Scheduling-Aware Real-Time Garbage Collection Using Dual Aperiodic Servers

2004 ◽  
pp. 1-17 ◽  
Author(s):  
Taehyoun Kim ◽  
Heonshik Shin
Keyword(s):  
Real Time ◽  
Garbage Collection

Parallel real-time garbage collection of multiple heaps in reconfigurable hardware

2015 ◽  
Vol 49 (11) ◽  
pp. 117-127 ◽  
Author(s):  
David F. Bacon ◽  
Perry Cheng ◽  
Sunil Shukla
Keyword(s):  
Real Time ◽  
Garbage Collection ◽  
Reconfigurable Hardware

scholarly journals Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3930 ◽  
Author(s):  
Ayaz Hussain ◽  
Umar Draz ◽  
Tariq Ali ◽  
Saman Tariq ◽  
Muhammad Irfan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Air Pollutants ◽  
Garbage Collection ◽  
Air Pollutant ◽  
Real Time Monitoring ◽  
Collection System ◽  
The Real ◽  
The Status

Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.

A Flexible Java-on-Chip Solution Eine flexible Java-on-Chip Lösung

2008 ◽  
Vol 50 (5) ◽  
Author(s):  
Sascha Uhrig
Keyword(s):  
Embedded Systems ◽  
Real Time ◽  
Garbage Collection ◽  
Runtime System ◽  
Programmable Logic ◽  
The Poor ◽  
Runtime Environment ◽  
Programmable Logic Devices ◽  
On Chip ◽  
Java Processor

AbstractJava is getting more and more important in embedded systems. However, there are several obstacles like the poor real-time capability, the garbage collection, and the need for a sufficient runtime environment. We present the jamuth Java framework which provides a multithreaded Java processor as IP core for programmable logic devices (FPGAs), a runtime system, and the necessary offline tools. The framework provides CDC compatibility and real-time support for embedded systems as well as a powerful garbage collection.

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