Rawshan Ara Begum

Radio Frequency Identification (RFID) and communication technologies for solid waste bin and truck monitoring system

This paper deals with a system of integration of Radio Frequency Identification (RFID) and communication technologies for solid waste bin and truck monitoring system. RFID, GPS, GPRS and GIS along with camera technologies have been integrated and developed the bin and truck intelligent monitoring system. A new kind of integrated theoretical framework, hardware architecture and interface algorithm has been introduced between the technologies for the successful implementation of the proposed system. In this system, bin and truck database have been developed such a way that the information of bin and truck ID, date and time of waste collection, bin status, amount of waste and bin and truck GPS coordinates etc. are complied and stored for monitoring and management activities. The results showed that the real-time image processing, histogram analysis, waste estimation and other bin information have been displayed in the GUI of the monitoring system. The real-time test and experimental results showed that the performance of the developed system was stable and satisfied the monitoring system with high practicability and validity.

Integrated technologies for solid waste bin monitoring system

The integration of communication technologies such as radio frequency identification (RFID), global positioning system (GPS), general packet radio system (GPRS), and geographic information system (GIS) with a camera are constructed for solid waste monitoring system. The aim is to improve the way of responding to customer’s inquiry and emergency cases and estimate the solid waste amount without any involvement of the truck driver. The proposed system consists of RFID tag mounted on the bin, RFID reader as in truck, GPRS/GSM as web server, and GIS as map server, database server, and control server. The tracking devices mounted in the trucks collect location information in real time via the GPS. This information is transferred continuously through GPRS to a central database. The users are able to view the current location of each truck in the collection stage via a web-based application and thereby manage the fleet. The trucks positions and trash bin information are displayed on a digital map, which is made available by a map server. Thus, the solid waste of the bin and the truck are being monitored using the developed system.

A review on technologies and their usage in solid waste monitoring and management systems: Issues and challenges.

In the backdrop of prompt advancement, information and communication technology (ICT) has become an inevitable part to plan and design of modern solid waste management (SWM) systems. This study presents a critical review of the existing ICTs and their usage in SWM systems to unfold the issues and challenges towards using integrated technologies based system. To plan, monitor, collect and manage solid waste, the ICTs are divided into four categories such as spatial technologies, identification technologies, data acquisition technologies and data communication technologies. The ICT based SWM systems classified in this paper are based on the first three technologies while the forth one is employed by almost every systems. This review may guide the reader about the basics of available ICTs and their application in SWM to facilitate the search for planning and design of a sustainable new system.

Solid waste monitoring system integration based on RFID, GPS and camera

The integration of communication technologies such as radio frequency identification (RFID), global positioning system (GPS), general packet radio system (GPRS), geographic information system (GIS) with a camera are constructed for solid waste monitoring system. The aim is to improve the way of responding to customers inquiry and emergency cases and estimate the solid waste amount without any involvement of the truck driver. The proposed system consists of RFID tag mounted on the bin, RFID reader as truck module, GPSR/GSM as web-server, GIS as map server, database server and control station server. The tracking devices mounted in the trucks collect location information in real-time via the GPS. This information is transferred continuously through GPRS to a central database. The users are able to view the current location of each truck in the collection stage via a web-based application, and thereby manage the fleet. The trucks positions and trash bin information are displayed on a digital map, which is made available by a map server. Thus, the solid waste of the bin and the truck are being monitored using the developed system.

Solid waste bin level detection using gray level co-occurrence matrix feature extraction approach

This paper presents solid waste bin level detection and classification using gray level co-occurrence matrix (GLCM) feature extraction methods. GLCM parameters, such as displacement, d, quantization, G, and the number of textural features, are investigated to determine the best parameter values of the bin images. The parameter values and number of texture features are used to form the GLCM database. The most appropriate features collected from the GLCM are then used as inputs to the multi-layer perceptron (MLP) and the K-nearest neighbor (KNN) classifiers for bin image classification and grading. The classification and grading performance for DB1, DB2 and DB3 features were selected with both MLP and KNN classifiers. The results demonstrated that the KNN classifier, at KNN = 3, d = 1 and maximum G values, performs better than using the MLP classifier with the same database. Based on the results, this method has the potential to be used in solid waste bin level classification and grading to provide a robust solution for solid waste bin level detection, monitoring and management.

Trends of dengue infections in Malaysia, 2000-2010

OBJECTIVE To analyze trends of dengue incidences and deaths in Malaysia from 2000 to 2010 as well as the predominant dengue virus serotypes during the last decade. METHODS We used the national data on annual reported cases, deaths, incidence rate, mortality rate, and case fatality rate of dengue fever (DF) and dengue hemorrhagic fever (DHF) as well as dengue virus serotypes prevalent in Malaysia during the last decade. Trend/ regression lines were fitted to investigate the trend of dengue incidences and deaths due to the disease for a 11-year period (2000-2010). For the distribution of national incidence rate, mortality rate, and case fatality rate of DF and DHF, descriptive statistics using mean and 95% confidence intervals (CI 39) for means, and range were applied. RESULTS The number of dengue cases and number of deaths have increased, on average, by 14% and 8% per year respectively. The average annual incidence rate of DF per 100 000 populations was higher as compared to that of DHF. Conversely, the yearly mean mortality rate of DHF per 100 000 populations was greater than that of DF. The simultaneous circulation of all four dengue serotypes has been found in Malaysia. But a particular dengue virus serotype predominates for at least two years before it becomes replaced by another serotype. CONCLUSIONS The dengue situation in Malaysia has worsened with an increasing number of reported cases and deaths during the last decade. The increasing trend of dengue highlights the need for a more systematic surveillance and reporting of the disease.

An automated solid waste bin level detection system using a gray level aura matrix

An advanced image processing approach integrated with communication technologies and a camera for waste bin level detection has been presented. The proposed system is developed to address environmental concerns associated with waste bins and the variety of waste being disposed in them. A gray level aura matrix (GLAM) approach is proposed to extract the bin image texture. GLAM parameters, such as neighboring systems, are investigated to determine their optimal values. To evaluate the performance of the system, the extracted image is trained and tested using multi-layer perceptions (MLPs) and K-nearest neighbor (KNN) classifiers. The results have shown that the accuracy of bin level classification reach acceptable performance levels for class and grade classification with rates of 98.98% and 90.19% using the MLP classifier and 96.91% and 89.14% using the KNN classifier, respectively. The results demonstrated that the system performance is robust and can be applied to a variety of waste and waste bin level detection under various conditions.

Energy consumption and emission projection for the road transport sector in Malaysia: an application of the LEAP model

This study has attempted to estimate the energy consumption and emission of pollutants namely carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx) and non-methane volatile organic compounds (NMVOC) from the road transport sector in Malaysia from the year 2012 till 2040. This was done using the long-range energy alternatives planning (LEAP) model. Estimates of energy consumption and emissions were evaluated and analysed under a business-as-usual scenario and three other alternative fuel policy scenarios of biodiesel vehicles (BIO), natural gas vehicles (NGV) and hybrid electric vehicles (HEV). The aim of this study has been to identify the potential alternative fuel policies that would be effective in reducing the future growth of road transport energy consumption and emission in Malaysia. Results indicate that the NGV scenario contributes towards the highest reduction in road transport energy consumption followed by BIO and HEV. The NGV scenario also achieves highest mitigation of emission of all the four pollutants. In the case of CO2 emission, BIO scenario attains second highest mitigation, whereas in the event of CO, NOx and NMVOC emission, HEV scenario achieves second highest mitigation.

Dynamic impact of household consumption on its CO 2 emissions in Malaysia

This article aims to measure the dynamic impact of household consumption (final household consumption expenditure, LHC) on CO2 emission from household’s energy consumption in Malaysia from 1971 to 2010. The estimation of autoregressive distributed lag (ARDL) bounds test confirms a non-monotonic relationship between LHC and residential CO2 emission. In the long run, there is a positive relationship between LHC and CO2 emission as well as a negative relationship between quadratic forms of LHC and CO2 emission which indicates the existence of an inverted U-shaped relationship between these two variables. The analysis also found a similar relationship in both the short and long run. To confirm the non-monotonous relationship, the U test of Sasabuchi–Lind–Mehlum (2010) approach has followed to obtain the sufficient conditions for the existence of inverted U relationship. Moreover, the U test of Sasabuchi–Lind–Mehlum (2010) found that CO2 emission increases with increasing LHC up to 6.5 units, but it declines with an additional increase of LHC which is also found by the ARDL model. However, the existence of environmental Kuznets curve implies that in the long run, household CO2 emission declines with the additional increase of household consumption in the Malaysian economy.

Backtracking search algorithm in CVRP models for efficient solid waste collection and route optimization

Waste collection is an important part of waste management that involves different issues, including environmental, economic, and social, among others. Waste collection optimization can reduce the waste collection budget and environmental emissions by reducing the collection route distance. This paper presents a modified Backtracking Search Algorithm (BSA) in capacitated vehicle routing problem (CVRP) models with the smart bin concept to find the best optimized waste collection route solutions. The objective function minimizes the sum of the waste collection route distances. The study introduces the concept of the threshold waste level (TWL) of waste bins to reduce the number of bins to be emptied by finding an optimal range, thus minimizing the distance. A scheduling model is also introduced to compare the feasibility of the proposed model with that of the conventional collection system in terms of travel distance, collected waste, fuel consumption, fuel cost, efficiency and CO2 emission. The optimal TWL was found to be between 70% and 75% of the fill level of waste collection nodes and had the maximum tightness value for different problem cases. The obtained results for four days show a 36.80% distance reduction for 91.40% of the total waste collection, which eventually increases the average waste collection efficiency by 36.78% and reduces the fuel consumption, fuel cost and CO2 emission by 50%, 47.77% and 44.68%, respectively. Thus, the proposed optimization model can be considered a viable tool for optimizing waste collection routes to reduce economic costs and environmental impacts.