Electroencephalographic Activities as Biomarker in the Accumulative Dose of Alcoholic Drinker: A Preliminary Study

The attentional processing of the human brain during the discrimination of supra-segmental features of Thai phonemes related to working memory was investigated. The electroencephalographic activities of accumulative dose of alcohol were investigated while the measurement of brain function of the cognitive task of discrimination of supra-segmental features of Thai phonemes. The cognitive effort caused changes by the difficulty in discriminating supra-segmental features of Thai phonemes were reflected in specific electroencephalographic signals. A 14-channels electroencephalogram (EMOTIV Inc. USA) was used to record the electrical activities. The electrode array was placed according to the international 10-20 system. Both earlobes were used as references. Electroencephalographic activities were recorded in two different periods; resting period and cognitive task. In the cognitive task, the participants were asked to perform the cognitive task in order to measure their brain function of discrimination of supra-segmental features of Thai phoneme

Cloud Based WiFi Multi-Sensor Network

A WiFi technology was the basis of the Internet of Things (IoT) and many popularity of the wireless communication in the social network. A smart device used any kinds of the detectors all of the analog and digital sensor. This study simulates on the scope of the home security system (HSS). There used three types of sensor; a temperature sensor, smoke-CO, and PIR sensor. This study aims to design a multi-sensor node. All of the sensors are connected on a microcontroller unit (MCU) with the general purpose input output (GPIO). After the connection, there got invalid multi-sensor data. This experiment tried to run over ten times. There appeared some invalid when the processor startup. First, the temperature sensor did not work. Second, the smoke-CO sensors read an invalid value there were higher than the actual. This problem can solve the situation by the sensor calibration methodology—to set the calibration time with the dynamic time follow up on the GPIO function of each sensor and self-calibrate by the finite impulse response (FIR) filter in the part of setup portion. When the system was running for a long time this should take the invalid data. There were high and low from the actual and there got the difference value suddenly a swinging value. During the system was running there had some noise and the heat collected on the device. There got the invalid value. This error is solved by the Full Scale Kalman Filter (FSKF) to fill and estimate the right value. Next, there used the OFF-Mode to save the power consumption and do not send sensor data to the Cloud all time. This method helps the device will be run as long time and work in long life. Finally, there got a high-performance WiFi multi-sensor network

Cloud Based WiFi Multi-Sensor Network

A WiFi technology was the basis of the Internet of Things (IoT) and many popularity of the wireless communication in the social network. A smart device used any kinds of the detectors all of the analog and digital sensor. This study simulates on the scope of the home security system (HSS). There used three types of sensor; a temperature sensor, smoke-CO, and PIR sensor. This study aims to design a multi-sensor node. All of the sensors are connected on a microcontroller unit (MCU) with the general purpose input output (GPIO). After the connection, there got invalid multi-sensor data. This experiment tried to run over ten times. There appeared some invalid when the processor startup. First, the temperature sensor did not work. Second, the smoke-CO sensors read an invalid value there were higher than the actual. This problem can solve the situation by the sensor calibration methodology—to set the calibration time with the dynamic time follow up on the GPIO function of each sensor and self-calibrate by the finite impulse response (FIR) filter in the part of setup portion. When the system was running for a long time this should take the invalid data. There were high and low from the actual and there got the difference value suddenly a swinging value. During the system was running there had some noise and the heat collected on the device. There got the invalid value. This error is solved by the Full Scale Kalman Filter (FSKF) to fill and estimate the right value. Next, there used the OFF-Mode to save the power consumption and do not send sensor data to the Cloud all time. This method helps the device will be run as long time and work in long life. Finally, there got a high-performance WiFi multi-sensor network.</p

A Smart WiFi Multi-Sensor Node for Fire Detection Mechanism Based on Social Network

A small device with WiFi multi-sensing element is very important under a social digital century. This study aims to implement the hardware and the power of the algorithm with WiFi technologies. Especially, the multi-sensors have to reinforce around a home area and support to any requirement in the term of digital society. This study focus to care the home security— on going to the fire detection with applying several technologies based on a Cloud. Firstly, the multi-sensor calibration has used calibration time and self-calibration as the Finite Impulse Response (FIR). Next, the Full-Scale Kalman Filter (FSKF) helps to fill data and estimate the accuracy data. After that, the fire detection mechanism has used Fuzzy logic to detect and send alert messages over an IFTTT process. There are changed following event-- the data range of fire proportion inside the home. Furthermore, The OFF-Mode has reduced the power consumption suddenly the WiFi module is sent the sensor data to the Cloud. Finally, the WiFi multi-sensor node will use more than one sensor as the same detector will be a high stability and high accuracy.</p

Biofeedback Assessment for Older People with Balance Impairment using a Low-cost Balance Board

This paper studies the feasibility of using a low-cost game device called Wii Fit Balance Board® to measure the static balance of older people for diagnosing a balance impairment, which is caused by muscle weakness in stroke patients. Sixty participants were invited to attend the risk assessment that included a clinical test. Four biofeedback testing patterns were tested with the participants. Two machine learning algorithms were selected to experiment using 10-fold cross validation scenario. The results show that Artificial Neuron Network has the best evaluation performance of 86.67%, 80%, and 80% in three out of four biofeedback testing patterns. This demonstrates that the application of static balance measurement together with Wii Fit Balance Board® could be implemented as a tool to replace high-cost force plate systems

Industry 4.0 and beyond. Present trends, emerging solutions and future technologies in the area of the industrial automation

The constant development of new technologies and production methods and their integration in the various fabrication and manufacturing processes has fuelled several waves of novelties and innovations in the industrial domain, which are also known as the industrial revolutions. These transformative processes have changed humanity forever and have shifted the economic models and the manufacturing methods of our societies - from the hand-based farming and manual production of goods to the total utilization of information systems, machines and robots for the large-scale fabrication of high-quality products and the delivery of modern services. The industrial revolutions have led to increased production volumes, higher manufacturing efficiency, lower prices, better working conditions, improved salaries and to significant improvements of the infrastructure, the railroad and the road networks. The recent technological advancements and the development of novel paradigms for the use of autonomous robots, cyber-physical systems, artificial intelligence algorithms, quantum computing and nanotechnologies are slowly changing the overall landscape and are pushing humanity towards the next wave of Industrial revolutions. While these innovations are offer significant benefits, they are also presenting a new set of challenges for the companies and their staff. In this paper we present and discuss the present trends, emerging solutions and future technologies in the area of the Industrial Automation. The Introduction section of the paper provides a brief overview of the past and present Industrial revolutions and highlights the advantages and the disadvantages of their underlying technologies. The next chapter of the paper presents in more details the trending technologies and the solutions of the present-day Industrial Automation domain. The paper continues with discussions about the emerging Industry 5.0 technologies and solutions. The next chapter of the manuscript is dedicated to the potential future Industrial Automation technologies, which will most likely constitute the Industry 6.0 paradigm. Last, but not least, we conclude the paper with some discussions about the automation technologies and the main challenges, opportunities and needs of the Industrial sector

Training of trainers for South-East Asia in the Industry 4.0 context: implementation of platforms

International audienceAsean-Factori 4.0 is a Key Action 2 of the European Union. This project, managed by Université Claude Bernard Lyon 1, regroups three European partners: Lyon (FR), Ruse (BG) and Grenoble (FR) together with 7 partners in SouthEast Asia (Asean) in Cambodia, Laos and Thailand. The main concept of the project is to train Asian teachers, around innovative platforms proposed by the European colleagues. The Asian institutions should create together a network of excellence; to this aim, each Asian institution will receive a specific platform with a specific focus and from a specific brand. The paper describes the state of advancement and the work achieved by the Grenoble partner (UGA, Univ. Grenoble Alpes) together with the Asian partners: ITC (Institute of Technology of Cambodia, KH), NUOL (National University of Laos, LA) and MFU (Mae Fah Luang University, TH). A first training was organized in Grenoble in the spring 2022. ITC and MFU attended on-site whereas NUOL attended on-line. A second training is organized on-site at the Asian partner institution, after the transfer of the platform in Asia; the purpose is for the partners to take their platform in hand

Training of trainers for South-East Asia in the Industry 4.0 context: implementation of platforms

International audienceAsean-Factori 4.0 is a Key Action 2 of the European Union. This project, managed by Université Claude Bernard Lyon 1, regroups three European partners: Lyon (FR), Ruse (BG) and Grenoble (FR) together with 7 partners in SouthEast Asia (Asean) in Cambodia, Laos and Thailand. The main concept of the project is to train Asian teachers, around innovative platforms proposed by the European colleagues. The Asian institutions should create together a network of excellence; to this aim, each Asian institution will receive a specific platform with a specific focus and from a specific brand. The paper describes the state of advancement and the work achieved by the Grenoble partner (UGA, Univ. Grenoble Alpes) together with the Asian partners: ITC (Institute of Technology of Cambodia, KH), NUOL (National University of Laos, LA) and MFU (Mae Fah Luang University, TH). A first training was organized in Grenoble in the spring 2022. ITC and MFU attended on-site whereas NUOL attended on-line. A second training is organized on-site at the Asian partner institution, after the transfer of the platform in Asia; the purpose is for the partners to take their platform in hand