scholarly journals IOT AND IT’S SMART APPLICATIONS

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Sanjograj Singh Ahuja ◽  
Vithal Kashkari
Keyword(s):  
Neural Network ◽  
Mobile Devices ◽  
Human Activity ◽  
Computer Technology ◽  
Radio Frequency Identification ◽  
Huge Amount ◽  
Internet Connectivity ◽  
Frequency Identification ◽  
Network Technologies ◽  
Alternative Machines

We have a tendency to enter an incredibly new age in computer technology. In the cloud, IoT can be a type of "universal world neural network" connecting various objects. IoT can be a display of intelligent connected devices and networks consisting of sensitive machines communicating with alternative machines, environments, artifacts, infrastructures, and human activity. Radio Frequency Identification (RFID) and detector network technologies may emerge to meet this new challenge. Therefore, a huge amount of data is produced, stored, and information is transformed into helpful acts that will make our lives much easier and safer. Internet connectivity is, however, provided to citizens on networks and their mobile devices in most nations, meaning that the transmission of data across the network is also much simpler and less costly.

scholarly journals The IoT (Internet of Things) Impact on Global Development

2020 ◽  
Vol 26 ◽  
pp. 111-118
Author(s):  
Virgil-Constantin FĂTU ◽  
◽  
Simona STANCU ◽  
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Radio Frequency ◽  
Sensor Network ◽  
Radio Frequency Identification ◽  
The Internet ◽  
Machine To Machine ◽  
Frequency Identification ◽  
Network Technologies ◽  
The Internet Of Things

Internet of Things (IoT) IOT is a kind of “universal global neural network” in the cloud which connects various devices. The Internet of Things - IoT is the sum of all the connected devices and systems which are comprised of machine-to-machine interacting and communicating, environments, objects and infrastructures and the Radio Frequency Identification (RFID) and sensor network technologies will rise to improve our day-to-day life.

An optimal measurement method for spatial distribution of radio frequency identification multi-tag based on image analysis and PSO

2019 ◽  
Vol 41 (12) ◽  
pp. 3331-3339
Author(s):  
Xiaolei Yu ◽  
Yujun Zhou ◽  
Zhenlu Liu ◽  
Zhimin Zhao
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Template Matching ◽  
Detection System ◽  
Optimization Method ◽  
Frequency Identification ◽  
Edge Detection Method ◽  
Reading Distance

In this paper, a multi-tag optimization method based on image analysis and particle swarm optimization (PSO) neural network is proposed to verify the effect of radio frequency identification (RFID) multi-tag distribution on the performance of the system. A RFID tag detection system is proposed with two charge coupled device (CCD). This system can automatically focus on the tag according to its position, so it can obtain the image information more accurately by template matching and edge detection method. Therefore, the spatial structure of multi-tag and the corresponding reading distance can be obtained for training. Because of its excellent performance in multi-objective optimization, the PSO neural network is used to train and predict multi-tag distribution at the maximum reading distance. Compared with other neural networks, PSO is more accurate and its uptime is shorter for RFID multi-tag analysis.

Controlling of Traffic Lights Using RFID Technology and Neural Network

2012 ◽  
Vol 433-440 ◽  
pp. 740-745 ◽  
Author(s):  
Sasan Mohammadi ◽  
Abolfazl Rajabi ◽  
Mostafa Tavassoli
Keyword(s):  
Neural Network ◽  
Radio Frequency Identification ◽  
Queue Length ◽  
New Technology ◽  
Weather Condition ◽  
Access Point ◽  
Rfid Technology ◽  
Average Speed ◽  
Traffic Lights ◽  
Frequency Identification

In this paper, the new technology of RDIF (Radio Frequency Identification) has been used in order to identify vehicles and also 3 significant parameters including the average speed of vehicles at any side of access point, the average time for waiting and the queue length. They have been used based on the data from neural network for making the best decision throughout the process of finding out duration of the cycle and percentage of green time for each of the access point. Implementation of this system is possible in the shortest time and it has a better function in any kind of weather condition, time or place compared to similar systems.

Managing Uncertain Inventory in Supply Chain with Neural Network and Radio Frequency Identification (RFID)

2012 ◽  
pp. 155-170
Author(s):  
CKM Lee ◽  
Ng Wenwei Benjamin ◽  
Shaligram Pokharel
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supply Chain ◽  
Radio Frequency ◽  
Real Time ◽  
Radio Frequency Identification ◽  
Bullwhip Effect ◽  
Distribution Centre ◽  
Frequency Identification ◽  
Artificial Neural

Demand uncertainty leads to fluctuations in inventory position at each echelon of a supply chain causing bullwhip effect, which can lead to significant cost and loss of efficiency and waste of resources. One of the aspects that can reduce potential bullwhip effect is the sharing of real time information for which the recently mass produced Radio Frequency Identification (RFID) can be of great value. The use of RFID technology can also help in increasing the visibility of the flow of goods and material, keeping track of the location and quantity at each distribution centre and warehouses. This will also help in the periodic and near real time optimization of inventory level of goods and material. The data collected with RFID can be analysed in artificial Neural Network (NN) to forecast the future demand. In this chapter, a framework is proposed by combining RFID with artificial neural network so that lean logistics can be realized in the supply chain.

The Evolution of Intelligent Classifiers into an Integrated Approach to Correct RFID Anomalies

2012 ◽  
pp. 41-73
Author(s):  
Peter Darcy ◽  
Bela Stantic ◽  
Abdul Sattar
Keyword(s):  
Neural Network ◽  
Radio Frequency Identification ◽  
False Positive ◽  
False Negative ◽  
Integrated Approach ◽  
Rfid Systems ◽  
Efficiency And Effectiveness ◽  
Frequency Identification ◽  
Wide Scale ◽  
Original Approach

Radio Frequency Identification (RFID) refers to wireless technology that is used to seamlessly and automatically track various amounts of items around an environment. This technology has the potential to improve the efficiency and effectiveness of tasks such as shopping and inventory saving commercial organisations both time and money. Unfortunately, the wide scale adoption of RFID systems have been hindered due to issues such as false-negative and false-positive anomalies that lower the integrity of captured data. In this chapter, we propose the utilisation three highly intelligent classifiers, specifically a Bayesian Network, Neural Network and Non-Monotonic Reasoning, to handle missing, wrong and duplicate observations. After discovering the potential from using Bayesian Networks, Neural Networks and Non-Monotonic Reasoning to correct captured data, we decided to improve upon the original approach by combining the three methodologies into an integrated classifier. From our experimental evaluation, we have shown the high results obtained from cleaning both false-negative and false-positive anomalies using each of our concepts, and the potential it holds to enhance physical RFID systems.

scholarly journals Detection of Waste Containers Using Computer Vision

2019 ◽  
Vol 2 (1) ◽  
pp. 11
Author(s):  
Miguel Valente ◽  
Hélio Silva ◽  
João Caldeira ◽  
Vasco Soares ◽  
Pedro Gaspar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Radio Frequency Identification ◽  
Current Method ◽  
Video Capture ◽  
Test Set ◽  
Feature Detectors ◽  
Different Types ◽  
Frequency Identification

This work is a part of an ongoing study to substitute the identification of waste containers via radio-frequency identification. The purpose of this paper is to propose a method of identification based on computer vision that performs detection using images, video, or real-time video capture to identify different types of waste containers. Compared to the current method of identification, this approach is more agile and does not require as many resources. Two approaches are employed, one using feature detectors/descriptors and other using convolutional neural networks. The former used a vector of locally aggregated descriptors (VLAD); however, it failed to accomplish what was desired. The latter used you only look once (YOLO), a convolutional neural network, and reached an accuracy in the range of 90%, meaning that it correctly identified and classified 90% of the pictures used on the test set.

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