Exploring the potential of offline cryptography techniques for securing ECG signals in healthcare

In the research, a software for ECG signal based on Chaos encryption based on C#-programmed and Kit of Microsoft Visual Studio Development was implemented. A chaos logic map (ChLMp ) and its initial value are utilized to create Level-1 ECG signal based on Chaos encryption bit streams. A ChLMp, an initial value, a ChLMp bifurcation parameter, and two encryption level parameters are utilized to create level-2 ECG signal based on Chaos encryption bit streams. The level-3 ECG signal based on Chaos encryption software utilizes two parameters for the level of encryption, a permutation mechanism, an initial value, a bifurcation parameter of the level of encryption, and a ChLMp. We assess 16-channel ECG signals with great resolution utilizing encryption software. The level-3 ECG signal based on Chaos encryption program has the slowest and most reliable encryption speed. The encryption effect is superior, according to test findings, and when the right decoding parameter is utilized, the ECG signals may be completely recovered. The high resolution 16-channel ECG signals (HRMCECG) won't be recovered if an invalid input parameter occurred, such as a 0.00001% initial point error, which will result in chaotic encryption bit streams

Matching based content discovery method on Geo-Centric Information Platform

We have proposed a concept of new information platform, Geo-Centric information platform (GCIP), that enables IoT data fusion based on geolocation. GCIP produces new and dynamic contents by combining cross-domain data in each geographic area and provides them to users. In this environment, it is difficult to find appropriate contents requested by a user because the user cannot recognize what contents are created in each area beforehand. In this paper, we propose a content discovery method for GCIP. This method evaluates the relevancy between topics specified in user requests and topics representing IoT data used for creating contents, called matching, and presents the candidates for the desired contents based on the relevancy. Simulation results showed that appropriate contents can reliably be discovered in response to user’s request.12th International Workshop on Information Network Design (WIND-2020), in conjunction with 12th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2020), August 31st - September 2nd, 2020, University of Victoria, Canada（新型コロナ感染拡大に伴い、現地開催中止

Pengembangan Prototype Pembelajaran Berbasis Mobile untuk Anak Berkebutuhan Khusus dengan Design Thinking

ABK memiliki tingkat IQ di bawah 70. Pembelajaran bagi ABK cenderung lebih sulit untuk dicerna karena mereka memiliki keterbatasan dalam fungsi organ secara permanen salah satunya dari sisi intelektual. Tujuan penelitian ini adalah untuk mengimplementasikan pembelajaran berupa aplikasi UDP (Unity Dyslexia Platform) untuk memudahkan guru, orang tua, dan siswa dalam proses pembelajaran. Metode penelitian ini menggunakan design thinking dengan tahapan emphasize, define, ideate, prototype, dan test. Hasil dari usability testing secara keseluruhan didapatkan persentase 61.23% dari direct success, 2.04% dari mission unfinished, 30% dari misclick rate, dan 37.62s dari average duration. Adapun rekomendasi yang perlu dilakukan untuk penelitian selanjutnya adalah memperbaiki beberapa fungsi button pada desain aplikasi, menelaah lebih dalam mengenai aksesibilitas desain, dan melakukan proses UX Design dengan lebih mendalam serta lebih terperinci agar menghasilkan peluang solusi yang lebih solutif

Biometric face authentication system for secure smart office environments

The use of the smart office concept in security has increased significantly recently. One of the areas of concern is the use of facial finger biometric technology for authentication systems, for example, authentication to enter a particular room in an office. This paper aims to describe a new prototype for office door automation and security that combines facial biometric technology and NodeMCU. Hopefully, this system will help increase the safety and comfort of office employees with easy installation and low cost. This system automatically controls (opens or closes) the door based on the biometrics of the user’s face registered in the database on the NodeMCU microcontroller. The main system comprises a NodeMCU microcontroller, face sensors, and a door lock system

Context-aware multi-head self-attentional neural network model for next location prediction

Accurate activity location prediction is a crucial component of many mobility applications and is particularly required to develop personalized, sustainable transportation systems. Despite the widespread adoption of deep learning models, next location prediction models lack a comprehensive discussion and integration of mobility-related spatio-temporal contexts. Here, we utilize a multi-head self-attentional (MHSA) neural network that learns location transition patterns from historical location visits, their visit time and activity duration, as well as their surrounding land use functions, to infer an individual's next location. Specifically, we adopt point-of-interest data and latent Dirichlet allocation for representing locations' land use contexts at multiple spatial scales, generate embedding vectors of the spatio-temporal features, and learn to predict the next location with an MHSA network. Through experiments on two large-scale GNSS tracking datasets, we demonstrate that the proposed model outperforms other state-of-the-art prediction models, and reveal the contribution of various spatio-temporal contexts to the model's performance. Moreover, we find that the model trained on population data achieves higher prediction performance with fewer parameters than individual-level models due to learning from collective movement patterns. We also reveal mobility conducted in the recent past and one week before has the largest influence on the current prediction, showing that learning from a subset of the historical mobility is sufficient to obtain an accurate location prediction result. We believe that the proposed model is vital for context-aware mobility prediction. The gained insights will help to understand location prediction models and promote their implementation for mobility applications.Comment: updated Discussion section; accepted by Transportation Research Part

Mapping and Testing Internet of Things Platforms for the Intelligent Maintenance of the Electrical Distribution Network

New technologies are crucial in the changing energy sector and the electricity network. The climate change and increasing dependence upon electricity are two main factors in this con-text. Consequently, there is a need to develop the reliability and quality of the electricity distribution system. The study was carried out in cooperation with Vaasan Sähköverkko. They wanted to explore and pilot possible alternatives to internet of things (IoT) technologies to be used in predictive maintenance of the electricity distribution network. The purpose of this study was to examine the features expected from good IoT platforms. Central to this study, was to demonstrate that IoT solutions could be built on these platforms in their operating environments connected to the distribution system. Internet of things platforms are a set of integrated software capabilities. The compared platforms in this study were M-Files, IoT-Ticket, Microsoft Azure, Amazon Web Services and Google Cloud Platform. When comparing the selected IoT platforms, data related to different features was collected by implementing four practical cases. The first case was monitoring air conditions at Vaasa primary substation using a Ruuvitag sensor. The second case was use CoreTec and CoreSense to import condition monitoring data from the power transformer at Purola primary substation. The third example was import measurement and status data from the DC system at Alskat primary substation to IoT platforms. In the final case, data was retrieved from MicroSCADA Historian to a comma separated value file and exported to IoT platforms using either the representational state transfer application programmable Interface (REST API) or a Python software development kit. The results of this study demonstrate that it is possible to install of IoT technology on significantly different platforms. M-Files was the IoT platform with largest amount of open questions still remaining. IoT-Ticket appeared to be the easiest option for installation and end use. If an organization were to choose Microsoft Azure, Amazon Web Services or Google Cloud Platform, they would need to find reliable partners to develop the platforms with end users. During this study, it became evident that IoT technology is relatively evolved and organizations should begin using to use it with a low threshold if suitable applications are found. For example, predictive maintenance can be considered as a particularly suitable option for the IoT platform further utilization by a distribution system operator