scholarly journals Cost effective Parking System Using Computer Vision

2020 ◽  
pp. 241-246
Author(s):  
Kaushal Shah ◽  
Shivang Rajbhoi ◽  
Nikhil Prasad ◽  
Charmi Patel ◽  
Roushan Raj
Keyword(s):  
Real Time ◽  
Cost Effective ◽  
Video Stream ◽  
Sensor Data ◽  
Practical Implementation ◽  
Wireless System ◽  
Central System ◽  
Static Images ◽  
High Processing ◽  
Changing Light

This paper presents an approach for detecting real-time parking slots which includes vision-based techniques. Traditional sensor-based systems are not cost effective as 'n' number of sensors are required for 'n' parking slots. Transmitting sensor data to central system is done by hardwiring or installing dedicated wireless system which is again costly. Our technique will overcome this problem by using camera instead of number of sensors which is expensive. For detection we are using a Convolutional Neural Networks (CNN) classifier which is custom trained. It is more robust and effective in changing light conditions and weather. The following system do not require high processing as detections are done on static images not on video stream. We have also demonstrated real-time parking scenario by constructing a small prototype which shows practical implementation of our system.

Edge Artificial Intelligence: A Multi-Camera Video Surveillance Application

2021 ◽  
Author(s):  
Daniele Berardini ◽  
Adriano Mancini ◽  
Primo Zingaretti ◽  
Sara Moccia
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Video Surveillance ◽  
Cost Effective ◽  
Fast Response ◽  
Video Stream ◽  
Single Shot ◽  
Central Unit ◽  
Surveillance Video ◽  
People Detection

Abstract Nowadays, video surveillance has a crucial role. Analyzing surveillance videos is, however, a time consuming and tiresome procedure. In the last years, artificial intelligence paved the way for automatic and accurate surveillance-video analysis. In parallel to the development of artificial-intelligence methodologies, edge computing is becoming an active field of research with the final goal to provide cost-effective and real time deployment of the developed methodologies. In this work, we present an edge artificial intelligence application to video surveillance. Our approach relies on a set of four IP cameras, which acquire video frames that are processed on the edge using the NVIDIA® Jetson Nano. A state-of-the-art deep-learning model, called Single Shot multibox Detector (SSD) MobileNetV2 network, is used to perform object and people detection in real-time. The proposed infrastructure obtained an inference speed of ∼10.0 Frames per Second (FPS) for each parallel video stream. These results prompt the possibility of translating our work into a real word scenario. The integration of the presented application into a wider monitoring system with a central unit could bring benefits to the overall infrastructure. Indeed our application could send only video-related high-level information to the central unit, allowing it to combine information with data coming from other sensing devices without unuseful data overload. This would ensure a fast response in case of emergency or detected anomalies. We hope this work will contribute to stimulate the research in the field of edge artificial intelligence for video surveillance.

scholarly journals Central Monitor Based on Personal Computer Design with SpO2 and Body Temperature Parameters Using Wireless Xbee Pro

2021 ◽  
Vol 3 (1) ◽  
pp. 35-43
Author(s):  
I KOMANG YOGI MAHARDIKA ◽  
Bambang Guruh Irianto ◽  
Torib Hamzah ◽  
Shubhrojit Misra
Keyword(s):  
Body Temperature ◽  
Real Time ◽  
Personal Computer ◽  
Health Workers ◽  
Sensor Data ◽  
Computer Design ◽  
Wireless System ◽  
Average Value ◽  
Patient Monitoring System ◽  
Multiple Patients

Central patient monitor that is not real-time and continues will cause inaccuracies monitoring results and also sending data that is still using cable will cause limited distance. The purpose of this research is to design a central monitoring based personal computer via Xbee Pro. The contribution of this research is,  the system works in real-time and continues, more parameters, using wireless, longer transmission distances. So that monitoring can be done in real-time and continue via wireless with more distance, then the wireless system uses the Xbee Pro module which has larger output power and uses the same number of wireless modules between transmitter and receiver. Body temperature was measured using the LM35 sensor and oxygen saturation in the blood was measured using the MAX30100 sensor. Data is sent using Xbee Pro and displayed on a personal computer. At the distance of receiving data approximately 25 meters with a wall divider, obtained results of smooth monitoring without any loss of data. The results showed that the average SpO2 error value was 0.34% in module 1 and 0.68% in module 2. The average value of body temperature error was 0.46% in module 1 and 0.72% in module 2. The results of this research can be implemented in a centralized patient monitoring system at the hospital, making it easier for health workers to monitor multiple patients, with the results of monitoring in real-time and continue, more parameters, via wireless with greater distance.

scholarly journals 3D Indoor Mapping System Using 2D LiDAR Sensor for Drones

2018 ◽  
Vol 7 (4.11) ◽  
pp. 179 ◽  
Author(s):  
M. R. Shahrin ◽  
F. H. Hashim ◽  
W. M.D.W. Zaki ◽  
A. Hussain ◽  
T. T. Raj
Keyword(s):  
Real Time ◽  
Autonomous Navigation ◽  
Point Cloud ◽  
Cost Effective ◽  
Sensor Data ◽  
3D Scanner ◽  
Mapping System ◽  
3D Scanners ◽  
Major Factors ◽  
3D Scans

Most 3D scanners are heavy, bulky and costly. These are the major factors that make them irrelevant to be attached to a drone for autonomous navigation. With modern technologies, it is possible to design a simple 3D scanner for autonomous navigation. The objective of this study is to design a cost effective 3D indoor mapping system using a 2D light detection and ranging (LiDAR) sensor for a drone. This simple 3D scanner is realised using a LiDAR sensor together with two servo motors to create the azimuth and elevation axes. An Arduino Uno is used as the interface between the scanner and computer for the real-time communication via serial port. In addition, an open source Point-Cloud Tool software is used to test and view the 3D scanner data. To study the accuracy and efficiency of the system, the LiDAR sensor data from the scanner is obtained in real-time in point-cloud form. The experimental results proved that the proposed system can perform the 2D and 3D scans with tolerable performance.  

scholarly journals Static, Portable and Smart Detection of Water Quality in Panchayath Distribution Tank

2019 ◽  
Vol 8 (2S5) ◽  
pp. 103-108
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Cost Effective ◽  
Daily Basis ◽  
Quality Parameters ◽  
Sensor Data ◽  
Sensor Technology ◽  
Real Time System ◽  
Quality Of Water ◽  
Physical And Chemical

The significant crunch in the Current world is Water pollution. It has created an abundant influence on the Environment. With the intention of the non-toxic distribution of the water and its eminence should be monitored at real time. This paper suggested the smart detection with low cost real time system which is used to monitor the quality of water through IOT(internet of things). The system entail of different sensors which are used to measure the physical and chemical parameters of the water. The quality parameters are temperature, pH, turbidity, conductivity and Total dissolved solids of the water are measured. Commercially available products capable of monitoring such parameters are usually somewhat expensive and the data’s are collected by mobile van. Using Sensor technology provides a cost-effective and pre-eminent reliable as they can provide real time output. The measured values from the sensors can be observed by the core controller. The controller was programmed to monitor the distribution tank on a daily basis to hour basis monitoring. The TIVA C series is used as a core controller. The Controller is mounted on the side of the distribution tank. Finally, the sensor data from the controller is sent to Wi-Fi module through UART protocol. Wi-fi Module is connected to a public Wi-Fi system through which data is seen by the locals who are all connected to that Wi-Fi network.

scholarly journals Road Accidents Detection, Data Collection and Data Analysis Using V2X Communication and Edge/Cloud Computing

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 896 ◽  
Author(s):  
Kishwer Abdul Khaliq ◽  
Omer Chughtai ◽  
Abdullah Shahwani ◽  
Amir Qayyum ◽  
Jürgen Pannek
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Sensor Data ◽  
Practical Implementation ◽  
Delayed Response ◽  
Care Providers ◽  
Road Accidents ◽  
Death Toll ◽  
Alert Message ◽  
Accident Detection

With the improvement in transportation infrastructure and in-vehicle technology in addition to a meteoric increase in the total number of commercial and non-commercial vehicles on the road, traffic accidents may occur, which usually cause a high death toll. More than half of these deaths occur due to a delayed response by medical care providers and rescue authorities. The chances of survival of an accident victim could increase drastically if immediate medical assistance is provided at an accident location. This work proposes a low-cost accident detection and notification system, which utilizes a multi-tier IoT-based vehicular environment; principally, it uses V2X Communication and Edge/Cloud computing. In this work, vehicles are equipped with an On-Board Unit (OBU) in addition to mechanical sensors (accelerometer, gyroscope) for reliable accident detection along with a Global Positioning System (GPS) module for identification of accident location. In addition to this, a camera module is implanted on the vehicle to capture the moment when an accident takes place. In order to facilitate inter-vehicle communication (IVC), OBU in each vehicle incorporates a wireless networking interface. Once an accident occurs, a vehicle detects it and generates an alert message. It then sends the message along with the accident location to an intermediate device, placed at the edge of the vehicular network, and therefore called an edge device. Upon receiving the notification, this edge device finds the nearest hospital and makes a request for an ambulance to be dispatched immediately. It also performs some preprocessing of data and effectively acts as a bridge between the sensors installed inside the vehicle and the distant server deployed in the cloud. A significant issue that the traffic authorities are currently facing is the real-time visualization of data obtained through such environments. Wireless interfaces are usually capable of forwarding real-time sensor data; however, this feature is not yet commercially available in the OBU of the vehicle; therefore, practical implementation is carried out using the Internet of things (IoT) in order to create a network among the vehicles, the edge node, and the central server. By performing analysis on the adequate acquired data of road accidents, the constructive plans of action can be devised that may limit the death toll. In order to assist the relevant authorities in performing wholesome analysis of refined and reliable data, a dynamic front-end visualization is proposed, which is hosted in the cloud. The generated charts and graphs help the personnel at relevant organizations to make appropriate decisions based on the conclusive analysis of processed and stored data.

scholarly journals Cost-Effective Design of IoT-Based Smart Household Distribution System

Designs ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 55
Author(s):  
Musse Mohamud Ahmed ◽  
Md Ohirul Qays ◽  
Ahmed Abu-Siada ◽  
S. M. Muyeen ◽  
Md Liton Hossain
Keyword(s):  
Real Time ◽  
Distribution System ◽  
Distribution Systems ◽  
Cost Effective ◽  
Practical Implementation ◽  
Main Function ◽  
Home Energy Management ◽  
Electrical Loads ◽  
Microprocessor Technology ◽  
And Control

The Internet of Things (IoT) plays an indispensable role in present-day household electricity management. Nevertheless, practical development of cost-effective intelligent condition monitoring, protection, and control techniques for household distribution systems is still a challenging task. This paper is taking one step forward into a practical implementation of such techniques by developing an IoT Smart Household Distribution Board (ISHDB) to monitor and control various household smart appliances. The main function of the developed ISHDB is collecting and storing voltage, current, and power data and presenting them in a user-friendly way. The performance of the developed system is investigated under various residential electrical loads of different energy consumption profiles. In this regard, an Arduino-based working prototype is employed to gather the collected data into the ThingSpeak cloud through a Wi-Fi medium. Blynk mobile application is also implemented to facilitate real-time monitoring by individual consumers. Microprocessor technology is adopted to automate the process, and reduce hardware size and cost. Experimental results show that the developed system can be used effectively for real-time home energy management. It can also be used to detect any abnormal performance of the electrical appliances in real-time through monitoring their individual current and voltage waveforms. A comparison of the developed system and other existing techniques reveals the superiority of the proposed method in terms of the implementation cost and execution time.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

scholarly journals A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Nicholas F. McCarthy ◽  
Ali Tohidi ◽  
Yawar Aziz ◽  
Matt Dennie ◽  
Mario Miguel Valero ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Real Time ◽  
Forest Fires ◽  
Wildland Fire ◽  
Fire Spread ◽  
Low Earth Orbit ◽  
Practical Implementation ◽  
Spatiotemporal Resolution ◽  
Active Fire

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.

scholarly journals Software Architecture of a Fog Computing Node for Industrial Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3715
Author(s):  
Ioan Ungurean ◽  
Nicoleta Cristina Gaitan
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Development Process ◽  
Fog Computing ◽  
Low Latency ◽  
Practical Implementation ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Starting Point ◽  
Hard Real Time

In the design and development process of fog computing solutions for the Industrial Internet of Things (IIoT), we need to take into consideration the characteristics of the industrial environment that must be met. These include low latency, predictability, response time, and operating with hard real-time compiling. A starting point may be the reference fog architecture released by the OpenFog Consortium (now part of the Industrial Internet Consortium), but it has a high abstraction level and does not define how to integrate the fieldbuses and devices into the fog system. Therefore, the biggest challenges in the design and implementation of fog solutions for IIoT is the diversity of fieldbuses and devices used in the industrial field and ensuring compliance with all constraints in terms of real-time compiling, low latency, and predictability. Thus, this paper proposes a solution for a fog node that addresses these issues and integrates industrial fieldbuses. For practical implementation, there are specialized systems on chips (SoCs) that provides support for real-time communication with the fieldbuses through specialized coprocessors and peripherals. In this paper, we describe the implementation of the fog node on a system based on Xilinx Zynq UltraScale+ MPSoC ZU3EG A484 SoC.

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