scholarly journals Sensor Based Industrial Kitchen Foodstuffs Monitoring System

2021 ◽  
Vol 3 (1) ◽  
pp. 26-43
Author(s):  
Sowndarya Palanisamy ◽  
Saranya N
Keyword(s):  
Artificial Intelligence ◽  
Monitoring System ◽  
Inventory Management ◽  
Mobile Application ◽  
Food Item ◽  
Early Stage ◽  
Ultrasonic Sensor ◽  
Specific Food

Artificial Intelligence based foodstuff monitoring system is used for inventory management in industrial kitchens, restaurants, canteens, vegetable stores and so on. If the store keeper is not available to monitor the grocery and orders, the process will be risky. The proposed method considers the level estimation detection using ultrasonic sensor and if the container is empty then the information is sent to the store keeper. By this method, the intimation about availability of specific food item can be found and items not available can be ordered for purchasing. The DHT11 sensor is used to monitor the humidity and temperature inside the container, if any of these two is high then the notification will be sent. Decomposed organic items are identified by MQ3 sensor based on detection of alcoholic gas produced by organic items. The sensors data such as grocery level, humidity, temperature and decomposition range of organic items are collected from the corresponding smart container. This data transaction happens via micro controller known as Node MCU. The level of the groceries present, and spoiled organic items can be identified and mail notification can be triggered in the early stage with the help of mobile application called IFTTT.

scholarly journals VOICE CONTROLLED VEHICLE USING MQTT SERVER

2021 ◽  
Vol 6 (4) ◽  
Author(s):  
N Ayush Ubale ◽  
Pranavya M U ◽  
Poli Guha Neogi ◽  
Hardik Jethava
Keyword(s):  
Artificial Intelligence ◽  
Open Source ◽  
Mobile Application ◽  
Ultrasonic Sensor ◽  
Operating Costs ◽  
Android Application ◽  
Specific Location ◽  
Human Voice ◽  
Robotic Vehicle ◽  
The Voice

The voice-controlled vehicle was created to make human work easier, since we live in an artificial intelligence-driven world where robots perform many tasks. The human voice is used to drive the vehicle. A stable android mobile application built with android studio software transmits the speech. It's essentially a Wi-Fi link. Using the mobile application, we can operate the vehicle with our voice from anywhere. The NodeMCU IoT framework is free and open source. It includes firmware for the ESP8266 Wi-Fi module, a Espressif Systems SoC, and ESP-12 module hardware. With the application that has been created, human work may become simpler. Since the vehicle will be linked to Wi-Fi, we will be able to access it from any location in our project. He or she will use the Android application to send commands or voice commands such as forward, backward, left, right, left forward, left backward, right forward, right backward, and right forward, right backward. The pins have been connected to the NodeMCU esp8266, and the code to control the car has been written. When an object or vehicle inhibits the car's movement, an ultrasonic sensor is used to stop it. The voice is recognised and forwarded to the HiveMQ server, where it is processed. HiveMQ's MQTT broker is optimised for cloud native deployments to take advantage of cloud resources. The use of MQTT reduces the amount of bandwidth used to transport data across the network. Low overall operating costs are a result of efficient IoT solutions. The hivemq server receives the command from the Android application and sends it to the NodeMCU microcontroller, which programmes the code to drive the vehicle. The voicecontrolled robot vehicle project has military, surveillance, and human applications in scope. It's a voice-activated wireless robot vehicle. The project's main goal is to guide the robotic vehicle to a specific location. In addition, the project's main goal is to use voice to control the robot. It is now possible to have

A study protocol: co-designing a mobile application to enhance communication, safety and wellbeing for people living at home with early stage dementia (Preprint)

2020 ◽  
Author(s):  
Karen Davies ◽  
Bie Nio Ong ◽  
Sudeh Cheraghi-Sohi ◽  
Katherine Perryman ◽  
Caroline Sanders
Keyword(s):  
Mobile Application ◽  
Design Research ◽  
Early Stage ◽  
Design Method ◽  
Mobile Apps ◽  
Care Service ◽  
Normalisation Process Theory ◽  
Policy And Practice ◽  
People With Dementia ◽  
At Home

BACKGROUND Background: There is a growing interest in using mobile applications in supporting health and wellbeing. Evidence directly from people with dementia regarding the acceptability, usability and usefulness of mobile apps is limited. It builds on ‘My Health Guide’ which was co-designed with people with cognitive disabilities. . OBJECTIVE Objective This paper describes the protocol of a study evaluating an app designed for supporting wellbeing with people living with dementia, specifically focusing on enhanced safety through improved communication METHODS Method: The study will employ design research, using participatory qualitative research methods over three cycles of evaluation with service users, their families and practitioners. The study will be developed in partnership with a specialist home care service in England. A purposive case selection will be used to ensure that the cases exemplify differences in experiences. The app will be evaluated in a ‘walkthrough’ workshop by people living with early stage dementia and then trialled at home by up to 12 families in a ‘try-out’ cycle. An amended version will be evaluated in a final ‘walkthrough’ workshop in cycle 3. Data will be collected from at least four data sources during the try-out phase and analysed thematically (people with dementia, carers, practitioners and app usage). An explanatory, multiple-case study design will be used to synthesise and present the evidence from the three cycles drawing on Normalisation Process Theory to support interpretation of the findings. RESULTS Results: The study is ready to be implemented but has been paused to protect vulnerable individuals during the Coronavirus in 2020. The findings will be particularly relevant for understanding how to support vulnerable people living in the community during social distancing and the period following the pandemic, as well as providing insight into the challenges of social isolation arising from living with dementia CONCLUSIONS Discussion: Evaluating a mobile application for enhancing communication, safety and wellbeing for people living with dementia contributes to key ambitions enshrined in policy and practice, championing the use of digital technology and supporting people with dementia to live safely in their own homes. The study uses a co-design method to enable the voice of users with dementia to highlight the benefits and challenges of technology and shape future development of apps that potentially enhances safety through improved communication.

scholarly journals Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 14
Author(s):  
Mei Dong ◽  
Hongyu Wu ◽  
Hui Hu ◽  
Rafig Azzam ◽  
Liang Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Monitoring System ◽  
Slope Instability ◽  
Mountainous Area ◽  
Probabilistic Forecasting ◽  
Intelligence Algorithm ◽  
Good Prediction Accuracy ◽  
Artificial Intelligence Algorithm ◽  
Steep Slopes

With increased urbanization, accidents related to slope instability are frequently encountered in construction sites. The deformation and failure mechanism of a landslide is a complex dynamic process, which seriously threatens people’s lives and property. Currently, prediction and early warning of a landslide can be effectively performed by using Internet of Things (IoT) technology to monitor the landslide deformation in real time and an artificial intelligence algorithm to predict the deformation trend. However, if a slope failure occurs during the construction period, the builders and decision-makers find it challenging to effectively apply IoT technology to monitor the emergency and assist in proposing treatment measures. Moreover, for projects during operation (e.g., a motorway in a mountainous area), no recognized artificial intelligence algorithm exists that can forecast the deformation of steep slopes using the huge data obtained from monitoring devices. In this context, this paper introduces a real-time wireless monitoring system with multiple sensors for retrieving high-frequency overall data that can describe the deformation feature of steep slopes. The system was installed in the Qili connecting line of a motorway in Zhejiang Province, China, to provide a technical support for the design and implementation of safety solutions for the steep slopes. Most of the devices were retained to monitor the slopes even after construction. The machine learning Probabilistic Forecasting with Autoregressive Recurrent Networks (DeepAR) model based on time series and probabilistic forecasting was introduced into the project to predict the slope displacement. The predictive accuracy of the DeepAR model was verified by the mean absolute error, the root mean square error and the goodness of fit. This study demonstrates that the presented monitoring system and the introduced predictive model had good safety control ability during construction and good prediction accuracy during operation. The proposed approach will be helpful to assess the safety of excavated slopes before constructing new infrastructures.

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