scholarly journals A Robust Artificial Intelligence: Smart Indoor Positioning System

2019 ◽  
Vol 8 (10) ◽  
pp. 1393-1398 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Human Life ◽  
Indoor Navigation ◽  
Cloud Services ◽  
Raspberry Pi ◽  
Positioning System ◽  
Navigation Systems ◽  
Computing Power ◽  
The Voice ◽  
Indoor Positioning System

Over the previous few centuries, technology has converted massively from being a desktop personal computer to handheld mobile phones, with lower energy consumption of raw computing power. This computability is now incorporated with other systems as well as isolated to a single device. This paradigm was first noted in cyber-physical systems with the introduction of cloud services. The evolution of Artificial Intelligence(AI) with cloud computing and the importance of this field in human life, induce us to make simple and efficient talkative assistant robot for indoor navigation. The navigation system in outdoor typically rely upon Global Positioning System (GPS) but the indoor navigation systems have to rely on different technologies, as GPS signals cannot be received indoors. Thus, several technologies have been proposed and implemented over the past decade to improve navigation in indoors. But they were costly and less effective. Therefore, we have proposed a system that assists humans to find their location in a conversational manner. The suggested system was constructed by introducing the advantages of a personal assistant device, Amazon Alexa, the cloud services of Amazon and its voice services for indoor navigation. A Raspberry Pi 3 Model B is used as the element of the hardware to provide our system with intelligent characteristics. You can trigger the speech service using the "Alexa" keyword. Using the voice command, the skill / application we created can be initiated. It operates a script on the cloud once Alexa is enabled, which runs a subroutine on the Raspberry Pi 3 in-turn to provide a path for that specific place. Once the Raspberry Pi calculation is finished, it sends the message back to Alexa. Alexa transforms the text into a voice and informs the user path.

scholarly journals SLAM-based Indoor Navigation in University Buildings

2021 ◽  
Author(s):  
Ekaterina Sukhareva ◽  
Tatiana Tomchinskaya ◽  
Ilya Serov
Keyword(s):  
Augmented Reality ◽  
Indoor Navigation ◽  
Positioning System ◽  
Navigation Systems ◽  
Mobile Platforms ◽  
Localization And Mapping ◽  
Mapping Technology ◽  
Augmented Reality Technology ◽  
University Buildings ◽  
Indoor Positioning System

The article discusses the use of SLAM (simultaneous localization and mapping) technology, with the help of which it is possible to build Indoor navigation systems using augmented reality technology, including on mobile platforms. The article also provides an overview of the positive and negative aspects of the SLAM technology its principle of operation for positioning a navigator using augmented reality in a university building within the framework of a student project are reviewed. The already implemented projects on similar topics, but using other technologies are considered their features are described. An example of the implementation of an indoor positioning system in a university using SLAM is given.

scholarly journals Digital Employee using Capabilities of Artificial Intelligence to Human Intelligence

2020 ◽  
pp. 208-211
Author(s):  
Shubham Jain ◽  
Yash Sharma ◽  
Nishi Sharma
Keyword(s):  
Artificial Intelligence ◽  
Speech Recognition ◽  
Human Life ◽  
Raspberry Pi ◽  
Human Intelligence ◽  
Text To Speech ◽  
Voice Input ◽  
Education Market ◽  
User Friendly

Using the concept of Artificial Intelligence, a virtual assistant named "Gabriel" has been Developed to aid in education, market, business and many other fields.[4] The uniqueness of this bot is that it is programmed in python and is stored in raspberry pi providing the user friendly environment by moving along with user. It uses numerous python Libraries to help perform various functions that enable the Assistant to assist its user in day to Day activities. The Assistant can convert text to speech and vice versa using pyttsx3 and speech Recognition libraries respectively. Also, it can scrape the information from Wikipedia and visit any Website. It can be used to surf YouTube and visit any YouTube channel. The Assistant Comes with games such as flappy bird, tic-tac-toe and snake developed by the programmer. Also, the Assistant has an in-built calculator that too developed by the programmer. This Research is to develop an assistant who is highly compatible with human life. This employee comes with a self-made library called ‘the_gmail_sender’ that sends Gmail taking Voice input from the user

Developing a Graphical User Interface for an Artificial Intelligence-Based Voice Assistant

2021 ◽  
Vol 11 (3) ◽  
pp. 49-67
Author(s):  
Subhash S. ◽  
Siddesh S. ◽  
Prajwal N. Srivatsa ◽  
Ullas A. ◽  
Santhosh B.
Keyword(s):  
Artificial Intelligence ◽  
User Interface ◽  
Graphical User Interface ◽  
Human Life ◽  
Human Machine Interaction ◽  
Audio File ◽  
Voice User ◽  
Text Response ◽  
Intelligent Personal Assistant ◽  
The Voice

Artificial intelligence machineries have been extensively active in human life in recent times. Self-governing devices are enhancing their way of interacting with both human and devices. Contemporary vision in this topic can pave the way for a new process of human-machine interaction in which users will get to know how people can understand human language, adapting and communicating through it. One such tool is voice assistant, which can be incorporated into many other brilliant devices. In this article, the voice assistant will receive the audio from the microphone and then convert that into text, later with the help of ‘pyttsx3', and then the text response will be converted into an audio file; then the audio file will be played. The audio is processed using the voice user interface (VUI). This article develops a functional intelligent personal assistant (IPA) and integrates it with a graphical user interface that can perform mental tasks such as ON/OFF of smart applications based on the user commands.

scholarly journals A Hybrid Marker-Based Indoor Positioning System for Pedestrian Tracking in Subway Stations

2020 ◽  
Vol 10 (21) ◽  
pp. 7421
Author(s):  
Gunwoo Lee ◽  
Hyun Kim
Keyword(s):  
Error Correction ◽  
Indoor Positioning ◽  
Indoor Navigation ◽  
Signal Interference ◽  
Measurement Unit ◽  
Subway Station ◽  
Positioning System ◽  
Correction Technique ◽  
Subway Stations ◽  
Indoor Positioning System

The use of smartphones for accurate navigation in underground spaces, such as subway stations, poses several challenges. This is because it is difficult to obtain a sure estimate of user location due to the radio signal interference caused by the entry and exit of trains, the infrastructure of the subway station installation, and changes in the internal facility environment. This study uses quick response markers and augmented reality to solve these difficulties using an error correction method. Specifically, a hybrid marker-based indoor positioning system (HMIPS) which provides accurate and efficient user-tracking results is proposed. The HMIPS performs hybrid localization by using marker images as well as inertial measurement unit data from smartphones. It utilizes the Viterbi tracking algorithm to solve the problem of tracking accuracy degradation that may occur when inertial sensors are used by adopting a sensor error correction technique. In addition, as an integrated system, the HMIPS provides a tool to easily carry out all the steps necessary for positioning. The results of experiments conducted in a subway station environment confirm that the HMIPS provides accurate and practical navigation services. The proposed system is expected to be useful for indoor navigation, even in poor indoor positioning environments.

scholarly journals Indore Navigation Mobile Application using Indore Positioning System (IPS)

2020 ◽  
Vol 2 (10) ◽  
pp. 5-7
Author(s):  
Anusha Sanampudi* ◽  
Keyword(s):  
Mobile Application ◽  
Smart Phone ◽  
Dead Reckoning ◽  
Steel Structures ◽  
Indoor Positioning ◽  
Indoor Navigation ◽  
Smart Phones ◽  
Positioning System ◽  
Shopping Malls ◽  
Indoor Positioning System

Indoor Positioning system (IPS) is the technology that is used to locate smart phones, people or other objects inside a building where Global Positioning System (GPS) doesn’t work or lack precision such as airports, underground locations, parking, multi-storey buildings etc…There is no fixed standard for implementing IPS rather it could be customized according to the location chosen. IPS in turn uses a number of technologies such as Wi-Fi, Bluetooth, Beacons, magnetic positioning, dead reckoning etc…Among the various technologies available studies prove that Magnetic localization provides a most efficient solution for Indoor positioning. Our paper focuses on building an indoor navigation mobile application for a retail store that allows users to search for a product and navigate them to the particular aisle in which the product is located. There by enabling the application to be location sensitive and context aware. In order to collect magnetic fingerprints and convert the obtained data into latitude and longitude values we make use of an API called IndoorAtlas, which helps in locating smart phones inside a building using the accelerometer, gyroscope, magnetometer and Bluetooth in a mobile. Magnetic localization is the concept where deflections of magnetic field from the steel structures inside the building will be captured by the magnetometer and other sensors within a mobile and that will be used to locate a smart phone inside a building. The same application could be utilized for various use cases such as Supermarkets & Hypermarkets, museums & galleries, Libraries, Hospitals, Airports & stations, Shopping malls, Exhibition and Conferences.

scholarly journals Machine Learning Medical Resource Allocation

2021 ◽  
Vol 2089 (1) ◽  
pp. 012082
Author(s):  
M. Sailaja ◽  
Abdul Ahad ◽  
K Sivaramakrishna ◽  
Ali Hussain
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Resource Allocation ◽  
Human Life ◽  
Learning Algorithms ◽  
Health Resources ◽  
Medical Resource ◽  
Computing Power ◽  
Data Elements ◽  
Key Indicators

Abstract In the last decade, machine learning has become very interesting, driven by cheaper computing power and costly storage—so that growing numbers of data can be saved, processed and analysed effectively. Enhanced algorithms are designed and used to identify hidden insights and correlations between non-human data elements in broad datasets. These insights help companies to better decide and optimize key indicators of interest. Machine learning is becoming more common because of the agnostic use of learning algorithms. The paper presents a number of machinery and auxiliary tumour processes to assign health resources, and proposes a number of new ways to use these resources at the time of artificial intelligence in order to make human life part of this process and explore the good conditions which are shared by both the medical and computer industries.

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