreactor with a tunable location of metal NPs. The catalytic performance of these nanoreactors can be controlled by both light irradiation and conventional heating, that is, electric heating. Interestingly, the location of the catalysts had a significant impact on the reduction kinetics of the nanoreactors; catalysts in the shell exhibited higher catalytic activity compared with those in the core, under conventional heating. When subjected to light irradiation, nanoreactors with catalysts loaded in the core demonstrated improved catalytic performance compared to direct heating, while nanoreactors with catalysts in the shell exhibited relatively similar activity. We attribute this enhancement in catalytic activity to the spatial distribution of the catalysts and the localized heating within the polydopamine cores of the nanoreactors. This research presents exciting prospects for the design of innovative smart nanoreactors and efficient photothermal-assisted catalysis.

Innovative Smart Dustbin for Managing Waste Disposal

The present paper introduces a novel approach to modern waste management by integrating technology with waste disposal systems. In response to the need for more engaging and efficient waste disposal methods, we propose an interactive smart dustbin. This dustbin, enhanced with robotic features and advanced sensors, aims to revolutionize how individuals perceive and participate in waste management practices. The design, implementation, and potential applications of the interactive smart dustbin are presented herein, with a focus on its potential to promote responsible waste disposal habits across diverse settings. Additionally, the integration of solar power as an alternative energy source is explored to enhance the system's sustainability.

SmarCyPad

Cycling is an efficient and effective way to improve one's overall fitness level, such as cardiovascular fitness, stamina, lower body strength, and body fat percentage. To improve fitness performance, real-time cycling fitness tracking can not only allow cyclists to better control their energy outputs but also help push workout intensity and keep users accountable for their fitness progress. However, existing bike sensors (e.g., the ones mounted to bike's wheel hub or crank arm) are only limited to measuring cycling cadence and speed. Although several recent studies relying on on-body sensors or cameras can provide more fine-grained information (e.g., riding position and knee joint angle), they would either require inconvenient setups or raise serious privacy concerns. To circumvent these limitations, in this paper, we propose SmarCyPad, an innovative smart seat pad that can continuously and unobtrusively track five cycling-specific metrics, including cadence, per-leg stability, leg strength balance, riding position, and knee joint angle of the cyclist. Specifically, we embed conductive fabric sensors in the seat pad to sense the pressure applied to the bike's seat exerted by the cyclist's gluteal muscles. A series of signal processing algorithms are developed to estimate the pedaling period from the sensed pressure signal and further derive the cycling cadence, per-leg stability, and leg strength balance. Additionally, we leverage a deep learning model to detect the cyclist's riding position and reconstruct the cyclist's knee joint angles via linear regression. The sensors and the system prototype are manufactured from scratch leveraging off-the-shelf materials, and the total cost is less than $50. Extensive experiments involving 15 participants demonstrate that SmarCyPad can accurately estimate the cycling cadence with an average error of 1.13 rounds per minute, quantify the cycling stability for each leg, detect cycling imbalance, distinguish five riding positions with an accuracy of 96.60%, and continuously track the knee joint angle with an average mean error as low as 9.58 degrees.

Dual-functional smart indicator for direct monitoring fruit freshness

Innovative smart packaging systems that inhibit bacterial growth and monitor food quality change are urgently needed in food industry. A dual-functional polyvinyl alcohol/polyethyleneimine/methylene blue (PVA/PEI/MB) nanofiber film was prepared for inhibiting bacterial growth and monitoring freshness of fruit. The nanofibers exhibited excellent mechanical property, breathability, water resistance and biocompatibility as characterized by the tensile test, air permeability test, water contact angle and cytotoxicity experiment. In vitro antibacterial experiment showed that the nanofibers had strong antibacterial activities against Escherichia coli and Staphylococcus aureus, and the antibacterial activities were improved under light irradiation. Nanofibers showed color variation from blue to colorless under bacterial infection in a range of 101–1010 CFU/mL, which can be used to detect the freshness of fresh-cut fruit. Effectiveness of nanofibers for freshness keeping and monitoring was confirmed via fruit tests, which suggests that the dual-functional nanofibers could potentially be used in food packaging.

Forecast of changes in the macroeconomic situation in Ukraine: smart economy of the future

Aims: to theoretical development of methodological tools for selecting national priorities for sustainable economic development based on the concept of a smart future economy. Methodology. This article uses a systematic approach to the study of change processes in the fields of macroeconomics, analysis and synthesis, induction and deduction, quantitative and qualitative scientific abstraction, comparison and analysis. Based on the modeling method, the article proposes a model for planning economic growth based on the indicators of socio-economic, investment, and innovation development of Ukraine and major countries of the world. Results. The paper proves the complexity of the problem of choosing the country's development priorities and its specialization, which is observed in many works of Ukrainian scientists and researchers from other countries. For this purpose, 40 scientific papers by foreign and domestic scholars were analyzed, and macroeconomic statistics data presented on official statistics websites were used. Based on the analyzed sources, the main problems of the socio-economic development of the country are determined, and the model provides a systematic approach to the formation and implementation of strategies for sustainable socio-economic development. The knowledge industry was developed, and a model was created for the selection of national innovative development priorities based on two main parameters: Overview of the national structure of innovative and specialized industries. The approach of information and communication platforms as a methodological tool for the formation of strategies for local socio-economic development is proposed. Scientific Novelty. With the help of the developed model, it is possible to determine the factors affecting the growth of Ukraine's GDP and forecast the growth of the country's economy. It is found that the transition to a smart economy and the use of the latest innovative smart technologies have the greatest impact on national economic growth. The proposed model for forecasting and evaluating the national socio-economic, investment, and innovation policy can provide the main directions for resource allocation and budgeting. Conclusion. The proposed proposals will contribute to the further development of an innovative program for socio-economic development of the country by applying the principles of the European concept of smart specialization to the Ukrainian economy. Implementation. The research materials can be used as methodological support for demonstrating the directions of regional socio-economic development.

Mechanisms of phototherapy of Alzheimer’s disease during sleep and wakefulness: the role of the meningeal lymphatics

With the increase in the aging population, the global number of people with Alzheimer’s disease (AD) progressively increased worldwide. The situation is aggravated by the fact that there is no the effective pharmacological therapy of AD. Photobiomodulation (PBM) is non-pharmacological approach that has shown very promising results in the therapy of AD in pilot clinical and animal studies. However, the mechanisms of therapeutic effects of PBM for AD are poorly understood. In this study on mice, we demonstrate that photodynamic effects of 5-aminolevulenic acid and laser 635 nm cause reduction of network of the meningeal lymphatic vessels (MLVs) leading to suppression of lymphatic removal of beta-amyloid (Aβ) from the right lateral ventricle and the hippocampus. Using the original protocol of PBM under electroencephalographic monitoring of wakefulness and sleep stages in non-anesthetized mice, we discover that the 7-day course of PBM during deep sleep vs. wakefulness provides better restoration of clearance of Aβ from the ventricular system of the brain and the hippocampus. Our results shed light on the mechanism of PBM and show the stimulating effects of PBM on the brain lymphatic drainage that promotes transport of Aβ via the lymphatic pathway. The effects of PBM on the brain lymphatics in sleeping brain open a new niche in the study of restorative functions of sleep as well as it is an important informative platform for the development of innovative smart sleep technologies for the therapy of AD.Graphical

Intelligent Materials and Nanomaterials Improving Physical Properties and Control Oriented on Electronic Implementations

The review highlights possible research topics matching the experimental physics of matter with advances in electronics to improve the intelligent design and control of innovative smart materials. Specifically, following the European research guidelines of Key Enabling Technologies (KETs), I propose different topics suitable for project proposals and research, including advances in nanomaterials, nanocomposite materials, nanotechnology, and artificial intelligence (AI), with a focus on electronics implementation. The paper provides a new research framework addressing the study of AI driving electronic systems and design procedures to determine the physical properties of versatile materials and to control dynamically the material’s “self-reaction” when applying external stimuli. The proposed research framework allows one to ideate new circuital solutions to be integrated in intelligent embedded systems formed of materials, algorithms and circuits. The challenge of the review is to bring together different research concepts and topics regarding innovative materials to provide a research direction for possible AI applications. The discussed research topics are classified as Technology Readiness Levels (TRL) 1 and 2.

An Overview of Urban Mobility: Revolutionizing with Innovative Smart Parking Systems

This feasibility study aims to propose the design of a smart parking architecture that is able to offer new services by exploiting the latest IoT technologies. This innovative solution is designed for use by both public authorities and private individuals who need to manage urban parking areas efficiently. The contribution of this work is to attempt to define the requirements and technical choices that can be made for the design of a system that adheres to the paradigm of innovation and efficiency in smart parking. Indeed, there is a practical limit between the existing models and the best solutions to apply. For each technology, the following details are provided: the problem to be managed, the current state of the art on the market, the main solutions, and also the related commercial policies. We proceed with the “Outline Design”, which integrates the technical specifications and defines the main information flows between the functional blocks. The results of experimentation show that the proposed reservation-based parking policy has the potential to simplify the operations of parking systems, as well as alleviate cities’ traffic congestion.

LEADERSHIP CREATIVE VILLAGE

The innovative leadership of the Chief District Officer of Cianjur District in the implementation of the Village Innovation was seen and measured to the extent to which the commitment and political will of the Head of the District in providing public services and providing public goods to its citizens. In addition, the Innovative Leadership Capacity of the Head of the District will be reviewed from the perspective of the innovative development visions built, the strategic steps taken to drive innovation, and the stability of his leadership. The research focuses on the leadership of regional heads in the implementation of smart innovation camp with a theoretical approach to leadership and innovation. This research uses a qualitative approach (qualitative approach). Data sources include people, events, places and documents. Data collection techniques include in-depth interviews, documentation and observations. Data analysis techniques are interactive models. (interactive model analysis). The results of the research show that Creative Village Innovation is a technology-based program that focuses on the village community as well as the implementation center in the village office. The program designs the village to have an integrated programme framework that blends the use of fiber-optic information and communication technology (ICT), productive economic activities, creative economic activity, improved education, health, and poverty alleviation efforts.

Innovative Smart Drilling with Critical Event Detection and Material Classification

This work presents a cyber-physical drilling machine that incorporates technologies discovered in the fourth industrial revolution. The machine is designed to realize its state by detecting whether it hits or breaks through the workpiece, without the need for additional sensors apart from the position sensor. Such self-recognition enables the machine to adapt and shift the controllers that handle position, velocity, and force, based on the workpiece and the drilling environment. In the experiment, the machine can detect and switch controls that follow the drilling events (HIT and BREAKHTROUGH) within 0.1 and 0.5 s, respectively. The machine’s high visibility design is beneficial for classification of the workpiece material. By using a support-vector-machine (SVM) on thrust force and feed rate, the authors are seen to achieve 92.86% accuracy for classification of material, such as medium-density fiberboard (MDF), acrylic, and glass.

Low Power IoT Electronics in Precision Irrigation

Agriculture is one of the drivers for societies and economies worldwide. There are several challenges to be addressed in this sector, such as food security and climate change. Agriculture 4.0 and the Internet of Things (IoT) technology paradigms aim to address these challenges, allowing for sustainable usage of natural resources and application of novel farming practices. This paper introduces an innovative smart IoT-based prototype system that supports precision irrigation of crops and enables real-time exploitation of data for minimization of errors, as well as forecasting. It is based on microprocessors and a Single-Board Computer (SBC) for data logging using various sensors that monitor soil moisture, air humidity, air temperature and UV light. Farmers’ Intelligent Actuation systems have full access to all relevant raw data, in order to choose when, where and how much to irrigate. The respective precision irrigation prototype system has been evaluated over series of experiments and the respective findings are presented in this paper.

Understanding place identity in urban scale Smart Heritage using a cross-case analysis method

PurposeSmart Heritage is a recently established discourse that entwines smartness and the heritage discipline. Studies have shown that place identity is at the core of value-based frameworks of built heritage. This study aims to unveil the role of identity in existing Smart Heritage frameworks, which is currently a gap in existing research.Design/methodology/approachTo better understand place identity in the Smart Heritage context and facilitate future framework establishments, this study uses a cross-case analysis method to scrutinise common trends in the identity development of seven current best practices.FindingsThe results show that current best practices involve smart technologies in sustaining or rebuilding heritage identities, mostly mapped on the local scale. Catered solutions are essential in this context due to historic cities’ variegated pursuits of identity. Most current Smart Heritage projects are at the transitioning stage from digital to smart, as the autonomous ability of smart innovations is yet to be fully realised on the city or the global scale. Researchers are encouraged to draw essence from existing heritage frameworks considering the built heritage’s place identity, which is at the core of culturally sustainable Smart Heritage transitions.Originality/valueThis study concludes with five recommendations for addressing heritage identity in Smart Heritage frameworks, targeting future research avenues. Also, this study furthers the discussion on the linkage of Smart Heritage, place identity and marketing strategy, contributing to the city branding and tourism management field. Future research should extend the case-study selection beyond Europe, which is a recognised limitation of this study.

A novel bionic decision-making mechanism for digital twin-based manufacturing system

As an innovative smart manufacturing system, the virtual entities-based decision-making process is the most typical difference between the digital twin-based manufacturing system (DTMS) and other smart manufacturing systems. Therefore, the accuracy of virtual entity-driven decision-making is the key to affecting the system reliability of the DTMS. Normally, the manufacturing process is often accompanied by complex state changes, which are collected by the perception module of the DTMS in the form of high-dimensional information. Then, the decision-making model needs to respond to these state changes in real-time and give reasonable decision results back to physical space, which has become an important scientific issue of DTMS. To fill this gap, a novel bionic decision-making mechanism for DTMS is put forward by introducing the biological sequential learning mechanism into the decision-making process. Subsequently, the systematic decision-making process imitates biological instinct and learning behavior mechanisms to explore the short-term and long-term process of decision-making. The bionic decision-making mode formed by combining the above two modes provides adaptive decision-making in different scenarios. It is believed that the bionic decision-making mechanism can help to quickly and accurately give decision-making feedback to guide on-site manufacturing and ensure product quality and manufacturing efficiency.

‘단골손님’과 한국적 외식서비스에 대한 접근

The purpose of this study was to conduct in-depth interviews with restaurant owners regarding the concepts of “eating out service” and “dangol customers” and academically analyze the inherent views and factors that are considered when providing eating out services. First, the study found, from the restaurant owner's perspective, the values and philosophies of restaurant services, the difficult reality of small self-employed businesses, and the responsibilities and missions of the restaurant industry. Second, in terms of restaurant operation, the results highlighted the importance of improving and maintaining food tastes and employees to the restaurant's reputation and longevity. Third, in terms of technology, themes included the imperfection of robots and innovative smart devices, as well as positive considerations of serving and cooking robots to fight labor shortages. Finally, regarding dangol customers, this study identified humane and affective feelings that the restaurant owners exchange with them. Food was stressed as fundamentally important in food service, and to overcome the difficult reality of small self-employed businesses, the restaurant owners tried to find an appropriate service position and demonstrate consistent, humble, and pro-customer attitudes. In addition, efforts were made to remember and recognize dangol customers, maintain their trust, and exchange positive sentiments with them, so the relationships can last long. Despite the current technological and physical limitations, a gradual introduction of restaurant service technologies may complement the weakness of the Korean restaurant service industry and enhance its operational efficiency.

Ultra-low power logic in memory with commercial grade memristors and FPGA-based smart-IMPLY architecture

Reducing power consumption in nowadays computer technologies represents an increasingly difficult challenge. Conventional computing architectures suffer from the so-called von Neumann bottleneck (VNB), which consists in the continuous need to exchange data and instructions between the memory and the processing unit, leading to significant and apparently unavoidable power consumption. Even the hardware typically employed to run Artificial Intelligence (AI) algorithms, such as Deep Neural Networks (DNN), suffers from this limitation. A change of paradigm is so needed to comply with the ever-increasing demand for ultra-low power, autonomous, and intelligent systems. From this perspective, emerging memristive non-volatile memories are considered a good candidate to lead this technological transition toward the next-generation hardware platforms, enabling the possibility to store and process information in the same place, therefore bypassing the VNB. To evaluate the state of current public-available devices, in this work commercial-grade packaged Self Directed Channel memristors are thoroughly studied to evaluate their performance in the framework of in-memory computing. Specifically, the operating conditions allowing both analog update of the synaptic weight and stable binary switching are identified, along with the associated issues. To this purpose, a dedicated yet prototypical system based on an FPGA control platform is designed and realized. Then, it is exploited to fully characterize the performance in terms of power consumption of an innovative Smart IMPLY (SIMPLY) Logic-in-Memory (LiM) computing framework that allows reliable in-memory computation of classical Boolean operations. The projection of these results to the nanoseconds regime leads to an estimation of the real potential of this computing paradigm. Although not investigated in this work, the presented platform can also be exploited to test memristor-based SNN and Binarized DNNs (i.e., BNN), that can be combined with LiM to provide the heterogeneous flexible architecture envisioned as the long-term goal for ubiquitous and pervasive AI.