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Wearable technologies will be extremely useful in the future life. This research proposes a textile wideband coplanar Vivaldi antenna constructed from a felt substrate and two distinct types of patches, shieldit, and copper tape integrated with a wearable device. This study also altered the slope of the tapered slot on the antenna's front and side to see how it affected the bandwidth and directivity antenna performance. An IoT wearable device that was connected to a microcontroller via a DS18B20 body temperature sensor and a MAX30100 sensor for heart rate and oxygen level monitoring was paired with the textile antenna. Based on the simulation findings, it was discovered that a 1 mm thick felt substrate material combined with a copper tape patch produces a workable frequency range of 2.6 GHz to 8.7 GHz, a minimum S11 of -44.93 dB at 3 GHz, and a fractional bandwidth up to 107%. According to the simulation results, the antenna's side and front tapered slots have an impact on directivity and return loss. Directivity at 3 GHz can be raised by 2.63 dBi, from 1.94 dBi to 4.57 dBi, by varying the Vivaldi antenna form on both sides of the patch. The data from the sensor was successfully conveyed by combining an IoT wearable device with a textile antenna. Thus, we deduce that the textile coplanar Vivaldi antenna is appropriate for Internet of Things applications. [ABSTRACT FROM AUTHOR]

Nowadays, with the common use of smartphones in daily lives, mobile applications have become popular around the world, which will lead to a rise in Flutter framework. Developed by Google, Flutter with Dart programming provides a cross-platform development environment to create visually appealing and responsive user interfaces across mobile, web, and desktop platforms using a single codebase. However, due to time and staff limitations, the Flutter/Dart programming course is not included in curricula, even in IT departments in universities. Therefore, independent learning environments for students are essential to meet this growing popularity. Previously, we have developed programming learning assistant system (PLAS) as a web-browser-based self-learning platform for novice students. PLAS offers various types of exercise problems designed to cultivate programming skills step-by-step through a lot of code reading and code writing practices. Among them, one particular type is the code modification problem (CMP), which asks to modify the given source code to satisfy the new specifications. CMP is expected to be solved by novices with little effort if they have knowledge of other programming languages. Thus, PLAS with CMP will be an excellent platform for independent learning. In this paper, we present PLAS with CMP for the independent learning of Flutter/Dart programming. To improve the readability of the source code by students, we provided rich comments on grammar or behaviors. Besides, the code can be downloaded so that students can check and run it on an IDE. For evaluations, we generated 38 CMP instances for basic and multimedia/storage topics in Flutter/Dart programming and assigned them to 21 master students at Okayama University, Japan, who have never studied it. The results confirm the validity of the proposal. [ABSTRACT FROM AUTHOR]

The study of indoor localization has been extensively studied, either in terms of wireless technologies or localization techniques. The accuracy is then challenged when the monitored object is actively moving. Previously, we studied a continuous and low-power fingerprint-based indoor localization system using IEEE 802.15.4 (FILS15.4), which has been integrated into a smart environmental IoT platform. Although fingerprint-based localization offers a great advantage in its simplicity, it relies on real-time signal strength measurements and databases. Thus, it suffers challenges in accuracy when the object is continuously moving. In this study, we focus on developing dynamic positioning, where users continuously move from one room to another. Due to human movements, the fluctuation of the link quality indicator (LQI) can affect the detection accuracy. To avoid false detection, we propose a movement validation method by checking the variance of the LQI and accelerometer to differentiate the cause of fluctuations and increase the detection accuracy. For experiments, we run the test-bed of FILS15.4 on a two-floor layout. Five to six receivers were allocated to detect multiple users. The results show that the system yields 96.2% accuracy using six receivers simultaneously. Thus, it gives sufficient detection accuracy even for dynamic conditions. [ABSTRACT FROM AUTHOR]

Parameters often take key roles in determining the accuracy of algorithms, logics, and models for practical applications. Previously, we have proposed a general-purpose parameter optimization algorithm, and studied its applications in various practical problems. This algorithm optimizes the parameter values by repeating small changes of them based on a local search method with hill-climbing capabilities. In this paper, we present three diverse applications of this algorithm to show the versatility and effectiveness. The first application is the fingerprint-based indoor localization system using IEEE802.15.4 devices called FILS15.4 that can detect the location of a user in an indoor environment. It is shown that the number of fingerprints for each detection point, the fingerprint values, and the detection interval are optimized together, and the average detection accuracy exceeds 99 % . The second application is the human face contour approximation model that is described by a combination of half circles, line segments, and a quadratic curve. It is shown that the simple functions can well approximate the face contour of various persons by optimizing the center coordinates, radii, and coefficients. The third application is the computational fluid dynamic (CFD) simulation to estimate temperature changes in a room. It is shown that the thermal conductivity is optimized to make the average temperature difference between the estimated and measured 0.22 ∘ C. [ABSTRACT FROM AUTHOR]

Nowadays, rapid developments of Internet of Things (IoT) technologies have increased possibilities of realizing smart cities where collaborations and integrations of various IoT application systems are essential. However, IoT application systems have often been designed and deployed independently without considering the standards of devices, logics, and data communications. In this paper, we present the design and implementation of the IoT server platform called Smart Environmental Monitoring and Analytical in Real-Time (SEMAR) for integrating IoT application systems using standards. SEMAR offers Big Data environments with built-in functions for data aggregations, synchronizations, and classifications with machine learning. Moreover, plug-in functions can be easily implemented. Data from devices for different sensors can be accepted directly and through network connections, which will be used in real-time for user interfaces, text files, and access to other systems through Representational State Transfer Application Programming Interface (REST API) services. For evaluations of SEMAR, we implemented the platform and integrated five IoT application systems, namely, the air-conditioning guidance system, the fingerprint-based indoor localization system, the water quality monitoring system, the environment monitoring system, and the air quality monitoring system. When compared with existing research on IoT platforms, the proposed SEMAR IoT application server platform offers higher flexibility and interoperability with the functions for IoT device managements, data communications, decision making, synchronizations, and filters that can be easily integrated with external programs or IoT applications without changing the codes. The results confirm the effectiveness and efficiency of the proposal. [ABSTRACT FROM AUTHOR]

Nowadays, human indoor localization services inside buildings or on underground streets are in strong demand for various location-based services. Since conventional GPS cannot be used, indoor localization systems using wireless technologies have been extensively studied. Previously, we studied a fingerprint-based indoor localization system using IEEE802.15.4 devices, called FILS15.4, to allow use of inexpensive, tiny, and long-life transmitters. However, due to the narrow channel band and the low transmission power, the link quality indicator (LQI) used for fingerprints easily fluctuates by human movements and other uncontrollable factors. To improve the localization accuracy, FILS15.4 restricts the detection granularity to one room in the field, and adopts multiple fingerprints for one room, considering fluctuated signals, where their values must be properly adjusted. In this paper, we present a fingerprint optimization method for finding the proper fingerprint parameters in FILS15.4 by extending the existing one. As the training phase using the measurement LQI, it iteratively changes fingerprint values to maximize the newly defined score function for the room detecting accuracy. Moreover, it automatically increases the number of fingerprints for a room if the accuracy is not sufficient. For evaluations, we applied the proposed method to the measured LQI data using the FILS15.4 testbed system in the no. 2 Engineering Building at Okayama University. The validation results show that it improves the average detection accuracy (at higher than 97%) by automatically increasing the number of fingerprints and optimizing the values. [ABSTRACT FROM AUTHOR]